GenAI4ELab/papercli-papers huggingface dataset:
PoSh: Using Scene Graphs to Guide LLMs-as-a-Judge for Detailed Image Descriptions
Authors: Amith Ananthram; Elias Stengel-Eskin; Lorena A. Bradford; Julia Demarest; Adam Purvis; Keith Krut; Robert Stein; Rina Elster Pantalony; Mohit Bansal; Kathleen McKeown
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While vision-language models (VLMs) have advanced into detailed image description, evaluation remains a challenge. Standard metrics (e.g. CIDEr, SPICE) were designed for short texts and tuned to recognize errors that are now uncommon, such as object misidentification. In contrast, long texts require sensitivity to attribute and relation attachments and scores that localize errors to particular text spans. In this work, we introduce PoSh, a metric for detailed image description that uses scene graphs as structured rubrics to guide LLMs-as-a-Judge, producing aggregate scores grounded in fine-grained errors (e.g. mistakes in compositional understanding). PoSh is replicable, interpretable and a better proxy for human raters than existing metrics (including GPT4o-as-a-Judge). To validate PoSh, we introduce a new dataset, DOCENT. This novel benchmark contains artwork, paired with expert-written references, and model-generated descriptions, augmented with granular and coarse judgments of their quality from art history students. Thus, DOCENT enables evaluating both detailed image description metrics and detailed image description itself in a challenging new domain. We show that PoSh achieves stronger correlations (+0.05 Spearman ρ) with the human judgments in DOCENT than the best open-weight alternatives, is robust to image type (using CapArena, an existing dataset of web imagery) and is a capable reward function, outperforming standard supervised fine-tuning. Then, using PoSh, we characterize the performance of open and closed models in describing the paintings, sketches and statues in DOCENT and find that foundation models struggle to achieve full, error-free coverage of images with rich scene dynamics, establishing a demanding new task to gauge VLM progress. Through both PoSh and DOCENT, we hope to enable advances in important areas such as assistive text generation.
MOBODY: Model-Based Off-Dynamics Offline Reinforcement Learning
Authors: Yihong Guo; Yu Yang; Pan Xu; Anqi Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study off-dynamics offline reinforcement learning, where the goal is to learn a policy from offline source and limited target datasets with mismatched dynamics. Existing methods either penalize the reward or discard source transitions occurring in parts of the transition space with high dynamics shift. As a result, they optimize the policy using data from low-shift regions, limiting exploration of high-reward states in the target domain that do not fall within these regions. Consequently, such methods often fail when the dynamics shift is significant or the optimal trajectories lie outside the low-shift regions. To overcome this limitation, we propose MOBODY, a Model-Based Off-Dynamics Offline RL algorithm that optimizes a policy using learned target dynamics transitions to explore the target domain, rather than only being trained with the low dynamics-shift transitions.  For the dynamics learning, built on the observation that achieving the same next state requires taking different actions in different domains, MOBODY employs separate action encoders for each domain to encode different actions to the shared latent space while sharing a unified representation of states and a common transition function. 
We further introduce a target Q-weighted behavior cloning loss in policy optimization to avoid out-of-distribution actions, which push the policy toward actions with high target-domain Q-values, rather than high source domain Q-values or uniformly imitating all actions in the offline dataset.  We evaluate MOBODY on a wide range of MuJoCo and Adroit benchmarks, demonstrating that it outperforms state-of-the-art off-dynamics RL baselines as well as policy learning methods based on different dynamics learning baselines, with especially pronounced improvements in challenging scenarios where existing methods struggle.
