GenAI4ELab/papercli-papers huggingface dataset:
From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning
Authors: Chen Shani; Liron Soffer; Dan Jurafsky; Yann LeCun; Ravid Shwartz-Ziv
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Humans organize knowledge into compact conceptual categories that balance compression with semantic richness. Large Language Models (LLMs) exhibit impressive linguistic abilities, but whether they navigate this same compression-meaning trade-off remains unclear. We apply an Information Bottleneck framework to compare human conceptual structure with embeddings from 40+ LLMs using classic categorization benchmarks (Rosch, 1973a; 1975; McCloskey & Glucksberg, 1978). 
We find that LLMs broadly agree with human category boundaries, yet fall short on fine-grained semantic distinctions. Unlike humans, who maintain "inefficient" representations that preserve contextual nuance, LLMs aggressively compress, achieving more optimal information-theoretic compression at the cost of semantic richness. Surprisingly, encoder models outperform much larger decoder models in agreement with human categories, suggesting that understanding and generation rely on distinct representational mechanisms.
Training-dynamics analysis reveals a two-phase trajectory: rapid initial concept formation followed by architectural reorganization, during which semantic processing migrates from deep to mid-network layers as the model discovers increasingly efficient, sparser encodings.
These divergent strategies, where LLMs optimize for compression and humans for adaptive utility, reveal fundamental differences between artificial and natural intelligence. This highlights the need for models that preserve the conceptual "inefficiencies" essential for human-like understanding.
On the Spectral Differences Between NTK and CNTK and Their Implications for Point Cloud Recognition
Authors: Yuanqu Mou; Chang Gou; Haiyang Bai; Jia Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The Convolutional Neural Tangent Kernel (CNTK) offers a principled framework for understanding convolutional architectures in the infinite-width regime. However, a comprehensive spectral comparison between CNTK and the classical Neural Tangent Kernel (NTK) remains underexplored. In this work, we present a detailed analysis of the spectral properties of CNTK and NTK, revealing that point cloud data exhibits a stronger alignment with the spectral bias of CNTK than images. This finding suggests that convolutional structures are inherently more suited to such geometric and irregular data formats. Based on this insight, we implement CNTK-based kernel regression for point cloud recognition tasks and demonstrate that it significantly outperforms NTK and other kernel baselines, especially in low-data settings. Furthermore, we derive a closed-form expression that connects CNTK with NTK in hybrid architectures. In addition, we introduce a closed-form of CNTK followed by NTK, while not the main focus, achieves strong empirical performance when applied to point-cloud tasks. Our study not only provides new theoretical understanding of spectral behaviors in neural tangent kernels but also shows that these insights can guide the practical design of CNTK-based regression for structured data such as point clouds.
Can Vision-Language Models Answer Face to Face Questions in the Real-World?
Authors: Reza Pourreza; Rishit Dagli; Apratim Bhattacharyya; Sunny Panchal; Guillaume Berger; Roland Memisevic
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: AI models have made significant strides in recent years in their ability to describe and answer questions about real-world images. They have also made progress in the ability to converse with users in real-time using audio input. This raises the question: have we reached the point where AI models, connected to a camera and microphone, can converse with users in real-time about scenes and events that are unfolding live in front of the camera? This has been a long-standing goal in AI and is a prerequisite for real-world AI assistants and humanoid robots to interact with humans in everyday situations. In this work, we introduce a new dataset and benchmark, the Interactive Video Dataset (IVD), which allows us to assess the extent to which existing models can support these abilities, and to what degree these capabilities can be instilled through fine-tuning. The dataset is based on a simple question-answering setup, where users ask questions that the system has to answer, in real-time, based on the camera and audio input. We show that existing models fall far behind human performance on this task, and we identify the main sources for the performance gap. However, we also show that for many of the required perceptual skills, fine-tuning on this form of data can significantly reduce this gap.
CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale
Authors: Zhun Wang; Tianneng Shi; Jingxuan He; Matthew Cai; Jialin Zhang; Dawn Song
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: AI agents have significant potential to reshape cybersecurity, making a thorough assessment of their capabilities critical.
However, existing evaluations fall short, because they are based on small-scale benchmarks and only measure static outcomes, failing to capture the full, dynamic range of real-world security challenges.
To address these limitations, we introduce CyberGym, a large-scale benchmark featuring 1,507 real-world vulnerabilities across 188 software projects.
Adjustable to different vulnerability analysis settings, CyberGym primarily tasks agents with generating a proof-of-concept test that reproduces a vulnerability, given only its text description and the corresponding codebase.
Our extensive evaluation highlights that CyberGym effectively differentiates agents' and models' cybersecurity capabilities.
Even the top-performing combinations only achieve a ~20% success rate, demonstrating the overall difficulty of CyberGym. 
