GenAI4ELab/papercli-papers huggingface dataset:
COOPERTRIM: Adaptive Data Selection for Uncertainty-Aware Cooperative Perception
Authors: Shilpa Mukhopadhyay; Amit Roy-Chowdhury; Hang Qiu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Cooperative perception enables autonomous agents to share encoded representations over wireless communication to enhance each other’s live situational awareness. However, the tension between the limited communication bandwidth and the rich sensor information hinders its practical deployment. Recent studies have explored selection strategies that share only a subset of features per frame while striving to keep the performance on par. Nevertheless, the bandwidth requirement still stresses current wireless technologies. To fundamentally ease the tension, we take a proactive approach, exploiting the temporal continuity to identify features that capture environment dynamics, while avoiding repetitive and redundant transmission of static information. By incorporating temporal awareness, agents are empowered to dynamically adapt the sharing quantity according to environment complexity. We instantiate this intuition into an adaptive selection framework, COOPERTRIM, which introduces a novel conformal temporal uncertainty metric to gauge feature relevance, and a data-driven mechanism to dynamically determine the sharing quantity. To evaluate COOPERTRIM, we take semantic segmentation and 3D detection as example tasks. Across multiple open-source cooperative segmentation and detection models, COOPERTRIM achieves up to 80.28% and 72.52% bandwidth reduction respectively while maintaining a comparable accuracy. Relative to other selection strategies, COOPERTRIM also improves IoU by as much as 45.54% with up to 72% less bandwidth. Combined with compression strategies, COOPERTRIM can further reduce bandwidth usage to as low as 1.46% without compromising IoU performance. Qualitative results show COOPERTRIM gracefully adapts to environmental dynamics, localization error, and communication latency, demonstrating flexibility and paving the way for real-world deployment.
CALM: Co-evolution of Algorithms and Language Model for Automatic Heuristic Design
Authors: Ziyao Huang; Weiwei Wu; Kui Wu; Wei-Bin Lee; Jianping Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Tackling complex optimization problems often relies on expert-designed heuristics, typically crafted through extensive trial and error. Recent advances demonstrate that large language models (LLMs), when integrated into well-designed evolutionary search frameworks, can autonomously discover high-performing heuristics at a fraction of the traditional cost. However, existing approaches predominantly rely on verbal guidance, i.e., manipulating the prompt generation process, to steer the evolution of heuristics, without adapting the underlying LLM. We propose a hybrid framework that combines verbal and numerical guidance, the latter achieved by fine-tuning the LLM via reinforcement learning (RL) based on the quality of generated heuristics. This joint optimization allows the LLM to co-evolve with the search process. Our method outperforms state-of-the-art (SOTA) baselines across various optimization tasks, running locally on a single 24GB GPU using a 7B model with INT4 quantization. It surpasses methods that rely solely on verbal guidance, even when those use significantly more powerful API-based models. The code is available at: https://github.com/whxru/CALM.
Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
Authors: Boyuan Li; Zhen Liu; Yicheng Luo; Qianli Ma
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Irregular Multivariate Time Series (IMTS) are characterized by uneven intervals between consecutive timestamps, which carry sampling pattern information valuable and informative for learning temporal and variable dependencies.
In addition, IMTS often exhibit diverse dependencies across multiple time scales.
However, many existing multi-scale IMTS methods use resampling to obtain the coarse series, which can alter the original timestamps and disrupt the sampling pattern information.
To address the challenge, we propose ReIMTS, a **Re**cursive multi-scale modeling approach for **I**rregular **M**ultivariate **T**ime **S**eries forecasting.
Instead of resampling, ReIMTS keeps timestamps unchanged and recursively splits each sample into subsamples with progressively shorter time periods.
Based on the original sampling timestamps in these long-to-short subsamples, an irregularity-aware representation fusion mechanism is proposed to capture global-to-local dependencies for accurate forecasting.
Extensive experiments demonstrate an average performance improvement of 27.1\% in the forecasting task across different models and real-world datasets.
Our code is available at [https://github.com/Ladbaby/PyOmniTS](https://github.com/Ladbaby/PyOmniTS).
SMOTE and Mirrors: Exposing Privacy Leakage from Synthetic Minority Oversampling
Authors: Georgi Ganev; MohammadReza Nazari; Rees Davison; Amirhassan Fallah Dizche; XINMIN WU; Ralph Abbey; Jorge G Silva; Emiliano De Cristofaro
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The Synthetic Minority Over-sampling Technique (SMOTE) is one of the most widely used methods for addressing class imbalance and generating synthetic data.