The Diffusion Duality, Chapter II: $\Psi$-Samplers
Authors: Justin Deschenaux; Caglar Gulcehre; Subham Sekhar Sahoo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Uniform-state discrete diffusion models excel at few-step generation and guidance due to their ability to self-correct, making them preferred over autoregressive or Masked diffusion models in these settings. However, their sampling quality plateaus with ancestral samplers as the number of steps increases. We introduce a family of Predictor-Corrector (PC) samplers for discrete diffusion that generalize prior methods and apply to arbitrary noise processes. When paired with uniform-state diffusion, our samplers outperform ancestral sampling on both language and image modeling, achieving lower generative perplexity at matched unigram entropy on OpenWebText and better FID/IS scores on CIFAR10. Crucially, unlike conventional samplers, our PC methods continue to improve with more sampling steps. **Taken together, these findings call into question the assumption that Masked diffusion is the inevitable future of diffusion-based language modeling.** Beyond sampling, we develop a memory-efficient curriculum for the Gaussian relaxation training phase, reducing training time by 25% and memory by 33% compared to Duo while maintaining comparable perplexity on OpenWebText and LM1B and strong downstream performance. We release code, checkpoints, and a video-tutorial on [https://s-sahoo.github.io/duo-ch2/](https://s-sahoo.github.io/duo-ch2/)
What Exactly Does Guidance Do in Masked Discrete Diffusion Models
Authors: Ye He; Kevin Rojas; Molei Tao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Masked discrete diffusion models have been gaining popularity recently, and classifier-free guidance, just like its continuous counterpart, has been proposed to enable efficacious conditional generation by discrete diffusion. To quantify the precise effect of discrete guidance, this article considers masked discrete diffusion with arbitrary data distribution in low dimension, so that 
   the distribution that guided masked discrete diffusion samples from, as well as the sampling dynamics, can be analytically and exactly quantified and interpreted. When the full data distribution is a mixture over classes and the goal is to sample from a specific class, guidance amplifies class-specific regions while suppresses regions shared with other classes. This effect depends on the guidance strength $w$ and induces distinct covariance structures in the sampled distribution. Notably, we observe quantitatively different behaviors in $1$D and $2$D.  We also show that for large $w$, the decay rate of the total variation ($\text{TV}$) along the reverse dynamics is double-exponential in $w$ for both $1$D and $2$D. These findings highlight the role of guidance, not just in shaping the output distribution, but also in controlling the dynamics of the sampling trajectory. Our theoretical analysis is supported by experiments that illustrate the geometric effects of guidance and its impact on convergence.
NeMo-map: Neural Implicit Flow Fields for Spatio-Temporal Motion Mapping
Authors: Yufei Zhu; Shih-Min Yang; Andrey Rudenko; Tomasz Piotr Kucner; Achim J. Lilienthal; Martin Magnusson
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Safe and efficient robot operation in complex human environments can benefit from good models of site-specific motion patterns. Maps of Dynamics (MoDs) provide such models by encoding statistical motion patterns in a map, but existing representations use discrete spatial sampling and typically require costly offline construction. We propose a continuous spatio-temporal MoD representation based on implicit neural functions that directly map coordinates to the parameters of a Semi-Wrapped Gaussian Mixture Model. This removes the need for discretization and imputation for unevenly sampled regions, enabling smooth generalization across both space and time. Evaluated on two public datasets with real-world people tracking data, our method achieves better accuracy of motion representation and smoother velocity distributions in sparse regions while still being computationally efficient, compared to available baselines. The proposed approach demonstrates a powerful and efficient way of modeling complex human motion patterns and high performance in the trajectory prediction downstream task. The code is publicly available at https://github.com/test-bai-cpu/nemo-map.
Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs
Authors: Vishal Pramanik; Maisha Maliha; Nathaniel D. Bastian; Sumit Kumar Jha
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Attribution methods seek to explain language model predictions by quantifying the contribution of input tokens to generated outputs. However, most existing techniques are designed for encoder-based architectures and rely on linear approximations that fail to capture the causal and semantic complexities of autoregressive generation in decoder-only models. To address these limitations, we propose **Hessian-Enhanced Token Attribution (HETA)**, a novel attribution framework tailored for decoder-only language models. HETA combines three complementary components: a semantic transition vector that captures token-to-token influence across layers, Hessian-based sensitivity scores that model second-order effects, and KL divergence to measure information loss when tokens are masked. This unified design produces context-aware, causally faithful, and semantically grounded attributions. Additionally, we introduce a **curated benchmark dataset** for systematically evaluating attribution quality in generative settings. Empirical evaluations across multiple models and datasets demonstrate that HETA consistently outperforms existing methods in attribution faithfulness and alignment with human annotations, establishing a new standard for interpretability in autoregressive language models.
