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版本更新 · v3arXiv:2611.08577

Meituan-Robotics-0: A Vision-Language-Action Foundation Model for Desktop Manipulation

Meituan Robotics Team, Caicheng Wang, Chen Zhao, Chengpeng Jin, Duo Xu, Fengjiao Chen, Guanglu Wan, Heqing Yang 等 38 位作者

原文摘要

We present Meituan-Robotics-0 (MR0), a vision-language-action foundation model and embodied-agent system for stationary-base desktop manipulation. MR0 combines 147 million vision-language samples with 15,060 hours of curated robot trajectories spanning 12 embodiments. A three-stage training recipe aligns multimodal representations with robot behavior through discrete action-token pre-training, transfers these priors to continuous cross-embodiment control through flow-matching mid-training, and adapts the policy through real-world post-training. We introduce Rollback, which pairs replaced policy segments with human corrections to provide positive and negative supervision for CFG-DAgger without value learning or advantage labeling. A unified end-effector interface supports heterogeneous embodiments, while asynchronous action-chunk merging maintains smooth execution under variable inference latency. An event-driven agent maintains persistent task events and reassesses plans when progress or feasibility changes, enabling cross-task retrieval and conditional task resumption. MR0 achieves 93.6% success on RoboTwin 2.0 and ranks fifth in the RoboDojo VLA track. Under matched HG-DAgger post-training, MR0 achieves higher average success rate and progress score than $π_{0.5}$ across three real-robot tasks. On Ethernet insertion, CFG-DAgger trained with Rollback data reaches 70% success and a progress score of 0.90. Qualitative agent deployments demonstrate cross-task event retrieval and recovery from temporarily unsatisfied task conditions.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15197

Anytime-Valid Empirical Likelihood Inference from LLM Annotations with Adaptive Human Auditing

Hongfu Gao, Jiang Hu, Pengfei Liu, Wang Zhou

原文摘要

Large language models (LLMs) are increasingly used to annotate unlabeled data as a fast, low-cost alternative to human annotation. However, their outputs can be biased and miscalibrated relative to human labels, potentially leading to misleading analysis and inference. We propose AAELI (Adaptive Human Auditing and Anytime-Valid Empirical Likelihood Inference), an online framework for estimating the population mean of human labels under a limited auditing budget. AAELI uses LLM outputs to guide adaptive human auditing and combines them with audited labels through predictable empirical-likelihood $e$-processes to construct anytime-valid confidence sequences. Our main theoretical result shows that a predictably learned efficient projection preserves the first-order profile empirical-likelihood statistic under adaptive auditing, without requiring a prescribed convergence rate for the projection estimator. This yields an exactly anytime-valid betting construction whose continuous-mixture form incurs a scalar rather than vector-dimensional mixing penalty. Supporting results establish adaptive empirical-likelihood asymptotics and show that a learned audit rule attains the oracle variance within the stated class of augmented estimating-equation designs under the same audit-probability floor and long-run expected budget. Experiments on synthetic data and three real-world datasets demonstrate that AAELI maintains empirical anytime coverage while requiring fewer human audits than the strongest competing baseline to reach a target precision.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.36637

Decompose Dynamics Before Learning Dependencies in Spatiotemporal Systems

Ziqi Wang, Daojiang Hu, Cheng Bao, Zhiwei Ling, Wenzhuo Qian, Jiahui Zhai, Hailiang Zhao

原文摘要

Relations in networked spatiotemporal systems are often learned from observations that entangle dynamics governed by different mechanisms, obscuring what evolves locally and how it propagates across nodes. We introduce Component-Aware Network Dynamics with Ordered Relations (CANDOR), which decomposes local dynamics before learning their dependencies. CANDOR represents each trajectory through a persistent background, gradual accumulation and release, and sparse shocks. Conditioned on these components, a delay-aware physical branch models edge and sample-dependent propagation over directed topology, while a topology-unconstrained functional branch discovers latent dependencies from background dynamics. Context-adaptive fusion combines functional, forward-propagation, and reverse-support forecasts, with training objectives encouraging specialized and semantically consistent representations. Experiments on two traffic benchmarks and three long-horizon water-quality datasets span two distinct spatiotemporal systems: human-driven urban traffic and naturally evolving river water quality. CANDOR achieves the lowest aggregate normalized error across five datasets, outperforming the overall runner-up by 7.25%. These results establish decomposition before dependency learning as an effective principle for spatiotemporal representation learning.

