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5,200
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Learning “Partner-aware” Collaborators in Multi-Party Collaboration
|
[
"Abhijnan Nath",
"Nikhil Krishnaswamy"
] |
Large Language Models (LLMs) are increasingly bring deployed in agentic settings where they act as collaborators with humans. Therefore, it is increasingly important to be able to evaluate their abilities to collaborate effectively in multi-turn, multi-party tasks. In this paper, we build on the AI-alignment and “safe interruptability” literature to offer novel theoretical insights on LLM-agent collaborative behavior between a collaborator agent and a intervention agent, where the goal is to learn an ideal “partner-aware” collaborator that increases the group’s common-ground (CG)—alignment on task-relevant propositions—by intelligently collecting information provided in interventions by a partner agent. We show how LLM agents trained using standard RLHF and related approaches are naturally inclined to ignore possibly well-meaning interventions, which makes increasing group common ground non-trivial in this setting. We employ a two-player Modified-action MDP to examine on this suboptimal behavior of standard AI agents, and then propose Interruptible Collaborative Roleplayer (ICR)—a novel “partner-aware” learning algorithm to train CG-optimal collaborators. Experiments on multiple collaborative task environments show that ICR, on average, is more capable of promoting successful CG convergence and exploring more diverse solutions in such tasks.
|
poster
| null | null | null |
[] |
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] |
5,201
|
Near-Optimal Sample Complexity for Online Constrained MDPs
|
[
"Chang Liu",
"Yunfan Li",
"Lin Yang"
] |
Safety is a fundamental challenge in reinforcement learning (RL), particularly in real-world applications such as autonomous driving, robotics, and healthcare. To address this, Constrained Markov Decision Processes (CMDPs) are commonly used to enforce safety constraints while optimizing performance. However, existing methods often suffer from significant safety violations or require a high sample complexity to generate near-optimal policies. We address two settings: relaxed feasibility, where small violations are allowed, and strict feasibility, where no violation is allowed. We propose a model-based primal-dual algorithm that balances regret and bounded constraint violations, drawing on techniques from online RL and constrained optimization. For relaxed feasibility, we prove that our algorithm returns an $\varepsilon$-optimal policy with $\varepsilon$-bounded violation with arbitrarily high probability, requiring $\tilde{O}\left(\frac{SAH^3}{\varepsilon^2}\right)$ learning episodes, matching the lower bound for unconstrained MDPs. For strict feasibility, we prove that our algorithm returns an $\varepsilon$-optimal policy with zero violation with arbitrarily high probability, requiring $\tilde{O}\left(\frac{SAH^5}{\varepsilon^2\zeta^2}\right)$ learning episodes, where $\zeta$ is the problem-dependent Slater constant characterizing the size of the feasible region. This result matches the lower bound for learning CMDPs with access to a generative model. Episodic tabular CMDPs serve as a crucial benchmark for safe RL, providing a structured environment for theoretical analysis and algorithmic validation. Our results demonstrate that learning CMDPs in an online setting is as easy as learning with a generative model and is no more challenging than learning unconstrained MDPs when small violations are allowed.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,202
|
Attractive Metadata Attack: Inducing LLM Agents to Invoke Malicious Tools
|
[
"Kanghua Mo",
"Li Hu",
"Yucheng Long",
"Zhihao Li"
] |
Large language model (LLM) agents have demonstrated remarkable capabilities in complex reasoning and decision-making by leveraging external tools. However, this tool-centric paradigm introduces a previously underexplored attack surface: adversaries can manipulate tool metadata—such as names, descriptions, and parameter schemas—to influence agent behavior. We identify this as a new and stealthy threat surface that allows malicious tools to be preferentially selected by LLM agents, without requiring prompt injection or access to model internals. To demonstrate and exploit this vulnerability, we propose the Attractive Metadata Attack (AMA), a black-box in-context learning framework that generates highly attractive but syntactically and semantically valid tool metadata through iterative optimization. Our attack integrates seamlessly into standard tool ecosystems and requires no modification to the agent’s execution framework. Extensive experiments across ten realistic, simulated tool-use scenarios and a range of popular LLM agents demonstrate consistently high attack success rates (81\%–95\%) and significant privacy leakage, with negligible impact on primary task execution.Moreover, the attack remains effective even under prompt-level defenses and structured tool-selection protocols such as the Model Context Protocol, revealing systemic vulnerabilities in current agent architectures.These findings reveal that metadata manipulation constitutes a potent and stealthy attack surface, highlighting the need for execution-level security mechanisms that go beyond prompt-level defenses. Code is available at https://anonymous.4open.science/r/AMA-A753.
|
poster
|
2508.02110
| null | null |
[] |
[] |
[] |
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] |
5,203
|
Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning
|
[
"Yuqi Jia",
"Minghong Fang",
"Hongbin Liu",
"Jinghuai Zhang",
"Neil Gong"
] |
Poisoning attacks compromise the training phase of federated learning (FL) such that the learned global model misclassifies attacker-chosen inputs called target inputs. Existing defenses mainly focus on protecting the training phase of FL such that the learnt global model is poison free. However, these defenses often achieve limited effectiveness when the clients' local training data is highly non-iid or the number of malicious clients is large, as confirmed in our experiments. In this work, we propose FLForensics, the first poison-forensics method for FL. FLForensics complements existing training-phase defenses. In particular, when training-phase defenses fail and a poisoned global model is deployed, FLForensics aims to trace back the malicious clients that performed the poisoning attack after a misclassified target input is identified. We theoretically show that FLForensics can accurately distinguish between benign and malicious clients under a formal definition of poisoning attack. Moreover, we empirically show the effectiveness of FLForensics at tracing back both existing and adaptive poisoning attacks on five benchmark datasets.
|
poster
|
2407.07221
| null | null |
[] |
[] |
[] |
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] |
5,204
|
Competitive Advantage Attacks to Decentralized Federated Learning
|
[
"Yuqi Jia",
"Minghong Fang",
"Neil Gong"
] |
Decentralized federated learning (DFL) enables clients (e.g., hospitals and banks) to jointly train machine learning models without a central orchestration server. In each global training round, each client trains a local model on its own training data and then they exchange local models for aggregation. In this work, we propose SelfishAttack, a new family of attacks to DFL. In SelfishAttack, a set of selfish clients aim to achieve competitive advantages over the remaining non-selfish ones, i.e., the final learnt local models of the selfish clients are more accurate than those of the non-selfish ones. Towards this goal, the selfish clients send carefully crafted local models to each remaining non-selfish one in each global training round. We formulate finding such local models as an optimization problem and propose methods to solve it when DFL uses different aggregation rules. Theoretically, we show that our methods find the optimal solutions to the optimization problem. Empirically, we show that SelfishAttack successfully increases the accuracy gap (i.e., competitive advantage) between the final learnt local models of selfish clients and those of non-selfish ones. Moreover, SelfishAttack achieves larger accuracy gaps than poisoning attacks when extended to increase competitive advantages.
|
poster
|
2310.13862
| null | null |
[] |
[] |
[] |
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] |
5,205
|
Constrained Optimization From a Control Perspective via Feedback Linearization
|
[
"Runyu Zhang",
"Arvind Raghunathan",
"Jeff Shamma",
"Na Li"
] |
Tools from control and dynamical systems have proven valuable for analyzing and developing optimization methods. In this paper, we establish rigorous theoretical foundations for using feedback linearization—a well-established nonlinear control technique—to solve constrained optimization problems. For equality-constrained optimization, we establish global convergence rates to first-order Karush-Kuhn-Tucker (KKT) points and uncover the close connection between the FL method and the Sequential Quadratic Programming (SQP) algorithm. Building on this relationship, we extend the FL approach to handle inequality-constrained problems. Furthermore, we introduce a momentum-accelerated feedback linearization algorithm and provide a rigorous convergence guarantee.
|
poster
|
2503.12665
| null | null |
[] |
[] |
[] |
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5,206
|
HyperMixup: Hypergraph-Augmented with Higher-order Information Mixup
|
[
"Kaixuan Yao",
"Zhuo Li",
"Jianqing Liang",
"Jiye Liang",
"Ming Li",
"Feilong Cao"
] |
Hypergraphs offer a natural paradigm for modeling complex systems with multi-way interactions. Hypergraph neural networks (HGNNs) have demonstrated remarkable success in learning from such higher-order relational data. While such higher-order modeling enhances relational reasoning, the effectiveness of hypergraph learning remains bottlenecked by two persistent challenges: the scarcity of labeled data inherent to complex systems, and the vulnerability to structural noise in real-world interaction patterns. Traditional data augmentation methods, though successful in Euclidean and graph-structured domains, struggle to preserve the intricate balance between node features and hyperedge semantics, often disrupting the very group-wise interactions that define hypergraph value. To bridge this gap, we present HyperMixup, a hypergraph-aware augmentation framework that preserves higher-order interaction patterns through structure-guided feature mixing. Specifically, HyperMixup contains three critical components: 1) Structure-aware node pairing guided by joint feature-hyperedge similarity metrics, 2) Context-enhanced hierarchical mixing that preserves hyperedge semantics through dual-level feature fusion, and 3) Adaptive topology reconstruction mechanisms that maintain hypergraph consistency while enabling controlled diversity expansion. Theoretically, we establish that our method induces hypergraph-specific regularization effects through gradient alignment with hyperedge covariance structures, while providing robustness guarantees against combined node-hyperedge perturbations. Comprehensive experiments across diverse hypergraph learning tasks demonstrate consistent performance improvements over state-of-the-art baselines, with particular effectiveness in low-label regimes. The proposed framework advances hypergraph representation learning by unifying data augmentation with higher-order topological constraints, offering both practical utility and theoretical insights for relational machine learning.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,207
|
Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach
|
[
"Yuting Huang",
"Ziquan Fang",
"Zhihao Zeng",
"Lu Chen",
"Yunjun Gao"
] |
Spatio-temporal prediction plays a crucial role in intelligent transportation, weather forecasting, and urban planning. While integrating multi-modal data has shown potential for enhancing prediction accuracy, key challenges persist: (i) inadequate fusion of multi-modal information, (ii) confounding factors that obscure causal relations, and (iii) high computational complexity of prediction models. To address these challenges, we propose E$^2$-CSTP, an Effective and Efficient Causal multi-modal Spatio-Temporal Prediction framework. E$^2$-CSTP leverages cross-modal attention and gating mechanisms to effectively integrate multi-modal data. Building on this, we design a dual-branch causal inference approach: the primary branch focuses on spatio-temporal prediction, while the auxiliary branch mitigates bias by modeling additional modalities and applying causal interventions to uncover true causal dependencies. To improve model efficiency, we integrate GCN with the Mamba architecture for accelerated spatio-temporal encoding. Extensive experiments on 4 real-world datasets show that E$^2$-CSTP significantly outperforms 9 state-of-the-art methods, achieving up to 9.66% improvements in accuracy as well as 17.37%-56.11% reductions in computational overhead. All code and data are publicly available at https://anonymous.4open.science/r/E2-CSTP.
|
poster
|
2505.17637
| null | null |
[] |
[] |
[] |
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] |
5,208
|
HyGen: Efficient LLM Serving via Elastic Online-Offline Request Co-location
|
[
"Ting Sun",
"Penghan Wang",
"Fan Lai"
] |
Large language models (LLMs) have facilitated a wide range of applications with distinct service-level objectives (SLOs), from latency-sensitive online tasks like interactive chatbots to throughput-oriented offline workloads like document summarization. The existing deployment model, which dedicates machines to each workload, simplifies SLO management but often leads to poor resource utilization. This paper introduces HyGen, an interference-aware LLM serving system that enables efficient co-location of online and offline workloads while preserving latency requirements. HyGen incorporates two key innovations: (1) performance control mechanisms, including a latency predictor to estimate batch execution time and an SLO-aware profiler to quantify latency interference, and (2) SLO-aware offline scheduling policies that maximize serving throughput and prevent starvation, without compromising online serving latency. Our evaluation on production workloads shows that HyGen achieves up to 3.87x overall throughput and 5.84x offline throughput gains over online and hybrid serving baselines, respectively, while strictly satisfying latency SLOs.
|
poster
|
2501.14808
| null | null |
[] |
[] |
[] |
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] |
5,209
|
Data Fusion for Partial Identification of Causal Effects
|
[
"Quinn Lanners",
"Cynthia Rudin",
"Alexander Volfovsky",
"Harsh Parikh"
] |
Data fusion techniques integrate information from heterogeneous data sources to improve learning, generalization, and decision-making across data sciences. In causal inference, these methods leverage rich observational data to improve causal effect estimation, while maintaining the trustworthiness of randomized controlled trials. Existing approaches often relax the strong "no unobserved confounding" assumption by instead assuming exchangeability of counterfactual outcomes across data sources. However, when both assumptions simultaneously fail—a common scenario in practice—current methods cannot identify or estimate causal effects. We address this limitation by proposing a novel partial identification framework that enables researchers to answer key questions such as: *Is the causal effect positive/negative?* and *How severe must assumption violations be to overturn this conclusion?* Our approach introduces interpretable sensitivity parameters that quantify assumption violations and derives corresponding causal effect bounds. We develop doubly robust estimators for these bounds and operationalize breakdown frontier analysis to understand how causal conclusions change as assumption violations increase. We apply our framework to the Project STAR study, which investigates the effect of classroom size on students’ third-grade standardized test performance. Our analysis reveals that the Project STAR results are robust to simultaneous violations of key assumptions, both on average and across various subgroups of interest. This strengthens confidence in the study's conclusions despite potential unmeasured biases in the data.
|
poster
|
2505.24296
| null | null |
[] |
[] |
[] |
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] |
5,210
|
Looking Beyond the Known: Towards a Data Discovery Guided Open-World Object Detection
|
[
"Anay Majee",
"Amitesh Gangrade",
"Rishabh Iyer"
] |
Open-World Object Detection (OWOD) enriches traditional object detectors by enabling continual discovery and integration of unknown objects via human guidance. However, existing OWOD approaches frequently suffer from semantic confusion between known and unknown classes, alongside catastrophic forgetting, leading to diminished unknown recall and degraded known-class accuracy. To overcome these challenges, we propose **C**ombinato**r**ial **O**pen-**W**orld **D**etection (**CROWD**), a unified framework reformulating unknown object discovery and adaptation as an interwoven combinatorial (set-based) data-discovery (CROWD-Discover) and representation learning (CROWD-Learn) task. CROWD-Discover strategically mines unknown instances by maximizing Submodular Conditional Gain (SCG) functions, selecting representative examples distinctly dissimilar from known objects. Subsequently, CROWD-Learn employs novel combinatorial objectives that jointly disentangle known and unknown representations while maintaining discriminative coherence among known classes, thus mitigating confusion and forgetting. Extensive evaluations on OWOD benchmarks illustrate that CROWD achieves improvements of 2.83% and 2.05% in known-class accuracy on M-OWODB and S-OWODB, respectively, and nearly 2.4$\times$ unknown recall compared to leading baselines.