The Tutor-Pupil Augmentation: Enhancing Learning and Interpretability via Input Corrections
Authors: Darya Biparva; Maarten Schoukens; Donatello Materassi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: State-of-the-art machine learning models often incorporate prior knowledge or structural information about the task or data distribution. In some tasks, such knowledge may arise from first principles or emerge as simplified, learned functions that distill essential aspects of the data distribution. Model augmentation has emerged as a strategy to leverage this structured knowledge by coupling it with an auxiliary model to improve predictive performance, while preserving the interpretability offered by the simpler component. In this work, we present a new augmentation framework called the Tutor-Pupil scheme, which is designed to enhance both performance and interpretability. The Pupil is a fixed model, structurally designed for the core task, while the Tutor is a more flexible model trained to apply minimal input-level corrections to improve the Pupil’s performance on the modified input. This strict separation of roles enables the Tutor not only to compensate for the Pupil’s limitations but also to act as a diagnostic instrument. By examining the Tutor’s targeted interventions, we can identify failure modes, detect regions where the Pupil struggles to generalize, and uncover residual patterns or higher-order structures in the data not captured by the original model.
DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Authors: Kevin Han; Bowen Deng; Amir Barati Farimani; Gerbrand Ceder
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large-scale atomistic simulations are essential to bridge computational materials and chemistry to realistic materials and drug discovery applications. In the past few years, rapid developments of machine learning interatomic potentials (MLIPs) have offered a solution to scale up quantum mechanical calculations. Parallelizing these interatomic potentials across multiple devices poses a challenging, but promising approach to further extending simulation scales to real-world applications. In this work, we present \textbf{DistMLIP}, an efficient distributed inference platform for MLIPs based on zero-redundancy, graph-level parallelization. In contrast to conventional spatial partitioning parallelization, DistMLIP enables efficient MLIP parallelization through graph partitioning, allowing multi-device inference on flexible MLIP model architectures like multi-layer graph neural networks. DistMLIP presents an easy-to-use, flexible, plug-in interface that enables distributed inference of pre-existing MLIPs. We demonstrate DistMLIP on four widely used and state-of-the-art MLIPs: CHGNet, MACE, TensorNet, and eSEN. We show that DistMLIP can simulate atomic systems 3.4x larger and up to 8x faster compared to previous multi-GPU methods. We show that existing foundation potentials can perform near-million-atom calculations at the scale of a few seconds on 8 GPUs with DistMLIP.
Scaling Multi-Task Bayesian Optimization with Large Language Models
Authors: Yimeng Zeng; Natalie Maus; Haydn Thomas Jones; Jeffrey Tao; Fangping Wan; Marcelo Der Torossian Torres; Cesar de la Fuente-Nunez; Ryan Marcus; Osbert Bastani; Jacob R. Gardner
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In multi-task Bayesian optimization, the goal is to leverage experience from optimizing existing tasks to improve the efficiency of optimizing new ones. While approaches using multi-task Gaussian processes or deep kernel transfer exist, the performance improvement is marginal when scaling beyond a moderate number of tasks. We introduce **BOLT**, an initialization-only transfer strategy that distills prior BO runs into an LLM which proposes candidates for new tasks, while the surrogate at test time remains single-task. The LLM is periodically fine-tuned on top solutions from completed runs, creating a closed loop where better BO outputs yield better initializations over time. This decoupled design scales to roughly 1500 tasks without the saturation observed for shared-surrogate MTBO and adds only a small, amortized overhead relative to the BO inner loops. We evaluate on two domains: database query optimization and antimicrobial peptide design. We demonstrate that LLM-generated initializations steadily improve and accelerate BO, and with sufficient fine-tuning, a few LLM samples often match or surpass full ''from-scratch'' BO with far fewer oracle calls.
