GenAI4ELab/papercli-papers huggingface dataset:
RMFlow: Refined Mean Flow by a Noise-Injection Step for Multimodal Generation
Authors: Yuhao Huang; Shih-Hsin Wang; Andrea L. Bertozzi; Bao Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mean flow (MeanFlow) enables efficient, high-fidelity image generation, yet its single-function evaluation (1-NFE) generation often cannot yield compelling results. We address this issue by introducing RMFlow, an efficient multimodal generative model that integrates a coarse 1-NFE MeanFlow transport with a subsequent tailored noise-injection refinement step. RMFlow approximates the average velocity of the flow path using a neural network trained with a new loss function that balances minimizing the Wasserstein distance between probability paths and maximizing sample likelihood. RMFlow achieves near state-of-the-art results on text-to-image, context-to-molecule, and time-series generation using only 1-NFE, at a computational cost comparable to the baseline MeanFlows.
Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Efficient Knowledge Grounding
Authors: Seong-Woong Shim; Myunsoo Kim; Jae Hyeon Cho; Byung-Jun Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Retrieval-Augmented Generation (RAG) is a framework for grounding Large Language Models (LLMs) in external, up-to-date information. However, recent advancements in context window size allow LLMs to process inputs of up to 128K tokens or more, offering an alternative strategy: supplying the full document context directly to the model, rather than relying on RAG to retrieve a subset of contexts. Nevertheless, this emerging alternative strategy has notable limitations: (i) it is token-inefficient to handle large and potentially redundant contexts; (ii) it exacerbates the “lost in the middle” phenomenon; and (iii) under limited model capacity, it amplifies distraction, ultimately degrading LLM output quality. In this paper, we propose LDAR (Learning Distraction-Aware Retrieval), an adaptive retriever that learns to retrieve contexts in a way that mitigates interference from distracting passages, thereby achieving significantly higher performance with reduced token usage compared to long-context approaches. Extensive experiments across diverse LLM architectures and six knowledge-intensive benchmarks demonstrate the effectiveness and robustness of our approach, highlighting the importance of balancing the trade-off between information coverage and distraction.
World-In-World: World Models in a Closed-Loop World
Authors: Jiahan Zhang; Muqing Jiang; Nanru Dai; TaiMing Lu; Arda Uzunoglu; Shunchi Zhang; Yana Wei; Jiahao Wang; Vishal M. Patel; Paul Pu Liang; Daniel Khashabi; Cheng Peng; Rama Chellappa; Tianmin Shu; Alan Yuille; Yilun Du; Jieneng Chen
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Generative world models (WMs) can now simulate worlds with striking visual realism, which naturally raises the question of whether they can endow embodied agents with predictive perception for decision making. Progress on this question has been limited by fragmented evaluation: most existing benchmarks adopt open-loop protocols that emphasize visual quality in isolation, leaving the core issue of embodied utility unresolved, i.e., *do WMs actually help agents succeed at embodied tasks?*
To address this gap, we introduce World-In-World, the first open platform that benchmarks WMs in a closed-loop setting that mirrors real agent-environment interactions. World-In-World provides a unified online planning strategy and a standardized action API, enabling heterogeneous WMs for decision making.
We curate four closed-loop environments that rigorously evaluate diverse WMs, prioritize task success as the primary metric, and move beyond the common focus on visual quality; we also present the first data scaling law for world models in embodied settings.
Our study uncovers three surprises: (1) visual quality alone does not guarantee task success—controllability matters more; (2) scaling post-training with action-observation data is more effective than upgrading the pretrained video generators; and (3) allocating more inference-time compute allows WMs to substantially improve closed-loop performance. By centering evaluation on closed-loop outcomes, World-In-World establishes a new benchmark for the systematic assessment of WMs.