Despite its popularity, little attention has been paid to its privacy implications; yet, it is used in the wild in many privacy-sensitive applications.
In this work, we conduct the first systematic study of privacy leakage in SMOTE:
We begin by showing that prevailing evaluation practices, i.e., naive distinguishing and distance-to-closest-record metrics, completely fail to detect any leakage and that membership inference attacks (MIAs) can be instantiated with high accuracy.
Then, by exploiting SMOTE's geometric properties, we build two novel attacks with very limited assumptions: DistinSMOTE, which perfectly distinguishes real from synthetic records in augmented datasets, and ReconSMOTE, which reconstructs real minority records from synthetic datasets with perfect precision and recall approaching one under realistic imbalance ratios.
We also provide theoretical guarantees for both attacks.
Experiments on eight standard imbalanced datasets confirm the practicality and effectiveness of these attacks.
Overall, our work reveals that SMOTE is inherently non-private and disproportionately exposes minority records, highlighting the need to reconsider its use in privacy-sensitive applications and as a baseline for assessing the privacy of modern generative models.
Evaluating and Improving Cultural Awareness of Reward Models for LLM Alignment
Authors: Hongbin Zhang; Kehai Chen; Xuefeng Bai; Yang Xiang; Min Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reward models (RMs) are crucial for aligning large language models (LLMs) with diverse cultures. Consequently, evaluating their cultural awareness is essential for further advancing global alignment of LLMs. However, existing RM evaluations fall short in assessing cultural awareness due to the scarcity of culturally relevant evaluation datasets.
To fill this gap, we propose Cultural Awareness Reward modeling Benchmark (CARB), covering 10 distinct cultures across 4 cultural domains.
Our extensive evaluation of state-of-the-art RMs reveals their deficiencies in modeling cultural awareness and demonstrates a positive correlation between performance on CARB and downstream multilingual cultural alignment tasks.
Further analysis identifies the spurious correlations within culture-aware reward modeling, wherein RM's scoring relies predominantly on surface-level features rather than authentic cultural nuance understanding.
To address these, we propose Think-as-Locals to elicit deeper culturally grounded reasoning from generative RMs via reinforcement learning from verifiable rewards (RLVR) and employ well-designed rewards to ensure accurate preference judgments and high-quality structured evaluation criteria generation. 
Experimental results validate its efficacy in mitigating spurious features interference and advancing culture-aware reward modeling.
Improving Attributed Long-form Question Answering with Intent Awareness
Authors: Xinran Zhao; Aakanksha Naik; Jay DeYoung; Joseph Chee Chang; Jena D. Hwang; Tongshuang Wu; Varsha Kishore
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) are increasingly being used to generate comprehensive, knowledge-intensive reports. However, while these models are trained on diverse academic papers and reports, they are not exposed to the reasoning processes and intents that guide authors in crafting these documents. We hypothesize that enhancing a model's intent awareness can significantly improve the quality of generated long-form reports. We develop and employ structured, tag-based schemes to better elicit underlying implicit intents to write or cite. We demonstrate that these extracted intents enhance both zero-shot generation capabilities in LLMs and enable the creation of high-quality synthetic data for fine-tuning smaller models. Our experiments reveal improved performance across various challenging scientific report generation tasks, with an average improvement of +2.9 and +12.3 absolute points for large and small models over baselines, respectively. Furthermore, our analysis illuminates how intent awareness enhances model citation usage and substantially improves report readability.
Towards Reliable Benchmarking: A Contamination Free, Controllable Evaluation Framework for Multi-step LLM Function Calling
Authors: Seiji Maekawa; Jackson Hassell; Pouya Pezeshkpour; Tom Mitchell; Estevam Hruschka
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As language models gain access to external tools through structured function calls, they become increasingly more capable of solving complex, multi-step tasks. However, existing benchmarks for tool-augmented language models (TaLMs) provide insufficient control over factors such as the number of functions accessible, task complexity, and input size, and remain vulnerable to data contamination.
We present FuncBenchGen, a unified, contamination-free framework that evaluates TaLMs by generating synthetic multi-step tool-use tasks to stress-test TaLMs.  
The key idea is to cast tool use as traversal over a hidden function-dependency DAG where nodes are function calls and an edge between nodes represents one function consuming the output of another.
Given a set of external function schemas, initial variable values, and a target variable, models must compose the correct call sequence to compute the target variable.  FuncBenchGen allows users to precisely control task difficulty (e.g., graph size, dependency depth, and distractor functions) while avoiding pretraining/test-time leakage.

