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版本更新 · v2arXiv:2611.12304

Evolving Infra: Agent-Adaptive LLM Serving at Scale

Hao Kang, Weili Xu, Ziyang Li, Zheng Du, Hanchen Li, Tian Xia, Song Bian, Tushar Krishna 等 11 位作者

原文摘要

Agentic workloads are a major source of LLM inference demand. Existing prefill--decode (PD) disaggregated serving systems offer limited flexibility for runtime adaptation of compute allocation and KV cache management, and rely on coarse signals that only partly capture the diverse and dynamic agent workload. These limitations lead to load imbalance between prefill and decode nodes and eviction of KV caches that agents will reuse. We present Evolving Infra, an agent-adaptive serving framework that adapts compute allocation to agent workloads and KV cache management to agent context management at runtime. We decouple compute allocation from key--value (KV) cache management, allowing nodes to switch efficiently between prefill and decode. Building on this design, an online planner matches the PD split to workload composition, while an agent-aware distributed KV manager guides cache eviction and transfer with signals from agent context management. Across coding, math, and computer-use tasks with GLM-5.2 and Kimi K2.6 on up to 256 GB300 GPUs, Evolving Infra reduces mean end-to-end request latency and P95 TTFT by up to 4.1x and 14x over Dynamo, NVIDIA's production framework. We open-source the system implementation of Evolving Infra at https://github.com/mit-han-lab/EvolvingInfra.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16463

Digitally Supported Transitional Care Pathways for Older Adults: A Multi-Site Co-Design Study

Camilla Gabrielsen, Roberta Bevilacqua, Lorena Rossi, Eleonora Bonifazi, Margherita Osimani, Ovidiu Anchidin, Ancuta Nemes, Ionut Anghel 等 13 位作者

原文摘要

During transitions from hospital to home, older adults are frequently affected by fragmented communication, limited coordination between levels of care, and insufficient post-discharge follow-up. Digital technologies, including remote patient monitoring (RPM), digital communication, and artificial intelligence (AI), offer opportunities to improve continuity of care. However, their integration needs to reflect the organizational and clinical contexts of different healthcare systems. This study presents the development and early implementation of digitally supported transitional care pathways within the TransCare project. Care pathways were analyzed and redesigned across three pilot sites in Italy, Romania, and Norway. Existing clinical and organizational processes were mapped to identify challenges and opportunities for improvement. Technology-augmented pathway matrices were developed to integrate RPM, digital communication tools, and AI-supported analytics across the stages of assessment, hospitalization, discharge, and post-discharge follow-up. The pathways were iteratively refined through a co-design process involving healthcare professionals, patients, caregivers, and technical partners. Early implementation experiences were subsequently analyzed across the three sites. Common challenges included fragmented communication and poor care coordination, particularly during discharge and post-discharge follow-up. However, the underlying cause and required solutions differed across the pilot sites, reflecting variations in patient profiles, healthcare organizations, available resources, and digital maturity.

cs.ET

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.37000

Cross-Organizational SysML Model Integration: A Survey of Challenges and AI-Supported Tasks

Zirui Li, Torsten Brix, Stephan Husung

原文摘要

Cross-organizational collaboration is widely regarded as a key promise of SysML-based Model-Based Systems Engineering (MBSE), yet practitioners still face persistent challenges when exchanging and integrating system models. In parallel, Large Language Models (LLMs) raise expectations for AI-assisted model understanding and integration, while reliability and required human oversight continue to pose challenges. This paper reports the results of an online questionnaire survey with 29 MBSE stakeholders involved in cross-organizational collaboration. Respondents rated eight predefined integration challenge categories and six AI-supported task types on five-point Likert scales. The results indicate that stakeholders perceive model integration as a multi-dimensional alignment problem across semantics, behavior, traceability, and exchange interoperability. These perceptions vary by organizational role and frequency of integration involvement. AI is rated highly useful for analysis tasks such as semantic structure analysis and inconsistency detection, and respondents predominantly prefer human-in-the-loop use with mandatory verification. These findings motivate AI support that enhances, rather than replaces, engineering responsibility in SysML-based integration.

cs.SE · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16461

Helgason-Fourier Random Features for Scalable Hyperbolic Latent Variables

Koshi Watanabe, Daichi Mochihashi, Hideitsu Hino, Takahiro Ogawa, Miki Haseyama

原文摘要

Hyperbolic geometry is well suited to embedding hierarchical data, capturing tree-like structure faithfully even in the low dimensions where Euclidean dimensionality reduction is severely constrained. We introduce Random Helgason--Fourier Features (RHFF), an explicit finite-dimensional feature map for kernels on hyperbolic space. We characterize the spectral density from which RHFF frequencies are drawn, allowing kernels such as the Matérn family to be specified directly in the frequency domain. We also prove a uniform approximation error bound for RHFF. Building on this representation, we develop a hyperbolic latent variable model called RHFF-LVM, which requires no numerical quadrature, variational bounds or auxiliary variables unlike previous hyperbolic GP-LVMs. Our RHFF-LVM recovers the metric of a synthetic tree more faithfully than all baselines and attains the best mean rank, driven by large gains on tree-structured atlases at $Q\!=\!2$. Furthermore, it fits $5\times10^{5}$ points in $505$ seconds, making it the first non-parametric hyperbolic LVM to reach this scale.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16460

