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Feature-aware Modulation for Learning from Temporal Tabular Data
|
[
"Haorun Cai",
"Han-Jia Ye"
] |
While tabular machine learning has achieved remarkable success, temporal distribution shifts pose significant challenges in real-world deployment, as the relationships between features and labels continuously evolve. Static models assume fixed mappings to ensure generalization, whereas adaptive models may overfit to transient patterns, creating a dilemma between robustness and adaptability.In this paper, we analyze key factors essential for constructing an effective dynamic mapping for temporal tabular data. We discover that evolving feature semantics—particularly objective and subjective meanings—introduce concept drift over time. Crucially, we identify that feature transformation strategies are able to mitigate discrepancies in feature representations across temporal stages.Motivated by these insights, we propose a feature-aware temporal modulation mechanism that conditions feature representations on temporal context, modulating statistical properties such as scale and skewness. By aligning feature semantics across time, our approach achieves a lightweight yet powerful adaptation, effectively balancing generalizability and adaptability.Benchmark evaluations validate the effectiveness of our method in handling temporal shifts in tabular data.
|
poster
| null | null | null |
[] |
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] |
1
|
Multimodal Tabular Reasoning with Privileged Structured Information
|
[
"Jun-Peng Jiang",
"Yu Xia",
"Hai-Long Sun",
"Shiyin Lu",
"Qingguo Chen",
"Weihua Luo",
"Kaifu Zhang",
"De-Chuan Zhan",
"Han-Jia Ye"
] |
Tabular reasoning involves multi-step information extraction and logical inference over tabular data. While recent advances have leveraged large language models (LLMs) for reasoning over structured tables, such high-quality textual representations are often unavailable in real-world settings, where tables typically appear as images. In this paper, we tackle the task of tabular reasoning from table images, leveraging privileged structured information available during training to enhance multimodal large language models (MLLMs). The key challenges lie in the complexity of accurately aligning structured information with visual representations, and in effectively transferring structured reasoning skills to MLLMs despite the input modality gap. To address these, we introduce TabUlar Reasoning with Bridged infOrmation (Turbo), a new framework for multimodal tabular reasoning with privileged structured tables. Turbo benefits from a structure-aware reasoning trace generator based on DeepSeek-R1, contributing to high-quality modality-bridged data. On this basis, Turbo repeatedly generates and selects the advantageous reasoning paths, further enhancing the model's tabular reasoning ability. Experimental results demonstrate that, with limited ($9$k) data, Turbo achieves state-of-the-art performance ($+7.2\%$ vs. previous SOTA) across multiple datasets.
|
poster
|
2506.04088
| null | null |
[] |
[] |
[] |
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] |
2
|
Hawk: Leveraging Spatial Context for Faster Autoregressive Text-to-Image Generation
|
[
"Zhi-Kai Chen",
"Jun-Peng Jiang",
"Han-Jia Ye",
"De-Chuan Zhan"
] |
Autoregressive (AR) image generation models can produce high-fidelity images but often struggle with slow inference due to their token-by-token, sequential decoding. Speculative decoding, which employs a draft model to approximate the AR model’s output, offers a promising way to reduce inference time. While this technique has been successfully applied to accelerate text-based AR models without sacrificing output quality, its application to image generation remains largely unexplored. Directly adapting this method to images is challenging because of the substantially larger sampling space, which complicates alignment between speculative and target model predictions, and the inadequate use of two-dimensional spatial information, which limits the exploitation of local image dependencies. To address these obstacles, we propose Spatial Speculative Decoding, a novel approach that leverages the inherent two-dimensional structure of images to guide a speculative model toward more accurate predictions and faster token generation. Experimental results on multiple text-to-image benchmarks demonstrate a 1.71× speedup over standard AR models, while preserving both image fidelity and diversity.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
3
|
AVR: Active Visual Reasoning for Multimodal Large Language Models in Physical Environments
|
[
"Weijie Zhou",
"Xuantang Xiong",
"Yi Peng",
"Manli Tao",
"Chaoyang Zhao",
"Honghui Dong",
"Ming Tang",
"Jinqiao Wang"
] |
