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版本更新 · v2arXiv:2610.34128

Semantic-Coupled Perception Framework for Autonomous Laparoscope Control

Enduo Zhao, Fangzhou Li, Yujie Zhu, Sicheng Lu, Fang Chen, Longfei Ma, Hongen Liao

原文摘要

In minimally invasive surgery (MIS), maintaining an appropriate field of view (FoV) is essential for surgical safety and efficiency. However, conventional autonomous laparoscope control methods often treat visual tracking and surgical phase recognition independently, limiting robustness under occlusion, entity overlap, and camera motion. This paper proposes a closed-loop autonomous laparoscope control framework that couples real-time surgical phase recognition with camera-motion-compensated visual perception. By exploiting phase-dependent relationships among surgical entities, the framework improves target state estimation under challenging visual conditions, supporting task-aware autonomous FoV control. Benchmark and phantom experiments evaluate the framework in surgical phase recognition, target tracking under occlusion and entity overlap, and autonomous FoV control, showing improved target visibility and task execution compared with fixed-FoV control. These results highlight the potential of incorporating task-level semantics into visual state estimation for autonomous laparoscope guidance.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15161

CineMME: From Dialogue Grounding to Narrative Reasoning in Multimodal Video Understanding

Jin Liu, Dawei Du, Yexiang Liu, Sijie Zhu, Fan Chen, Junxian Duan, Huaibo Huang, Ran He 等 10 位作者

原文摘要

Narrative video understanding requires more than recognizing visible events: models must extract evidence from dialogue, bind it to speakers, and use it to infer narrative meaning. Existing video benchmarks evaluate aspects of this problem, including video question answering, audio-visual reasoning, and temporal grounding, but rarely test whether these abilities form a coherent dialogue-to-plot evidence chain within the same clips. We introduce \textbf{CineMME}, a human-verified benchmark designed to diagnose dialogue-grounded plot understanding, supported by approximately $1,600$ expert person-hours of annotation. CineMME contains $975$ curated short narrative video clips and two linked tracks: \textit{CineMME-Grounding}, with $13,554$ timestamped speech segments and $33,083$ speaker face boxes for temporal dialogue localization, transcription, and active-speaker grounding; and \textit{CineMME-Reasoning}, with $1,600$ multiple-choice and $316$ open-ended questions spanning perception, audio, narrative, social, and scene-presentation understanding. Our main evaluation compares $20$ MLLMs on reasoning and $9$ omni-modal models on grounding. The results reveal a pronounced who-what-when decoupling: Gemini-3 Pro localizes dialogue in time ($68.34\%$ tIoU) and transcribes it with a WER of $28.99\%$, yet achieves only $7.74\%$ vIoU for active-speaker grounding while achieving $67.66\%$ MCQ accuracy on questions with extractable answers. Modality ablations further show that vision is not uniformly beneficial: it improves performance on perception-heavy questions but can hurt dialogue-centric reasoning. CineMME provides a compact diagnostic testbed for evaluating how multimodal video models connect dialogue, speakers, visual evidence, and narrative meaning.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14969

Scaling VLA Serving Under Robotic Execution Constraints

Yue Zhang, Zicong Ye, Lufang Chen, Dance Yang, Mingyang Li, Zhenwei Duan, Michael Zhao, Ziye Deng 等 17 位作者

原文摘要

Vision-language-action (VLA) models have emerged as a promising paradigm for general-purpose robotic manipulation. In our production setting, hundreds of VLA-powered robots are already operating in real-world environments, creating an immediate need for efficient fleet serving. We present XInfer, a production-deployed VLA serving system designed to reduce the inference infrastructure cost of large-scale robotic deployments. XInfer jointly coordinates heterogeneous VLA computation with the evolving response requirements of robotic execution. By exploiting computational parallelism while managing interference, XInfer improves GPU efficiency while protecting time-critical robotic feedback. We evaluate XInfer across three VLA models using production-derived workloads, achieving 1.26-3.75 times the effective robot capacity (ERC) of state-of-the-art systems under physical-execution constraints. Our implementation is publicly available at https://github.com/X-Square-Robot/xinfer.

