GenAI4ELab/papercli-papers huggingface dataset:
Behavioral Embeddings of Programs: A Quasi-Dynamic Approach for Optimization Prediction
Authors: Haolin Pan; Dong Jinyuan; Hongbin Zhang; Hongyu Lin; Mingjie Xing; Yanjun Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Learning effective numerical representations, or embeddings, of programs is a fundamental prerequisite for applying machine learning to automate and enhance compiler optimization. Prevailing paradigms, however, present a dilemma. Static representations, derived from source code or intermediate representation (IR), are efficient and deterministic but offer limited insight into how a program will behave or evolve under complex code transformations. Conversely, dynamic representations, which rely on runtime profiling, provide profound insights into performance bottlenecks but are often impractical for large-scale tasks due to prohibitive overhead and inherent non-determinism. This paper transcends this trade-off by proposing a novel quasi-dynamic framework for program representation. The core insight is to model a program's optimization sensitivity. We introduce the Program Behavior Spectrum, a new representation generated by probing a program's IR with a diverse set of optimization sequences and quantifying the resulting changes in its static features. To effectively encode this high-dimensional, continuous spectrum, we pioneer a compositional learning approach. Product Quantization is employed to discretize the continuous reaction vectors into structured, compositional sub-words. Subsequently, a multi-task Transformer model, termed PQ-BERT, is pre-trained to learn the deep contextual grammar of these behavioral codes. Comprehensive experiments on two representative compiler optimization tasks---Best Pass Prediction and -Oz Benefit Prediction---demonstrate that our method outperforms strong static baselines. Our code is publicly available at https://github.com/Panhaolin2001/PREP/.
HeurekaBench: A Benchmarking Framework for AI Co-scientist
Authors: Siba Smarak Panigrahi; Jovana Videnović; Maria Brbic
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: LLM-based reasoning models have enabled the development of agentic systems that act as co-scientists, assisting in multi-step scientific analysis. However, evaluating these systems is challenging, as it requires realistic, end-to-end research scenarios that integrate data analysis, interpretation, and the generation of new insights from the experimental data. To address this limitation, we introduce HeurekaBench, a framework to create benchmarks with *exploratory, open-ended research questions* for experimental datasets.
Each such question is grounded in a scientific study and its corresponding code repository, and is created using a semi-automated pipeline that leverages multiple LLMs to extract insights and generate candidate workflows, which are then verified against reported findings. We instantiate the framework in single-cell biology to obtain sc-HeurekaBench benchmark and use it to compare state-of-the-art single-cell agents. We further showcase the benefits of our benchmark for quantitatively analyzing current design choices in agentic systems. We find that the addition of a *critic* module can improve ill-formed responses for open-source LLM-based agents by up to 22% and close the gap with their closed-source counterparts. Overall, HeurekaBench sets a path toward rigorous, end-to-end evaluation of scientific agents, grounding benchmark construction in real scientific workflows.
From Natural Alignment to Conditional Controllability in Multimodal Dialogue
Authors: Zeyu Jin; Songtao Zhou; Haoyu Wang; Minghao Tian; Kaifeng Yun; Zhuo Chen; Xiaoyu Qin; Jia Jia
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The recent advancement of Artificial Intelligence Generated Content (AIGC) has led to significant strides in modeling human interaction, particularly in the context of multimodal dialogue. 
While current methods impressively generate realistic dialogue in isolated modalities like speech or vision, challenges remain in controllable Multimodal Dialogue Generation (MDG). 
This paper focuses on the natural alignment between speech, vision, and text in human interaction, aiming for expressive dialogue generation through multimodal conditional control. 
To address the insufficient richness and diversity of dialogue expressiveness in existing datasets, we introduce a novel multimodal dialogue annotation pipeline to curate dialogues from movies and TV series with fine-grained annotations in interactional characteristics.
The resulting MM-Dia dataset (360+ hours, 54,700 dialogues) facilitates explicitly controlled MDG, specifically through style-controllable dialogue speech synthesis. 
