GenAI4ELab/papercli-papers huggingface dataset:
FHE-Coder: Benchmarking Secure Agentic Code Generation for Fully Homomorphic Encryption
Authors: Mayank Kumar; Jiaqi Xue; Mengxin Zheng; Qian Lou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fully Homomorphic Encryption (FHE) is a foundational technology for confidential computing, yet its practical adoption remains limited by the need for specialized cryptographic expertise and error-prone parameter configuration. To lower this barrier, we investigate whether Large Language Model (LLM) agents can reliably generate secure FHE code from natural-language specifications. We present FHE-Coder, a three-phase agentic framework that addresses the key failure modes of FHE code generation: semantic ambiguity, API misuse, and cryptographic insecurity. The framework integrates (1) a Prompt Formalizer that structures user intent and enforces secure parameterization, (2) a specialized retrieval-augmented generation (RAG) module that supplies scheme-specific API and documentation knowledge, and (3) an automated Security Verifier that performs iterative validation and feedback to detect and correct cryptographic flaws. We evaluate FHE-Coder across four leading LLMs on a benchmark of ten FHE programming tasks spanning increasing functional and security complexity. While baseline agents frequently produce code that compiles and passes functional tests, they often violate security constraints or misuse cryptographic parameters. In contrast, FHE-Coder consistently generates solutions that are compilable, functionally correct, and verifiably secure across schemes including TFHE and CKKS. Our work establishes a systematic methodology and benchmark for agentic FHE code generation, providing a practical step toward democratizing secure computation without compromising cryptographic guarantees.
A Joint Diffusion Model with Pre-Trained Priors for RNA Sequence-Structure Co-Design
Authors: Xiner Li; Masatoshi Uehara; Xingyu Su; Gabriele Scalia; Shuiwang Ji
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: RNA molecules underlie regulation, catalysis, and therapeutics in biological systems, yet de novo RNA design remains difficult with the tight and highly non-linear sequence-structure coupling. 
The RNA sequence-structure co-design problem generates nucleotide sequences and 3D conformations jointly, which is challenging due to RNA’s conformational flexibility, non-canonical base pairing, and the scarcity of 3D data. 
We introduce a joint generative framework that embeds RoseTTAFold2NA as the denoiser into a dual diffusion model, injecting rich cross-molecular priors while enabling sample-efficient learning from limited RNA data. Our method couples a discrete diffusion process for sequences with an $SE(3)$-equivariant diffusion for rigid-frame translations and rotations over all-atom coordinates. The architecture supports flexible conditioning,
and is further enhanced at inference via lightweight RL techniques that optimize task-aligned rewards. 
Across de novo RNA design as well as complex and protein-conditioned design tasks, our approach yields high self-consistency and confidence scores, improving over recent diffusion/flow baselines trained from scratch. Results demonstrate that leveraging pre-trained structural priors within a joint diffusion framework is a powerful paradigm for RNA design under data scarcity, enabling high-fidelity generation of standalone RNAs and functional RNA-protein interfaces.
CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
Authors: Xianglong Hou; Xinquan Huang; Paris Perdikaris
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neural operator surrogates for time-dependent partial differential equations (PDEs) conventionally employ autoregressive (AR) prediction schemes, which accumulate error over long rollouts and require uniform temporal discretization.
We introduce the Continuous Flow Operator (CFO), a framework that learns continuous-time PDE dynamics without the computational burden of standard continuous approaches, e.g., neural ODE. The key insight is repurposing flow matching to directly learn the right-hand side of PDEs without backpropagating through ODE solvers. CFO fits temporal splines to trajectory data, using finite-difference estimates of time derivatives at knots to construct probability paths whose velocities closely approximate the true PDE dynamics. A neural operator is then trained via flow matching to predict these analytic velocity fields. This approach is inherently time-resolution invariant: training accepts trajectories sampled on trajectory-specific, non-uniform time grids while inference queries solutions at any temporal resolution through ODE integration. 