|
poster
|
2510.00303
| null | null |
[] |
[] |
[] |
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] |
5,211
|
DeltaFormer: Unlock the state space of Transformer
|
[
"Mingyu Xu",
"Tenglong Ao",
"Jiaao He",
"Jianqiao Lu",
"Guang Shi",
"Shu Zhong"
] |
In recent years, large language models with Transformer architecture as the core have made breakthrough progress in many fields. At the same time, there are also some weaknesses in the large language model that have prompted people to reflect, among which the most fundamental one is the reflection on the Transformer architecture. The Transformer architecture has high parallelism and can fully utilize the computing power of GPUs, thus replacing models such as LSTM in the past few years. However, high parallelism is not a free lunch, as it fundamentally limits the performance of models. Especially, the problems that logarithmic precision Transformer architecture can solve are strictly limited to the $TC^0$. And there are many important issues that are usually considered out of $TC^0$, such as Python code evaluation, entity tracking, chess, and other state tracking tasks. Meanwhile, some recent state space methods based on Delta Rule have been able to break through the $TC^0$ architecture, but they are limited by fixed size state spaces and perform poorly on many tasks. To this end, we have re-examined the Transformer from the perspective of a state space with kernel functions and propose an improved Transformer called DeltaFormer. We have theoretically and practically demonstrated that the proposed new architecture can break through the limitation of the inherent $TC^0$ expressivity of Transformers and verified that it is not weaker than standard Transformer in language modeling tasks. We hope our work can provide inspiration for designing more expressive models.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,212
|
Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions
|
[
"Kehan Long",
"Jorge Cortes",
"Nikolay Atanasov"
] |
We study the problem of certifying the stability of closed-loop systems under control policies derived from optimal control or reinforcement learning (RL). Classical Lyapunov methods require a strict step-wise decrease in the Lyapunov function but such a certificate is difficult to construct for a learned control policy. The value function associated with an RL policy is a natural Lyapunov function candidate but it is not clear how it should be modified. To gain intuition, we first study the linear quadratic regulator (LQR) problem and make two key observations. First, a Lyapunov function can be obtained from the value function of an LQR policy by augmenting it with a residual term related to the system dynamics and stage cost. Second, the classical Lyapunov decrease requirement can be relaxed to a generalized Lyapunov condition requiring only decrease on average over multiple time steps. Using this intuition, we consider the nonlinear setting and formulate an approach to learn generalized Lyapunov functions by augmenting RL value functions with neural network residual terms. Our approach successfully certifies the stability of RL policies trained on Gymnasium and DeepMind Control benchmarks. We also extend our method to jointly train neural controllers and stability certificates using a multi-step Lyapunov loss, resulting in larger certified inner approximations of the region of attraction compared to the classical Lyapunov approach. Overall, our formulation enables stability certification for a broad class of systems with learned policies by making certificates easier to construct, thereby bridging classical control theory and modern learning-based methods.
|
poster
|
2505.10947
| null | null |
[] |
[] |
[] |
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] |
5,213
|
Tail-Optimized Caching for LLM Inference
|
[
"Wenxin Zhang",
"Yueying Li",
"Tianyi Peng",
"Ciamac C Moallemi"
] |
Prompt caching is critical for reducing latency and cost in LLM inference---OpenAI and Anthropic report up to 50–90\% cost savings through prompt reuse. Despite its widespread success, little is known about what constitutes an optimal prompt caching policy, particularly when optimizing tail latency—a metric of central importance to practitioners. The widely used Least Recently Used (LRU) policy can perform arbitrarily poor on this metric, as it is oblivious to the heterogeneity of conversation lengths. To address this gap, we propose Tail-Optimized LRU, a simple two-line modification that reallocates KV cache capacity to prioritize high-latency conversations by evicting cache entries unlikely to affect future turns. Though the implementation is simple, we prove its optimality under a natural stochastic model of conversation dynamics, providing the first theoretical justification for LRU in this setting---a result that may be of independent interest to the caching community. Experimentally, on real conversation data WildChat~\cite{zhao2024wildchat}, Tail-Optimized LRU achieves up to 27.5\% reduction in P90 tail Time to First Token latency and 23.9\% in P95 tail latency compared to LRU, along with up to 40\% decrease in SLO violations of 200ms. We believe this provides a practical and theoretically grounded option for practitioners seeking to optimize tail latency in real-world LLM deployments.
|
poster
|
2510.15152
| null | null |
[] |
[] |
[] |
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5,214
|
CodeGEMM: A Codebook-Centric Approach to Efficient GEMM in Quantized LLMs
|
[
"Gunho Park",
"Jeongin Bae",
"Byeongwook Kim",
"Baeseong Park",
"Jiwon Ryu",
"Hoseung Kim",
"Se Jung Kwon",
"Dongsoo Lee"
] |
Codebook-based quantization delivers superior performance in extreme low-bit environments but is hindered by latency overhead in existing dequantization-based kernels, which require loading the entire codebook into programmable cache memory. We propose CodeGEMM, a matrix multiplication kernel optimized for computing codebook-based quantized models by precomputing and storing inner product results in a Psumbook, thereby eliminating the need for traditional dequantization. The kernel supports a wide range of hyperparameter configurations, enabling exploration of trade-offs between latency, memory usage, and accuracy to achieve optimal performance. CodeGEMM obtains a $2.27\times$ speed-up compared to the state-of-the-art codebook-based quantization method with comparable accuracy in the 2-bit configuration.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,215
|
DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing
|
[
"Zixiang Li",
"Haoyu Wang",
"Wei Wang",
"Chuangchuang Tan",
"Yunchao Wei",
"Yao Zhao"
] |
Diffusion models have achieved remarkable success in image generation and editing tasks. Inversion within these models aims to recover the latent noise representation for a real or generated image, enabling reconstruction, editing, and other downstream tasks. However, to date, most inversion approaches suffer from an intrinsic trade-off between reconstruction accuracy and editing flexibility. This limitation arises from the difficulty of maintaining both semantic alignment and structural consistency during the inversion process. In this work, we introduce **Dual-Conditional Inversion (DCI)**, a novel framework that jointly conditions on the source prompt and reference image to guide the inversion process. Specifically, DCI formulates the inversion process as a dual-condition fixed-point optimization problem, minimizing both the latent noise gap and the reconstruction error under the joint guidance. This design anchors the inversion trajectory in both semantic and visual space, leading to more accurate and editable latent representations. Our novel setup brings new understanding to the inversion process. Extensive experiments demonstrate that DCI achieves state-of-the-art performance across multiple editing tasks, significantly improving both reconstruction quality and editing precision. Furthermore, we also demonstrate that our method achieves strong results in reconstruction tasks, implying a degree of robustness and generalizability approaching the ultimate goal of the inversion process.
|
poster
|
2506.02560
| null | null |
[] |
[] |
[] |
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] |
5,216
|
Test-Time Adaptation by Causal Trimming
|
[
"Yingnan Liu",
"Rui Qiao",
"Mong-Li Lee",
"Wynne Hsu"
] |
Test-time adaptation aims to improve model robustness under distribution shifts by adapting models with access to unlabeled target samples. A primary cause of performance degradation under such shifts is the model’s reliance on features that lack a direct causal relationship with the prediction target. We introduce Test-time Adaptation by Causal Trimming (TACT), a method that identifies and removes non-causal components from representations for test distributions. TACT applies data augmentations that preserve causal features while varying non-causal ones. By analyzing the changes in the representations using Principal Component Analysis, TACT identifies the highest variance directions associated with non-causal features. It trims the representations by removing their projections on the identified directions, and uses the trimmed representations for the predictions. During adaptation, TACT continuously tracks and refines these directions to get a better estimate of non-causal features. We theoretically analyze the effectiveness of this approach and empirically validate TACT on real-world out-of-distribution benchmarks. TACT consistently outperforms state-of-the-art methods by a significant margin.
|
poster
|
2510.11133
| null | null |
[] |
[] |
[] |
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] |
5,217
|
Entropy-Calibrated Label Distribution Learning
|
[
"Yunan Lu",
"Bowen Xue",
"Xiuyi Jia",
"Lei Yang"
] |
Label Distribution Learning (LDL) has emerged as a powerful framework for estimating complete conditional label distributions, providing crucial reliability for risk-sensitive decision-making tasks. While existing LDL algorithms exhibit competent performance under the conventional LDL performance evaluation methods, two key limitations remain: (1) current algorithms systematically underperform on the samples with low-entropy label distributions, which can be particularly valuable for decision making, and (2) the conventional performance evaluation methods are inherently biased due to the numerical imbalance of samples. In this paper, through empirical and theoretical analyses, we find that excessive cohesion between anchor vectors contributes significantly to the observed entropy bias phenomenon in LDL algorithms. Accordingly, we propose an inter-anchor angular regularization term that mitigates cohesion among anchor vectors by penalizing over-small angles. Besides, to alleviate the numerical imbalance of high-entropy samples in test set, we propose an entropy-calibrated aggregation strategy that obtains the overall model performance by evaluating performance on the low-entropy and high-entropy subsets of the overall test set separately. Finally, we conduct extensive experiments on various real-world datasets to demonstrate the effectiveness of our proposal.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,218
|
Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints
|
[
"Hyungki Im",
"Wyame Benslimane",
"Paul Grigas"
] |
We study an extension of contextual stochastic linear optimization (CSLO) that, in contrast to most of the existing literature, involves inequality constraints that depend on uncertain parameters predicted by a machine learning model. To handle the constraint uncertainty, we use contextual uncertainty sets constructed via methods like conformal prediction. Given a contextual uncertainty set method, we introduce the "Smart Predict-then-Optimize with Robust Constraints" (SPO-RC) loss, a feasibility-sensitive adaptation of the SPO loss that measures decision error of predicted objective parameters. We also introduce a convex surrogate, SPO-RC+, and prove Fisher consistency with SPO-RC. To enhance performance, we train on truncated datasets where true constraint parameters lie within the uncertainty sets, and we correct the induced sample selection bias using importance reweighting techniques. Through experiments on fractional knapsack and alloy production problem instances, we demonstrate that SPO-RC+ effectively handles uncertainty in constraints and that combining truncation with importance reweighting can further improve performance.
|
poster
|
2505.22881
| null | null |
[] |
[] |
[] |
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] |
5,219
|
EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test
|
[
"Yuhui Li",
"Fangyun Wei",
"Chao Zhang",
"Hongyang Zhang"
] |
The sequential nature of modern LLMs makes them expensive and slow, and speculative sam- pling has proven to be an effective solution to this problem. Methods like EAGLE perform autoregression at the feature level, reusing top- layer features from the target model to achieve better results than vanilla speculative sampling. A growing trend in the LLM community is scaling up training data to improve model intelligence without increasing inference costs. However, we observe that scaling up data provides limited improvements for EAGLE. We identify that this limitation arises from EAGLE’s feature prediction constraints. In this paper, we introduce EAGLE-3, which abandons feature prediction in favor of direct token prediction and replaces reliance on top-layer features with multi-layer feature fusion via a technique named training-time test. These improvements significantly enhance performance and enable the draft model to fully benefit from scaling up training data. Our experiments include both chat models and reasoning models, evaluated on five tasks. The results show that EAGLE-3 achieves a speedup ratio up to 6.5x, with about 1.4x improvement over EAGLE-2. In the SGLang framework, EAGLE- 3 achieves a 1.38x throughput improvement at a batch size of 64.
|
poster
| null | null | null |
[] |
[] |
[] |
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5,220
|
LaRes: Evolutionary Reinforcement Learning with LLM-based Adaptive Reward Search
|
[
"Pengyi Li",
"Jianye Hao",
"Hongyao Tang",
"Jinbin Qiao",
"YAN ZHENG"
] |
The integration of evolutionary algorithms (EAs) with reinforcement learning (RL) has shown superior performance compared to standalone methods. However, previous research focuses on exploration in policy parameter space, while overlooking the reward function search. To bridge this gap, we propose LaRes, a novel hybrid framework that achieves efficient policy learning through reward function search. LaRes leverages large language models (LLMs) to generate the reward function population, guiding RL in policy learning. The reward functions are evaluated by the policy performance and improved through LLMs. To improve sample efficiency, LaRes employs a shared experience buffer that collects experiences from all policies, with each experience containing rewards from all reward functions. Upon reward function updates, the rewards of experiences are relabeled, enabling efficient use of historical data. Furthermore, we introduce a Thompson sampling-based selection mechanism that enables more efficient elite interaction. To prevent policy collapse when improving reward functions, we propose a reward scaling and parameter constraint mechanism to efficiently coordinate reward search with policy learning. Across 16 robot manipulation tasks, LaRes outperforms other strong RL and ERL baselines guided by expert rewards in terms of both sample efficiency and final performance.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,221
|
Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models
|
[
"Haoyi Song",
"Ruihan Ji",
"Naichen Shi",
"Fan Lai",
"Raed AL Kontar"
] |
Large language models (LLMs) have transformed natural language processing, but their reliable deployment requires effective uncertainty quantification (UQ). Existing UQ methods are often heuristic and lack a fully probabilistic foundation. This paper begins by providing a theoretical justification for the role of perturbations in UQ for LLMs. We then introduce a dual random walk perspective, modeling input–output pairs as two Markov chains with transition probabilities defined by semantic similarity. Building on this, we propose a fully probabilistic framework based on an inverse model, which quantifies uncertainty by evaluating the diversity of the input space conditioned on a given output through systematic perturbations. Within this framework, we define a new uncertainty measure, Inv-Entropy. A key strength of our framework is its flexibility: it supports various definitions of uncertainty measures, embeddings, perturbation strategies, and similarity metrics. We also propose GAAP, a perturbation algorithm based on genetic algorithms, which enhances the diversity of sampled inputs. In addition, we introduce a new evaluation metric, Temperature Sensitivity of Uncertainty (TSU), which directly assesses uncertainty without relying on correctness as a proxy. Extensive experiments demonstrate that Inv-Entropy outperforms existing semantic UQ methods.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,222
|
Model Merging in Pre-training of Large Language Models
|
[
"Yunshui Li",
"Yiyuan Ma",
"Shen Yan",
"Chaoyi Zhang",
"Jing Liu",
"Jianqiao Lu",
"Ziwen Xu",
"Mengzhao Chen",
"Minrui Wang",
"Shiyi Zhan",
"Jin Ma",
"Xunhao Lai",
"Yao Luo",
"Xingyan Bin",
"Hongbin Ren",
"Mingji Han",
"Wenhao Hao",
"Bairen Yi",
"LingJun Liu",
"Bole Ma",
"Xiaoying Jia",
"zhou Xun",
"liang xiang",
"Yonghui Wu"
] |
Model merging has emerged as a promising technique for enhancing large language models, though its application in large-scale pre-training remains relatively unexplored. In this paper, we present a comprehensive investigation of model merging techniques during the pre-training process. Through extensive experiments with both dense and Mixture-of-Experts (MoE) architectures ranging from millions to over 100 billion parameters, we demonstrate that merging checkpoints trained with constant learning rates not only achieves significant performance improvements but also enables accurate prediction of annealing behavior. These improvements lead to both more efficient model development and significantly lower training costs. Our detailed ablation studies on merging strategies and hyperparameters provide new insights into the underlying mechanisms while uncovering novel applications. Through comprehensive experimental analysis, we offer the open-source community practical pre-training guidelines for effective model merging.
