GenAI4ELab/papercli-papers huggingface dataset:
Mixture-of-Visual-Thoughts: Exploring Context-Adaptive Reasoning Mode Selection for General Visual Reasoning
Authors: Zejun Li; Yingxiu Zhao; Jiwen Zhang; Siyuan Wang; Yang Yao; Runzhou Zhao; Jun Song; Bo Zheng; zhongyu wei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Current visual reasoning methods mainly focus on exploring specific reasoning modes. Although improvements can be achieved in particular domains, they struggle to develop general reasoning capabilities. Inspired by this, we propose a novel adaptive reasoning paradigm, $\underline{\text{M}}$ixture-$\underline{\text{o}}$f-$\underline{\text{V}}$isual-$\underline{\text{T}}$houghts (**MoVT**), which unifies different reasoning modes within a single model and guides it to select the appropriate mode based on context. To achieve this, we introduce **AdaVaR**, a two-stage $\underline{\text{Ada}}$ptive $\underline{\text{V}}$isu$\underline{\text{a}}$l $\underline{\text{R}}$easoning learning framework: different modes are unified and learned during the supervised cold-start stage, and the mode selection capability is induced via an RL process with a carefully designed AdaGRPO algorithm. Extensive experiments show that AdaVaR effectively guides the model to learn and differentiate multiple modes and perform context-adaptive mode selection, achieving consistent improvement across various scenarios, highlighting MoVT as an effective solution for building general visual reasoning models.
OptimSyn: Influence-Guided Rubrics Optimization for Synthetic Data Generation
Authors: Zhiting Fan; Ruizhe Chen; Tianxiang Hu; Ru Peng; Zenan Huang; Haokai Xu; Yixin Chen; Jian Wu; Junbo Zhao; Zuozhu Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) achieve strong downstream performance largely due to abundant supervised fine-tuning (SFT) data that imparts problem-solving capabilities. However, as applications expand, high-quality SFT data in knowledge-intensive verticals (e.g., humanities and social sciences, medicine, law, finance) is exceedingly scarce: expert curation is costly, privacy constraints are strict, and label consistency is hard to guarantee. Recent work turns to synthetic data, typically prompting a teacher model over domain documents and filtering with handcrafted rubrics. Yet, rubric design is expert-dependent and rarely transfers across domains; moreover, prevalent heuristic optimization follows a brittle loop (write rubric $\rightarrow$ synthesize $\rightarrow$ train $\rightarrow$ inspect $\rightarrow$ guess tweaks) that lacks reliable, quantitative feedback about a rubric's true contribution to downstream performance.
We argue for assessing synthetic data quality through its causal impact on the target model, using this feedback to guide data generation. Inspired by classic influence functions, we repurpose an optimizer-aware estimator that uses gradient information to quantify each synthetic sample's contribution to the objective of a given target model on specific tasks. Our analysis reveals a gap: although synthetic and real samples may be close in embedding space, their influence on learning can differ substantially. Building on this insight, we propose an optimization-based synthetic data framework that adapts rubrics with target-model feedback. Instead of manually engineering domain rubrics, we supply lightweight guiding text and delegate rubric generation to a rubric-specialized model conditioned on the task; crucially, rubric (and data) selection is supervised by estimated downstream impact rather than proxy formality. Empirically, the framework yields consistent gains across domains (HSS and health), target models (e.g., Qwen and Llama families), and data generators, demonstrating broad generalization and engineering portability without task-specific tuning.
Learning in Prophet Inequalities with Noisy Observations
Authors: Jung-hun Kim; Vianney Perchet
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study the prophet inequality, a fundamental problem in online decision-making and optimal stopping, in a practical setting where rewards are observed only through noisy realizations and reward distributions are unknown. At each stage, the decision-maker receives a noisy reward whose true value follows a linear model with an unknown latent parameter, and observes a feature vector drawn from a distribution. To address this challenge, we propose algorithms that integrate learning and decision-making via lower-confidence-bound (LCB) thresholding. In the i.i.d. setting, we establish that both an Explore-then-Decide strategy and an $\varepsilon$-Greedy variant achieve the sharp competitive ratio of $1 - 1/e$, under a mild condition on the optimal value. For non-identical distributions, we show that a competitive ratio of $1/2$ can be guaranteed against a relaxed benchmark. Moreover, with limited window access to past rewards, the tight ratio of $1/2$ against the optimal benchmark is achieved.