Across four benchmarks (Lorenz, 1D Burgers, 2D diffusion-reaction, 2D shallow water), CFO demonstrates superior long-horizon stability and remarkable data efficiency. 
CFO trained on only 25% of irregularly subsampled time points outperforms autoregressive baselines trained on complete data, with relative error reductions up to 87%. 
Despite requiring numerical integration at inference, CFO achieves competitive efficiency, exceeding AR accuracy using only half the AR rollout step budget, while uniquely enabling reverse-time inference and arbitrary temporal querying. Code is available at https://github.com/shannon-hou/CFO_official
Contextual Multi-Armed Bandits with Minimum Aggregated Revenue Constraints
Authors: Ahmed Ben Yahmed; Hafedh El Ferchichi; Marc Abeille; Vianney Perchet
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We examine a multi-armed bandit problem with contextual information, where the objective is to ensure that each arm receives a minimum aggregated reward across contexts while simultaneously maximizing the total cumulative reward. This framework captures a broad class of real-world applications where fair revenue allocation is critical and contextual variation is inherent. The cross-context aggregation of minimum reward constraints, while enabling better performance and easier feasibility, introduces significant technical challenges—particularly the absence of closed-form optimal allocations typically available in standard MAB settings. We design and analyze algorithms that either optimistically prioritize performance or pessimistically enforce constraint satisfaction. For each algorithm, we derive problem-dependent upper bounds on both regret and constraint violations. Furthermore, we establish a lower bound demonstrating that the dependence on the time horizon in our results is optimal in general and revealing fundamental limitations of the free exploration principle leveraged in prior work.
SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors
Authors: Tiancheng Hu; Joachim Baumann; Lorenzo Lupo; Nigel Collier; Dirk Hovy; Paul Röttger
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language model (LLM) simulations of human behavior have the potential to revolutionize the social and behavioral sciences, if and only if they faithfully reflect real human behaviors. Current evaluations of simulation fidelity are fragmented, based on bespoke tasks and metrics, creating a patchwork of incomparable results. To address this, we introduce SimBench, the first large-scale, standardized benchmark for a robust, reproducible science of LLM simulation. By unifying 20 diverse datasets covering tasks from moral decision-making to economic choice across a large global participant pool, SimBench provides the necessary foundation to ask fundamental questions about when, how, and why LLM simulations succeed or fail. We show that the best LLMs today achieve meaningful but modest simulation fidelity (score: 40.80/100), with performance scaling log-linearly with model size but not with increased inference-time compute. We discover an alignment-simulation tradeoff: instruction tuning improves performance on low-entropy (consensus) questions but degrades it on high-entropy (diverse) ones. Models particularly struggle when simulating specific demographic groups. Finally, we demonstrate that simulation ability correlates most strongly with knowledge-intensive reasoning (MMLU-Pro, $r=0.939$). By making progress measurable, we aim to accelerate the development of more faithful LLM simulators.
Count Bridges enable Modeling and Deconvolving Transcriptomic Data
Authors: Nic Fishman; Gokul Gowri; Tanush Kumar; Jiaqi Lu; Valentin De Bortoli; Jonathan S. Gootenberg; Omar Abudayyeh
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Many modern biological assays, including RNA sequencing, yield integer-valued counts that reflect the number of molecules detected. These measurements are often not at the desired resolution: while the unit of interest is typically a single cell, many measurement technologies produce counts aggregated over sets of cells. 
Although recent generative frameworks such as diffusion and flow matching have been extended to non-Euclidean and discrete settings, it remains unclear how best to model integer-valued data or how to systematically deconvolve aggregated observations.
We introduce Count Bridges, a stochastic bridge process on the integers that provides an exact, tractable analogue of diffusion-style models for count data, with closed-form conditionals for efficient training and sampling. We extend this framework to enable direct training from aggregated measurements via an Expectation-Maximization approach that treats unit-level counts as latent variables.