Beyond static benchmarking, we show that CyberGym leads to the discovery of 34 zero-day vulnerabilities and 18 historically incomplete patches.
These results underscore that CyberGym is not only a robust benchmark for measuring AI's progress in cybersecurity but also a platform for creating direct, real-world security impact.
Go-Browse: Training Web Agents with Structured Exploration
Authors: Apurva Gandhi; Graham Neubig
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: One of the fundamental problems in digital agents is their lack of understanding of their environment. For instance, a web browsing agent may get lost in unfamiliar websites, uncertain what pages must be visited to achieve its goals. To address this, we propose Go-Browse, a method for automatically collecting diverse and realistic web agent data at scale through structured exploration of web environments. Go-Browse achieves efficient exploration by framing data collection as a graph search, enabling reuse of information across exploration episodes. We instantiate our method on the WebArena benchmark, collecting a dataset of 10K successful task-solving trajectories and 40K interaction steps across 100 URLs. Fine-tuning a 7B parameter language model on this dataset achieves a success rate of 21.7% on the WebArena benchmark, beating GPT-4o mini by 2.4% and exceeding current state-of-the-art results for sub-10B parameter models by 2.9%.
LLMS ON TRIAL: Evaluating Judicial Fairness For Large Language Models
Authors: Yiran HU; Zongyue Xue; Haitao Li; Siyuan Zheng; Qingjing Chen; Shaochun Wang; Xihan Zhang; Ning Zheng; Yun Liu; Qingyao Ai; Yiqun LIU; Charles L. A. Clarke; Weixing Shen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) are increasingly used in high-stakes fields, such as law, where their decisions can directly impact people's lives. When LLMs act as judges, the ability to fairly resolve judicial issues is necessary to ensure their trustworthiness. Based on theories of judicial fairness, we construct a comprehensive framework to measure LLM fairness, leading to a selection of 65 labels and 161 corresponding values. We further compile an extensive dataset, JudiFair, comprising 177,100 unique case facts. To achieve robust statistical inference, we develop three evaluation metrics—inconsistency, bias, and imbalanced inaccuracy—and introduce a method to assess the overall fairness of multiple LLMs across various labels. Through experiments with 16 LLMs, we uncover pervasive inconsistency, bias, and imbalanced inaccuracy across models, underscoring severe LLM judicial unfairness. 
Particularly, LLMs display notably more pronounced biases on demographic labels, with slightly less bias on substance labels compared to procedure ones. Interestingly, increased inconsistency correlates with reduced biases, but more accurate predictions exacerbate biases. While we find that adjusting the temperature parameter can influence LLM fairness, model size, release date, and country of origin do not exhibit significant effects on judicial fairness. Accordingly, we introduce a publicly available toolkit to support future research in evaluating and improving LLM fairness, along with a full technical analysis included as an appendix.
Controllable diffusion-based generation for multi-channel biological data
Authors: Haoran Zhang; Mingyuan Zhou; Wesley Tansey
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Biological profiling technologies, such as imaging mass cytometry (IMC) and spatial transcriptomics (ST), generate multi-channel data with strong spatial alignment and complex inter-channel relationships. 
Modeling such data requires generative frameworks that jointly model spatial structure and inter-channel dependencies and generalize across arbitrary subsets of observed and missing channels. Existing generative models typically assume low-dimensional inputs (e.g., RGB images) and rely on simple conditioning mechanisms that disrupt spatial correspondence and overlook inter-channel dependencies. This work proposes a unified multi-channel diffusion (MCD) framework for controllable generation of structured biological data with complex inter-channel relationships. Our model introduces two key innovations: (1) a hierarchical feature injection mechanism that enables multi-resolution conditioning on spatially aligned observed channels, and (2) two complementary channel attention modules to capture inter-channel relationships and recalibrate latent features. To support flexible conditioning and generalization to arbitrary sets of observed channels, we train the model using a random channel masking strategy, enabling it to reconstruct missing channels given any combination of observed channels as the spatial condition. We demonstrate state-of-the-art performance across both spatial and non-spatial biological data generation tasks, including imputation in spatial proteomics and clinical imaging, as well as gene-to-protein translation in single-cell datasets, and show strong generalizability to unseen conditional configurations.
Bradley-Terry and Multi-Objective Reward Modeling Are Complementary
Authors: Zhiwei Zhang; Hui Liu; Xiaomin Li; Zhenwei Dai; Jingying Zeng; Fali Wang; Minhua Lin; Ramraj Chandradevan; Linlin Wu; Zhen Li; Chen Luo; Zongyu Wu; Xianfeng Tang; Qi He; Suhang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reward models trained on human preference data have demonstrated strong effectiveness in aligning Large Language Models (LLMs) with human intent under the framework of Reinforcement Learning from Human Feedback (RLHF). However, RLHF remains vulnerable to reward hacking, where the policy exploits imperfections in the reward function rather than genuinely learning the intended behavior. 
