GenAI4ELab/papercli-papers huggingface dataset:
HAMLET: Switch Your Vision-Language-Action Model into a History-Aware Policy
Authors: Myungkyu Koo; Daewon Choi; Taeyoung Kim; Kyungmin Lee; Changyeon Kim; Younggyo Seo; Jinwoo Shin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Inherently, robotic manipulation tasks are history-dependent: leveraging past context could be beneficial. However, most existing Vision-Language-Action models (VLAs) have been designed without considering this aspect, i.e., they rely solely on the current observation, ignoring preceding context. In this paper, we propose HAMLET, a scalable framework to adapt VLAs to attend to the historical context during action prediction. Specifically, we introduce moment tokens that compactly encode perceptual information at each timestep. Their representations are initialized with time-contrastive learning, allowing them to better capture temporally distinctive aspects. Next, we employ a lightweight memory module that integrates the moment tokens across past timesteps into memory features, which are then leveraged for action prediction. Through empirical evaluation, we show that HAMLET successfully transforms a state-of-the-art VLA into a history-aware policy, especially demonstrating significant improvements on long-horizon tasks that require historical context. In particular, on top of GR00T N1.5, HAMLET achieves an average success rate of 76.4% on history-dependent real-world tasks, surpassing the baseline performance by 47.2%. Furthermore, HAMLET pushes prior art performance from 64.1% to 66.4% on RoboCasa Kitchen (100-demo setup) and from 95.6% to 97.6% on LIBERO, highlighting its effectiveness even under generic
robot-manipulation benchmarks.
Fixing the Broken Compass: Diagnosing and Improving Inference-Time Reward Modeling
Authors: Jiachun Li; Pengfei Cao; Zhuoran Jin; Yubo Chen; Jiexin Xu; Huaijun Li; Xiaojian Jiang; Kang Liu; Jun Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Inference-time scaling techniques have shown promise in enhancing the reasoning capabilities of large language models (LLMs). While recent research has primarily focused on training-time optimization, our work highlights inference-time reward model (RM)-based reasoning as a critical yet overlooked avenue. In this paper, we conduct a systematic analysis of RM behavior across downstream reasoning tasks, revealing three key limitations: (1) RM can impair performance on simple questions, (2) its discriminative ability declines with increased sampling, and (3) high search diversity undermines RM performance. To address these issues, we propose CRISP (Clustered Reward Integration with Stepwise Prefixing), a novel inference-time algorithm that clusters generated reasoning paths by final answers, aggregates reward signals at the cluster level, and adaptively updates prefix prompts to guide generation. Experimental results demonstrate that CRISP significantly enhances LLM reasoning performance, achieving up to 5% accuracy improvement over other RM-based inference methods and an average of 10% gain over advanced reasoning models.
RepSpec: Structural Re-parameterized Draft Model Training for Speculative Decoding
Authors: Feiye Huo; Jianchao Tan; Jiahao Liu; Zixu Jiang; Jiacheng Li; Jingang Wang; Xunliang Cai; Shengli Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As the parameter size of large language models (LLMs) continues to grow, the latency of autoregressive inference increases due to memory-bound computational inefficiency. To address this, speculative decoding has been proposed, where a large target model verifies multiple tokens generated in parallel by a smaller draft model. However, the performance of speculative decoding is fundamentally limited by the draft model’s capacity, which stems from the parameter gap between the two models. To overcome this limitation, we propose RepSpec, which combines structural re-parameterization with draft model training. During training, redundant linear structures are introduced and later merged into the backbone network during inference, thus enhancing the draft model’s training effectiveness without increasing inference cost. By applying our method to improve the current state-of-the-art approach, EAGLE, we achieve a significant improvement in accepted sequence length. Furthermore, considering the specific characteristics of the speculative decoding scenario, we explore a hybrid training strategy that combines linear and nonlinear structures, which yields a further improvement in acceptance length.
Out-of-Distribution Graph Models Merging
Authors: Yidi Wang; Ziyue Qiao; Jiawei Gu; Xubin Zheng; Pengyang Wang; pei Xiaobing; Xiao Luo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper studies a novel problem of out-of-distribution graph models merging, which aims to construct a generalized model from multiple graph models pre-trained on different domains with distribution discrepancy. This problem is challenging because of the difficulty in learning domain-invariant knowledge implicitly in model parameters and consolidating expertise from potentially heterogeneous GNN backbones. In this work, we propose a graph generation strategy that instantiates the mixture distribution of multiple domains. Then, we merge and fine-tune the pre-trained graph models via a MoE module and a masking mechanism for generalized adaptation. Our framework is architecture-agnostic and can operate without any source/target domain data. Both theoretical analysis and experimental results demonstrate the effectiveness of our approach in addressing the model generalization problem.