Musculoskeletal simulation of limb movement biomechanics in Drosophila melanogaster
Authors: Pembe Gizem Ozdil; Chuanfang Ning; Jasper S. Phelps; Sibo Wang-Chen; Guy Elisha; Auke Ijspeert; Pavan Ramdya
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Computational models are critical for advancing our understanding of how neural, biomechanical, and physical systems interact to orchestrate animal behavior. Despite the availability of near-complete reconstructions of the Drosophila melanogaster central nervous system, musculature, and exoskeleton, anatomically and physically grounded models of fly leg muscles are still missing. Such models provide an indispensable bridge between motor neuron activity and joint movements.
Here, we introduce the first 3D, data-driven musculoskeletal model of Drosophila legs, implemented in both OpenSim and MuJoCo simulation environments. Our model incorporates a Hill-type muscle representation based on high-resolution X-ray scans from multiple fixed specimens. We present a pipeline for constructing muscle models from morphological imaging data and for optimizing unknown muscle parameters specific to the fly.
We then combine our musculoskeletal models with detailed 3D pose estimation data from behaving flies to achieve muscle-actuated behavioral replay in OpenSim. Simulations of muscle activity across diverse walking and grooming behaviors predict coordinated muscle synergies that can be tested experimentally. Furthermore, by training imitation learning policies in MuJoCo, we examine the effect of different passive joint properties on learning speed and find that damping and stiffness facilitate learning.
Overall, our model enables the investigation of motor control in an experimentally tractable model organism, providing insight into how biomechanics contributes to the generation of complex limb movements. Moreover, it can be used to control embodied artificial agents to generate naturalistic and compliant locomotion in simulated environments.
Learnable Fractional Superlets with a Spectro-Temporal Emotion Encoder for Speech Emotion Recognition
Authors: Alaa Nfissi; Wassim Bouachir; Nizar Bouguila; Brian L Mishara
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Speech emotion recognition (SER) hinges on front-ends that expose informative time-frequency (TF) structure from raw speech. Classical short-time Fourier and wavelet transforms impose fixed resolution trade-offs, while prior ”superlet” variants rely on integer orders and hand-tuned hyperparameters. We revisit TF analysis from first principles and formulate a learnable continuum of superlet transforms. Starting from DC-corrected analytic Morlet wavelets, we define superlets as multiplicative ensembles of wavelet responses and realize learnable fractional orders via softmax-normalized weights over discrete orders, computed as a logdomain geometric mean. We establish admissibility (zero mean) and continuity in order and frequency, and characterize approximate analyticity by bounding negative-frequency leakage as a function of an effective cycle parameter. Building on these results, we introduce the Learnable Fractional Superlet Transform (LFST), a fully differentiable front-end that jointly optimizes (i) a monotone, logspaced frequency grid, (ii) frequency-dependent base cycles, and (iii) learnable fractional-order weights, all trained end-to-end. LFST further includes a learnable asymmetric hard-thresholding (LAHT) module that promotes sparse, denoised TF activations while preserving transients; we provide sufficient conditions for boundedness and stability under mild cycle and grid constraints. To exploit LFST for SER, we design a compact Spectro-Temporal Emotion Encoder (STEE), achieving strong performance with a parameter budget that is orders of magnitude smaller than large self-supervised models, at the cost of additional frontend computation compared to STFT- or LEAF-based baselines. STEE consumes two-channel TF maps, magnitude S and phase-congruency κ, through a compact multi-scale stack with residual temporal and depthwise-frequency blocks, Adaptive FiLM gating, axial (time-axis) self-attention, global attentive pooling, and a lightweight classifier. The full LFST+STEE system is trained in a standard train-validate-test regime using focal loss with optional class rebalancing, and is validated on IEMOCAP, EMO-DB, and the private NSPL-CRISE dataset under standard protocols. By unifying a principled, learnable TF transform with a compact encoder, LFST+STEE replaces ad hoc front-ends with a mathematically grounded alternative that is differentiable, stable, and adaptable to data, enabling systematic ablations over frequency grids, cycle schedules, and fractional orders within a single end-to-end model. The source code of this paper is shared on the GitHub repository: https://github.com/alaaNfissi/LFST-for-SER.
