GenAI4ELab/papercli-papers huggingface dataset:
Can we generate portable representations for clinical time series data using LLMs?
Authors: Zongliang Ji; Yifei Sun; Andre Carlos Kajdacsy-Balla Amaral; Anna Goldenberg; Rahul G Krishnan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under distribution shifts at the next. In this work, we study a simple question -- can large language models (LLMs) create portable patient embeddings i.e. representations of patients enable a downstream predictor built on one hospital to be used elsewhere with minimal-to-no retraining and fine-tuning.
To do so, we map from irregular ICU time series onto concise natural language summaries using a frozen LLM, then embed each summary with a frozen text embedding model to obtain a fixed length vector capable of serving as input to a variety of downstream predictors.
Across three cohorts (MIMIC-IV, HIRID, PPICU), on multiple clinically grounded forecasting and classification tasks, we find that our approach is simple, easy to use and competitive with in-distribution with grid imputation, self-supervised representation learning, and time series foundation models, while exhibiting smaller relative performance drops when transferring to new hospitals.
We study the variation in performance across prompt design, with structured prompts being crucial to reducing the variance of the predictive models without altering mean accuracy. We find that using these portable representations improves few-shot learning and does not increase demographic recoverability of age or sex relative to baselines, suggesting little additional privacy risk.
Our work points to the potential that LLMs hold as tools to enable the scalable deployment of production grade predictive models by reducing the engineering overhead.
Cognitive models can reveal interpretable value trade-offs in language models
Authors: Sonia Krishna Murthy; Rosie Zhao; Jennifer Hu; Sham M. Kakade; Markus Wulfmeier; Peng Qian; Tomer Ullman
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Value trade-offs are an integral part of human decision-making and language use, however, current tools for interpreting such dynamic and multi-faceted notions of values in language models are limited. In cognitive science, so-called "cognitive models" provide formal accounts of such trade-offs in humans, by modeling the weighting of a speaker's competing utility functions in choosing an action or utterance. Here, we show that a leading cognitive model of polite speech can be used to systematically evaluate alignment-relevant trade-offs in language models via two encompassing settings: degrees of reasoning "effort" and system prompt manipulations in closed-source frontier models, and RL post-training dynamics of open-source models. Our results show that LLMs' behavioral profiles under the cognitive model a) shift predictably when they are prompted to prioritize certain goals, b) are amplified by a small reasoning budget, and c) can be used to diagnose other social behaviors such as sycophancy. Our findings from LLMs' post-training dynamics reveal large shifts in values early on in training and persistent effects of the choice of base model and pretraining data, compared to feedback dataset or alignment method. Our framework offers a flexible tool for probing behavioral profiles across diverse model types and gaining insights for shaping training regimes that better control trade-offs between values during model development.
SigmaDock: Untwisting Molecular Docking with Fragment-Based SE(3) Diffusion
Authors: Alvaro Prat; Leo Zhang; Charlotte Deane; Yee Whye Teh; Garrett M Morris
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Determining the binding pose of a ligand to a protein, known as molecular docking, is a fundamental task in drug discovery. Generative approaches promise faster, improved, and more diverse pose sampling than physics-based methods, but are often hindered by chemically implausible outputs, poor generalisability, and high computational cost. To address these challenges, we introduce a novel fragmentation scheme, leveraging inductive biases from structural chemistry, to decompose ligands into rigid-body fragments. Building on this decomposition, we present SigmaDock, an SE(3) Riemannian diffusion model that generates poses by learning to reassemble these rigid bodies within the binding pocket. By operating at the level of fragments in SE(3), SigmaDock exploits well-established geometric priors while avoiding overly complex diffusion processes and unstable training dynamics. Experimentally, we show SigmaDock achieves state-of-the-art performance, reaching Top-1 success rates (RMSD $<2$ \&  PB-valid) above 79.9\% on the PoseBusters set, compared to 12.7-32.8\% reported by recent deep learning approaches, whilst demonstrating consistent generalisation to unseen proteins.  SigmaDock is the first deep learning approach to surpass classical physics-based docking under the PB train-test split, marking a significant leap forward in the reliability and feasibility of deep learning for molecular modelling.