Human or Machine? A Preliminary Turing Test for Speech-to-Speech Interaction
Authors: Xiang Li; Jiabao Gao; Sipei Lin; Xuan Zhou; Chi Zhang; Bo Cheng; Jiale Han; Benyou Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The pursuit of human-like conversational agents has long been guided by the Turing test. For modern speech-to-speech (S2S) systems, a critical yet unanswered question is whether they can converse like humans. To tackle this, we conduct the first Turing test for S2S systems, collecting 2,968 human judgments on dialogues between 9 state-of-the-art S2S systems and 28 human participants. Our results deliver a clear finding: no existing evaluated S2S system passes the test, revealing a significant gap in human-likeness. To diagnose this failure, we develop a fine-grained taxonomy of 18 human-likeness dimensions and crowd-annotate our collected dialogues accordingly. Our analysis shows that the bottleneck is not semantic understanding but stems from paralinguistic features, emotional expressivity, and conversational persona. Furthermore, we find that off-the-shelf AI models perform unreliably as Turing test judges. In response, we propose an interpretable model that leverages the fine-grained human-likeness ratings and delivers accurate and transparent human-vs-machine discrimination, offering a powerful tool for automatic human-likeness evaluation. Our work establishes the first human-likeness evaluation for S2S systems and moves beyond binary outcomes to enable detailed diagnostic insights, paving the way for human-like improvements in conversational AI systems.
GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care
Authors: Yanke Li; Ruirui Wang; Manuel Günther; Diego Paez-Granados
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness. However, clinical applications demand predictive models that are both accurate and interpretable. We present our Graph Attention-based Relational Learning for Intensive Care (GARLIC) model, a novel neural network architecture that imputes missing data through a learnable exponential-decay encoder, captures inter-sensor dependencies via time-lagged summary graphs, and fuses global patterns with cross-dimensional sequential attention. All attention weights and graph edges are learned end-to-end to serve as built-in observation-, signal-, and edge-level explanations. To reconcile auxiliary reconstruction and primary classification objectives, we developed an alternating decoupled optimization scheme that stabilizes training. On three ICU benchmarks (PhysioNet 2012 \& 2019, MIMIC-III), GARLIC sets the new state of the art in outcome prediction, significantly improving AUROC and AUPRC over best-performing baselines at comparable computational cost. Ablation studies confirm the contribution of each module, and feature-removal trials validate the fidelity of importance attribution through a monotonic performance drop (full > top 50\% > random 50\% > bottom 50\%). Real-time case studies demonstrate actionable risk warnings with transparent explanations, marking a significant advance toward accurate, explainable deep learning for irregularly sampled ICU time series data. Moreover, we demonstrated GARLIC's superiority in data imputation and classification on various time-series datasets beyond the ICU domain, showing its generalizability and applicability to broader tasks.
BeyondBench: Contamination-Resistant Evaluation of Reasoning in Language Models
Authors: Gaurav Srivastava; Aafiya Shamshad Hussain; Zhenyu Bi; Swastik Roy; Priya Pitre; Meng Lu; Morteza Ziyadi; Xuan Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Evaluating language models fairly is becoming harder as static benchmarks risk contamination by training data, making it unclear whether models are truly reasoning or just recalling answers. We introduce **BeyondBench**, an evaluation framework that avoids this problem by using **algorithmic problem generation**. Unlike traditional benchmarks that risk contamination from internet-scale training data, BeyondBench creates mathematically grounded problems on the fly, ensuring each test remains fresh and uncontaminated. Our framework covers **44 algorithmic tasks** with a total of **117 variations**, grouped into three difficulty levels: the *Easy Suite* (29 tasks) for basic arithmetic and statistics, the *Medium Suite* (5 tasks, 49 variations) for sequence patterns and reasoning, and the *Hard Suite* (10 tasks, 68 variations) tackling NP-complete and constraint satisfaction problems. Each task generates problems from a combinatorial space larger than $10^{15}$ unique instances, with solutions verified deterministically by mathematical proofs. We evaluated **101 language models**, including 85 open-source and 16 closed-source models, spanning sizes from 0.5B to 141B parameters and multiple quantization schemes. All evaluations use three-fold evaluation to ensure statistical robustness. Our results show consistent reasoning deficiencies across model families, with performance degrading sharply as problem complexity increases from polynomial to exponential. In our Hard Suite evaluations, models such as Gemini-2.5-pro, Llama-3.3-70B, and Qwen2.5-72B achieved average accuracies of **56.21%, 27.16%, and 33.37%,** respectively. Moreover, we observe that performance drops drastically without tool usage, with GPT-5, GPT-5-mini, and GPT-5-nano showing a **decline** of **16.81%, 15.86%, and 43.95%** in overall accuracy without tool access. The contamination resistance of BeyondBench rests on three guarantees: (i) the problem space is vastly larger than any static dataset, (ii) every instance has a deterministically verifiable solution (unique or fully enumerated), and (iii) isomorphic transformations generate semantically equivalent but syntactically new problems. BeyondBench redefines reasoning evaluation through genuine algorithmic problem-solving, ensuring fair and meaningful evaluation. Our public leaderboard is available at https://ctrl-gaurav.github.io/BeyondBench/. Our open-source Python package is available at https://pypi.org/project/beyondbench/, and the codebase can be found at https://github.com/ctrl-gaurav/BeyondBench for easy and reproducible evaluation.