Fine-Tuned Small Language Model for Synthetic Clinical Data Generation and Predictive Healthcare Analytics

Mihnea Sebastian Burlacu, Ionut Anghel, Tudor Cioara

原文摘要

The reduced availability of medical datasets represents an important challenge in the development of predictive models for remote healthcare solutions, especially when the available data are imbalanced or difficult to extend through traditional methods. Small Language Models (SLMs) have become a promising alternative to Large Language Models (LLMs) for addressing these limitations due to their lower computational requirements and adaptability to specialized tasks. However, their performance remains highly dependent on the quality and representativeness of the available training data. In this paper, we propose a method for clinical data augmentation based on a fine-tuned Microsoft Phi-2 model that is used to generate synthetic instances, with the purpose of improving deep learning-based model for predicting remotely monitored vital signs. The Phi-2 model was adapted to the medical domain through supervised fine-tuning on the MedQuad dataset, through a Quantized Low-Rank Adaptation (QLoRA)-based method and by applying few-shot prompting techniques. The resulting model was used afterwards in the augmentation of a small-sized dataset, composed of vital sign values and labels associated with pathological and physiological states. The experimental results demonstrate that fine-tuned SLMs improved the performance on medical domain, achieving higher scores for both lexical (ROUGE) and semantic (BERTScore) evaluation metrics compared to the pretrained model. Moreover, the fine-tuned SLM is effective for generating structured synthetic clinical data. When used for data augmentation, the generated dataset improved the performance of a LSTM predictive model, increasing the classification accuracy from 89.0% to 93.7% and showing consistent improvements in precision, recall, and F1-score.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.38694

Geometry Matters in Packed 3D Attention

Amil Khan, Anantajit Subrahmanya, B. S. Manjunath

原文摘要

Packing a 3D field into a token sequence gives every token two indices: a coordinate in the sample and a position in memory. Raster, Morton, and block-contiguous layouts change the position and leave the coordinate alone. A positional mechanism indexed by sequence position can therefore learn the memory layout rather than the object, and accuracy on the training layout cannot reveal that it has. We introduce PACK3D, a local-global attention operator that separates memory layout from attention geometry. It attends exactly within a coordinate-defined neighborhood, sees the whole field through pooled summaries, and rotates queries and keys with axial RoPE at token-grid coordinates. Because keys are rotated before pooling, each query-to-summary logit is a weighted sum of query-key terms evaluated at the summarized tokens' displacements from the query. The operator is equivariant to any repacking that carries coordinates, neighborhoods, summary memberships, and within-block slots along with the tokens. When evaluated on Allen WTC-11 microscopy at 262k tokens, PACK3D and a matched packed-sequence RoPE control reach comparable Dice on the training layout. Repacking the same validation volumes then costs the packed control 0.048 macro Dice on average, with a loss in all six seeds, while PACK3D changes by 0.0001. On MSD Heart and Spleen, sequence indexing again changes more predictions under repacking, at little cost in Dice. PACK3D also exceeds a parameter-matched 3D U-Net by 0.009 Dice over six seeds, and a full PACK3D layer runs within 7-31% of window-attention latency while adding a global pathway. Geometry, not storage order, should index attention over packed 3D fields. Code is available at https://github.com/amilworks/Pack3D.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16452

Can an LVLM Perceive Event Boundaries Like a Human?

Hyolim Kang, Jungsoo Lee, Yeji Song, Whie Jung, Janghoon Cho, Juntae Lee

原文摘要

Large Vision-Language Models (LVLMs) reason effectively about video content, but whether they perceive its event boundaries in a manner consistent with humans remains unclear. We first investigate this through Generic Event Boundary Detection (GEBD), which identifies event boundaries naturally perceived by humans in continuous video, without relying on predefined event categories. On Kinetics-GEBD dataset, LVLMs miss many boundaries when predicting directly from frames, but effectively refine candidates proposed by an unsupervised discovery method, achieving state-of-the-art unsupervised GEBD performance. We extend this discovery-refinement approach to hour-long egocentric video, where boundary discovery requires a broader semantic scale and whole-video LVLM refinement becomes impractical. We address these challenges through coarse-to-fine temporal discovery and localized LVLM accept/reject judgments. Human evaluation shows that the LVLM's agreement with group consensus falls within the range observed among human annotators, with disagreements concentrated on low-consensus cases. Using this pipeline, we construct EgoBoundary, a benchmark for event boundary detection in hour-scale egocentric video. Representative supervised and unsupervised methods degrade substantially on EgoBoundary, highlighting the challenge of long-horizon event boundary detection.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16451

HIFC-IQA: Train-Free Cross-Domain Image Quality Assessment via Dual-Process Cognition

Yu Li, Zhengran Shen, Puchao Zhou, Yukang Ding, Yachun Mi, Boyuan Yang, Xi Zhai, Shaohui Liu

