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StegoZip: Enhancing Linguistic Steganography Payload in Practice with Large Language Models
[ "Jun Jiang", "Kejiang Chen", "Zijin Yang", "Weiming Zhang", "Nenghai Yu" ]
Generative steganography has emerged as an active research area, yet its practical system is constrained by the inherent secret payload limitation caused by low entropy in generating stego texts. This payload limitation necessitates the use of lengthy stego texts or frequent transmissions, which increases the risk of suspicion by adversaries. Previous studies have mainly focused on payload enhancement through optimized entropy utilization while overlooking the crucial role of secret message processing. To address this gap, we propose StegoZip, a framework that leverages large language models to optimize secret message processing. StegoZip consists of two core components: semantic redundancy pruning and index-based compression coding. The former dynamically prunes the secret message to extract a low-semantic representation, whereas the latter further compresses it into compact binary codes. When integrated with state-of-the-art steganographic methods under lossless decoding, StegoZip achieves 2.5$\times$ the payload of the baselines while maintaining comparable processing time in practice. This enhanced payload significantly improves covertness by mitigating the risks associated with frequent transmissions while maintaining provable content security.
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301
Avoiding exp(R) scaling in RLHF through Preference-based Exploration
[ "Mingyu Chen", "Yiding Chen", "Wen Sun", "Xuezhou Zhang" ]
Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for large language model (LLM) alignment.This paper studies the setting of online RLHF and focuses on improving its sample efficiency.All existing algorithms for online RLHF, whether doing passive exploration or active exploration, suffer from a sample complexity that scales exponentially with the range of the reward function.This statistical inefficiency hinders their effectiveness in scenarios with heavily skewed preferences, e.g. questions with objectively correct answers.To address this, we introduce Self-Exploring Preference-Incentive Online Preference Optimization (SE-POPO), an online RLHF algorithm that for the first time achieves a sample complexity that scales polynomially with the reward range, answering an open problem raised by Xie et al. [2024].Theoretically, we demonstrate that the sample complexity of SE-POPO dominates that of existing exploration algorithms. Empirically, our systematic evaluation confirms that SE-POPO is more sample-efficient than both exploratory and non-exploratory baselines, in two primary application scenarios of RLHF as well as on public benchmarks, marking a significant step forward in RLHF algorithm design.
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302
Physics-Constrained Flow Matching: Sampling Generative Models with Hard Constraints
[ "Utkarsh Utkarsh", "Pengfei Cai", "Alan Edelman", "Rafael Gomez-Bombarelli", "Christopher Rackauckas" ]
Deep generative models have recently been applied to physical systems governed by partial differential equations (PDEs), offering scalable simulation and uncertainty-aware inference. However, enforcing physical constraints, such as conservation laws (linear and nonlinear) and physical consistencies, remains challenging. Existing methods often rely on soft penalties or architectural biases that fail to guarantee hard constraints. In this work, we propose Physics-Constrained Flow Matching (PCFM), a zero-shot inference framework that enforces arbitrary nonlinear constraints in pretrained flow-based generative models. PCFM continuously guides the sampling process through physics-based corrections applied to intermediate solution states, while remaining aligned with the learned flow and satisfying physical constraints. Empirically, PCFM outperforms both unconstrained and constrained baselines on a range of PDEs, including those with shocks, discontinuities, and sharp features, while ensuring exact constraint satisfaction at the final solution. Our method provides a general framework for enforcing hard constraints in both scientific and general-purpose generative models, especially in applications where constraint satisfaction is essential.
poster
2506.04171
null
null
[]
[]
[]
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303
A Cramér–von Mises Approach to Incentivizing Truthful Data Sharing
[ "Alex Clinton", "Thomas Zeng", "Yiding Chen", "Jerry Zhu", "Kirthevasan Kandasamy" ]
Modern data marketplaces and data sharing consortia increasingly rely on incentive mechanisms to encourage agents to contribute data. However, schemes that reward agents based on the quantity of submitted data are vulnerable to manipulation, as agents may submit fabricated or low-quality data to inflate their rewards. Prior work has proposed comparing each agent’s data against others’ to promote honesty: when others contribute genuine data, the best way to minimize discrepancy is to do the same. Yet prior implementations of this idea rely on very strong assumptions about the data distribution (e.g. Gaussian), limiting their applicability.In this work, we develop reward mechanisms based on a novel, two-sample test inspired by the Cramér-von Mises statistic. Our methods strictly incentivize agents to submit more genuine data, while disincentivizing data fabrication and other types of untruthful reporting.We establish that truthful reporting constitutes a (possibly approximate) Nash equilibrium in both Bayesian and prior-agnostic settings. We theoretically instantiate our method in two canonical data sharing problems and show that it relaxes key assumptions made by prior work.Empirically, we demonstrate that our mechanism incentivizes truthful data sharing via simulations and on real-world language and image data.
poster
2506.07272
null
null
[]
[]
[]
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End-to-End Low-Light Enhancement for Object Detection with Learned Metadata from RAWs
[ "Xuelin Shen", "Haifeng Jiao", "Yitong Wang", "Yulin HE", "Wenhan Yang" ]
Although RAW images offer advantages over sRGB by avoiding ISP-induced distortion and preserving more information in low-light conditions, their widespread use is limited due to high storage costs, transmission burdens, and the need for significant architectural changes for downstream tasks. To address the issues, this paper explores a new raw-based machine vision paradigm, termed Compact RAW Metadata-guided Image Refinement (CRM-IR). In particular, we propose a Machine Vision-oriented Image Refinement (MV-IR) module that refines sRGB images to better suit machine vision preferences, guided by learned raw metadata. Such a design allows the CRM-IR to focus on extracting the most essential metadata from raw images to support downstream machine vision tasks, while remaining plug-and-play and fully compatible with existing imaging pipelines, without any changes to model architectures or ISP modules. We implement our CRM-IR scheme on various object detection networks, and extensive experiments under low-light conditions demonstrate that it can significantly improve performance with an additional bitrate cost of less than $10^{-3}$ bits per pixel.
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305
Flow based approach for Dynamic Temporal Causal models with non-Gaussian or Heteroscedastic Noises
[ "Abdellah Rahmani", "Pascal Frossard" ]
Understanding causal relationships in multivariate time series is crucial in many scenarios, such as financial and neurological data. Many such time series exhibit multiple regimes, i.e., consecutive segments with unknown boundaries. Each regime has its own causal structure. Detecting these causal dependencies and regime shifts is critical for revealing regime indices and the underlying processes. However, causal structure learning in this setting is challenging due to (1) non-stationarity, i.e. each regime can have its own causal graph and mixing function, and (2) complex noise distributions, which may be non-Gaussian or heteroscedastic, with variance depending on one or more influencing variables. Existing causal discovery approaches often assume stationarity or Gaussian noise with constant variance. To address these challenges, We introduce FANTOM, a unified framework that tackles non-stationarity along with non-Gaussian and heteroscedastic noises. FANTOM simultaneously learns each regime’s Directed Acyclic Graph (DAG) and infers the number and indices of these regimes, using a Bayesian Expectation Maximization algorithm that maximizes the evidence lower bound of the data log likelihood. On the theoretical side, we prove, under common assumptions, that temporal heteroscedastic causal models are identifiable in both stationary and non-stationary settings. In addition, extensive experiments on synthetic and real data show that FANTOM outperforms existing methods.
poster
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306
Efficient Prompt Compression with Evaluator Heads for Long-Context Transformer Inference
[ "Weizhi Fei", "Xueyan Niu", "XIE GUOQING", "Yingqing Liu", "Bo Bai", "Wei Han" ]
Although applications involving long-context inputs are crucial for the effective utilization of large language models (LLMs), they also result in increased computational costs and reduced performance. To address this challenge, we propose an efficient, training-free prompt compression method that retains key information within compressed prompts. We identify specific attention heads in transformer-based LLMs, which we designate as evaluator heads, that are capable of selecting tokens in long inputs that are most significant for inference. Building on this discovery, we develop EHPC, an Evaluator Head-based Prompt Compression method, which enables LLMs to rapidly "skim through'' input prompts by leveraging only the first few layers with evaluator heads during the pre-filling stage, subsequently passing only the important tokens to the model for inference. EHPC achieves state-of-the-art results across two mainstream benchmarks: prompt compression and long-context inference acceleration. Consequently, it effectively improves performance with the reduced costs associated with commercial API calls compared to prompt compressing methods. We further demonstrate that EHPC attains competitive results compared to key-value cache-based acceleration methods, thereby highlighting its potential to enhance the efficiency of LLMs for long-context tasks.
spotlight
2501.12959
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307
Multiscale guidance of AlphaFold3 with heterogeneous cryo-EM data
[ "Rishwanth Raghu", "Axel Levy", "Gordon Wetzstein", "Ellen Zhong" ]
Trained on decades of accumulated high-resolution atomic models, protein structure prediction models are now capable of generating accurate 3D structural hypotheses from sequence alone. However, they routinely fail to capture the conformational diversity of dynamic biomolecular complexes, often requiring heuristic MSA subsampling approaches for generating alternative states. In parallel, cryo-electron microscopy (cryo-EM) has emerged as a powerful tool for imaging near-native structural heterogeneity, but is challenged by arduous pipelines to go from raw experimental data to atomic models. Here, we bridge the gap between these modalities, combining cryo-EM density maps with the rich sequence and biophysical priors learned by protein structure prediction models. Our method, CryoBoltz, guides the sampling trajectory of a pretrained protein structure prediction model using both global and local structural constraints derived from density maps, driving predictions towards conformational states consistent with the experimental data. We demonstrate that this flexible yet powerful inference-time approach allows us to build atomic models into heterogeneous cryo-EM maps across a variety of dynamic biomolecular systems including transporters and antibodies.
poster
2506.04490
null
null
[]
[]
[]
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308
Forecasting in Offline Reinforcement Learning for Non-stationary Environments
[ "Suzan Ece Ada", "Georg Martius", "Emre Ugur", "Erhan Oztop" ]
Offline Reinforcement Learning (RL) provides a promising avenue for training policies from pre-collected datasets when gathering additional interaction data is infeasible. However, existing offline RL methods often assume stationarity or only consider synthetic perturbations at test time—assumptions that often fail in real-world scenarios characterized by abrupt, time-varying offsets. These offsets can lead to partial observability, causing agents to misperceive their true state and degrade performance. To overcome this challenge, we introduce Forecasting in Offline RL (FORL), a framework that unifies (i) conditional diffusion-based candidate state generation, trained without presupposing any specific form of future non-stationarity, and (ii) zero-shot time-series foundation models. FORL targets environments prone to unexpected, potentially non-Markovian offsets, requiring robust agent performance from the onset of each episode. Empirical evaluations on offline RL benchmarks, augmented with real-world time-series data to simulate realistic non-stationarity, demonstrate that FORL consistently improves performance compared to competitive baselines. By integrating zero-shot forecasting with the agent’s experience we aim to bridge the gap between offline RL and the complexity of real-world, non-stationary environments.
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309
Mitigating Forgetting in LLM Fine-Tuning via Low-Perplexity Token Learning
[ "Chao-Chung Wu", "Zhi Rui Tam", "Chieh-Yen Lin", "Yun-Nung (Vivian) Chen", "Shao-Hua Sun", "Hung-yi Lee" ]
Maintaining consistent model performance across domains is a fundamental challenge in machine learning. While recent work has explored using LLM-generated data for fine-tuning, its impact on cross-domain generalization remains poorly understood. This paper presents a systematic analysis revealing that fine-tuning with LLM-generated data not only improves target task performance but also reduces non-target task degradation compared to fine-tuning with ground truth data. Through analyzing the data sequence in tasks of various domains, we demonstrate that this enhancement of non-target task robustness stems from the reduction of high perplexity tokens found in LLM-generated sequences. Following our findings, we showed that masking high perplexity tokens in ground truth training data achieves similar non-target task performance preservation, comparable to using LLM-generated data. Extensive experiments across different model families and scales, including Gemma 2 IT 2B, Llama 3 8B Instruct, and 3 additional models, agree with our findings. To the best of our knowledge, this is the first work to provide an empirical explanation based on token perplexity reduction to mitigate catastrophic forgetting in LLMs after fine-tuning, offering valuable insights for developing more robust fine-tuning strategies.
poster
2501.14315
null
null
[]
[]
[]
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310
UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens
[ "Ruichuan An", "Sihan Yang", "Renrui Zhang", "zijun shen", "Ming Lu", "Gaole Dai", "Hao Liang", "Ziyu Guo", "Shilin Yan", "Yulin Luo", "Bocheng Zou", "Chaoqun Yang", "Wentao Zhang" ]
Personalized models have demonstrated remarkable success in understanding and generating concepts provided by users. However, existing methods use separate concept tokens for understanding and generation, treating these tasks in isolation. This may result in limitations for generating images with complex prompts. For example, given the concept $\langle bo\rangle$, generating "$\langle bo\rangle$ wearing its hat" without additional textual descriptions of its hat. We call this kind of generation personalized knowledge-driven generation. To address the limitation, we present UniCTokens, a novel framework that effectively integrates personalized information into a unified vision language model (VLM) for understanding and generation. UniCTokens trains a set of unified concept tokens to leverage complementary semantics, boosting two personalized tasks. Moreover, we propose a progressive training strategy with three stages: understanding warm-up, bootstrapping generation from understanding, and deepening understanding from generation to enhance mutual benefits between both tasks. To quantitatively evaluate the unified VLM personalization, we present UnifyBench, the first benchmark for assessing concept understanding, concept generation, and knowledge-driven generation. Experimental results on UnifyBench indicate that UniCTokens shows competitive performance compared to leading methods in concept understanding, concept generation, and achieving state-of-the-art results in personalized knowledge-driven generation. Our research demonstrates that enhanced understanding improves generation, and the generation process can yield valuable insights into understanding. Our code and dataset will be released.
poster
2505.14671
https://github.com/arctanxarc/UniCTokens
null
[]
[]
[]
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311
Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in Robotics
[ "Dongyoung Kim", "Huiwon Jang", "Sumin Park", "Jaehyung Kim", "Younggyo Seo", "Jinwoo Shin" ]
Large Vision-Language Models (LVLMs) have recently shown great promise in advancing robotics by combining embodied reasoning with robot control. A common approach involves training on embodied reasoning tasks related to robot control using Supervised Fine-Tuning (SFT). However, SFT datasets are often heuristically constructed and not explicitly optimized for improving robot control. Furthermore, SFT often leads to issues such as catastrophic forgetting and reduced generalization performance. To address these limitations, we introduce Robot-R1, a novel framework that leverages reinforcement learning to enhance embodied reasoning specifically for robot control. Robot-R1 learns to predict the next keypoint state required for task completion, conditioned on the current scene image and environment metadata derived from expert demonstrations. Inspired by the DeepSeek-R1 learning approach, Robot-R1 samples reasoning-based responses and reinforces those that lead to more accurate predictions. Our experiments show that models trained with Robot-R1 outperform SFT methods on embodied reasoning tasks. Despite having only 7B parameters, Robot-R1 even surpasses GPT-4o on reasoning tasks related to low-level action control, such as spatial and primitive movement reasoning.
poster
2506.00070
null
null
[]
[]
[]
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Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo
[ "Zachary Charles", "Gabriel Teston", "Lucio Dery", "John Rush", "Nova Fallen", "Zachary Garrett", "Arthur Szlam", "Arthur Douillard" ]
As we scale to more massive machine learning models, the frequent synchronization demands inherent in data-parallel approaches create significant slowdowns, posing a critical challenge to further scaling. Recent work develops an approach (DiLoCo) that relaxes synchronization demands without compromising model quality. However, these works do not carefully analyze how DiLoCo's behavior changes with model size. In this work, we study the scaling law behavior of DiLoCo when training LLMs under a fixed compute budget. We focus on how algorithmic factors, including number of model replicas, hyperparameters, and token budget affect training in ways that can be accurately predicted via scaling laws. We find that DiLoCo scales both predictably and robustly with model size. When well-tuned, DiLoCo scales better than data-parallel training with model size, and can outperform data-parallel training even at small model sizes. Our results showcase a more general set of benefits of DiLoCo than previously documented, including increased optimal batch sizes, improved downstream generalization with scale, and improved evaluation loss for a fixed token budget.
spotlight
2503.09799
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313
CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding
[ "Yuchen Zhou", "Jiamin Wu", "Zichen Ren", "Zhouheng Yao", "Weiheng Lu", "Kunyu Peng", "Qihao Zheng", "Chunfeng Song", "Wanli Ouyang", "Chao Gou" ]
Understanding and decoding human brain activity from electroencephalography (EEG) signals is a fundamental problem in neuroscience and artificial intelligence, with applications ranging from cognition and emotion recognition to clinical diagnosis and brain–computer interfaces. While recent EEG foundation models have made progress in generalized brain decoding by leveraging unified architectures and large-scale pretraining, they inherit a scale-agnostic dense modeling paradigm from NLP and vision. This design overlooks an intrinsic property of neural activity—cross-scale spatiotemporal structure. Different EEG task patterns span a broad range of temporal and spatial scales, from brief neural activations to slow-varying rhythms, and from localized cortical activations to large-scale distributed interactions. Ignoring this diversity may lead to suboptimal representations and weakened generalization ability. To address these limitations, we propose CSBrain, a Cross-scale Spatiotemporal Brain foundation model for generalized EEG decoding. CSBrain introduces two key components: (i) Cross-scale Spatiotemporal Tokenization (CST), which aggregates multi-scale features within localized temporal windows and anatomical brain regions into compact scale-aware token representations; and (ii) Structured Sparse Attention (SSA), which models cross-window and cross-region dependencies for diverse decoding tasks, further enriching scale diversities while eliminating the spurious dependencies. CST and SSA are alternately stacked to progressively integrate cross-scale spatiotemporal dependencies. Extensive experiments across 11 representative EEG tasks and 16 datasets demonstrate that CSBrain consistently outperforms both task-specific models and strong foundation baselines. These results establish cross-scale modeling as a key inductive bias for generalized EEG decoding and highlight CSBrain as a robust backbone for future brain–AI research. The code and model will be released.