Variational Reasoning for Language Models
Authors: Xiangxin Zhou; Zichen Liu; Haonan Wang; Chao Du; Min Lin; Chongxuan Li; Liang Wang; Tianyu Pang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce a **variational reasoning** framework for language models that treats thinking traces as latent variables and optimizes them through variational inference. Starting from the evidence lower bound (ELBO), we extend it to a multi-trace objective for tighter bounds and propose a forward-KL formulation that stabilizes the training of the variational posterior. We further show that rejection sampling finetuning and binary-reward RL, including GRPO, can be interpreted as local forward-KL objectives, where *an implicit weighting by model accuracy* naturally arises from the derivation and reveals a previously unnoticed bias toward easier questions. We empirically validate our method on the Qwen 2.5 and Qwen 3 model families across a wide range of reasoning tasks. Overall, our work provides a principled probabilistic perspective that unifies variational inference with RL-style methods and yields stable objectives for improving the reasoning ability of language models.
CloDS: Visual-Only Unsupervised Cloth Dynamics Learning in Unknown Conditions
Authors: Yu-Liang Zhan; Jian Li; Wenbing Huang; Yang Liu; Hao Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep learning has demonstrated remarkable capabilities in simulating complex dynamic systems. However, existing methods require known physical properties as supervision or inputs, limiting their applicability under unknown conditions. To explore this challenge, we introduce Cloth Dynamics Grounding (CDG), a novel scenario for unsupervised learning of cloth dynamics from multi-view visual observations. We further propose Cloth Dynamics Splatting (CloDS), an unsupervised dynamic learning framework designed for CDG. CloDS adopts a three-stage pipeline that first performs video-to-geometry grounding and then trains a dynamics model on the grounded meshes. To cope with large non-linear deformations and severe self-occlusions during grounding, we introduce a dual-position opacity modulation that supports bidirectional mapping between 2D observations and 3D geometry via mesh-based Gaussian splatting in video-to-geometry grounding stage. It jointly considers the absolute and relative position of Gaussian components. Comprehensive experimental evaluations demonstrate that CloDS effectively learns cloth dynamics from visual data while maintaining strong generalization capabilities for unseen configurations. Our code is available at https://github.com/whynot-zyl/CloDS. Visualization results are available at https://github.com/whynot-zyl/CloDS_video.
Incentivizing Consistent, Effective and Scalable Reasoning Capability in Audio LLMs via Reasoning Process Rewards
Authors: Jiajun Fan; Roger Ren; Jingyuan Li; Rahul Pandey; Prashanth Gurunath Shivakumar; Ivan Bulyko; Ankur Gandhe; Ge Liu; Yile Gu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The role of reasoning in Audio Large Language Models remains widely underexplored, as introducing a reasoning process often degrades rather than improves performance during inference, a phenomenon we term test-time inverse scaling, where longer reasoning chains yield progressively worse results. We demonstrate that this stems not from fundamental limitations of reasoning itself, but from inadequate training: models without proper guidance for the reasoning process produce hallucinatory, inconsistent reasoning that accumulates errors over longer chains. To address these challenges, we introduce CESAR (Consistent, Effective, and Scalable Audio Reasoners), shifting from outcome verification to rewarding the reasoning process. Our online reinforcement learning framework employs Group Relative Policy Optimization with a multi-faceted reward suite that incentivizes not only correctness and format but also consistency, structured analytical patterns, causal reasoning, domain-knowledge integration, and calibrated reasoning depth. CESAR resolves test-time inverse scaling, transforming reasoning from  detriments into gains while revealing model-specific "reasoning sweet spots", where performance peaks during test-time scaling. We achieve state-of-the-art results on MMAU Test-mini, substantially outperforming Gemini 2.5 Pro and GPT-4o Audio, and near-human-level performance on MMSU reasoning tasks. Through AI-as-judge evaluations and qualitative comparisons, we provide both quantitative and qualitative validation of our improved reasoning quality. Importantly, enhanced reasoning creates synergistic effects, simultaneously improving multimodal reasoning and perception capabilities. Overall, CESAR establishes a principled method for developing robust and scalable reasoning in Audio LLMs.
