GenAI4ELab/papercli-papers huggingface dataset:
MoL: Adaptive Mixture-of-Length Reasoning for Efficient Question Answering with Context
Authors: Guocong Li; Jinjian Zhang; Ping Wang; Dongnan Liu; Tian Liang; Qiuyi Qi; Hao Huang; Siyan Guo; Mutian Bao; Wei Zhou; Linjian Mo; Hongxia Xu; Jian Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present Mixture-of-Length (MoL), an approach for Question Answering (QA) with context that aims to improve the balance between reasoning quality and response efficiency. Our method introduces a principled difficulty assessment based on information-theoretic principles and a dual-objective reward mechanism that adaptively modulates response length. In our experiments, MoL exhibits an emergent behavior termed "intelligent brevity": the model tends to produce shorter responses for simpler queries and longer ones for more complex inputs. This property is desirable for human-computer interaction and can reduce inference costs. A post-hoc analysis of internal activations suggests a correlation between this output adaptivity and the effective number of layers that contribute during inference. On multiple QA benchmarks, MoL demonstrates competitive accuracy while substantially reducing tokens compared to baselines, indicating that difficulty-aware length modulation is a promising direction for efficient QA with context.
VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use
Authors: Mingyuan Wu; Jingcheng Yang; Jize Jiang; Meitang Li; Kaizhuo Yan; Hanchao Yu; Minjia Zhang; ChengXiang Zhai; Klara Nahrstedt
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning finetuning (RFT) has significantly advanced the reasoning capabilities of large language models (LLMs) by enabling long chains of thought, multi-turn self-correction, and effective tool use. While recent works attempt to extend RFT to vision-language models (VLMs), these efforts largely focus on text-only reasoning conditioned on original image inputs, and do not incorporate visual reasoning in the response. In contrast, test-time methods like Visual Sketchpad incorporate visual steps but lack training mechanisms.

We introduce VTool-R1, the first RFT framework that trains VLMs to generate multimodal chains of thought by interleaving text and intermediate visual reasoning steps. VTool-R1 integrates Python-based visual editing tools into the RFT process, enabling VLMs to learn when and how to generate visual reasoning steps that enhance the final output quality. Trained with outcome-based rewards, our approach elicits strategic visual tool use for multi-modal reasoning without relying on process-based supervision. Extensive experiments on structured visual reasoning over charts and tables show that VTool-R1 enhances reasoning performance by teaching VLMs to "think with images" and generate multimodal chain of thoughts with tools. To support future research in multi-turn multi-modal reasoning, we open-source our code at https://github.com/VTOOL-R1/vtool-r1.
When would Vision-Proprioception Policies Fail in Robotic Manipulation?
Authors: Jingxian Lu; Wenke Xia; Yuxuan Wu; Zhiwu Lu; Di Hu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Proprioceptive information is critical for precise servo control by providing real-time robotic states. Its collaboration with vision is highly expected to enhance performances of the manipulation policy in complex tasks. However, recent studies have reported inconsistent observations on the generalization of vision-proprioception policies. 
In this work, we investigate this by conducting temporally controlled experiments. We found that during task sub-phases that robot's motion transitions, which require target localization, the vision modality of the vision-proprioception policy plays a limited role. Further analysis reveals that the policy naturally gravitates toward concise proprioceptive signals that offer faster loss reduction when training, thereby dominating the optimization and suppressing the learning of the visual modality during motion-transition phases. 
To alleviate this, we propose the Gradient Adjustment with Phase-guidance (GAP) algorithm that adaptively modulates the optimization of proprioception, enabling dynamic collaboration within the vision-proprioception policy. Specifically, we leverage proprioception to capture robotic states and estimate the probability of each timestep in the trajectory belonging to motion-transition phases. During policy learning, we apply fine-grained adjustment that reduces the magnitude of proprioception's gradient based on estimated probabilities, leading to robust and generalizable vision-proprioception policies. 