Visual reasoning in multimodal large language models (MLLMs) has primarily been studied in static, fully observable settings, limiting their effectiveness in real-world environments where information is often incomplete due to occlusion or limited field of view. Humans, in contrast, actively explore and interact with their environment—moving, examining, and manipulating objects—to gather information through a closed-loop process integrating perception, reasoning, and action. Inspired by this human capability, we introduce the Active Visual Reasoning (AVR) task, extending visual reasoning to partially observable, interactive environments. AVR necessitates agents to: (1) actively acquire information via sequential physical actions, (2) integrate observations across multiple steps for coherent reasoning, and (3) dynamically adjust decisions based on evolving visual feedback. To rigorously evaluate AVR, we introduce CLEVR-AVR, a simulation benchmark featuring multi-round interactive environments designed to assess both reasoning correctness and information-gathering efficiency. We present AVR-152k, a large-scale dataset offers rich Chain-of-Thought (CoT) annotations detailing iterative reasoning for uncertainty identification, action-conditioned information gain prediction, and information-maximizing action selection, crucial for training agents in a higher-order Markov Decision Process. Building on this, we develop PhysVLM-AVR, an MLLM achieving state-of-the-art performance on CLEVR-AVR, embodied reasoning (OpenEQA, RoboVQA), and passive visual reasoning (GeoMath, Geometry30K). Our analysis also reveals that current embodied MLLMs, despite detecting information incompleteness, struggle to actively acquire and integrate new information through interaction, highlighting a fundamental gap in active reasoning capabilities.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
4
|
StelLA: Subspace Learning in Low-rank Adaptation using Stiefel Manifold
|
[
"Zhizhong Li",
"Sina Sajadmanesh",
"Jingtao Li",
"Lingjuan Lyu"
] |
Low-rank adaptation (LoRA) has been widely adopted as a parameter-efficient technique for fine-tuning large-scale pre-trained models. However, it still lags behind full fine-tuning in performance, partly due to its insufficient exploitation of the geometric structure underlying low-rank manifolds. In this paper, we introduce a geometry-aware extension of LoRA that uses a three-factor decomposition $USV^\top$, separating the adapter's input and output subspaces $V$ and $U$ from the scaling component $S$, in the spirit of singular value decomposition (SVD). Our method constrains $U$ and $V$ to lie on the Stiefel manifold, ensuring their orthonormality throughout training. To optimize on the Stiefel manifold, we employ a flexible geometric optimization design that converts any Euclidean optimizer to a Riemannian optimizer via a modular interface. This enables principled and stable subspace learning while remaining compatible with existing fine-tuning pipelines. Empirical results across a wide range of downstream tasks, including commonsense reasoning, math and code generation, image classification, and image generation, demonstrate the superior performance of our approach against the vanilla LoRA and recent state-of-the-art variants.
|
spotlight
|
2510.01938
| null | null |
[] |
[] |
[] |
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] |
5
|
Continuous Subspace Optimization for Continual Learning
|
[
"Quan Cheng",
"Yuanyu Wan",
"Lingyu Wu",
"Chenping Hou",
"Lijun Zhang"
] |
Continual learning aims to learn multiple tasks sequentially while preserving prior knowledge, but faces the challenge of catastrophic forgetting when acquiring new knowledge. Recently, approaches leveraging pre-trained models have gained increasing popularity to mitigate this issue, due to the strong generalization ability of foundation models. To adjust pre-trained models for new tasks, existing methods usually employ low-rank adaptation, which restricts parameter updates to a fixed low-rank subspace. However, constraining the optimization space inherently compromises the model's learning capacity, resulting in inferior performance. To address the limitation, we propose Continuous Subspace Optimization for Continual Learning (CoSO) to fine-tune the model in a series of subspaces rather than a single one. These sequential subspaces are dynamically determined through the singular value decomposition of gradients. CoSO updates the model by projecting gradients into these subspaces, ensuring memory-efficient optimization. To mitigate forgetting, the optimization subspaces of each task are set to be orthogonal to the historical task subspace. During task learning, CoSO maintains a task-specific component that captures the critical update directions associated with the current task. Upon completing a task, this component is used to update the historical task subspace, laying the groundwork for subsequent learning. Extensive experiments on multiple datasets demonstrate that CoSO significantly outperforms state-of-the-art methods, especially in challenging scenarios with long task sequences.