|
poster
|
2505.12082
| null | null |
[] |
[] |
[] |
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] |
5,223
|
Delving into Cascaded Instability: A Lipschitz Continuity View on Image Restoration and Object Detection Synergy
|
[
"Qing Zhao",
"Weijian Deng",
"Pengxu Wei",
"ZiYi Dong",
"hannan lu",
"Xiangyang Ji",
"Liang Lin"
] |
To improve detection robustness in adverse conditions (e.g., haze and low light), image restoration is commonly applied as a pre-processing step to enhance image quality for the detector. However, the functional mismatch between restoration and detection networks can introduce instability and hinder effective integration---an issue that remains underexplored. We revisit this limitation through the lens of Lipschitz continuity, analyzing the functional differences between restoration and detection networks in both the input space and the parameter space. Our analysis shows that restoration networks perform smooth, continuous transformations, while object detectors operate with discontinuous decision boundaries, making them highly sensitive to minor perturbations. This mismatch introduces instability in traditional cascade frameworks, where even imperceptible noise from restoration is amplified during detection, disrupting gradient flow and hindering optimization. To address this, we propose Lipschitz-regularized object detection (LROD), a simple yet effective framework that integrates image restoration directly into the detector’s feature learning, harmonizing the Lipschitz continuity of both tasks during training. We implement this framework as Lipschitz-regularized YOLO (LR-YOLO), extending seamlessly to existing YOLO detectors. Extensive experiments on haze and low-light benchmarks demonstrate that LR-YOLO consistently improves detection stability, optimization smoothness, and overall accuracy.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,224
|
Cost-aware LLM-based Online Dataset Annotation
|
[
"Eray Can Elumar",
"Cem Tekin",
"Osman Yagan"
] |
Recent advances in large language models (LLMs) have enabled automated dataset labeling with minimal human supervision. While majority voting across multiple LLMs can improve label reliability by mitigating individual model biases, it incurs high computational costs due to repeated querying. In this work, we propose a novel online framework, Cost-aware Majority Voting (CaMVo), for efficient and accurate LLM-based dataset annotation. CaMVo adaptively selects a subset of LLMs for each data instance based on contextual embeddings, balancing confidence and cost without requiring pre-training or ground-truth labels. Leveraging a LinUCB-based selection mechanism and a Bayesian estimator over confidence scores, CaMVo estimates a lower bound on labeling accuracy for each LLM and aggregates responses through weighted majority voting. Our empirical evaluation on the MMLU and IMDB Movie Review datasets demonstrates that CaMVo achieves comparable or superior accuracy to full majority voting while significantly reducing labeling costs. This establishes CaMVo as a practical and robust solution for cost-efficient annotation in dynamic labeling environments.
|
spotlight
|
2505.15101
| null | null |
[] |
[] |
[] |
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] |
5,225
|
Establishing Linear Surrogate Regret Bounds for Convex Smooth Losses via Convolutional Fenchel–Young Losses
|
[
"Yuzhou Cao",
"Han Bao",
"Lei Feng",
"Bo An"
] |
Surrogate regret bounds, also known as excess risk bounds, bridge the gap between the convergence rates of surrogate and target losses, with linear bounds favorable for their lossless regret transfer. While convex smooth surrogate losses are appealing in particular due to the efficient estimation and optimization, the existence of a trade-off between the smoothness and linear regret bound has been believed in the community. That being said, the better optimization and estimation properties of convex smooth surrogate losses may inevitably deteriorate after undergoing the regret transfer onto a target loss. Strikingly, we overcome this dilemma for arbitrary discrete target losses by constructing a convex smooth surrogate loss, which entails a linear surrogate regret bound composed with a tailored prediction link. The construction is based on Fenchel--Young losses generated by the $\textit{convolutional negentropy}$,which is relevant to the infimal convolution of a generalized negentropy and the target Bayes risk. Consequently, the infimal convolution enables us to derive a smooth loss while maintaining the regret bound linear. We additionally benefit from the infimal convolution to have a consistent estimator of the linear property, which serves as prediction confidence. Our results are overall a novel demonstration of how convex analysis penetrates into optimization and statistical efficiency in risk minimization.
|
spotlight
|
2505.09432
| null | null |
[] |
[] |
[] |
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] |
5,226
|
FALQON: Accelerating LoRA Fine-tuning with FP8 Arithmetic
|
[
"Kanghyun Choi",
"Hyeyoon Lee",
"Sunjong Park",
"Dain Kwon",
"Jinho Lee"
] |
Low-bit floating-point (FP) formats, such as FP8, provide significant acceleration and memory savings in model training thanks to native hardware support on modern GPUs and NPUs. However, we analyze that FP8 quantization offers speedup primarily for large-dimensional matrix multiplications, while inherent quantization overheads diminish speedup when applied to low-rank adaptation (LoRA), which uses small-dimensional matrices for efficient fine-tuning of large language models (LLMs). To address this limitation, we propose FALQON, a novel framework that eliminates the quantization overhead from separate LoRA computational paths by directly merging LoRA adapters into an FP8-quantized backbone during fine-tuning. Furthermore, we reformulate the forward and backward computations for merged adapters to significantly reduce quantization overhead, and introduce a row-wise proxy update mechanism that efficiently integrates substantial updates into the quantized backbone. Experimental evaluations demonstrate that FALQON achieves approximately a 3$\times$ training speedup over existing quantized LoRA methods with a similar level of accuracy, providing a practical solution for efficient large-scale model fine-tuning. Moreover, FALQON’s end-to-end FP8 workflow removes the need for post-training quantization, facilitating efficient deployment.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,227
|
VFRTok: Variable Frame Rates Video Tokenizer with Duration-Proportional Information Assumption
|
[
"Tianxiong Zhong",
"Xingye Tian",
"Boyuan Jiang",
"Xuebo Wang",
"Xin Tao",
"Pengfei Wan",
"Zhiwei Zhang"
] |
Modern video generation frameworks based on Latent Diffusion Models suffer from inefficiencies in tokenization due to the Frame-Proportional Information Assumption.Existing tokenizers provide fixed temporal compression rates, causing the computational cost of the diffusion model to scale linearly with the frame rate.The paper proposes the Duration-Proportional Information Assumption: the upper bound on the information capacity of a video is proportional to the duration rather than the number of frames.Based on this insight, the paper introduces VFRTok, a Transformer-based video tokenizer, that enables variable frame rate encoding and decoding through asymmetric frame rate training between the encoder and decoder.Furthermore, the paper proposes Partial Rotary Position Embeddings (RoPE) to decouple position and content modeling, which groups correlated patches into unified tokens.The Partial RoPE effectively improves content-awareness, enhancing the video generation capability.Benefiting from the compact and continuous spatio-temporal representation, VFRTok achieves competitive reconstruction quality and state-of-the-art generation fidelity while using only $1/8$ tokens compared to existing tokenizers.
|
poster
|
2505.12053
|
https://github.com/KwaiVGI/VFRTok
| null |
[] |
[
"KwaiVGI/VFRTok"
] |
[] |
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] |
5,228
|
You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering
|
[
"Hanyang Li",
"Yuheng Jia",
"Hui LIU",
"Junhui Hou"
] |
Recent deep clustering models have produced impressive clustering performance. However, a common issue with existing methods is the disparity between global and local feature structures. While local structures typically show strong consistency and compactness within class samples, global features often present intertwined boundaries and poorly separated clusters. Motivated by this observation, we propose **DCBoost**, a parameter-free plug-in designed to enhance the global feature structures of current deep clustering models. By harnessing reliable local structural cues, our method aims to elevate clustering performance effectively. Specifically, we first identify high-confidence samples through adaptive $k$-nearest neighbors-based consistency filtering, aiming to select a sufficient number of samples with high label reliability to serve as trustworthy anchors for self-supervision. Subsequently, these samples are utilized to compute a discriminative loss, which promotes both intra-class compactness and inter-class separability, to guide network optimization. Extensive experiments across various benchmark datasets showcase that our DCBoost significantly improves the clustering performance of diverse existing deep clustering models. Notably, our method improves the performance of current state-of-the-art baselines (e.g., ProPos) by more than 3\% and amplifies the silhouette coefficient by over $7\times$. **The source code is included in the Supplementary Material.**
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,229
|
Mask Image Watermarking
|
[
"Runyi Hu",
"Jie Zhang",
"Shiqian Zhao",
"Nils Lukas",
"Jiwei Li",
"Qing Guo",
"Han Qiu",
"Tianwei Zhang"
] |
We present MaskMark, a simple, efficient, and flexible framework for image watermarking. MaskMark has two variants: (1) MaskMark-D, which supports global watermark embedding, watermark localization, and local watermark extraction for applications such as tamper detection; (2) MaskMark-ED, which focuses on local watermark embedding and extraction, offering enhanced robustness in small regions to support fine-grined image protection. MaskMark-D builds on the classical encoder-distortion layer-decoder training paradigm. In MaskMark-D, we introduce a simple masking mechanism during the decoding stage that enables both global and local watermark extraction. During training, the decoder is guided by various types of masks applied to watermarked images before extraction, helping it learn to localize watermarks and extract them from the corresponding local areas. MaskMark-ED extends this design by incorporating the mask into the encoding stage as well, guiding the encoder to embed the watermark in designated local regions, which improves robustness under regional attacks. Extensive experiments show that MaskMark achieves state-of-the-art performance in global and local watermark extraction, watermark localization, and multi-watermark embedding. It outperforms all existing baselines, including the recent leading model WAM for local watermarking, while preserving high visual quality of the watermarked images. In addition, MaskMark is highly efficient and adaptable. It requires only 20 hours of training on a single A6000 GPU, achieving 15× computational efficiency compared to WAM. By simply adjusting the distortion layer, MaskMark can be quickly fine-tuned to meet varying robustness requirements.
|
poster
|
2504.12739
|
https://github.com/hurunyi/maskmark
| null |
[] |
[
"Runyi-Hu/MaskWM"
] |
[] |
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] |
5,230
|
Aligning Text-to-Image Diffusion Models to Human Preference by Classification
|
[
"Longquan Dai",
"Xiaolu Wei",
"wang he",
"Shaomeng Wang",
"Jinhui Tang"
] |
Text-to-image diffusion models are typically trained on large-scale web data, often resulting in outputs that misalign with human preferences. Inspired by preference learning in large language models, we propose ABC (Alignment by Classification), a simple yet effective framework for aligning diffusion models with human preferences. In contrast to prior DPO-based methods that depend on suboptimal supervised fine-tuned (SFT) reference models, ABC assumes access to an ideal reference model perfectly aligned with human intent and reformulates alignment as a classification problem. Under this view, we recognize that preference data naturally forms a semi-supervised classification setting. To address this, we propose a data augmentation strategy that transforms preference comparisons into fully supervised training signals. We then introduce a classification-based ABC loss to guide alignment. Our alignment by classification approach could effectively steer the diffusion model toward the behavior of the ideal reference. Experiments on various diffusion models show that our ABC consistently outperforms existing baselines, offering a scalable and robust solution for preference-based text-to-image fine-tuning.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
5,231
|
Self-Improving Embodied Foundation Models
|
[
"Seyed Kamyar Seyed Ghasemipour",
"Ayzaan Wahid",
"Jonathan Tompson",
"Pannag R Sanketi",
"Igor Mordatch"
] |
Foundation models trained on web-scale data have revolutionized robotics, but their application to low-level control remains largely limited to behavioral cloning. Drawing inspiration from the success of the reinforcement learning stage in fine-tuning large language models, we propose a two-stage post-training approach for robotics. The first stage, Supervised Fine-Tuning (SFT), fine-tunes pretrained foundation models using both: a) behavioral cloning, and b) steps-to-go prediction objectives. In the second stage, Self-Improvement, steps-to-go prediction enables the extraction of a well-shaped reward function and a robust success detector, enabling a fleet of robots to autonomously practice downstream tasks with minimal human supervision. Through extensive experiments on real-world and simulated robot embodiments, our novel post-training recipe unveils significant results on Embodied Foundation Models. First, we demonstrate that the combination of SFT and Self-Improvement is significantly more sample-efficient than scaling imitation data collection for supervised learning, and that it leads to policies with significantly higher success rates. Further ablations highlight that the combination of web-scale pretraining and Self-Improvement is the key to this sample-efficiency. Next, we demonstrate that our proposed combination uniquely unlocks a capability not possible by current methods: autonomously practicing and acquiring novel skills that generalize far beyond the behaviors observed in the imitation learning datasets used during training. These findings highlight the transformative potential of combining pretrained foundation models with online Self-Improvement to enable autonomous skill acquisition in robotics.
|
poster
|
2509.15155
| null |
https://self-improving-efms.github.io/
|
[] |
[] |
[] |
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] |
5,232
|
Multi-View Oriented GPLVM: Expressiveness and Efficiency
|
[
"Zi Yang",
"Ying Li",
"Zhidi Lin",
"Michael Minyi Zhang",
"Pablo Martinez Olmos"
] |
The multi-view Gaussian process latent variable model (MV-GPLVM) aims to learn a unified representation from multi-view data but is hindered by challenges such as limited kernel expressiveness and low computational efficiency. To overcome these issues, we first introduce a new duality between the spectral density and the kernel function. By modeling the spectral density with a bivariate Gaussian mixture, we then derive a generic and expressive kernel termed Next-Gen Spectral Mixture (NG-SM) for MV-GPLVMs. To address the inherent computational inefficiency of the NG-SM kernel, we propose a random Fourier feature approximation. Combined with a tailored reparameterization trick, this approximation enables scalable variational inference for both the model and the unified latent representations. Numerical evaluations across a diverse range of multi-view datasets demonstrate that our proposed method consistently outperforms state-of-the-art models in learning meaningful latent representations.
|
poster
|
2502.08253
| null | null |
[] |
[] |
[] |
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] |
5,233
|
HyRF: Hybrid Radiance Fields for Memory-efficient and High-quality Novel View Synthesis
|
[
"Zipeng Wang",
"Dan Xu"
] |
Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful alternative to NeRF-based approaches, enabling real-time, high-quality novel view synthesis through explicit, optimizable 3D Gaussians. However, 3DGS suffers from significant memory overhead due to its reliance on per-Gaussian parameters to model view-dependent effects and anisotropic shapes. While recent works propose compressing 3DGS with neural fields, these methods struggle to capture high-frequency spatial variations in Gaussian properties, leading to degraded reconstruction of fine details. We present Hybrid Radiance Fields (HyRF), a novel scene representation that combines the strengths of explicit Gaussians and neural fields. HyRF decomposes the scene into (1) a compact set of explicit Gaussians storing only critical high-frequency parameters and (2) grid-based neural fields that predict remaining properties. To enhance representational capacity, we introduce a decoupled neural field architecture, separately modeling geometry (scale, opacity, rotation) and view-dependent color. Additionally, we propose a hybrid rendering scheme that composites Gaussian splatting with a neural field-predicted background, addressing limitations in distant scene representation.Experiments demonstrate that HyRF achieves state-of-the-art rendering quality while reducing model size by over 20× compared to 3DGS and maintaining real-time performance.