Path Channels and Plan Extension Kernels: a Mechanistic Description of Planning in a Sokoban RNN
Authors: Mohammad Taufeeque; Aaron David Tucker; Adam Gleave; Adrià Garriga-Alonso
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We partially reverse-engineer a convolutional recurrent neural network (RNN) trained with model-free reinforcement learning to play the box-pushing game Sokoban. We find that the RNN stores future moves (plans) as activations in particular channels of the hidden state, which we call *path channels*. A high activation in a particular location means that, when a box is in that location, it will get pushed in the channel's assigned direction.
We examine the convolutional kernels between path channels and find that they encode the change in position resulting from each possible action, thus representing part of a learned *transition model*.
The RNN constructs plans by starting at the boxes and goals.
These kernels, *extend* activations in path channels forwards from boxes and backwards from the goal.
Negative values are placed in channels at obstacles. This causes the extension kernels to propagate the negative value in reverse, thus pruning the last few steps and letting an alternative plan emerge; a form of backtracking. 
Our work shows that, a precise understanding of the plan representation allows us to directly understand the bidirectional planning-like algorithm learned by model-free training in more familiar terms.
ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory
Authors: Siru Ouyang; Jun Yan; I-Hung Hsu; Yanfei Chen; Ke Jiang; Zifeng Wang; Rujun Han; Long Le; Samira Daruki; Xiangru Tang; Vishy Tirumalashetty; George Lee; Mahsan Rofouei; Hangfei Lin; Jiawei Han; Chen-Yu Lee; Tomas Pfister
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With the growing adoption of large language model agents in persistent real-world roles, they naturally encounter continuous streams of tasks. A key limitation, however, is their failure to learn from the accumulated interaction history, forcing them to discard valuable insights and repeat past errors. We propose ReasoningBank, a novel memory framework that distills generalizable reasoning strategies from an agent's self-judged successful and failed experiences. At test time, an agent retrieves relevant memories from ReasoningBank to inform its interaction and then integrates new learnings back, enabling it to become more capable over time. Building on this powerful experience learner, we further introduce memory-aware test-time scaling (MaTTS), which accelerates and diversifies this learning process by scaling up the agent's interaction experience. By allocating more compute to each task, the agent generates abundant, diverse experiences that provide rich contrastive signals for synthesizing higher-quality memory. The better memory in turn guides more effective scaling, establishing a powerful synergy between memory and test-time scaling. Across web browsing and software engineering benchmarks, ReasoningBank consistently outperforms existing memory mechanisms that store raw trajectories or only successful task routines, improving both effectiveness and efficiency; MaTTS further amplifies these gains. These findings establish memory-driven experience scaling as a new scaling dimension, enabling agents to self-evolve with emergent behaviors naturally arise. Our code can be found at https://github.com/google-research/reasoning-bank.
WorldGym: World Model as An Environment for Policy Evaluation
Authors: Julian Hector Quevedo; Ansh Kumar Sharma; Yixiang Sun; Varad Suryavanshi; Percy Liang; Sherry Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Evaluating robot control policies is difficult: real-world testing is costly, and handcrafted simulators require manual effort to improve in realism and generality. We propose a world-model-based policy evaluation environment (WorldGym), an autoregressive, action-conditioned video generation model which serves as a proxy to real world environments. Policies are evaluated via Monte Carlo rollouts in the world model, with a vision-language model providing rewards. We evaluate a set of VLA-based real-robot policies in the world model using only initial frames from real robots, and show that policy success rates within the world model highly correlate with real-world success rates. Moreoever, we show that WorldGym is able to preserve relative policy rankings across different policy versions, sizes, and training checkpoints. Due to requiring only a single start frame as input, the world model further enables efficient evaluation of robot policies' generalization ability on novel tasks and environments. We find that modern VLA-based robot policies still struggle to distinguish object shapes and can become distracted by adversarial facades of objects. While generating highly realistic object interaction remains challenging, WorldGym faithfully emulates robot motions and offers a practical starting point for safe and reproducible policy evaluation before deployment.