Preference Leakage: A Contamination Problem in LLM-as-a-judge
Authors: Dawei Li; Renliang Sun; Yue Huang; Ming Zhong; Bohan Jiang; Jiawei Han; Xiangliang Zhang; Wei Wang; huan liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) as judges and LLM-based data synthesis have emerged as two fundamental LLM-driven data annotation methods in model development. While their combination significantly enhances the efficiency of model training and evaluation, little attention has been given to the potential contamination brought by this new model development paradigm. In this work, we expose preference leakage, a contamination problem in LLM-as-a-judge caused by the relatedness between the synthetic data generators and LLM-based evaluators. To study this issue, we first define three common relatednesses between the data generator LLM and the judge LLM: being the same model, having an inheritance relationship, and belonging to the same model family. Through extensive experiments, we empirically confirm the bias of judges towards their related student models caused by preference leakage across multiple LLM baselines and benchmarks. Further analysis suggests that preference leakage is a pervasive and real-world problem that is harder to detect compared to previously identified biases in LLM-as-a-judge scenarios. All of these findings imply that preference leakage is a widespread and challenging problem in the area of LLM-as-a-judge. We release all codes and data at: \url{https://github.com/David-Li0406/Preference-Leakage}.
GeoDiv: Framework for Measuring Geographical Diversity in Text-to-Image Models
Authors: Abhipsa Basu; Mohana Singh; Shashank Agnihotri; Margret Keuper; Venkatesh Babu Radhakrishnan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Text-to-image (T2I) models are rapidly gaining popularity, yet their outputs often lack geographical diversity, reinforce stereotypes, and misrepresent regions. Given their broad reach, it is critical to rigorously evaluate how these models portray the world. Existing diversity metrics either rely on curated datasets or focus on surface-level visual similarity, limiting interpretability. We introduce *GeoDiv*, a framework leveraging large language and vision-language models to assess geographical diversity along two complementary axes: the Socio-Economic Visual Index (SEVI), capturing economic and condition-related cues, and the Visual Diversity Index (VDI), measuring variation in primary entities and backgrounds. Applied to images generated by models such as Stable Diffusion and FLUX.1-dev across $10$ entities and $16$ countries, *GeoDiv* reveals a consistent lack of diversity and identifies fine-grained attributes where models default to biased portrayals. Strikingly, depictions of countries like India, Nigeria, and Colombia are disproportionately impoverished and worn, reflecting underlying socio-economic biases. These results highlight the need for greater geographical nuance in generative models. *GeoDiv* provides the first systematic, interpretable framework for measuring such biases, marking a step toward fairer and more inclusive generative systems. Project page: https://abhipsabasu.github.io/geodiv
Enhancing Persona Following at Decoding Time via Dynamic Importance Estimation for Role-Playing Agents
Authors: Yuxin Liu; Mingye Zhu; Siyuan Liu; Bo Hu; Lei Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The utility of Role-Playing Language Agents in sociological research is growing alongside the adoption of Large Language Models. For realism in social simulation, these agents must adhere to their personas defined by character profiles, yet existing strategies—static prompt engineering or costly fine-tuning—fail to adapt personas to dynamic scenarios. Psychological theories, such as the Cognitive-Affective Personality Systems, provide a crucial explanation for this failure: a persona's influence on behavior is not static but varies with the scenarios. This context-dependence highlights the critical need for adaptive persona management. To address this gap, we propose a novel, theory-driven method that dynamically estimates context-dependent persona importance and integrates it into weighted reward-guided decoding, enabling inference-time persona following. Specifically, we introduce Persona Dynamic Decoding (PDD) framework that consists of two key components:
(1) Persona Importance Estimation (PIE) module, which dynamically quantifies the contextual importance of persona attributes without requiring ground-truth supervision; and (2) Persona-Guided Inference-Time Alignment (PIA) paradigm, which leverages these importance scores to construct weighted multi-objective rewards and modulate generation probabilities during inference. Extensive experiments show the effectiveness of our method in utterance consistency and behavioral fidelity.