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314
PaceLLM: Brain-Inspired Large Language Models for Long-Context Understanding
[ "Kangcong Li", "Peng Ye", "Chongjun Tu", "Lin Zhang", "Chunfeng Song", "Jiamin Wu", "Tao Yang", "Qihao Zheng", "Tao Chen" ]
While Large Language Models (LLMs) demonstrate strong performance across domains, their long-context capabilities are limited by transient neural activations causing information decay and unstructured feed-forward network (FFN) weights leading to semantic fragmentation. Inspired by the brain’s working memory and cortical modularity, we propose PaceLLM, featuring two innovations: (1) a Persistent Activity (PA) Mechanism that mimics prefrontal cortex (PFC) neurons’ persistent firing by introducing an activation-level memory bank to dynamically retrieve, reuse, and update critical FFN states, addressing contextual decay; and (2) Cortical Expert (CE) Clustering that emulates task-adaptive neural specialization to reorganize FFN weights into semantic modules, establishing cross-token dependencies and mitigating fragmentation. Extensive evaluations show that PaceLLM achieves 6% improvement on LongBench’s Multi-document QA and 12.5–17.5% performance gains on $\infty$-Bench tasks, while extending measurable context length to 200K tokens in Needle-In-A-Haystack (NIAH) tests. This work pioneers brain-inspired LLM optimization and is complementary to other works. Besides, it can be generalized to any model and enhance their long-context performance and interpretability without structural overhauls.
poster
2506.17310
null
null
[]
[]
[]
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315
Offline Goal Conditioned Reinforcement Learning with Temporal Distance Representations
[ "Vivek Myers", "Bill Zheng", "Benjamin Eysenbach", "Sergey Levine" ]
Learned successor features provide a powerful framework for learning goal-reaching policies. These representations are constructed such that similarity in the representation space predicts future outcomes, allowing goal-reaching policies to be extracted. Representations learned for forward inference have some practical limitations - stitching of behaviors does not arise naturally with forward objectives like contrastive classification, and additional regularization is required to enable valid policy extraction. In this work, we propose a new representation learning objective that enables extraction of goal-reaching policies. Our key insight is that rather than learning representations of the future, we should really learn representations that can associate outcomes with preceding states. We show that when combined with existing quasimetric network parameterization and the right invariances, these representations let us learn optimal goal-reaching policies from offline data. On existing offline GCRL benchmarks, the hindsight classification objective improves performance with a simpler algorithm and fewer independent networks/parameters to learn relative to past methods.
poster
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316
HopaDIFF: Holistic-Partial Aware Fourier Conditioned Diffusion for Referring Human Action Segmentation in Multi-Person Scenarios
[ "Kunyu Peng", "Junchao Huang", "Xiangsheng Huang", "Di Wen", "Junwei Zheng", "Yufan Chen", "Kailun Yang", "Jiamin Wu", "Chongqing Hao", "Rainer Stiefelhagen" ]
Action segmentation is a core challenge in high-level video understanding, aiming to partition untrimmed videos into segments and assign each a label from a predefined action set. Existing methods primarily address single-person activities with fixed action sequences, overlooking multi-person scenarios. In this work, we pioneer textual reference-guided human action segmentation in multi-person settings, where a textual description specifies the target person for segmentation. We introduce the first dataset for Referring Human Action Segmentation, i.e., RHAS133, built from 133 movies and annotated with 137 fine-grained actions with 33h video data, together with textual descriptions for this new task. Benchmarking existing action recognition methods on RHAS133 using VLM-based feature extractors reveals limited performance and poor aggregation of visual cues for the target person. To address this, we propose aholistic-partial aware Fourier-conditioned diffusion framework, i.e., HopaDIFF, leveraging a novel cross-input gate attentional xLSTM to enhance holistic-partial long-range reasoning and a novel Fourier condition to introduce more fine-grained control to improve the action segmentation generation. HopaDIFF achieves state-of-the-art results on RHAS133 in diverse evaluation settings. The dataset and code are available in the supplementary.
spotlight
2506.09650
https://github.com/KPeng9510/HopaDIFF
null
[]
[]
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317
Wukong's 72 Transformations: High-fidelity 3D Morphing via Flow Models
[ "Minghao Yin", "Yukang Cao", "Kai Han" ]
We present WUKONG, a novel training-free framework for high-fidelity textured 3D morphing that takes a pair of source and target prompts (text or images) as input. Unlike conventional methods -- which rely on manual correspondence matching and deformation trajectory estimation (limiting generalization and requiring costly preprocessing) -- WUKONG leverages the generative prior of flow-based transformers to produce high-fidelity 3D transitions with rich texture details. To ensure smooth shape transitions, we exploit the inherent continuity of flow-based generative processes and formulate morphing as an optimal transport barycenter problem. We further introduce a sequential initialization strategy to prevent abrupt geometric distortions and preserve identity coherence. For faithful texture preservation, we propose a similarity-guided semantic consistency mechanism that selectively retains high-frequency details and enables precise control over blending dynamics. This avoids common artifacts like oversmoothing while maintaining semantic fidelity. Through extensive quantitative and qualitative evaluations, we demonstrate that WUKONG significantly outperforms state-of-the-art methods, achieving superior results across diverse geometry and texture variations.
poster
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318
Removing Concepts from Text-to-Image Models with Only Negative Samples
[ "Hanwen Liu", "Yadong Mu" ]
This work introduces Clipout, a method for removing a target concept in pre-trained text-to-image models. By randomly clipping units from the learned data embedding and using a contrastive objective, models are encouraged to differentiate these clipped embedding vectors. Our goal is to remove private, copyrighted, inaccurate, or harmful concepts from trained models without the need for retraining. This is achieved by considering only negative samples and generating them in a bootstrapping-like manner, requiring minimal prior knowledge. Additionally, theoretical analyses are provided to further understand our proposed Clipout. Extensive experiments on text-to-image show that Clipout is simple yet highly effective and efficient compared with previous state-of-the-art approaches.
poster
null
null
null
[]
[]
[]
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319
From Bytes to Ideas: Language Modeling with Autoregressive U-Nets
[ "Mathurin VIDEAU", "Badr Youbi Idrissi", "Alessandro Leite", "Marc Schoenauer", "David Lopez-Paz", "Olivier Teytaud" ]
Tokenization imposes a fixed granularity on the input text, freezing how a language model operates on data and how far in the future it predicts. Byte Pair Encoding (BPE) and similar schemes split text once, build a static vocabulary, and leave the model stuck with that choice. We relax this rigidity by introducing an autoregressive U-Net that learns to embed its own tokens as it trains. The network reads raw bytes, pools them into words, then pairs of words, then up to 4 words, giving it a multi-scale view of the sequence. At deeper stages, the model must predict further into the future -- anticipating the next few words rather than the next byte -- so deeper stages focus on broader semantic patterns while earlier stages handle fine details. When carefully tuning and controlling pretraining compute, shallow hierarchies tie strong BPE baselines, and deeper hierarchies have a promising trend. Because tokenization now lives inside the model, the same system can handle character-level tasks and carry knowledge across low-resource languages.
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320
Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery
[ "Jiyeon Kang", "Songseong Kim", "Chanhui Lee", "Doyeong Hwang", "Joanie Chung", "Yunkyung Ko", "Sumin Lee", "Sungwoong Kim", "Sungbin Lim" ]
Ordering-based approaches to causal discovery identify topological orders of causal graphs, providing scalable alternatives to combinatorial search methods. Under the Additive Noise Models (ANMs) assumption, recent causal ordering methods based on score matching require an accurate estimation of the Hessian diagonal of the log-densities. However, previous approaches mainly use Stein gradient estimators, which are computationally expensive and memory-intensive. Although DiffAN addresses these limitations by substituting kernel-based estimates with diffusion models, it remains numerically unstable due to the second-order derivatives of score models. To alleviate these problems, we propose Score-informed Neural Operator (SciNO), a probabilistic generative model in smooth function spaces designed to stably approximate the Hessian diagonal and to preserve structural information during the score modeling. Empirical results show that SciNO reduces order divergence by 42.7% on synthetic graphs and by 31.5% in real-world datasets on average compared to DiffAN, while maintaining memory efficiency and scalability. Furthermore, we propose a probabilistic control algorithm for causal reasoning with autoregressive models that integrates SciNO's probability estimates with autoregressive model priors, enabling reliable data-driven causal ordering informed by semantic information. Consequently, the proposed method enhances causal reasoning abilities of LLMs without additional fine-tuning or prompt engineering.
poster
2508.12650
null
null
[]
[]
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321
Siegel Neural Networks
[ "Xuan Son Nguyen", "Aymeric Histace", "Nistor Grozavu" ]
Riemannian symmetric spaces (RSS) such as hyperbolic spaces and symmetric positive definite (SPD) manifolds have become popular spaces for representation learning. In this paper, we propose a novel approach for building discriminative neural networks on Siegel spaces, a family of RSS that is largely unexplored in machine learning tasks. For classification applications, one focus of recent works is the construction of multiclass logistic regression (MLR) and fully-connected (FC) layers for hyperbolic and SPD neural networks. Here we show how to build such layers for Siegel neural networks. Our approach relies on the quotient structure of those spaces and the notation of vector-valued distance onRSS. We demonstrate the relevance of our approach on two applications, i.e., radar signal classification and node classification. Our results successfully demonstrate state-of-the-art performance across all datasets.
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322
Neural Hamiltonian Diffusions for Modeling Structured Geometric Dynamics
[ "Sungwoo Park" ]
We introduce Neural Hamiltonian Diffusion, a unified framework for learning stochastic Hamiltonian dynamics on differentiable manifolds. While Hamiltonian Neural Networks (HNNs) model conservative systems in flat Euclidean space, they fail to account for geometric structure and intrinsic stochasticity. Conversely, diffusion models on Riemannian manifolds offer geometry-aware stochastic modeling but lack physical inductive biases. Our method parameterizes a Hamiltonian with a neural network and defines its dynamics as a stochastic differential equation on a (pseudo-)Riemannian manifold equipped with a Poisson structure. This enables physically consistent modeling of dynamics on curved, periodic, or causally structured spaces. We demonstrate that the proposed geometric dynamics generalizes existing approaches and applies to systems ranging from molecular dynamics to relativistic n-body problems.
poster
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323
Mechanism Design for LLM Fine-tuning with Multiple Reward Models
[ "Haoran Sun", "Yurong Chen", "Siwei Wang", "Chu Xu", "Wei Chen", "Xiaotie Deng" ]
Fine-tuning large language models (LLMs) to aggregate multiple preferences has attracted considerable research attention. With aggregation algorithms advancing, a potential economic scenario arises where fine-tuning services are provided to agents with different preferences. In this context, agents may benefit from strategically misreporting their preferences, but this could harm the aggregation performance. This paper addresses such incentive issues by framing it as a mechanism design problem: an LLM provider determines the fine-tuning objective (training rule) and the pricing scheme (payment rule) for agents. We primarily focus on training rules that maximize social welfare subject to certain regularizations, referred to as SW-Max rules. First, we show that under most circumstances, truthful reporting is sub-optimal with simply a SW-Max rule, thereby highlighting the necessity of payments. Second, we extend the VCG payment to implement SW-Max rules in dominant-strategy incentive compatibility (DSIC). We characterize sufficient conditions for payment equivalence and derive the necessary conditions for a payment rule to implement a SW-Max rule in DSIC and other principles. Third, we demonstrate that our mechanism is approximately DSIC with perturbed input, showcasing its robustness against the inevitable errors in real-world applications. Experiments on real LLM training results further confirm the practical implications of our results.
poster
2405.16276
null
null
[]
[]
[]
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324
On Efficiency-Effectiveness Trade-off of Diffusion-based Recommenders
[ "Wenyu Mao", "Jiancan Wu", "Guoqing Hu", "Zhengyi Yang", "Wei Ji", "Xiang Wang" ]
Diffusion models have emerged as a powerful paradigm for generative sequential recommendation, which typically generate next items to recommend guided by user interaction histories with a multi-step denoising process. However, the multi-step process relies on discrete approximations, introducing discretization error that creates a trade-off between computational efficiency and recommendation effectiveness.To address this trade-off, we propose TA-Rec, a two-stage framework that achieves one-step generation by smoothing the denoising function during pretraining while alleviating trajectory deviation by aligning with user preferences during fine-tuning. Specifically, to improve the efficiency without sacrificing the recommendation performance, TA-Rec pretrains the denoising model with Temporal Consistency Regularization (TCR), enforcing the consistency between the denoising results across adjacent steps. Thus, we can smooth the denoising function to map the noise as oracle items in one step with bounded error. To further enhance effectiveness, TA-Rec introduces Adaptive Preference Alignment (APA) that aligns the denoising process with user preference adaptively based on preference pair similarity and timesteps. Extensive experiments prove that TA-Rec’s two-stage objective effectively mitigates the discretization errors-induced trade-off, enhancing both efficiency and effectiveness of diffusion-based recommenders.
poster
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325
XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation
[ "Bowen Chen", "Brynn zhao", "Haomiao Sun", "Li Chen", "Xu Wang", "Daniel Du", "Xinglong Wu" ]
Achieving fine-grained control over subject identity and semantic attributes (pose, style, lighting) in text-to-image generation, particularly for multiple subjects, often undermines the editability and coherence of Diffusion Transformers (DiTs). Many approaches introduce artifacts or suffer from attribute entanglement. To overcome these challenges, we propose a novel multi-subject controlled generation model XVerse. By transforming reference images into offsets for token-specific text-stream modulation, XVerse allows for precise and independent control for specific subject without disrupting image latents or features. Consequently, XVerse offers high-fidelity, editable multi-subject image synthesis with robust control over individual subject characteristics and semantic attributes. This advancement significantly improves personalized and complex scene generation capabilities.
poster
2506.21416
https://github.com/bytedance/XVerse
https://bytedance.github.io/XVerse/
[ "ByteDance/XVerse" ]
[ "ByteDance/XVerse" ]
[]
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326
Temporal Representation Alignment: Successor Features Enable Emergent Compositionality in Robot Instruction Following
[ "Vivek Myers", "Bill Zheng", "Anca Dragan", "Kuan Fang", "Sergey Levine" ]
Effective task representations should facilitate compositionality, such that after learning a variety of basic tasks, an agent can perform compound tasks consisting of multiple steps simply by composing the representations of the constituent steps together. While this is conceptually simple and appealing, it is not clear how to automatically learn representations that enable this sort of compositionality. We show that learning to associate the representations of current and future states with a temporal alignment loss can improve compositional generalization, even in the absence of any explicit subtask planning or reinforcement learning. We evaluate our approach across diverse robotic manipulation tasks as well as in simulation, showing substantial improvements for tasks specified with either language or goal images.
poster
2502.05454
null
null
[]
[]
[]
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327
Anatomically inspired digital twins capture hierarchical object representations in visual cortex
[ "Emanuele Luconi", "Dario Liscai", "Carlo Baldassi", "Alessandro Marin Vargas", "Alessandro Sanzeni" ]
Invariant object recognition-the ability to identify objects despite changes in appearance-is a hallmark of visual processing in the brain, yet its understanding remains a central challenge in systems neuroscience. Artificial neural networks trained to predict neural responses to visual stimuli (“digital twins”) could provide a powerful framework for studying such complex computations in silico. However, while current models accurately capture single-neuron responses within individual visual areas, their ability to reproduce how populations of neurons represent object identity, and how these representations transform across the cortical hierarchy, remains largely unexplored. Here we examine key functional signatures observed experimentally and find that current models account for hierarchical changes in basic single-neuron properties, such as receptive field size, but fail to capture more complex population-level phenomena, particularly invariant object representations. To address this gap, we introduce a biologically inspired hierarchical readout scheme that mirrors cortical anatomy, modeling each visual area as a projection from a distinct depth within a shared core network. This approach significantly improves the prediction of population-level representational transformations, outperforming standard models that use only the final layer, as well as alternatives with modified architecture, regularization, and loss function. Our results suggest that incorporating anatomical information provides a strong inductive bias in digital twin models, enabling them to better capture general principles of brain function.