We demonstrate state-of-the-art performance on integer distribution matching benchmarks, comparing against flow matching and discrete flow matching baselines across various metrics. We then apply Count Bridges to two large-scale problems in biology: modeling single-cell gene expression data at the nucleotide resolution, with applications to deconvolving bulk RNA-seq, and resolving multicellular spatial transcriptomic spots into single-cell count profiles. Our methods offer a principled foundation for generative modeling and deconvolution of biological count data across scales and modalities.
High-dimensional limit theorems for SGD: Momentum and Adaptive Step-sizes
Authors: Aukosh Jagannath; Taj Jones-McCormick; Varnan Sarangian
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We develop a high-dimensional scaling limit for Stochastic Gradient Descent with Polyak Momentum (SGD-M) and adaptive step-sizes. This provides a framework to rigourously compare online SGD with some of its popular variants. We show that the scaling limits of SGD-M coincide with those of online SGD after an appropriate time rescaling and a specific choice of step-size. However, if the step-size is kept the same between the two algorithms, SGD-M will amplify high-dimensional effects, potentially degrading performance relative to online SGD. We demonstrate our framework on two popular learning problems: Spiked Tensor PCA and Single Index Models. In both cases, we also examine online SGD with an adaptive step-size based on normalized gradients. In the high-dimensional regime, this algorithm yields multiple benefits: its dynamics admit fixed points closer to the population minimum and widens the range of admissible step-sizes for which the iterates converge to such solutions. These examples provide a rigorous account, aligning with empirical motivation, of how early preconditioners can stabilize and improve dynamics in settings where online SGD fails.
One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
Authors: Zaid Khan; Archiki Prasad; Elias Stengel-Eskin; Jaemin Cho; Mohit Bansal
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Symbolic world modeling is the task of inferring and representing the transitional dynamics of an environment as an executable program. Previous research
on symbolic world modeling has focused on simple, deterministic environments
with abundant data and human-provided guidance. We address the more realistic and challenging problem of learning a symbolic world model in a complex, stochastic environment with severe constraints: a limited interaction budget
where the agent has only “one life” to explore a hostile environment and no external guidance in the form of human-provided, environment-specific rewards or
goals. We introduce OneLife, a framework that models world dynamics through
conditionally-activated programmatic laws within a probabilistic programming
framework. Each law operates through a precondition-effect structure, allowing
it to remain silent on irrelevant aspects of the world state and predict only the attributes it directly governs. This creates a dynamic computation graph that routes
both inference and optimization only through relevant laws for each transition,
avoiding the scaling challenges that arise when all laws must contribute to predictions about a complex, hierarchical state space, and enabling accurate learning
of stochastic dynamics even when most rules are inactive at any given moment.
To evaluate our approach under these demanding constraints, we introduce a new
evaluation protocol that measures (a) state ranking, the ability to distinguish plausible future states from implausible ones, and (b) state fidelity, the ability to generate future states that closely resemble reality. We develop and evaluate our framework on Crafter-OO, our reimplementation of the popular Crafter environment
that exposes a structured, object-oriented symbolic state and and a pure transition function that operates on that state alone. OneLife can successfully learn
key environment dynamics from minimal, unguided interaction, outperforming a
strong baseline on 16 out of 23 scenarios tested. 
We also demonstrate
the world model’s utility for planning, where rollouts simulated within the world
model successfully identify superior strategies in multi-step goal-oriented tasks.
Our work establishes a foundation for autonomously constructing programmatic world models of unknown,
complex environments.