cs.RO · cs.DC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.00763

Analyzing and Improving Training-Free Fast Sampling of Text-to-Image Diffusion Models

Zhenyu Zhou, Defang Chen, Siwei Lyu, Chun Chen, Can Wang

原文摘要

Text-to-image diffusion models have achieved unprecedented success but still struggle to produce high-quality results under limited sampling budgets. Existing training-free sampling acceleration methods are typically developed independently, leaving the overall performance and compatibility among these methods unexplored. In this paper, we bridge this gap by systematically elucidating the design space, and our comprehensive experiments identify the sampling time schedule as the most pivotal factor. Inspired by the geometric properties of diffusion models revealed through the Frenet-Serret formulas, we propose constant total rotation schedule (TORS), a scheduling strategy that ensures uniform geometric variation along the sampling trajectory. TORS outperforms previous training-free acceleration methods and produces high-quality images with 10 sampling steps on Flux.1-Dev and Stable Diffusion 3.5. Extensive experiments underscore the adaptability of our method to unseen models, hyperparameters, and downstream applications. Code is available at https://github.com/zju-pi/TORS.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13503

FaiSonic MMD: A popular music dataset with temporally aligned multidimensional annotations for multitask music analysis

Tieyao Zhang, Yuke Liu, Jiaxing Yu, Xinda Wu, Genfang Chen

原文摘要

Temporal analysis in music information retrieval concerns the positions of musical events and structures within recordings. Joint modeling has demonstrated the value of combining musical descriptions, but existing resources differ in joint coverage and expert verification across dimensions, constraining detailed joint supervision and the analysis of relationships on the same recordings. To support multitask modeling and the study of relationships among musical dimensions in popular music, we introduce FaiSonic Multidimensional Music Dataset (FaiSonic MMD), containing 500 songs totaling approximately 32.86 hours, comprising 250 Chinese-language and 250 English-language songs. Professional musicians annotate phrases, sections, chords, and rhythm in real recordings, followed by independent review. Events and intervals across dimensions share a recording-based time reference while retaining their respective musical granularity and boundaries, allowing direct comparison across musical descriptions. We describe the annotations, collection procedure, and statistics, and establish a comprehensive multitask baseline for phrase boundaries, section boundaries, section functions, chords, beats, and downbeats. We report reference performance for all prediction targets. FaiSonic MMD provides shared expert annotations and accompanying benchmarks for temporal music analysis.

cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10338

On the universal structure in deterministic generative diffusion

Defang Chen, Zhenyu Zhou, Can Wang, Zeyu Zheng, Siwei Lyu

原文摘要

Diffusion-based generative modeling has emerged as a general computational framework for simulating high-dimensional, structured data across various domains. Understanding its generative mechanisms is paramount for advancing model training and inference in a principled way. Here, we demonstrate that deterministic sampling trajectories share a universal geometric structure invariant to data modalities and diffusion model implementations. This structure can be largely explained by an exactly solvable model based on the optimal linear denoiser.

cs.LG · cond-mat.stat-mech · cs.CV · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08162

RouteAlign: Aligning Task Requirements with Model Profiles for LLM Service Selection

Jihao Zhao, Ding Chen, Yanfang Chen, Bo Tang, Feiyu Xiong, Junchi Yan, Zhiyu Li

原文摘要

LLM service selection requires performance evidence that reflects the demands of the current task. Aggregate scores obscure differences in capability and difficulty, while semantically related examples can involve different combinations of skills. We present RouteAlign, which uses task requirements to structure both the measurement of model performance and the selection of services. Task requirements describe capability demand and difficulty, enabling alignment with corresponding model performance measurements. Historical requirements determine how outcomes contribute to global profiles and local capability estimates; incoming requirements determine how those measurements inform the current decision. A shared scorer integrates the evidence, and a generation-cost preference controls selection among eligible services. Evaluation across diverse tasks and heterogeneous LLM services shows that RouteAlign extends the empirical quality-cost frontier toward higher task quality. Comparisons with open Jev-style decision backends show consistent quality gains, with lower generation cost than their strongest tested configuration. Ablations support the contribution of local performance evidence, while catalog-update experiments demonstrate effective reuse of the fitted scorer with refreshed model measurements.

