GenAI4ELab/papercli-papers huggingface dataset:
DAMR: Efficient and Adaptive Context-Aware Knowledge Graph Question Answering with LLM-Guided MCTS
Authors: Yingxu Wang; Shiqi Fan; Mengzhu Wang; Siyang Gao; Chao Wang; Nan Yin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Knowledge Graph Question Answering (KGQA) aims to interpret natural language queries and perform structured reasoning over knowledge graphs by leveraging their relational and semantic structures to retrieve accurate answers. Existing methods primarily follow either the retrieve-then-reason paradigm, which relies on Graph Neural Networks (GNNs) or heuristic rules to extract static candidate paths, or dynamic path generation strategies that employ large language models (LLMs) with prompting to jointly perform retrieval and reasoning. However, the former lacks adaptability due to static path extraction and the absence of contextual refinement, while the latter suffers from high computational costs and limited evaluation accuracy because of their dependence on fixed scoring functions and repeated LLM calls. To address these issues, this paper proposes Dynamically Adaptive MCTS-based Reasoning (DAMR), a novel framework that integrates LLM-guided Monte Carlo Tree Search (MCTS) with adaptive path evaluation to enable efficient and context-aware KGQA. DAMR leverages MCTS as a backbone, where an LLM-based planner selects the top-k semantically relevant relations at each expansion step to effectively reduce the search space. To enhance evaluation accuracy, we introduce a lightweight Transformer-based scorer that performs context-aware plausibility estimation by jointly encoding the question and relation sequence through cross-attention, thereby capturing fine-grained semantic shifts during multi-hop reasoning. Furthermore, to mitigate the scarcity of high-quality supervision, DAMR incorporates a dynamic pseudo-path refinement mechanism that periodically generates training signals from partial paths explored during search, enabling the scorer to continually adapt to the evolving distribution of reasoning trajectories. Extensive experiments on multiple KGQA benchmarks show that DAMR significantly outperforms state-of-the-art methods.
VideoAnchor: Reinforcing Subspace-Structured Visual Cues for Coherent Visual-Spatial Reasoning
Authors: Zhaozhi Wang; Tong Zhang; Mingyue Guo; Yaowei Wang; Qixiang Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal Large Language Models (MLLMs) have achieved impressive progress in vision–language alignment, yet they remain limited in visual–spatial reasoning. We first identify that this limitation arises from the attention mechanism: visual tokens are overshadowed by language tokens, preventing the model from consistently recognizing the same visual cues across frames. To address this challenge, we draw a novel connection between the self-expressiveness property in sparse subspace clustering and the attention mechanism in Transformers. Building on this insight, we propose VideoAnchor, a plug-and-play module that leverages subspace affinities to reinforce visual cues across frames without retraining, effectively anchoring attention to shared visual structures. Extensive experiments across benchmarks and backbone models show consistent performance gains — e.g., 3.2% and 4.6% improvements on VSI-Bench and Video-MME (spatial-related tasks) with InternVL2-8B and Qwen2.5VL-72B—while qualitative analyses demonstrate more coherent subspace partitions and stronger visual grounding.
DemoGrasp: Universal Dexterous Grasping from a Single Demonstration
Authors: Haoqi Yuan; Ziye Huang; Ye Wang; Chuan Mao; Chaoyi Xu; Zongqing Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Universal grasping with multi-fingered dexterous hands is a fundamental challenge in robotic manipulation. While recent approaches successfully learn closed-loop grasping policies using reinforcement learning (RL), the inherent difficulty of high-dimensional, long-horizon exploration necessitates complex reward and curriculum design, often resulting in suboptimal solutions across diverse objects. We propose DemoGrasp, a simple yet effective method for learning universal dexterous grasping. We start from a single successful demonstration trajectory of grasping a specific object and adapt to novel objects and poses by editing the robot actions in this trajectory: changing the wrist pose determines where to grasp, and changing the hand joint angles determines how to grasp. We formulate this trajectory editing as a single-step Markov Decision Process (MDP) and use RL to optimize a universal policy across hundreds of objects in parallel in simulation, with a simple reward consisting of a binary success term and a robot–table collision penalty. In simulation, DemoGrasp achieves a 95% success rate on DexGraspNet objects using the Shadow Hand, outperforming previous state-of-the-art methods. It also shows strong transferability, achieving an average success rate of 84.6% across diverse dexterous hand embodiments on six unseen object datasets, while being trained on only 175 objects. Through vision-based imitation learning, our policy successfully grasps 110 unseen real-world objects, including small, thin items. It generalizes to spatial, background, and lighting changes, supports both RGB and depth inputs, and extends to language-guided grasping in cluttered scenes.
