GenAI4ELab/papercli-papers huggingface dataset:
Data-to-Energy Stochastic Dynamics
Authors: Kirill Tamogashev; Nikolay Malkin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The Schrödinger bridge problem is concerned with finding a
  stochastic dynamical system bridging two marginal distributions
  that minimises a certain transportation cost.
  This problem, which represents a generalisation of optimal
  transport to the stochastic case, has received attention due to its
  connections to diffusion models and flow matching, as well as its
  applications in the natural sciences.
  However, all existing algorithms enable the inference of such
  dynamics only for cases where samples from both distributions are available.
  In this paper, we propose the first general method for modelling
  Schrödinger bridges when one (or both) distributions are given by
  their unnormalised densities, with no access to data samples.
  Our algorithm relies on a generalisation of the iterative
  proportional fitting (IPF) procedure to the data-free case,
  inspired by recent developments in off-policy reinforcement
  learning for training of diffusion samplers.
  We demonstrate the efficacy of the proposed data-to-energy
  IPF on synthetic problems, finding that it can successfully learn
  transports between multimodal distributions.
  As a secondary consequence of our reinforcement learning
  formulation, which assumes a fixed time discretisation scheme for
  the dynamics, we find that existing data-to-data Schrödinger bridge
  algorithms can be substantially improved by learning the diffusion
  coefficient of the dynamics.
  Finally, we apply the newly developed algorithm to the problem of
  sampling posterior distributions in latent spaces of generative
  models, thus creating a data-free image-to-image translation method.
Splat and Distill: Augmenting Teachers with Feed-Forward 3D Reconstruction For 3D-Aware Distillation
Authors: David Shavin; Sagie Benaim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision Foundation Models (VFMs) have achieved remarkable success when applied to various downstream 2D tasks. Despite their effectiveness, they often exhibit a critical lack of 3D awareness. To this end, we introduce Splat and Distill, a framework that instills robust 3D awareness into 2D VFMs by augmenting the teacher model with a fast, feed-forward 3D reconstruction pipeline. Given 2D features produced by a teacher model, our method first lifts these features into an explicit 3D Gaussian representation, in a feedforward manner. These 3D features are then "splatted" onto novel viewpoints, producing a set of novel 2D feature maps used to supervise the student model, "distilling" geometrically grounded knowledge. By replacing slow per-scene optimization of prior work with our feed-forward lifting approach, our framework avoids feature-averaging artifacts, creating a dynamic learning process where the teacher’s consistency improves alongside that of the student. We conduct a comprehensive evaluation on a suite of downstream tasks, including monocular depth estimation, surface normal estimation, multi-view correspondence, and semantic segmentation. Our method significantly outperforms prior works, not only achieving substantial gains in 3D awareness but also enhancing the underlying semantic richness of 2D features. Our project page and code are available [here](https://davidshavin4.github.io/Splat-and-Distill/)
ReCAPA: Hierarchical Predictive Correction to Mitigate Cascading Failures
Authors: Xiyin Zeng; Yuyu Sun; Haoyang Li; Shouqiang Liu; Hao Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision–Language–Action (VLA) systems follow instructions to execute multi-step tasks in multimodal environments. Recent VLA approaches typically rely on post-hoc correction mechanisms or operate under fixed task decompositions and alignment schemes. However, once an intermediate step is mis-specified, local errors propagate through subsequent steps and eventually accumulate into cascading failures. To mitigate this compounding effect, we propose Predictive Alignment and Planning Architecture (ReCAPA), a framework that uses prediction and contrast to adjust deviations across three levels: actions, subgoals, and trajectories. Semantic alignment is enforced at all levels using a Sinkhorn-based module and a Score-field module. The predictive correction and alignment, jointly updates the action-generator in the training phase, enabling it to adjust fine-grained steps to remain aligned with the overall intent. We further introduce two new metrics to quantify error propagation and recovery processes in tasks, capturing how mistakes spread and fade over long-horizon execution.  Experiments show that ReCAPA achieves competitive results on embodied agent benchmarks such as VisualAgentBench, MineDojo, and AI2-THOR, outperforming strong proprietary and open-source Large Language Model (LLM) baselines.