GoalRank: Group-Relative Optimization for a Large Ranking Model
Authors: Kaike Zhang; Xiaobei Wang; Shuchang Liu; HailanYang; Xiang Li; Lantao Hu; Han Li; Qi Cao; Fei Sun; Kun Gai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mainstream ranking approaches typically follow a Generator–Evaluator two-stage paradigm, where a generator produces candidate lists and an evaluator selects the best one. Recent work has attempted to enhance performance by expanding the number of candidate lists, for example, through multi-generator settings. However, ranking involves selecting a recommendation list from a combinatorially large space, simply enlarging the candidate set remains ineffective, and performance gains quickly saturate. At the same time, recent advances in large recommendation models have shown that end-to-end one-stage models can achieve promising performance with the expectation of scaling laws. Motivated by this, we revisit ranking from a generator-only one-stage perspective. We theoretically prove that, for any (finite Multi-)Generator–Evaluator model, there always exists a generator-only model that achieves strictly smaller approximation error to the optimal ranking policy, while also enjoying a scaling law as its size increases. Building on this result, we derive an evidence upper bound of the one-stage optimization objective, from which we find that one can leverage a reward model trained on real user feedback to construct a reference policy in a group-relative manner. This reference policy serves as a practical surrogate of the optimal policy, enabling effective training of a large generator-only ranker. Based on these insights, we propose GoalRank, a generator-only ranking framework. Extensive offline experiments on public benchmarks and large-scale online A/B tests demonstrate that GoalRank consistently outperforms state-of-the-art methods.
Multimodal Aligned Semantic Knowledge for Unpaired Image-text Matching
Authors: Laiguo Yin; Yixin Zhang; YUQING SUN; Lizhen Cui
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: While existing approaches address unpaired image-text matching by constructing cross-modal aligned knowledge, they often fail to identify semantically corresponding visual representations for Out-of-Distribution (OOD) words. Moreover, the distributional variance of visual representations associated with different words varies significantly, which negatively impacts matching accuracy. To address these issues, we propose a novel method namely Multimodal Aligned Semantic Knowledge (MASK), which leverages word embeddings as bridges to associate words with their corresponding prototypes, thereby enabling semantic knowledge alignment between the image and text modalities. For OOD words, the representative prototypes are constructed by leveraging the semantic relationships encoded in word embeddings. Beyond that, we introduce a prototype consistency contrastive loss to structurally regularize the feature space, effectively mitigating the adverse effects of variance. Experimental results on the Flickr30K and MSCOCO datasets demonstrate that MASK achieves superior performance in unpaired matching.
GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables
Authors: Zhengyu Li; Xiangfei Qiu; Yuhan Zhu; Xingjian Wu; Jilin Hu; Chenjuan Guo; Bin Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Exogenous variables offer valuable supplementary information for predicting future endogenous variables. Forecasting with exogenous variables needs to consider both past-to-future dependencies (i.e., temporal correlations) and the influence of exogenous variables on endogenous variables (i.e., channel correlations). This is pivotal when future exogenous variables are available, because they may directly affect the future endogenous variables. Many methods have been proposed for time series forecasting with exogenous variables, focusing on modeling temporal and channel correlations. However, most of them use a two-step strategy, modeling temporal and channel correlations separately, which limits their ability to capture joint correlations across time and channels. Furthermore, in real-world scenarios, recorded time series are frequently affected by various forms of noises, underscoring the critical importance of robustness in such correlations modeling. To address these limitations, we propose GCGNet, a Graph-Consistent Generative Network for time series forecasting with exogenous variables. Specifically, GCGNet first employs a Variational Generator to produce coarse predictions. A Graph Structure Aligner then further guides it by evaluating the consistency between the generated and true correlations, where the correlations are represented as graphs, and are robust to noises. Finally, a Graph Refiner is proposed to refine the predictions to prevent degeneration and improve accuracy. Extensive experiments on 12 real-world datasets demonstrate that GCGNet outperforms state-of-the-art baselines.