原文摘要

The gap between controlled synthetic distortions and complex real-world degradations introduces a critical domain shift challenge for No-Reference Image Quality Assessment. Although Unsupervised Domain Adaptation (UDA) approaches attempt to address this issue, they remain constrained by substantial retraining overhead and cumulative error propagation inherent in iterative pseudo-labeling. Inspired by the dual-process theory of human cognition for remarkable generalization ability, we propose Hierarchical Intuitive and Fuzzy Consensus (HIFC), a completely train-free Cross-Domain IQA framework. HIFC employs asymmetric perceptual decomposition to disentangle semantic content from distortion-sensitive representations. The final quality score is fused through the synergy of two complementary branches: a memory consensus branch that retrieves a stable Fuzzy Quality Centroid from a synthetic reference gallery, and an intuitive branch that captures aesthetic polarization via zero-shot vision-language alignment. Extensive experiments show that HIFC achieves state-of-the-art performance across multiple benchmarks, attaining the highest average SRCC and PLCC among competing UDA methods. Most notably, in the challenging synthetic-to-authentic cross-domain setting, HIFC achieves a substantial performance margin over competing methods, demonstrating remarkable robustness and generalization.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16450

A Recipe for Physical Action Model Training

Shaojiang Wang, Geer Yang

原文摘要

Choosing robot model capacity and demonstration budgets also requires decisions about data composition, representation, optimization, and deployment. We connect these decisions through a common conditional-distribution target. Nested learning oracles give exact risk accounting; local information geometry, conditional mutual information, and empirical convergence provide conditional training prescriptions. Known-target numerical checks expose capacity-data coupling, representation-induced mixture mismatch, and an example where deployment KL increases while negative log-likelihood decreases. An analysis of 206 public PushT trajectories evaluates fixed-feature information-design criteria without fitting a policy. Under asymmetric evaluation targets, the selected allocations reduce this criterion by 4.5-6.2% relative to target matching; balanced targets show no benefit. These results clarify which quantities can support training recommendations and when an apparently favorable predictive metric has a different interpretation.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16447

A $Θ(n)$-Multiplicative Improvement of the Gilbert-Varshamov Bound for Linear and Quantum Codes

Chen Yuan, Ruiqi Zhu

原文摘要

The Gilbert-Varshamov (GV) bound is a fundamental benchmark for the existence of error-correcting codes. For non-linear $q$-ary codes of code length $n$, its finite length form is known to admit a multiplicative improvement of order $Θ(n)$. In contrast, for $q$-ary linear codes of arbitrary rates, the best previously known general improvement was only of order $Θ(\sqrt n)$. This leaves open whether the algebraic constraint of linearity inherently prevents a linear-order refinement. We show that it does not. For every fixed prime power $q$ and relative distance $0<δ<1-1/q$, we obtain a $Θ(n)$-multiplicative improvement of the GV bound for $q$-ary linear codes of arbitrary rate, matching the corresponding result for nonlinear codes. Our proof is based on a simple change of viewpoint. Rather than requiring the initial random linear construction to contain no low-weight vectors, we allow $O(n)$ low-weight points, show that they are independent in the underlying projective space, and then eliminate all of them simultaneously at the cost of a single dimension. The key probabilistic ingredient is an exponential bound on nontrivial linear dependencies among low-weight vectors. We further show that this approach can be extended to quantum codes despite the symplectic self-orthogonality constraint. We obtain a $Θ(n)$-multiplicative improvement of the quantum GV bound for every fixed $0<δ<1-1/q^2$ satisfying $H_{q^2}(δ)<1/2$. Thus, the same $Θ(n)$ finite length improvement is applicable to both general linear structure and symplectic self-orthogonality.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16446

A Serious Game for Training Civil Participation in Flood Crisis Management

Solmaz Goodarzi, Arseni Ivanov, David Maruscsák, Diana Quitéria Sousa

原文摘要

Floods are among the most frequent and destructive natural disasters, requiring citizens to make critical decisions under rapidly changing conditions. Most serious games related to flooding emphasize awareness, preparedness, and education in flood management, but there is still a research gap on how to train citizens to act under pressure, uncertainty, and stress. In this paper, we position Virtual Reality (VR) serious games as a promising approach to addressing this gap by providing experiential and immersive training opportunities for citizens. We propose a conceptual VR serious game that enables participants to experience realistic flood scenarios, practice decision-making, rehearse appropriate response behaviors, and develop response-related skills in a safe and controlled environment. In addition, we suggest a research agenda and an evaluation framework focused on flood risk awareness, response capabilities, and stress-related outcomes. The framework thoughtfully integrates subjective, objective, and physiological measures to assess the effectiveness of immersive flood-response training. Finally, we present design, methodological, ethical, and user-experience considerations in VR-based disaster training, and outline future research directions for the development and evaluation of immersive serious games for flood emergency response. The present study aims to contribute to the growing intersection of serious games, extended reality, and disaster risk reduction by establishing citizen-centered response training as a significant yet under-researched field.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2508.05167

PhysPatch: A Physically Realizable and Transferable Adversarial Patch Attack for Multimodal Large Language Models-based Autonomous Driving Systems

Qi Guo, Xiaojun Jia, Shanmin Pang, Simeng Qin, Lin Wang, Ju Jia, Yang Liu, Qing Guo