We apply our FuncBenchGen framework to evaluate seven open and closed LLMs on tool use tasks of varying difficulty.  Reasoning-optimized models consistently outperform general-purpose models with GPT-5 significantly outperforming other available models. Performance declines sharply as dependency depth increases.  Furthermore, connected distractors---irrelevant functions sharing type-compatible variables with relevant functions---prove especially difficult to handle. 

We find that strong models often make syntactically valid function calls but propagate incorrect or stale argument values across steps, revealing brittle state tracking by LLMs in multi-turn tool use.
Motivated by this observation, we introduce a simple mitigation strategy that explicitly restates prior variable values to the agent at each step.  Surprisingly, this lightweight change yields substantial gains across models. e.g., yielding an improvement in success rate from 62.5\% to 81.3\% for GPT-5, without modifying the underlying architectures or training.
SCRIBES: Web-Scale Script-Based Semi-Structured Data Extraction with Reinforcement Learning
Authors: Shicheng Liu; Kai Sun; Lisheng Fu; Xilun Chen; Xinyuan Zhang; Zhaojiang Lin; Rulin Shao; Yue Liu; Anuj Kumar; Wen-tau Yih; Xin Luna Dong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complicates usage, and reliably extracting structured information from them remains challenging. Existing methods either lack generalization or are resource-intensive due to per-page LLM inference. In this paper, we introduce SCRIBES (**SCRI**pt-**B**ased Semi-Structured Content **E**xtraction at Web-**S**cale), a novel reinforcement learning framework that leverages layout similarity across webpages within the same site as a reward signal. Instead of processing each page individually, SCRIBES generates reusable extraction scripts that can be applied to groups of structurally similar webpages. Our approach further improves by iteratively training on synthetic annotations from in-the-wild CommonCrawl data. Experiments show that our approach outperforms strong baselines by over 13\% in script quality and boosts downstream question answering accuracy by more than 4\% for GPT-4o, enabling scalable and resource-efficient web information extraction.
ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
Authors: Zhen Li; Duan Li; Yukai Guo; Xinyuan Guo; Bowen Li; Lanxi Xiao; Shenyu Qiao; Jiashu Chen; Zijian Wu; Hui Zhang; Xinhuan Shu; Shixia Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Infographic charts are a powerful medium for communicating abstract data by combining visual elements (e.g., charts, images) with textual information. However, their visual and structural richness poses challenges for large vision-language models (LVLMs), which are typically trained on plain charts. To bridge this gap, we introduce ChartGalaxy, a million-scale dataset designed to advance the understanding and generation of infographic charts. The dataset is constructed through an inductive process that identifies 75 chart types, 440 chart variations, and 68 layout templates from real infographic charts and uses them to create synthetic ones programmatically. We showcase the utility of this dataset through: 1) improving infographic chart understanding via fine-tuning, 2) benchmarking code generation for infographic charts, and 3) enabling example-based infographic chart generation. By capturing the visual and structural complexity of real design, ChartGalaxy provides a useful resource for enhancing multimodal reasoning and generation in LVLMs.
Differentially Private Equilibrium Finding in Polymatrix Games
Authors: Mingyang Liu; Gabriele Farina; Asuman E. Ozdaglar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study equilibrium finding in polymatrix games under differential privacy constraints. Prior work in this area fails to achieve both high-accuracy equilibria and a low privacy budget. To better understand the fundamental limitations of differential privacy in games, we show hardness results establishing that no algorithm can simultaneously obtain high accuracy and a vanishing privacy budget as the number of players tends to infinity. This impossibility holds in two regimes: (i) We seek to establish equilibrium approximation guarantees in terms of Euclidean \emph{distance} to the equilibrium set, and (ii) The adversary has access to all communication channels. We then consider the more realistic setting in which the adversary can access only a bounded number of channels and propose a new distributed algorithm that: recovers strategies with simultaneously vanishing \emph{Nash gap} (in expected utility, also referred to as \emph{exploitability}) and \emph{privacy budget} as the number of players increases. Our approach leverages structural properties of polymatrix games. To our knowledge, this is the first paper that can achieve this in equilibrium computation. Finally, we also provide numerical results to justify our algorithm.