When Machine Learning Gets Personal: Evaluating Prediction and Explanation
Authors: Louisa Cornelis; Guillermo Bernardez; Haewon Jeong; Nina Miolane
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In high-stakes domains like healthcare, users often expect that sharing personal information with machine learning systems will yield tangible benefits, such as more accurate diagnoses and clearer explanations of contributing factors. However, the validity of this assumption remains largely unexplored. We propose a unified framework to quantify how personalizing a model influences both prediction and explanation. We show that its impacts on prediction and explanation can diverge: a model may become more or less explainable even when prediction is unchanged. For practical settings, we study a standard hypothesis test for detecting personalization effects on demographic groups. We derive a finite-sample lower bound on its probability of error as a function of group sizes, number of personal attributes, and desired benefit from personalization. This provides actionable insights, such as which dataset characteristics are necessary to test an effect, or the maximum effect that can be tested given a dataset. We apply our framework to real-world tabular datasets using feature-attribution methods, uncovering scenarios where effects are fundamentally untestable due to the dataset statistics. Our results highlight the need for joint evaluation of prediction and explanation in personalized models and the importance of designing models and datasets with sufficient information for such evaluation.
Weak-to-Strong Generalization with Failure Trajectories
Authors: Ruimeng Ye; Zihan Wang; Yang Xiao; Zinan Ling; Manling Li; Bo Hui
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Weak-to-Strong generalization (W2SG) is a new trend to elicit the full capabilities of a strong model with supervision from a weak model. While existing W2SG studies focus on simple tasks like binary classification, we extend this paradigm to complex interactive decision-making environments.  
Specifically, we fine-tune a strong model with trajectories of intermediate actions generated by a weak model. Motivated by the human learning process, we propose to generalize not only successful knowledge but also failed experiences so that the strong model can learn from the failed trajectories accumulated by weak models. To effectively and efficiently elicit the potential of strong agents, we further construct ``trajectory trees," a hierarchical representation that organizes weak model-generated action trajectories, coupled with Monte Carlo Tree Search (MCTS) to optimize the strong model. Through theoretical analysis, we provide formal guarantees for the effectiveness of our method in improving W2SG performance. Our empirical evaluations demonstrate substantial improvements in reasoning and decision-making capabilities across diverse task domains, validating the scalability and robustness of our proposed framework. Our code is available at: https://github.com/yeruimeng/TraTree.git.
The Markovian Thinker: Architecture-Agnostic Linear Scaling of Reasoning
Authors: Milad Aghajohari; Kamran Chitsaz; Amirhossein Kazemnejad; Sarath Chandar; Alessandro Sordoni; Aaron Courville; Siva Reddy
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reasoning LLMs suffer from quadratic compute growth as their context length increases, making reinforcement learning with verifiable rewards (RLVR) and test-time scaling prohibitively expensive. Prior work has tried to lighten the computational burden by shortening reasoning traces through pruning, summarization, or multi-stage training, but these methods remain bound to quadratic costs. We introduce Delethink, a thinking algorithm that realizes the Markovian Thinking Paradigm. Instead of producing one long monolithic reasoning trace,  Delethink thinks in a sequence of chunks, the Delethink trace. Each chunk continues reasoning by referring only to a fixed number of prior tokens, which functions as a Markovian state sufficient for progressing reasoning, while deleting the rest. This preserves continuity without carrying the quadratic baggage. As a result, compute scales linearly and peak memory remains constant. In experiments, we show that Delethink can be applied directly to off-the-shelf reasoning models ranging from $1.5\textnormal{B}$ to $30\textnormal{B}$ parameters, with no loss in performance. Extended reasoning becomes possible under fixed memory and linear compute, while enabling efficient RL training on new tasks. On the DeepScaleR dataset, Delethink trains R1DistillQwen1.5B to the same benchmark performance as a standard long chain-of-thought (LongCoT) approach, where both models generate up to $24\textnormal{k}$ thinking tokens. The difference is efficiency. Delethink reasons $40\%$ faster with $70\%$ less memory footprint. By decoupling reasoning length from context length, the Markovian Thinking paradigm opens the door to next-generation reasoning LLMs that can scale to millions of tokens with linear compute and constant memory.
