GenAI4ELab/papercli-papers huggingface dataset:
Hyper-SET: Designing Transformers via Hyperspherical Energy Minimization
Authors: Yunzhe Hu; Difan Zou; Dong Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformer-based models have achieved remarkable success, but their core components, Transformer layers, are largely heuristics-driven and engineered from the bottom up, calling for a prototypical model with high interpretability and practical competence. To this end, we conceptualize a principled, top-down approach grounded in energy-based interpretation. Specifically, we formalize token dynamics as a joint maximum likelihood estimation on the hypersphere, featuring two properties: semantic alignment in the high-dimensional space and distributional uniformity in the low-dimensional space. By quantifying them with extended Hopfield energy functions, we instantiate this idea as a constrained energy minimization problem, which enables designs of symmetric attention and feedforward modules with RMS normalization. We further present *Hyper-Spherical Energy Transformer* (Hyper-SET), a recurrent-depth alternative to vanilla Transformers naturally emerging from iterative energy optimization on the hypersphere. With shared parameters across layers, Hyper-SET can scale to arbitrary depth with fewer parameters. Theoretically grounded and compact, it achieves competitive or superior performance across diverse tasks, including Sudoku solving, image classification, and masked image modeling. We also design novel variations under the proposed general principle, such as linear attention and gated feedforward layer, and showcase its scalability with depth-wise LoRA. Our results highlight Hyper-SET as a step toward interpretable and principled Transformer design. Code is available at https://github.com/huyunzhe/hyper-set.
Bi-Criteria Metric Distortion
Authors: Kiarash Banihashem; Diptarka Chakraborty; Shayan Chashm Jahan; Iman Gholami; MohammadTaghi Hajiaghayi; Mohammad Mahdavi; Max Springer
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Selecting representatives based on voters' preferences is a fundamental problem in social choice theory. While cardinal utility functions offer a detailed representation of preferences, voters often cannot precisely quantify their affinity towards a given candidate. As a result, modern voting systems rely on ordinal rankings to simplistically represent preference profiles. In quantifying the suboptimality of solutions due to the loss of information when using ordinal preferences, the metric distortion framework models voters and candidates as points in a metric space, with distortion bounding the efficiency loss. Prior works within this framework use the distance between a voter and a candidate in the underlying metric as the cost of selecting the candidate for the given voter, with a goal of minimizing the sum (utilitarian) or maximum (egalitarian) of costs across voters. For deterministic election mechanisms selecting a single winning candidate, the best possible distortion is known to be 3 for any metric, as established by Gkatzelis, Halpern, and Shah (FOCS'20). In contrast, for randomized mechanisms, distortions cannot be lower than $2.112$, as shown by Charikar and Ramakrishnan (SODA'22), and there exists a mechanism with a distortion guarantee of $2.753$, according to Charikar, Ramakrishnan, Wang, and Wu (SODA'24 Best Paper Award). Our work asks: can one obtain a better approximation compared to an optimal candidate by selecting a committee of $k$ candidates ($k \ge 1$), where the cost of a voter is defined to be its distance to the closest candidate in the committee? We affirmatively answer this question by introducing the concept of bi-criteria approximation within the metric distortion framework. In the line metric, it is possible to achieve optimal cost with only $O(1)$ candidates. In contrast, we also prove that in both the two-dimensional and tree metrics -- which naturally generalize the line metric -- achieving optimal cost is impossible unless all candidates are selected. These results apply to both utilitarian and egalitarian objectives. Our results establish a stark separation between the line metric and the 2D or tree metric in the context of the metric distortion problem.
LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures
Authors: Hai Huang; Yann LeCun; Randall Balestriero
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Model (LLM) pretraining, finetuning, and evaluation rely on input-space reconstruction and generative capabilities. Yet, it has been observed in vision that embedding-space training objectives, e.g., with Joint Embedding Predictive Architectures (JEPAs), are far superior to their input-space counterpart. That mismatch in how training is achieved between language and vision opens up a natural question: {\em can language training methods learn a few tricks from the vision ones?} The lack of JEPA-style LLM is a testimony of the challenge in designing such objectives for language. In this work, we propose a first step in that direction where we develop LLM-JEPA, a JEPA based solution for LLMs applicable both to finetuning and pretraining. Thus far, LLM-JEPA is able to outperform the standard LLM training objectives by a significant margin across models, all while being robust to overfiting. Those findings are observed across numerous datasets (NL-RX, GSM8K, Spider, RottenTomatoes) and various models from the Llama3, OpenELM, Gemma2 and Olmo families. Code: \url{https://github.com/galilai-group/llm-jepa}.