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328
Binary Quadratic Quantization: Beyond First-Order Quantization for Real-Valued Matrix Compression
[ "Kyo Kuroki", "Yasuyuki Okoshi", "Thiem Van Chu", "Masato Motomura", "Kazushi Kawamura" ]
This paper proposes a novel matrix quantization method, Binary Quadratic Quantization (BQQ). In contrast to conventional first-order quantization approaches—such as uniform quantization and binary coding quantization—that approximate real-valued matrices via linear combinations of binary bases, BQQ leverages the expressive power of binary quadratic expressions while maintaining an extremely compact data format.We validate our approach with two experiments: a matrix compression benchmark and post-training quantization (PTQ) on pretrained Vision Transformer-based models.Experimental results demonstrate that BQQ consistently achieves a superior trade-off between memory efficiency and reconstruction error than conventional methods for compressing diverse matrix data. It also delivers strong PTQ performance, even though we neither target state-of-the-art PTQ accuracy under tight memory constraints nor rely on PTQ-specific binary matrix optimization.For example, our proposed method outperforms the state-of-the-art PTQ method by up to 2.0\% and 59.1\% on the ImageNet dataset under the calibration-based and data-free scenarios, respectively, with quantization equivalent to 2 bits.These findings highlight the surprising effectiveness of binary quadratic expressions for efficient matrix approximation and neural network compression.
poster
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329
Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling
[ "Hao Chen", "Guanxi Lu", "Yasuyuki Okoshi", "Zhiwen Mo", "Masato Motomura", "Hongxiang Fan" ]
Test-time scaling (TTS) has proven effective in enhancing the reasoning capabilities of large language models (LLMs). Verification plays a key role in TTS, simultaneously influencing (1) reasoning performance and (2) compute efficiency, due to the quality and computational cost of verification. In this work, we challenge the conventional paradigms of verification, and make the first attempt toward systematically investigating the impact of verification granularity—that is, how frequently the verifier is invoked during generation, beyond verifying only the final output or individual generation steps.To this end, we introduce Variable Granularity Search (VG-Search), a unified algorithm that generalizes beam search and Best-of-N sampling via a tunable granularity parameter $g$. Extensive experiments with VG-Search under varying compute budgets, generator-verifier configurations, and task attributes reveal that dynamically selecting $g$ can improve the compute efficiency and scaling behavior. Building on these findings, we propose adaptive VG-Search strategies that achieve accuracy gains of up to 3.1\% over Beam Search and 3.6\% over Best-of-N, while reducing FLOPs by over 52\%. We will open-source the code to support future research.
poster
2505.11730
null
null
[]
[]
[]
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330
Beyond Scalars: Concept-Based Alignment Analysis in Vision Transformers
[ "Johanna Vielhaben", "Dilyara Bareeva", "Jim Berend", "Wojciech Samek", "Nils Strodthoff" ]
Measuring the alignment between representations lets us understand similarities between the feature spaces of different models, such as Vision Transformers trained under diverse paradigms. However, traditional measures for representational alignment yield only scalar values that obscure how these spaces agree in terms of learned features. To address this, we combine alignment analysis with concept discovery, allowing a fine-grained breakdown of alignment into individual concepts. This approach reveals both universal concepts across models and each representation’s internal concept structure. We introduce a new definition of concepts as non-linear manifolds, hypothesizing they better capture the geometry of the feature space. A sanity check demonstrates the advantage of this manifold-based definition over linear baselines for concept-based alignment. Finally, our alignment analysis of four different ViTs shows that increased supervision tends to reduce semantic organization in learned representations.
poster
2412.06639
null
null
[]
[]
[]
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331
ALTo: Adaptive-Length Tokenizer for Autoregressive Mask Generation
[ "Lingfeng Wang", "Hualing Lin", "Senda Chen", "Tao Wang", "Changxu Cheng", "Yangyang Zhong", "Dong Zheng", "Wuyue Zhao" ]
While humans effortlessly draw visual objects and shapes by adaptively allocating attention based on their complexity, existing multimodal large language models (MLLMs) remain constrained by rigid token representations. Bridging this gap, we propose ALTo, an adaptive length tokenizer for autoregressive mask generation. To achieve this, a novel token length predictor is designed, along with a length regularization term and a differentiable token chunking strategy. We further build ALToLLM that seamlessly integrates ALTo into MLLM. Preferences on the trade-offs between mask quality and efficiency is implemented by group relative policy optimization (GRPO). Experiments demonstrate that ALToLLM achieves state-of-the-art performance with adaptive token cost on popular segmentation benchmarks. Code and models will be released.
poster
2505.16495
https://github.com/yayafengzi/ALToLLM
null
[]
[ "yayafengzi/ALToLLM-8B" ]
[ "yayafengzi/Multi-Class-OV" ]
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332
NestedFP: High-Performance, Memory-Efficient Dual-Precision Floating Point Support for LLMs
[ "Haeun Lee", "Omin Kwon", "Yeonhong Park", "Jae Lee" ]
Large Language Models (LLMs) are increasingly deployed in latency-critical, high-throughput services such as virtual assistants, code generation, and multi-modal applications. To satisfy stringent service-level objectives (SLOs), techniques such as continuous batching, paged attention, and hybrid scheduling of prefill and decode phases have been developed. While weight and activation quantization has been widely explored to accelerate inference, methods for adapting precision dynamically and efficiently at runtime remain largely underexplored. Recent hardware support for FP8 arithmetic—offering up to 2× the throughput of FP16—makes it an attractive option for interactive LLM serving. However, naively co-deploying separate FP8 and FP16 models increases storage overhead and may sacrifice accuracy or performance.We present Dual-FP LLM, a precision-adaptive serving technique that enables both FP8 and FP16 inference from a single 16-bit model representation. By decomposing each FP16 weight into two 8-bit components, our method enables efficient FP8 execution while preserving full FP16 accuracy—without incurring additional memory cost. We implement a custom CUTLASS-based GEMM kernel that reconstructs FP16 operands on-the-fly and integrated it into the vLLM serving framework. Dual-FP LLM delivers up to 1.55x throughput improvement in FP8 mode, with negligible accuracy degradation, and introduces only up to 4.51% average overhead for FP16 inference. This enables dynamic, SLO-aware precision selection and offers a flexible foundation for scalable LLM serving under bursty, heterogeneous workloads.
poster
2506.02024
null
null
[]
[]
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333
Unified Scaling Laws for Learning over Compressed Representations
[ "Andrei Panferov", "Alexandra Volkova", "Ionut-Vlad Modoranu", "Vage Egiazarian", "Mher Safaryan", "Dan Alistarh" ]
Scaling laws have shaped recent advances in machine learning by enabling predictable scaling of model performance based on model size, computation, and data volume. Concurrently, the rise in computational cost for AI has motivated model compression techniques, notably quantization and sparsification, which have emerged to mitigate the steep computational demands associated with large-scale training and inference. This paper investigates the interplay between scaling laws and compression strategies, exploring whether a unified scaling framework can accurately predict model performance when training occurs over various compressed representations, such as sparse, scalar-quantized, sparse-quantized or even vector-quantized formats. Our key contributions include proposing and validating a general scaling law formulation applicable both individually but also composably across compression types. We demonstrate both theoretically and empirically that a simple metric based on Gaussian mean squared error fitting can robustly predict parameter efficiency across compressed models. Additionally, we extend our formulation to directly compare the accuracy potential of different compressed formats, and to derive better algorithms for training over sparse-quantized formats. Finally, we identify conditions under which these unified scaling laws fail.
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334
Revisiting Logit Distributions for Reliable Out-of-Distribution Detection
[ "Jiachen Liang", "RuiBing Hou", "Minyang Hu", "Hong Chang", "Shiguang Shan", "Xilin Chen" ]
Out-of-distribution (OOD) detection is critical for ensuring the reliability of deep learning models in open-world applications. While post-hoc methods are favored for their efficiency and ease of deployment, existing approaches often underexploit the rich information embedded in the model’s logits space. In this paper, we propose LogitGap, a novel post-hoc OOD detection method that explicitly exploits the relationship between the maximum logit and the remaining logits to enhance the separability between in-distribution (ID) and OOD samples. To further improve its effectiveness, we refine LogitGap by focusing on a more compact and informative subset of the logit space. Specifically, we introduce a training-free strategy that automatically identifies the most informative logits for scoring. We provide both theoretical analysis and empirical evidence to validate the effectiveness of our approach. Extensive experiments on both vision-language and vision-only models demonstrate that LogitGap consistently achieves state-of-the-art performance across diverse OOD detection scenarios and benchmarks.
poster
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335
Zero-Shot Context Generalization in Reinforcement Learning from Few Training Contexts
[ "James Chapman", "Kedar Karhadkar", "Guido Montufar" ]
Deep reinforcement learning (DRL) has achieved remarkable success across multiple domains, including competitive games, natural language processing, and robotics. Despite these advancements, policies trained via DRL often struggle to generalize to evaluation environments with different parameters. This challenge is typically addressed by training with multiple contexts and/or by leveraging additional structure in the problem. However, obtaining sufficient training data across diverse contexts can be impractical in real-world applications. In this work, we consider contextual Markov decision processes (CMDPs) with transition and reward functions that exhibit regularity in context parameters. We introduce the context-enhanced Bellman equation (CEBE) to improve generalization when training on a single context. We prove both analytically and empirically that the CEBE yields a first-order approximation to the Q function trained across multiple contexts. We then derive context sample enhancement (CSE) as an efficient data augmentation method for approximating the CEBE in deterministic control environments. We numerically validate the performance of CSE in simulation environments, showcasing its potential to improve generalization in DRL.
poster
2507.07348
null
null
[]
[]
[]
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336
Hogwild! Inference: Parallel LLM Generation via Concurrent Attention
[ "Gleb Rodionov", "Roman Garipov", "Alina Shutova", "George Yakushev", "Erik Schultheis", "Vage Egiazarian", "Anton Sinitsin", "Denis Kuznedelev", "Dan Alistarh" ]
Large Language Models (LLMs) have demonstrated the ability to tackle increasingly complex tasks through advanced reasoning, long-form content generation, and tool use. Solving these tasks often involves long inference-time computations. In human problem solving, a common strategy to expedite work is collaboration: by dividing the problem into sub-tasks, exploring different strategies concurrently, etc. Recent research has shown that LLMs can also operate in parallel by implementing explicit cooperation frameworks, such as voting mechanisms or the explicit creation of independent sub-tasks that can be executed in parallel. However, each of these frameworks may not be suitable for all types of tasks, which can hinder their applicability. In this work, we propose a different design approach: we run LLM "workers" in parallel , allowing them to synchronize via a concurrently-updated attention cache and prompt these workers to decide how best to collaborate. Our approach allows the instances to come up with their own collaboration strategy for the problem at hand, all the while "seeing" each other's partial progress in the concurrent cache. We implement this approach via Hogwild! Inference: a parallel LLM inference engine where multiple instances of the same LLM run in parallel with the same attention cache, with "instant" access to each other's generated tokens. Hogwild! inference takes advantage of Rotary Position Embeddings (RoPE) to avoid recomputation while improving parallel hardware utilization. We find that modern reasoning-capable LLMs can perform inference with shared Key-Value cache out of the box, without additional fine-tuning.
spotlight
2504.06261
https://github.com/eqimp/hogwild_llm
https://eqimp.github.io/hogwild_llm/
[]
[]
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337
Unlocking Multimodal Mathematical Reasoning via Process Reward Model
[ "Chufan Shi", "Zhuofan Zheng", "Yifan Wang", "Xinzhe Ni", "Zicheng Lin", "Songtao Jiang", "Yiyao Yu", "Chufan Shi", "Ruihang Chu", "Lei Wang", "Jin zeng", "Yujiu Yang" ]
Process Reward Models (PRMs) have shown promise in enhancing the mathematical reasoning capabilities of Large Language Models (LLMs) through Test-Time Scaling (TTS). However, their integration into multimodal reasoning remains largely unexplored. In this work, we take the first step toward unlocking the potential of PRMs in multimodal mathematical reasoning. We identify three key challenges: (i) the scarcity of high-quality reasoning data constrains the capabilities of foundation Multimodal Large Language Models (MLLMs), which imposes further limitations on the upper bounds of TTS and reinforcement learning (RL); (ii) a lack of automated methods for process labeling within multimodal contexts persists; (iii) the employment of process rewards in unimodal RL faces issues like reward hacking, which may extend to multimodal scenarios. To address these issues, we introduce URSA, a three-stage Unfolding multimodal pRocess-Supervision Aided training framework. We first construct MMathCoT-1M, a high-quality large-scale multimodal Chain-of-Thought (CoT) reasoning dataset, to build a stronger math reasoning foundation MLLM, URSA-8B. Subsequently, we go through an automatic process to synthesize process supervision data, which emphasizes both logical correctness and perceptual consistency. We introduce DualMath-1.1M to facilitate the training of URSA-8B-RM. Finally, we propose Process-Supervised Group-Relative-Policy-Optimization (PS-GRPO), pioneering a multimodal PRM-aided online RL method that outperforms vanilla GRPO. With PS-GRPO application, URSA-8B-PS-GRPO outperforms Gemma3-12B and GPT-4o by 8.4% and 2.7% on average across 6 benchmarks.
poster
2501.04686
https://github.com/URSA-MATH
https://ursa-math.github.io/
[]
[ "URSA-MATH/URSA-8B-PS-GRPO", "URSA-MATH/URSA-8B", "URSA-MATH/URSA-RM-8B" ]
[ "URSA-MATH/DualMath-1.1M", "URSA-MATH/MMathCoT-1M", "URSA-MATH/URSA_Alignment_860K" ]
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338
Adaptive 3D Reconstruction via Diffusion Priors and \\Forward Curvature-Matching Likelihood Updates
[ "Seunghyeok Shin", "Dabin Kim", "Hongki Lim" ]
Reconstructing high-quality point clouds from images remains challenging in computer vision. Existing generative model, particularly diffusion model, based approaches that directly learn the posterior may suffer from inflexibility—they require conditioning signals during training, support only fixed numbers of input views, and need complete retraining for different measurements. Recent diffusion-based methods have attempted to address this by combining prior models with likelihood updates, but they rely on heuristic fixed step sizes for likelihood update that lead to slow convergence and suboptimal reconstruction quality. We advance this line of approach by integrating our novel Forward Curvature-Matching(FCM) update method with diffusion sampling. Our method dynamically determines optimal step sizes using only forward automatic differentiation and finite-difference curvature estimates, enabling precise optimization of the likelihood update. This formulation enables high-fidelity reconstruction from both single and multi-view inputs, and supports various input modalities through simple operator substitution—all without retraining. Experiments on ShapeNet and CO3D datasets demonstrate that our method achieves superior reconstruction quality with 10.2\% and 12.8\% improvement in F-score, respectively, validating its efficiency and adaptability for practical applications.