cs.SE · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.07084

DMuon-2: Scaling Matrix Optimizers in Data Parallelism with Topology-Aware Matrix Ownership and Communication

Starrick Liu, Ruizhi Chengze, Lufang Chen, Michael Zhao, Ran Yu, Xiaofan Li, Dance Kuzi, Yue Zhang 等 13 位作者

原文摘要

Matrix optimizers such as Muon update whole weight matrices, which conflicts with the element- or row-wise sharding of data-parallel runtimes. Once parameters are sharded as well, matrix optimizers incur extra communication or repeated computation. We present DMuon, a matrix-granular optimizer runtime for PyTorch FSDP (ZeRO-2/3) and HSDP. DMuon assigns each matrix a unique owner, which performs the Newton--Schulz (NS) iteration and optimizer update on the full matrix, and balances the load across owners by computational cost. On HSDP, ownership is global across replicas, so the cluster keeps a single copy of gradients and optimizer state, and updated parameters are published across nodes as soon as they are ready, overlapping subsequent computation. This layout makes every collective sparse and variable in length, so DMuon schedules these collectives as their data becomes ready and routes them along the cluster's two-level network. On up to 256 H100 GPUs, across three dense LLMs and two VLA models, DMuon matches or exceeds the training throughput of PyTorch FSDP2 with AdamW, reaching 1.05$\times$ that of FSDP2 with AdamW at the median on 256 GPUs. The optimized parameter gathering and gradient reduction absorb the extra communication and computation a matrix optimizer requires. Against a gather-then-replicate Muon baseline, DMuon's throughput is 2.1 times as high at the median.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07604

FisherDistill: Diffusion Distribution Distillation via Closed-Form Fisher Critics

Mahesh Bhosale, Yuanhao Zhai, Siwei Lyu, Junsong Yuan, David Doermann, Defang Chen

原文摘要

Recent advances in diffusion distillation have enabled high-quality image generation in only one or a few network function evaluations (NFEs). Despite their efficiency, aggressively distilled generators often retain a residual distribution gap from their multi-step teachers, motivating recent methods to introduce adversarial refinement with a learned discriminator. In this work, we ask whether such refinement necessarily requires a learned adversarial discriminator. We introduce \textbf{FisherDistill}, a simple alternative based on Fisher linear discriminant analysis. Using intermediate representations from the frozen teacher, we estimate the first- and second-order statistics of real and generated samples and derive a closed-form Fisher direction that maximally separates their feature distributions relative to within-class variation. This direction provides a lightweight refinement signal that complements the original distillation objective without requiring an additional adversarial discriminator or its optimization loop. Across multiple distillation frameworks, our approach consistently improves generation quality over their respective distilled baselines and substantially closes the gap to adversarially refined counterparts. Code: https://bhosalems.github.io/FisherDistill/

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07473

Echoes of Error: Residual-Directional Local Rollout Consistency for Timeseries Forecasting