原文摘要

Multimodal Large Language Models (MLLMs) are becoming integral to autonomous driving (AD) systems due to their strong vision-language reasoning capabilities. However, MLLMs are vulnerable to adversarial attacks, particularly adversarial patch attacks, which can pose serious threats in real-world scenarios. Existing patch-based attack methods are primarily designed for object detection models and perform poorly when transferred to MLLM-based systems due to the latter's complex architectures and reasoning abilities. To address these limitations, we propose PhysPatch, a physically realizable and transferable adversarial patch framework tailored for MLLM-based AD systems. PhysPatch jointly optimizes patch location, shape, and content to enhance attack effectiveness and real-world applicability. It introduces a semantic-based mask initialization strategy for realistic placement, an SVD-based local alignment loss with patch-guided crop-resize to improve transferability, and a potential field-based mask refinement method. Extensive experiments across open-source, commercial, and reasoning-capable MLLMs demonstrate that PhysPatch significantly outperforms prior methods in steering MLLM-based AD systems toward target-aligned perception and planning outputs. Moreover, PhysPatch consistently places adversarial patches in physically feasible regions of AD scenes, ensuring strong real-world applicability and deployability.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16442

DesignTaste: Benchmarking AI-Generator Visual Design Against Award-Wining Works

Zhou Chen, JiaXiang Li, DanYing Pan, Ye Li, TianRui Yang, EnBao Zhang, YaNan Xiao, LiXin Sun 等 17 位作者

原文摘要

Text-to-image benchmarks commonly assess text-image alignment or overall preference. Whether models can produce good designs when a brief leaves the appearance open remains less well understood. We introduce DesignTaste, a visual design benchmark that compares generated images with award-winning works. From these works and their publicly available context, we derive 403 design briefs: 100 poster tasks from 100 Beste Plakate 2025 and 303 tasks spanning 20 categories from ADC 2026. Six leading commercial text-to-image models each generate an image from the same brief. A single model judge scores each output and the corresponding award-winning work independently on five evaluation dimensions. On each dataset, only ArtArk achieves a higher mean overall score than the award-winning works. All six models score lower in Creative Originality on both datasets. These results show that producing finished, visually appealing images does not imply that a model can develop good design solutions.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16439

HALO: Human-Aligned Data Collection for WhoLe-body MObile Manipulation

Shuai Yang, Fangyi Xu, Yishu Shen, Kecheng Zheng, Yinghao Xu

原文摘要

Learning whole-body mobile manipulation requires demonstrations that coordinate bimanual actions with locomotion across extended workspaces. However, existing interfaces trade off direct robot control, perception and action alignment, and natural operator mobility, making such demonstrations difficult to collect. We introduce HALO, a human-aligned data collection system for whole-body mobile manipulation. The system aligns the operator's perception and action space with the robot while preserving direct physical feedback and natural locomotion. Operators guide the robot end-effectors through kinesthetic teaching and move a low-resistance platform without the deployed mobile base while performing bimanual manipulation. This interface enables more natural whole-body demonstrations, but the close human-robot coupling introduces discrepancies between the demonstration and deployment settings. We address these discrepancies with deployment-aligned demonstration processing, which calibrates and temporally aligns platform motion with the deployed base response and masks and inpaints demonstrator hands in recorded observations. Compared with Mobile ALOHA and VR teleoperation, HALO enables up to 2.9x faster demonstration collection while maintaining comparable or higher success rates. Policies trained with HALO perform precise manipulation tasks, including zip-tie fastening, and achieve a 73.3% success rate on a six-stage long-horizon mobile manipulation task involving repeated coordinated base and arm motion. Project page: https://robbyant-research.github.io/Lingbot-ALOHA/

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.18230

Allocating Recurrent Compute in Looped Language Models

Ruhai Lin, Yiyang Guo, Rui-Jie Zhu, Hao Ye, Jason K. Eshraghian

原文摘要

Looped language models improve reasoning and knowledge manipulation by applying shared computation repeatedly. Existing systems usually repeat an entire layer stack, although a mixer and a dense feed-forward network (FFN) perform different operations and have different costs. We ask a narrower question: what should loop? We view recurrence as repeated composition of a state update and argue that an application is valuable when it exposes a new cross-position influence direction that remains observable at the task readout. Iterative Transport Rank (ITR) describes the cumulative influence trajectory; marginal ITR describes the nonredundant influence contributed by successive applications. This view motivates MixerLoop, which repeats each Gated DeltaNet mixer while applying its dense FFN once. We compare MixerLoop with no recurrence and full-block recurrence at 15M and 110M parameters under the same data, initialization, and architecture. A finite context-off intervention tests whether later mixer applications produce distinct, non-negligible, and beneficial changes at the final language-model readout. MixerLoop surpasses FullLoop on aggregate CORE at 15M and retains 41.5% of its CORE improvement at 110M while reducing recurrent-backbone projection FLOPs by 45.9%. These results show that the benefits of recurrent depth can be retained without repeatedly executing the dense FFN.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16433

daVinci-Prover: Agent-Native Mid-Training for Formal Theorem Proving

Wenhong Zhu, Xuefeng Li, Jiashuo Zhang, Yu Chen, Tiantian Mi, Qipeng Guo, Pengfei Liu

原文摘要

Advances in large language models (LLMs) are rapidly transforming formal theorem proving. Yet further progress remains constrained by the lack of a transparent and effective data recipe for systematically improving theorem-proving models. In this work, we argue that effective agentic provers require strong mathematical skill priors to ground theorem proving and agentic trajectories to enable long-horizon, interactive proof construction. To this end, we synthesize an agent-native 33.7B-token mid-training corpus covering decomposition, search, self-verification, and revision, together with verifier-grounded agentic trajectories. We further introduce the daVinci-Prover harness, which enables models to construct proofs and interact with a Lean verifier in an isolated workspace, and use it to collect approximately 36K high-quality agent trajectories for supervised fine-tuning. Our mid-training recipe yields an average improvement of 13.7 points over the matched Qwen3-8B SFT-only baseline across MiniF2F, FATE-M, and ProofNet. When applied to the Qwen3.5 series, our approach also yields strong performance across theorem-proving benchmarks and achieves near-saturating MiniF2F performance with memory compaction. The results highlight agent-native mid-training as an effective foundation for developing stronger interactive theorem-proving agents.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.11854