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339
A Theoretical Framework for Grokking: Interpolation followed by Riemannian Norm Minimisation
[ "Etienne Boursier", "Scott Pesme", "Radu-Alexandru Dragomir" ]
We study the dynamics of gradient flow with small weight decay on general training losses $F: \mathbb{R}^d \to \mathbb{R}$. Under mild regularity assumptions and assuming convergence of the unregularised gradient flow, we show that the trajectory with weight decay $\lambda$ exhibits a two-phase behaviour as $\lambda \to 0$. During the initial fast phase, the trajectory follows the unregularised gradient flow and converges to a manifold of critical points of $F$. Then, at time of order $1/\lambda$, the trajectory enters a slow drift phase and follows a Riemannian gradient flow minimising the $\ell_2$-norm of the parameters. This purely optimisation-based phenomenon offers a natural explanation for the \textit{grokking} effect observed in deep learning, where the training loss rapidly reaches zero while the test loss plateaus for an extended period before suddenly improving. We argue that this generalisation jump can be attributed to the slow norm reduction induced by weight decay, as explained by our analysis. We validate this mechanism empirically on several synthetic regression tasks.
poster
2505.20172
null
null
[]
[]
[]
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340
Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models
[ "Wancong Zhang", "Uladzislau Sobal", "Kyunghyun Cho", "Randall Balestriero", "Tim G. J. Rudner", "Yann LeCun" ]
A long-standing goal in AI is to develop agents capable of solving diverse tasks across a range of environments, including those never seen during training. Two dominant paradigms address this challenge: (i) reinforcement learning (RL), which learns policies via trial and error, and (ii) optimal control, which plans actions using a known or learned dynamics model. However, their comparative strengths in the offline setting—where agents must learn from reward-free trajectories—remain underexplored. In this work, we systematically evaluate RL and control-based methods on a suite of navigation tasks, using offline datasets of varying quality. On the RL side, we consider goal-conditioned and zero-shot approaches. On the control side, we train a latent dynamics model using the Joint Embedding Predictive Architecture (JEPA) and employ it for planning. We investigate how factors such as data diversity, trajectory quality, and environment variability influence the performance of these approaches. Our findings reveal that model-free RL excels when abundant, high-quality data is available, while model-based planning demonstrates superior generalization to novel layouts, better trajectory stitching, and greater data efficiency. Notably, planning with a latent dynamics model proves to be a strong approach for handling suboptimal offline data and adapting to diverse environment configurations.
poster
2502.14819
null
null
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[]
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341
Certifying Concavity and Monotonicity in Games via Sum-of-Squares Hierarchies
[ "Vincent Leon", "Iosif Sakos", "Ryann Sim", "Antonios Varvitsiotis" ]
Concavity and its refinements underpin tractability in multiplayer games, where players independently choose actions to maximize their own payoffs which depend on other players’ actions. In *concave* games, where players' strategy sets are compact and convex, and their payoffs are concave in their own actions, strong guarantees follow: Nash equilibria always exist and decentralized algorithms converge to equilibria. If the game is furthermore *monotone*, an even stronger guarantee holds: Nash equilibria are unique under strictness assumptions. Unfortunately, we show that *certifying* concavity or monotonicity is NP-hard, already for games where utilities are multivariate polynomials and compact, convex basic semialgebraic strategy sets -- an expressive class that captures extensive-form games with imperfect recall. On the positive side, we develop two hierarchies of sum-of-squares programs that certify concavity and monotonicity of a given game, and each level of the hierarchies can be solved in polynomial time. We show that almost all concave/monotone games are certified at some finite level of the hierarchies. Subsequently, we introduce the classes of SOS-concave/monotone games, which globally approximate concave/monotone games, and show that for any given game we can compute the closest SOS-concave/monotone game in polynomial time. Finally, we apply our techniques to canonical examples of extensive-form games with imperfect recall.
poster
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[]
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342
SAO-Instruct: Free-form Audio Editing using Natural Language Instructions
[ "Michael Ungersböck", "Florian Grötschla", "Luca Lanzendörfer", "June Young Yi", "Changho Choi", "Roger Wattenhofer" ]
Generative models have made significant progress in synthesizing high-fidelity audio from short textual descriptions. However, editing existing audio using natural language has remained largely underexplored. Current approaches either require the complete description of the edited audio or are constrained to predefined edit instructions that lack flexibility. In this work, we introduce SAO-Instruct, a model based on Stable Audio Open capable of editing audio clips using any free-form natural language instruction. To train our model, we create a dataset of audio editing triplets (input audio, edit instruction, output audio) using Prompt-to-Prompt, DDPM inversion, and a manual editing pipeline. Although partially trained on synthetic data, our model generalizes well to real in-the-wild audio clips and unseen edit instructions. We demonstrate that SAO-Instruct achieves competitive performance on objective metrics and outperforms other audio editing approaches in a subjective listening study. To encourage future research, we release our code and model weights.
poster
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343
AI-Generated Video Detection via Perceptual Straightening
[ "Christian Internò", "Robert Geirhos", "Markus Olhofer", "Sunny Liu", "Barbara Hammer", "David Klindt" ]
The rapid advancement of generative AI enables highly realistic synthetic video, posing significant challenges for content authentication and raising urgent concerns about misuse. Existing detection methods often struggle with generalization and capturing subtle temporal inconsistencies. We propose $ReStraV$ ($Re$presentation $Stra$ightening for $V$ideo), a novel approach to distinguish natural from AI-generated videos. Inspired by the ``perceptual straightening'' hypothesis—which suggests real-world video trajectories become more straight in neural representation domain—we analyze deviations from this expected geometric property. Using a pre-trained self-supervised vision transformer (DINOv2), we quantify the temporal curvature and stepwise distance in the model's representation domain. We aggregate statistical and signals descriptors of these measures for each video and train a classifier. Our analysis shows that AI-generated videos exhibit significantly different curvature and distance patterns compared to real videos. A lightweight classifier achieves state-of-the-art detection performance (e.g., $97.17$ % accuracy and $98.63$ % AUROC on the VidProM benchmark, substantially outperforming existing image- and video-based methods. ReStraV is computationally efficient, it is offering a low-cost and effective detection solution. This work provides new insights into using neural representation geometry for AI-generated video detection.
poster
2507.00583
null
null
[]
[]
[]
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344
Training the Untrainable: Introducing Inductive Bias via Representational Alignment
[ "Vighnesh Subramaniam", "David Mayo", "Colin Conwell", "Tomaso Poggio", "Boris Katz", "Brian Cheung", "Andrei Barbu" ]
We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another architecture. We call a network untrainable when they overfit, underfit, or converge to poor results even when tuning their hyperparameters. For example, fully connected networks overfit on object recognition while deep convolutional networks without residual connections underfit. The traditional answer is to change the architecture to impose some inductive bias, although the nature of that bias is unknown. We introduce guidance, where a guide network steers a target network using a neural distance function. The target minimizes its task loss plus a layerwise representational similarity against the frozen guide. If the guide is trained, this transfers over the architectural prior and knowledge of the guide to the target. If the guide is untrained, this transfers over only part of the architectural prior of the guide. We show that guidance prevents FCN overfitting on ImageNet, narrows the vanilla RNN–Transformer gap, boosts plain CNNs toward ResNet accuracy, and aids Transformers on RNN-favored tasks. We further identify that guidance-driven initialization alone can mitigate FCN overfitting. Our method provides a mathematical tool to investigate priors and architectures, and in the long term, could automate architecture design.
poster
2410.20035
null
https://untrainable-networks.github.io/
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345
On the creation of narrow AI: hierarchy and nonlocality of neural network skills
[ "Eric Michaud", "Asher Parker-Sartori", "Max Tegmark" ]
We study the problem of creating strong, yet narrow, AI systems. While recent AI progress has been driven by the training of large general-purpose foundation models, the creation of smaller models specialized for narrow domains could be valuable for both efficiency and safety. In this work, we explore two challenges involved in creating such systems, having to do with basic properties of how neural networks learn and structure their representations. The first challenge regards when it is possible to train narrow models from scratch. Through experiments on a synthetic task, we find that it is sometimes necessary to train networks on a wide distribution of data to learn certain narrow skills within that distribution. This effect arises when skills depend on each other hierarchically, and training on a broad distribution introduces a curriculum which substantially accelerates learning. The second challenge regards how to transfer particular skills from large general models into small specialized models. We find that model skills are often not perfectly localized to a particular set of prunable components. However, we find that methods based on pruning can still outperform distillation. We investigate the use of a regularization objective to align desired skills with prunable components while unlearning unnecessary skills.
poster
2505.15811
null
null
[]
[]
[]
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346
Bayesian Concept Bottleneck Models with LLM Priors
[ "Jean Feng", "Avni Kothari", "Lucas Zier", "Chandan Singh", "Yan Shuo Tan" ]
Concept Bottleneck Models (CBMs) have been proposed as a compromise between white-box and black-box models, aiming to achieve interpretability without sacrificing accuracy.The standard training procedure for CBMs is to predefine a candidate set of human-interpretable concepts, extract their values from the training data, and identify a sparse subset as inputs to a transparent prediction model.However, such approaches are often hampered by the tradeoff between exploring a sufficiently large set of concepts versus controlling the cost of obtaining concept extractions, resulting in a large interpretability-accuracy tradeoff.This work investigates a novel approach that sidesteps these challenges: BC-LLM iteratively searches over a potentially infinite set of concepts within a Bayesian framework, in which Large Language Models (LLMs) serve as both a concept extraction mechanism and prior.Even though LLMs can be miscalibrated and hallucinate, we prove that BC-LLM can provide rigorous statistical inference and uncertainty quantification.Across image, text, and tabular datasets, BC-LLM outperforms interpretable baselines and even black-box models in certain settings, converges more rapidly towards relevant concepts, and is more robust to out-of-distribution samples.
poster
2410.15555
null
null
[]
[]
[]
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347
HQA-VLAttack: Towards High Quality Adversarial Attack on Vision-Language Pre-Trained Models
[ "Han Liu", "Jiaqi Li", "Zhi Xu", "Xiaotong Zhang", "Xiaoming Xu", "Fenglong Ma", "Yuanman Li", "Hong Yu" ]
Black-box adversarial attack on vision-language pre-training models is a practical and challenging task, as text and image perturbations need to be considered simultaneously, and only the predicted results are accessible. Research on this problem is in its infancy and only a handful of methods are available. Nevertheless, existing methods either rely on complex iterative cross-search strategy which inevitably consume numerous queries, or only consider to reduce the similarity of positive image-text pairs but ignore that of negative ones will also be implicitly diminished, thus inevitably affecting the attack performance. To alleviate the above issues, we propose a simple yet effective framework to generate high quality adversarial examples on vision-language pre-trained models, named HQA-VLAttack, which consists of text and image attack stages. For text perturbation generation, it leverages the counter-fitting word vector to generate the substitute word set, thus guaranteeing the semantic consistency between the substitute word and the original word. For image perturbation generation, it first initializes the image adversarial example via the layer-Importance guided strategy, and then utilizes contrastive learning to optimize the image adversarial perturbation, which ensures that the similarity of positive image-text pairs is decreased while that of negative image-text pairs is increased. In this way, the optimized adversarial images and texts are more likely to retrieve negative examples, thereby enhancing the attack success rate. Experimental results on three benchmark datasets demonstrate that HQA-VLAttack outperforms other strong baselines in attack success rate significantly.
poster
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348
HoliGS: Holistic Gaussian Splatting for Embodied View Synthesis
[ "Xiaoyuan Wang", "Yizhou Zhao", "Botao Ye", "Shan Xiaojun", "Weijie Lyu", "Lu Qi", "Kelvin Chan", "Yinxiao Li", "Ming-Hsuan Yang" ]
We propose HoliGS, a novel deformable Gaussian splatting framework that addresses embodied view synthesis from long monocular RGB videos. Unlike prior 4D Gaussian splatting and dynamic NeRF pipelines, which struggle with training overhead in minute-long captures, our method leverages invertible Gaussian Splatting deformation networks to reconstruct large-scale, dynamic environments accurately. Specifically, we decompose each scene into a static background plus time-varying objects, each represented by learned Gaussian primitives undergoing global rigid transformations, skeleton-driven articulation, and subtle non-rigid deformations via an invertible neural flow. This hierarchical warping strategy enables robust free-viewpoint novel-view rendering from various embodied camera trajectories by attaching Gaussians to a complete canonical foreground shape (\eg, egocentric or third-person follow), which may involve substantial viewpoint changes and interactions between multiple actors. Our experiments demonstrate that \ourmethod~ achieves superior reconstruction quality on challenging datasets while significantly reducing both training and rendering time compared to state-of-the-art monocular deformable NeRFs. These results highlight a practical and scalable solution for EVS in real-world scenarios. The source code will be released.
poster
2506.19291
null
null
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[]
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349
SSR: Enhancing Depth Perception in Vision-Language Models via Rationale-Guided Spatial Reasoning
[ "Yang Liu", "Ming Ma", "Xiaomin Yu", "Pengxiang Ding", "Han Zhao", "Mingyang Sun", "Siteng Huang", "Donglin Wang" ]
Despite impressive advancements in Visual-Language Models (VLMs) for multi-modal tasks, their reliance on RGB inputs limits precise spatial understanding. Existing methods for integrating spatial cues, such as point clouds or depth, either require specialized sensors or fail to effectively exploit depth information for higher-order reasoning. To this end, we propose a novel Spatial Sense and Reasoning method, dubbed SSR, a novel framework that transforms raw depth data into structured, interpretable textual rationales. These textual rationales serve as meaningful intermediate representations to significantly enhance spatial reasoning capabilities. Additionally, we leverage knowledge distillation to compress the generated rationales into compact latent embeddings, which facilitate resource-efficient and plug-and-play integration into existing VLMs without retraining. To enable comprehensive evaluation, we introduce a new dataset named SSR-CoT, a million-scale visual-language reasoning dataset enriched with intermediate spatial reasoning annotations, and present SSRBench, a comprehensive multi-task benchmark. Extensive experiments on multiple benchmarks demonstrate SSR substantially improves depth utilization and enhances spatial reasoning, thereby advancing VLMs toward more human-like multi-modal understanding.