Decoding Open-Ended Information Seeking Goals from Eye Movements in Reading
Authors: Cfir Avraham Hadar; Omer Shubi; Yoav Meiri; Amit Heshes; Yevgeni Berzak
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: When reading, we often have specific information that interests us in a text. For example, you might be reading this paper because you are curious about LLMs for eye movements in reading, the experimental design, or perhaps you wonder ``This sounds like science fiction. Does it actually work?''. More broadly, in daily life, people approach texts with any number of text-specific goals that guide their reading behavior. In this work, we ask whether open-ended reading goals can be automatically decoded solely from eye movements in reading. To address this question, we introduce goal decoding tasks and evaluation frameworks using large-scale eye tracking for reading data in English with hundreds of text-specific information seeking tasks and auxiliary annotations of task-critical information. We develop and compare several discriminative and generative multimodal text and eye movements LLMs for these tasks. Our experiments show considerable success on selecting the correct goal among several options, and even progress towards free-form textual reconstruction of the precise goal formulation. We further tie model performance to cognitively interpretable aspects of human gaze behavior. These results open the door for further scientific investigation of goal driven reading, as well as the development of educational and assistive technologies that will rely on real-time decoding of reader goals from their eye movements.
Spotlight on Token Perception for Multimodal Reinforcement Learning
Authors: Siyuan Huang; Xiaoye Qu; Yafu Li; Yun Luo; Zefeng He; Daizong Liu; Yu Cheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capabilities of Large Vision-Language Models (LVLMs), most existing methods in multimodal reasoning neglect the critical role of visual perception within the RLVR optimization process. In this paper, we undertake a pioneering exploration of multimodal RLVR through the novel perspective of token perception, which measures the visual dependency of each generated token. With a granular analysis of Chain-of-Thought (CoT) processes, we uncover two key insights: first, token perception in a rollout trajectory is sparsely distributed,  where only a small fraction of tokens have high visual dependency for visually-grounded reasoning; second, different trajectories exhibit significant divergence in their overall visual dependency. Based on these observations, we propose **V**isually-**P**erceptive **P**olicy **O**ptimization (**VPPO**), a novel policy gradient algorithm that explicitly leverages token perception to refine the learning signal. Specifically, VPPO achieves this through a dual mechanism: it reweights a trajectory's advantage by its overall visual dependency, and focuses policy updates exclusively on perceptually pivotal tokens. On a comprehensive suite of eight perception and reasoning benchmarks, VPPO demonstrates substantial gains over leading open-source RL-tuned models, with its effectiveness consistently validated across 7B and 32B model scales. Our findings not only establish a new token-level perceptual perspective for analyzing multimodal RLVR but also present a novel and effective optimization strategy to significantly enhance the multimodal reasoning capabilities of LVLMs.
Multi-Marginal Flow Matching with Adversarially Learnt Interpolants
Authors: Oskar Kviman; Kirill Tamogashev; Nicola Branchini; Víctor Elvira; Jens Lagergren; Nikolay Malkin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Learning the dynamics of a process given sampled observations at
  several time points is an important but difficult task in many
  scientific applications.
  When no ground-truth trajectories are available, but one has only
  snapshots of data taken at discrete time steps, the problem of
  modelling the dynamics, and thus inferring the underlying
  trajectories, can be solved by multi-marginal generalisations of
  flow matching algorithms.
  This paper introduces a novel flow matching method that overcomes
  the limitations of existing multi-marginal trajectory inference algorithms.
  Our proposed method, ALI-CFM, uses a GAN-inspired adversarial loss
  to fit neurally parameterised interpolant curves between source and
  target points such that the marginal distributions at intermediate
  time points are close to the observed distributions.
  The resulting interpolants are smooth trajectories that, as we
  show, are unique under mild assumptions.
  These interpolants are subsequently marginalised by a flow matching
  algorithm, yielding a trained vector field for the underlying dynamics.
  ALI-CFM outperforms existing baselines on spatial transcriptomics and
  cell tracking problems, while performing on par with them on
  single-cell trajectory prediction, which showcases its versatility and scalability.
Architecture-Agnostic Test-Time Adaptation via Backprop-Free Embedding Alignment
Authors: MA Xiao; Young D. Kwon; Pan Zhou; Dong Ma
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Test-Time Adaptation (TTA) adapts a deployed model during online inference to mitigate the impact of domain shift. While achieving strong accuracy, most existing methods rely on backpropagation, which is memory and computation intensive, making them unsuitable for resource-constrained devices. Recent attempts to reduce this overhead often suffer from high latency or are tied to specific architectures such as ViT-only or CNN-only.
In this work, we revisit domain shift from an embedding perspective. Our analysis reveals that domain shift induces three distinct structural changes in the embedding space: translation (mean shift), scaling (variance shift), and rotation (covariance shift). Based on this insight, we propose Progressive Embedding Alignment (PEA), a backpropagation-free and architecture-agnostic TTA approach. By applying a novel covariance alignment procedure at each intermediate layer, PEA efficiently corrects the embedding distortions with only two forward passes. Extensive experiments demonstrate that PEA achieves state-of-the-art performance in both accuracy and efficiency, while also proving versatile across different architectures including ViTs and CNNs.