Disentangled Hierarchical VAE for 3D Human-Human Interaction Generation
Authors: Zichen Geng; Zeeshan Hayder; Bo Miao; Jian Liu; Wei Liu; Ajmal Saeed Mian
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generating realistic 3D Human-Human Interaction (HHI) requires coherent modeling of the physical plausibility of the agents and their interaction semantics. Existing methods compress all motion information into a single latent representation, limiting their ability to capture fine-grained actions and inter-agent interactions. This often leads to semantic misalignment and physically implausible artifacts, such as penetration or missed contact. We propose Disentangled Hierarchical Variational Autoencoder (DHVAE) based latent diffusion for structured and controllable HHI generation. DHVAE explicitly disentangles the global interaction context and individual motion patterns into a decoupled latent structure by employing a CoTransformer module. To mitigate implausible and physically inconsistent contacts in HHI, we incorporate contrastive learning constraints with our DHVAE to promote a more discriminative and physically plausible latent interaction space. For high-fidelity interaction synthesis, DHVAE employs a DDIM-based diffusion denoising process in the hierarchical latent space, enhanced by a skip-connected AdaLN-Transformer denoiser. Extensive evaluations show that DHVAE achieves superior motion fidelity, text alignment, and physical plausibility with greater computational efficiency.
Text2Grad: Reinforcement Learning from Natural Language Feedback
Authors: Hanyang Wang; Lu Wang; Chaoyun Zhang; Tianjun Mao; Si Qin; Qingwei Lin; Saravan Rajmohan; Dongmei Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Traditional RLHF optimizes language models with coarse, scalar rewards that mask the fine-grained reasons behind success or failure, leading to slow, opaque learning. Recent work augments RL with textual critiques through prompting or reflection, improving interpretability but leaving model parameters untouched. We introduce **Text2Grad**, a reinforcement-learning paradigm that *turns free-form textual feedback into span-level gradients*. Given human (or programmatic) critiques, Text2Grad aligns each feedback phrase with the relevant token spans, converts these alignments into differentiable reward signals, and performs gradient updates that directly refine the offending portions of the model's policy. This yields precise, feedback-conditioned adjustments instead of global nudges. Text2Grad is realized through three components: (1) a high-quality feedback–annotation pipeline that pairs critiques with token spans; (2) a fine-grained reward model that predicts span-level reward on answers while generating explanatory critiques; and (3) a span-level policy optimizer that back-propagates *natural-language gradients*. Across summarization, code generation, and question answering, Text2Grad consistently surpasses scalar-reward RL and prompt-only baselines, providing both higher task metrics and richer interpretability. Our results suggest that natural-language feedback can serve not only as explanations, but also as actionable training signals for fine-grained alignment. The code for our method is available at *[https://github.com/microsoft/Text2Grad](https://github.com/microsoft/Text2Grad)*.
Many Eyes, One Mind: Temporal Multi-Perspective and Progressive Distillation for Spiking Neural Networks
Authors: Kai Sun; Peibo Duan; Yongsheng Huang; Nanxu Gong; Levin Kuhlmann
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spiking Neural Networks (SNNs), inspired by biological neurons, are attractive for their event-driven energy efficiency but still fall short of Artificial Neural Networks (ANNs) in accuracy. Knowledge distillation (KD) has emerged as a promising approach to narrow this gap by transferring ANN knowledge into SNNs. Temporal-wise distillation (TWD) leverages the temporal dynamics of SNNs by providing supervision across timesteps, but it applies a constant teacher output to all timesteps, mismatching the inherently evolving temporal process of SNNs. Moreover, while TWD improves per-timestep accuracy, truncated inference still suffers from full-length temporal information loss due to the progressive accumulation process. We propose **MEOM** (**M**any **E**yes, **O**ne **M**ind), a unified KD framework that enriches supervision with diverse temporal perspectives through mask-weighted teacher features and progressively aligns truncated predictions with the full-length prediction, thereby enabling more reliable inference across all timesteps. Extensive experiments and theoretical analyses demonstrate that MEOM achieves state-of-the-art performance on multiple benchmarks. Code is available at https://github.com/KaiSUN1/MEOM.