poster
2505.12448
https://github.com/yliu-cs/SSR
https://yliu-cs.github.io/SSR/
[]
[ "yliu-cs/SSR-MIDI-7B", "yliu-cs/SSR-VLM-7B" ]
[ "yliu-cs/SSR-CoT", "yliu-cs/SSRBench" ]
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350
EvolvedGRPO: Unlocking Reasoning in LVLMs via Progressive Instruction Evolution
[ "Zhebei Shen", "Qifan Yu", "Juncheng Li", "Wei Ji", "Qizhi Chen", "Siliang Tang", "Yueting Zhuang" ]
Recent advances in reinforcement learning (RL) methods such as Grouped Relative Policy Optimization (GRPO) have strengthened the reasoning capabilities of Large Vision-Language Models (LVLMs). However, due to the inherent entanglement between visual and textual modalities, applying GRPO to LVLMs often leads to reward convergence across different responses to the same sample as training progresses, hindering effective gradient updates and causing the enhancement of chain-of-thought reasoning to stagnate or even collapse.To address this issue, we propose a progressive instruction evolution framework, EvolvedGRPO, to gradually generate more complex questions via editing instructions in an adversarial way, progressively aligned with the model’s evolving capabilities. Specifically, we design two instruction editing strategies across modalities, incorporating incrementally increasing editing instructions and RL-based adversarial data augmentation to improve the effectiveness of model training. To address GRPO's limitations on overly difficult problems, we first train on basic subproblem versions of complex multi-modal questions in both the visual and textual modalities, progressively increasing difficulty to enable prefix-style process rewards, effectively combining the strengths of both process rewards and group-wise relative rewards. Finally, EvolvedGRPO achieves state-of-the-art performance among open-source RL models on multi-modal reasoning tasks, even approaching the closed-source GPT-4o in reasoning capabilities, and demonstrates better performance on unseen LVLM general benchmarks.
poster
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351
VLMs have Tunnel Vision: Evaluating Nonlocal Visual Reasoning in Leading VLMs
[ "Shmuel Berman", "Jia Deng" ]
Visual Language Models (VLMs) excel at complex visual tasks such as VQA and chart understanding, yet recent work suggests they struggle with simple perceptual tests. We present an evaluation that tests vision-language models’ capacity for \emph{nonlocal visual reasoning}- reasoning that requires chaining evidence collected from multiple, possibly distant, regions of an image. We isolate three distinct forms of non‑local vision: \emph{comparative perception}, which demands holding two images in working memory and comparing them; \emph{saccadic search}, which requires making discrete, evidence‑driven jumps to locate successive targets; and \emph{smooth visual search}, which involves searching smoothly along a continuous contour. Flagship models (e.g., Gemini 2.5 Pro, Claude Vision 3.7, GPT‑o4-mini), even those that perform well on prior primitive‑vision benchmarks, fail these tests and barely exceed random accuracy on two variants of our tasks that are trivial for humans. Our structured evaluation suite allows us if VLMs can perform similar visual algorithms to humans. Our findings show that despite gains in raw visual acuity, current models lack core visual reasoning capabilities.
spotlight
2507.13361
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null
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352
Amortized Variational Transdimensional Inference
[ "Laurence Davies", "Daniel MacKinlay", "Rafael Oliveira", "Scott SIsson" ]
The expressiveness of flow-based models combined with stochastic variational inference (SVI) has, in recent years, expanded the application of optimization-based Bayesian inference to include problems with complex data relationships. However, until now, SVI using flow-based models has been limited to problems of fixed dimension. We introduce CoSMIC normalizing flows (COntextually-Specified Masking for Identity-mapped Components), an extension to neural autoregressive conditional normalizing flow architectures that enables using a single amortized variational density for inference over a transdimensional target distribution. We propose a combined stochastic variational transdimensional inference approach to training CoSMIC flows using techniques from Bayesian optimization and Monte Carlo gradient estimation. Numerical examples are provided to demonstrate the proposed methodology on challenging problems that scale to high-cardinality model spaces.
spotlight
2506.04749
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353
Diff4Seg: Unveiling Open-Vocabulary Semantic Segmentation in Text-to-Image Diffusion Transformers
[ "Chaehyun Kim", "Heeseong Shin", "Heeji Yoon", "Eunbeen Hong", "Anurag Arnab", "Paul Hongsuck Seo", "Sunghwan Hong", "Seungryong Kim" ]
Text-to-image diffusion models excel at translating language prompts into photorealistic images by implicitly grounding textual concepts in their cross-modal attention mechanisms. Prior work has exploited these attention maps for downstream tasks such as editing, inpainting, and zero-shot open-vocabulary semantic segmentation (OVSS), but a detailed understanding of how these maps contribute to image generation remains limited. Recent architectural advances like Multi-Modal Diffusion Transformers (MM-DiTs) introduce joint self-attention over concatenated image and text tokens, enabling richer and more scalable cross-modal alignment. In this work, we systematically analyze the attention structures of MM-DiT, focusing on how specific heads and layers propagate semantic information and influence generation quality. By decomposing attention score distributions and attention norms across layers, we identify a subset of heads that consistently align text tokens with spatially coherent image regions, naturally yielding high-quality zero-shot segmentation masks. We then introduce a lightweight LoRA-based fine-tuning method to enhance the semantic grouping capabilities of these heads without degrading— and often improving—image fidelity. Our findings demonstrate that semantic alignment is an emergent property of diffusion transformers and can be selectively amplified to improve both dense recognition and generative performance, paving the way toward unified models that bridge generation and perception.
poster
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354
Counterfactual Evolution of Multimodal Datasets via Visual Programming
[ "Minghe Gao", "Zhongqi Yue", "Wenjie Yan", "Yihao Hu", "Wei Ji", "Siliang Tang", "Jun Xiao", "Tat-Seng Chua", "Yueting Zhuang", "Juncheng Li" ]
The rapid development of Multimodal Large Language Models (MLLMs) poses increasing demands on the diversity and complexity of multimodal datasets. Yet manual annotation pipelines can no longer keep pace. Existing augmentation methods often follow fixed rules and lack verifiable control over sample diversity and reasoning complexity. To address this, we introduce Scalable COunterfactual Program Evolution (SCOPE), a framework that uses symbolic Visual Programming to guide program evolution via counterfactual reasoning. SCOPE performs the three steps of counterfactual inference: (1) Abduction, by generating verifiable programs to model reasoning associations; (2) Action, by intervening on program structure along three axes—reasoning path, visual context, and cross-instance composition; and (3) Prediction, by categorizing evolved instances by difficulty, structure, and input multiplicity. Based on this process, we build SCOPE-Train and SCOPE-Test, evolving benchmarks with expert validation. To support training, we propose MAP, a curriculum learning strategy that aligns model capacity with sample difficulty. Experiments show that SCOPE improves reasoning performance, exposes model blind spots, and enhances visual dialog capabilities.
poster
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355
CausalVTG: Towards Robust Video Temporal Grounding via Causal Inference
[ "Qiyi Wang", "Ying Shen", "Senda Chen" ]
Video Temporal Grounding (VTG) aims to localize relevant segments in untrimmed videos based on natural language queries and has seen notable progress in recent years.However, most existing methods suffer from two critical limitations. First, they are prone to learning superficial co-occurrence patterns—such as associating specific objects or phrases with certain events—induced by dataset biases, which ultimately degrades their semantic understanding abilities.Second, they typically assume that relevant segments always exist in the video, an assumption misaligned with real-world scenarios where queried content may be absent.Fortunately, causal inference offers a natural solution to the above-mentioned issues by disentangling dataset-induced biases and enabling counterfactual reasoning about query relevance. To this end, we propose CausalVTG, a novel framework that explicitly integrates causal reasoning into VTG. Specifically, we introduce a causality-aware disentangled encoder (CADE) based on front-door adjustment to mitigate confounding biases in visual and textual modalities. To better capture temporal granularity, we design a multi-scale temporal perception module (MSTP) that reconstructs query-conditioned video features at multiple resolutions. Additionally, a counterfactual contrastive learning objective is employed to help the model discern whether a query is truly grounded in a video.Extensive experiments on five widely-used benchmarks demonstrate that CausalVTG outperforms state-of-the-art methods, achieving higher localization precision under stricter IoU thresholds and more accurately identifying whether a query is truly grounded in the video. These results demonstrate both the effectiveness and generalizability of proposed CausalVTG.
poster
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356
Active Target Discovery under Uninformative Priors: The Power of Permanent and Transient Memory
[ "Anindya Sarkar", "Binglin Ji", "Yevgeniy Vorobeychik" ]
In many scientific and engineering fields, where acquiring high-quality data is expensive—such as medical imaging, environmental monitoring, and remote sensing—strategic sampling of unobserved regions based on prior observations is crucial for maximizing discovery rates within a constrained budget. The rise of powerful generative models, such as diffusion models, has enabled active target discovery in partially observable environments by leveraging learned priors—probabilistic representations that capture underlying structure from data. With guidance from sequentially gathered task-specific observations, these models can progressively refine exploration and efficiently direct queries toward promising regions. However, in domains where learning a strong prior is infeasible due to extremely limited data or high sampling cost (such as rare species discovery, diagnostics for emerging diseases, etc.), these methods struggle to generalize. To overcome this limitation, we propose a novel approach that enables effective active target discovery even in settings with uninformative priors, ensuring robust exploration and adaptability in complex real-world scenarios. Our framework is theoretically principled and draws inspiration from neuroscience to guide its design. Unlike black-box policies, our approach is inherently interpretable, providing clear insights into decision-making. Furthermore, it guarantees a strong, monotonic improvement in prior estimates with each new observation, leading to increasingly accurate sampling and reinforcing both reliability and adaptability in dynamic settings. Through comprehensive experiments and ablation studies across various domains, including species distribution modeling and remote sensing, we demonstrate that our method substantially outperforms baseline approaches.
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357
State Size Independent Statistical Error Bound for Discrete Diffusion Models
[ "Shintaro Wakasugi", "Taiji Suzuki" ]
Diffusion models operating in discrete state spaces have emerged as powerful approaches, demonstrating remarkable efficacy across diverse domains, including reasoning tasks and molecular design. Despite their promising applications, the theoretical foundations of these models remain substantially underdeveloped, with the existing literature predominantly focusing on continuous-state diffusion models.A critical gap persists in the theoretical understanding of discrete diffusion modeling: the absence of a rigorous framework for quantifying estimation error with finite data. Consequently, the fundamental question of how precisely one can reconstruct the true underlying distribution from a limited training set remains unresolved. In this work, we analyze the estimation error induced by a score estimation of the discrete diffusion models. One of the main difficulties in the analysis stems from the fact that the cardinality of the state space can be exponentially large with respect to its dimension, which results in an intractable error bound by a naive approach. To overcome this difficulty, we make use of a property that the state space can be smoothly embedded in a continuous Euclidean space that enables us to derive a cardinality independent bound, which is more practical in real applications. In particular, we consider a setting where the state space is structured as a hypercube graph, and another where the induced graph Laplacian can be asymptotically well approximated by the ordinary Laplacian defined on the continuous space, and then derive state space size independent bounds.
poster
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358
Unlocking Aha Moments with CoT: Advancing Multimodal LLMs for Unified Image Generation
[ "Kaihang Pan", "Yang Wu", "Wendong Bu", "Shen Kai", "Juncheng Li", "Yingting Wang", "Yunfei Li", "Siliang Tang", "Jun Xiao", "Fei Wu", "ZhaoHang", "Yueting Zhuang" ]
Recent endeavors in Multimodal Large Language Models (MLLMs) aim to unify visual comprehension and generation. However, these two capabilities remain largely independent, as if they are two separate functions encapsulated within the same model. Consequently, visual comprehension does not enhance visual generation, and the reasoning mechanisms of LLMs have not been fully integrated to revolutionize image generation. In this paper, we propose to enable the collaborative co-evolution of visual comprehension and generation, advancing image generation into an iterative introspective process. We introduce a two-stage training approach: supervised fine-tuning teaches the MLLM with the foundational ability to generate genuine CoT for visual generation, while reinforcement learning activates its full potential via an exploration-exploitation trade-off. Ultimately, we unlock the Aha moment in visual generation, advancing MLLMs from text-to-image tasks to unified image generation. Extensive experiments demonstrate the superior performance of our model in both text-to-image generation and image editing. Anonymous Project: https://anonymous.4open.science/r/RefineDraw_Anonym-8714.
poster
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359
MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query
[ "Wei Chow", "Yuan Gao", "Linfeng Li", "XIAN WANG", "Qi Xu", "Hang Song", "Lingdong Kong", "Ran Zhou", "Yi Zeng", "Yidong Cai", "Botian Jiang", "Shilin Xu", "Jiajunzhang", "Minghui Qiu", "Xiangtai Li", "Tianshu Yang", "Siliang Tang", "Juncheng Li" ]
Semantic retrieval is crucial for modern applications yet remains underexplored in current research. Existing datasets are limited to single languages, single images, or singular retrieval conditions, often failing to fully exploit the expressive capacity of visual information as evidenced by maintained performance when images are replaced with captions. However, practical retrieval scenarios frequently involve interleaved multi-condition queries with multiple images.Hence, this paper introduces MERIT, the first multilingual dataset for interleaved multi-condition semantic retrieval, comprising 320,000 queries with 135,000 products in 5 languages, covering 7 distinct product categories.Extensive experiments on MERIT identify existing models's critical limitation: focusing solely on global semantic information while neglecting specific conditional elements in queries.Consequently, we propose Coral, a novel fine-tuning framework that adapts pre-trained MLLMs by integrating embedding reconstruction to preserve fine-grained conditional elements and contrastive learning to extract comprehensive global semantics.Experiments demonstrate that Coral achieves a 45.9% performance improvement over conventional approaches on MERIT, with strong generalization capabilities validated across 8 established retrieval benchmarks. Collectively, our contributions—a novel dataset, identification of critical limitations in existing approaches, and an innovative fine-tuning framework—establish a foundation for future research in interleaved multi-condition semantic retrieval. Anonymous Project Page:[https://anoy1314.github.io](https://anoy1314.github.io).
poster
2506.03144
https://github.com/weichow23/merit
https://merit-2025.github.io/
[]
[ "Bia/CORAL" ]
[ "WeiChow/merit" ]
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360
SAP: Exact Sorting in Splatting via Screen-Aligned Primitives
[ "Zhanke Wang", "Zhiyan Wang", "Kaiqiang Xiong", "Wu Jiahao", "Yang Deng", "Ronggang Wang" ]
Recently, 3D Gaussian Splatting (3DGS) has achieved state-of-the-art rendering results. However, its efficiency relies on simplifications that disregard the thickness of Gaussian primitives and their overlapping interactions. These simplifications can lead to popping artifacts due to inaccurate sorting, thereby affecting the rendering quality. In this paper, we propose Screen-Aligned Primitives (SAP), an anisotropic kernel that generates primitives parallel to the image plane for each view. Our rasterization pipeline enables full per-pixel ordering in real time. Since the primitives are parallel for a given viewpoint, a single global sorting operation suffices for correct per-pixel depth ordering. We formulate 3D reconstruction as a combination of a 3D-consistent decoder and 2D view-specific primitives, and further propose a highly efficient decoder to ensure 3D consistency. Moreover, within our framework, the primitive function values remain consistent between view space and screen space, allowing arbitrary radial basis functions (RBFs) to represent the scene without introducing projection errors. Experiments on diverse datasets demonstrate that our method achieves state-of-the-art rendering quality while maintaining real-time performance.