In parallel, MM-Dia-Bench (309 highly expressive dialogues with visible single-/dual-speaker scenes) serves as a rigorous testbed for implicit cross-modal MDG control, evaluating audio-visual style consistency across modalities. 
Extensive experiments demonstrate that training on MM-Dia significantly enhances fine-grained controllability, while benchmarks on MM-Dia-Bench reveal limitations in current frameworks to replicate the nuanced expressiveness of human interaction. 
These findings provides new insights and challenges for multimodal conditional dialogue generation.
FIRE: Frobenius-Isometry Reinitialization for Balancing the Stability–Plasticity Tradeoff
Authors: Isaac Han; Sangyeon Park; Seungwon Oh; Donghu Kim; Hojoon Lee; KyungJoong Kim
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Deep neural networks trained on nonstationary data must balance stability (i.e., retaining prior knowledge) and plasticity (i.e., adapting to new tasks). Standard reinitialization methods, which reinitialize weights toward their original values, are widely used but difficult to tune: conservative reinitializations fail to restore plasticity, while aggressive ones erase useful knowledge. We propose FIRE, a principled reinitialization method that explicitly balances the stability–plasticity tradeoff. FIRE quantifies stability through Squared Frobenius Error (SFE), measuring proximity to past weights, and plasticity through Deviation from Isometry (DfI), reflecting weight isotropy. The reinitialization point is obtained by solving a constrained optimization problem, minimizing SFE subject to DfI being zero, which is efficiently approximated by Newton–Schulz iteration. FIRE is evaluated on continual visual learning (CIFAR-10 with ResNet-18), language modeling (OpenWebText with GPT-0.1B), and reinforcement learning (HumanoidBench with SAC and Atari games with DQN). Across all domains, FIRE consistently outperforms both naive training without intervention and standard reinitialization methods, demonstrating effective balancing of the stability–plasticity tradeoff.
Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning
Authors: Chang Huang; Shatong Zhu; Junqiao Zhao; Hongtu Zhou; Hai Zhang; Di Zhang; Chen Ye; Ziqiao Wang; Guang Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Value function factorization is widely used in cooperative multi-agent reinforcement learning (MARL).
Existing approaches often impose monotonicity constraints between the joint action value and individual action values to enable decentralized execution.
However, such constraints limit the expressiveness of value factorization, restricting the range of joint action values that can be represented and hindering the learning of optimal policies.
To address this, we propose Potentially Optimal Joint Actions Weighting (POW), a method that ensures optimal policy recovery where existing approximate weighting strategies may fail.
POW iteratively identifies potentially optimal joint actions and assigns them higher training weights through a theoretically grounded iterative weighted training process. We prove that this mechanism guarantees recovery of the true optimal policy, overcoming the limitations of prior heuristic weighting strategies.
POW is architecture-agnostic and can be seamlessly integrated into existing value factorization algorithms.
Extensive experiments on matrix games, difficulty-enhanced predator-prey tasks, SMAC, SMACv2, and a highway-env intersection scenario show that POW substantially improves stability and consistently surpasses state-of-the-art value-based MARL methods.
Reliable Fine-Grained Evaluation of Natural Language Math Proofs
Authors: Wenjie Ma; Andrei Cojocaru; Neel Kolhe; Haihan Zhang; Vincent Zhuang; Matei Zaharia; Sewon Min
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in large language models (LLMs) for mathematical reasoning have largely focused on tasks with easily verifiable final answers while generating and verifying natural language math proofs remains an open challenge. We identify the absence of a reliable, fine-grained evaluator for LLM-generated math proofs as a critical gap.
To address this, we propose a systematic methodology for developing and validating evaluators that assign fine-grained scores on a 0-7 scale to model-generated math proofs. 
To enable this study, we introduce ProofBench, the first expert-annotated dataset of fine-grained proof ratings, spanning 145 problems from six major math competitions (USAMO, IMO, Putnam, etc) and 435 LLM-generated solutions from Gemini-2.5-Pro, o3, and DeepSeek-R1. 