Although significant efforts have been made to mitigate reward hacking, they predominantly focus on and evaluate in-distribution scenarios, where the training and testing data for the reward model share the same distribution.
In this paper, we empirically show that state-of-the-art methods struggle in more challenging out-of-distribution (OOD) settings. We further demonstrate that incorporating fine-grained multi-attribute scores helps address this challenge. However, the limited availability of high-quality data often leads to weak performance of multi-objective reward functions, which can negatively impact overall performance and become the bottleneck. To address this issue, we propose a unified reward modeling framework that jointly trains Bradley-Terry (BT) single-objective and multi-objective regression-based reward functions using a shared embedding space. We theoretically establish a connection between the BT loss and the regression objective and highlight their complementary benefits. Specifically, the regression task enhances the single-objective reward function’s ability to mitigate reward hacking in challenging OOD settings, while BT-based training improves the scoring capability of the multi-objective reward function, enabling a 7B model to outperform a 70B baseline.
Extensive experimental results demonstrate that our framework significantly improves both the robustness and the scoring performance of reward models.
Antithetic Noise in Diffusion Models
Authors: Jing Jia; Sifan Liu; Bowen Song; Wei Yuan; Liyue Shen; Guanyang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We systematically study antithetic initial noise in diffusion models, discovering that pairing each noise sample with its negation consistently produces strong negative correlation. This universal phenomenon holds across datasets, model architectures, conditional and unconditional sampling, and even other generative models such as VAEs and Normalizing Flows. To explain it, we combine experiments and theory and propose a \textit{symmetry conjecture} that the learned score function is approximately affine antisymmetric (odd symmetry up to a constant shift), supported by empirical evidence.
This negative correlation leads to substantially more reliable uncertainty quantification with up to $90\%$ narrower confidence intervals.  We demonstrate these gains on tasks including estimating pixel-wise statistics and evaluating diffusion inverse solvers.  We also provide extensions with randomized quasi-Monte Carlo noise designs for uncertainty quantification, and explore additional applications of the antithetic noise design to improve image editing and generation diversity. Our framework is training-free, model-agnostic, and adds no runtime overhead. Code is available at https://github.com/jjia131/Antithetic-Noise-in-Diffusion-Models-page.
Not-a-Bandit: Provably No-Regret Drafter Selection in Speculative Decoding for LLMs
Authors: Hongyi Liu; Jiaji Huang; Zhen Jia; Youngsuk Park; Yu-Xiang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Speculative decoding is widely used in accelerating large language model (LLM) inference. In this work, we focus on the online draft model selection problem in speculative decoding.  We design an algorithm that provably competes with the best draft model in hindsight for each query in terms of either the token acceptance probability or expected acceptance length. In particular, we show that we can accurately evaluate all draft models, instead of only the chosen model without incurring additional queries to the target model, which allows us to improve exponentially over the existing bandit-based approach as the number of draft models increases. Our approach is generically applicable with any speculative decoding methods (single draft, multi-drafts and draft-trees). Moreover, we design system-efficient versions of online learners and demonstrate that the overhead in computation and latency can be substantially reduced. We conduct extensive experiments on open-source LLMs and diverse datasets, demonstrating that our methods substantially outperform the state-of-the-art EAGLE3 and the BanditSpec baseline in a variety of domains where specialized domain-expert drafters are available, especially when long reasoning chains are required.
Scalable Chain of Thoughts via Elastic Reasoning
Authors: Yuhui Xu; Hanze Dong; Lei Wang; Doyen Sahoo; Junnan Li; Caiming Xiong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large reasoning models (LRMs) have achieved remarkable progress on complex tasks by generating extended chains of thought (CoT). However, their uncontrolled output lengths pose significant challenges for real-world deployment, where inference-time budgets on tokens, latency, or compute are strictly constrained. We propose Elastic Reasoning, a novel framework for scalable chain of thoughts that explicitly separates reasoning into two phases—thinking and solution—with independently allocated budgets. At test time, Elastic Reasoning prioritizes the completeness of solution segments, significantly improving reliability under tight resource constraints. To train models that are robust to truncated thinking, we introduce a lightweight budget-constrained rollout strategy, integrated into GRPO, which teaches the model to reason adaptively when the thinking process is cut short and generalizes effectively to unseen budget constraints without additional training. Empirical results on mathematical (AIME, MATH500) and programming (LiveCodeBench, Codeforces) benchmarks demonstrate that Elastic Reasoning performs robustly under strict budget constraints, while incurring significantly lower training cost than baseline methods. Remarkably, our approach also produces more concise and efficient reasoning even in unconstrained settings. Elastic Reasoning offers a principled and practical solution to the pressing challenge of controllable reasoning at scale. Code is available in the supplementary material.