Zheng Wang, Kaixuan Zhang, Xiaonan Lu, Wanfang Chen

原文摘要

In time-series forecasting, rollout errors are not merely one-step fitting discrepancies: once fed back into the evolving state, they can perturb future predictions. We formalize this effect through local rollout consistency, which compares the next prediction produced under teacher forcing with that produced under free running. In principle, this consistency can be enforced directly by tracking the residual-induced perturbation of the next state. In practice, however, the residual-to-state alignment operator is often difficult to design when the input state and output block have different dimensions, heterogeneous components, or architecture-dependent layouts. We therefore use a first-order local surrogate that penalizes the Jacobian-vector product of the predictor along the actual feedback perturbation, suppressing precisely the directions through which self-generated errors remain visible to future predictions. We further give the same loss a geometric interpretation near a predictive manifold: by controlling residual-induced directions that probe the normal bundle, the objective yields theoretical conditions under which free-running trajectories remain inside a stability tube around the manifold. Experiments on time series forecasting tasks support this view, showing that the proposed regularization improves predictive accuracy. More broadly, our results suggest a training principle for time-series forecasters and sequential predictors: a model should not only make small one-step errors, but also learn not to let its own errors echo into the future. Code is available at https://github.com/Wangzzzzzzzz/Echoes-Of-Error.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39491

Automated Verification of Hamiltonian Simulation by Quantum Signal Processing

Min Lai, Yu-Fang Chen, Shih-Han Hung

原文摘要

We present, to the best of our knowledge, the first fully automated verification framework for QSP-based Hamiltonian simulation. Our framework exploits the structure of the simulation algorithm: Hamiltonians are given as weighted sums of Pauli strings, embedded into larger unitaries through block encodings, and transformed into time-evolution operators by quantum signal processing (QSP). Using a symbolic Pauli-sum semantics for ancilla-indexed circuit blocks, our verifier proves that the distinguished all-zero ancilla block is proportional to the target Hamiltonian and infers the subnormalization factor automatically. It then verifies the QSP layer by extracting the implemented polynomial transformation and certifying its approximation error. This structure-aware abstraction is essential for scalability. Composing our verified block encoding and QSP wrapper yields an estimated macro-expanded size of about 1.52 million gates. The verifier certifies the block encoding once and checks the QSP layer through its abstract interface, without expanding the oracle calls. Our implementation verifies block encodings of structured Pauli-sum Hamiltonians with up to 256 system qubits and verifies QSP Hamiltonian-simulation instances by extracting their implemented polynomials and checking the resulting approximation bounds. These results show that fully automated verification can handle common Hamiltonian-simulation circuits at substantial scale when it exploits their algorithmic structure.

cs.PL · cs.LO · quant-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34435

CyberDual: AI Attackers versus AI Code Reviewers in Real Software Maintenance

Chenyuan Yang, Hwiwon Lee, Jongseong Kim, Yihan Yang, Dongjun Kim, Yinfang Chen, Lingming Zhang

原文摘要

Open-source projects now let AI agents review incoming code, but the code under review may itself come from an AI attacker. We present CyberDual, an arena for this dual role of AI: attacker agents hide an exploitable bug in real maintenance work on widely used open-source projects, and reviewer agents try to find and repair it. Execution decides each match: we rerun the attacker's proof of concept (PoC) on the code that comes out of review. Every attack is new, and the windows can be rebuilt each quarter from the latest upstream history. Three agentic reviewers catch 85-91% of 1,088 valid attacks from six attackers. Yet when the attacker spreads the bug over real upstream commits, about one in six attack slots still ends with a working exploit after review. The strongest attacker has open weights: GLM 5.3 almost never refuses, and its attacks make up 13 of the 28 that get past all three reviewers. When they miss, Claude reviewers mostly approve the attack, while GPT-6-Sol mostly repairs the wrong code; a reviewer from a second vendor catches about two thirds of a Claude reviewer's misses. Beyond a leaderboard, CyberDual has three uses. First, PoC execution gives both sides a reward that agent reports cannot game, so its surviving attacks can train reviewers against stronger attackers. Second, it can gate a release on both scores: Claude Sonnet 5 refuses one in five attack slots, yet attacks about as well as GPT-6-Sol, so refusal alone does not remove attack capability. Third, it can warn the teams behind a deployed reviewer when a new attacker crosses a threshold: GLM 5.3 in Claude Code already gets an exploit past Sonnet 5 in 17% of its attack slots.

cs.CR · cs.AI · cs.SE

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arXiv ↗PDF ↗