Decoupled Asynchronous System for Humanoids (DASH): Latency-Agnostic Scaling of Vision-Language-Action Models for Humanoid Manipulation

Bingzhang Wang, Nathan Zhao, Jialeng Ni, Jiayin Zheng, Yin Zhang, Kehua Chen, Jian Yao

原文摘要

Vision-language-action (VLA) models pair a vision-language backbone with an action head, but in closed-loop control the backbone sits on the control-critical path: while it computes, the robot keeps moving, so each executed action is conditioned on an increasingly stale observation. A larger backbone therefore directly blocks reactivity, especially in humanoid dexterous manipulation. We present DASH, a Decoupled Asynchronous System for Humanoids that removes this coupling. DASH splits the policy into a slow vision-language model (System 2) and a fast flow-matching action head (System 1) that run as concurrent processes over the LCM communication protocol, so the control rate is set by the action head alone, independent of backbone size. The action head fuses stale semantics from a frozen backbone with fresh vision from its own ResNet encoder, desynchronized training exposes it to the staleness it meets at deployment, and an adaptive temporal aggregator aligns overlapping action chunks under variable inference latency. On an asynchronous RoboTwin benchmark that charges inference latency in simulated time, DASH holds 20 Hz control rate from 0.8B to 8B backbones while the synchronous rate falls from 9.7 to 4.1 Hz, with higher success at every size. On a real humanoid with dexterous hands, DASH achieves clearly higher pick-and-place success than the synchronous pipeline.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.09670

Goodhart in the Planner: Sampling-Based Control Exploits Rewards Learned from Human Preferences

Soovadeep Bakshi, Apurba Bose

原文摘要

Robots increasingly plan by searching against a reward model learned from human preferences; we measure what this search does to the true objective. On HalfCheetah and Hopper with crowd-sourced labels, and on Meta-World drawer-open and plate-slide with synthetic labels and blind labels from the two authors, harder MPPI search cuts true return by up to 76% while held-out label accuracy stays at 0.75-0.97: with human labels the drawer opens in 0 of 20 runs, against 10 of 10 for the cloned policy the planner starts from. The effect persists with a learned dynamics model and with an image scorer. True return roughly follows how far the planner's weights move from its proposals ($\sqrt{\mathrm{KL}}$), and best-of-$N$ action selection with a verifier trained on synthetic labels falls below a single noisy action. We prove that $χ^2$-regularized MPPI at a temperature set by the scorer's error spread on the proposals is worst-case optimal, that no gain can be certified once that spread exceeds the scorer's own, and that an episode's return splits exactly into per-step terms, only some of which the guarantee covers. Calibrated from 100 proposal pairs ranked by true return (an oracle), this rule is the safest fix we test: it keeps on average 86-94% of the cloned policy's return on drawer-open and plate-slide, though it is cautious on window-open, where search helps. Calibrated from a person's own comparisons, it cannot detect a bias the scorer shares with that person; a second person, or correcting for pairs people cannot tell apart, limits the damage.

cs.RO · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16426

SoScale: Second-Order Scale Search for FP4 Quantization

Pengxiang Zhao, Xing Li, Hui-Ling Zhen, Weizhe Lin, Xianzhi Yu, Zhenhua Dong

原文摘要

Low-bit quantization reduces the memory footprint and inference cost of large language models (LLMs), yet accurate FP4 quantization remains challenging. Practical formats such as MXFP4, NVFP4, and HiF4 pair a 4-bit floating-point payload with block-wise or hierarchical scale codes, making accuracy highly sensitive to how each local distribution is scaled. Default maximum-absolute-value scaling preserves dynamic range, but can spend scarce FP4 levels on rarely occupied outlier regions and leave dense tensor values poorly resolved. Existing scale-search methods minimize local element-wise reconstruction error, optimizing tensor fidelity rather than layer-output fidelity. We propose SoScale, a second-order weight scale-code search method for hierarchical FP4 quantization. SoScale searches hardware-valid scale codes under a Hessian-aware layer reconstruction objective and couples this search with GPTQ-style error recovery, selecting scales by their impact on layer outputs. Across Qwen3 models, SoScale consistently reduces downstream quantization degradation under MXFP4, NVFP4, and HiF4.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.30022

Density Constraints Overcome the Energy-Density Trade-Off in Exchange-Correlation Functionals