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361
Strategic Cost Selection in Participatory Budgeting
[ "Piotr Faliszewski", "Łukasz Janeczko", "Andrzej Kaczmarczyk", "Grzegorz Lisowski", "Piotr Skowron", "Stanisław Szufa", "Mateusz Szwagierczak" ]
We study strategic behavior of project proposers in the context of approval-basedparticipatory budgeting (PB). In our model we assume that the votes are fixed andknown and the proposers want to set as high project prices as possible, providedthat their projects get selected and the prices are not below the minimum costs oftheir delivery. We study the existence of pure Nash equilibria (NE) in such games,focusing on the AV/Cost, Phragmen, and Method of Equal Shares rules. We alsoprovide an experimental study of cost selection on real-life PB election data.
poster
2407.18092
null
null
[]
[]
[]
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362
Rethinking Approximate Gaussian Inference in Classification
[ "Bálint Mucsányi", "Nathaël Da Costa", "Philipp Hennig" ]
In classification tasks, softmax functions are ubiquitously used as output activations to produce predictive probabilities. Such outputs only capture aleatoric uncertainty. To capture epistemic uncertainty, approximate Gaussian inference methods have been proposed. We develop a common formalism to describe such methods, which we view as outputting Gaussian distributions over the logit space. Predictives are then obtained as the expectations of the Gaussian distributions pushed forward through the softmax. However, such softmax Gaussian integrals cannot be solved analytically, and Monte Carlo (MC) approximations can be costly and noisy. We propose to replace the softmax activation by element-wise normCDF or sigmoid, which allows for the accurate sampling-free approximation of predictives. This also enables the approximation of the Gaussian pushforwards by Dirichlet distributions with moment matching. This approach entirely eliminates the runtime and memory overhead associated with MC sampling. We evaluate it combined with several approximate Gaussian inference methods (Laplace, HET, SNGP) on large- and small-scale datasets (ImageNet, CIFAR-100, CIFAR-10), demonstrating improved uncertainty quantification capabilities compared to softmax MC sampling.
poster
2502.03366
null
null
[]
[]
[]
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363
KLASS: KL-Adaptive Stability Sampling for Fast Inference in Masked Diffusion Models
[ "Seo Hyun Kim", "Sunwoo Hong", "Youngrok Park", "Hojung Jung", "Se-Young Yun" ]
Masked diffusion models have demonstrated competitive results on various tasks including language generation. However, due to its multiple refinement process, the inference is often bottlenecked by slow and static sampling speed. To overcome this problem, we introduce `KL-Adaptive Stability Sampling' (KLASS), a fast yet effective sampling method that exploits token-level KL divergence to identify stable, high-confidence predictions. By unmasking multiple tokens in each iteration without any additional model training, our approach speeds up generation significantly while maintaining sample quality. We test our sampler on diverse domains including $2\times$ faster in wall-clock time while improving performance in reasoning benchmarks (GSM8K and MATH) compared to standard greedy decoding, achieving state-of-the-art result among diffusion sampler.
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364
OpenMMEgo: Enhancing Egocentric Understanding for LMMs with Open Weights and Data
[ "Hao Luo", "Zihao Yue", "Wanpeng Zhang", "Yicheng Feng", "Sipeng Zheng", "Deheng Ye", "Zongqing Lu" ]
Recent advances in large multimodal models (LMMs) have significantly advanced video comprehension, yet their performance remains limited in first-person scenarios. The interactive nature of egocentric videos is critical for applications like embodied intelligence, but introduces complex visual contexts that conventional models struggle to capture. To bridge this gap, we introduce OpenMMEgo withinnovations across three dimensions: data, model, and training strategy. To provide rich spatiotemporal visual knowledge, we curate a large-scale, high-quality dataset named OME10M, comprising over 8.2M egocentric video QA pairs synthesized from Ego4D series. We also establish OMEBench, a comprehensive benchmark for rigorous egocentric understanding assessment. To alleviate the frequent viewpoint shifts inherent in egocentric videos, we implement semantic-aware visual token compression. Further, a curriculum learning strategy is complemented to foster stable learning across various data complexities. OpenMMEgo consistently improves the performance of LMMs on egocentric benchmarks without sacrificing general video understanding performance. Notably, Qwen2.5-VL tuned with OpenMMEgo substantially outperforms other models of the same size in egocentric video understanding. All components will be open-sourced.
poster
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365
SEMPO: Lightweight Foundation Models for Time Series Forecasting
[ "Hui He", "Guansong Pang", "Kun Yi", "Yuanchi Ma", "Qi Zhang", "Zhendong Niu" ]
The recent boom of large pre-trained models witnesses remarkable success in developing foundation models (FMs) for time series forecasting. Despite impressive performance across diverse downstream forecasting tasks, existing time series FMs possess massive network architectures and require substantial pre-training on large-scale datasets, which significantly hinders their deployment in resource-constrained environments. In response to this growing tension between versatility and affordability, we propose **SEMPO**, a novel lightweight foundation model that requires pretraining on relatively small-scale data, yet exhibits strong general time series forecasting. Concretely, SEMPO comprises two key modules: 1) energy-aware **S**p**E**ctral decomposition module, that substantially improves the utilization of pre-training data by modeling not only the high-energy frequency signals but also the low-energy yet informative frequency signals that are ignored in current methods; and 2) **M**ixture-of-**P**r**O**mpts enabled Transformer, that learns heterogeneous temporal patterns through small dataset-specific prompts and adaptively route time series tokens to these prompt-based experts for parameter-efficient model adaptation across different datasets and domains. Equipped with these modules, SEMPO significantly reduces both pre-training data scale and model size, while achieving strong generalization. Extensive experiments on two large-scale benchmarks covering 16 datasets demonstrate the superior performance of SEMPO in zero-shot and few-shot forecasting scenarios compared with state-of-the-art methods.
poster
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366
Refinement Methods for Distributed Distribution Estimation under $\ell^p$-Losses
[ "Deheng Yuan", "Tao Guo", "Zhongyi Huang" ]
Consider the communication-constrained estimation of discrete distributions under $\ell^p$ losses, where each distributed terminal holds multiple independent samples and uses limited number of bits to describe the samples. We obtain the minimax optimal rates of the problem for most parameter regimes. As a result, an elbow effect of the optimal rates at $p=2$ is clearly identified. In order to achieve the optimal rates for different parameter regimes, we introduce refinement methods and develop additional customized techniques in the estimation protocols. The general idea of the refinement methods is to first generate rough estimate by partial information and then establish refined estimate in subsequent steps guided by the rough estimate. Then customized techniques such as successive refinement, sample compression, thresholding and random hashing are leveraged to achieve the optimal rates in different parameter regimes. The optimality of the estimation protocols is shown by deriving compatible minimax lower bounds.
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367
Statistical Analysis of the Sinkhorn Iterations for Two-Sample Schr\"{o}dinger Bridge Estimation
[ "Ibuki Maeda", "Yao", "Atsushi Nitanda" ]
The Schrödinger bridge problem seeks the optimal stochastic process that connects two given probability distributions with minimal energy modification. While the Sinkhorn algorithm is widely used to solve the static optimal transport problem, a recent work (Pooladian and Niles-Weed, 2024) proposed the *Sinkhorn bridge*, which estimates Schrödinger bridges by plugging optimal transport into the time-dependent drifts of SDEs, with statistical guarantees in the one-sample estimation setting where the true source distribution is fully accessible. In this work, to further justify this method, we study the statistical performance of intermediate Sinkhorn iterations in the two-sample estimation setting, where only finite samples from both source and target distributions are available. Specifically, we establish a statistical bound on the squared total variation error of Sinkhorn bridge iterations: $\mathcal{O}(1/m+1/n + r^{2k})~(r \in (0,1))$, where $m$ and $n$ are the sample sizes from the source and target distributions, respectively, and $k$ is the number of Sinkhorn iterations. This result provides a theoretical guarantee for the finite-sample performance of the Schrödinger bridge estimator and offers practical guidance for selecting sample sizes and the number of Sinkhorn iterations. Notably, our theoretical results apply to several representative methods such as [SF]$^2$M, DSBM-IMF, BM2, and lightSB(-M) under specific settings, through the previously unnoticed connection between these estimators.
poster
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368
Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs
[ "ChangHao Li", "Yuchen Zhuang", "Rushi Qiang", "Haotian Sun", "Hanjun Dai", "Chao Zhang", "Bo Dai" ]
Despite the impressive generative abilities of black-box large language models (LLMs), their inherent opacity hinders further advancements in capabilities such as reasoning, planning, and personalization. Existing works aim to enhance LLM capabilities via domain-specific adaptation, which require additional training on accessible model parameters, an infeasible option for black-box LLMs. To address this challenge, we introduce Matryoshka Pilot (M-Pilot), a lightweight white-box LLM controller that guides a large-scale black-box LLM generator by decomposing complex tasks into a series of intermediate outputs.Specifically, we consider the black-box LLM as an environment, with M-Pilot serving as a policy to provide intermediate guidance through prompts for driving the black-box LLM. M-Pilot is trained to pivot the outputs of the black-box LLM aligning with preferences during iterative interaction, which enables controllable multi-turn generation and self-improvement in optimizing intermediate guidance. Empirical evaluations on diverse tasks demonstrate that our method effectively enhances the capabilities of black-box LLMs in complex, long-horizon tasks.
poster
2410.20749
null
null
[]
[]
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369
Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
[ "Ting Wei", "Biao Mei", "Junliang Lyu", "Renquan Zhang", "Feng Zhou", "Yifan Sun" ]
Personalized Bayesian federated learning (PBFL) handles non-i.i.d. client data and quantifies uncertainty by combining personalization with Bayesian inference. However, current PBFL methods face two main limitations: posterior inference on clients often assumes restrictive parametric forms, and server-side posterior aggregation typically relies on naive parameter averaging. To overcome these issues, we propose FedWBA, a novel PBFL method that enhances both local inference and global aggregation. At the client level, we use particle-based variational inference for nonparametric posterior representation. At the server level, we introduce particle-based Wasserstein barycenter aggregation, offering a more geometrically meaningful approach. Theoretically, we provide local and global convergence guarantees for FedWBA. Locally, we prove a KL divergence decrease lower bound per iteration for variational inference convergence. Globally, we show that the Wasserstein barycenter converges to the true parameter as the client data size increases. Empirically, experiments show that FedWBA outperforms baselines in prediction accuracy, uncertainty calibration, and convergence rate, with ablation studies confirming its robustness.
poster
2505.14161
null
null
[]
[]
[]
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370
Tight Generalization Bounds for Large-Margin Halfspaces
[ "Kasper Green Larsen", "Natascha Schalburg" ]
We prove the first generalization bound for large-margin halfspaces that is asymptotically tight in the tradeoff between the margin, the fraction of training points with the given margin, the failure probability and the number of training points.
spotlight
2502.13692
null
null
[]
[]
[]
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371
HIDISC: A Hyperbolic Framework for Domain Generalization with Generalized Category Discovery
[ "Vaibhav Rathore", "Divyam Gupta", "Biplab Banerjee" ]
Generalized Category Discovery (GCD) aims to classify test-time samples into either seen categories—available during training—or novel ones, without relying on label supervision. Most existing GCD methods assume simultaneous access to labeled and unlabeled data during training and arising from the same domain, limiting applicability in open-world scenarios involving distribution shifts. Domain Generalization with GCD (DG-GCD) lifts this constraint by requiring models to generalize to unseen domains containing novel categories, without accessing target-domain data during training.The only prior DG-GCD method, DG$^2$CD-Net~\cite{dg2net}, relies on episodic training with multiple synthetic domains and task vector aggregation, incurring high computational cost and error accumulation. We propose \textsc{HiDISC}, a hyperbolic representation learning framework that achieves domain and category-level generalization without episodic simulation. To expose the model to minimal but diverse domain variations, we augment the source domain using GPT-guided diffusion, avoiding overfitting while maintaining efficiency.To structure the representation space, we introduce \emph{Tangent CutMix}, a curvature-aware interpolation that synthesizes pseudo-novel samples in tangent space, preserving manifold consistency. A unified loss—combining penalized Busemann alignment, hybrid hyperbolic contrastive regularization, and adaptive outlier repulsion—facilitates compact, semantically structured embeddings. A learnable curvature parameter further adapts the geometry to dataset complexity.\textsc{HiDISC} achieves state-of-the-art results on PACS~\cite{pacs}, Office-Home~\cite{officehome}, and DomainNet~\cite{domainnet}, consistently outperforming the existing Euclidean and hyperbolic (DG)-GCD baselines.
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372
Semi-supervised Graph Anomaly Detection via Robust Homophily Learning
[ "GUOGUO AI", "Hezhe Qiao", "Hui Yan", "Guansong Pang" ]
Semi-supervised graph anomaly detection (GAD) utilizes a small set of labeled normal nodes to identify abnormal nodes from a large set of unlabeled nodes in a graph. Current methods in this line posit that 1) normal nodes share a similar level of homophily and 2) the labeled normal nodes can well represent the homophily patterns in the normal class. However, this assumption often does not hold well since normal nodes in a graph can exhibit diverse homophily in real-world GAD datasets. In this paper, we propose RHO, namely Robust Homophily Learning, to adaptively learn such homophily patterns. RHO consists of two novel modules, adaptive frequency response filters (AdaFreq) and graph normality alignment (GNA). AdaFreq learns a set of adaptive spectral filters that capture different frequency components of the labeled normal nodes with varying homophily in the channel-wise and cross-channel views of node attributes. GNA is introduced to enforce consistency between the channel-wise and cross-channel homophily representations to robustify the normality learned by the filters in the two views. Experiments on eight real-world GAD datasets show that RHO can effectively learn varying, often under-represented, homophily in the small normal node set and substantially outperforms state-of-the-art competing methods. Code is available at \url{https://anonymous.4open.science/r/RHO-5C57}.
poster
2506.15448
https://github.com/mala-lab/RHO
null
[]
[]
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373
DGSolver: Diffusion Generalist Solver with Universal Posterior Sampling for Image Restoration
[ "Hebaixu Wang", "Jing Zhang", "Haonan Guo", "Di Wang", "Jiayi Ma", "Bo Du" ]
Diffusion models have achieved remarkable progress in universal image restoration. However, existing methods perform naive inference in the reverse process, which leads to cumulative errors under limited sampling steps and large step intervals. Moreover, they struggle to balance the commonality of degradation representations with restoration quality, often depending on complex compensation mechanisms that enhance fidelity at the expense of efficiency. To address these challenges, we introduce DGSolver, a diffusion generalist solver with universal posterior sampling. We first derive the exact ordinary differential equations for generalist diffusion models to unify degradation representations and design tailored high-order solvers with a queue-based accelerated sampling strategy to improve both accuracy and efficiency. We then integrate universal posterior sampling to better approximate manifold-constrained gradients, yielding a more accurate noise estimation and correcting errors in inverse inference. Extensive experiments demonstrate that DGSolver outperforms state-of-the-art methods in restoration accuracy, stability, and scalability, both qualitatively and quantitatively.
poster
2504.21487
https://github.com/MiliLab/DGSolver
null
[]
[]
[]
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374
Gated Integration of Low-Rank Adaptation for Continual Learning of Language Models
[ "Yan-Shuo Liang", "Jiarui Chen", "Wu-Jun Li" ]
Continual learning (CL), which requires the model to learn multiple tasks sequentially, is crucial for language models (LMs). Recently, low-rank adaptation (LoRA), one of the most representative parameter-efficient fine-tuning (PEFT) methods, has gained increasing attention in CL of LMs. However, most existing CL methods based on LoRA typically expand a new LoRA branch to learn each new task and force the new and old LoRA branches to contribute equally to old tasks, potentially leading to forgetting. In this work, we propose a new method, called gated integration of low-rank adaptation (GainLoRA), for CL of LMs. GainLoRA expands a new LoRA branch for each new task and introduces gating modules to integrate the new and old LoRA branches. Furthermore, GainLoRA leverages the new gating module to minimize the contribution from the new LoRA branch to old tasks, effectively mitigating forgetting and improving the model's overall performance. Experimental results on CL benchmarks demonstrate that GainLoRA outperforms existing state-of-the-art methods.
poster
2505.15424
null
null
[]
[]
[]
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375
MiniMax-Remover: Taming Bad Noise Helps Video Object Removal
[ "Bojia Zi", "Weixuan Peng", "Xianbiao Qi", "Jianan Wang", "Shihao Zhao", "Rong Xiao", "Kam-Fai Wong" ]
Recent advances in video diffusion models have driven rapid progress in video editing techniques. However, video object removal, a critical subtask of video editing, remains challenging due to issues such as hallucinated objects and visual artifacts. Furthermore, existing methods often rely on computationally expensive sampling procedures and classifier-free guidance (CFG), resulting in slow inference. To address these limitations, we propose ***MiniMax-Remover***, a novel two-stage video object removal approach. Motivated by the observation that text condition is not best suited for this task, we simplify the pretrained video generation model by removing textual input and cross-attention layers, resulting in a more lightweight and efficient model architecture in the first stage. In the second stage, we distilled our remover on successful videos produced by the stage-1 model and curated by human annotators, using a minimax optimization strategy to further improve editing quality and inference speed. Specifically, the inner maximization identifies adversarial input noise ("bad noise") that makes failure removals, while the outer minimization step trains the model to generate high-quality removal results even under such challenging conditions. As a result, our method achieves a state-of-the-art video object removal results with as few as 6 sampling steps and doesn't rely on CFG, significantly improving inference efficiency. Extensive experiments demonstrate the effectiveness and superiority of MiniMax-Remover compared to existing methods.