RewardBench 2: Advancing Reward Model Evaluation
Authors: Saumya Malik; Valentina Pyatkin; Sander Land; Jacob Morrison; Noah A. Smith; Hannaneh Hajishirzi; Nathan Lambert
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reward models are used throughout the post-training of language models to capture nuanced signals from preference data and provide a training target for optimization across instruction following, reasoning, safety, and more domains. The community has begun establishing best practices for evaluating reward models, from the development of benchmarks that test capabilities in specific skill areas to others that test agreement with human preferences. At the same time, progress in evaluation has not been mirrored by the effectiveness of reward models in downstream tasks -- simpler direct alignment algorithms are reported to work better in many cases. This paper introduces RewardBench 2, a new multi-skill reward modeling benchmark designed to bring new, challenging data for accuracy-based reward model evaluation -- models score about 20 points on average lower on RewardBench 2 compared to RewardBench, a widely-used existing reward model evaluation-- while being highly correlated with downstream performance. Compared to most other benchmarks, RewardBench 2 sources new human prompts instead of existing prompts from downstream evaluations, facilitating more rigorous evaluation practices. In this paper, we describe our benchmark construction process and report how existing models perform on it, while quantifying and providing new insights on how performance on the benchmark correlates with downstream use of the models in both inference-time scaling algorithms, like best-of-N sampling, and RLHF training algorithms like proximal policy optimization.
RAVEN: End-to-end Equivariant Robot Learning with RGB Cameras
Authors: David Klee; Boce Hu; Andrew Cole; Heng Tian; Dian Wang; Robert Platt; Robin Walters
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent work has shown that equivariant policy networks can achieve strong performance on robot manipulation tasks with limited human demonstrations.  However, existing equivariant methods typically require structured inputs, such as 3D point clouds or top-down camera views, which prevents their use in low-cost setups or dynamic environments.  In this work, we propose the first $\mathrm{SE}(3)$-equivariant policy learning framework that operates with only RGB image observations.  The key insight is to treat image-based data as collections of rays that, unlike 2D pixels, transform under 3D roto-translations. Extensive experiments in both simulation with diverse robot configurations and real-world settings demonstrate that our method consistently surpasses strong baselines in both performance and efficiency.
Oracle-efficient Hybrid Learning with Constrained Adversaries
Authors: Princewill Okoroafor; Robert Kleinberg; Michael P. Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The Hybrid Online Learning Problem, where features are drawn i.i.d. from an unknown distribution but labels are generated adversarially, is a well-motivated setting positioned between statistical and fully-adversarial online learning. Prior work has presented a dichotomy: algorithms that are statistically-optimal, but computationally intractable \citep{wu2023expected}, and algorithms that are computationally-efficient (given an ERM oracle), but statistically-suboptimal \citep{pmlr-v247-wu24a}.

This paper takes a significant step towards achieving statistical optimality and computational efficiency \emph{simultaneously} in the Hybrid Learning setting. To do so, we consider a structured setting, where the Adversary is constrained to pick labels from an expressive, but fixed, class of functions $\mathcal{R}$. Our main result is a new learning algorithm, which runs efficiently given an ERM oracle and obtains regret scaling with the Rademacher complexity of a class derived from the Learner's hypothesis class $\mathcal{H}$ and the Adversary's label class $\mathcal{R}$. As a key corollary, we give an oracle-efficient algorithm for computing equilibria in stochastic zero-sum games when action sets may be high-dimensional but the payoff function exhibits a type of low-dimensional structure. Technically, we develop a number of novel tools for the design and analysis of our learning algorithm, including a novel Frank-Wolfe reduction with "truncated entropy regularizer" and a new tail bound for sums of "hybrid'' martingale difference sequences.
Inpainting-Guided Policy Optimization for Diffusion Large Language Models
Authors: Siyan Zhao; Mengchen Liu; Jing Huang; Miao Liu; Chenyu Wang; Bo Liu; Yuandong Tian; Guan Pang; Sean Bell; Aditya Grover; Feiyu Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Masked diffusion large language models (dLLMs) are emerging as promising alternatives to autoregressive LLMs, offering competitive performance while supporting unique generation capabilities such as inpainting. We explore how inpainting can inform RL algorithm design for dLLMs. Aligning LLMs with reinforcement learning faces an exploration challenge: sparse reward signals and sample waste when models fail to discover correct solutions. While this inefficiency affects LLMs broadly, dLLMs offer a distinctive opportunity—their inpainting ability can guide exploration. We introduce IGPO (Inpainting Guided Policy Optimization), an RL framework that strategically inserts partial ground-truth reasoning traces during online sampling. Unlike providing full solutions, inpainting steers exploration toward promising trajectory spaces while preserving self-generated reasoning, bridging supervised fine-tuning and reinforcement learning.