ContextNav: Towards Agentic Multimodal In-Context Learning
Authors: Honghao Fu; Yuan Ouyang; Kai-Wei Chang; Yiwei Wang; Zi Huang; Yujun Cai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances demonstrate that multimodal large language models (MLLMs) exhibit strong multimodal in-context learning (ICL) capabilities, enabling them to adapt to novel vision-language tasks from a few contextual examples. However, existing ICL approaches face challenges in reconciling generalization with robustness across diverse tasks and noisy contextual examples: manually selecting examples produces clean contexts but is labor-intensive and task-specific, while similarity-based retrieval improves scalability but could introduce irrelevant or structurally inconsistent samples that degrade ICL performance. To address these limitations, we propose ContextNav, the first agentic framework that integrates the scalability of automated retrieval with the quality and adaptiveness of human-like curation, enabling noise-robust and dynamically optimized contextualization for multimodal ICL. ContextNav unifies context management and noise-robust contextualization within a closed-loop workflow driven by graph-based tool orchestration. Specifically, it builds a resource-aware multimodal embedding pipeline, maintains a retrievable vector database, and applies agentic retrieval and structural alignment to construct noise-resilient contexts. An Operational Grammar Graph (OGG) further supports adaptive toolchain planning and optimization, enabling the agent to refine its strategies based on downstream feedback. Experimental results demonstrate that ContextNav achieves state-of-the-art performance across various datasets, underscoring the promise of agentic workflows for advancing scalable and robust contextualization in multimodal ICL.
CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning
Authors: Long Xing; Xiaoyi Dong; Yuhang Zang; Yuhang Cao; Jianze Liang; Qidong Huang; Jiaqi Wang; Feng Wu; Dahua Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Image captioning is a fundamental task that bridges the visual and linguistic
domains, playing a critical role in pre-training Large Vision-Language Models
(LVLMs). Current state-of-the-art captioning models are typically trained with
Supervised Fine-Tuning (SFT), a paradigm that relies on expensive, non-scalable
data annotated by humans or proprietary models. This approach often leads to
models that memorize specific ground-truth answers, limiting their generality and
ability to generate diverse, creative descriptions. To overcome the limitation of
SFT, we propose applying the Reinforcement Learning with Verifiable Rewards
(RLVR) paradigm to the open-ended task of image captioning. A primary challenge,
however, is designing an objective reward function for the inherently subjective
nature of what constitutes a "good" caption. We introduce Captioning Reinforce-
ment Learning (CapRL), a novel training framework that redefines caption quality
through its utility: a high-quality caption should enable a non-visual language
model to accurately answer questions about the corresponding image. CapRL
employs a decoupled two-stage pipeline where an LVLM generates a caption, and
the objective reward is derived from the accuracy of a separate, vision-free LLM
answering Multiple-Choice Questions based solely on that caption. As the first
study to apply RLVR to the subjective image captioning task, we demonstrate
that CapRL significantly enhances multiple settings. Pretraining on the CapRL-
5M caption dataset annotated by CapRL-3B results in substantial gains across 12
benchmarks. Moreover, within the Prism Framework for caption quality evaluation,
CapRL achieves performance comparable to Qwen2.5-VL-72B, while exceeding
the baseline by an average margin of 8.4%. Results validate that our CapRL effec-
tively trains models to produce a more general and accurate image descriptions,
moving beyond the limitations of traditional SFT-based image captioning models.