A Bayesian Nonparametric Framework For Learning Disentangled Representations
Authors: Vaishnavi S Patil; Siddhi Patil; Matthew Evanusa; Amit Kumar Kundu; Cornelia Fermuller; Joseph JaJa
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Disentangled representation learning aims to identify and organize the underlying sources of variation in observed data. However, learning disentangled representations from observational data alone without any additional supervision necessitates inductive biases to solve the fundamental identifiability problem of uniquely recovering the true latent structure and parameters of the data-generating process. Existing methods address this by imposing heuristic inductive biases that typically lack these theoretical identifiability guarantees. Additionally, these methods rely on strong regularization to impose these inductive biases, creating an inherent trade-off in which stronger regularization improves disentanglement but limits the latent capacity to represent underlying variations. To address both challenges, we propose a principled generative model with a Bayesian nonparametric hierarchical mixture prior that embeds inductive biases within a provably identifiable framework for unsupervised disentanglement. Specifically, the hierarchical mixture prior imposes the structural constraints necessary for identifiability guarantees, while the nonparametric formulation allows the latent representation to scale with infinite capacity to faithfully represent the complete set of underlying variations without violating these structural constraints. To enable tractable inference under this nonparametric hierarchical prior, we develop a structured variational inference framework with a nested variational family that both preserves the hierarchical structure of the identifiable generative model and approximates the expressiveness of the nonparametric prior. We evaluate our proposed probabilistic model on standard disentanglement benchmarks, 3DShapes and MPI3D datasets characterized by diverse source variation distributions, to demonstrate that our method consistently outperforms strong baseline models through structural biases and a unified objective function, obviating the need for auxiliary regularization constraints or careful hyperparameter tuning.
Unveiling the Potential of Diffusion Large Language Model in Controllable Generation
Authors: Zhen Xiong; Yujun Cai; Zhecheng Li; Yiwei Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Controllable generation is a fundamental task in NLP with many applications, providing a basis for function calling to agentic communication. However, even state-of-the-art autoregressive Large Language Models (LLMs) today exhibit unreliability when required to generate structured output. Inspired by the current new diffusion-based large language models (dLLM), we realize that the architectural difference, especially the global information-sharing mechanism for language modeling, may be the key to unlock next-level controllable generation. To explore the possibility, we propose Self-adaptive Schema Scaffolding ($S^3$), a novel framework that enables dLLM to stably generate reliable structured outputs (e.g., JSON) by utilizing its innate reverse reasoning capability and global context awareness. $S^3$ initiates a schematic template directly in the output context as a starting state for dLLM, offering a more robust and general method than intricate prompt optimization. Experiments demonstrate that our method substantially unlocks the dLLM’s potential in controllable generation in terms of structure adherence, content fidelity, and faithfulness. These results establish new perspectives and practical pathways for deploying language models in controllable generation tasks.
Representing local protein environments with machine learning force fields
Authors: Meital Bojan; Sanketh Vedula; Sai Advaith Maddipatla; Nadav Bojan; Anar Rzayev; Federico Napoli; Paul Schanda; Alexander Bronstein
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The local structure of a protein strongly impacts its function and interactions with other molecules. Representing local biomolecular environments remains a key challenge while applying machine learning approaches over protein structures. The structural and chemical variability of these environments makes them challenging to model, and performing representation learning on these objects remains largely under-explored.  In this work, we propose representations for local protein environments that leverage intermediate features from machine learning force fields (MLFFs). We extensively benchmark state-of-the-art MLFFs—comparing their performance across latent spaces and downstream tasks—and show that their embeddings capture local structural (e.g., secondary motifs) and chemical features (e.g., amino acid identity and protonation state), organizing protein environments into a structured manifold. We show that these representations enable zero-shot generalization and transfer across diverse downstream tasks. As a case study, we build a physics-informed, uncertainty-aware chemical shift predictor that achieves state-of-the-art accuracy in biomolecular NMR spectroscopy. Our results establish MLFFs as general-purpose, reusable representation learners for protein modeling, opening new directions in representation learning for structured physical systems.