Using ProofBench as a testbed, we systematically explore the evaluator design space across key axes: the backbone model, input context, instructions and evaluation workflow.
Our analysis delivers ProofGrader, an evaluator that combines a strong reasoning backbone LM, rich context from reference solutions and marking schemes, and a simple ensembling method; it achieves a low Mean Absolute Error (MAE) of 0.926 against expert scores, significantly outperforming naive baselines.
Finally, we demonstrate its practical utility in a best-of-$n$ selection task: at $n=16$, ProofGrader achieves an average score of 4.14/7, closing 78\% of the gap between a naive binary evaluator (2.48) and the human oracle (4.62), highlighting its potential to advance downstream proof generation.
RAEE: A Robust Retrieval-Augmented Early Exit Framework for Efficient Inference
Authors: LIANMING HUANG; Shangyu Wu; Yufei Cui; Ying Xiong; Haibo Hu; Xue Liu; Tei-Wei Kuo; Nan Guan; Chun Jason Xue
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deploying large language model inference remains challenging due to their high computational overhead.
Early exit optimizes model inference by adaptively reducing the number of inference layers.
Current methods typically train internal classifiers or use heuristic methods to determine the exit layer.
However, those methods either introduce significant training overheads or lead to performance degradation.
To address these limitations, this paper proposes RAEE, a robust Retrieval-Augmented Early Exit framework that not only enables early exit but also enhances model performance through corrective exit information at intermediate layers.
This paper first demonstrates that the early exit problem can be effectively modeled as a distribution prediction problem, in which the distribution can be further approximated through the exit information of similar data. 
Subsequently, this paper introduces the process of collecting exit information of correct predictions and the steps to construct the retrieval database.
Finally, leveraging the pre-constructed retrieval database, RAEE utilizes the exit information from retrieved similar data to guide the backbone model's exit.
Experimental results demonstrate that RAEE can not only accelerate inference while achieving robust zero-shot performance across eight downstream tasks.
Adaptive Rollout Allocation for Online Reinforcement Learning with Verifiable Rewards
Authors: Hieu Trung Nguyen; Bao Nguyen; Wenao Ma; Yuzhi Zhao; Ruifeng She; Viet Anh Nguyen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Sampling efficiency is a key bottleneck in reinforcement learning with verifiable rewards. Existing group-based policy optimization methods, such as GRPO, allocate a fixed number of rollouts for all training prompts. This uniform allocation implicitly treats all prompts as equally informative, and could lead to inefficient computational budget usage and impede training progress. We introduce VIP, a Variance-Informed Predictive allocation strategy that allocates a given rollout budget to the prompts in the incumbent batch to minimize the expected gradient variance of the policy update. At each iteration, VIP uses a lightweight Gaussian process model to predict per-prompt success probabilities based on recent rollouts. These probability predictions are translated into variance estimates, which are then fed into a convex optimization problem to determine the optimal rollout allocations under a hard compute budget constraint. Empirical results show that VIP consistently improves sampling efficiency and achieves higher performance than uniform or heuristic allocation strategies in multiple benchmarks.
Robustness of Probabilistic Models to Low-Quality Data: A Multi-Perspective Analysis
Authors: Liu Peng; Yaochu Jin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper investigates a critical challenge in modern machine learning: 
how different probabilistic models withstand low-quality training data. 
Through a systematic, comparative investigation, 
we reveal a stark spectrum of robustness. 
Empirically, we find that autoregressive language models 
exhibit remarkable resilience against both token-level noise 
and structural corruption (for GPT-2, test NLL increases modestly from 
2.87 to 3.59 despite 50\% corruption). 
By sharp contrast, class-conditional diffusion models degrade catastrophically 
under identical noise levels (image-label consistency plummets by 56.81\%), 
while image classifiers show a moderate vulnerability that diminishes with dataset scale.
To explain these discrepancies, 
we analyze the results through a multi-perspective lens 
integrating information theory, PAC learning, and gradient dynamics. 