R1-Code-Interpreter: LLMs Reason with Code via Supervised and Multi-stage Reinforcement Learning
Authors: Yongchao Chen; Yueying Liu; Junwei Zhou; Yilun Hao; Jingquan Wang; Yang Zhang; Na Li; Chuchu Fan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Practical guidance on training Large Language Models (LLMs) to leverage Code Interpreter across diverse tasks remains lacking. We present R1-Code-Interpreter, an extension of a text-only LLM trained via multi-turn supervised fine-tuning (SFT) and reinforcement learning (RL) to autonomously generate multiple code queries during step-by-step reasoning. Unlike prior RL + tool-use efforts focused on narrow domains such as math or retrieval, we curate 144 diverse reasoning and planning tasks and show that training a general-purpose Code Interpreter across them presents significant challenges due to task heterogeneity and scarcity of effective samples. To address this, we introduce a multi-stage curriculum learning approach that partitions training samples by measured improvement potential. The RL training prioritizes samples with higher potential and gradually shifts to lower-potential ones, increasing the average RL gains from merely +3.4\% to +9.3\% across Qwen-2.5 models (3/7/14B). Our final model, R1-CI-14B, improves average accuracy on the 37 test tasks from 44.1\% to 72.4\%, outperforming text-only GPT-4o (58.6\%) and GPT-4o with Code Interpreter (70.9\%). Notably, R1-CI-14B also exhibits emergent self-checking behavior through code generation. Datasets, Codes, and Models are available at https://github.com/yongchao98/R1-Code-Interpreter and https://huggingface.co/yongchao98.
CONCUR: A Framework for Continual Constrained and Unconstrained Routing
Authors: Peter Baile Chen; Weiyue Li; Dan Roth; Mike Cafarella; Samuel Madden; Jacob Andreas
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: AI tasks differ in complexity and are best addressed with different computation strategies (e.g., combinations of models and decoding methods). Hence, an effective routing system that maps tasks to the appropriate strategies is crucial.
Most prior methods build the routing framework by training a *single* model across *all* strategies, which demands full retraining whenever new strategies appear and leads to high overhead. Attempts at such continual routing, however, often face difficulties with generalization.
Prior models also typically use a *single* input representation, limiting their ability to capture the full complexity of the routing problem and leading to sub-optimal routing decisions.
To address these gaps, we propose CONCUR, a **con**tinual routing framework that supports both **c**onstrained and **u**nconstrained **r**outing (i.e., routing with or without a budget).
Our *modular* design trains a separate predictor model for each strategy, enabling seamless incorporation of new strategies with low additional training cost.
Our predictors also leverage *multiple* representations of both tasks and computation strategies to better capture overall problem complexity.
Experiments on both in-distribution and out-of-distribution, knowledge- and reasoning-intensive tasks show that our method outperforms the best single strategy and strong existing routing techniques with higher end-to-end accuracy and lower inference cost in both continual and non-continual settings, while also reducing training cost in the continual setting.
Efficient Multimodal Spatial Reasoning via Dynamic and Asymmetric Routing
Authors: Yixian Shen; Qi Bi; Zihan Wang; Zhiheng Yang; Changshuo Wang; Zhi Zhang; Prayag Tiwari; Andy D. Pimentel; Anuj Pathania
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recently, visualization-of-thought (VoT) has unlocked new opportunities for complex spatial reasoning in multimodal large language models (MLLMs) by complementing verbal reasoning with visual thinking.
However, the autoregressive accumulation of lengthy and redundant tokens substantially increases computation and memory costs.
In this paper, we present a new efficient framework for multimodal spatial reasoning, named *DARE*, designed to adaptively prune multimodal tokens across different network depths, reasoning hops, and modalities. 
First, *DARE* devises an intra- and inter-hop-aware differentiable retention mechanism to dynamically estimate token importance both within each reasoning step and across successive hops. 
Recognizing that deeper network layers encode visual cues into verbal streams, *DARE* introduces an asymmetric compression strategy that prunes tokens according to modality-specific redundancy and semantic importance.
Furthermore, *DARE* incorporates a progressive KV-cache retention policy aligned with cross-modal fusion dynamics, further reducing memory overhead during autoregressive reasoning.
Our method delivers substantial reductions in computation and memory footprint, averaging a 40.37\% reduction in FLOPs and 46.07\% reduction in KV caches usage, 
while consistently preserving or even improving reasoning performance across seven multimodal spatial reasoning benchmarks, and further generalizing to broader multimodal reasoning tasks, 
establishing a scalable and robust recipe for efficient multimodal reasoning.