Interpretable 3D Neural Object Volumes for Robust Conceptual Reasoning
Authors: Nhi Pham; Artur Jesslen; Bernt Schiele; Adam Kortylewski; Jonas Fischer
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With the rise of deep neural networks, especially in safety-critical applications, robustness and interpretability are crucial to ensure their trustworthiness. Recent advances in 3D-aware classifiers that map image features to volumetric representation of objects, rather than relying solely on 2D appearance, have greatly improved robustness on out-of-distribution (OOD) data. Such classifiers have not yet been studied from the perspective of interpretability. Meanwhile, current concept-based XAI methods often neglect OOD robustness. We aim to address both aspects with CAVE - Concept Aware Volumes for Explanations - a new direction that unifies interpretability and robustness in image classification. We design CAVE as a robust and inherently interpretable classifier that learns sparse concepts from 3D object representation. We further propose 3D Consistency (3D-C), a metric to measure spatial consistency of concepts. Unlike existing metrics that rely on human-annotated parts on images, 3D-C leverages ground-truth object meshes as a common surface to project and compare explanations across concept-based methods. CAVE achieves competitive classification performance while discovering consistent and meaningful concepts across images in various OOD settings.
Smooth Reading: Bridging the Gap of Recurrent LLM to Self-Attention LLM on Long-Context Understanding
Authors: Kai Liu; Zhan Su; Peijie Dong; Fengran Mo; Jianfei Gao; Shaoting Zhang; Kai Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recurrent large language models (Recurrent LLMs) offer linear computational complexity as efficient alternatives to quadratic self-attention-based LLMs (Self-Attention LLMs). However, Recurrent LLMs underperform on long-context tasks due to limited fixed-size memory. Previous research focused on architectural innovations to enhance memory capacity, but failed to match Self-Attention LLM performance. We argue this limitation stems from processing entire contexts at once being ill-suited for Recurrent LLMs. We propose Smooth Reading, a co-design of recurrent architecture and inference method. It introduces a end-to-end multi-round inference method that processes context incrementally and iteratively summarizes information, reducing memory demands.
Methodologically, we reveal architecture-inference interactions play an important role for performance, efficiency and scalability, shedding light on future Recurrent LLM design.
Besides, our method substantially bridges the performance gap between Recurrent and Self-Attention LLMs on long-context tasks while preserving efficiency advantages.
Smooth Reading boosts SWA-3B-4k from 5.68% lower to 3.61% higher performance than Self-Attention LLMs on LongBench, while maintaining 2.5× faster training and 2× faster inference at 64k context.
BioBO: Biology-informed Bayesian Optimization for Perturbation Design
Authors: Yanke Li; Tianyu Cui; Tommaso Mansi; Mangal Prakash; Rui Liao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Efficient design of genomic perturbation experiments is crucial for accelerating drug discovery and therapeutic target identification, yet exhaustive perturbation of the human genome remains infeasible due to the vast search space of potential genetic interactions and experimental constraints. Bayesian optimization (BO) has emerged as a powerful framework for selecting informative interventions, but existing approaches often fail to exploit domain-specific biological prior knowledge. We propose Biology-Informed Bayesian Optimization (BioBO), a method that integrates Bayesian optimization with multimodal gene embeddings and enrichment analysis, a widely used tool for gene prioritization in biology, to enhance surrogate modeling and acquisition strategies. 
BioBO combines biologically grounded priors with acquisition functions in a principled framework, which biases the search toward promising genes while maintaining the ability to explore uncertain regions. 
Through experiments on established public benchmarks and datasets, we demonstrate that BioBO improves labeling efficiency by 25-40\%, and consistently outperforms conventional BO by identifying top-performing perturbations more effectively. Moreover, by incorporating enrichment analysis, BioBO yields pathway-level explanations for selected perturbations, offering mechanistic interpretability that links designs to biologically coherent regulatory circuits.
Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics
Authors: Tai Hoang; Alessandro Trenta; Alessio Gravina; Niklas Freymuth; Philipp Becker; Davide Bacciu; Gerhard Neumann
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Learning to simulate complex physical systems from data has emerged as a promising way to overcome the limitations of traditional numerical solvers, which often require prohibitive computational costs for high-fidelity solutions. Recent Graph Neural Simulators (GNSs) accelerate simulations by learning dynamics on graph-structured data, yet often struggle to capture long-range interactions and suffer from error accumulation under autoregressive rollouts. To address these challenges, we propose Information-preserving Graph Neural Simulators (IGNS), a graph-based neural simulator built on the principles of Hamiltonian dynamics. This structure guarantees preservation of information across the graph, while extending to port-Hamiltonian systems allows the model to capture a broader class of dynamics, including non-conservative effects. IGNS further incorporates a warmup phase to initialize global context, geometric encoding to handle irregular meshes, and a multi-step training objective that facilitates PDE matching, where the trajectory produced by integrating the port-Hamiltonian core aligns with the ground-truth trajectory, thereby reducing rollout error. To evaluate these properties systematically, we introduce new benchmarks that target long-range dependencies and challenging external forcing scenarios. Across all tasks, IGNS consistently outperforms state-of-the-art GNSs, achieving higher accuracy and stability under challenging and complex dynamical systems. Our project page: https://thobotics.github.io/neural_pde_matching.
Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMs
Authors: Zhihe Yang; Xufang Luo; Zilong Wang; Dongqi Han; Zhiyuan He; Dongsheng Li; Yunjian Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning (RL) has become a cornerstone for enhancing the reasoning capabilities of large language models (LLMs), with recent innovations such as Group Relative Policy Optimization (GRPO) demonstrating exceptional effectiveness. In this study, we identify a critical yet underexplored issue in RL training: low-probability tokens disproportionately influence model updates due to their large gradient magnitudes. This dominance hinders the effective learning of high-probability tokens, whose gradients are essential for LLMs' performance but are substantially suppressed. To mitigate this interference, we propose two novel methods: Advantage Reweighting and Low-Probability Token Isolation (Lopti), both of which effectively attenuate gradients from low-probability tokens while emphasizing parameter updates driven by high-probability tokens. Our approaches promote balanced updates across tokens with varying probabilities, thereby enhancing the efficiency of RL training. Experimental results demonstrate that they substantially improve the performance of GRPO-trained LLMs, achieving up to a 46.2% improvement in K&K Logic Puzzle reasoning tasks. Our implementation is available at https://github.com/zhyang2226/AR-Lopti.
Attention, Please! Revisiting Attentive Probing Through the Lens of Efficiency
Authors: Bill Psomas; Dionysis Christopoulos; Eirini Baltzi; Ioannis Kakogeorgiou; Tilemachos Aravanis; Nikos Komodakis; Konstantinos Karantzalos; Yannis Avrithis; Giorgos Tolias
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As fine-tuning becomes impractical at scale, probing is emerging as the preferred evaluation protocol. However, standard linear probing can understate the capability of models whose pre-training optimizes local representations rather than an explicit global representation. This motivates attentive probing, an alternative that uses attention to selectively aggregate patch-level features. Despite growing adoption, attentive probing is still underexplored: existing approaches are often over-parameterized and computationally inefficient. In this work, we revisit attentive probing through the lens of the accuracy vs. parameter-efficiency trade-off. We present the first comprehensive study of existing methods, analyzing their design choices and benchmarking their performance. Building on these insights, we propose efficient probing (EP), a lightweight yet effective multi-query cross-attention mechanism that eliminates redundant projections and reduces the number of trainable parameters. Across multiple benchmarks and pre-training paradigms, EP consistently outperforms linear probing and previous attentive probing methods, and remains effective when combined with parameter-efficient fine-tuning. Beyond evaluation, our analysis uncovers emerging properties of EP, including complementary attention maps, which open new directions for leveraging probing beyond protocol design. Project page: https://vrg.fel.cvut.cz/ep/.
Beyond Magic Words: Sharpness-Aware Prompt Evolving for Robust Large Language Models with TARE
Authors: Guancheng Wan; Lucheng Fu; Haoxin Liu; Yiqiao Jin; Hui Yi Leong; Eric Hanchen Jiang; Hejia Geng; Jinhe Bi; Yunpu Ma; Xiangru Tang; B. Aditya Prakash; Yizhou Sun; Wei Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The performance of Large Language Models (LLMs) hinges on carefully engineered prompts. However, prevailing prompt optimization methods, ranging from heuristic edits and reinforcement learning to evolutionary search, primarily target point-wise accuracy. They seldom enforce paraphrase invariance or searching stability, and therefore cannot remedy this brittleness in practice. Automated prompt search remains brittle: small, semantically preserving paraphrases often cause large performance swings. We identify this brittleness as the **textual sharpness** of the **prompt landscape**. In this work, we provide the first formal treatment of textual sharpness in the discrete, semantic space of prompts, together with an operational robustness criterion over a semantic neighborhood; the design is black-box or API-only, requiring no gradients to update the model's parameters. Then we introduce **TARE** (Textual Sharpness-Aware Evolving), a derivative-free framework that alternates between an inner, sampling-based adversarial search that stresses a prompt with hard paraphrases and an outer, robust selection that prefers candidates whose neighborhoods remain strong. We further propose **ATARE**, which learns anisotropic weights to shape the semantic neighborhood and adapts its radius over time to balance exploration and fidelity. Diverse tasks evaluate our methods, whose design for minimizing textual sharpness gap leads to prompts that preserve accuracy under paraphrasing, outperforming accuracy-only prompt search while remaining computationally practical. The code is available for anonymous access at https://anonymous.4open.science/r/ATARE_TARE/.