|
poster
|
2509.17083
|
https://github.com/wzpscott/hybrid-radiance-fields
|
https://wzpscott.github.io/hyrf/
|
[] |
[] |
[] |
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] |
5,234
|
Partition to Evolve: Niching-enhanced Evolution with LLMs for Automated Algorithm Discovery
|
[
"Qinglong Hu",
"Qingfu Zhang"
] |
Large language model-assisted Evolutionary Search (LES) has emerged as a promising approach for Automated Algorithm Discovery (AAD). While many evolutionary search strategies have been developed for classic optimization problems, LES operates in abstract language spaces, presenting unique challenges for applying these strategies effectively. To address this, we propose a general LES framework that incorporates feature-assisted niche construction within abstract search spaces, enabling the seamless integration of niche-based search strategies from evolutionary computation. Building on this framework, we introduce PartEvo, an LES method that combines niche collaborative search and advanced prompting strategies to improve algorithm discovery efficiency. Experiments on both synthetic and real-world optimization problems show that PartEvo outperforms human-designed baselines and surpasses prior LES methods, such as Eoh and Funsearch. In particular, on resource scheduling tasks, PartEvo generates meta-heuristics with low design costs, achieving up to 90.1\% performance improvement over widely-used baseline algorithms, highlighting its potential for real-world applications.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,235
|
Class-aware Domain Knowledge Fusion and Fission for Continual Test-Time Adaptation
|
[
"Jiahuan Zhou",
"Chao Zhu",
"Zhenyu Cui",
"Zichen Liu",
"Xu Zou",
"Gang Hua"
] |
Continual Test-Time Adaptation (CTTA) aims to quickly fine-tune the model during the test phase so that it can adapt to multiple unknown downstream domain distributions without pre-acquiring downstream domain data. To this end, existing advanced CTTA methods mainly reduce the catastrophic forgetting of historical knowledge caused by irregular switching of downstream domain data by restoring the initial model or reusing historical models. However, these methods are usually accompanied by serious insufficient learning of new knowledge and interference from potentially harmful historical knowledge, resulting in severe performance degradation. To this end, we propose a class-aware domain Knowledge Fusion and Fission method for continual test-time adaptation, called KFF, which adaptively expands and merges class-aware domain knowledge in old and new domains according to the test-time data from different domains, where discriminative historical knowledge can be dynamically accumulated. Specifically, considering the huge domain gap within streaming data, a domain Knowledge FIssion (KFI) module is designed to adaptively separate new domain knowledge from a paired class-aware domain prompt pool, alleviating the impact of negative knowledge brought by old domains that are distinct from the current domain. Besides, to avoid the cumulative computation and storage overheads from continuously fissioning new knowledge, a domain Knowledge FUsion (KFU) module is further designed to merge the fissioned new knowledge into the existing knowledge pool with minimal cost, where a greedy knowledge dynamic merging strategy is designed to improve the compatibility of new and old knowledge while keeping the computational efficiency.
|
poster
|
2510.12150
| null | null |
[] |
[] |
[] |
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] |
5,236
|
BTL-UI: Blink-Think-Link Reasoning Model for GUI Agent
|
[
"Shaojie Zhang",
"Ruoceng Zhang",
"Pei Fu",
"Shaokang Wang",
"Jiahui Yang",
"Xin Du",
"ShiqiCui",
"Bin Qin",
"Ying Huang",
"Zhenbo Luo",
"Jian Luan"
] |
In the field of AI-driven human-GUI interaction automation, while rapid advances in multimodal large language models and reinforcement fine-tuning techniques have yielded remarkable progress, a fundamental challenge persists: their interaction logic significantly deviates from natural human-GUI communication patterns. To fill this gap, we propose ``Blink-Think-Link'' (BTL), a brain-inspired framework for human-GUI interaction that mimics the human cognitive process between users and graphical interfaces. The system decomposes interactions into three biologically plausible phases: (1) \textbf{Blink} - rapid detection and attention to relevant screen areas, analogous to saccadic eye movements; (2) \textbf{Think} - higher-level reasoning and decision-making, mirroring cognitive planning; and (3) \textbf{Link} - generation of executable commands for precise motor control, emulating human action selection mechanisms. Additionally, we introduce two key technical innovations for BTL framework:(1) Blink Data Generation - an automated annotation pipeline specifically optimized for blink data, and(2) {BTL Reward – the first rule-based reward mechanism that enables reinforcement learning driven by both process and outcome.}Building upon this framework, we develop a GUI agent model named BTL-UI, which demonstrates consistent state-of-the-art performance across both static GUI understanding and dynamic interaction tasks in comprehensive benchmarks. These results provide conclusive empirical validation of the framework's efficacy in developing advanced GUI Agents. We will soon release the relevant data and models.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,237
|
Adaptable Safe Policy Learning from Multi-task Data with Constraint Prioritized Decision Transformer
|
[
"Ruiqi Xue",
"Ziqian Zhang",
"Lihe Li",
"Cong Guan",
"Lei Yuan",
"Yang Yu"
] |
Learning safe reinforcement learning (RL) policies from offline multi-task datasets without direct environmental interaction is crucial for efficient and reliable deployment of RL agents. Benefiting from their scalability and strong in-context learning capabilities, recent approaches attempt to utilize Decision Transformer (DT) architectures for offline safe RL, demonstrating promising adaptability across varying safety budgets. However, these methods primarily focus on single-constraint scenarios and struggle with diverse constraint configurations across multiple tasks. Additionally, their reliance on heuristically defined Return-To-Go (RTG) inputs limits flexibility and reduces learning efficiency, particularly in complex multi-task environments. To address these limitations, we propose CoPDT, a novel DT-based framework designed to enhance adaptability to diverse constraints and varying safety budgets. Specifically, CoPDT introduces a constraint prioritized prompt encoder, which leverages sparse binary cost signals to accurately identify constraints, and a constraint prioritized Return-To-Go (CPRTG) token mechanism, which dynamically generates RTGs based on identified constraints and corresponding safety budgets. Extensive experiments on the OSRL benchmark demonstrate that CoPDT achieves superior efficiency and significantly enhanced safety compliance across diverse multi-task scenarios, surpassing state-of-the-art DT-based methods by satisfying safety constraints in more than twice as many tasks.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,238
|
Per-Architecture Training-Free Metric Optimization for Neural Architecture Search
|
[
"Mingzhuo Lin",
"Jianping Luo"
] |
Neural Architecture Search (NAS) aims to identify high-performance networks within a defined search space. Training-free metrics have been proposed to estimate network performance without actual training, reducing NAS deployment costs. However, individual training-free metrics often capture only partial architectural features, and their estimation capabilities are different in various tasks. Combining multiple training-free metrics has been explored to enhance scalability across tasks. Yet, these methods typically optimize global metric combinations over the entire search space, overlooking the varying sensitivities of different architectures to specific metrics, which may limit the final architectures' performance. To address these challenges, we propose the Per-Architecture Training-Free Metric Optimization NAS (PO-NAS) algorithm. This algorithm: (a) Integrates multiple training-free metrics as auxiliary scores, dynamically optimizing their combinations using limited real-time training data, without relying on benchmarks; (b) Individually optimizes metric combinations for each architecture; (c) Integrates an evolutionary algorithm that leverages efficient predictions from surrogate models, enhancing search efficiency in large search spaces. Notably, PO-NAS combines the efficiency of training-free search with the robust performance of training-based evaluations. Extensive experiments demonstrate the effectiveness of our approach. Our code has been made publicly available at https://anonymous.4open.science/r/PO-NAS-2953.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,239
|
Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking
|
[
"Pengxiang Li",
"Shilin Yan",
"Jiayin Cai",
"Renrui Zhang",
"Ruichuan An",
"Ziyu Guo",
"Xiaowei Gao"
] |
Classifier-Free Guidance (CFG) significantly enhances controllability in generative models by interpolating conditional and unconditional predictions. However, standard CFG often employs a static unconditional input, which can be suboptimal for iterative generation processes where model uncertainty varies dynamically. We introduce Adaptive Classifier-Free Guidance (A-CFG), a novel method that tailors the unconditional input by leveraging the model's instantaneous predictive confidence. At each step of an iterative (masked) diffusion language model, A-CFG identifies tokens in the currently generated sequence for which the model exhibits low confidence. These tokens are temporarily re-masked to create a dynamic, localized unconditional input. This focuses CFG's corrective influence precisely on areas of ambiguity, leading to more effective guidance. We integrate A-CFG into a state-of-the-art masked diffusion language model and demonstrate its efficacy. Experiments on diverse language generation benchmarks show that A-CFG yields substantial improvements over standard CFG, achieving, for instance, a 3.9 point gain on GPQA. Our work highlights the benefit of dynamically adapting guidance mechanisms to model uncertainty in iterative generation.
|
poster
|
2505.20199
|
https://github.com/pixeli99/a-cfg
| null |
[] |
[] |
[] |
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] |
5,240
|
HOComp: Interaction-Aware Human-Object Composition
|
[
"Dong Liang",
"Jinyuan Jia",
"Yuhao LIU",
"Rynson Lau"
] |
While existing image‑guided composition methods may help insert a foreground object onto a user-specified region of a background image, achieving natural blending inside the region with the rest of the image unchanged, we observe that these existing methods often struggle in synthesizing seamless interaction-aware compositions when the task involves human-object interactions.In this paper, we first propose HOComp, a novel approach for compositing a foreground object onto a human-centric background image, while ensuring harmonious interactions between the foreground object and the background person and their consistent appearances. Our approach includes two key designs: (1) MLLMs-driven Region-based Pose Guidance (MRPG), which utilizes MLLMs to identify the interaction region as well as the interaction type (e.g., holding and lefting) to provide coarse-to-fine constraints to the generated pose for the interaction while incorporating human pose landmarks to track action variations and enforcing fine-grained pose constraints; and (2) Detail-Consistent Appearance Preservation (DCAP), which unifies a shape-aware attention modulation mechanism, a multi-view appearance loss, and a background consistency loss to ensure consistent shapes/textures of the foreground and faithful reproduction of the background human. We then propose the first dataset, named Interaction-aware Human-Object Composition (IHOC), for the task.Experimental results on our dataset show that HOComp effectively generates harmonious human-object interactions with consistent appearances, and outperforms relevant methods qualitatively and quantitatively.
|
poster
|
2507.16813
| null |
https://dliang293.github.io/HOComp-project
|
[] |
[] |
[] |
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] |
5,241
|
Less is More: Improving LLM Alignment via Preference Data Selection
|
[
"Xun Deng",
"Han Zhong",
"Rui Ai",
"Fuli Feng",
"Zheng Wang",
"Xiangnan He"
] |
Direct Preference Optimization (DPO) has emerged as a promising approach for aligning large language models with human preferences. While prior work mainly extends DPO from the aspect of the objective function, we instead improve DPO from the largely overlooked but critical aspect of data selection. Specifically, we address the issue of parameter shrinkage caused by noisy data by proposing a novel margin-maximization principle for dataset curation in DPO training. To further mitigate the noise in different reward models, we propose a Bayesian Aggregation approach that unifies multiple margin sources (external and implicit) into a single preference probability. Extensive experiments in diverse settings demonstrate the consistently high data efficiency of our approach. Remarkably, by using just 10\% of the Ultrafeedback dataset, our approach achieves 3\% to 8\% improvements across various Llama, Mistral, and Qwen models on the AlpacaEval2 benchmark. Furthermore, our approach seamlessly extends to iterative DPO, yielding a roughly 3\% improvement with 25\% online data, revealing the high redundancy in this presumed high-quality data construction manner. These results highlight the potential of data selection strategies for advancing preference optimization.
|
spotlight
|
2502.14560
| null | null |
[] |
[] |
[] |
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] |
5,242
|
Risk-aware Direct Preference Optimization under Nested Risk Measure
|
[
"Lijun Zhang",
"Lin Li",
"Yajie Qi",
"Huizhong Song",
"Yaodong Yang",
"Jun Wang",
"Wei Wei"
] |
When fine-tuning pre-trained Large Language Models (LLMs) to align with human values and intentions, maximizing the estimated reward can lead to superior performance, but it also introduces potential risks due to deviations from the reference model's intended behavior. Most existing methods typically introduce KL divergence to constrain deviations between the trained model and the reference model; however, this may not be sufficient in certain applications that require tight risk control. In this paper, we introduce Risk-aware Direct Preference Optimization (Ra-DPO), a novel approach that incorporates risk-awareness by employing a class of nested risk measures. This approach formulates a constrained risk-aware advantage function maximization problem and then converts the Bradley-Terry model into a token-level representation. The objective function maximizes the likelihood of the policy while suppressing the deviation between a trained model and the reference model using a sequential risk ratio, thereby enhancing the model's risk-awareness. Experimental results across three open-source datasets: IMDb Dataset, Anthropic HH Dataset, and AlpacaEval, demonstrate the proposed method's superior performance in balancing alignment performance and model drift.
|
poster
|
2505.20359
|
https://github.com/zlj123-max/Ra-DPO
| null |
[] |
[] |
[] |
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] |
5,243
|
Co-Regularization for Multi-Source Knowledge Transfer in High Dimensions
|
[
"Shuo Shuo Liu",
"Haotian Lin",
"Matthew Reimherr",
"Runze Li"
] |
Most existing transfer learning algorithms for high-dimensional models employ a two-step regularization framework, whose success heavily hinges on the assumption that the pre-trained model closely resembles the target. To address this challenge, we propose a co-regularization process to directly exploit beneficial knowledge from the source domain for high-dimensional generalized linear models. The proposed method learns the target parameter by constraining the source parameters to be close to the target one, thereby preventing fine-tuning failures caused by significantly deviated pre-trained parameters. Our theoretical analysis demonstrates that the proposed method accommodates a broader range of sources than existing two-step frameworks, thus being more robust to less similar sources. Its effectiveness is validated through extensive empirical studies.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,244
|
FRAM: Frobenius-Regularized Assignment Matching with Mixed-Precision Computing
|
[
"Binrui Shen",
"LiangYuan",
"Shengxin Zhu"
] |
Graph matching, typically formulated as a Quadratic Assignment Problem (QAP), seeks to establish node correspondences between two graphs. To address the NP-hardness of QAP, some existing methods adopt projection-based relaxations that embed the problem into the convex hull of the discrete domain. However, these relaxations inevitably enlarge the feasible set, introducing two sources of error: numerical scale sensitivity and geometric misalignment between the relaxed and original domains. To alleviate these errors, we propose a novel relaxation framework by reformulating the projection step as a Frobenius-regularized Linear Assignment (FRA) problem, where a tunable regularization term mitigates feasible region inflation. This formulation enables normalization-based operations to preserve numerical scale invariance without compromising accuracy. To efficiently solve FRA, we propose the Scaling Doubly Stochastic Normalization (SDSN) algorithm. Building on its favorable computational properties, we develop a theoretically grounded mixed-precision architecture to achieve substantial acceleration.Comprehensive CPU-based benchmarks demonstrate that FRAM consistently outperforms all baseline methods under identical precision settings. When combined with a GPU-based mixed-precision architecture, FRAM achieves up to 370× speedup over its CPU-FP64 counterpart, with negligible loss in solution accuracy.