Revisiting Nonstationary Kernel Design for Multi-Output Gaussian Processes
Authors: Qiaochu Xu; Zi Yang; Ying Li; Michael Minyi Zhang; Pablo M. Olmos
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-output Gaussian processes (MOGPs) provide a Bayesian framework for modeling non-linear functions with multiple outputs, in which nonstationary kernels are essential for capturing input-dependent variations in observations. However, from a spectral (dual) perspective, existing nonstationary kernels inherit the inflexibility and over-parameterization of their spectral densities due to the restrictive spectral–kernel duality. To overcome this, we establish a generalized spectral–kernel duality that enables fully flexible matrix-valued spectral densities — albeit at the cost of quadratic parameter growth in the number of outputs. To achieve linear scaling while retaining sufficient expressiveness, we propose the multi-output low-rank nonstationary (MO-LRN) kernel: by modeling the spectral density through a low-rank matrix whose rows are independently parameterized by bivariate Gaussian mixtures. Experiments on synthetic and real-world datasets demonstrate that MO-LRN consistently outperforms existing MOGP kernels in regression, missing-data interpolation, and imputation tasks.
Relative Entropy Pathwise Policy Optimization
Authors: Claas A Voelcker; Axel Brunnbauer; Marcel Hussing; Michal Nauman; Pieter Abbeel; Radu Grosu; Eric Eaton; Amir-massoud Farahmand; Igor Gilitschenski
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Score-function based methods for policy learning, such as REINFORCE and PPO, have delivered strong results in game-playing and robotics, yet their high variance often undermines training stability. Improving a policy through state-action value functions, for example by differentiating Q with regard to the policy, alleviates the variance issues. However, this requires an accurate action-conditioned value
function, which is notoriously hard to learn without relying on replay buffers for reusing past off-policy data. We present Relative Entropy Pathwise Policy Optimization, an algorithm that trains Q-value models purely from on-policy trajectories, unlocking the use of Q function derivatives to compute policy updates in the context of on-policy learning. We show how to combine stochastic policies
for exploration with constrained updates for stable training, and evaluate important architectural components that stabilize value function learning. This results in an efficient on-policy algorithm that combines the stability of Q-based policy gradients with the simplicity and minimal memory footprint of standard on-policy learning. Compared to state-of-the-art on two standard GPU-parallelized benchmarks, REPPO provides strong empirical performance at superior sample efficiency, wall-clock time, memory footprint, and hyperparameter robustness.
SurfSplat: Conquering Feedforward 2D Gaussian Splatting with Surface Continuity Priors
Authors: Bing He; Jingnan Gao; Yunuo Chen; Ning Cao; Gang Chen; Zhengxue Cheng; Li Song; Wenjun Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reconstructing 3D scenes from sparse images remains a challenging task due to the difficulty of recovering accurate geometry and texture without optimization.
Recent approaches leverage generalizable models to generate 3D scenes using 3D Gaussian Splatting (3DGS) primitive. 
However, they often fail to produce continuous surfaces and instead yield discrete, color-biased point clouds that appear plausible at normal resolution but reveal severe artifacts under close-up views. 
To address this issue, we present SurfSplat, a feedforward framework based on 2D Gaussian Splatting (2DGS) primitive, which provides stronger anisotropy and higher geometric precision. By incorporating a surface continuity prior and a forced alpha blending strategy, SurfSplat reconstructs coherent geometry together with faithful textures.
Furthermore, we introduce High-Resolution Rendering Consistency (HRRC), a new evaluation metric designed to evaluate high-resolution reconstruction quality. Extensive experiments on RealEstate10K, DL3DV, and ScanNet demonstrate that SurfSplat consistently outperforms prior methods on both standard metrics and HRRC, establishing a robust solution for high-fidelity 3D reconstruction from sparse inputs.
Frequency-aware Dynamic Gaussian Splatting
Authors: Qiaowei Miao; JinSheng Quan; Kehan Li; Yichao Xu; Yi Yang; Yawei Luo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present \textbf{Frequency-Aware Dynamic Gaussian Splatting (FAGS)}, a novel approach to mitigating motion blur in 4D reconstruction, particularly under novel viewpoints. This blur stems from a fundamental spectral conflict in existing methods, which struggle to \textbf{balance  high-frequency rendering details with high-frequency motion.}
FAGS addresses this challenge with two key innovations. First, we introduce a frequency-differentiated Gaussian kernel that refines the alpha-blending process of 3D Gaussian Splatting. By adaptively classifying Gaussians into two types—a slowly varying kernel for smooth, low-frequency regions and a sharp-transitioning kernel for high-frequency boundaries—our method explicitly separates representation responsibilities, preserving fine details without sacrificing continuity.