BEP: A Binary Error Propagation Algorithm for Binary Neural Networks Training
Authors: Luca Colombo; Fabrizio Pittorino; Daniele Zambon; Carlo Baldassi; Manuel Roveri; Cesare Alippi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Binary Neural Networks (BNNs), which constrain both weights and activations to binary values, offer substantial reductions in computational complexity, memory footprint, and energy consumption. These advantages make them particularly well suited for deployment on resource-constrained devices. However, training BNNs via gradient-based optimization remains challenging due to the discrete nature of their variables. The dominant approach, quantization-aware training, circumvents this issue by employing surrogate gradients. Yet, this method requires maintaining latent full-precision parameters and performing the backward pass with floating-point arithmetic, thereby forfeiting the efficiency of binary operations during training. While alternative approaches based on local learning rules exist, they are unsuitable for global credit assignment and for back-propagating errors in multi-layer architectures. This paper introduces Binary Error Propagation (BEP), the first learning algorithm to establish a principled, discrete analog of the backpropagation chain rule. This mechanism enables error signals, represented as binary vectors, to be propagated backward through multiple layers of a neural network. BEP operates entirely on binary variables, with all forward and backward computations performed using only bitwise operations. Crucially, this makes BEP the first solution to enable end-to-end binary training for recurrent neural network architectures. We validate the effectiveness of BEP on both multi-layer perceptrons and recurrent neural networks, demonstrating gains of up to $+6.89$% and $+10.57$% in test accuracy, respectively. The proposed algorithm is released as an open-source repository.
Geometric-Mean Policy Optimization
Authors: Yuzhong Zhao; Yue Liu; Junpeng Liu; Jingye Chen; Xun Wu; Yaru Hao; Tengchao Lv; Shaohan Huang; Lei Cui; Qixiang Ye; Fang Wan; Furu Wei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Group Relative Policy Optimization (GRPO) has significantly enhanced the reasoning capability of large language models by optimizing the arithmetic mean of token-level rewards. Unfortunately, GRPO is observed to suffer from unstable policy updates when facing tokens with outlier importance-weighted rewards, which manifest as extreme importance sampling ratios during training. In this study, we propose Geometric-Mean Policy Optimization (GMPO), with the aim to improve the stability of GRPO through suppressing token reward outliers. GMPO is plug-and-play—simply replacing GRPO's arithmetic mean with the geometric mean of token-level rewards, as the latter is inherently less sensitive to outliers. GMPO is theoretically plausible—analysis reveals that both GMPO and GRPO are weighted forms of the policy gradient while the former enjoys more stable weights, which consequently benefits policy optimization and performance. Experiments on multiple mathematical reasoning benchmarks show that \Ours-7B improves the average Pass@1 of GRPO by up to 4.1%, outperforming many state-of-the-art approaches. Code is available at https://github.com/callsys/GMPO and verl.
Sharpness-Aware Machine Unlearning
Authors: Haoran Tang; Rajiv Khanna
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We characterize the effectiveness of Sharpness-aware minimization (SAM) under machine unlearning scheme, where unlearning forget signals interferes with learning retain signals. While previous work prove that SAM improves generalization with noise memorization prevention, we show that SAM abandons such denoising property when fitting the forget set, leading to altered generalization depending on signal strength. We further characterize the signal surplus of SAM in the order of signal strength, which enables learning from less retain signals to maintain model performance and putting more weight on unlearning the forget set. Empirical studies show that SAM outperforms SGD with relaxed requirement for retain signals and can enhance various unlearning methods either as pretrain or unlearn algorithm. Motivated by our refined characterization of SAM unlearning and observing that overfitting can benefit more stringent sample-specific unlearning, we propose Sharp MinMax, which splits the model into two to learn retain signals with SAM and unlearn forget signals with sharpness maximization, achieving best performance. Extensive experiments show that SAM enhances unlearning across varying difficulties measured by memorization, yielding decreased feature entanglement between retain and forget sets, stronger resistance to membership inference attacks, and a flatter loss landscape. Our observations generalize to more noised data, different optimizers, and different architectures.
SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization
Authors: Huashan Sun; Shengyi Liao; Yansen Han; Yu Bai; Yang Gao; Cheng Fu; Weizhou Shen; Fanqi Wan; Ming Yan; Ji Zhang; Fei Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite advances in pretraining with extended context sizes, large language models (LLMs) still face challenges in effectively utilizing real-world long-context information, primarily due to insufficient long-context alignment caused by data quality issues, training inefficiencies, and the lack of well-designed optimization objectives. To address these limitations, we propose a framework named **S**h**o**rt-to-**Lo**ng **P**reference **O**ptimization (**SoLoPO**), decoupling long-context preference optimization (PO) into two components: short-context PO and short-to-long reward alignment (SoLo-RA), supported by both theoretical and empirical evidence. Specifically, short-context PO leverages preference pairs sampled from short contexts to enhance the model's contextual knowledge utilization ability. Meanwhile, SoLo-RA explicitly encourages reward score consistency for the responses when conditioned on both short and long contexts that contain identical task-relevant information. This facilitates transferring the model's ability to handle short contexts into long-context scenarios. SoLoPO is compatible with mainstream preference optimization algorithms, while substantially improving the efficiency of data construction and training processes. Experimental results show that SoLoPO enhances all these algorithms with respect to stronger length and domain generalization abilities across various long-context benchmarks, while achieving notable improvements in both computational and memory efficiency.
Plan and Budget: Effective and Efficient Test-Time Scaling on Reasoning Large Language Models
Authors: Junhong Lin; Xinyue Zeng; Jie Zhu; Song Wang; Julian Shun; Jun Wu; Dawei Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks, but their inference remains computationally inefficient. We observe a common failure mode in many prevalent LLMs, overthinking, where models generate verbose and tangential reasoning traces even for simple queries. Recent work has tried to mitigate this by enforcing fixed token budgets, however, this can lead to underthinking, especially on harder problems. Through empirical analysis, we identify that this inefficiency often stems from unclear problem-solving strategies. To formalize this, we develop a theoretical model, BAM (Budget Allocation Model), which models reasoning as a sequence of sub-questions with varying uncertainty, and introduce the E3 metric to capture the trade-off between correctness and computation efficiency. Building on theoretical results from BAM, we propose Plan-and-Budget, a model-agnostic, test-time framework that decomposes complex queries into sub-questions and allocates token budgets based on estimated complexity using adaptive scheduling. Plan-and-Budget improves reasoning efficiency across a range of tasks and models, achieving up to 70% accuracy gains, 39% token reduction, and 193.8% improvement in E3. Notably, it improves the efficiency of a smaller model (DS-Qwen-32B) to match the efficiency of a larger model (DS-LLaMA-70B), demonstrating Plan-and-Budget’s ability to close performance gaps without retraining. Our code is available at https://github.com/junhongmit/P-and-B.
QLCoder: A Query Synthesizer For Static Analysis of Security Vulnerabilities
Authors: Claire Wang; Ziyang Li; Saikat Dutta; Mayur Naik
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: CodeQL is a powerful static analysis engine that represents programs’ abstract syntax trees as databases that can be queried to detect security vulnerabilities. While CodeQL supports expressive interprocedural dataflow queries, the coverage and precision of its existing security queries remain limited, and writing new queries is challenging even for experts. Automatically synthesizing CodeQL queries from known vulnerabilities (CVEs) can provide fine-grained vulnerability signatures, enabling both improved detection and systematic variant analysis. We present QLCoder, an agentic framework for synthesizing CodeQL queries from known CVE descriptions. QLCoder leverages the Model Context Protocol (MCP) for agentic tool use, integrates abstract syntax tree guidance, and incorporates CodeQL’s language infrastructure and documentation into the synthesis loop. A key challenge is that state-of-the-art large language models hallucinate deprecated CodeQL syntax due to limited training data and outdated knowledge. QLCoder addresses this by combining contextual engineering, iterative query feedback, and structured tool interaction to reliably generate executable, up-to-date queries.