This framework identifies what informational properties drive robustness, 
why they are required for generalization, 
and how the optimization process achieves this resilience.
These analyses suggest that robustness is heavily influenced by two key principles: 
the richness of conditioning information, which constrains the learning problem, 
and the absolute information content of the training data, which allows the signal from correct information to dominate statistical noise.
Frozen Policy Iteration: Computationally Efficient RL under Linear $Q^{\pi}$ Realizability for Deterministic Dynamics
Authors: Yijing Ke; Zihan Zhang; Ruosong Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study computationally and statistically efficient reinforcement learning under the linear $Q^{\pi}$ realizability assumption, where any policy's $Q$-function is linear in a given state-action feature representation. Prior methods in this setting are either computationally intractable, or require (local) access to a simulator. In this paper, we propose a computationally efficient online RL algorithm, named *Frozen Policy Iteration*, under the linear $Q^{\pi}$ realizability setting that works for Markov Decision Processes (MDPs) with stochastic initial states, stochastic rewards and deterministic transitions. Our algorithm achieves a regret bound of $\widetilde{O}(\sqrt{d^2H^6T})$, where $d$ is the dimensionality of the feature space, $H$ is the horizon length, and $T$ is the total number of episodes. Our regret bound is optimal for linear (contextual) bandits which is a special case of our setting with $H = 1$. 


Existing policy iteration algorithms under the same setting heavily rely on repeatedly sampling the same state by access to the simulator, which is not implementable in the online setting with stochastic initial states studied in this paper.  In contrast, our new algorithm circumvents this limitation by strategically using only high-confidence part of the trajectory data and freezing the policy for well-explored states, which ensures that all data used by our algorithm remains effectively *on-policy* during the whole course of learning. 
We further demonstrate the versatility of our approach by extending it to the Uniform-PAC setting and to  function classes with bounded eluder dimension.
WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
Authors: Zhengwei Tao; Jialong Wu; Wenbiao Yin; Pu Wu; Junkai Zhang; Baixuan Li; Haiyang SHEN; Kuan Li; Liwen Zhang; Xinyu Wang; Wentao Zhang; Yong Jiang; Pengjun Xie; Fei Huang; Jingren Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The advent of Large Language Model (LLM)-powered agents has revolutionized artificial intelligence by enabling solutions to complex, open-ended tasks through web-based information-seeking (IS) capabilities. The scarcity of high-quality training data has limited the development of IS agents.  Existing data synthesis approaches typically adopt an information-driven paradigm that first collects information and then refines question-answer pairs through retrieval. However, this may lead to inconsistency between information structure and reasoning structure, as well as between the question and the corresponding answer. To mitigate, we propose a formalization-driven IS data synthesis framework WebShaper, which systematically formalizes IS tasks using set-theoretic constructs.
Central to the formalization is the concept of Knowledge Projections (KP), which enables precise control over reasoning structure by KP operation compositions.  During synthesis, we begin by creating seed tasks, then use a multi-step expansion process.
At each step, an agentic Expander expands the current formal question more complex through retrieval and validation tools grounded in our formalization. We train our model on the synthesized dataset. Experiment results demonstrate that WebShaper achieves state-of-the-art performance among open-sourced IS agents on competitive benchmarks.
Epistemic Uncertainty Quantification To Improve Decisions From Black-Box Models
Authors: Sébastien Melo; Gaël Varoquaux; Marine Le Morvan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Distinguishing epistemic uncertainty (model ignorance) from aleatoric uncertainty (task randomness) is critical for reliable AI systems, yet standard confidence evaluation metrics capture different and incomplete aspects of uncertainty. While AUC and accuracy measure predictive signal, proper scoring rules assess overall uncertainty, and calibration metrics isolate part of the epistemic uncertainty but ignore within-bin heterogeneity of errors, known as grouping loss. We bridge this evaluation gap by introducing asymptotically consistent and sample-efficient estimators of the grouping loss and excess decision risk, providing a fine-grained assessment of epistemic uncertainty that complements existing calibration metrics. Applied to LLM question-answering with inherent aleatoric noise, our estimators reveal substantial grouping loss which decreases with model scale and instruction tuning. Their local nature enables automatic identification of subgroups with systematic over- or under-confidence, supporting interpretable confidence audits. Finally, we leverage these estimates to design LLM cascades that defer high excess decision risk predictions to stronger models, achieving higher accuracy at lower cost than competing approaches.