Second, we propose a Fourier-Deformation Network that enhances motion expressiveness. This network employs high-frequency Fourier embeddings to capture diverse motion patterns by learning amplitudes across frequency components. To further improve accuracy, we integrate a frequency-aware gate in fusion module, which predicts and regulates the relative deformation of each Gaussian.
Extensive experiments on both synthetic and real-world 4D benchmarks demonstrate that FAGS significantly reduces motion blur and enhances structural details, achieving state-of-the-art performance.
Primary-Fine Decoupling for Action Generation in Robotic Imitation
Authors: Xiaohan Lei; Min Wang; Wengang Zhou; Xingyu Lu; Houqiang Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-modal distribution in robotic manipulation action sequences poses critical challenges for imitation learning. 
To this end, existing approaches often model the action space as either a discrete set of tokens or a continuous, latent-variable distribution.
However, both approaches present trade-offs: some methods discretize actions into tokens and therefore lose fine-grained action variations, while others generate continuous actions in a single stage tend to produce unstable mode transitions.
To address these limitations, we propose Primary-Fine Decoupling for Action Generation (PF-DAG), a two-stage framework that decouples coarse action consistency from fine-grained variations. First, we compress action chunks into a small set of discrete modes, enabling a lightweight policy to select consistent coarse modes and avoid mode bouncing. Second, a mode conditioned MeanFlow policy is learned to generate high-fidelity continuous actions.
Theoretically, we prove PF-DAG’s two-stage design achieves a strictly lower MSE bound than single-stage generative policies. 
Empirically, PF-DAG outperforms state-of-the-art baselines across 56 tasks from Adroit, DexArt, and MetaWorld benchmarks. It further generalizes to real-world tactile dexterous manipulation tasks. Our work demonstrates that explicit mode-level decoupling enables both robust multi-modal modeling and reactive closed-loop control for robotic manipulation.
SPICE: Submodular Penalized Information–Conflict Selection for Efficient Large Language Model Training
Authors: Powei Chang; Jinpeng Zhang; Bowen Chen; Chenyu Wang; Chenlu Guo; Yixing Zhang; Yukang Gao; JianXiang Xiang; Yue Gao; Chaoqun Sun; Yiyi Chen; Dongying kong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Information-based data selection for instruction tuning is compelling: maximizing the log-determinant of the Fisher information yields a monotone submodular objective, enabling greedy algorithms to achieve a $(1-1/e)$ approximation under a cardinality budget. In practice, however, we identify alleviating gradient conflicts, misalignment between per-sample gradients, is a key factor that slows down the decay of marginal log-determinant information gains, thereby preventing significant loss of information. We formalize this via an $\varepsilon$-decomposition that quantifies the deviation from ideal submodularity as a function of conflict statistics, yielding data-dependent approximation factors that tighten as conflicts diminish. Guided by this analysis, we propose SPICE, a conflict-aware selector that maximizes information while penalizing misalignment, and that supports early stopping and proxy models for efficiency. Empirically, SPICE selects subsets with higher log-determinant information than original criteria, and these informational gains translate into performance improvements: across 8 benchmarks with LLaMA2-7B and Qwen2-7B, SPICE uses only 10% of the data, yet matches or exceeds 6 methods including full-data tuning. This achieves performance improvements with substantially lower training cost.
Code is available at https://github.com/Chang-pw/SPICE#.