|
poster
|
2508.00887
| null | null |
[] |
[] |
[] |
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] |
5,245
|
WorldMem: Long-term Consistent World Simulation with Memory
|
[
"Zeqi Xiao",
"Yushi LAN",
"Yifan Zhou",
"Wenqi Ouyang",
"Shuai Yang",
"Yanhong Zeng",
"Xingang Pan"
] |
World simulation has gained increasing popularity due to its ability to model virtual environments and predict the consequences of actions. However, the limited temporal context window often leads to failures in maintaining long-term consistency, particularly in preserving 3D spatial consistency. In this work, we present WorldMem, a framework that enhances scene generation with a memory bank consisting of memory units that store memory frames and states (e.g., poses and timestamps). By employing state-aware memory attention that effectively extracts relevant information from these memory frames based on their states, our method is capable of accurately reconstructing previously observed scenes, even under significant viewpoint or temporal gaps. Furthermore, by incorporating timestamps into the states, our framework not only models a static world but also captures its dynamic evolution over time, enabling both perception and interaction within the simulated world. Extensive experiments in both virtual and real scenarios validate the effectiveness of our approach.
|
poster
|
2504.12369
|
https://github.com/xizaoqu/WorldMem
|
https://xizaoqu.github.io/worldmem/
|
[
"yslan/worldmem"
] |
[] |
[] |
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] |
5,246
|
A Simple Linear Patch Revives Layer-Pruned Large Language Models
|
[
"Xinrui Chen",
"Haoli Bai",
"Tao Yuan",
"ruikang liu",
"Kang Zhao",
"Xianzhi Yu",
"Lu Hou",
"Tian Guan",
"Yonghong He",
"Chun Yuan"
] |
Layer pruning has become a popular technique for compressing large language models (LLMs) due to its simplicity. However, existing layer pruning methods often suffer from significant performance drops. We identify that \textit{this degradation stems from the mismatch of activation magnitudes across layers and tokens at the pruning interface}. To address this, we propose \textsc{LinearPatch}, a simple plug-and-play technique to revive the layer-pruned LLMs. The proposed method adopts Hadamard transformation to suppress massive outliers in particular tokens, and channel-wise scaling to align the activation magnitudes. These operations can be fused into a single matrix, which functions as a patch to bridge the pruning interface with negligible inference overhead. \textsc{LinearPatch} retains up to \textbf{94.15\%} performance of the original model when pruning 5 layers of LLaMA-3-8B on the question answering benchmark, surpassing existing state-of-the-art methods by \textbf{4\%}. In addition, the patch matrix can be further optimized with memory efficient offline knowledge distillation. With only 5K samples, the retained performance of \textsc{LinearPatch} can be further boosted to \textbf{95.16\%} within 30 minutes on a single computing card.
|
poster
|
2505.24680
| null | null |
[] |
[] |
[] |
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] |
5,247
|
A Closed-Form Solution for Fast and Reliable Adaptive Testing
|
[
"Yan Zhuang",
"Chenye Ke",
"Zirui Liu",
"Qi Liu",
"Yuting Ning",
"Zhenya Huang",
"Weizhe Huang",
"Qingyang Mao",
"Shijin Wang"
] |
Human ability estimation is essential for educational assessment, career advancement, and professional certification. Adaptive Testing systems can improve estimation efficiency by selecting fewer, targeted questions, and are widely used in exams, e.g., GRE, GMAT, and Duolingo English Test. However, selecting an optimal subset of questions remains a challenging nested optimization problem. Existing methods rely on costly approximations or data-intensive training, making them unsuitable for today's large-scale and complex testing environments. Thus, we propose a Closed-Form solution for question subset selection in Adaptive Testing. It directly minimizes ability estimation error by reducing ability parameter's gradient bias while maintaining Hessian stability, which enables a simple greedy algorithm for question selection. Moreover, it can quantify the impact of human behavioral perturbations on ability estimation. Extensive experiments on large-scale educational datasets demonstrate that it reduces the number of required questions by 10% compared to SOTA methods, while maintaining the same estimation accuracy.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,248
|
Continual Optimization with Symmetry Teleportation for Multi-Task Learning
|
[
"Zhipeng Zhou",
"Ziqiao Meng",
"Pengcheng Wu",
"Peilin Zhao",
"Chunyan Miao"
] |
Multi-task learning (MTL) is a widely explored paradigm that enables the simultaneous learning of multiple tasks using a single model. Despite numerous solutions, the key issues of optimization conflict and task imbalance remain under-addressed, limiting performance. Unlike existing optimization-based approaches that typically reweight task losses or gradients to mitigate conflicts or promote progress, we propose a novel approach based on Continual Optimization with Symmetry Teleportation (COST). During MTL optimization, when an optimization conflict arises, we seek an alternative loss-equivalent point on the loss landscape to reduce conflict. Specifically, we utilize a low-rank adapter (LoRA) to facilitate this practical teleportation by designing convergent, loss-invariant objectives. Additionally, we introduce a historical trajectory reuse strategy to continually leverage the benefits of advanced optimizers. Extensive experiments on multiple mainstream datasets demonstrate the effectiveness of our approach. COST is a plug-and-play solution that enhances a wide range of existing MTL methods. When integrated with state-of-the-art methods, COST achieves superior performance.
|
poster
|
2503.04046
| null | null |
[] |
[] |
[] |
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] |
5,249
|
Exploring Tradeoffs through Mode Connectivity for Multi-Task Learning
|
[
"Zhipeng Zhou",
"Ziqiao Meng",
"Pengcheng Wu",
"Peilin Zhao",
"Chunyan Miao"
] |
Nowadays deep models are required to be versatile due to the increasing realistic needs. Multi-task learning (MTL) offers an efficient way for this purpose to learn multiple tasks simultaneously with a single model. However, prior MTL solutions often focus on resolving conflicts and imbalances during optimization, which may not outperform simple linear scalarization strategies~\citep{xin2022current}. Instead of altering the optimization trajectory, this paper leverages mode connectivity to efficiently approach the Pareto front and identify the desired trade-off point. Unlike Pareto Front Learning (PFL), which aims to align with the entire Pareto front, we focus on effectively and efficiently exploring optimal trade-offs. However, three challenges persist: (1) the low-loss path can neither fully traverse trade-offs nor align with user preference due to its randomness, (2) commonly adopted Bézier curves in mode connectivity are ill-suited to navigating the complex loss landscapes of deep models, and (3) poor scalability to large-scale task scenarios. To address these challenges, we adopt non-uniform rational B-Splines (NURBS) to model mode connectivity, allowing for more flexible and precise curve optimization. Additionally, we introduce an order-aware objective to explore task loss trade-offs and employ a task grouping strategy to enhance scalability under massive task scenarios. Extensive experiments on key MTL datasets demonstrate that our proposed method, *EXTRA* (EXplore TRAde-offs), effectively identifies the desired point on the Pareto front and achieves state-of-the-art performance. *EXTRA* is also validated as a plug-and-play solution for mainstream MTL approaches.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,250
|
EGGS: Exchangeable 2D/3D Gaussian Splatting for Geometry-Appearance Balanced Novel View Synthesis
|
[
"Yancheng Zhang",
"Guangyu Sun",
"Chen Chen"
] |
Novel view synthesis (NVS) is crucial in computer vision and graphics, with wide applications in AR, VR, and autonomous driving. While 3D Gaussian Splatting (3DGS) enables real-time rendering with high appearance fidelity, it suffers from multi-view inconsistencies, limiting geometric accuracy. In contrast, 2D Gaussian Splatting (2DGS) enforces multi-view consistency but compromises texture details. To address these limitations, we propose Exchangeable Gaussian Splatting (EGGS), a hybrid representation that integrates 2D and 3D Gaussians to balance appearance and geometry. To achieve this, we introduce Hybrid Gaussian Rasterization for unified rendering, Adaptive Type Exchange for dynamic adaptation between 2D and 3D Gaussians, and Frequency-Decoupled Optimization that effectively exploits the strengths of each type of Gaussian representation. Our CUDA-accelerated implementation ensures efficient training and inference. Extensive experiments demonstrate that EGGS outperforms existing methods in rendering quality, geometric accuracy, and efficiency, providing a practical solution for high-quality NVS.
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] |
5,251
|
Infrequent Exploration in Linear Bandits
|
[
"Harin Lee",
"Min-hwan Oh"
] |
We study the problem of infrequent exploration in linear bandits, addressing a significant yet overlooked gap between fully adaptive exploratory methods (e.g., UCB and Thompson Sampling), which explore potentially at every time step, and purely greedy approaches, which require stringent diversity assumptions to succeed. Continuous exploration can be impractical or unethical in safety-critical or costly domains, while purely greedy strategies typically fail without adequate contextual diversity. To bridge these extremes, we introduce a novel and practical framework, INFEX, explicitly designed for infrequent exploration. INFEX executes a base exploratory policy according to a given schedule while predominantly choosing greedy actions in between. Despite its simplicity, our theoretical analysis demonstrates that INFEX achieves instance-dependent regret matching standard provably efficient algorithms, provided the exploration frequency exceeds a logarithmic threshold. Additionally, INFEX is highly generic, allowing seamless integration of any fully adaptive exploratory method, thus facilitating wide applicability and ease of adoption. By restricting intensive exploratory computations to infrequent intervals, our approach can also enhance computational efficiency. Empirical evaluations confirm our theoretical findings, showing state-of-the-art regret performance and runtime improvements over existing methods.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,252
|
Neural Correlates of Serial Dependence: Synaptic Short-term Plasticity Orchestrates Repulsion and Attraction
|
[
"Xiuning Zhang",
"Xincheng Lu",
"Nihong Chen",
"Yuanyuan Mi"
] |
Serial dependence reflects how recent sensory history shapes current perception, producing two opposing biases: repulsion, where perception is repelled from recent stimuli, and attraction, where perception is drawn toward them. Repulsion typically occurs at the sensory perception stage, while attraction arises at the post-perception stage. To uncover the neural basis of these effects, we developed a two-layer continuous attractor neural network model incorporating synaptic short-term plasticity (STP). The lower layer, dominated by synaptic depression, models sensory processing and drives repulsion due to sustained neurotransmitter depletion. The higher layer, dominated by synaptic facilitation, models post-perception processing and drives attraction by sustained high neurotransmitter release probability. Our model successfully explains the serial dependence phenomena observed in the visual orientation judgment experiments, highlighting STP as the critical mechanism, with its time constants defining the temporal windows of repulsion and attraction. Furthermore, the model provides a neural foundation for the Bayesian interpretation of serial dependence. This study advances our understanding of how the neural system leverages STP to balance sensitivity in sensory perception with stability in post-perceptual cognition.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,253
|
Shaping Sequence Attractor Schema in Recurrent Neural Networks
|
[
"Zhikun Chu",
"Bo Ho",
"xiaolong zou",
"Yuanyuan Mi"
] |
Sequence schemas are abstract, reusable knowledge structures that facilitate rapid adaptation and generalization in novel sequential tasks. In both animals and humans, shaping is an efficient way for acquiring such schemas, particularly in complex sequential tasks. As a form of curriculum learning, shaping works by progressively advancing from simple subtasks to integrated full sequences, and ultimately enabling generalization across different task variations. Despite the importance of schemas in cognition and shaping in schema acquisition, the underlying neural dynamics at play remain poorly understood. To explore this, we train recurrent neural networks on an odor-sequence task using a shaping protocol inspired by well-established paradigms in experimental neuroscience. Our model provides the first systematic reproduction of key features of schema learning observed in the orbitofrontal cortex, including rapid adaptation to novel tasks, structured neural representation geometry, and progressive dimensionality compression during learning. Crucially, analysis of the trained RNN reveals that the learned schema is implemented through sequence attractors. These attractor dynamics emerge gradually through the shaping process: starting with isolated discrete attractors in simple tasks, evolving into linked sequences, and eventually abstracting into generalizable attractors that capture shared task structure. Moreover, applying our method to a keyword spotting task shows that shaping facilitates the rapid development of sequence attractor-like schemas, leading to enhanced learning efficiency. In summary, our work elucidates a novel attractor-based mechanism underlying schema representation and its evolution via shaping, with the potential to provide new insights into the acquisition of abstract knowledge across biological and artificial intelligence.
|
poster
| null | null | null |
[] |
[] |
[] |
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5,254
|
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)
|
[
"Zhenjie Yang",
"Xiaosong Jia",
"Qifeng Li",
"Xue Yang",
"Maoqing Yao",
"Junchi Yan"
] |
Reinforcement Learning (RL) can mitigate the causal confusion and distribution shift inherent to imitation learning (IL). However, applying RL to end-to-end autonomous driving (E2E-AD) remains an open problem for its training difficulty, and IL is still the mainstream paradigm in both academia and industry. Recently Model-based Reinforcement Learning (MBRL) have demonstrated promising results in neural planning; however, these methods typically require privileged information as input rather than raw sensor data. We fill this gap by designing Raw2Drive, a dual-stream MBRL approach. Initially, we efficiently train an auxiliary privileged world model paired with a neural planner that uses privileged information as input. Subsequently, we introduce a raw sensor world model trained via our proposed Guidance Mechanism, which ensures consistency between the raw sensor world model and the privileged world model during rollouts. Finally, the raw sensor world model combines the prior knowledge embedded in the heads of the privileged world model to effectively guide the training of the raw sensor policy. Raw2Drive is so far the only RL based end-to-end method on CARLA Leaderboard 2.0, and Bench2Drive and it achieves state-of-the-art performance.