Yuhao Chen, Donald G. Truhlar, Xiao He

原文摘要

Exchange--correlation functionals are trained mainly on energies, and their densities often degrade as their energies improve. We present a supervised-learning algorithm that trains functionals on energies and accurate densities without self-consistent-field (SCF) solutions. From one Fock build at a fixed accurate density, it predicts the density that the functional would reach self-consistently and penalizes its deviation from the reference; for functionals linear in their parameters, training is convex. Refitting the MN15 form to 1,500 reaction energies under a constraint on 98 coupled-cluster densities gives the density-constrained hybrid DCH-D4. On 19 held-out energy sets its mean normalized error ratio is 0.96, against 1.76 for MN15, and on 145 densities outside the fit its mean density error is 22 times lower than that of MN15. Deep-learning neural GGA and meta-GGA functionals (NN-GGA and NN-mGGA) trained on only 6 energies and 11 densities lower the atomization-energy error on 103 molecules from 14.6 to 5.6~kcal/mol relative to PBE and from 3.7 to 3.3~kcal/mol relative to SCAN, while roughly halving the density error of each base functional. Accurate densities can thus enter functional training at the scale of modern energy databases.

physics.chem-ph · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.03681

WISE: World-model-guided Imagination Scheduling for Efficient Post-training of Vision-Language-Action Models

Chenhao Zhang, Hanyu Zhao, Zihan Que, Hang Cheng, Tengfei Pan, Long Zeng

原文摘要

Post-training VLA policies typically rely on supervised fine-tuning with costly expert demonstrations or reinforcement learning with expensive and potentially unstable real-world exploration. World models offer a promising alternative by evaluating candidate behaviors through imagined futures, yet effective post-training requires more than accurate prediction: imagination must be scheduled where it is useful, bounded within reliable horizons, and translated into trustworthy policy supervision. In robotic manipulation, the value of imagination varies substantially across execution stages, while extended rollouts can accumulate prediction errors and introduce unreliable learning signals. We introduce WISE (World-model-guided Imagination Scheduling for Efficient Post-training of Vision-Language-Action Models), a unified framework that coordinates when and how world-model imagination is used during policy refinement. WISE selectively invokes imagination at interaction-relevant states, performs bounded multi-view rollouts, evaluates candidate futures using progress and completion signals, and uses their relative outcomes to refine actions generated from real interaction contexts. Extensive experiments with both $π_0$ and $π_{0.5}$ demonstrate consistent improvements across diverse manipulation tasks while reducing GPU computation time by approximately 80% compared with full imagination. Real-world evaluations further show substantial gains in robustness and generalization under diverse real-world distribution shifts.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.14281

UniFlow-RS: Unified Generative Prediction for Heterogeneous Remote-Sensing Tasks

Jianlong He, Liang Liao, Jing Xiao, Chaofeng Chen, Gui-Song Xia

原文摘要

Remote-sensing foundation models increasingly unify representations across sensing modalities and downstream tasks, yet their prediction mechanisms remain largely task-specific. Separate decoders, prediction heads, and objectives leave prediction fragmented across tasks. This naturally raises a question: can heterogeneous remote-sensing tasks share the prediction process? UniFlow-RS answers this question with a single latent flow-matching predictor shared across tasks. This requires addressing two key challenges: unifying heterogeneous prediction spaces and harmonizing joint optimization across tasks. First, we cast heterogeneous task outputs into structured visual targets in a common latent space, allowing them to be modeled by the same pretrained flow predictor. Task-dependent conditioning further accommodates differences in input configurations. Second, we optimize all tasks with a common Flow Matching objective, eliminating task-specific prediction losses. To mitigate optimization mismatch across tasks and modalities, we introduce Task-Modality Adaptive Timestep Sampling (TMAS), which dynamically allocates training timesteps according to the online learning state of each task-modality group. Using a single shared model, UniFlow-RS handles six tasks and thirteen datasets, surpassing recent task-specific state-of-the-art methods on several benchmarks while remaining competitive on the others. Beyond these benchmarks, the same checkpoint demonstrates strong zero-shot transfer across unseen datasets, task settings, occlusion conditions, and near-infrared false-color imagery, without target-specific adaptation. Codes are available at https://github.com/MindVision-AI/UniFlow-RS.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16424

PhysCapBench: A Cross-Domain Benchmark for Physically Grounded Video Captioning

Haofeng Wang, Pengcheng Wen, Ruiqi Li, Yuhao Zhang, Tiange Zhang, Boren Zheng, Xuanyi Liu, Siyu Tao 等 15 位作者

原文摘要

Physical AI aims to learn transferable physical knowledge from real-world experience to understand, predict, and act in the physical world. Internet videos and embodied demonstrations provide complementary observations of motion, interaction, and state evolution. A foundational data challenge is to measure and preserve this physical information as training supervision. In video-language pretraining, captions express observed processes in language, yet a semantically correct description can omit the interactions and transitions that define those processes. Rich physical experience in video does not automatically translate into sufficient language supervision. This gap calls for a yardstick to assess annotation quality, compare captioning strategies, and guide training-data refinement. Motivated by our data construction for world-model pretraining, we introduce PhysCapBench, to our knowledge the first unified physical-caption benchmark spanning all three domains: third-person scenes, egocentric manipulation, and handheld-gripper demonstrations. It provides human-verified annotations for 1,500 video sequences and evaluates accuracy and coverage through seven physical dimensions. We examine downstream utility through video reconstruction and manipulation-policy fine-tuning. Physical captions reduce reconstruction LPIPS by 8.6-9.5% on third-person videos and 26.9-30.9% on egocentric videos relative to metadata captions. Training-instruction enrichment improves both PhysCapBench scores and policy success, including a 7.5-percentage-point gain for pi0.5 on a RoboTwin 2.0 subtask. PhysCapBench makes the physical content of language supervision inspectable and comparable, providing a cross-domain basis for its evaluation and improvement.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16423