MedAgentGym: A Scalable Agentic Training Environment for Code-Centric Reasoning in Biomedical Data Science
Authors: Ran Xu; Yuchen Zhuang; Yishan Zhong; Yue Yu; Zifeng Wang; Xiangru Tang; Hang Wu; May Dongmei Wang; Peifeng Ruan; Donghan Yang; Tao Wang; Guanghua Xiao; Xin Liu; Carl Yang; Yang Xie; Wenqi Shi
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: We introduce MedAgentGym, a scalable and interactive training environment designed to enhance coding-based biomedical reasoning capabilities in large language model (LLM) agents. MedAgentGym comprises 72,413 task instances across 129 categories derived from 12 authentic real-world biomedical scenarios. Tasks are encapsulated within executable sandbox environments, each featuring detailed task specifications, interactive feedback mechanisms, verifiable ground truth annotations, and scalable training trajectory generation. Extensive benchmarking of 29 LLMs reveals substantial performance disparities in biomedical data science between commercial and open-source LLMs. Leveraging efficient multi-threaded and multi-turn trajectory sampling in MedAgentGym, Med-Copilot achieves performance gains of +43.02% and +45.28% from offline and online reinforcement learning, respectively, demonstrating MedAgentGym as an effective training ground while establishing itself as a cost-effective, privacy-preserving alternative competitive with proprietary LLMs (gpt-4o). By offering a unified execution environment with a comprehensive benchmark and accessible, extensible training resources, MedAgentGym delivers an integrated platform to develop LLM-based coding assistants for advanced biomedical data science.
Optimistic Task Inference for Behavior Foundation Models
Authors: Thomas Rupf; Marco Bagatella; Marin Vlastelica; Andreas Krause
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Behavior Foundation Models (BFMs) are capable of retrieving high-performing policy for any reward function specified directly at test-time, commonly referred to as zero-shot reinforcement learning (RL). While this is a very efficient process in terms of compute, it can be less so in terms of data: as a standard assumption, BFMs require computing rewards over a non-negligible inference dataset, assuming either access to a functional form of rewards, or significant labeling efforts. To alleviate these limitations, we tackle the problem of task inference purely through interaction with the environment at test-time. We propose OpTI-BFM, an optimistic decision criterion that directly models uncertainty over reward functions and guides BFMs in data collection for task inference. Formally, we provide a regret bound for well- trained BFMs through a direct connection to upper-confidence algorithms for linear bandits. Empirically, we evaluate OpTI-BFM on established zero-shot benchmarks, and observe that it enables successor-features-based BFMs to identify and optimize an unseen reward function in a handful of episodes with minimal compute overhead.
Annotation-Efficient Honesty Alignment via Confidence Elicitation and Calibration
Authors: Shiyu Ni; Keping Bi; Jiafeng Guo; Minghao Tang; Jingtong wu; Zengxin Han; Xueqi Cheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Honesty alignment—the ability of large language models (LLMs) to recognize their knowledge boundaries and express calibrated confidence—is essential for trustworthy deployment. Existing methods either rely on training-free confidence estimation (e.g., token probabilities, self-consistency) or training-based calibration with correctness annotations. While effective, the latter demands costly, large-scale labeling.
We introduce Elicitation-Then-Calibration (EliCal), a two-stage framework that first elicits internal confidence using inexpensive self-consistency supervision, then calibrates this confidence with a small set of correctness annotations. This design substantially reduces annotation requirements while improving generalization across tasks. To support a large-scale study, we release HonestyBench, a benchmark covering ten free-form QA datasets with 560k training and 70k evaluation instances annotated with correctness and self-consistency signals.
Experiments show that EliCal achieves near-optimal alignment with only 1k correctness annotations ($\sim$0.18\% of full supervision) and better alignment performance on unseen MMLU tasks than the calibration-only baseline, offering a scalable solution toward universal honesty alignment in LLMs.
MicroVerse: A Preliminary Exploration Toward a Micro-World Simulation
Authors: Rongsheng Wang; Minghao Wu; Hongru Zhou; Zhihan Yu; Zhenyang Cai; Junying Chen; Benyou Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in video generation have opened new avenues for macroscopic simulation of complex dynamic systems, but their application to microscopic phenomena remains largely unexplored. Microscale simulation holds great promise for biomedical applications such as drug discovery, organ-on-chip systems, and disease mechanism studies, while also showing potential in education and interactive visualization. In this work, we introduce **MicroWorldBench**, a multi-level rubric-based benchmark for microscale simulation tasks. MicroWorldBench enables systematic, rubric-based evaluation through 459 unique expert-annotated criteria spanning multiple microscale simulation task (e.g., organ-level processes, cellular dynamics, and subcellular molecular interactions) and evaluation dimensions (e.g., scientific fidelity, visual quality, instruction following). MicroWorldBench reveals that current SOTA video generation models fail in microscale simulation, showing violations of physical laws, temporal inconsistency, and misalignment with expert criteria.