cs.CE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.05229

Santa Claus and Weighted Nash Social Welfare with Subadditive Valuations

Yuhang Chen, Yuda Feng, Weijiang Hu, Shi Li, Ruilong Zhang

原文摘要

We study max-min allocation (the Santa Claus problem) and weighted Nash social welfare for $n$ agents with subadditive valuations over $m$ indivisible items. Using demand queries, we give randomized polynomial-time approximation algorithms with factors $O(\sqrt n)$ and $O(Γ(w))$, respectively, where $Γ(w)=\exp((\ln n-H(w))/2)$ and $H(w)=-\sum_i w_i\ln w_i$ for positive weights summing to one. Both ratios are independent of $m$. The max-min result improves the $2n-1$ bound of Khot and Ponnuswami (2007). We also give simpler $O(\sqrt n)$ algorithms for additive valuations and for submodular valuations with value queries, improving the $O(\sqrt{n\log n})$ bound of Saha and Srinivasan (2018) in the additive case. For Nash welfare, $Γ(w)=1$ for equal weights and $Γ(w)\le\sqrt n$ in general. We prove a configuration-LP integrality gap of $Θ(Γ(w))$ for every prescribed weight vector, even for XOS valuations. For every fixed $D>0$, we also prove information-theoretic approximation lower bounds of $Ω_D(\sqrt{n/\ln n})$ and $Ω_D(Γ(w)/\sqrt{\ln n})$, respectively, for randomized algorithms making at most $n^D$ adaptive queries. These hold on XOS instances with $m=Θ(n)$ items, even with simultaneous access to value, demand, and XOS supporting-clause oracles. They apply both to guarantees with success probability at least $2/3$ and to guarantees in expected welfare. The weighted bound holds for every prescribed weight vector and is vacuous below one. Thus the oracle lower bounds are within an $O(\sqrt{\ln n})$ factor of our upper bounds, while the configuration-LP gaps match them up to constants. Our algorithms build on random matchings for large items and charge the aggregate loss across configurations to the at most $n$ matched items.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.01493

OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference

Zhuoyuan Wang, Hanjiang Hu, Xiyu Deng, Saviz Mowlavi, Yorie Nakahira

原文摘要

Solving diverse partial differential equations (PDEs) is fundamental in science and engineering. Large language models (LLMs) have demonstrated strong capabilities in code generation, symbolic reasoning, and tool use, but reliably solving PDEs across heterogeneous settings remains challenging. Prior work on LLM-based code generation and transformer-based foundation models for PDE learning has shown promising advances. However, a persistent trade-off between execution success rate and numerical accuracy arises, particularly when generalization to unseen parameters and boundary conditions is required. In this work, we propose OpInf-LLM, an LLM parametric PDE solving framework via operator inference. The proposed framework leverages small amounts of solution data to enable accurate prediction of diverse PDE instances, including unseen parameters and configurations, and provides seamless integration with LLMs for natural language task specification and physics-based reasoning of proper feature parameterization. Its low computational demands and unified solution pipeline further enable a high execution success rate across heterogeneous settings, opening new possibilities for generalizable reduced-order modeling in LLM-based PDE solving.

cs.LG · cs.AI

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1287ACL Anthology · ACL 2026

MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning

Weikang Shi, Aldrich Yu Information Engineering, Chinese University of Hong Kong, Rongyao Fang The Chinese University of Hong Kong, Houxing Ren Beijing University of Aeronautics and Astronautics, Ke Wang The Chinese University of Hong Kong, Aojun Zhou, Changyao Tian, Xinyu Fu Huawei Technologies Ltd. 等 14 位作者