poster
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376
Learning Dense Hand Contact Estimation from Imbalanced Data
[ "Daniel Jung", "Kyoung Mu Lee" ]
Hands are essential to human interaction, and understanding contact between hands and the world can promote comprehensive understanding of their function. Recently, there have been growing number of hand interaction datasets that cover interaction with object, other hand, scene, and body. Despite the significance of the task and increasing high-quality data, how to effectively learn dense hand contact estimation remains largely underexplored. There are two major challenges for learning dense hand contact estimation. First, there exists class imbalance issue from hand contact datasets where majority of samples are not in contact. Second, hand contact datasets contain spatial imbalance issue with most of hand contact exhibited in finger tips, resulting in challenges for generalization towards contacts in other hand regions. To tackle these issues, we present a framework that learns dense HAnd COntact estimation (HACO) from imbalanced data. To resolve the class imbalance issue, we introduce balanced contact sampling, which builds and samples from multiple sampling groups that fairly represent diverse contact statistics for both contact and non-contact samples. Moreover, to address the spatial imbalance issue, we propose vertex-level class-balanced (VCB) loss, which incorporates spatially varying contact distribution by separately reweighting loss contribution of each vertex based on its contact frequency across dataset. As a result, we effectively learn to predict dense hand contact estimation with large-scale hand contact data without suffering from class and spatial imbalance issue. The codes will be released.
poster
2505.11152
https://github.com/dqj5182/HACO_RELEASE
https://haco-release.github.io/
[ "dqj5182/HACO" ]
[]
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377
Manipulating Feature Visualizations with Gradient Slingshots
[ "Dilyara Bareeva", "Marina Höhne", "Alexander Warnecke", "Lukas Pirch", "Klaus-Robert Müller", "Konrad Rieck", "Sebastian Lapuschkin", "Kirill Bykov" ]
Deep Neural Networks (DNNs) are capable of learning complex and diverse representations; however, the semantic nature of the concepts they capture remains poorly understood. A widely used technique for interpreting the functional role of concepts within a model is Feature Visualization (FV), which synthesizes an input pattern that maximally activates a given feature. In this paper, we examine the vulnerability of FV to adversarial fine-tuning of the model. We introduce a novel technique, Gradient Slingshots, that enables manipulation of FV without changing the model architecture. By shaping new trajectories in the off-distribution regions of the activation landscape of a feature, we coerce the optimization process to converge in a predefined visualization. Crucially, this manipulation largely preserves the model’s external behavior and internal mechanisms, enabling deceptive explanations while maintaining functional integrity. We validate the effectiveness of our technique across several neural network models, demonstrating its ability to conceal the true functionality of arbitrary neurons by replacing their original explanations with selected target explanations during model auditing.
poster
2401.06122
null
null
[]
[]
[]
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378
TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning
[ "Andreas Auer", "Patrick Podest", "Daniel Klotz", "Sebastian Böck", "Günter Klambauer", "Sepp Hochreiter" ]
In-context learning, the ability of large language models to perform tasks using only examples provided in the prompt, has recently been adapted for time series forecasting. This paradigm enables zero-shot prediction, where past values serve as context for forecasting future values, making powerful forecasting tools accessible to non-experts and increasing the performance when training data are scarce.Most existing zero-shot forecasting approaches rely on transformer architectures, which, despite their success in language, often fall short of expectations in time series forecasting, where recurrent models like LSTMs frequently have the edge. Conversely, while LSTMs are well-suited for time series modeling due to their state-tracking capabilities, they lack strong in-context learning abilities.We introduce *TiRex* that closes this gap by leveraging xLSTM, an enhanced LSTM with competitive in-context learning skills. Unlike transformers, state-space models, or parallelizable RNNs such as RWKV, TiRex retains state tracking, a critical property for long-horizon forecasting. To further facilitate its state-tracking ability, we propose a training-time masking strategy called CPM.TiRex sets a new state of the art in zero-shot time series forecasting on the Hugging Face benchmarks *GiftEval* and *Chronos-ZS*, outperforming significantly larger models including *TabPFN-TS* (Prior Labs), *Chronos Bolt* (Amazon), *TimesFM* (Google), and *Moirai* (Salesforce) across both short- and long-term forecasts.
poster
2505.23719
https://github.com/nx-ai/tirex
null
[ "NX-AI/TiRex-demo" ]
[ "NX-AI/TiRex", "NX-AI/TiRex-1.1-gifteval" ]
[]
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379
KeeA*: Epistemic Exploratory A* Search via Knowledge Calibration
[ "Dengwei Zhao", "Shikui Tu", "Yanan Sun", "Lei Xu" ]
In recent years, neural network-guided heuristic search algorithms, such as Monte-Carlo tree search and A$^*$ search, have achieved significant advancements across diverse practical applications. Due to the challenges stemming from high state-space complexity, sparse training datasets, and incomplete environmental modeling, heuristic estimations manifest uncontrolled inherent biases towards the actual expected evaluations, thereby compromising the decision-making quality of search algorithms. Sampling exploration enhanced A$^*$ (SeeA$^*$) was proposed to improve the efficiency of A$^*$ search by constructing an dynamic candidate subset through random sampling, from which the expanded node was selected. However, uniform sampling strategy utilized by SeeA$^*$ facilitates exploration exclusively through the injection of randomness, which completely neglects the heuristic knowledge relevant to open nodes. Moreover, the theoretical support of cluster sampling remains ambiguous. Despite the existence of potential biases, heuristic estimations still encapsulate certain valuable information. In this paper, epistemic exploratory A$^*$ search (KeeA$^*$) is proposed to integrate heuristic knowledge for calibrating the sampling process. We first theoretically demonstrate that SeeA$^*$ with cluster sampling outperforms uniform sampling due to the distribution-aware selection with higher variance. Building on this insight, cluster scouting and path-aware sampling are introduced in KeeA$^*$ to further exploit heuristic knowledge to increase the sampling mean and variance, respectively, thereby generating higher-quality extreme candidates and enhancing overall decision-making performance. Finally, empirical results on retrosynthetic planning and logic synthesis demonstrate superior performance of KeeA$^*$ compared to state-of-the-art heuristic search algorithms.
poster
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380
Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer
[ "Zechuan Zhang", "Ji Xie", "Yu Lu", "Zongxin Yang", "Yi Yang" ]
Instruction-based image editing enables precise modifications via natural language prompts, but existing methods face a precision-efficiency tradeoff: fine-tuning demands massive datasets (>10M) and computational resources, while training-free approaches suffer from weak instruction comprehension. We address this by proposing \textbf{ICEdit}, which leverages the inherent comprehension and generation abilities of large-scale Diffusion Transformers (DiTs) through three key innovations: (1) An in-context editing paradigm without architectural modifications; (2) Minimal parameter-efficient fine-tuning for quality improvement; (3) Early Filter Inference-Time Scaling, which uses VLMs to select high-quality noise samples for efficiency. Experiments show that ICEdit achieves state-of-the-art editing performance with only 0.1\% of the training data and 1\% trainable parameters compared to previous methods. Our approach establishes a new paradigm for balancing precision and efficiency in instructional image editing.
poster
2504.20690
null
https://river-zhang.github.io/ICEdit-gh-pages/
[ "RiverZ/ICEdit" ]
[ "sanaka87/ICEdit-MoE-LoRA", "RiverZ/normal-lora" ]
[]
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381
CLiFT: Compressive Light-Field Tokens for Compute Efficient and Adaptive Neural Rendering
[ "Zhengqing Wang", "Yuefan Wu", "Jiacheng Chen", "Fuyang Zhang", "Yasutaka Furukawa" ]
This paper proposes a neural rendering approach that represents a scene as "compressed light-field tokens (CLiFTs)", retaining rich appearance and geometric information of a scene. CLiFT enables compute-efficient rendering by compressed tokens, while being capable of changing the number of tokens to represent a scene or render a novel view with one trained network. Concretely, given a set of images, multi-view encoder tokenizes the images with the camera poses. Latent-space K-means selects a reduced set of rays as cluster centroids using the tokens. The multi-view ``condenser'' compresses the information of all the tokens into the centroid tokens to construct CLiFTs. At test time, given a target view and a compute budget (i.e., the number of CLiFTs), the system collects the specified number of nearby tokens and synthesizes a novel view using a compute-adaptive renderer. trained to handle a variable number of tokens. Extensive experiments on RealEstate10K and DL3DV datasets quantitatively and qualitatively validate our approach, achieving significant data reduction with comparable rendering quality and the highest overall rendering score, while providing trade-offs of data size, rendering quality, and rendering speed.
spotlight
2507.08776
https://github.com/eric-zqwang/CLiFT
https://clift-nvs.github.io/
[]
[]
[]
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382
LaM-SLidE: Latent Space Modeling of Spatial Dynamical Systems via Linked Entities
[ "Florian Sestak", "Artur Toshev", "Andreas Fürst", "Günter Klambauer", "Andreas Mayr", "Johannes Brandstetter" ]
Generative models are spearheading recent progress in deep learning, showcasing strong promise for trajectory sampling in dynamical systems as well. However, whereas latent space modeling paradigms have transformed image and video generation, similar approaches are more difficult for most dynamical systems. Such systems -- from chemical molecule structures to collective human behavior -- are described by interactions of entities, making them inherently linked to connectivity patterns, entity conservation, and the traceability of entities over time. Our approach, LaM-SLidE (Latent Space Modeling of Spatial Dynamical Systems via Linked Entities), bridges the gap between: (1) keeping the traceability of individual entities in a latent system representation, and (2) leveraging the efficiency and scalability of recent advances in image and video generation, where pre-trained encoder and decoder enable generative modeling directly in latent space. The core idea of LaM-SLidE is the introduction of identifier representations (IDs) that enable the retrieval of entity properties and entity composition from latent system representations, thus fostering traceability.Experimentally, across different domains, we show that LaM-SLidE performs favorably in terms of speed, accuracy, and generalizability. Code is available at https://anonymous.4open.science/r/lam-slide-B38B.
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383
AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction
[ "Niklas Freymuth", "Tobias Würth", "Nicolas Schreiber", "Balázs Gyenes", "Andreas Boltres", "Johannes Mitsch", "Aleksandar Taranovic", "Tai Hoang", "Philipp Dahlinger", "Philipp Becker", "Luise Kärger", "Gerhard Neumann" ]
The cost and accuracy of simulating complex physical systems using the Finite Element Method (FEM) scales with the resolution of the underlying mesh. Adaptive meshes improve computational efficiency by refining resolution in critical regions, but typically require task-specific heuristics or cumbersome manual design by a human expert. We propose Adaptive Meshing By Expert Reconstruction (AMBER), a supervised learning approach to mesh adaptation. Starting from a coarse mesh, AMBER iteratively predicts the sizing field, i.e., a function mapping from the geometry to the local element size of the target mesh, and uses this prediction to produce a new intermediate mesh using an out-of-the-box mesh generator. This process is enabled through a hierarchical graph neural network, and relies on data augmentation by automatically projecting expert labels onto AMBER-generated data during training. We evaluate AMBER on 2D and 3D datasets, including classical physics problems, mechanical components, and real-world industrial designs with human expert meshes. AMBER generalizes to unseen geometries and consistently outperforms multiple recent baselines, including ones using Graph and Convolutional Neural Networks, and Reinforcement Learning-based approaches.
poster
2505.23663
null
null
[]
[]
[]
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384
Understanding Bias Terms in Neural Representations
[ "Weixiang Zhang", "Boxi Li", "Shuzhao Xie", "Chengwei Ren", "Yuan Xue", "Zhi Wang" ]
In this paper, we examine the impact and significance of bias terms in Implicit Neural Representations (INRs). While bias terms are known to enhance nonlinear capacity by shifting activations in typical neural networks, we discover their functionality differs markedly in neural representation networks.Our analysis reveals that INR performance neither scales with increased number of bias terms nor shows substantial improvement through bias term gradient propagation. We demonstrate that bias terms in INRs primarily serve to eliminate \textit{spatial aliasing} caused by symmetry from both coordinates and activation functions, with input-layer bias terms yielding the most significant benefits.These findings challenge the conventional practice of implementing full-bias INR architecture. We propose using freezing bias terms exclusively in input layers, which consistently outperforms fully biased networks in signal fitting tasks.Furthermore, we introduce Feature-Biased INRs~(Feat-Bias), which initialize input-layer bias with high-level features extracted from pre-trained models. This feature-biasing approach effectively addresses the limited performance in INR post-processing tasks due to neural parameter uninterpretability, achieving superior accuracy while reducing parameter count and improving reconstruction quality.We will release the code.
poster
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385
Adversarial Training for Generalized and Invariant Single-Neuron In-Vivo Activity Representation
[ "Wei Wu", "Yuxing Lu", "Zhengrui Guo", "Chi Zhang", "Can Liao", "Yifan Bu", "Fangxu Zhou", "Jinzhuo Wang" ]
In computational neuroscience, models representing single-neuron in-vivo activity have become essential for understanding the functional identities of individual neurons. These models, such as implicit representation methods based on Transformer architectures, contrastive learning frameworks, and variational autoencoders, aim to capture the invariant and intrinsic computational features of single neurons. The learned single-neuron computational role representations should remain invariant across changing environment and are affected by their molecular expression and location. Thus, the representations allow for in vivo prediction of the molecular cell types and anatomical locations of single neurons, facilitating advanced closed-loop experimental designs. However, current models face the problem of limited generalizability. This is due to batch effects caused by differences in experimental design, animal subjects, and recording platforms. These confounding factors often lead to overfitting, reducing the robustness and practical utility of the models across various experimental scenarios. Previous studies have not rigorously evaluated how well the models generalize to new animals or stimulus conditions, creating a significant gap in the field. To solve this issue, we present a comprehensive experimental protocol that explicitly evaluates model performance on unseen animals and stimulus types. Additionally, we propose a model-agnostic adversarial training strategy. In this strategy, a discriminator network is used to eliminate batch-related information from the learned representations. The adversarial framework forces the representation model to focus on the intrinsic properties of neurons, thereby enhancing generalizability. Our approach is compatible with all major single-neuron representation models and significantly improves model robustness. This work emphasizes the importance of generalization in single-neuron representation models and offers an effective solution, paving the way for the practical application of computational models in vivo. It also shows potential for building unified atlases based on single-neuron in vivo activity.
poster
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386
KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment
[ "Yuxing Lu", "Wei Wu", "Xukai Zhao", "Rui Peng", "Jinzhuo Wang" ]
Maintaining comprehensive and up-to-date knowledge graphs (KGs) is critical for modern AI systems, but manual curation struggles to scale with the rapid growth of scientific literature. This paper presents KARMA, a novel framework employing multi-agent large language models (LLMs) to automate KG enrichment through structured analysis of unstructured text. Our approach employs nine collaborative agents, spanning entity discovery, relation extraction, schema alignment, and conflict resolution that iteratively parse documents, verify extracted knowledge, and integrate it into existing graph structures while adhering to domain-specific schema. Experiments on 1,200 PubMed articles from three different domains demonstrate the effectiveness of KARMA in knowledge graph enrichment, with the identification of up to 38,230 new entities while achieving 83.1\% LLM-verified correctness and reducing conflict edges by 18.6\% through multi-layer assessments.