The comprehensive experiments demonstrate GAP is applicable in both simulated and real-world environments, across one-arm and dual-arm setups, and compatible with both conventional and Vision-Language-Action models. We believe this work can offer valuable insights into the development of vision-proprioception policies in robotic manipulation.
Videos and code are available at https://gewu-lab.github.io/GAP/.
Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models
Authors: Shilei Cao; Hehai Lin; Jiashun Cheng; Yang Liu; Guowen Li; Xuehe Wang; Juepeng Zheng; Haoyuan Liang; Meng Jin; Chengwei Qin; Hong Cheng; Haohuan Fu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While recent advances in machine learning have equipped Weather Foundation Models (WFMs) with substantial generalization capabilities across diverse downstream tasks, the escalating computational requirements associated with their expanding scale increasingly hinder practical deployment. Current Parameter-Efficient Fine-Tuning (PEFT) methods, designed for vision or language tasks, fail to address the unique challenges of weather downstream tasks, such as variable heterogeneity, resolution diversity, and spatiotemporal coverage variations, leading to suboptimal performance when applied to WFMs. To bridge this gap, we introduce WeatherPEFT, a novel PEFT framework for WFMs incorporating two synergistic innovations. First, during the forward pass, Task-Adaptive Dynamic Prompting (TADP) dynamically injects the embedding weights within the encoder to the input tokens of the pre-trained backbone via internal and external pattern extraction, enabling context-aware feature recalibration for specific downstream tasks. Furthermore, during backpropagation, Stochastic Fisher-Guided Adaptive Selection (SFAS) not only leverages Fisher information to identify and update the most task-critical parameters, thereby preserving invariant pre-trained knowledge, but also introduces randomness to stabilize the selection. We demonstrate the effectiveness and efficiency of WeatherPEFT on three downstream tasks, where existing PEFT methods show significant gaps versus Full-Tuning, and WeatherPEFT achieves performance parity with Full-Tuning using fewer trainable parameters. The code of this work is available at https://github.com/ShileiCao/WeatherPEFT.
Beyond Text-Only: Towards Multimodal Table Retrieval in Open-World
Authors: Da Li; Keping Bi; Jiafeng Guo; Wei Yuan; Fan Yang; Tingting Gao; Xueqi Cheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Open-domain table retrieval aims to retrieve semantically relevant structured tables from a large-scale corpus in response to natural language queries. Unlike unstructured text, tables store information not only through their textual or numerical content but also through their structural properties, including hierarchical relationships between headers and cells, as well as complex spatial arrangements within the table layout. Existing methods predominantly treat table retrieval as a variant of text retrieval. They struggle to accurately preserve the rich structural semantics of diverse table formats during text serialization. Existing methods typically flatten tables into linear text sequences through row-wise or column-wise serialization, inadvertently discarding structural information. The problem becomes particularly acute when processing complex table layouts containing merged cells or irregular alignments, ultimately compromising retrieval performance. Moreover, existing methods struggle to handle embedded images within table cells. Notably, visual representations inherently preserve both structural and content information while being format-agnostic. This insight motivates our exploration of image-based table retrieval, as it can naturally overcome the challenges faced by existing methods. In this paper, we introduce TaR-ViR (Table Retrieval via Visual Representations), a new benchmark that reformulates table retrieval as a multimodal task by treating tables as images. Experiments on TaR-ViR show that this paradigm shift achieved more effective and efficient retrieval performance. Crucially, it eliminates the need for error-prone text conversion, enabling scalable collection and utilization of open-world tables. Our data are available at https://github.com/Trustworthy-Information-Access/Tab-ViR.