Decision Aggregation under Quantal Response
Authors: Zhihuan Huang; Yichong Xia; Yuqing Kong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The effectiveness of collective decision-making is often challenged by the bounded rationality and inherent stochasticity of individual agents. We investigate this by analyzing how to aggregate decisions from $n$ experts, each receiving a private signal about an unknown state. Assuming signals are conditionally independent and identically distributed, we depart from the fully rational paradigm and model expert behavior using quantal response—a stochastic choice model capturing bounded rationality. Within a minimax regret framework, we show that majority voting is the optimal robust aggregator when individual rationality falls below a certain threshold. Interestingly, such groups can outperform perfectly rational agents, as their decision randomness encodes weak but informative signals lost in deterministic behavior. We validate these findings using large language models (LLMs), which naturally exhibit quantal response via their temperature parameter. Aggregating moderately stochastic LLM outputs significantly improves accuracy on complex reasoning tasks, highlighting bounded rationality not as a limitation, but as a potential strength in collective intelligence.
DaVinci: Reinforcing Visual-Structural Syntax in MLLMs for Generalized Scientific Diagram Parsing
Authors: Xingchen ZENG; Zhewei Su; Hengming Zhang; Juyong Jiang; Jiazhi Xia; Wei Zeng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Parsing raster-based scientific diagrams into structured representations is critical for editability and reusability. However, existing multimodal LLMs (MLLMs) struggle with the diverse visual primitives, complex structural layouts, and strict syntax involved. To address this, we introduce DaVinci, a novel MLLM that learns diagram parsing based on a two-stage framework—supervised learning of visual primitives followed by reinforcement learning of their structural relationships. Our model learns visual-structural syntax through supervised training on TikZ30K, a newly curated dataset of high-quality diagram-TikZ code pairs that features abundant visual primitives and structurally optimized drawing sequences. We further refine the model via reinforcement learning, guided by a hybrid reward function that jointly optimizes for visual fidelity, structural consistency, and code correctness. Extensive experiments show that DaVinci significantly outperforms existing open-source MLLMs and surpasses leading proprietary models like GPT-5 and Claude-Sonnet-4.
Pyramid Patchification Flow for Visual Generation
Authors: Hui Li; Baoyou Chen; Li jiaye; Jingdong Wang; Siyu Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion Transformers (DiTs) typically use the same patch size for $\operatorname{Patchify}$ across timesteps, enforcing a constant token budget across timesteps. In this paper, we introduce Pyramidal Patchification Flow (PPFlow), which reduces the number of tokens for high-noise timesteps to improve the sampling efficiency. The idea is simple: use larger patches at higher-noise timesteps and smaller patches at lower-noise timesteps. The implementation is easy: share the DiT's transformer blocks across timesteps, and learn separate linear projections for different patch sizes in $\operatorname{Patchify}$ and $\operatorname{Unpatchify}$. Unlike Pyramidal Flow that operates on pyramid representations,, our approach operates over full latent representations, eliminating trajectory ``jump points'', and thus avoiding re-noising tricks for sampling. Training from pretrained SiT-XL/2 requires only $+8.9\%$ additional training FLOPs and delivers $2.02\times$ denoising speedups with image generation quality kept; training from scratch achieves comparable sampling speedup, e.g., $2.04\times$ speedup in SiT-B. Training from text-to-image model FLUX.1, PPFlow can achieve $1.61 - 1.86 \times$ speedup from 512 to 2048 resolution with comparable quality.
Beyond Raw Detection Scores: Markov-Informed Calibration for Boosting Machine-Generated Text Detection
Authors: Chenwang Wu; Yiu-ming Cheung; Shuhai Zhang; Bo Han; Defu Lian
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While machine-generated texts (MGTs) offer great convenience, they also pose risks such as disinformation and phishing, highlighting the need for reliable detection. Metric-based methods, which extract statistically distinguishable features of MGTs, are often more practical than complex model-based methods that are prone to overfitting. Given their diverse designs, we first place representative metric-based methods within a unified framework, enabling a clear assessment of their advantages and limitations. Our analysis identifies a core challenge across these methods: the token-level detection score is easily biased by the inherent randomness of the MGTs generation process. To address this,  we theoretically and empirically reveal two relationships of context detection scores that may aid calibration: Neighbor Similarity and Initial Instability. We then propose a Markov-informed score calibration strategy that models these relationships using Markov random fields, and implements it as a lightweight component via a mean-field approximation, allowing our method to be seamlessly integrated into existing detectors. Extensive experiments in various real-world scenarios, such as cross-LLM and paraphrasing attacks, demonstrate significant gains over baselines with negligible computational overhead. The code is available at \url{ https://github.com/tmlr-group/MRF_Calibration}.