GenAI4ELab/papercli-papers huggingface dataset:
The First Impression Problem: Internal Bias Triggers Overthinking in Reasoning Models
Authors: Renfei Dang; Zhening Li; Shujian Huang; Jiajun Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reasoning models often exhibit overthinking, characterized by redundant reasoning steps. We identify \emph{internal bias} elicited by the input question as a key trigger of such behavior. Upon encountering a problem, the model immediately forms a preliminary guess about the answer, which we term an internal bias since it may not be explicitly generated, and it arises without systematic reasoning. When this guess conflicts with its subsequent reasoning, the model tends to engage in excessive reflection, resulting in wasted computation. We validate the association between internal bias and overthinking across multiple models and diverse reasoning tasks. To demonstrate the causal relationship more rigorously, we conduct two counterfactual interventions, showing that removing the input question after the model reduces the redundant reasoning across various complex reasoning tasks, and manually injecting bias affects overthinking accordingly. Further interpretability experiments suggest that excessive attention to the input question serves as a key mechanism through which internal bias influences subsequent reasoning trajectories. Finally, we evaluated several methods aimed at mitigating overthinking, yet the influence of internal bias persisted under all conditions.
GeomMotif: A Benchmark for Arbitrary Geometric Preservation in Protein Generation
Authors: Pavel Strashnov; Andrey Shevtsov; Viacheslav Meshchaninov; Olga Kardymon; Dmitry Vetrov
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Motif scaffolding in protein design involves generating complete protein structures while preserving the 3D geometry of designated structural fragments, analogous to image outpainting in computer vision. Current benchmarks focus on functional motifs, leaving general geometric preservation capabilities largely untested. We introduce GeomMotif, a systematic benchmark that evaluates arbitrary structural fragment preservation without requiring functional specificity. We construct 57 benchmark tasks, each containing one or two motifs with up to 7 continuous fragments, by sampling from the Protein Data Bank (PDB) to ensure a ground-truth, solvable conformation for every problem. The tasks are characterized by comprehensive structural and physicochemical properties: size, geometric context, secondary structure, hydrophobicity, charge, and degree of burial. These features enable detailed performance analysis beyond simple success rates, revealing model-specific strengths and limitations. We evaluate models using scRMSD and pLDDT for geometric fidelity and clustering for structural diversity and novelty. Our results show that sequence-based and structure-based approaches find different tasks challenging, and that geometric preservation varies significantly with structural and physicochemical context. GeomMotif provides insights complementary to function-focused benchmarks and establishes a foundation for improving protein generative models.
OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction
Authors: Emily Jin; Andrei Cristian Nica; Mikhail Galkin; Jarrid Rector-Brooks; Kin Long Kelvin Lee; Santiago Miret; Frances H. Arnold; Michael M. Bronstein; Joey Bose; Alexander Tong; Cheng-Hao Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurately predicting experimentally realizable 3D molecular crystal structures from their 2D chemical graphs is a long-standing open challenge in computational chemistry called crystal structure prediction (CSP). Efficiently solving this problem has implications ranging from pharmaceuticals to organic semiconductors, as crystal packing directly governs the physical and chemical properties of organic solids. In this paper, we introduce OXtal, a large-scale 100M parameter all-atom diffusion model that directly learns the conditional joint distribution over intramolecular conformations and periodic packing. To efficiently scale OXtal, we abandon explicit equivariant architectures imposing inductive bias arising from crystal symmetries in favor of data augmentation strategies. We further propose a novel crystallization-inspired lattice-free training scheme, Stoichiometric Stochastic Shell Sampling ($S^4$), that efficiently captures long-range interactions while sidestepping explicit lattice parametrization---thus enabling more scalable architectural choices at all-atom resolution.