We apply IGPO to group-based optimization methods such as GRPO, where exploration failures cause zero advantages and gradients. IGPO restores meaningful gradients while improving sample efficiency. We also propose supervised fine-tuning on synthetically rewritten concise traces that better align with dLLM generation patterns. With additional techniques including entropy-based filtering, our training recipe yields substantial gains across four mathematical benchmarks—GSM8K, Math500, AMC and Minerva—achieving new state-of-the-art results for full-attention masked dLLMs.
Graph-of-Agents: A Graph-based Framework for Multi-Agent LLM Collaboration
Authors: Sukwon Yun; Jie Peng; Pingzhi Li; Wendong Fan; Jie Chen; James Zou; Guohao Li; Tianlong Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With an ever-growing zoo of LLMs and benchmarks, the need to orchestrate multiple models for improved task performance has never been more pressing. While frameworks like Mixture-of-Agents (MoA) attempt to coordinate LLMs, they often fall short in terms of (1) selecting relevant agents, (2) facilitating effective intra-agent communication, and (3) integrating responses efficiently. In this work, we propose Graph-of-Agents (GoA), a new graph-based framework for modeling multi-agent LLM communication. Our approach begins with node sampling, selecting only the most relevant agents by leveraging model cards that summarize each model’s domain, task specialization, and other characteristics. Next, we construct edges between the selected agents by evaluating their responses against one another to determine relevance ordering. Directed message passing is then performed from highly relevant agents to less relevant ones to enhance their responses, followed by reverse message passing to refine the original responses of the more relevant agents. Finally, the updated responses are aggregated via graph-based pooling (e.g., max or mean pooling) to produce a single, unified answer. We evaluate GoA on diverse multi-domain benchmarks (MMLU, MMLU-Pro, GPQA) and domain-specific benchmarks (MATH, HumanEval, MedMCQA), with an agent pool of 6 LLMs spanning multiple domains. Surprisingly, GoA achieves superior performance18 using only 3 selected agents, outperforming recent multi-agent LLM baselines that utilize all 6 agents simultaneously. By adopting a graph structure, GoA offers both scalability and effectiveness through structured message passing—positioning it as a strong candidate for navigating the challenges of the ever-growing LLM zoo.
Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions
Authors: Wang Bill Zhu; Tian-qi Chen; Xinyan Velocity Yu; Ching Ying Lin; Jade Law; Mazen Jizzini; Jorge J. Nieva; Ruishan Liu; Robin Jia
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Cancer patients are increasingly turning to large language models (LLMs) for medical information, making it critical to assess how well these models handle complex, personalized questions. 
However, current medical benchmarks focus on medical exams or consumer-searched questions and do not evaluate LLMs on real patient questions with patient details. 
In this paper, we first have three hematology-oncology physicians evaluate cancer-related questions drawn from real patients. 
While LLM responses are generally accurate, the models frequently fail to recognize or address false presuppositions} in the questions, posing risks to safe medical decision-making.
To study this limitation systematically, we introduce Cancer-Myth, an expert-verified adversarial dataset of 585 cancer-related questions with false presuppositions.
On this benchmark, no frontier LLM---including GPT-5, Gemini-2.5-Pro, and Claude-4-Sonnet---corrects these false presuppositions more than $43\%$ of the time.
To study mitigation strategies, we further construct a 150-question Cancer-Myth-NFP set, in which physicians confirm the absence of false presuppositions.
We find typical mitigation strategies, such as adding precautionary prompts with GEPA optimization, can raise accuracy on Cancer-Myth to $80\%$, but at the cost of misidentifying presuppositions in $41\%$ of Cancer-Myth-NFP questions and causing a $10\%$ relative performance drop on other medical benchmarks.
These findings highlight a critical gap in the reliability of LLMs, show that prompting alone is not a reliable remedy for false presuppositions, and underscore the need for more robust safeguards in medical AI systems.