|
poster
|
2505.11816
| null | null |
[] |
[] |
[] |
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] |
6
|
Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings
|
[
"Xingguang Wei",
"Haomin Wang",
"Shenglong Ye",
"Ruifeng Luo",
"Zhang",
"Lixin Gu",
"Jifeng Dai",
"Yu Qiao",
"Wenhai Wang",
"Hongjie Zhang"
] |
We study the task of panoptic symbol spotting, which involves identifying both individual instances of countable \textit{things} and the semantic regions of uncountable \textit{stuff} in computer-aided design (CAD) drawings composed of vector graphical primitives.Existing methods typically rely on image rasterization, graph construction, or point-based representation, but these approaches often suffer from high computational costs, limited generality, and loss of geometric structural information. In this paper, we propose \textit{VecFormer}, a novel method that addresses these challenges through \textit{line-based representation} of primitives. This design preserves the geometric continuity of the original primitive, enabling more accurate shape representation while maintaining a computation-friendly structure, making it well-suited for vector graphic understanding tasks. To further enhance prediction reliability, we introduce a \textit{Branch Fusion Refinement} module that effectively integrates instance and semantic predictions, resolving their inconsistencies for more coherent panoptic outputs. Extensive experiments demonstrate that our method establishes a new state-of-the-art, achieving 91.1 PQ, with Stuff-PQ improved by 9.6 and 21.2 points over the second-best results under settings with and without prior information, respectively—highlighting the strong potential of line-based representation as a foundation for vector graphic understanding.
|
poster
|
2505.23395
| null | null |
[] |
[] |
[] |
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] |
7
|
HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations
|
[
"Shuaicheng Zhang",
"Haohui Wang",
"Junhong Lin",
"Xiaojie Guo",
"Yada Zhu",
"Si Zhang",
"Dongqi Fu",
"Dawei Zhou"
] |
Graph heterophily, where connected nodes have different labels, has attracted significant interest recently. Most existing works adopt a simplified approach - using low-pass filters for homophilic graphs and high-pass filters for heterophilic graphs. However, we discover that the relationship between graph heterophily and spectral filters is more complex - the optimal filter response varies across frequency components and does not follow a strict monotonic correlation with heterophily degree. This finding challenges conventional fixed filter designs and suggests the need for adaptive filtering to preserve expressiveness in graph embeddings. Formally, natural questions arise: Given a heterophilic graph $\mathcal{G}$ , how and to what extent will the varying heterophily degree of $\mathcal{G}$ affect the performance of GNNs? How can we design adaptive filters to fit those varying heterophilic connections? Our theoretical analysis reveals that the average frequency response of GNNs and graph heterophily degree do not follow a strict monotonic correlation, necessitating adaptive graph filters to guarantee good generalization performance. Hence, we propose HeroFilter, a simple yet powerful GNN, which extracts information across the heterophily spectrum and combines salient representations through adaptive mixing. HeroFilter's superior performance achieves up to 9.2% accuracy improvement over leading baselines across homophilic and heterophilic graphs.
