GenAI4ELab/papercli-papers huggingface dataset:
Provable and Practical In-Context Policy Optimization for Self-Improvement
Authors: Tianrun Yu; yuxiao Yang; Zhaoyang Wang; Kaixiang Zhao; Porter Jenkins; Xuchao Zhang; Chetan Bansal; Huaxiu Yao; Weitong Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study test-time scaling, where a model improves its answer through multi-round self-reflection at inference. We introduce In-Context Policy Optimization (ICPO), in which an agent optimizes its response in context using self-assessed or externally observed rewards without modifying its parameters. 
To explain this ICPO process, we theoretically show that with sufficient pretraining under a novel Fisher-weighted logit-matching objective, a single-layer linear self-attention model can provably imitate policy-optimization algorithm for linear bandits. Building on this theory, we propose Minimum-Entropy ICPO (ME-ICPO), a practical algorithm that iteratively uses its response and self-assessed reward to refine its response in-context at inference time. 
By selecting the responses and their rewards with minimum entropy, ME-ICPO ensures the robustness of the self-assessed rewards via majority voting. 
Across standard mathematical reasoning tasks, ME-ICPO attains competitive, top-tier performance while keeping inference costs affordable compared with other inference-time algorithms. Overall, ICPO provides a principled understanding of self-reflection in LLMs and yields practical benefits for test-time scaling for mathematical reasoning.
Generalizable Heuristic Generation Through LLMs with Meta-Optimization
Authors: Yiding Shi; Jianan Zhou; Wen Song; Jieyi Bi; Yaoxin Wu; Zhiguang Cao; Jie Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Heuristic design with large language models (LLMs) has emerged as a promising approach for tackling combinatorial optimization problems (COPs). However, existing approaches often rely on manually predefined evolutionary computation (EC) heuristic-optimizers and single-task training schemes, which may constrain the exploration of diverse heuristic algorithms and hinder the generalization of the resulting heuristics. To address these issues, we propose Meta-Optimization of Heuristics (MoH), a novel framework that operates at the optimizer level, discovering effective heuristic-optimizers through the principle of meta-learning. Specifically, MoH leverages LLMs to iteratively refine a meta-optimizer that autonomously constructs diverse heuristic-optimizers through (self-)invocation, thereby eliminating the reliance on a predefined EC heuristic-optimizer. These constructed heuristic-optimizers subsequently evolve heuristics for downstream tasks, enabling broader heuristic exploration. Moreover, MoH employs a multi-task training scheme to promote its generalization capability. Experiments on classic COPs demonstrate that MoH constructs an effective and interpretable meta-optimizer, achieving state-of-the-art performance across various downstream tasks, particularly in cross-size settings. Our code is available at: \url{https://github.com/yiding-s/MoH}.
Strictly Constrained Generative Modeling via Split Augmented Langevin Sampling
Authors: Matthieu Blanke; Yongquan Qu; Sara Shamekh; Pierre Gentine
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep generative models hold great promise for representing complex physical systems, but their deployment is currently limited by the lack of guarantees on the physical plausibility of the generated outputs. Ensuring that known physical constraints are enforced is therefore critical when applying generative models to scientific and engineering problems. We address this limitation by developing a principled framework for sampling from a target distribution while rigorously satisfying physical constraints. Leveraging the variational formulation of Langevin dynamics, we propose Split Augmented Langevin (CASAL), a novel primal-dual sampling algorithm that enforces constraints progressively through variable splitting, with convergence guarantees. While the method is developed theoretically for Langevin dynamics, we demonstrate its effective applicability to diffusion models. We apply our method to diffusion-based data assimilation on a complex physical system, where enforcing physical constraints substantially improves both forecast accuracy and the preservation of critical conserved quantities. We also demonstrate the potential of CASAL for challenging feasibility problems in optimal control.
HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model
Authors: Jiaming Liu; Hao Chen; Zhuoyang Liu; Pengju An; Renrui Zhang; Chenyang Gu; Xiaoqi Li; Ziyu Guo; Sixiang Chen; Mengzhen Liu; Chengkai Hou; Mengdi Zhao; KC alex Zhou; Pheng-Ann Heng; Shanghang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A central objective of manipulation policy design is to enable robots to comprehend human instructions and predict generalized actions in unstructured environments. Recent autoregressive vision-language-action (VLA) approaches discretize actions into bins to exploit the pretrained reasoning and generation paradigms of vision-language models (VLMs). While these models achieve efficient and scalable training, the discretization undermines the continuity required for precise control. In contrast, diffusion-based VLA methods incorporate an additional diffusion head to predict continuous actions, but they rely solely on feature representations extracted from the VLM, without leveraging the pretrained large language model (LLM) as an expert for iterative action generation. To integrate the complementary strengths of autoregressive and diffusion generation, we introduce HybridVLA, which innovatively leverages a shared LLM backbone to perform iterative action prediction through both paradigms. Specifically, a collaborative training recipe is proposed, incorporating diffusion denoising into the next-token prediction process and mitigating interference between the two generation paradigms. With this recipe, we find these two action prediction methods not only reinforce each other but also exhibit varying strengths across different scenarios. Therefore, we design a collaborative action ensemble mechanism that adaptively fuses both predictions, leading to more robust control. HybridVLA outperforms previous state-of-the-art VLA methods by 17\% and 19\% in mean success rate on simulation and real-world tasks, respectively, while demonstrating generalization to unseen configurations.