To address these limitations, we construct **MicroSim-10K**, a high-quality, expert-verified simulation dataset. Leveraging this dataset, we train **MicroVerse**, a video generation model tailored for microscale simulation. MicroVerse can accurately reproduce complex microscale mechanism. Our work first introduce the concept of **Micro-World Simulation** and present a **proof of concept**, paving the way for applications in biology, education, and scientific visualization. Our work demonstrates the potential of educational microscale simulations of biological mechanisms.
Flow Matching with Injected Noise for Offline-to-Online Reinforcement Learning
Authors: Yongjae Shin; Jongseong Chae; Jongeui Park; Youngchul Sung
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generative models have recently demonstrated remarkable success across diverse domains, motivating their adoption as expressive policies in reinforcement learning (RL).
While they have shown strong performance in offline RL, particularly where the target distribution is well defined, their extension to online fine-tuning has largely been treated as a direct continuation of offline pre-training, leaving key challenges unaddressed.
In this paper, we propose Flow Matching with Injected Noise for Offline-to-Online RL (FINO), a novel method that leverages flow matching-based policies to enhance sample efficiency for offline-to-online RL.
FINO facilitates effective exploration by injecting noise into policy training, thereby encouraging a broader range of actions beyond those observed in the offline dataset.
In addition to exploration-enhanced flow policy training, we combine an entropy-guided sampling mechanism to balance exploration and exploitation, allowing the policy to adapt its behavior throughout online fine-tuning.
Experiments across diverse, challenging tasks demonstrate that FINO consistently achieves superior performance under limited online budgets.
Chessformer: A Unified Architecture for Chess Modeling
Authors: Daniel Monroe; George Eilender; Philip Chalmers; Zhenwei Tang; Ashton Anderson
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Chess has long served as a canonical testbed for artificial intelligence, but
modeling approaches for its central tasks have diverged. Maximizing playing
strength, predicting human play, and enabling interpretability are typically
solved with disparate architectures, and these designs are often misaligned
with the geometry of the domain. This raises the natural question of whether
these objectives require separate modeling paradigms, or if there exists a single
architecture that supports them simultaneously.

We introduce Chessformer, a unified architecture that advances the state of
the art on all three central goals in chess modeling. Chessformer is an encoder-
only transformer that represents board squares as tokens, augments self-attention
with a novel dynamic positional encoding called Geometric Attention Bias
(GAB) that adapts to domain-specific geometry, and predicts actions with an
attention-based source-destination policy head. We evaluate Chessformer on
each front. First, we develop MAIA-3, a family of models for human move
prediction that reaches 57.1% move-matching accuracy, significantly surpassing
the previous state of the art with fewer than a quarter of the parameters. Second,
we integrate Chessformer into Leela Chess Zero, a leading open-source engine,
adding over 100 Elo of playing strength and resulting in tournament victories
over Stockfish in major computer chess competitions. Third, we show that
Chessformer’s square-token design makes attention patterns and activations
directly attributable to board squares, enabling granular interpretability analyses
that prior architectures do not naturally support. More broadly, our results
demonstrate that aligning a model’s tokenization, positional encoding, and output
design with the underlying structure of a domain can yield simultaneous gains in
performance, human compatibility, and interpretability.
Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large Language Models
Authors: yanjiang liu; Shuheng Zhou; Yaojie Lu; Huijia Zhu; Weiqiang Wang; Hongyu Lin; Ben He; Xianpei Han; Le Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Automated red-teaming has emerged as an essential approach for identifying vulnerabilities in large language models (LLMs). However, most existing methods rely on fixed attack templates and focus primarily on individual high-severity flaws,limiting their adaptability to evolving defenses and their ability to detect complex, high-exploitability vulnerabilities. To address these limitations, we propose AUTO-RT, a reinforcement learning framework designed for automatic jailbreak strategy exploration, i.e., discovering diverse and effective prompts capable of bypassing the safety restrictions of LLMs. AUTO-RT autonomously explores and optimizes attack strategies by interacting with the target model and generating crafted queries that trigger security failures. Specifically, AUTO-RT introduces two key techniques to improve exploration efficiency and attack effectiveness: 1) Dynamic Strategy Pruning, which focuses exploration on high-potential strategies by eliminating highly redundant paths early, and 2) Progressive Reward Tracking, which leverages intermediate downgrade models and a novel First Inverse Rate (FIR) metric to smooth sparse rewards and guide learning. Extensive experiments across diverse white-box and black-box LLM settings demonstrate that AUTO-RT significantly improves success rates (by up to 16.63%), expands vulnerability coverage, and accelerates discovery compared to existing methods.