|
poster
|
2505.16394
| null | null |
[] |
[] |
[] |
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] |
5,255
|
Environment Inference for Learning Generalizable Dynamical System
|
[
"Shixuan Liu",
"Yue He",
"Haotian Wang",
"Wenjing Yang",
"Yunfei Wang",
"Peng Cui",
"Zhong Liu"
] |
Data-driven methods offer efficient and robust solutions for analyzing complex dynamic systems but rely on the assumption of IID data, driving the development of generalization techniques for environmental differences. These techniques, however, are limited by their dependence on environment labels, which are often unavailable during training due to data acquisition challenges, privacy concerns, and environmental variability, particularly in large public datasets and privacy-sensitive domains. In response, we propose DynaInfer, a novel method that infers environment specifications by analyzing prediction errors from fixed neural networks within each training round, enabling environment assignments directly from data. We prove our algorithm effectively solves the alternate optimization problem in unlabeled scenarios and validate it through extensive experiments across diverse dynamic systems. Results show that DynaInfer outperforms existing environment assignment techniques, converges rapidly to true labels, and even achieves superior performance when environment labels are available.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
5,256
|
RFMPose: Generative Category-level Object Pose Estimation via Riemannian Flow Matching
|
[
"Wenzhe Ouyang",
"Qi Ye",
"Jinghua Wang",
"Zenglin Xu",
"Jiming Chen"
] |
We introduce RFMPose, a novel generative framework for category-level 6D object pose estimation that learns deterministic pose trajectories through Riemannian Flow Matching (RFM). Existing discriminative approaches struggle with multi-hypothesis predictions (e.g., symmetry ambiguities) and often require specialized network architectures. RFMPose advances this paradigm through three key innovations:(1) Ensuring geometric consistency via geodesic interpolation on Riemannian manifolds combined with bi-invariant metric constraints;(2) Alleviating symmetry-induced ambiguities through Riemannian Optimal Transport for probability mass redistribution without ad-hoc design;(3) Enabling end-to-end likelihood estimation through Hutchinson trace approximation, thereby eliminating auxiliary model dependencies.Extensive experiments on the Omni6DPose demonstrate state-of-the-art performance of the proposed method, with significant improvements of $\textbf{+4.1}$ in $\mathrm{\textbf{IoU}_{25}}$ and $\textbf{+2.4}$ in $\textbf{5°2cm}$ metrics compared to prior generative approaches. Furthermore, the proposed RFM framework exhibits robust sim-to-real transfer capabilities and facilitates pose tracking extensions with minimal architectural adaptation.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,257
|
Imitation Learning with Temporal Logic Constraints
|
[
"Zining Fan",
"He Zhu"
] |
Designing reinforcement learning agents to satisfy complex temporal objectives expressed in Linear Temporal Logic (LTL), presents significant challenges, particularly in ensuring sample efficiency and task alignment over infinite horizons. Recent works have shown that by leveraging the corresponding Limit Deterministic Büchi Automaton (LDBA) representation, LTL formulas can be translated into variable discounting schemes over LDBA-accepting states to maximize a lower bound on the probability of formula satisfaction. However, the resulting reward signals are inherently sparse, making exploration of LDBA-accepting states increasingly difficult as task horizons lengthen to infinity. In this work, we address these challenges by leveraging finite-length demonstrations to overcome the exploration bottleneck for LTL objectives over infinite horizons. We segment demonstrations and agent exploratory trajectories at LDBA-accepting states and iteratively guide the agent within each segment to learn to reach these accepting states. By incentivizing the agent to visit LDBA-accepting states from arbitrary states, our approach increases the probability of LTL formula satisfaction without the need for extensive or lengthy demonstrations. We demonstrate the applicability of our method across a variety of high-dimensional continuous control domains. It achieves faster convergence and consistently outperforms baseline approaches.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,258
|
DSCS: Fast CPDAG-Based Verification of Collapsible Submodels in High-Dimensional Bayesian Networks
|
[
"Wentao Wu",
"Shiyuan He",
"Jianhua Guo"
] |
Bayesian networks (BNs), represented by directed acyclic graphs (DAGs), provide a principled framework for modeling complex dependencies among random variables. As data dimensionality increases into the tens of thousands, fitting and marginalizing a full BN becomes computationally prohibitive—particularly when inference is only needed for a small subset of variables. Estimation-collapsibility addresses this challenge by ensuring that directly fitting a submodel, obtained by ignoring non-essential variables, still yields exact inference on target variables. However, current DAG-based criterion for checking estimation-collapsibility is computationally intensive, involving exhaustive vertex searches and iterative removals. Additionally, practical applications typically identify the underlying DAG only up to its Markov equivalence class, represented by a completed partially directed acyclic graph (CPDAG). To bridge this gap, we introduce sequential $c$-simplicial sets—a novel graphical characterization of estimation-collapsibility applicable directly to CPDAGs. We further propose DSCS, a computationally efficient algorithm for verifying estimation-collapsibility within CPDAG framework that scales effectively to high-dimensional BNs. Extensive numerical experiments demonstrate the practicality, scalability, and efficiency of our proposed approach.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,259
|
DETree: DEtecting Human-AI Collaborative Texts via Tree-Structured Hierarchical Representation Learning
|
[
"Yongxin He",
"Shan Zhang",
"Yixuan Cao",
"Lei Ma",
"Ping Luo"
] |
Detecting AI-involved text is essential for combating misinformation, plagiarism, and academic misconduct. However, AI text generation includes diverse collaborative processes (AI-written text edited by humans, human-written text edited by AI, and AI-generated text refined by other AI), where various or even new LLMs could be involved. Texts generated through these varied processes exhibit complex characteristics, presenting significant challenges for detection. Current methods model these processes rather crudely, primarily employing binary classification (purely human vs. AI-involved) or multi-classification (treating human-AI collaboration as a new class).We observe that representations of texts generated through different processes exhibit inherent clustering relationships.So, we propose DETree, a novel approach that models the relationships among different processes as a Hierarchical Affinity Tree structure, and introduces a specialized loss function that aligns representations to this tree. To facilitate this learning, we developed RealBench, a comprehensive benchmark dataset that automatically incorporates a wide spectrum of hybrid texts produced through various human-AI collaboration processes.Our method improves performance in hybrid text detection tasks and significantly enhances robustness and generalization in out-of-distribution scenarios, particularly in few-shot learning conditions, further demonstrating the promise of training-based approaches in OOD settings. We will publicly release all code, model checkpoints, and the constructed dataset.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,260
|
Scaling Language-centric Omni-modal Representation Learning
|
[
"Chenghao Xiao",
"Hou Pong (Ken) Chan",
"Hao Zhang",
"Weiwen Xu",
"Mahani Aljunied",
"Yu Rong"
] |
Recent multi-modal embedding approaches leveraging multi-modal large language models (MLLMs) fine-tuned with contrastive learning (CL) have shown promising results. However, the underlying reasons behind their superiority remain underexplored. This work argues that a crucial advantage of MLLM-based approaches stems from implicit cross-modal alignment achieved during generative pretraining, where the language decoder learns to leverage multimodal information within a shared representation space for generating unimodal outputs. We empirically demonstrate that latent alignment emerges within MLLM representations, allowing CL to serve as a lightweight refinement stage. Leveraging this insight, we propose a language-centric omnimodal representation learning approach. Extensive experiments across diverse backbones and benchmarks demonstrate the effectiveness of our approach. Furthermore, we identify a Generation-Representation Scaling Law (GRSL), demonstrating that the representational capacity gained through contrastive refinement scales positively with the MLLM's generative capabilities. This suggests that improving generative abilities evolves as an effective paradigm for boosting representation quality. Finally, we provide a theoretical explanation for the GRSL based on mutual information discrepancy
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,261
|
Efficient Training-Free Online Routing for High-Volume Multi-LLM Serving
|
[
"Fangzhou Wu",
"Sandeep Silwal"
] |
Increasing demand for Large Language Models (LLMs) services imposes substantial deployment and computation costs on providers. LLM routing offers a cost-efficient solution by directing queries to the optimal LLM based on model and query features.However, existing works primarily focus on offline scenarios and struggle to adapt to online settings with high query volume and constrained token budgets.In this work, we introduce the first training-free algorithm for online routing scenarios.Our algorithm leverages approximate nearest neighbor search to efficiently estimate the features of queries and performs a one-time optimization over a small set of initial queries to learn a set of routing weights that guide future routing.We provide a theoretical guarantee that the algorithm achieves a competitive ratio of $1 - o(1)$ under natural assumptions, which is further validated by extensive experiments across 3 benchmark datasets and 8 baselines, showing an average improvement of 3.55$\times$ in performance, 1.85$\times$ in cost efficiency, and nearly 4.25$\times$ in throughput.
|
poster
|
2509.02718
| null | null |
[] |
[] |
[] |
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5,262
|
UniLumos: Fast and Unified Image and Video Relighting with Physics-Plausible Feedback
|
[
"Pengwei Liu",
"Hangjie Yuan",
"Bo Dong",
"Jiazheng Xing",
"Jinwang Wang",
"Rui Zhao",
"Weihua Chen",
"Fan Wang"
] |
Image and video relighting remains a challenging task, where controlling illumination under ensures physical consistency, and efficiency under diverse lighting conditions.However, existing relighting methods lack explicit physical grounding, often resulting in perceptually implausible illumination under diverse lighting conditions, such as overexposed highlights or inconsistent shadow placement.In addition, similar to general-purpose generative models, their reliance on multi-iterative sampling leads to slow inference. This is particularly problematic considering the strong conditioning in relighting tasks, which should intuitively enable much faster generation, especially for real-time applications.To address these challenges, we propose UniLumos, a unified framework for fast and physically plausible relighting of both images and videos. First, we introduce LumosData, a lightweight pipeline for constructing diverse relighting pairs from the illumination augmentations of real-world data. Leveraging the strong conditioning provided by LumosData, UniLumos extends a flow-matching backbone with path consistency learning to enable fast inference without multi-step sampling.Second, inspired by the inherent geometric consistency in dense estimation, we introduce a physics-plausible feedback mechanism that supervises the latent diffusion process through depth and normal alignment with the labeled references, encouraging physically realistic illumination.Experiments demonstrate that UniLumos achieves high-quality and physically plausible relighting with significantly faster inference for both image and video relighting.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,263
|
Whole-Body-Conditioned Ego-Centric Video Prediction
|
[
"Yutong Bai",
"Danny Tran",
"Amir Bar",
"Trevor Darrell",
"Yann LeCun",
"Jitendra Malik"
] |
We train models to predict ego-centric video from human actions (PEVA), given the past video and an action represented by the relative 3D body pose. By conditioning on kinematic pose trajectories, structured by the joint hierarchy of the body, our model learns to simulate how physical human actions shape the environment from a first-person point of view. We train an auto-regressive conditional diffusion transformer on Nymeria, a large-scale dataset of real-world egocentric video and body pose capture. We further design a hierarchical evaluation protocol with increasingly challenging tasks, enabling a comprehensive analysis of the model’s embodied prediction and control abilities. Our work represents an initial attempt to tackle the challenges of modeling complex real-world environments and embodied agent behaviors with video prediction from the perspective of a human.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,264
|
Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning
|
[
"Danni Yang",
"Zhikang Chen",
"Sen Cui",
"Mengyue Yang",
"Ding Li",
"Abudukelimu Wuerkaixi",
"Haoxuan Li",
"Jinke Ren",
"Mingming Gong"
] |
Federated continual learning (FCL) has garnered increasing attention for its ability to support distributed computation in environments with evolving data distributions. However, the emergence of new tasks introduce both temporal and cross-client shifts, making catastrophic forgetting a critical challenge. Most existing works aggregate knowledge from clients into a global model, which may not enhance client performance since irrelevant knowledge could introduce interference, especially in heterogeneous scenarios. Additionally, directly applying decentralized approaches to FCL suffer from ineffective group formation caused by task changes. To address these challenges, we propose a decentralized dynamic cooperation framework for FCL, where clients establish dynamic cooperative learning coalitions to balance the acquisition of new knowledge and the retention of prior learning, thereby obtaining personalized models. To maximize model performance, each client engages in selective cooperation, dynamically allying with others who offer meaningful performance gains. This results in non-overlapping, variable coalitions at each stage of the task. Moreover, we use coalitional affinity game to simulate coalition relationships between clients. By assessing both client gradient coherence and model similarity, we quantify the client benefits derived from cooperation. We also propose a merge-blocking algorithm and a dynamic cooperation evolution algorithm to achieve cooperative and dynamic equilibrium. Comprehensive experiments demonstrate the superiority of our method compared to various baselines. Code is available at: https://anonymous.4open.science/r/DCFCL-0372.
|
poster
|
2509.23683
|
https://github.com/ydn3229/DCFCL
| null |
[] |
[] |
[] |
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] |
5,265
|
DOVTrack: Data-Efficient Open-Vocabulary Tracking
|
[
"Zekun Qian",
"Ruize Han",
"Zhixiang Wang",
"Junhui Hou",
"Wei Feng"
] |
Open-Vocabulary Multi-Object Tracking (OVMOT) aims to detect and track multi-category objects that have both been seen and not seen during training.Currently, a significant challenge in this domain is the lack of large-scale annotated video data for training.To address this challenge, this work aims to effectively train the OV tracker using only the existing limited and sparse-annotated video data.Specifically, on the one hand, for the association task, we apply the diffusion models to complement the missing objects at the gaps between annotated frames, which is achieved directly in the implicit embedding space and enhances the coherence and accuracy of association features.On the other hand, for the object detection task, we develop a dynamic group contrastive learning strategy for object recognition and a simple yet effective loss, namely adaptive localization loss.Extensive results verify that the proposed method achieves promising results on the OVMOT benchmark, without additional data or annotations.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,266
|
U-REPA: Aligning Diffusion U-Nets to ViTs
|
[
"Yuchuan Tian",
"Hanting Chen",
"Mengyu Zheng",
"Yuchen Liang",
"Chao Xu",
"Yunhe Wang"
] |
Representation Alignment (REPA) that aligns Diffusion Transformer (DiT) hidden-states with ViT visual encoders has proven highly effective in DiT training, demonstrating superior convergence properties, but it has not been validated on the canonical diffusion U-Net architecture that shows faster convergence compared to DiTs. However, adapting REPA to U-Net architectures presents unique challenges: (1) different block functionalities necessitate revised alignment strategies; (2) spatial-dimension inconsistencies emerge from U-Net's spatial downsampling operations; (3) space gaps between U-Net and ViT hinder the effectiveness of tokenwise alignment. To encounter these challenges, we propose U-REPA, a representation alignment paradigm that bridges U-Net hidden states and ViT features as follows: Firstly, we propose via observation that due to skip connection, the middle stage of U-Net is the best alignment option. Secondly, we propose upsampling of U-Net features after passing them through MLPs. Thirdly, we observe difficulty when performing tokenwise similarity alignment, and further introduces a manifold loss that regularizes the relative similarity between samples. Experiments indicate that the resulting U-REPA could achieve excellent generation quality and greatly accelerates the convergence speed. With CFG guidance interval, U-REPA could reach FID<1.5 in 200 epochs or 1M iterations on ImageNet 256 $\times$ 256, and needs only half the total epochs to perform better than REPA under \textit{sd-vae-ft-ema}.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,267
|
A Reinforcement Learning-based Bidding Strategy for Data Consumers in Auction-based Federated Learning
|
[
"Xiaoli Tang",
"Han Yu",
"Xiaoxiao Li"
] |
Auction-based Federated Learning (AFL) fosters collaboration among self-interested data consumers (DCs) and data owners (DOs). A major challenge in AFL pertains to how DCs select and bid for DOs. Existing methods are generally static, making them ill-suited for dynamic AFL markets. To address this issue, we propose the R}einforcement Learning-based Bidding Strategy for DCs in Auction-based Federated Learning (RLB-AFL). We incorporate historical states into a Deep Q-Network to capture sequential information critical for bidding decisions. To mitigate state space sparsity, where specific states rarely reoccur for each DC during auctions, we incorporate the Gaussian Mixture Model into RLB-AFL. This facilitates soft clustering on sequential states, reducing the state space dimensionality and easing exploration and action-value function approximation. In addition, we enhance the $\epsilon$-greedy policy to help the RLB-AFL agent balance exploitation and exploration, enabling it to be more adaptable in the AFL decision-making process. Extensive experiments under 6 widely used benchmark datasets demonstrate that RLB-AFL achieves superior performance compared to 8 state-of-the-art approaches. It outperforms the best baseline by 10.56% and 3.15% in terms of average total utility
|
poster
| null | null | null |
[] |
[] |
[] |
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5,268
|
CrossSpectra: Exploiting Cross-Layer Smoothness for Parameter-Efficient Fine-Tuning
|
[
"Yifei Zhang",
"Hao Zhu",
"Junhao Dong",
"Haoran Shi",
"Ziqiao Meng",
"Piotr Koniusz",
"Han Yu"
] |
Parameter-efficient fine-tuning (PEFT) methods are essential for adapting large foundation models to specific tasks without prohibitive storage costs. However, current approaches like LoRA treat each layer's adaptations independently, overlooking cross-layer relationships and resulting in parameter counts that scale linearly with model depth. We present a theoretical analysis showing that skip connections in transformers induce smooth gradient propagation across layers, leading to weight adaptations that naturally concentrate in low-frequency spectral components—particularly in the cross-layer dimension. Empirical analysis confirms this insight, revealing that nearly 70\% of adaptation energy concentrates in low frequencies. Building on this finding, we propose CrossSpectra, which parameterizes all attention weight adaptations (Q, K, V) across layers as a unified 3D tensor and decomposes it using sparse spectral coefficients. By employing $k_1$ non-zero coefficients within each layer's frequency space and truncating to $k_2$ frequencies across layers, CrossSpectra achieves parameter requirements of $O(k_1 k_2)$ compared to LoRA's $O(Lrd)$, where $L$ is the layer count and $r$ is the rank. Extensive experiments demonstrate that CrossSpectra matches or exceeds baseline performance while using 275× fewer parameters than LoRA—achieving 0.36\% of LoRA's parameters when fine-tuning LLaMA-7B on instruction-following tasks. Our approach shows that leveraging architectural properties through spectral analysis can yield dramatic efficiency gains in parameter-efficient fine-tuning.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,269
|
Gradient-Variation Online Adaptivity for Accelerated Optimization with Hölder Smoothness
|
[
"Yuheng Zhao",
"Yu-Hu Yan",
"Kfir Y. Levy",
"Peng Zhao"
] |
Smoothness is known to be crucial for acceleration in offline optimization, and for problem-dependent regret that scale with gradient variations in online learning. Interestingly, these two problems are actually closely connected -- accelerated optimization can be understood from the lens of gradient-variation online learning. In this paper, we systematically investigate online learning with \emph{Hölder smooth} functions, a class encompassing both smooth and non-smooth (Lipschitz) functions, and further explore its implications for offline optimization. First, we propose an online algorithm with optimal gradient-variation regret for convex functions, which implies an optimal accelerated method for stochastic convex optimization under Hölder smoothness. Then, we extend the results in three aspects: \rom{1} strongly convex functions, \rom{2} non-stationary online learning where the comparator sequence changes over time; \rom{3} universal online learning where the curvature of functions is unknown. In all three cases, we have achieved the first gradient-variation regret that can well interpolate the results between the smooth and Lipschitz regimes, and recover the optimal results in each case. Notably, our proposed algorithms do not require prior knowledge of the Hölder smoothness parameter, greatly improving the adaptivity of existing methods that depend on this parameter, even when designed specifically for smooth functions. Finally, we demonstrate several implications for offline optimization through carefully tailored online-to-batch conversions.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
5,270
|
Monotone and Separable Set Functions: Characterizations and Neural Models
|
[
"Soutrik Sarangi",
"Yonatan Sverdlov",
"Nadav Dym",
"Abir De"
] |
Motivated by applications for set containment problems, we consider the following fundamental problem: can we design set-to-vector functions so that the natural partial order on sets is preserved, namely $S\subseteq T \text{ if and only if } F(S)\leq F(T) $. We call functions satisfying this property \emph{Monotone and Separating (MAS)} set functions. We establish lower and upper bounds for the vector dimension necessary to obtain MAS functions, as a function of the cardinality of the multisets and the underlying ground set. In the important case of an infinite ground set, we show that MAS functions do not exist, but provide a model called \our\ which provably enjoys a relaxed MAS property we name 'weakly MAS' and is stable in the sense of Holder continuity. We also show that MAS functions can be used to construct universal models which are monotone by construction, and can approximate all monotone set functions. Experimentally, we consider a variety of set containtment tasks. The experiments show the benefit of using our \our\ model, in comparsion with standard set models which do not incorporate set containment as an inductive bias.