spotlight
2502.06472
https://github.com/YuxingLu613/KARMA
null
[]
[]
[]
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387
Tree-Sliced Entropy Partial Transport
[ "Viet-Hoang Tran", "Thanh Tran", "Thanh Chu", "Tam Le", "Tan Nguyen" ]
Optimal Transport (OT) has emerged as a fundamental tool in machine learning for comparing probability distributions in a geometrically meaningful manner. However, a key limitation of classical OT is its requirement that the source and target distributions have equal total mass, limiting its use in real-world settings involving imbalanced data, noise, outliers, or structural inconsistencies. Partial Transport (PT) addresses this limitation by allowing only a fraction of the mass to be transported, offering greater flexibility and robustness. Nonetheless, similar to OT, PT remains computationally expensive, as it typically involves solving large-scale linear programs—especially in high-dimensional spaces. To alleviate this computational burden, several emerging works have introduced the Tree-Sliced Wasserstein (TSW) distance, which projects distributions onto tree-metric spaces where OT problems admit closed-form solutions. Building on this line of research, we propose a novel framework that extends the tree-sliced approach to the PT setting, introducing the Partial Tree-Sliced Wasserstein (PartialTSW) distance. Our method is based on the key observation that, within tree-metric spaces, the PT problem can be equivalently reformulated as a standard balanced OT problem between suitably modified measures. This reformulation enables efficient computation while preserving the adaptability and robustness of partial transport. Our method proves effective across challenging tasks such as outlier removal and addressing class imbalance in image-to-image translation.
poster
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388
Causal Discovery and Inference through Next-Token Prediction
[ "Eivinas Butkus", "Nikolaus Kriegeskorte" ]
Some argue that deep neural networks are fundamentally _statistical_ systems that fail to capture the causal generative processes behind their training data. Here we demonstrate that a GPT-style transformer trained for next-token prediction can simultaneously discover instances of linear Gaussian structural causal models (SCMs) and learn to answer counterfactual queries about them. First, we show that the network generalizes to counterfactual queries about SCMs for which it saw _only_ strings describing noisy interventional data. Second, we decode the implicit SCM from the network's residual stream activations and use gradient descent to intervene on that "mental" SCM with predictable effects on the model's output. Our results suggest that neural networks trained using statistical prediction objectives on passively observed data may nevertheless discover and learn to use causal models of the world.
poster
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[]
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389
GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs
[ "Advik Basani", "Xiao Zhang" ]
LLMs have demonstrated impressive capabilities across various natural language processing tasks yet remain vulnerable to prompts, known as jailbreak attacks, carefully designed to bypass safety guardrails and elicit harmful responses. Traditional methods rely on manual heuristics that suffer from limited generalizability. Despite being automatic, optimization-based attacks often produce unnatural jailbreak prompts that can be easily detected by safety filters or require high computational costs due to discrete token optimization. This paper introduces *Generative Adversarial Suffix Prompter* (GASP), a novel automated framework that can efficiently generate human-readable jailbreak prompts in a fully black-box setting. In particular, GASP leverages latent Bayesian optimization to craft adversarial suffixes by efficiently exploring continuous latent spaces, gradually optimizing the suffix generator to improve attack efficacy while balancing prompt coherence via a targeted iterative refinement procedure. Through comprehensive experiments, we show that GASP can produce natural adversarial prompts, significantly improving jailbreak success, reducing training times, and accelerating inference speed, thus making it an efficient and scalable solution for red-teaming LLMs.
poster
2411.14133
https://github.com/TrustMLRG/GASP
null
[]
[]
[]
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390
State Space Prompting via Gathering and Spreading Spatio-Temporal Information for Video Understanding
[ "Jiahuan Zhou", "Kai Zhu", "Zhenyu Cui", "Zichen Liu", "Xu Zou", "Gang Hua" ]
Recently, pre-trained state space models have shown great potential for video classification, which sequentially compresses visual tokens in videos with linear complexity, thereby improving the processing efficiency of video data while maintaining high performance. To apply powerful pre-trained models to downstream tasks, prompt learning is proposed to achieve efficient downstream task adaptation with only a small number of fine-tuned parameters. However, the sequentially compressed visual prompt tokens fail to capture the spatial and temporal contextual information in the video, thus limiting the effective propagation of spatial information within a video frame and temporal information between frames in the state compression model and the extraction of discriminative information. To tackle the above issue, we proposed a State Space Prompting (SSP) method for video understanding, which combines intra-frame and inter-frame prompts to aggregate and propagate key spatiotemporal information in the video. Specifically, an Intra-Frame Gathering (IFG) module is designed to aggregate spatial key information within each frame. Besides, an Inter-Frame Spreading (IFS) module is designed to spread discriminative spatio-temporal information across different frames. By adaptively balancing and compressing key spatio-temporal information within and between frames, our SSP effectively propagates discriminative information in videos in a complementary manner. Extensive experiments on four video benchmark datasets verify that our SSP significantly outperforms existing SOTA methods by 2.76\% on average while reducing the overhead of fine-tuning parameters.
poster
2510.12160
null
null
[]
[]
[]
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391
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol
[ "Pai Liu", "Lingfeng Zhao", "Shivangi Agarwal", "Jinghan Liu", "Audrey Huang", "Philip Amortila", "Nan Jiang" ]
Holdout validation and hyperparameter tuning from data is a long-standing problem in offline reinforcement learning (RL). A standard framework is to use off-policy evaluation (OPE) methods to evaluate and select the policies, but OPE either incurs exponential variance (e.g., importance sampling) or has hyperparameters on their own (e.g., FQE and model-based). In this work we focus on hyperparameter tuning for OPE itself, which is even more under-investigated. Concretely, we select among candidate value functions ("model-free") or dynamics ("model-based") to best assess the performance of a target policy. We develop: (1) new model-free and model-based selectors with theoretical guarantees, and (2) a new experimental protocol for empirically evaluating them. Compared to the model-free protocol in prior works, our new protocol allows for more stable generation of candidate value functions, better control of misspecification, and evaluation of model-free and model-based methods alike. We exemplify the protocol on Gym-Hopper, and find that our new model-free selector, LSTD-Tournament, demonstrates promising empirical performance.
poster
2502.08021
null
null
[]
[]
[]
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392
A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees
[ "Yuhao Zhou", "Jintao Xu", "Bingrui Li", "Chenglong Bao", "Chao Ding", "Jun Zhu" ]
Finding an $\epsilon$-stationary point of a nonconvex function with a Lipschitz continuous Hessian is a central problem in optimization. Regularized Newton methods are a classical tool and have been studied extensively, yet they still face a trade‑off between global and local convergence. Whether a parameter-free algorithm of this type can simultaneously achieve optimal global complexity and quadratic local convergence remains an open question. To bridge this long-standing gap, we propose a new class of regularizers constructed from the current and previous gradients, and leverage the conjugate gradient approach with a negative curvature monitor to solve the regularized Newton equation. The proposed algorithm is adaptive, requiring no prior knowledge of the Hessian Lipschitz constant, and achieves a global complexity of $O(\epsilon^{-\frac{3}{2}})$ in terms of the second-order oracle calls, and $\tilde O(\epsilon^{-\frac{7}{4}})$ for Hessian-vector products, respectively. When the iterates converge to a point where the Hessian is positive definite, the method exhibits quadratic local convergence. Preliminary numerical results, including training the physics-informed neural networks, illustrate the competitiveness of our algorithm.
poster
2502.04799
null
null
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[]
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393
Cached Token Similarity Is a Strong Prior for Fine-grained Visual Question Answering
[ "Liangyu Zhong", "Fabio Rosenthal", "Joachim Sicking", "Fabian Hüger", "Thorsten Bagdonat", "Hanno Gottschalk", "Leo Schwinn" ]
While Multimodal Large Language Models (MLLMs) offer great perception and reasoning capabilities for image-text input, fine-grained Visual Question Answering (VQA) focusing on small details still remains a challenge. Although visual cropping techniques seem promising, recent approaches have several limitations: the need for task-specific fine-tuning, low efficiency due to uninformed exhaustive search, or incompatibility with efficient attention implementations. We address these shortcomings by proposing a training-free visual cropping method, dubbed FOCUS, that leverages MLLM-internal representations to guide the search for the most relevant image region. This is accomplished in four steps: first, we identify the target object(s) in the prompt; second, we compute an object relevance map using the key-value (KV) cache; third, we propose and rank relevant image regions based on the map; and finally, we perform the fine-grained VQA task using the top-ranked region. As a result of this informed search strategy, our method achieves strong performance across four fine-grained VQA datasets and two types of MLLM. It outperforms three existing visual cropping methods in both accuracy and efficiency, and matches the best-performing baseline, ZoomEye, with 3 - 6.5 x higher efficiency. Finally, we perform an ablation study to assess the impact of key design choices.We plan to release our code upon acceptance.
poster
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394
T-SHIRT: Token-Selective Hierarchical Data Selection for Instruction Tuning
[ "Yanjun Fu", "Faisal Hamman", "Sanghamitra Dutta" ]
Instruction tuning is essential for Large Language Models (LLMs) to effectively follow user instructions. To improve training efficiency and reduce data redundancy, recent works use LLM-based scoring functions, e.g., Instruction-Following Difficulty (IFD), to select high–quality instruction-tuning data with scores above a threshold. While these data selection methods often lead to models that can match or even exceed the performance of models trained on the full datasets, we identify two key limitations: (i) they assess quality at the sample level, ignoring token-level informativeness; and (ii) they overlook the robustness of the scoring method, often selecting a sample due to superficial lexical features instead of its true quality. In this work, we propose Token-Selective HIeRarchical Data Selection for Instruction Tuning (T-SHIRT), a novel data selection framework that introduces a new scoring method to include only informative tokens in quality evaluation and also promote robust and reliable samples whose neighbors also show high quality with less local inconsistencies. We demonstrate that models instruction-tuned on a curated dataset (only 5% of the original size) using T-SHIRT can outperform those trained on the entire large-scale dataset by up to 5.48 points on average across eight benchmarks. Across various LLMs and training set scales, our method consistently surpasses existing state-of-the-art data selection techniques, while also remaining both cost-effective and highly efficient. For instance, by using GPT-2 for score computation, we are able to process a dataset of 52k samples using 40 minutes on a single GPU.
poster
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[]
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[]
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395
SimulMEGA: MoE Routers are Advanced Policy Makers for Simultaneous Speech Translation
[ "Chenyang Le", "Bing Han", "Jinshun Li", "Songyong Chen", "Yanmin Qian" ]
Simultaneous Speech Translation (SimulST) enables real-time cross-lingual communication by jointly optimizing speech recognition and machine translation under strict latency constraints. Existing systems struggle to balance translation quality, latency, and semantic coherence, particularly in multilingual many-to-many scenarios where divergent read/write policies hinder unified strategy learning. In this paper, we present SimulMEGA(Simultaneous Generation by Mixture-of-Experts GAting), an unsupervised policy learning framework that combines prefix-based training with a Mixture-of-Experts refiner to learn effective read/write decisions in an implicit manner, without adding inference-time overhead. Our design requires only minimal modifications to standard transformer architectures and generalizes across both speech-to-text and text-to-speech streaming tasks. Through comprehensive evaluation on six language pairs, our 500 M-parameter speech-to-text model outperforms the Seamless baseline, achieving under 7% BLEU degradation at 1.5 s average lag and under 3% at 3 s. We further demonstrate SimulMEGA’s versatility by extending it to streaming TTS via a unidirectional backbone, yielding superior latency–quality trade-offs.
poster
2509.01200
null
null
[]
[]
[]
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396
Few-Shot Knowledge Distillation of LLMs With Counterfactual Explanations
[ "Faisal Hamman", "Pasan Dissanayake", "Yanjun Fu", "Sanghamitra Dutta" ]
Knowledge distillation is a promising approach to transfer capabilities from resource-intensive teacher models to smaller, resource-efficient student models that can be deployed easily, particularly in task-aware scenarios. However, existing methods of task-aware distillation typically require substantial quantities of data which may be unavailable or expensive to obtain in many practical scenarios. In this paper, we address this challenge by introducing a novel strategy called CoD for *few-shot task-aware knowledge distillation by systematically infusing counterfactual explanations*. Counterfactual explanations (CFE) refer to inputs that can flip the output prediction of the teacher model with minimum input perturbation. Our strategy CoD, short for **Co**unterfactual-explanation-infused **D**istillation leverages these CFEs to precisely map the teacher's decision boundary with significantly fewer samples. We provide theoretical guarantees for motivating the role of CFEs in distillation, from both statistical and geometric perspectives. We mathematically show that CFEs can improve parameter estimation by providing more informative examples near the teacher’s decision boundary. We also derive geometric insights on how CFEs effectively act as *knowledge probes*, helping the students mimic the teacher's decision boundaries more effectively than standard data. We perform experiments across various datasets and LLMs to show that CoD outperforms standard distillation approaches in few-shot regimes (8 - 512 samples), achieving improved performance under equal number of shots which is essentially half of the original samples used by the baselines, infused with their corresponding CFEs.
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397
Towards Doctor-Like Reasoning: Medical RAG Fusing Knowledge with Patient Analogy through Textual Gradients
[ "Yuxing Lu", "Gecheng Fu", "Wei Wu", "Xukai Zhao", "Sin Yee Goi", "Jinzhuo Wang" ]
Existing medical RAG systems mainly leverage knowledge from medical knowledge bases, neglecting the crucial role of experiential knowledge derived from similar patient cases - a key component of human clinical reasoning. To bridge this gap, we propose DoctorRAG, a RAG framework that emulates doctor-like reasoning by integrating both explicit clinical knowledge and implicit case-based experience. DoctorRAG enhances retrieval precision by first allocating conceptual tags for queries and knowledge sources, together with a hybrid retrieval mechanism from both relevant knowledge and patient. In addition, a Med-TextGrad module using multi-agent textual gradients is integrated to ensure that the final output adheres to the retrieved knowledge and patient query. Comprehensive experiments on multilingual, multitask datasets demonstrate that DoctorRAG significantly outperforms strong baseline RAG models and gains improvements from iterative refinements. Our approach generates more accurate, relevant, and comprehensive responses, taking a step towards more doctor-like medical reasoning systems.
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398
Straight-Line Diffusion Model for Efficient 3D Molecular Generation
[ "Yuyan Ni", "Shikun Feng", "Haohan Chi", "Bowen Zheng", "Huan-ang Gao", "Wei-Ying Ma", "Zhi-Ming Ma", "Yanyan Lan" ]
Diffusion-based models have shown great promise in molecular generation but often require a large number of sampling steps to generate valid samples. In this paper, we introduce a novel Straight-Line Diffusion Model (SLDM) to tackle this problem, by formulating the diffusion process to follow a linear trajectory. The proposed process aligns well with the noise sensitivity characteristic of molecular structures and uniformly distributes reconstruction effort across the generative process, thus enhancing learning efficiency and efficacy. Consequently, SLDM achieves state-of-the-art performance on 3D molecule generation benchmarks, delivering a 100-fold improvement in sampling efficiency.
poster
2503.02918
null
null
[]
[]
[]
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399
Efficient Large Language Model Inference with Neural Block Linearization
[ "Mete Erdogan", "Francesco Tonin", "Volkan Cevher" ]
The high inference demands of transformer-based Large Language Models (LLMs) pose substantial challenges in their deployment. To this end, we introduce *Neural Block Linearization* (NBL), a novel framework for accelerating transformer model inference by replacing self-attention layers with linear approximations derived from Linear Minimum Mean Squared Error estimators. NBL leverages Canonical Correlation Analysis to compute a theoretical upper bound on the approximation error. Then, we use this bound as a criterion for substitution, selecting the LLM layers with the lowest linearization error. NBL can be efficiently applied to pre-trained LLMs without the need for fine-tuning. In experiments, NBL achieves notable computational speed-ups while preserving competitive accuracy on multiple reasoning benchmarks. For instance, applying NBL to 12 self-attention layers in *DeepSeek-R1-Distill-Llama-8B* increases the inference speed by 32% with less than 1% accuracy trade-off, making it a flexible and promising solution to improve the inference efficiency of LLMs.
poster
2505.21077
null
null
[]
[]
[]
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