Flow-based Conformal Prediction for Multi-dimensional Time Series
Authors: Junghwan Lee; Chen Xu; Yao Xie
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Time series prediction underpins a broad range of downstream tasks across many scientific domains. Recent advances and increasing adoption of black-box machine learning models for time series prediction highlight the critical need for uncertainty quantification. While conformal prediction has gained attention as a reliable uncertainty quantification method, conformal prediction for time series faces two key challenges: (1) \textbf{leveraging correlations in observations and non-conformity scores to overcome the exchangeability assumption}, and (2) \textbf{constructing prediction sets for multi-dimensional outcomes}. To address these challenges, we propose a novel conformal prediction method for time series using flow with classifier-free guidance. We provide coverage guarantees by establishing exact non-asymptotic marginal coverage and a finite-sample bound on conditional coverage for the proposed method. Evaluations on real-world time series datasets demonstrate that our method constructs significantly smaller prediction sets than existing conformal prediction methods, maintaining target coverage.
OmniNav: A Unified Framework for Prospective Exploration and Visual-Language Navigation
Authors: Xinda Xue; Junjun Hu; Minghua Luo; Xie Shichao; Jintao Chen; Zixun Xie; Quan Kuichen; GuoWei; Zedong Chu; Mu Xu; Zhengzhou Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Embodied navigation is a foundational challenge for intelligent robots, demanding the ability to comprehend visual environments, follow natural language instructions, and explore autonomously. However, existing models struggle to provide a unified solution across heterogeneous navigation paradigms, often yielding low success rates and limited generalization. We present OmniNav, a unified framework that handles instruct-goal, object-goal, point-goal navigation, and frontier-based exploration within a single architecture. First, we introduce a lightweight, low-latency policy that predicts continuous-space waypoints (coordinates and orientations) with high accuracy, outperforming action-chunk methods in precision and supporting real-world deployment with control frequencies up to 5 Hz. Second, at the architectural level, OmniNav proposes a fast-slow system design: a fast module performs waypoint generation from relatively short-horizon visual context and subtasks, while a slow module conducts deliberative planning using long-horizon observations and candidate frontiers to select the next subgoal and subtask. This collaboration improves path efficiency and maintains trajectory coherence in exploration and memory-intensive settings. Notably, we find that the primary bottleneck lies not in navigation policy learning per se, but in robust understanding of general instructions and objects. To enhance generalization, we incorporate large-scale general-purpose training datasets including those used for image captioning and referring/grounding into a joint multi-task regimen, which substantially boosts success rates and robustness. Extensive experiments demonstrate state-of-the-art performance across diverse navigation benchmarks, and real-world deployment further validates the approach. OmniNav offers practical insights for embodied navigation and points to a scalable path toward versatile, highly generalizable robotic intelligence.
Purrception: Variational Flow Matching for Vector-Quantized Image Generation
Authors: Răzvan-Andrei Matișan; Vincent Tao Hu; Grigory Bartosh; Björn Ommer; Cees G. M. Snoek; Max Welling; Jan-Willem van de Meent; Mohammad Mahdi Derakhshani; Floor Eijkelboom
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce Purrception, a variational flow matching approach for vector-quantized image generation that provides explicit categorical supervision while maintaining continuous transport dynamics. Our method adapts Variational Flow Matching to vector-quantized latents by learning categorical posteriors over codebook indices while computing velocity fields in the continuous embedding space. This combines the geometric awareness of continuous methods with the discrete supervision of categorical approaches, enabling uncertainty quantification over plausible codes and temperature-controlled generation. We evaluate Purrception on ImageNet-1k $256 \times 256$ generation. Training converges faster than both continuous flow matching and discrete flow matching baselines while achieving competitive FID scores with state-of-the-art models. This demonstrates that Variational Flow Matching can effectively bridge continuous transport and discrete supervision for improved training efficiency in image generation.
Fastcar: Cache Attentive Replay for Fast Auto-Regressive Video Generation on the Edge
Authors: Xuan Shen; Weize Ma; Yufa Zhou; Enhao Tang; Yanyue Xie; Zhengang Li; Yifan Gong; Quanyi Wang; Henghui Ding; Yiwei Wang; Pu Zhao; Jun Lin; Jiuxiang Gu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Auto-regressive (AR) models, initially successful in language generation, have recently shown promise in visual generation tasks due to their superior sampling efficiency.