VOKE: Visual Object Kinematics Estimator for Dexterous In-Hand Manipulation

Shengjia Zhu, Wenqiang Xu, Lin Shao

原文摘要

Vision-based dexterous in-hand manipulation requires knowing not only where an object is, but also how it moves. However, most robotic manipulation systems rely on off-the-shelf visual perception models whose outputs are limited to static object masks, geometry, or 6D poses, leaving downstream controllers to infer object dynamics implicitly from noisy visual observations. We present VOKE, a kinematics-aware perception framework that augments existing object and hand-object pose estimators to recover a complete object kinematic state comprising pose, linear and angular velocity, and linear and angular acceleration. VOKE contains two core perception modules and a downstream application study. First, a Pluggable Kinematics Module (PKM) attaches to the output of a standard pose estimator and predicts the object's linear and angular velocity and acceleration from temporal visual features. Second, a Kinematics-guided Pose Refinement Module (KPRM) converts this motion vector into a second-order, motion-prior reference pose and refines the per-frame pose estimates. We evaluate VOKE on DexYCB and OakInk, where it consistently improves object pose estimation across three different backbones and substantially reduces velocity and acceleration estimation error compared with pose differencing. As a downstream application, we use Kinematics-Augmented Reinforcement Learning (KARL) to compare a kinematics-augmented policy with a pose-history baseline. After training the policies in simulation, we transfer the kinematics-augmented policy to the physical robot without policy fine-tuning, demonstrating the complete perception-to-control pathway in the target domain.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16422

BlinkMimic: Training a Humanoid Motion Tracker in Minutes on a Single GPU

Fuyi Chen

原文摘要

Whole-body motion tracking lets a humanoid robot follow reference motions, but training a tracking policy for a library of motions has usually taken hours of GPU time. SuperMimic showed that the policy can instead be trained by backpropagating through a world model learned from parallel simulation, and InstantTrack added Muon updates, a residual policy around the reference and an averaged deployed policy. We build on both and present BlinkMimic, a recipe that makes this training faster. On a simulated Unitree G1, BlinkMimic trains one policy for all 40 LAFAN1 clips in 3.4 minutes on a single RTX 4090, measured until three consecutive checkpoints reach a normalized return of at least 0.82 with no failed clip (mean of three seeds). We split training time into the cost of an iteration, the iterations needed to fit the motions, and the iterations needed to remove the last failures, and assign each component, inherited or new, to one of these terms. The new components are a step size that decreases once training has progressed, resets directed by failures of the deployed policy under deployment conditions, and GPU graph capture of the whole iteration. A leave-one-out ablation with three seeds per component shows which term each component affects, and further measurements of the gradient, the update rule and the failure cases explain these effects. Code and per-seed results are available at https://github.com/fuyichen0725/blinkmimic.

cs.RO · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.32955

Measure Before You Train: Typed Decision Models vs. a Classical Baseline vs. a Generative LLM for Routing a Personal Multilingual Inbox

Federico Lucca

原文摘要

Personal email agents decide, for every message, its type and urgency and whether to draft a reply, create a task or ignore it. In production these decisions are delegated to a generative LLM or to rules, and their error rate is rarely measured: the stored labels were written by the system itself. We describe Foyer, a self-hosted personal inbox agent, and a measurement protocol (stratified sampling, frozen snapshots, thread-grouped splits, secret redaction, human labels in the review loop) that produces ground truth before any training. On 231 emails in Italian and English we compare eleven systems: the stored production type, a TF-IDF + logistic-regression baseline, local and hosted typed decision models (Tev1, Nimble, Jeva, Jev) and generative LLMs. Four results. The stored production type scores 28% against a 57% majority class. With about two hundred labels a logistic regression has the highest point accuracy on every field (type 0.77 +/- 0.02 over five seeds) at a thousandth of the latency, but on every field at least one hosted model is statistically indistinguishable from it (exact McNemar, Holm-corrected). A generative model recovers part of the sender's relationship to the owner from the text (0.60); inheritance from the contact is right 98.6% of the time where the sender was seen before: it belongs in the contact graph, not in the prompt. Per-email accuracy is not the product metric: composed into actions, the classifier ignores 18% of the emails that needed action, against 4% for the best generative model; a cost-chosen threshold cuts that to 10%, indistinguishable from the hosted decision model's; on the adversarial corpus the thresholded classifier is the cheapest system above a missed-email price of 4. Two blind annotators agree with the reference at kappa 0.81 and 0.86 on type. v4 corrects threshold results computed on a truncated corpus in v1-v3.