Improving Feasibility via Fast Autoencoder-Based Projections
Authors: Maria Chzhen; Priya L. Donti
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Enforcing complex (e.g., nonconvex) operational constraints is a critical challenge in real-world learning and control systems. However, existing methods struggle to efficiently enforce general classes of constraints. To address this, we propose a novel data-driven amortized approach that uses a trained autoencoder as an approximate projector to provide fast corrections to infeasible predictions. Specifically, we train an autoencoder using an adversarial objective to learn a structured, convex latent representation of the feasible set. This enables rapid correction of neural network outputs by projecting their associated latent representations onto a simple convex shape before decoding into the original feasible set. We test our approach on a diverse suite of constrained optimization and reinforcement learning problems with challenging nonconvex constraints. Results show that our method effectively enforces constraints at a low computational cost, offering a practical alternative to expensive feasibility correction techniques based on traditional solvers.
LitmusValues: Will AI Tell Lies to Save Sick Children? Litmus-Testing AI Values Prioritization with AIRiskDilemmas
Authors: Yu Ying Chiu; Zhilin Wang; Sharan Maiya; Yejin Choi; Kyle Fish; Sydney Levine; Evan J Hubinger
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Detecting AI risks becomes more challenging as stronger models emerge and find novel methods such as Alignment Faking to circumvent these detection attempts. Inspired by how risky behaviors in humans (i.e., illegal activities that may hurt others) are sometimes guided by strongly-held values, we believe that identifying values within AI models can be an early warning system for AI's risky behaviors. We create LitmusValues, an evaluation pipeline to reveal AI models' priorities on a range of AI value classes. Then, we collect AIRiskDilemmas, a diverse collection of dilemmas that pit values against one another in scenarios relevant to AI safety risks such as Power Seeking. By measuring an AI model's value prioritization using its aggregate choices, we obtain a self-consistent set of predicted value priorities that uncover potential risks. We show that values in LitmusValues (including seemingly innocuous ones like Care) can predict for both seen risky behaviors in AIRiskDilemmas and unseen risky behaviors in HarmBench.
Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Models
Authors: Lujain Ibrahim; Canfer Akbulut; Rasmi Elasmar; Charvi Rastogi; Minsuk Kahng; Meredith Ringel Morris; Kevin R. McKee; Verena Rieser; Murray Shanahan; Laura Weidinger
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The tendency of users to anthropomorphise large language models (LLMs) is of growing societal interest. Here, we present AnthroBench: a novel empirical method and tool for evaluating anthropomorphic LLM behaviours in realistic settings. Our work introduces three key advances; first, we develop a multi-turn evaluation of 14 distinct anthropomorphic behaviours, moving beyond single-turn assessment. Second, we present a scalable, automated approach by leveraging simulations of user interactions, enabling efficient and reproducible assessment. Third, we conduct an interactive, large-scale human subject study (N=1101) to empirically validate that the model behaviours we measure predict real users’ anthropomorphic perceptions. We find that all evaluated LLMs exhibit similar behaviours, primarily characterised by relationship-building (e.g., empathy and validation) and first-person pronoun use. Crucially, we observe that the majority of these anthropomorphic behaviors only first occur after multiple turns, underscoring the necessity of multi-turn evaluations for understanding complex social phenomena in human-AI interaction. Our work provides a robust empirical foundation for investigating how design choices influence anthropomorphic model behaviours and for progressing the ethical debate on the desirability of these behaviours.