From Data Statistics to Feature Geometry: How Correlations Shape Superposition
Authors: Lucas Prieto; Edward Stevinson; Melih Barsbey; Tolga Birdal; Pedro A. M. Mediano
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A central idea in mechanistic interpretability is that neural networks represent more features than they have dimensions, arranging them in superposition to form an over-complete basis. This framing has been influential, motivating dictionary learning approaches such as sparse autoencoders. However, superposition has mostly been studied in idealized settings where features are sparse and uncorrelated. In these settings, superposition is typically understood as introducing interference that must be minimized geometrically and filtered out by non-linearities such as ReLUs, yielding local structures like regular polytopes. We show that this account is incomplete for realistic data by introducing Bag-of-Words Superposition (BOWS), a controlled setting to encode binary bag-of-words representations of internet text in superposition. Using BOWS, we find that when features are correlated, interference can be constructive rather than just noise to be filtered out. This is achieved by arranging features according to their co-activation patterns, making interference between active features constructive, while still using ReLUs to avoid false positives. We show that this kind of arrangement is more prevalent in models trained with weight decay and naturally gives rise to semantic clusters and cyclical structures which have been observed in real language models yet were not explained by the standard picture of superposition. Code for this paper can be found at: https://github.com/LucasPrietoAl/correlations-feature-geometry.
A Federated Generalized Expectation-Maximization Algorithm for Mixture Models with an Unknown Number of Components
Authors: Michael Ibrahim; Nagi Gebraeel; Weijun Xie
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study the problem of federated clustering when the total number of clusters $K$ across clients is unknown, and the clients have heterogeneous but potentially overlapping cluster sets in their local data. To that end, we develop FedGEM: a federated generalized expectation-maximization algorithm for the training of mixture models with an unknown number of components. Our proposed algorithm relies on each of the clients performing EM steps locally, and constructing an uncertainty set around the maximizer associated with each local component. The central server utilizes the uncertainty sets to learn potential cluster overlaps between clients, and infer the global number of clusters via closed-form computations. We perform a thorough theoretical study of our algorithm, presenting probabilistic convergence guarantees under common assumptions. Subsequently, we study the specific setting of isotropic GMMs, providing tractable, low-complexity computations to be performed by each client during each iteration of the algorithm, as well as rigorously verifying assumptions required for algorithm convergence. We perform various numerical experiments, where we empirically demonstrate that our proposed method achieves comparable performance to centralized EM, and that it outperforms various existing federated clustering methods.
TrimR: Verifier-based Training-Free Thinking Trimming for Efficient Test-Time Scaling
Authors: Weizhe Lin; Xing Li; Zhiyuan Yang; Xiaojin Fu; Hui-Ling Zhen; Yaoyuan Wang; Xianzhi Yu; Wulong Liu; Xiaosong Li; Mingxuan Yuan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Reasoning Models (LRMs) demonstrate exceptional capability in tackling complex mathematical, logical, and coding tasks by leveraging extended Chain-of-Thought (CoT) reasoning. Test-time scaling methods—such as prolonging CoT with explicit token-level exploration—can push LRMs’ accuracy boundaries, but they incur significant decoding overhead. A key inefficiency source is LRMs often generate redundant thinking CoTs, which demonstrate clear structured overthinking and underthinking patterns. Inspired by human cognitive reasoning processes and numerical optimization theories, we propose TrimR, a verifier-based, training-free, efficient framework to trim reasoning and enhance test-time scaling, explicitly tailored for production-level deployment. Our method employs a lightweight, pretrained, instruction-tuned verifier to detect and truncate redundant intermediate thoughts of LRMs without any LRM or verifier fine-tuning. We present both the core algorithm and asynchronous online system engineered for high-throughput industrial applications. Empirical evaluations on Ascend NPUs and vLLM show that our framework delivers substantial gains in inference efficiency under large-batch workloads. In particular, on the four MATH500, AIME24/25, and GPQA benchmarks, the reasoning runtime of QwQ-32B, DeepSeek-R1-Distill-Qwen-32B, and Pangu-R-38B is improved by up to 70% with negligible impact on accuracy.
GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching
Authors: Guinan Su; Li Shen; Lu Yin; Shiwei Liu; Yanwu Yang; Jonas Geiping
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. However, such impressive capability typically comes with a substantial model size, which presents significant challenges in deployment and inference. While structured pruning of model parameters offers a promising way to reduce computational costs at deployment time, current methods primarily focus on single model pruning. In this work, we develop a novel strategy to compress models by strategically combining or merging layers from finetuned model variants, which preserves the original model's abilities by aggregating capabilities accentuated in different finetunes. We pose the optimal tailoring of these LLMs as a zero-order optimization problem, adopting a search space that supports three different operations: (1) Layer removal, (2) Layer selection from different candidate models, and (3) Layer merging. Our experiments demonstrate that this approach leads to competitive model pruning, for example, for the Llama2-13B model families, our compressed models maintain approximately 97.3\% of the original performance while removing ～25\% of parameters, significantly outperforming previous state-of-the-art methods.
SAIR: Enabling Deep Learning for Protein-Ligand Interactions with a Synthetic Structural Dataset
Authors: Pablo Lemos; Zane Beckwith; Sasaank Bandi; Maarten Van Damme; Jordan Crivelli-Decker; Benjamin J. Shields; Thomas Merth; Punit K Jha; Nicola De Mitri; Tiffany Callahan; AJ Nish; Paul Abruzzo; Romelia Salomon-Ferrer; Martin Ganahl
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurate prediction of protein-ligand binding affinities remains a cornerstone problem in drug discovery. While binding affinity is inherently dictated by the 3D structure and dynamics of protein-ligand complexes, current deep learning approaches are limited by the lack of high-quality experimental structures with annotated binding affinities. To address this limitation, we introduce the Structurally Augmented IC50 Repository (SAIR), the largest publicly available dataset of protein-ligand 3D structures with associated activity data. The dataset comprises $5,244,285$ structures across $1,048,857$ unique protein-ligand systems, curated from the ChEMBL and BindingDB databases, which were then computationally folded using the Boltz-1x model. We provide a comprehensive characterization of the dataset, including distributional statistics of proteins and ligands, and evaluate the structural fidelity of the folded complexes using PoseBusters. Our analysis reveals that approximately $3 \%$ of structures exhibit physical anomalies, predominantly related to internal energy violations. As an initial demonstration, we benchmark several binding affinity prediction methods, including empirical scoring functions (Vina, Vinardo), a 3D convolutional neural network (Onionnet-2), and a graph neural network (AEV-PLIG). While machine learning-based models consistently outperform traditional scoring function methods, neither exhibit a high correlation with ground truth affinities, highlighting the need for models specifically fine-tuned to synthetic structure distributions. This work provides a foundation for developing and evaluating next-generation structure and binding-affinity prediction models and offers insights into the structural and physical underpinnings of protein-ligand interactions. 
The link to the data will be added upon publication, to preserve anonymity of the submission.
Dragging with Geometry: From Pixels to Geometry-Guided Image Editing
Authors: Xinyu Pu; Hongsong Wang; Jie Gui; Pan Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Interactive point-based image editing serves as a controllable editor, enabling precise and flexible manipulation of image content. However, most drag-based methods operate primarily on the 2D pixel plane with limited use of 3D cues. As a result, they often produce imprecise and inconsistent edits, particularly in geometry-intensive scenarios such as rotations and perspective transformations. To address these limitations, we propose a novel geometry-guided drag-based image editing method—GeoDrag, which addresses three key challenges: 1) incorporating 3D geometric cues into pixel-level editing, 2) mitigating discontinuities caused by geometry-only guidance, and 3) resolving conflicts arising from multi-point dragging. Built upon a unified displacement field that jointly encodes 3D geometry and 2D spatial priors, GeoDrag enables coherent, high-fidelity, and structure-consistent editing in a single forward pass. In addition, a conflict-free partitioning strategy is introduced to isolate editing regions, effectively preventing interference and ensuring consistency. Extensive experiments across various editing scenarios validate the effectiveness of our method, showing superior precision, structural consistency, and reliable multi-point editability. Project page: https://xinyu-pu.github.io/projects/geodrag.