Latent-to-Data Cascaded Diffusion Models for Unconditional Time Series Generation
Authors: Lifeng Shen; Kai Syun Hou; Weiyu Chen; James Kwok
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Synthetic time series generation (TSG) is crucial for applications such as privacy preservation, data augmentation, and anomaly detection. A key challenge in TSG lies in modeling the multi-modal distributions of time series, which requires simultaneously capturing diverse high-level representation distributions and preserving local temporal fidelity. Most existing diffusion models, however, are constrained by their single-space focus: latent-space models capture representation distributions but often compromise local fidelity, while data-space models preserve local details in the data space but struggle to learn high-level representations essential for multi-modal time series. To address these limitations, we propose L2D-Diff, a dual-space diffusion framework for synthetic time series generation. Specifically, L2D-Diff first compresses input sequences into a latent space to efficiently model the distribution of time series representations. The distribution then guides a data-space diffusion model to refine local data details, enabling faithful generation of time series distribution without relying on external conditions. Experiments on both single-modal and multi-modal datasets demonstrate the effectiveness of L2D-Diff in tackling unconditional TSG tasks. Ablation studies further highlight the necessity and impact of its dual-space design, showcasing its capability to achieve representation coherence and local fidelity.
Balancing the Experts: Unlocking LoRA-MoE for GRPO via Mechanism-Aware Rewards
Authors: Changlian Ma; Zizheng Huang; Xiangyu Zeng; Yi Wang; Cheng Liang; Kun Tian; Xinhai Zhao; Limin Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Parameter-efficient Mixture-of-Experts (MoE) architectures, such as LoRA-MoE, enable strong and generalizable fine-tuning. However, a critical problem arises when fine-tuning these architectures with advanced reinforcement learning algorithms such as Group Relative Policy Optimization (GRPO). Traditional supervised techniques are not naturally compatible with
the GRPO  objective, and naive combinations fail to effectively address routing collapse and the underutilization of MoE adapter parameters. To resolve this disconnect, we introduce Routing-Optimized Group Relative Policy Optimization (RO-GRPO), a mechanism-aware framework. It turns internal expert routing statistics collected during training into a direct reward signal, seamlessly integrating routing supervision into the reinforcement fine-tuning (RFT) process. This enables effective optimization of parameter utilization and improves performance on both unimodal and multimodal mathematical reasoning tasks, all without extra training stages. Our work provides the first demonstration that a scalar reward in GRPO can be engineered from a model's own internal mechanics to explicitly guide its optimization, extending alignment from mere behavior tuning to holistic mechanism alignment.
EIP: Weighted Ranking of LLMs by Quantifying Question Difficulty
Authors: Xingjian Hu; Ziqian Zhang; Yue Huang; Kai Zhang; Ruoxi Chen; Yixin Liu; Qingsong Wen; Kaidi Xu; Xiangliang Zhang; Neil Zhenqiang Gong; Lichao Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitating objective comparisons and driving advancements in the field. However, existing benchmarks fail to differentiate question difficulty, limiting their ability to effectively distinguish models' capabilities. To address this limitation, we propose Empirical Interaction Propagation (EIP), a novel framework designed to quantify both question difficulty and model competency. EIP introduces difficulty as the primary criterion for differentiation, enabling a more fine-grained evaluation of LLM capabilities. EIP's core mechanism facilitates bidirectional score propagation between models and questions. The core intuition of EIP is that a model earns a competency score when it correctly answers a question, while a question's difficulty score increases when it challenges a model. Using this framework, we evaluate 30 models on 35,550 questions across multiple domains. EIP achieves 90\% agreement with human judgments and consistently outperforms strong baselines such as IRT. It also exhibits strong stability, fast convergence, and high computational efficiency, making it a practical solution for large-scale, difficulty-aware LLM evaluation. Code is available at https://github.com/Leozz04/EIP.
On the Limits of Sparse Autoencoders: A Theoretical Framework and Reweighted Remedy
Authors: Jingyi Cui; Qi Zhang; Yifei Wang; Yisen Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Sparse autoencoders (SAEs) have recently emerged as a powerful tool for interpreting the features learned by large language models (LLMs). By reconstructing features with sparsely activated networks, SAEs aim to recover complex superposed polysemantic features into interpretable monosemantic ones. Despite their wide applications, it remains unclear under what conditions SAEs can fully recover the ground truth monosemantic features from the superposed polysemantic ones. In this paper, we provide the first theoretical analysis with a closed-form solution for SAEs, revealing that they generally fail to fully recover the ground truth monosemantic features unless the ground truth features are extremely sparse. To improve the feature recovery of SAEs in general cases, we propose a reweighting strategy targeting at enhancing the reconstruction of the ground truth monosemantic features instead of the observed polysemantic ones. We further establish a theoretical weight selection principle for our proposed weighted SAE (WSAE). Experiments across multiple settings validate our theoretical findings and demonstrate that our WSAE significantly improves feature monosemanticity and interpretability.