Adaptive gradient descent on Riemannian manifolds and its applications to Gaussian variational inference
Authors: Jiyoung Park; Jaewook J. Suh; Bofan Wang; Anirban Bhattacharya; Shiqian Ma
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose RAdaGD, a novel family of adaptive gradient descent methods on general Riemannian manifolds. RAdaGD adapts the step size parameter without line search, and includes instances that achieve a non-ergodic convergence guarantee, $f(x_k) - f(x_\star) \le \mathcal{O}(1/k)$, under local geodesic smoothness and generalized geodesic convexity. A core application of RAdaGD is Gaussian Variational Inference, where our method provides the first convergence guarantee in the absence of $L$-smoothness of the target log-density, under additional technical assumptions. We also investigate the empirical performance of RAdaGD in numerical simulations and demonstrate its competitiveness in comparison to existing algorithms.
KL-Regularized Reinforcement Learning for Generative Modelling is Designed to Mode Collapse
Authors: Anthony GX-Chen; Jatin Prakash; Jeff Guo; Rob Fergus; Rajesh Ranganath
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Classical intuitions cast minimizing reverse KL as "mode seeking" and forward KL as "mass covering". In KL-regularized reinforcement learning, however, the regularizer determines _both_ the target distribution's shape _and_ the divergence being implicitly minimized, making its role more nuanced than simply inducing generic mode-seeking or mass-covering behaviour. Specifically, the target distribution is defined jointly by the reward function, the reference model, the type of regularizer, and the regularization strength. We show that under common settings—such as low regularization strength and equal verifiable rewards—both forward and reverse KL regularization tend to specify target distributions whose mass concentrates on a single high-reward region. Thus, the objective itself _by construction_ induces diversity collapse, regardless of the policy optimization algorithm used.

Building on this perspective, we introduce a simple and scalable modification that rescales rewards to induce target distributions assigning substantial probability across _all_ high-reward regions. This yields a principled objective that maintains high solution quality while achieving broad reward-mode coverage. Empirically, this approach improves post-training diversity and performance for Large Language Models and Chemical Language Models, and is effective with either forward or reverse KL regularization, while using either naively fails.
PropensityBench: Evaluating Latent Safety Risks in Large Language Models via an Agentic Approach
Authors: Udari Madhushani Sehwag; Shayan Shabihi; Alex McAvoy; Vikash Sehwag; Yuancheng Xu; Dalton towers; Furong Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in Large Language Models (LLMs) have sparked concerns over their potential to acquire and misuse dangerous capabilities, posing frontier risks to society. Current safety evaluations primarily test for what a model *can* do—its capabilities—without assessing what it *would* do if endowed with high-risk capabilities. This leaves a critical blind spot: models may strategically conceal capabilities or rapidly acquire them, while harboring latent inclinations toward misuse.  We argue that **propensity**—the likelihood of a model to pursue harmful actions if empowered—is a critical, yet underexplored, axis of safety evaluation. We present 
**PropensityBench**, a novel benchmark framework that assesses the proclivity of models to engage in risky behaviors when equipped with simulated dangerous capabilities using proxy tools. Our framework includes 5,874 scenarios with 6,648 tools spanning four high-risk domains: cybersecurity, self-proliferation, biosecurity, and chemical security. We simulate access to powerful capabilities via a controlled agentic environment and evaluate the models' choices under varying operational pressures that reflect real-world constraints or incentives models may encounter, such as resource scarcity or gaining more autonomy. Across open-source and proprietary frontier models, we uncover alarming signs of propensity: models frequently choose high-risk tools when under pressure, despite lacking the capability to execute such actions unaided. These findings call for a shift from static capability audits toward dynamic propensity assessments as a prerequisite for deploying frontier AI systems safely. Our code and data is available at https://github.com/scaleapi/propensity-evaluation.