Flower: A Flow-Matching Solver for Inverse Problems
Authors: Mehrsa Pourya; Bassam El Rawas; Michael Unser
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce Flower, a solver for linear inverse problems. It leverages a pre-trained flow model to produce reconstructions that are consistent with the observed measurements. Flower operates through an iterative procedure over three steps: (i) a flow-consistent destination estimation, where the velocity network predicts a denoised target; (ii) a refinement step that projects the estimated destination onto a feasible set defined by the forward operator; and (iii) a time-progression step that re-projects the refined destination along the flow trajectory.  We provide a theoretical analysis that demonstrates how Flower approximates Bayesian posterior sampling, thereby unifying perspectives from plug-and-play methods and generative inverse solvers. On the practical side, Flower achieves state-of-the-art reconstruction quality while using nearly identical hyperparameters across various linear inverse problems. Our code is available at https://github.com/mehrsapo/Flower.
e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs
Authors: Amrith Setlur; Matthew Y. R. Yang; Charlie Victor Snell; Jeremiah Greer; Ian Wu; Virginia Smith; Max Simchowitz; Aviral Kumar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Test-time scaling offers a promising path to improve LLM reasoning by utilizing more compute at inference time; however, the true promise of this paradigm lies in extrapolation (i.e., improvement in performance on hard problems as LLMs keep "thinking" for longer, beyond the maximum token budget they were trained on). Surprisingly, we find that most existing reasoning models do not extrapolate well. We show that one way to enable extrapolation is by training the LLM to perform in-context exploration: training the LLM to effectively spend its test time budget by chaining operations (such as generation, verification, refinement, etc.), or testing multiple hypotheses before it commits to an answer. To enable in-context exploration, we identify three key ingredients as part of our recipe e3: (1) chaining skills that the base LLM has asymmetric competence in, e.g., chaining verification (easy) with generation (hard), as a way to implement in-context search; (2) leveraging "negative" gradients from incorrect traces to amplify exploration during RL, resulting in longer search traces that chains additional asymmetries; and (3) coupling task difficulty with training token budget during training via a specifically-designed curriculum to structure in-context exploration. Our recipe e3 produces the best known 1.7B model according to AIME'25 and HMMT'25 scores, and extrapolates to 2x the training token budget. Our e3-1.7B model not only attains high pass@1 scores, but also improves pass@k over the base model.
Human-Object Interaction via Automatically Designed VLM-Guided Motion Policy
Authors: Zekai Deng; Ye Shi; Kaiyang Ji; Lan Xu; Shaoli Huang; Jingya Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Human-object interaction (HOI) synthesis is crucial for applications in animation, simulation, and robotics. However, existing approaches either rely on expensive motion capture data or require manual reward engineering, limiting their scalability and generalizability. In this work, we introduce the first unified physics-based HOI framework that leverages Vision-Language Models (VLMs) to enable long-horizon interactions with diverse object types — including static, dynamic, and articulated objects. We introduce VLM-Guided Relative Movement Dynamics (RMD), a fine-grained spatio-temporal bipartite representation that automatically constructs goal states and reward functions for reinforcement learning. By encoding structured relationships between human and object parts, RMD enables VLMs to generate semantically grounded, interaction-aware motion guidance without manual reward tuning. To support our methodology, we present Interplay, a novel dataset with thousands of long-horizon static and dynamic interaction plans. Extensive experiments demonstrate that our framework outperforms existing methods in synthesizing natural, human-like motions across both simple single-task and complex multi-task scenarios. For more details, please refer to our project webpage: https://vlm-rmd.github.io/.
Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling
Authors: Xiaolong Tang; Meina Kan; Shiguang Shan; Xilin Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Safe and feasible trajectory planning is critical for real-world autonomous driving systems.
However, existing learning-based planners rely heavily on expert demonstrations, which not only lack explicit safety awareness but also risk inheriting undesirable behaviors such as speeding from suboptimal human driving data.
Inspired by the success of large language models, we propose Plan-R1, a two-stage trajectory planning framework that decouples principle alignment from behavior learning.
In the first stage, a general trajectory predictor is pre-trained on expert data to capture diverse, human-like driving behaviors.
In the second stage, the model is fine-tuned with rule-based rewards using Group Relative Policy Optimization (GRPO), explicitly aligning ego planning with principles such as safety, comfort, and traffic rule compliance.
This two-stage paradigm retains human-like behaviors while enhancing safety awareness and discarding undesirable patterns from demonstrations.
Furthermore, we identify a key limitation of directly applying GRPO to planning: group-wise normalization erases cross-group scale differences, causing rare, high-variance safety-violation groups to have similar advantages as abundant low-variance safe groups, thereby  suppressing optimization for safety-critical objectives.
To address this, we propose Variance-Decoupled GRPO (VD-GRPO), which replaces normalization with centering and fixed scaling to preserve absolute reward magnitudes, ensuring that safety-critical objectives remain dominant throughout training.
Experiments on the nuPlan benchmark demonstrate that Plan-R1 significantly improves planning safety and feasibility, achieving state-of-the-art performance, particularly in realistic reactive settings.
Our code is available at https://github.com/XiaolongTang23/Plan-R1.