Unlike image generation, 
video generation requires a substantially larger number of tokens to produce coherent temporal frames, resulting in significant overhead during  decoding. 
We first make specific key observations: (i) MLP modules in the decode phase dominate the inference latency, and (ii) there exists high temporal redundancy in MLP outputs of adjacent frames. 
With the insights, we propose **FastCar** to accelerate the decode phase for the AR video generation  by exploring the temporal redundancy. The Temporal Attention Score (TAS) is proposed to determine whether to apply the replay strategy (i.e., reusing cached MLP outputs from the previous frame to reduce redundant computations)  with detailed theoretical analysis and justification.
Furthermore, we develop a hardware accelerator on FPGA with Dynamic Resource Scheduling  based on TAS to enable better resource utilization and faster inference.
Experimental results demonstrate the effectiveness of our method, which outperforms traditional sparse attention approaches with more than 2.1x decoding speedup and higher energy efficiency on the edge.
Furthermore, by combining FastCar and sparse attention, FastCar can boost the performance of sparse attention with alleviated drifting, demonstrating our unique advantages for high-resolution and long-duration video generation.
Code: https://github.com/shawnricecake/fast-car
SASFT: Sparse Autoencoder-guided Supervised Finetuning to Mitigate Unexpected Code-Switching in LLMs
Authors: Boyi Deng; Yu Wan; Baosong Yang; Fei Huang; Wenjie Wang; Fuli Feng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have impressive multilingual capabilities, but they suffer from unexpected code-switching, also known as language mixing, which involves switching to unexpected languages in the model response. This problem leads to poor readability and degrades the usability of model responses.
However, existing work on this issue lacks a mechanistic analysis and shows limited effectiveness.
In this paper, we first provide an in-depth analysis of unexpected code-switching using sparse autoencoders and find that when LLMs switch to a language, the features of that language exhibit excessive pre-activation values. Based on our findings, we propose $\textbf{S}$parse $\textbf{A}$utoencoder-guided $\textbf{S}$upervised $\textbf{F}$ine$\textbf{t}$uning (SASFT), which teaches LLMs to maintain appropriate pre-activation values of specific language features during training. Experiments on five models across three languages demonstrate that SASFT consistently reduces unexpected code-switching by more than 50\% compared to standard supervised fine-tuning, with complete elimination in one case. Moreover, SASFT maintains or even improves the models' performance on six multilingual benchmarks, showing its effectiveness in addressing code-switching while preserving multilingual capabilities. The code and data are available at https://github.com/Aatrox103/SASFT.
MCP-SafetyBench: A Benchmark for Safety Evaluation of Large Language Models with Real-World MCP Servers
Authors: Xuanjun Zong; Zhiqi Shen; Lei Wang; Yunshi Lan; Chao Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) are evolving into agentic systems that reason, plan, and operate external tools. 
The Model Context Protocol (MCP) is a key enabler of this transition, offering a standardized interface for connecting LLMs with heterogeneous tools and services. 
Yet MCP's openness and multi-server workflows introduce new safety risks that existing benchmarks fail to capture, as they focus on isolated attacks or lack real-world coverage. 
We present **MCP-SafetyBench**, a comprehensive benchmark built on real MCP servers that supports realistic multi-turn evaluation across five domains—browser automation, financial analysis, location navigation, repository management, and web search. 
It incorporates a unified taxonomy of 20 MCP attack types spanning server, host, and user sides, and includes tasks requiring multi-step reasoning and cross-server coordination under uncertainty. 
Using MCP-SafetyBench, we systematically evaluate leading open- and closed-source LLMs, revealing that all models remain vulnerable to MCP attacks, with a notable safety-utility trade-off. 
Our results highlight the urgent need for stronger defenses and establish MCP-SafetyBench as a foundation for diagnosing and mitigating safety risks in real-world MCP deployments.
Our benchmark is available at https://github.com/xjzzzzzzzz/MCPSafety.