Sparling: End-to-End Spatial Concept Learning via Extremely Sparse Activations
Authors: Kavi Gupta; Osbert Bastani; Armando Solar-Lezama
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real-world processes often contain intermediate state that can be modeled as an extremely sparse activation tensor. In this work, we analyze the identifiability of such sparse and local latent intermediate variables, which we call motifs. We prove our Motif Identifiability Theorem, stating that under certain assumptions it is possible to precisely identify these motifs exclusively by reducing end-to-end error. Notably, we do not assume identifiability of parameters, but rather of a latent intermediate representation output by a local model, thus allowing these representations to be arbitrarily complex functions of the input. Additionally, we provide the Sparling algorithm, which uses a new kind of informational bottleneck that enforces levels of activation sparsity unachievable using other techniques. We confirm empirically that extreme sparsity is necessary to achieve good intermediate state modeling. On synthetic domains, we are able to precisely localize the intermediate states up to feature permutation with $>90\%$ accuracy, even though we only train end-to-end.
Fast and Interpretable Protein Substructure Alignment via Optimal Transport
Authors: Zhiyu Wang; Bingxin Zhou; Weishu Zhao; Yang Tan; Jing Wang; Pietro Lio; Liang Hong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Proteins are essential biological macromolecules that execute life functions. Local motifs within protein structures, such as active sites, are the most critical components for linking structure to function and are key to understanding protein evolution and enabling protein engineering. Existing computational methods struggle to identify and compare these local structures, which leaves a significant gap in understanding protein structures and harnessing their functions. This study presents PLASMA, the first deep learning framework for efficient and interpretable residue-level protein substructure alignment. We reformulate the problem as a regularized optimal transport task and leverage differentiable Sinkhorn iterations. For a pair of input protein structures, PLASMA outputs a clear alignment matrix with an interpretable overall similarity score. Through extensive quantitative evaluations and three biological case studies, we demonstrate that PLASMA achieves accurate, lightweight, and interpretable residue-level alignment. Additionally, we introduce PLASMA-PF, a training-free variant that provides a practical alternative when training data are unavailable. Our method addresses a critical gap in protein structure analysis tools and offers new opportunities for functional annotation, evolutionary studies, and structure-based drug design. Reproducibility is ensured via our official implementation at https://github.com/ZW471/PLASMA-Protein-Local-Alignment.git.
Inconsistency Biases in Dynamic Data Pruning
Authors: Qing Zhou; Tao Yang; Bingxuan Zhao; Hongyuan Zhang; Junyu Gao; Qi Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Dynamic data pruning accelerates training by focusing on informative samples. However, comparing importance scores across different model states introduces inconsistency (score context drift), and variable selection rates bias gradient dynamics over time (temporal gradient bias). We introduce RePB (Resolving Pruning Biases), a framework addressing these issues. RePB performs pruning decisions within local windows (short sequences of batches) during training, using loss scores computed with a near-constant model state within each window to ensure valid comparisons. These decisions determine the data subset used in the subsequent training phase. To counteract temporal gradient bias arising from non-uniform sample inclusion, cumulative temporal rescaling reweights sample losses during training based on their historical selection frequency. We provide theoretical grounding for RePB's consistency in score comparison and gradient alignment. Experiments show RePB achieves near-full-dataset accuracy using reduced data (most above 30%) across 16 datasets, 17 models and 13 tasks, offering a robust and scalable approach to efficient deep learning. Code is available at https://github.com/mrazhou/RePB.