The Price of Robustness:  Stable Classifiers Need Overparameterization
Authors: Jonas von Berg; Adalbert Fono; Massimiliano Datres; Sohir Maskey; Gitta Kutyniok
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The relationship between overparameterization, stability, and generalization remains incompletely understood in the setting of discontinuous classifiers. We address this gap by establishing a generalization bound for finite function classes that improves inversely with _class stability_, defined as the expected distance to the decision boundary in the input domain (margin). Interpreting class stability as a quantifiable notion of robustness, we derive as a corollary a _law of robustness_ for classification that extends the results of Bubeck and Selke beyond smoothness assumptions to discontinuous functions. In particular, any interpolating model with $p \approx n$ parameters on $n$ data points must be _unstable_, implying that substantial overparameterization is necessary to achieve high stability. We obtain analogous results for (parameterized) infinite function classes by analyzing a stronger robustness measure derived from the margin in the co-domain, which we refer to as the _normalized co-stability_. Experiments support our theory: stability increases with model size and correlates with test performance, while traditional norm-based measures remain largely uninformative.
Rethinking Code Similarity for Automated Algorithm Design with LLMs
Authors: Rui Zhang; Zhichao Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The rise of Large Language Model-based Automated Algorithm Design (LLM-AAD) has transformed algorithm development by autonomously generating code implementations of expert-level algorithms. Unlike traditional expert-driven algorithm development, in the LLM-AAD paradigm, the algorithm's ideas are often implicitly embedded in the generated code. Therefore, assessing algorithmic similarity directly from code, distinguishing genuine algorithmic innovation from mere syntactic variation, becomes essential. While code similarity metrics exist, they fail to capture algorithmic similarity, as they focus on surface-level syntax or output equivalence rather than problem-solving behavior.

We propose BehaveSim, a novel method to measure algorithmic similarity through the lens of problem-solving trajectories (PSTrajs)—sequences of intermediate solutions produced during execution. By quantifying the alignment between PSTrajs using dynamic time warping (DTW), BehaveSim distinguishes algorithms with divergent logic despite syntactic or output-level similarities. We demonstrate its utility in two key applications: (i) Enhancing LLM-AAD: Integrating BehaveSim into existing LLM-AAD frameworks (e.g., FunSearch, EoH) promotes behavioral diversity, significantly improving performance on three AAD tasks. (ii) Algorithm analysis: BehaveSim clusters generated algorithms by behavior, enabling systematic analysis of problem-solving strategies—a crucial tool for the growing ecosystem of AI-generated algorithms. Data and code of this work are open-sourced at https://github.com/RayZhhh/behavesim.
Exchangeability of GNN Representations  with Applications to Graph Retrieval
Authors: Kartik Nair; Indradyumna Roy; Soumen Chakrabarti; Anirban Dasgupta; Abir De
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: In this work, we discover a probabilistic symmetry, called as exchangeability in graph neural networks (GNNs). Specifically, we show that the trained node embedding computed using a large family of graph neural networks, learned under standard optimization tools,  are exchangeable random variables. This implies that the probability density of the node embeddings remains invariant with respect to a permutation applied on their dimension axis. This results in identical distribution across the elements of the graph representations.  Such a property enables approximation of transportation-based graph similarities by Euclidean similarities between order statistics. Leveraging this reduction, we propose a unified locality-sensitive hashing (LSH) framework that supports diverse relevance measures, including subgraph matching and graph edit distance. Experiments show that our method helps to do LSH more effectively than baselines.