Learning Dynamics Feature Representation via Policy Attention for Dynamic Path Planning in Urban Road Networks
Authors: Kai Zhang; Jingjing Gu; Qiuhong Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Dynamic Path Planning (DPP) in urban road networks faces fundamental challenges, as traffic conditions change rapidly over time and often render planned routes ineffective. Reinforcement Learning (RL) provides an effective way to adaptively handle such uncertainties by incorporating traffic dynamics into state, but its performance crucially depends on how these dynamics are represented. Existing approaches either rely on global traffic information, which ensures decision completeness but suffers from redundancy and high computational cost, or oversimplified local features, which are efficient but often omit critical dynamics and lead to suboptimal paths. To address this, we propose a Dynamics Feature Representation (DFR) framework that progressively refines global traffic dynamics into compact features for RL-based DPP. Specifically, we introduce a policy attention mechanism that identifies a core subset of global dynamics by extracting the top-k shortest paths, and further constructs node-related local features by intersecting with n-hop neighborhoods, enabling near-optimal policy learning. Theoretical analysis demonstrates that DFR guarantees state completeness, while empirical results confirm that, compared to classical baselines and standard RL methods, DFR significantly improves path planning performance and accelerates convergence. This work highlights the central role of feature representation in RL-based DPP and proposes a general framework that balances information sufficiency with computational efficiency, paving the way for scalable dynamic decision-making in real-world transportation systems.
NFT: Bridging Supervised Learning and Reinforcement Learning in Math Reasoning
Authors: Huayu Chen; Kaiwen Zheng; Qinsheng Zhang; Ganqu Cui; Yin Cui; Haotian Ye; Tsung-Yi Lin; Ming-Yu Liu; Jun Zhu; Haoxiang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning (RL) has played a central role in the recent surge of LLMs' math abilities by enabling verification-driven training through binary verifier signals. In contrast, Supervised Learning (SL) is rarely considered for such verification-driven training, largely due to its heavy reliance on reference answers and inability to reflect on mistakes. In this work, we challenge the prevailing notion that self-improvement is exclusive to RL and propose Negative-aware Fine-Tuning (NFT) --- a supervised approach that enables LLMs to reflect on their failures and improve autonomously with no external teachers. In online training, instead of throwing away self-generated negative answers, NFT constructs an \textit{implicit} negative policy to model them. This implicit policy is parameterized with the same positive LLM we target to optimize on positive data, enabling direct policy optimization on all LLMs' generations. 
We conduct experiments on 7B and 32B models in math reasoning tasks. Results consistently show that through the additional leverage of negative feedback, NFT significantly improves over SL baselines like rejection fine-tuning, matching, or even surpassing leading RL algorithms like GRPO and DAPO. Furthermore, we demonstrate that NFT and GRPO are actually equivalent in strict-on-policy training, even though they have entirely different theoretical foundations. Our experiments and theoretical findings bridge the gap between SL and RL methods in binary-feedback learning systems.
Adaptive Width Neural Networks
Authors: Federico Errica; Henrik Christiansen; Viktor Zaverkin; Mathias Niepert; Francesco Alesiani
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: For almost 70 years, researchers have typically selected the width of neural networks’ layers either manually or through automated hyperparameter tuning methods such as grid search and, more recently, neural architecture search. This paper challenges the status quo by introducing an easy-to-use technique to learn an \textit{unbounded} width of a neural network's layer \textit{during training}. The method jointly optimizes the width and the parameters of each layer via standard backpropagation. We apply the technique to a broad range of data domains such as tables, images, text, sequences, and graphs, showing how the width adapts to the task's difficulty. A by product of our width learning approach is the easy truncation of the trained network at virtually zero cost, achieving a smooth trade-off between performance and compute resources. Alternatively, one can dynamically compress the network until performances do not degrade. 
In light of recent foundation models trained on large datasets, requiring billions of parameters and where hyper-parameter tuning is unfeasible due to huge training costs, our approach introduces a viable alternative for width learning.