|
poster
|
2510.10864
| null | null |
[] |
[] |
[] |
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] |
8
|
Learning to Plan Like the Human Brain via Visuospatial Perception and Semantic-Episodic Synergistic Decision-Making
|
[
"Tianyuan Jia",
"Ziyu Li",
"Qing Li",
"Xiuxing Li",
"Xiang Li",
"Chen Wei",
"Li Yao",
"Xia Wu"
] |
Motion planning in high-dimensional continuous spaces remains challenging due to complex environments and computational constraints. Although learning-based planners, especially graph neural network (GNN)-based, have significantly improved planning performance, they still struggle with inaccurate graph construction and limited structural reasoning, constraining search efficiency and path quality. The human brain exhibits efficient planning through a two-stage Perception-Decision model. First, egocentric spatial representations from visual and proprioceptive input are constructed, and then semantic–episodic synergy is leveraged to support decision-making in uncertainty scenarios. Inspired by this process, we propose NeuroMP, a brain-inspired planning framework that learns to plan like the human brain. NeuroMP integrates a Perceptive Segment Selector inspired by visuospatial perception to construct safer graphs, and a Global Alignment Heuristic guide search in weakly connected graphs by modeling semantic-episodic synergistic decision-making. Experimental results demonstrate that NeuroMP significantly outperforms existing planning methods in efficiency and quality while maintaining a high success rate.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
9
|
Cognitive Predictive Processing: A Human-like Framework for Adaptive Exploration in Open-World Reinforcement Learning
|
[
"boheng liu",
"Ziyu Li",
"Chenghua Duan",
"YuTian Liu",
"Zhuo Wang",
"Xiuxing Li",
"Qing Li",
"Xia Wu"
] |
Open-world reinforcement learning challenges agents to develop intelligent behavior in vast exploration spaces. Recent approaches like LS-Imagine have advanced the field by extending imagination horizons through jumpy state transitions, yet remain limited by fixed exploration mechanisms and static jump thresholds that cannot adapt across changing task phases, resulting in inefficient exploration and lower completion rates. Humans demonstrate remarkable capabilities in open-world decision-making through a chain-like process of task decomposition, selective memory utilization, and adaptive uncertainty regulation. Inspired by human decision-making processes, we present Cognitive Predictive Processing (CPP), a novel framework that integrates three neurologically-inspired systems: a phase-adaptive cognitive controller that dynamically decomposes tasks into exploration, approach, and completion phases with adaptive parameters; a dual-memory integration system implementing dual-modal memory that balances immediate context with selective long-term storage; and an uncertainty-modulated prediction regulator that continuously updates environmental predictions to modulate exploration behavior. Comprehensive experiments in MineDojo demonstrate that these human-like decision-making strategies enhance performance over recent techniques, with success rates improving by an average of 4.6\% across resource collection tasks while reducing task completion steps by an average of 7.1\%. Our approach bridges cognitive neuroscience and reinforcement learning, excelling in complex scenarios that require sustained exploration and strategic adaptation while demonstrating how neural-inspired models can solve key challenges in open-world AI systems. Our main code has been anonymously uploaded to \url{https://anonymous.4open.science/r/CPP} without any author information.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
10
|
FlexWorld: Progressively Expanding 3D Scenes for Flexible-View Exploration
|
[
"Luxi Chen",
"Zihan Zhou",
"Min Zhao",
"Yikai Wang",
"Ge Zhang",
"Wenhao Huang",
"Hao Sun",
"Ji-Rong Wen",
"Chongxuan LI"
] |
Generating flexible-view 3D scenes, including 360° rotation and zooming, from single images is challenging due to a lack of 3D data. To this end, we introduce FlexWorld, a novel framework that progressively constructs a persistent 3D Gaussian splatting representation by synthesizing and integrating new 3D content. To handle novel view synthesis under large camera variations, we leverage an advanced pre-trained video model fine-tuned on accurate depth-estimated training pairs. By combining geometry-aware scene integration and optimization, FlexWorld refines the scene representation, producing visually consistent 3D scenes with flexible viewpoints. Extensive experiments demonstrate the effectiveness of FlexWorld in generating high-quality novel view videos and flexible-view 3D scenes from single images, achieving superior visual quality under multiple popular metrics and datasets compared to existing state-of-the-art methods. Additionally, FlexWorld supports extrapolating from existing 3D scenes, further extending its applicability. Qualitatively, we highlight that FlexWorld can generate high-fidelity scenes that enable 360° rotations and zooming exploration.
|
poster
| null | null | null |
[] |
[] |
[] |
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