cs.CL · cs.HC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.14894

Pro-t-VAE: Closed-Form Student's t Mixtures for Robust Prototype Discovery

Zhikang Xu, Jiarui Xing, Jian Wang

原文摘要

We present a robust deep generative framework for unsupervised image prototype discovery, built on a heavy-tailed Student's t mixture prior, that bridges statistical robustness and mathematical tractability. Existing generative approaches typically rely on noise-prone Gaussian priors or computationally intensive diffusion models; neither simultaneously provides explicit latent structure nor exact analytic optimization. While diffusion models can yield strong priors, iterative sampling and intractable likelihoods limit scalability and interpretability for prototype discovery. We address these issues by constructing a Student's t latent space via a Bayesian scale-mixture formulation. This yields a fully closed-form augmented ELBO for unsupervised Student's t mixture variational autoencoders, which, to our knowledge, is the first such objective applied to robust image prototype discovery, avoiding biased variational approximations and high computational costs in prior heavy-tailed or diffusion-based frameworks. Treating precision as a latent variable automatically modulates each data point's influence, downweighting outliers to learn a data-dependent measure of trust. The result is improved robustness, accuracy, and efficiency, with well-separated latent representations and more stable manifold learning than state-of-the-art methods. We validate our model on standard vision benchmarks and challenging neuroimaging tasks. In brain MRI-driven neurodegenerative disease analysis, our model recovers clean, anatomically coherent prototypes where clinical noise often collapses rare subtypes. These results highlight the ability of our framework to preserve anatomy-associated patterns for precise discovery in clinical settings. Our code is available at: https://github.com/xuzhikangnba/torch-tvae.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08286

Interactive Visual Gym: Evaluating Visual Interaction Models with Simulated Users in Interactive Worlds

Chenhao Zheng, Zixian Ma, Kai Li, Kanchana Ranasinghe, Jieyu Zhang, Artemis Panagopoulou, Weikai Huang, Jianxu Shangguan 等 14 位作者

原文摘要

Visual interaction models that support users during ongoing physical tasks require evaluation in closed loop, where assistant responses can change subsequent user behavior and task outcomes. Existing benchmarks largely rely on offline replay and therefore cannot capture these effects. We introduce Interactive Visual Gym, a framework for closed-loop evaluation and training of visual interaction models through joint simulation of executable task environments and human users. We contribute three simulators that recreate everyday tasks and human-aligned action spaces across cooking, household manipulation, and mobile-interface domains, together with a persona-conditioned user simulation harness that collaborates with the assistant while incorporating controlled execution errors and other out-of-plan behaviors. We instantiate the framework as IVG-Bench, a benchmark with 315 interaction scenarios. Evaluating state-of-the-art VLMs as visual interaction models reveals substantial limitations: the best-performing model, Claude Opus 5.5, completes only 53.5% of tasks within the time limit, exhibits significant weaknesses in factual grounding and actionable guidance, and is often too slow to prevent errors when response latency is considered. We verify our evaluation protocol with a human study which finds 78.5% agreement between our interaction-quality metric and participant preferences. We further show that the environments provide useful learning signals: in a proof-of-concept GRPO experiment, closed-loop training improves both task success and interaction quality for Qwen3.5-4B model in our cooking environment.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.22321

AVC-Reward: Event-Centric Audio-Video Consistency Evaluation

Yuruo Wu, Kaihang Pan, Kai Shen, Jiacheng Li, Shuyuan Tu, Li Tuoyu, Juncheng Li, Siliang Tang

原文摘要

As joint audio-video generation models become more capable, they can generate audio-visual scenes exhibiting three forms of complexity: multi-event composition, multi-layer audio mixture, and asymmetric cross-modal relations. However, current evaluations largely reuse metrics from earlier simple and unidirectional tasks. They typically evaluate an audio-video clip as a whole and rely on simple predefined relations, making them unsuitable for these increasingly complex scenes. We propose an event-centric evaluation framework that disentangles audio and visual events at the event level, analyzes each event independently, cross-validates relations across modalities, and aggregates the judgments into a coarse-to-fine, three-level consistency metric. The metric assesses global narrative harmony, event-level temporal synchronization, and fine-grained attribute alignment, providing a general formulation for audio-video consistency evaluation across diverse generation settings and cross-modal relations. We instantiate the metric as AVC-Reward, a trainable audio-video reward model. To evaluate the agreement between automatic judgments and human perception across generation paradigms and relation complexities, we construct AVC-Benchmark, which is primarily built from diverse positive and negative samples generated by various types of audio-video generation models. AVC-Reward achieves state-of-the-art alignment with human judgment among automatic methods, provides interpretable rationales that localize failures, and improves the audio-video consistency of a joint generation model when used as an online reinforcement-learning signal. Project page: https://tsuki0512.github.io/AVC-Reward.github.io/

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16418

Model Distribution-Aware Multimodal Dataset Distillation

Jiajun Shen, Yonglin Wu, Ruonan Yu, Xinchao Wang

原文摘要

Multimodal dataset distillation (MDD) aims to reduce the cost of vision-language pre-training by synthesizing compact image-text datasets. Although recent feature-matching methods have improved distillation efficiency, synthetic data may become specialized to the model representations encountered during optimization, limiting generalization to unseen model states. We propose Model Distribution-Aware Multimodal Dataset Distillation (MDA-DD), which leverages supervision from a dynamic staggered queue of models at different training stages. We further introduce asymmetric dual projection for complementary alignment in visual and linguistic spaces and an uncentered second-order moment objective that retains mean-related cross-modal information. Experiments on Flickr30K and MS-COCO demonstrate consistent retrieval improvements over existing methods across synthetic-data budgets. With only 100 synthetic pairs, MDA-DD improves IR@1 and TR@1 on Flickr30K by 12.9 and 18.2 percentage points, respectively, over the strongest baseline at the same budget, outperforming all compared methods using 500 pairs on both datasets across all six retrieval metrics.

cs.CV

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arXiv ↗PDF ↗