Mathesis: Towards Formal Theorem Proving from Natural Languages
Authors: Yu Xuejun; Jianyuan Zhong; Zijin Feng; Pengyi Zhai; Roozbeh Yousefzadeh; Wei Chong Ng; Haoxiong Liu; Ziyi Shou; Jing Xiong; Yudong Zhou; Claudia Beth Ong; Austen Jeremy Sugiarto; Yaoxi Zhang; Wai Ming Tai; Huan Cao; Dongcai Lu; Jiacheng Sun; Qiang Xu; SHEN XIN; Zhenguo Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in large language models (LLMs) show strong promise for formal reasoning. However, most LLM-based theorem provers remain constrained by the need for expert-written formal statements as inputs, limiting their applicability to real-world problems expressed in natural language. We address this gap by focusing on autoformalization, the task of translating informal problems into formal statements. We propose Mathesis, the first pipeline for the systematic study of formal theorem proving from natural language. It contributes the first autoformalizer trained with reinforcement learning, which integrates syntactic, semantic, and prover feedback as reward signals to yield accurate and verifiable formalizations. This is further supported by our novel LeanScorer framework for evaluating semantic correctness. To assess real-world applicability, we introduce Gaokao-Formal, a benchmark of 495 complex proof problems from the college entrance exams. Experiments demonstrate that our autoformalizer improves pass rates by 45% on Gaokao-Formal and 6% on MiniF2F compared to state-of-the-art baselines. Paired with provers, our autoformalizer consistently enhances proving accuracy, including a 42% gain for DeepSeek-Prover-V2 on Gaokao-Formal. Our code is available at https://github.com/Huawei-AI4Math/Mathesis.
CMT: Mid-Training for Efficient Learning of Consistency, Mean Flow, and Flow-Map Models
Authors: Zheyuan Hu; Chieh-Hsin Lai; Yuki Mitsufuji; Stefano Ermon
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Flow map models such as Consistency Models (CM) and Mean Flow (MF) enable few-step generation by learning the long jump of the ODE solution of diffusion models, yet training remains unstable, sensitive to hyperparameters, and costly. Initializing from a pre-trained diffusion model helps, but still requires converting infinitesimal steps into a long-jump map, leaving instability unresolved. We introduce *mid-training*, the first concept and practical method that inserts a lightweight intermediate stage between the (diffusion) pre-training and the final flow map training (i.e., post-training) for vision generation. Concretely, *Consistency Mid-Training* (CMT) is a compact and principled stage that trains a model to map points along a solver trajectory from a pre-trained model, starting from a prior sample, directly to the solver-generated clean sample. It yields a trajectory-consistent and stable initialization. This initializer outperforms random and diffusion-based baselines and enables fast, robust convergence without heuristics. Initializing post-training with CMT weights further simplifies flow map learning.  Empirically, CMT achieves state-of-the-art two-step FIDs of 1.97 (CIFAR-10), 1.32 (ImageNet $64\times64$), and 1.84 (ImageNet $512\times512$), using up to $98$\% less training data and GPU time than CMs. On ImageNet $256\times256$, it attains 1-step FID 3.34 with $\sim50$\% less training than MF from scratch (FID 3.43). On MSCOCO T2I, CMT reaches the best FID with $\sim47$\% less training. This establishes CMT as a principled, efficient, and general framework for training flow map models. Code and models are available at https://github.com/sony/cmt.
Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-Language Navigation
Authors: Meng Wei; Chenyang Wan; Jiaqi Peng; Xiqian Yu; Yuqiang Yang; Delin Feng; Wenzhe Cai; Chenming Zhu; Tai Wang; Jiangmiao Pang; Xihui Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While recent large vision-language models (VLMs) have improved generalization in vision-language navigation (VLN), existing methods typically rely on end-to-end pipelines that map vision-language inputs directly to short-horizon discrete actions. Such designs often produce fragmented motions, incur high latency, and struggle with real-world challenges like dynamic obstacle avoidance.
We propose DualVLN, the first dual-system VLN foundation model that synergistically integrates high-level reasoning with low-level action execution. System 2, a VLM-based global planner, "grounds slowly" by predicting mid-term waypoint goals via image-grounded reasoning. System 1, a lightweight, multi-modal conditioning Diffusion Transformer policy, "moves fast" by leveraging both explicit pixel goals and latent features from System 2 to generate smooth and accurate trajectories.
The dual-system design enables robust real-time control and adaptive local decision-making in complex, dynamic environments. By decoupling training, the VLM retains its generalization, while System 1 achieves interpretable and effective local navigation. DualVLN outperforms prior methods across all VLN benchmarks and real-world experiments demonstrate robust long-horizon planning and real-time adaptability in dynamic environments.