By leveraging a large dataset of 600K experimentally validated crystal structures (including rigid and flexible molecules, co-crystals, and solvates), OXtal achieves orders-of-magnitude improvements over prior ab initio machine learning CSP methods, while remaining orders of magnitude cheaper than traditional quantum-chemical approaches. Specifically, OXtal recovers experimental structures with conformer $\mathrm{RMSD}_1<0.5$ Å and attains over 80% packing similarity rate, demonstrating its ability to model both thermodynamic and kinetic regularities of molecular crystallization.
DeepSADR: Deep Transfer Learning with Subsequence Interaction and Adaptive Readout for Cancer Drug Response Prediction
Authors: Yuanpeng Zhang; Zhijian Huang; Ziyu Fan; Siyuan Shen; Yahan Li; Shangqian Wu; Min Wu; Lei Deng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Cancer treatment efficacy exhibits high inter-patient heterogeneity due to genomic variations. While large-scale in vitro drug response data from cancer cell lines exist, predicting patient drug responses remains challenging due to genomic distribution shifts and the scarcity of clinical response data. Existing transfer learning methods primarily align global genomic features between cell lines and patients. However, they often ignore two critical aspects. First, drug response depends on specific drug substructures and genomic pathways. Second, drug response mechanisms differ in vitro and in vivo settings due to factors such as the immune system and tumor microenvironment. To address these limitations, we propose DeepSADR, a novel deep transfer learning framework for enhanced drug response prediction based on subsequence interaction and adaptive readout. In particular, DeepSADR models drug responses as interpretable bipartite interaction graphs between drug substructures and enriched genomic pathways. Subsequently, a supervised graph autoencoder was designed to capture latent interactions between drugs and gene subsequences within these interaction graphs. In addition, DeepSADR treats the drug response process as a transferable domain. A Set Transformer-based adaptive readout (AR) function learns domain-invariant response representations, enabling effective knowledge transfer from abundant cell line data to scarce patient data. Extensive experiments on clinical patient cohorts demonstrate that DeepSADR significantly outperforms state-of-the-art methods, and ablation experiments have validated the effectiveness of each module.
LEXam: Benchmarking Legal Reasoning on 340 Law Exams
Authors: Yu Fan; Jingwei Ni; Jakob Merane; Yang Tian; Yoan Hermstrüwer; Yinya Huang; Mubashara Akhtar; Etienne Salimbeni; Florian Geering; Oliver Dreyer; Daniel Brunner; Markus Leippold; Mrinmaya Sachan; Alexander Stremitzer; Christoph Engel; Elliott Ash; Joel Niklaus
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Long-form legal reasoning remains a key challenge for large language models (LLMs) in spite of recent advances in test-time scaling. To address this, we introduce ***LEXam***, a novel benchmark derived from 340 law exams spanning 116 law school courses across a range of subjects and degree levels. The dataset comprises 7,537 law exam questions in English and German. It includes both long-form, open-ended questions and multiple-choice questions with varying numbers of options. Besides reference answers, the open questions are also accompanied by explicit guidance outlining the expected legal reasoning approach such as issue spotting, rule recall, or rule application. 
Our evaluation on both open-ended and multiple-choice questions present significant challenges for current LLMs; in particular, they notably struggle with open questions that require structured, multi-step legal reasoning. Moreover, our results underscore the effectiveness of the dataset in differentiating between models with varying capabilities. 
Deploying an ensemble LLM-as-a-Judge paradigm with rigorous human expert validation, we demonstrate how model-generated reasoning steps can be evaluated consistently and accurately, closely aligning with human expert assessments. Our evaluation setup provides a scalable method to assess legal reasoning quality beyond simple accuracy metrics. Project page: https://lexam-benchmark.github.io/.
Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport
Authors: Harry Amad; Mihaela van der Schaar
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Neural networks (NNs) often have critical behavioural trade-offs that are set at design time with hyperparameters—such as reward weights in reinforcement learning or quantile targets in regression. 
Post-deployment, however, user preferences can evolve, making initial settings undesirable, necessitating potentially expensive retraining. 
To circumvent this, we introduce the task of Hyperparameter Trajectory Inference (HTI): to learn, from observed data, how a NN's conditional output distribution changes with its hyperparameters, and construct a surrogate model that approximates the NN at unobserved hyperparameter settings. 