FSD-CAP: Fractional Subgraph Diffusion with Class-Aware Propagation for Graph Feature Imputation
Authors: Xin Qiao; Shijie Sun; Anqi Dong; Cong Hua; Xia Zhao; Longfei Zhang; Guangming Zhu; zhang liang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Imputing missing node features in graphs is challenging, particularly under high missing rates. Existing methods based on latent representations or global diffusion often fail to produce reliable estimates, and may propagate errors across the graph. We propose FSD-CAP, a two-stage framework designed to improve imputation quality under extreme sparsity. In the first stage, a graph-distance-guided subgraph expansion localizes the diffusion process. A fractional diffusion operator adjusts propagation sharpness based on local structure. In the second stage, imputed features are refined using class-aware propagation, which incorporates pseudo-labels and neighborhood entropy to promote consistency. We evaluated FSD-CAP on multiple datasets. With 99.5% of features missing across five benchmark datasets, FSD-CAP achieves average accuracies of 80.06% (structural) and 81.01% (uniform) in node classification, close to the 81.31% achieved by a standard GCN with full features. For link prediction under the same setting, it reaches AUC scores of 91.65% (structural) and 92.41% (uniform), compared to 95.06% for the fully observed case. Furthermore, FSD-CAP demonstrates superior performance on both large-scale and heterophily datasets when compared to other models. Codes are available at https://github.com/ssjcode/FSD-CAP.
Lean Finder: Semantic Search for Mathlib That Understands User Intents
Authors: Jialin Lu; Kye Emond; Kaiyu Yang; Swarat Chaudhuri; Weiran Sun; Wuyang Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present Lean Finder, a semantic search engine for Lean and mathlib that understands and aligns with the intents of mathematicians. Progress in formal theorem proving is often hindered by the difficulty of locating relevant theorems and the steep learning curve of the Lean 4 language, making advancement slow and labor-intensive. Existing Lean search engines, though helpful, rely primarily on informalizations (natural language translation of the formal statements), while largely overlooking the mismatch with real-world user queries. In contrast, we propose a user-centered semantic search tailored to the needs of mathematicians. Our approach begins by analyzing and clustering the semantics of public Lean discussions, then fine-tuning text embeddings on synthesized queries that emulate user intents. We further align Lean Finder with mathematicians’ preferences using diverse feedback signals, encoding it with a rich awareness of their goals from multiple perspectives. Evaluations on real-world queries, informalized statements, and proof states demonstrate that our Lean Finder achieves over 30% relative improvement compared to previous search engines and GPT-4o. In addition, Lean Finder is compatible with LLM-based theorem provers, bridging retrieval with formal reasoning. Lean Finder is available at: https://leanfinder.github.io.
DiscoX: Benchmarking Discourse-Level Translation in Expert Domains
Authors: Xiying ZHAO; Zhoufutu Wen; Zhixuan Chen; Jingzhe Ding; Jianpeng Jiao; Shuai Li; Xi Li; Danni Liang; Shengda Long; Qianqian Liu; Xianbo Wu; Hongwan Gao; Xiang Gao; LIANG HU; Jiashuo Liu; Liumengyun; Weiran Shi; Chenghao Yang; Qianyu Yang; Xuanliang Zhang; Ge Zhang; Wenhao Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The evaluation of discourse-level translation in expert domains remains inadequate, despite its centrality to knowledge dissemination and cross-lingual scholarly communication. While these translations demand discourse-level coherence and strict terminological precision, current evaluation methods predominantly focus on segment-level accuracy and fluency. To address this limitation, we introduce DiscoX, a new benchmark for discourse-level and expert-level Chinese-English translation. It comprises 200 professionally-curated texts from 7 domains, with an average length exceeding 1700 tokens. To evaluate performance on DiscoX, we also develop Metric-S, a reference-free system that provides fine-grained automatic assessments across accuracy, fluency, and appropriateness. Metric-S demonstrates strong consistency with human judgments, significantly outperforming existing metrics. Our experiments reveal a remarkable performance gap: even the most advanced LLMs still trail human experts on these tasks. This finding validates the difficulty of DiscoX and underscores the challenges that remain in achieving professional-grade machine translation. The proposed benchmark and evaluation system provide a robust framework for more rigorous evaluation, facilitating future advancements in LLM-based translation. Our data and code are available at https://github.com/ByteDance-Seed/DiscoX.