|
poster
| null | null | null |
[] |
[] |
[] |
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5,271
|
SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking
|
[
"Shubo Lin",
"Yutong Kou",
"Zirui Wu",
"Shaoru Wang",
"Bing Li",
"Weiming Hu",
"Jin Gao"
] |
While existing query-based 3D end-to-end visual trackers integrate detection and tracking via the $\textit{tracking-by-attention}$ paradigm, these two chicken-and-egg tasks encounter optimization difficulties when sharing the same parameters. Our findings reveal that these difficulties arise due to two inherent constraints on the self-attention mechanism, i.e., over-deduplication for object queries and self-centric attention for track queries. In contrast, removing self-attention mechanism not only minimally impacts regression predictions of the tracker, but also tends to generate more latent candidate boxes. Based on these analyses, we present SynCL, a novel plug-and-play synergistic training strategy designed to co-facilitate multi-task learning for detection and tracking. Specifically, we propose a Task-specific Hybrid Matching module for a weight-shared cross-attention-based decoder that matches the targets of track queries with multiple object queries to exploit promising candidates overlooked by the self-attention mechanism. To flexibly select optimal candidates for the one-to-many matching, we also design a Dynamic Query Filtering module controlled by model training status. Moreover, we introduce Instance-aware Contrastive Learning to break through the barrier of self-centric attention for track queries, effectively bridging the gap between detection and tracking. Without additional inference costs, SynCL consistently delivers improvements in various benchmarks and achieves state-of-the-art performance with $58.9\%$ AMOTA on the nuScenes dataset. Code and raw results will be publicly available.
|
poster
|
2411.06780
| null | null |
[] |
[] |
[] |
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] |
5,272
|
Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation
|
[
"Muquan Li",
"Hang Gou",
"Dongyang Zhang",
"Shuang Liang",
"Xiurui Xie",
"Deqiang Ouyang",
"Ke Qin"
] |
The growing demand for efficient deep learning has positioned dataset distillation as a pivotal technique for compressing training dataset while preserving model performance. However, existing inner-loop optimization methods for dataset distillation typically rely on random truncation strategies, which lack flexibility and often yield suboptimal results. In this work, we observe that neural networks exhibit distinct learning dynamics across different training stages—early, middle, and late—making random truncation ineffective. To address this limitation, we propose Automatic Truncated Backpropagation Through Time (AT-BPTT), a novel framework that dynamically adapts both truncation positions and window sizes according to intrinsic gradient behavior. AT-BPTT introduces three key components: (1) a probabilistic mechanism for stage-aware timestep selection, (2) an adaptive window sizing strategy based on gradient variation, and (3) a low-rank Hessian approximation to reduce computational overhead. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet-1K show that AT-BPTT achieves state-of-the-art performance, improving accuracy by an average of 6.16\% over baseline methods. Moreover, our approach accelerates inner-loop optimization by 3.9 × while saving 63\% memory cost.
|
poster
|
2510.04838
| null | null |
[] |
[] |
[] |
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] |
5,273
|
Learning to Zoom with Anatomical Relations for Medical Structure Detection
|
[
"Bin Pu",
"Liwen Wang",
"Xingbo Dong",
"Xingguo Lv",
"ZHE JIN"
] |
Accurate anatomical structure detection is a critical preliminary step for diagnosing diseases characterized by structural abnormalities. In clinical practice, radiologists frequently adjust the zoom level of medical images to obtain comprehensive views for diagnosis. This common interaction results in significant variations in the apparent scale of anatomical structures across different images or fields of view. However, the information embedded in these zoom-induced scale changes is often overlooked by existing detection algorithms. To overcome this limitation, we propose ZR-DETR, a zoom-aware probabilistic framework tailored for medical object detection. ZR-DETR uniquely incorporates scale-sensitive zoom embeddings, anatomical relation constraints, and a Gaussian Process-based detection head. This architecture enables the framework to jointly model semantic context, enforce anatomical plausibility, and quantify detection uncertainty. Empirical validation across three diverse medical imaging benchmarks (FCS, EPV, and MM-WHS) demonstrates that ZR-DETR consistently outperforms strong baselines in both single-domain and unsupervised domain adaptation scenarios.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,274
|
Reinforcing the Diffusion Chain of Lateral Thought with Diffusion Language Models
|
[
"Zemin Huang",
"Zhiyang Chen",
"Zijun Wang",
"Tiancheng Li",
"Guo-Jun Qi"
] |
We introduce the Diffusion Chain of Lateral Thought (DCoLT), a reasoning framework for diffusion language models. DCoLT treats each intermediate step in the reverse diffusion process as a latent "thinking" action and optimizes the entire reasoning trajectory to maximize the reward on the correctness of the final answer with outcome-based Reinforcement Learning (RL).Unlike traditional Chain-of-Thought (CoT) methods that follow a causal, linear thinking process, DCoLT allows bidirectional, non-linear reasoning with no strict rule on grammatical correctness amid its intermediate steps of thought.We implement DCoLT on two representative Diffusion Language Models (DLMs). First, we choose SEDD as a representative continuous-time discrete diffusion model, where its concrete score derives a probabilistic policy to maximize the RL reward over the entire sequence of intermediate diffusion steps. We further consider the discrete-time masked diffusion language model -- LLaDA, and find that the order to predict and unmask tokens plays an essential role to optimize its RL action resulting from the ranking-based Unmasking Policy Module (UPM) defined by the Plackett-Luce model.Experiments on both math and code generation tasks show that using only public data and 16 H800 GPUs, DCoLT-reinforced DLMs outperform other DLMs trained by SFT or RL or even both. Notably, DCoLT-reinforced LLaDA boosts its reasoning accuracy by +9.8%, +5.7%, +11.4%, +19.5% on GSM8K, MATH, MBPP, and HumanEval.
|
poster
|
2505.10446
| null | null |
[] |
[] |
[] |
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] |
5,275
|
RAT: Bridging RNN Efficiency and Attention Accuracy in Language Modeling
|
[
"Xiuying Wei",
"Anunay Yadav",
"Razvan Pascanu",
"Caglar Gulcehre"
] |
Transformers have become the cornerstone of modern large-scale language models; however, their dependence on softmax attention poses a major computational bottleneck, particularly in long-context settings. In this work, rather than following prevalent approaches such as linear attention (or SSMs) and local attention, we introduce an intermediate design called RAT between recurrence and attention mechanisms. It partitions the input into chunks, applies a simple linear recurrence within each chunk to capture local dependencies, and then performs softmax attention across chunks to model long-range interactions. By adjusting the size of the chunk, RAT enables flexible trade-offs, combining the strengths of RNN and attention. Empirically, with a chunk size of 16, the RAT layer achieves a $7\times$ improvement in training speed with 1M token sequences and $9\times$ in generation at 4K sequence length, while maintaining similar or sometimes even better accuracy compared to standard attention. We demonstrate this by training 1.3B parameter models from scratch and performing large-scale evaluations, including short- and long-context benchmarks, as well as supervised fine-tuning~(SFT). We further propose a hybrid architecture that interleaves RAT with local attention. By combining efficient long-range modeling with strong local interactions, this hybrid design not only improves inference speed and reduces cache memory usage compared to attention, but also consistently enhances performance, for example, achieving an average 1 point gain in commonsense reasoning tasks, up to 4 points on code tasks, and a 1 point Rouge-L increase in a summarization SFT task.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,276
|
GUI-Reflection: Empowering Multimodal GUI Models with Self-Reflection Behavior
|
[
"Penghao Wu",
"Shengnan Ma",
"Bo Wang",
"Jiaheng Yu",
"Lewei Lu",
"Ziwei Liu"
] |
Multimodal Large Language Models (MLLMs) have shown great potential in revolutionizing Graphical User Interface (GUI) automation. However, existing GUI models mostly rely on learning from nearly error-free offline trajectories, thus lacking reflection and error recovery capabilities. To bridge this gap, we propose GUI-Reflection, a novel framework that explicitly integrates self-reflection and error correction capabilities into end-to-end multimodal GUI models throughout dedicated training stages: GUI-specific pre-training, offline supervised fine-tuning (SFT), and online reflection tuning. GUI-reflection enables self-reflection behavior emergence with fully automated data generation and learning processes without requiring any human annotation. Specifically, 1) we first propose scalable data pipelines to automatically construct reflection and error correction data from existing successful trajectories. While existing GUI models mainly focus on grounding and UI understanding ability, we propose the GUI-Reflection Task Suite to learn and evaluate reflection-oriented abilities explicitly. 2) Furthermore, we built a diverse and efficient environment for online training and data collection of GUI models on mobile devices. 3) We also present an iterative online reflection tuning algorithm leveraging the proposed environment, enabling the model to continuously enhance its reflection and error correction abilities. Our framework equips GUI agents with self-reflection and correction capabilities, paving the way for more robust, adaptable, and intelligent GUI automation, with all data, models, environments, and tools to be released publicly.
|
poster
|
2506.08012
|
https://github.com/penghao-wu/GUI_Reflection
|
https://penghao-wu.github.io/GUI_Reflection/
|
[] |
[] |
[
"craigwu/GUI_Reflection_Task_Suite_Benchmark",
"craigwu/GUI_Reflection_SFT_train",
"craigwu/GUI_Reflection_Task_Suite_train"
] |
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] |
5,277
|
Representational Alignment under Flexible Evaluation: When the Best-Fitting Model Isn’t the Right One
|
[
"Itamar Avitan",
"Tal Golan"
] |
Linearly transforming stimulus representations of deep neural networks yieldshigh-performing models of behavioral and neural responses to complex stimuli.But does the test accuracy of such predictions identify genuine representationalalignment? We addressed this question through a large-scale model-recovery study.Twenty diverse vision models were linearly aligned to 4.5 million behavioral judg-ments from the THINGS odd-one-out dataset and calibrated to reproduce humanresponse variability. For each model in turn, we sampled synthetic responses fromits probabilistic predictions, fitted all candidate models to the synthetic data, andtested whether the data-generating model would re-emerge as the best predictorof the simulated data. Model recovery accuracy improved with training-set sizebut plateaued below 80%, even at millions of simulated trials. Regression analyseslinked misidentification primarily to shifts in representational geometry inducedby the linear transformation, as well as to the effective dimensionality of the trans-formed features. These findings demonstrate that, even with massive data, overlyflexible alignment metrics may fail to guide us toward artificial representationsthat are genuinely more human-aligned. Model comparison experiments must bedesigned to balance the trade-off between predictive accuracy and identifiability—ensuring the best-fitting model is also the right one.