OpenAgentSafety: A Comprehensive Framework For Evaluating Real-World AI Agent Safety
Authors: Sanidhya Vijayvargiya; Aditya Bharat Soni; Xuhui Zhou; Zora Zhiruo Wang; Nouha Dziri; Graham Neubig; Maarten Sap
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in LLM agents capable of solving complex, everyday tasks, ranging from software engineering to customer service, have enabled deployment in real-world scenarios, but their possibilities for unsafe behavior demands rigorous evaluation. While prior benchmarks have attempted to evaluate safety of LLM agents, most fall short by relying on simulated environments, narrow task domains, or unrealistic tool abstractions. We introduce OpenAgentSafety, a comprehensive and modular framework for evaluating agent behavior across eight critical risk categories. Unlike prior work, our framework evaluates agents that interact with real tools, including web browser, code execution environment, file system, bash terminal, and messaging platform; and supports over 350 multi-turn, multi-user tasks spanning both benign and adversarial user intents. OpenAgentSafety is designed for extensibility, allowing researchers to add tools, tasks, web environments, and adversarial strategies with minimal effort. It combines rule-based evaluation with LLM-as-judge assessments to detect both overt and subtle unsafe behaviors. Empirical analysis of seven prominent LLMs in agentic scenarios reveals unsafe behavior in 49% of safety-vulnerable tasks with Claude Sonnet 4, to 73% with o3-mini, highlighting critical risks and the need for stronger safeguards before real-world deployment of LLM agents.
Neural Posterior Estimation with Latent Basis Expansions
Authors: Declan McNamara; Yicun Duan; Jeffrey Regier
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neural posterior estimation (NPE) is a likelihood-free amortized variational inference method that approximates projections of the posterior distribution. To date, NPE variational families have been either simple and interpretable (such as the Gaussian family) or highly flexible but black-box and potentially difficult to optimize (such as normalizing flows). In this work, we parameterize variational families via basis expansions of the latent variables. The log density of our variational distribution is a linear combination of latent basis functions (LBFs), which may be fixed a priori or adapted to the problem class of interest. 
Our training and inference procedures are computationally efficient even for problems with high-dimensional latent spaces, provided only a low-dimensional projection of the posterior is of interest, owing to NPE's automatic marginalization capabilities.
In numerous inference problems, the proposed variational family exhibits better performance than existing variational families used with NPE, including mixtures of Gaussians (mixture density networks) and normalizing flows, as well as outperforming an existing basis expansion method for variational inference.
EigenBench: A Comparative Behavioral Measure of Value Alignment
Authors: Jonathn Chang; Leonhard Piff; Suvadip Sana; Jasmine Xinze Li; Lionel Levine
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Aligning AI with human values is a pressing unsolved problem. To address the lack of quantitative metrics for value alignment, we propose EigenBench: a black-box method for comparatively benchmarking language models’ values. Given an ensemble of models, a constitution describing a value system, and a dataset of scenarios, our method returns a vector of scores quantifying each model’s alignment to the given constitution. To produce these scores, each model judges the outputs of other models across many scenarios, and these judgments are aggregated with EigenTrust (Kamvar et al., 2003), yielding scores that reflect a weighted consensus judgment of the whole ensemble. EigenBench uses no ground truth labels, as it is designed to quantify subjective traits for which reasonable judges may disagree on the correct label. Hence, to validate our method, we collect human judgments on the same ensemble of models and show that EigenBench’s judgments align closely with those of human evaluators. We further demonstrate that EigenBench can recover model rankings on the GPQA benchmark without access to objective labels, supporting its viability as a framework for evaluating subjective values for which no ground truths exist. The code is available at https://github.com/jchang153/EigenBench.