Compactness and Consistency: A Conjoint Framework for Deep Graph Clustering
Authors: Wei Ju; Siyu Yi; Kangjie Zheng; Yifan Wang; Ziyue Qiao; Li Shen; Yongdao Zhou; Xiaochun Cao; Jiancheng Lv
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Graph clustering is a fundamental task in data analysis, aiming at grouping nodes with similar characteristics in the graph into clusters. This problem has been widely explored using graph neural networks (GNNs) due to their ability to leverage node attributes and graph topology for effective cluster assignments. However, representations learned through GNNs typically struggle to capture global relationships between nodes via local message-passing mechanisms. Moreover, the redundancy and noise inherently present in graph data may easily result in node representations lacking compactness and robustness. To address these issues, we propose a conjoint framework CoCo, which captures compactness and consistency in the learned node representations for deep graph clustering. Technically, our CoCo leverages graph convolutional filters to learn robust node representations from both local and global views, and then encodes them into low-rank compact embeddings, thus effectively removing the redundancy and noise as well as uncovering the intrinsic underlying structure. To further enrich the node semantics, we develop a consistency learning strategy based on compact embeddings to facilitate knowledge transfer from the two perspectives. Our experimental results indicate that our CoCo outperforms state-of-the-art counterparts on various datasets. The code is available at https://github.com/juweipku/CoCo.
Interleaving Reasoning for Better Text-to-Image Generation
Authors: Wenxuan Huang; Shuang Chen; Zheyong Xie; Shaosheng Cao; SHIXIANG TANG; Yufan Shen; Qingyu Yin; Wenbo Hu; Xiaoman Wang; Yuntian Tang; Junbo Qiao; Hangyu Guo; Yao Hu; Zhenfei Yin; Philip Torr; Yu Cheng; Wanli Ouyang; Shaohui Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unified multimodal understanding and generation models recently have achieve significant improvement in image generation capability, yet a large gap remains in instruction following and detail preservation compared to systems that tightly couple comprehension with generation such as GPT-4o.
Motivated by recent advances in interleaving reasoning, we explore whether such reasoning can further improve text-to-image (T2I) generation. 
We introduce Interleaving Reasoning Generation (IRG), a framework that alternates between text-based thinking and image synthesis: the model first produces a text-based thinking to guide an initial image, then reflects on the result to refine fine-grained details, visual quality, and aesthetics while preserving semantics. 
To train IRG effectively, we propose Interleaving Reasoning Generation Learning (IRGL), which targets two sub-goals: (1) strengthening the initial think-and-generate stage to establish core content and base quality, and (2) enabling high-quality textual reflection and faithful implementation of those refinements in a subsequent image. 
We curate IRGL-300K, a 300K-scale dataset organized into six decomposed learning modes that jointly cover learning text-based thinking, and full thinking–image trajectories. 
Starting from a unified foundation model that natively emits interleaved text–image outputs, our two-stage training first builds robust thinking and reflection, then efficiently tunes the IRG pipeline in the full thinking–image trajectory data. 
Extensive experiments show SoTA performance, yielding absolute gains of 5–10 points on GenEval, WISE, TIIF, GenAI-Bench, and OneIG-EN, alongside substantial improvements in visual quality and fine-grained fidelity. 
As an early exploration, our results demonstrate that interleaving reasoning is a powerful paradigm for advancing T2I.
The code, model weights and datasets will be released in: https://github.com/Osilly/Interleaving-Reasoning-Generation.
UniSplat: Unified Spatio-Temporal Fusion via 3D Latent Scaffolds for Dynamic Driving Scene Reconstruction
Authors: Chen Shi; Shaoshuai Shi; Xiaoyang Lyu; Chunyang Liu; Kehua Sheng; Bo Zhang; Li Jiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Feed-forward 3D reconstruction for autonomous driving has advanced rapidly, yet existing methods struggle with the joint challenges of sparse, non-overlapping camera views and complex scene dynamics. 
We present UniSplat, a general feed-forward framework that learns robust dynamic scene reconstruction through unified latent spatio-temporal fusion. UniSplat constructs a 3D latent scaffold, a structured representation that captures geometric and semantic scene context by leveraging pretrained foundation models. To effectively integrate information across spatial views and temporal frames, we introduce an efficient fusion mechanism that operates directly within the 3D scaffold, enabling consistent spatio-temporal alignment. To ensure complete and detailed reconstructions, we design a dual-branch decoder that generates dynamic-aware Gaussians from the fused scaffold by combining point-anchored refinement with voxel-based generation, and maintain a persistent memory of static Gaussians to enable streaming scene completion beyond current camera coverage. Extensive experiments on real-world datasets demonstrate that UniSplat achieves state-of-the-art performance in novel view synthesis, while providing robust and high-quality renderings even for viewpoints outside the original camera coverage.