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poster
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] |
5,278
|
Efficient Low Rank Attention for Long-Context Inference in Large Language Models
|
[
"Li Tenghui",
"Guoxu Zhou",
"Xuyang Zhao",
"Yuning Qiu",
"Qibin Zhao"
] |
As the length of input text grows, the key-value (KV) cache in LLMs imposes prohibitive GPU memory costs and limits long‐context inference on resource‐constrained devices. Existing approaches, such as KV quantization and pruning, reduce memory usage but suffer from numerical precision loss or suboptimal retention of key-value pairs. We introduce Low Rank Query and Key attention (LRQK), a two‐stage framework that jointly decomposes the full‐precision query and key matrices into compact rank-$r$ factors during the prefill stage, and then uses these low-dimensional projections to compute proxy attention scores in $\mathcal{O}(lr)$ time at each decode step. By selecting only the top-$k$ tokens and a small fixed set of recent tokens, LRQK employs a hierarchical GPU–CPU cache with a hit-and-miss mechanism that transfers only missing full-precision KV pairs, thereby preserving exact attention outputs while reducing CPU–GPU data movement. Extensive experiments on the RULER and LongBench benchmarks with LLaMA-3-8B and Qwen2.5-7B demonstrate that LRQK matches or surpasses leading sparse-attention methods in long context settings, while delivering significant memory savings with minimal loss in accuracy.
|
poster
| null | null | null |
[] |
[] |
[] |
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5,279
|
Linear Differential Vision Transformer: Learning Visual Contrasts via Pairwise Differentials
|
[
"Yifan Pu",
"JIXUAN YING",
"Tianzhu Ye",
"Dongchen Han",
"Ziyi Wang",
"Qixiu Li",
"shao xinyu",
"Xiaochen Wang",
"Gao Huang",
"Xiu Li"
] |
Vision Transformers (ViTs) have become a universal backbone for both image recognition and image generation. Yet their Multi–Head Self–Attention (MHSA) layer still performs a quadratic query–key interaction for \emph{every} token pair, spending the bulk of computation on visually weak or redundant correlations. We introduce \emph{Visual–Contrast Attention} (VCA), a drop-in replacement for MHSA that injects an explicit notion of discrimination while reducing the theoretical complexity from $\mathcal{O}(N^{2}C)$ to $\mathcal{O}(N n C)$ with $n\!\ll\!N$. VCA first distils each head’s dense query field into a handful of spatially pooled \emph{visual–contrast tokens}, then splits them into a learnable \emph{positive} and \emph{negative} stream whose differential interaction highlights what truly separates one region from another. The module adds fewer than $0.3$\,M parameters to a DeiT-Tiny backbone, requires no extra FLOPs, and is wholly architecture-agnostic. Empirically, VCA lifts DeiT-Tiny top-1 accuracy on ImageNet-1K from $72.2\%$ to \textbf{$75.5\%$} (+$3.3$) and improves three strong hierarchical ViTs by up to $2.9$\%, while in class-conditional ImageNet generation it lowers FID-50K by $2$ to $7.5$ points across both diffusion (DiT) and flow (SiT) models. Extensive ablations confirm that (i) spatial pooling supplies low-variance global cues, (ii) dual positional embeddings are indispensable for contrastive reasoning, and (iii) combining the two in both stages yields the strongest synergy. VCA therefore offers a simple path towards faster and sharper Vision Transformers.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,280
|
Scalable Best-of-N Selection for Large Language Models via Self-Certainty
|
[
"Zhewei Kang",
"Xuandong Zhao",
"Dawn Song"
] |
Best-of-N selection is a key technique for improving the reasoning performance of Large Language Models (LLMs) through increased test-time computation. Current state-of-the-art methods often employ computationally intensive reward models for response evaluation and selection. Reward-free alternatives, like self-consistency and universal self-consistency, are limited in their ability to handle open-ended generation tasks or scale effectively. To address these limitations, we propose self-certainty, a novel and efficient metric that leverages the inherent probability distribution of LLM outputs to estimate response quality without requiring external reward models. We hypothesize that higher distributional self-certainty, aggregated across multiple samples, correlates with improved response accuracy, as it reflects greater confidence in the generated output. Through extensive experiments on various reasoning tasks, we demonstrate that self-certainty (1) scales effectively with increasing sample size $N$, akin to reward models but without the computational overhead; (2) complements chain-of-thought, improving reasoning performance beyond greedy decoding; and (3) generalizes to open-ended tasks where traditional self-consistency methods fall short. Our findings establish self-certainty as a practical and efficient way for improving LLM reasoning capabilities.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,281
|
Scalable Valuation of Human Feedback through Provably Robust Model Alignment
|
[
"Masahiro Fujisawa",
"Masaki Adachi",
"Michael A Osborne"
] |
Despite the importance of aligning language models with human preferences, crowd-sourced human feedback is often noisy---for example, preferring less desirable responses---posing a fundamental challenge to alignment. A truly robust alignment objective should yield identical model parameters even under severe label noise, a property known as redescending. We prove that no existing alignment methods satisfy this property. To address this, we propose Hölder-DPO, the first principled alignment loss with a provable redescending property, enabling approximation of the clean data distribution from noisy feedback. The aligned model estimates the likelihood of clean data, providing a theoretically grounded metric for dataset valuation that identifies the location and fraction of mislabels. This metric is gradient-free, enabling scalable and automated human feedback valuation without costly manual verification or clean validation dataset. Hölder-DPO achieves state-of-the-art robust alignment performance while accurately detecting mislabels in controlled datasets. Finally, we apply Hölder-DPO to widely used alignment datasets, revealing substantial noise levels and demonstrating that removing these mislabels significantly improves alignment performance across methods.
|
poster
|
2505.17859
| null | null |
[] |
[] |
[] |
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] |
5,282
|
Seeing through Uncertainty: Robust Task-Oriented Optimization in Visual Navigation
|
[
"Yiyuan Pan",
"Yunzhe XU",
"Zhe Liu",
"Hesheng Wang"
] |
Visual navigation is essential for autonomous agents, yet data scarcity poses a fundamental bottleneck that severely impairs the generalization of learned policies to unseen scenarios. Existing visual navigation agents depend on complex predictive or reasoning modules that become counterproductive and data-hungry in such limited-data regimes. This paper introduces NeuRO, a novel hybrid framework that pioneers the integration of networks with downstream task-based optimization to tackle this critical problem. NeuRO addresses core difficulties in this integration: (i) it empowers the network to translate inherently unreliable visual predictions under data scarcity into calibrated convex uncertainty sets, which then directly inform and constrain the downstream optimization problem, using Partially Input Convex Neural Networks (PICNNs) via a conformal method; and (ii) it reformulates the partially observable task as a generalizable robust optimization problem to effectively leverage these uncertainty-aware representations to derive robust policies. Extensive experiments on both unordered and sequential MultiON tasks demonstrate that NeuRO establishes state-of-the-art performance, particularly in generalization to unseen environments. Our work thus presents a significant advancement for developing robust, generalizable autonomous agents.
|
poster
|
2510.00441
| null | null |
[] |
[] |
[] |
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] |
5,283
|
Scalable Policy-Based RL Algorithms for POMDPs
|
[
"Ameya Anjarlekar",
"S. Rasoul Etesami",
"R. Srikant"
] |
The continuous nature of belief states in POMDPs presents significant computational challenges in learning the optimal policy. In this paper, we consider an approach that solves a Partially Observable Reinforcement Learning (PORL) problem by approximating the corresponding POMDP model into a finite-state Markov Decision Process (MDP) (called Superstate MDP). We first derive theoretical guarantees that improve upon prior work that relate the optimal value function of the transformed Superstate MDP to the optimal value function of the original POMDP. Next, we propose a policy-based learning approach with linear function approximation to learn the optimal policy for the Superstate MDP. Consequently, our approach shows that a POMDP can be approximately solved using TD-learning followed by Policy Optimization by treating it as an MDP, where the MDP state corresponds to a finite history. We show that the approximation error decreases exponentially with the length of this history. To the best of our knowledge, our finite-time bounds are the first to explicitly quantify the error introduced when applying standard TD learning to a setting where the true dynamics are not Markovian.
|
poster
|
2510.06540
| null | null |
[] |
[] |
[] |
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] |
5,284
|
Bisecle: Binding and Separation in Continual Learning for Video Language Understanding
|
[
"Yue Tan",
"Xiaoqian Hu",
"Hao Xue",
"Celso de Melo",
"Flora Salim"
] |
Frontier vision-language models (VLMs) have made remarkable improvements in video understanding tasks. However, real-world videos typically exist as continuously evolving data streams (e.g., dynamic scenes captured by wearable glasses), necessitating models to continually adapt to shifting data distributions and novel scenarios. Considering the prohibitive computational costs of fine-tuning models on new tasks, usually, a small subset of parameters is updated while the bulk of the model remains frozen. This poses new challenges to existing continual learning frameworks in the context of large multimodal foundation models, i.e., catastrophic forgetting and update conflict. While the foundation models struggle with parameter-efficient continual learning, the hippocampus in the human brain has evolved highly efficient mechanisms for memory formation and consolidation. Inspired by the rapid **Bi**nding and pattern **se**paration mechanisms in the hippocampus, in this work, we propose **Bisecle** for video-language **c**ontinual **le**arning, where a multi-directional supervision module is used to capture more cross-modal relationships and a contrastive prompt learning scheme is designed to isolate task-specific knowledge to facilitate efficient memory storage. Binding and separation processes further strengthen the ability of VLMs to retain complex experiences, enabling robust and efficient continual learning in video understanding tasks. We perform a thorough evaluation of the proposed Bisecle, demonstrating its ability to mitigate forgetting and enhance cross-task generalization on several VideoQA benchmarks.
|
poster
|
2507.00469
| null | null |
[] |
[] |
[] |
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] |
5,285
|
LoRA-EnVar: Parameter-Efficient Hybrid Ensemble Variational Assimilation for Weather Forecasting
|
[
"Yi Xiao",
"Hang Fan",
"Kun Chen",
"Ye Cao",
"Ben Fei",
"Wei Xue",
"LEI BAI"
] |
Accurate estimation of background error (i.e., forecast error) distribution is critical for effective data assimilation (DA) in numerical weather prediction (NWP). In state-of-the-art operational DA systems, it is common to account for the temporal evolution of background errors by employing hybrid methods, which blend a static climatological covariance with a flow-dependent ensemble-derived component. While effective to some extent, these methods typically assume Gaussian-distributed errors and rely heavily on hand-crafted covariance structures and domain expertise, limiting their ability to capture the complex, non-Gaussian nature of atmospheric dynamics. In this work, we propose LoRA-EnVar, a novel hybrid ensemble variational DA algorithm that integrates low-rank adaptation (LoRA) into a deep generative modeling framework. We first learn a climatological background error distribution using a variational autoencoder (VAE) trained on historical data. To incorporate flow-dependent uncertainty, we introduce LoRA modules that efficiently adapt the learned distribution in response to flow-dependent ensemble perturbations. Our approach supports online finetuning, enabling dynamic updates of the background error distribution without catastrophic forgetting. We validate LoRA-EnVar in high-resolution assimilation settings using the FengWu forecast model and simulated observations from ERA5 reanalysis. Experimental results show that LoRA-EnVar significantly improves assimilation accuracy over models assuming static background error distribution and achieves comparable or better performance than full finetuning while reducing the number of trainable parameters by three orders of magnitude. This demonstrates the potential of parameter-efficient adaptation for scalable, non-Gaussian DA in operational meteorology.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
5,286
|
A Unified Framework on the Universal Approximation of Transformer-type Architectures
|
[
"Jingpu Cheng",
"Qianxiao Li",
"Ting Lin",
"Zuowei Shen"
] |
We investigate the universal approximation property (UAP) of transformer-type architectures, providing a unified theoretical framework that extends prior results on residual networks to models incorporating attention mechanisms. Our work identifies token distinguishability as a fundamental requirement for UAP and introduces a general sufficient condition that applies to a broad class of architectures. Leveraging an analyticity assumption on the attention layer, we can significantly simplify the verification of this condition, providing a non-constructive approach in establishing UAP for such architectures. We demonstrate the applicability of our framework by proving UAP for transformers with various attention mechanisms, including kernel-based and sparse ones. The corollaries of our results either generalize prior works or establish UAP for architectures not previously covered. Furthermore, our framework offers a principled foundation for designing novel transformer architectures with inherent UAP guarantees, including those with specific functional symmetries. We propose examples to illustrate these insights.
|
poster
|
2506.23551
| null | null |
[] |
[] |
[] |
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] |
5,287
|
Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization
|
[
"Yiran Yang",
"Rui Chen"
] |
In this paper, we consider a score-based Integer Programming (IP) approach for solving the Bayesian Network Structure Learning (BNSL) problem. State-of-the-art BNSL IP formulations suffer from the exponentially large number of variables and constraints. A standard approach in IP to address such challenges is to employ row and column generation techniques, which dynamically generate rows and columns, while the complex pricing problem remains a computational bottleneck for BNSL. For the general class of $\ell_0$-penalized likelihood scores, we show how the pricing problem can be reformulated as a difference of submodular optimization problem, and how the Difference of Convex Algorithm (DCA) can be applied as an inexact method to efficiently solve the pricing problems. Empirically, we show that, for continuous Gaussian data, our row and column generation approach yields solutions with higher quality than state-of-the-art score-based approaches, especially when the graph density increases, and achieves comparable performance against benchmark constraint-based and hybrid approaches, even when the graph size increases.
|
poster
|
2505.11089
| null | null |
[] |
[] |
[] |
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] |
5,288
|
Efficient Conditional Gradient Methods for Solving Stochastic Convex Bilevel Optimization Problems
|
[
"Khanh-Hung (Bruce) Giang-Tran",
"Soroosh Shafiee",
"Nam Ho-Nguyen"
] |
We propose efficient methods for solving stochastic convex bilevel optimization problems, where the goal is to minimize an outer stochastic objective function subject to the solution set of an inner stochastic optimization problem. Existing methods often rely on costly projection or linear optimization oracles over complex sets, which limits scalability. To overcome this, we propose an iteratively regularized conditional gradient framework that leverages efficient linear optimization oracles exclusively over the base feasible set. Our proposed methods employ a vanishing regularization sequence that progressively emphasizes the inner problem while biasing towards desirable minimal outer objective solutions. Under standard convexity assumptions, we establish non-asymptotic convergence rates of $O(t^{-({1}/{2}-p)})$ for the outer objective and $O(t^{-p})$ for the inner objective, where $p \in (0,1/2)$ controls the regularization decay, in the one-sample stochastic setting, and $O(t^{-(1-p)})$ and $O(t^{-p})$ in the finite-sum setting using a mini-batch scheme, where $p \in (0,1)$. Experimental results on over-parametrized regression and $\ell_1$-constrained logistics regression tasks demonstrate the practical advantages of our approach over existing methods, confirming our theoretical findings.
|
poster
|
2505.18037
| null | null |
[] |
[] |
[] |
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5,289
|
VL-SAE: Interpreting and Enhancing Vision-Language Alignment with a Unified Concept Set
|
[
"Shufan Shen",
"Junshu Sun",
"Qingming Huang",
"Shuhui Wang"
] |
The alignment of vision-language representations endows current Vision-Language Models (VLMs) with strong multi-modal reasoning capabilities. However, the interpretability of the alignment component remains uninvestigated due to the difficulty in mapping the semantics of multi-modal representations into a unified concept set. To address this problem, we propose VL-SAE, a sparse autoencoder that encodes vision-language representations into its hidden activations. Each neuron in the hidden layer correlates to a concept represented by semantically similar images and texts, thereby interpreting these representations with a unified concept set. To establish the neuron-concept correlation, we encourage semantically similar representations to exhibit consistent neuron activations during self-supervised training. First, to measure the semantic similarity of multi-modal representations, we perform their alignment in an explicit form based on cosine similarity. Second, we construct the VL-SAE with a distance-based encoder and two modality-specific decoders to ensure the activation consistency of semantically similar representations. Experiments across multiple VLMs (e.g., CLIP, LLaVA) demonstrate the superior capability of VL-SAE in interpreting and enhancing the vision-language alignment. For interpretation, the alignment between vision and language representations can be understood by comparing their semantics with concepts. For enhancement, the alignment can be strengthened by aligning vision-language representations at the concept level, contributing to performance improvements in downstream tasks, including zero-shot image classification and hallucination elimination. Codes are provided in the supplementary and will be released to GitHub.
|
poster
| null | null | null |
[] |
[] |
[] |
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