HTI requires extending existing trajectory inference approaches to incorporate conditions, exacerbating the challenge of ensuring inferred paths are feasible.
We propose an approach based on conditional Lagrangian optimal transport, jointly learning the Lagrangian function governing hyperparameter-induced dynamics along with the associated optimal transport maps and geodesics between observed marginals, which form the surrogate model.
We incorporate inductive biases based on the manifold hypothesis and least-action principles into the learned Lagrangian, improving surrogate model feasibility.
We empirically demonstrate that our approach reconstructs NN outputs across various hyperparameter spectra better than other alternatives.
CGSA: Class-Guided Slot-Aware Adaptation for Source-Free Object Detection
Authors: Boyang Dai; Zeng Fan; Zihao Qi; Meng Lou; Yizhou Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Source-Free Domain Adaptive Object Detection (SF-DAOD) aims to adapt a detector trained on a labeled source domain to an unlabeled target domain without retaining any source data. Despite recent progress, most popular approaches focus on tuning pseudo-label thresholds or refining the teacher-student framework, while overlooking object-level structural cues within cross-domain data. In this work, we present CGSA, the first framework that brings Object-Centric Learning (OCL) into SF-DAOD by integrating slot-aware adaptation into the DETR-based detector. Specifically, our approach integrates a Hierarchical Slot Awareness (HSA) module into the detector to progressively disentangle images into slot representations that act as visual priors. These slots are then guided toward class semantics via a Class-Guided Slot Contrast (CGSC) module, maintaining semantic consistency and prompting domain-invariant adaptation. Extensive experiments on multiple cross-domain datasets demonstrate that our approach outperforms previous SF-DAOD methods, with theoretical derivations and experimental analysis further demonstrating the effectiveness of the proposed components and the framework, thereby indicating the promise of object-centric design in privacy-sensitive adaptation scenarios. Code is released at https://github.com/Michael-McQueen/CGSA.
RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours
Authors: Rafael Pablos Sarabia; Joachim Nyborg; Morten Birk; Jeppe Liborius Sjørup; Anders Lillevang Vesterholt; Ira Assent
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present a deep learning model for high-resolution probabilistic precipitation forecasting over an 8-hour horizon in Europe, overcoming the limitations of radar-only deep learning models with short forecast lead times. Our model efficiently integrates multiple data sources - including radar, satellite, and physics-based numerical weather prediction (NWP) - while capturing long-range interactions, resulting in accurate forecasts with robust uncertainty quantification through consistent probabilistic maps. Featuring a compact architecture, it enables more efficient training and faster inference than existing models. Extensive experiments demonstrate that our model surpasses current operational NWP systems, extrapolation-based methods, and deep-learning nowcasting models, setting a new standard for high-resolution precipitation forecasting in Europe, ensuring a balance between accuracy, interpretability, and computational efficiency.
DRIFT: Decompose, Retrieve, Illustrate, then Formalize Theorems
Authors: Meiru Zhang; Philipp Borchert; Milan Gritta; Gerasimos Lampouras
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Automating the formalization of mathematical statements for theorem proving remains a major challenge for Large Language Models (LLMs). LLMs struggle to identify and utilize the prerequisite mathematical knowledge and its corresponding formal representation in languages like Lean. Current retrieval-augmented autoformalization methods query external libraries using the informal statement directly, but overlook a fundamental limitation: informal statements lack direct mappings to mathematical theorems and lemmata, nor do those theorems translate trivially into the formal primitives of languages like Lean.
To address this, we introduce DRIFT, a novel framework that enables LLMs to decompose informal mathematical statements into smaller, more tractable "sub-components". This facilitates targeted retrieval of premises from mathematical libraries such as Mathlib. Additionally, DRIFT retrieves illustrative theorems to help models use premises more effectively in formalization tasks. 