原文摘要

While Large Language Models (LLMs) have excelled in textual reasoning, they struggle with mathematical domains like geometry that intrinsically rely on visual aids. Existing approaches to Visual Chain-of-Thought (VCoT) are often limited by rigid external tools or fail to generate the high-fidelity, strategically-timed diagrams necessary for complex problem-solving. To bridge this gap, we introduce MathCanvas, a comprehensive framework designed to endow unified Large Multimodal Models (LMMs) with intrinsic VCoT capabilities for mathematics. Our approach consists of two phases. First, a Visual Manipulation stage pre-trains the model on a novel 15.2M-pair corpus, comprising 10M caption-to-diagram pairs (MathCanvas-Imagen) and 5.2M step-by-step editing trajectories (MathCanvas-Edit), to master diagram generation and editing. Second, a Strategic Visual-Aided Reasoning stage fine-tunes the model on MathCanvas-Instruct, a new 219K-example dataset of interleaved visual-textual reasoning paths, teaching it when and how to leverage visual aids. To facilitate rigorous evaluation, we introduce MathCanvas-Bench, a challenging benchmark with 3K problems that require models to produce interleaved visual-textual solutions. Our model, BAGEL-Canvas, trained under this framework, achieves an 86% relative improvement over strong LMM baselines on MathCanvas-Bench, demonstrating excellent generalization to other public math benchmarks. Our work provides a complete toolkit—framework, datasets, and benchmark—to unlock complex, human-like visual reasoning in LMMs.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1327ACL Anthology · ACL 2026

Latent Attention Denoising: A Training-Free Energy-Based Framework for Mitigating Hallucinations in Vision-Language Models

Zhiwen Luo, Siyu Jiang, Weilong Jiang Huazhong University of Science and Technology, Kun He Huazhong University of Sceince and Technology

原文摘要

Visual hallucination remains a major obstacle to the reliability of Large Vision-Language Models (LVLMs). We argue that this issue originates from a fundamental statistical misspecification: the conventional softmax attention implicitly assumes i.i.d. noise, yet real LVLM attention patterns exhibit structured and competitive biases (e.g., attention sinks) that violate this assumption. To address this mismatch, we introduce Latent Attention Denoising (LAD), a principled and training-free framework that recasts attention calibration as a one-step score-based denoising process. LAD employs an interpretable energy function to derive an analytic score and applies a single Langevin-inspired update to actively steer corrupted attention logits toward more faithful configurations. This intervention imposes negligible computational overhead and operates at a speed comparable to standard greedy decoding. Extensive evaluations across diverse architectures confirm that LAD achieves superior performance on both generative and discriminative tasks, effectively mitigating hallucinations while maintaining efficiency comparable to standard decoding.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.767ACL Anthology · ACL 2026

Analyzing and Internalizing Complex Policy Documents for LLM Agents

Jiateng Liu, Zhenhailong Wang, Xiaojiang Huang Amazon, Yingjie Li Amazon, Xiang Li Amazon, Chenlei Guo Amazon, Xing Fan, Ruhi Sarikaya 等 9 位作者

原文摘要

Large language model agents rely on in-context policy documents encoding diverse business rules. As businesses scale, these documents grow, creating substantial computational overhead and motivating internalization methods that embed policy into model priors. Prior work focuses on generic prompts, but we find agentic policies span multiple complexity levels and demand heavier reasoning, posing greater challenges. We introduce an agentic benchmark generator with Controllable Complexity in agent policy across four levels, enabling systematic evaluation of agents under increasing complexity and providing a testbed for policy internalization. Our analysis shows that workflow-governing policy specifications are the hardest to reason over, and that SFT on gold trajectories with chain-of-thought is data-hungry and struggles at high complexity. We propose Category-Aware Policy Continued Pretraining, an automated pipeline that analyzes policies, extracts key specifications, categorizes them into factual, behavioral, and conditional types, and isolates those driving workflow complexity. This enables targeted “therapy” by synthesizing specialized training data for each type and improving internalization via an autoregressive pretraining loss. Extensive experiments show our synthetic data and objective consistently improve performance. Combined with SFT, our method outperforms the baseline across different settings, especially in data-sparse and high-complexity regimes, with gains up to 41% and 22% on Qwen-3-32B. Overall, we achieve 97.3% prompt reduction on our benchmark, and on \tau-Bench we further improve performance while reducing prompt requirements with very limited SFT data.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表