We evaluate DRIFT across diverse benchmarks (ProofNet, ConNF, and MiniF2F-test) and find that it consistently improves premise retrieval, nearly doubling the F1 score compared to the DPR baseline on ProofNet. Notably, DRIFT demonstrates strong performance on the out-of-distribution ConNF benchmark, with BEq+@10 improvements of 42.25% and 37.14% using GPT-4.1 and DeepSeek-V3.1, respectively. Our analysis shows that retrieval effectiveness in mathematical autoformalization depends heavily on model-specific knowledge boundaries, highlighting the need for adaptive retrieval strategies aligned with each model's capabilities.
DHG-Bench: A Comprehensive Benchmark for Deep Hypergraph Learning
Authors: Fan Li; Xiaoyang Wang; Wenjie Zhang; Ying Zhang; Xuemin Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep graph models have achieved great success in network representation learning. However, their focus on pairwise relationships restricts their ability to learn pervasive higher-order interactions in real-world systems, which can be naturally modeled as hypergraphs.
To tackle this issue, Hypergraph Neural Networks (HNNs) have
garnered substantial attention in recent years. 
Despite the proposal of numerous HNNs, the absence of consistent experimental protocols and multi-dimensional empirical analysis impedes deeper understanding and further development of HNN research. While several toolkits for deep hypergraph learning (DHGL) have been introduced to facilitate algorithm evaluation, they provide only limited quantitative evaluation results and insufficient coverage of advanced algorithms, datasets, and benchmark tasks.
To fill the gap, we introduce DHG-Bench, the first comprehensive benchmark for HNNs.
Specifically, DHG-Bench systematically investigates the characteristics of HNNs in terms of four dimensions: effectiveness, efficiency, robustness, and fairness. 
We comprehensively evaluate 17 state-of-the-art HNN algorithms on 22 diverse datasets spanning node-, edge-, and graph-level tasks, under unified experimental settings.
Extensive experiments reveal both the strengths and limitations of existing algorithms, offering valuable insights and directions for future research. 
Furthermore, to facilitate reproducible research, we have developed an easy-to-use library for training and evaluating different HNN methods.
The DHG-Bench library is available at: 
https://github.com/Coco-Hut/DHG-Bench.
Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders
Authors: Junqi Jiang; Francesco Leofante; Antonio Rago; Francesca Toni
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Counterfactual explanations (CEs) provide recourse recommendations for individuals affected by algorithmic decisions. A key challenge is generating CEs that are robust against various perturbation types (e.g. input and model perturbations) while simultaneously satisfying other desirable properties. These include plausibility, ensuring CEs reside on the data manifold, and diversity, providing multiple distinct recourse options for single inputs. Existing methods, however, mostly struggle to address these multifaceted requirements in a unified, model-agnostic manner. We address these limitations by proposing a novel generative framework. First, we introduce the Label-conditional Gaussian Mixture Variational Autoencoder (L-GMVAE), a model trained to learn a structured latent space where each class label is represented by a set of Gaussian components with diverse, prototypical centroids. Building on this, we present LAPACE (LAtent PAth Counterfactual Explanations), a model-agnostic algorithm that synthesises entire paths of CE points by interpolating from inputs' latent representations to those learned latent centroids. This approach inherently ensures robustness to input changes, as all paths for a given target class converge to the same fixed centroids. Furthermore, the generated paths provide a spectrum of recourse options, allowing users to navigate the trade-off between proximity and plausibility while also encouraging robustness against model changes. In addition, user-specified actionability constraints can also be easily incorporated via lightweight gradient optimisation through the L-GMVAE's decoder. Comprehensive experiments show that LAPACE is computationally efficient and achieves competitive performance across eight quantitative metrics.
Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management
Authors: Mo Li; L.H. Xu; Qitai Tan; Long Ma; Flood Sung; Ting Cao; Yunxin Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) suffer from significant performance degradation when processing long contexts due to proactive interference, where irrelevant information in earlier parts of the context disrupts reasoning and memory recall. While most research focuses on external memory systems to augment LLMs' capabilities, we propose a complementary approach: empowering LLMs with Active Context Management (ACM) tools to actively sculpt their internal working memory. We introduce Sculptor, a framework that equips LLMs with three categories of tools: (1) context fragmentation, (2) summary, hide, and restore, and (3) precise search. Our approach enables LLMs to proactively manage their attention and working memory, analogous to how humans selectively focus on relevant information while filtering out distractions. Experimental evaluation on diverse long-context benchmarks demonstrates that Sculptor significantly improves performance even without specific training, leveraging LLMs' inherent tool-calling and instruction-following capabilities. To further optimize these strategies, we introduce a novel dynamic context-aware reinforcement learning (RL) approach, advancing the training of an agent that actively modifies its own conversational history. By enabling Active Context Management, Sculptor not only mitigates proactive interference but also provides a cognitive foundation for more reliable reasoning across diverse long-context tasks—highlighting that explicit context-control strategies, rather than merely larger token windows, are key to robustness at scale.
Learning Global Hypothesis Space for Enhancing Synergistic Reasoning Chain
Authors: Jiaquan Zhang; Chaoning Zhang; Shuxu Chen; Xudong Wang; Zhenzhen Huang; Pengcheng Zheng; Shuai Yuan; Sheng Zheng; Qigan Sun; Jie Zou; LIK-HANG LEE; Yang Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Chain-of-Thought (CoT) has been shown to significantly improve the reasoning accuracy of large language models (LLMs) on complex tasks. However, due to the autoregressive, step-by-step generation paradigm, existing CoT methods suffer from two fundamental limitations. First, the reasoning process is highly susceptible to early-stage errors, which tend to propagate and amplify without a global coordination and correction mechanism, thereby distorting the overall reasoning chain. Second, current CoT methods lack structured analytical frameworks for pruning redundant reasoning and identifying critical reasoning features, resulting in instability and reduced interpretability. To address these issues, we propose Global Hypothesis Structure via Topological Data Analysis (GHS-TDA), which constructs a semantically enriched global hypothesis graph that integrates and coordinates multiple candidate reasoning paths, thereby supporting global consistency refinement and error mitigation. GHS-TDA applies persistent homology-based topological data analysis to capture stable multi-scale structures, remove redundancy and inconsistencies, and extract a more reliable reasoning skeleton. By jointly leveraging reasoning diversity and topological stability, GHS-TDA achieves self-adaptive convergence, produces high-confidence and interpretable reasoning paths, and consistently outperforms strong baselines in terms of both accuracy and robustness across multiple reasoning benchmarks.
Multi-Action Self-Improvement For Neural Combinatorial Optimization
Authors: Laurin Luttmann; Lin Xie
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Self-improvement has emerged as a state-of-the-art paradigm in Neural Combinatorial Optimization (NCO), where models iteratively refine their policies by generating and imitating high-quality solutions. Despite strong empirical performance, existing methods face key limitations. Training is computationally expensive, as policy updates require sampling numerous candidate solutions per instance to extract a single expert trajectory. More fundamentally, these approaches fail to exploit the structure of combinatorial problems involving the coordination of multiple agents, such as vehicles in min-max routing or machines in scheduling. By supervising on single-action trajectories,  they fail to exploit agent-permutation symmetries, where distinct sequences of actions yield identical solutions, hindering generalization and the ability to learn coordinated behavior.

We address these challenges by extending self-improvement to operate over joint multi-agent actions. Our model architecture predicts complete agent-task assignments jointly at each decision step. To explicitly leverage symmetries, we employ a set-prediction loss, which supervises the policy on multiple expert assignments for any given state. This approach enhances sample efficiency and the model's ability to learn coordinated behavior. Furthermore, by generating multi-agent actions in parallel, it drastically accelerates the solution generation phase of the self-improvement loop. Empirically, we validate our method on several combinatorial problems, demonstrating consistent improvements in the quality of the final solution and a reduced generation latency compared to standard self-improvement.