跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+0新增论文
+0其中 AI 相关
+2已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-08 16:36 · 全站最近入库：2026-11-08 06:02各信源进度 →

统计口径与数据新鲜度

统计 2026-11-07 16:51 — 2026-11-08 16:51（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-08 16:36最近发现新增版本：2026-11-08 06:02库内论文最新更新时间：2026-11-06 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

17,100 已收录论文14,661 AI 相关4,057 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 17,100 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-08 16:33（北京时间）
新论文 · v1arXiv:2611.02452

Adjustment control usability and dynamic accommodation in office chairs: A comparative study of two contemporary designs

Yejin Kang, Dusan Baek, Dongwoo Park, Kitae Hwang, Woojin Park

原文摘要

Adjustability is a core determinant of ergonomic fit in office chairs, but its benefits depend on users being able to operate the controls and, on the chair, continuing to support the body as posture changes during work. We compared two high-end office chairs, the T90 (Sidiz; Chair A) and the Aeron (Herman Miller; Chair B), in two experiments to explore which design features may explain differences between them. In Experiment 1, 20 participants adjusted ten controls common to both chairs and rated perceived adjustment difficulty on four items. Chair A had significantly lower overall difficulty (Wilcoxon signed-rank test, p = 0.008), with control-level advantages for headrest angle, armrest height, armrest rotation angle and lumbar support position; Chair B was easier for seat height, possibly because its side-mounted lever is more visible than Chair A's underside button. The headrest comparisons involved a third-party headrest fitted to Chair B, and control- and item-level tests were not corrected for multiple comparisons. In Experiment 2, the same participants completed a 15-minute multi-device task requiring repeated posture changes, reach and reorientation, then rated body-part discomfort, general comfort and satisfaction with five components. Discomfort and comfort did not differ; Chair A had higher satisfaction for the seat-pan and lumbar support (exploratory, uncorrected). Interviews suggested links to mechanism properties: Chair B's rear lumbar dial was often described as producing little perceptible change, and for controls without significant differences each chair appeared to leave a different aspect of the same need unmet. These findings suggest mechanical and interface properties that designers of adjustable office chairs may consider for dynamic, multi-device work.

cs.HC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02451

Vectorizing Recurrences over Semirings

Yihong Zhang, Bala Vinaithirthan, Karima Ma, Zachary Tatlock, Maaz Bin Safeer Ahmad, Andrew Adams

原文摘要

Recurrences are ubiquitous in signal and image processing, bioinformatics, numerical computing, combinatorial optimization, and many other domains. Because recurrences seem inherently sequential, they are often implemented serially and thus do not leverage vector instructions in modern hardware. This leaves much of the available, often highly parallel, hardware underutilized. In this paper, we present an algebra of recurrences over semirings and study algebraic identities that enable vectorization. Using our algebraic framework, we present a number of vectorizing transformations that yield efficient, vectorized implementations across various settings. These transformations naturally extend to 2D recurrences and mutual recurrences. We implement the recurrence algebra and the vectorizing transformations in a compiler called VecRec.VecRec compiles recurrence programs to efficient vectorized C++ implementations. The generated kernels can achieve up to an order-of-magnitude speedup over serial implementations across a wide variety of recurrences. A case study in bioinformatics shows that they are competitive with, and in many cases better than, existing libraries with manually-crafted vectorization.

cs.PL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02449

Turbo-GMM: FlashAttention-Inspired GPU-Accelerated Gaussian Mixture Model

Momin Abbas, Sadia Asif, Asad Ul Haq, Kaoutar El Maghraoui

原文摘要

Expectation-maximization (EM) for Gaussian mixture models (GMMs) requires scoring $N$ data points against $K$ mixture components. Prior fused GPU implementations, notably Flash-GMM, eliminate materialization of the $N \times K$ responsibility matrix but traverse the component dimension twice to compute normalization terms and accumulate sufficient statistics. We introduce Turbo-GMM, a single-sweep fused EM kernel that completes responsibility normalization and sufficient-statistic accumulation within one traversal of the component dimension. By retaining the complete component axis on chip, Turbo-GMM avoids the second score computation required by the released two-sweep Flash-GMM implementation. Its applicability is limited by register pressure, occupancy, and the component and feature dimensions, making it particularly suitable for large datasets with relatively small numbers of components. For larger configurations, Turbo-GMM employs a two-pass fallback following the established fused design. We establish algebraic equivalence to standard EM in real arithmetic and evaluate sixteen configurations on an NVIDIA A100. Under the reported benchmarking protocol, Turbo-GMM achieves a median $2.15\times$ speedup over the evaluated Flash-GMM implementation and up to $5.3\times$ and $9.9\times$ over PyTorch and \texttt{scikit-learn}, respectively. The results demonstrate the potential of single-sweep component traversal while identifying on-chip resource constraints as the principal limitation of this approach.

cs.LG · cs.AI · cs.AR · cs.DC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02447

DruMR: A Feedback Driven Mixed Reality Air Drumming System

Omar Elgohary, Viktor Radev, James Herbert, Akul Manoj

原文摘要

This paper introduces DruMR, a mixed reality (MR) drumming system designed to make learning drums more accessible, responsive, and feedback-driven. By combining Aerodrums hardware with the Meta Quest 3 headset, DruMR allows users to drum in a virtual environment without a physical drum set. Unlike existing VR drumming applications, DruMR emphasizes instructional value through quantitative feedback and future integration of AI-generated insights, refined quantitative and qualitative visualizations, reactive environment modes and more. Overall, this paper reviews the current state of the DruMR system, related works that influenced the systems development, planned user study comparing physical and virtual training, and future work to be added to the system.

cs.HC

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.15321

Rethinking the Readout: Unlocking Video Backbones for AI-Generated Video Detection

Manni Cui, Ziheng Qin, ZiAn Wang, Ruiqi Liu, Dianyuan Zou, Jianglan Wei, Han Zhou, Yu Liu 等 11 位作者

原文摘要

AI-generated videos (AIGVs) typically contain subtle temporal artifacts that arise from inter-frame inconsistencies rather than within individual frames. A detector that captures such artifacts should therefore benefit from video pretrained backbones over image only ones. In practice, however, video backbones with standard global readouts often fail to outperform strong image pretrained probes on AIGV benchmarks. We attribute this gap to excessive spatiotemporal aggregation in the readout. Video pretrained backbones tend to compress each frame into a single global descriptor. This compression suppresses local patch level temporal dynamics and discards inter patch relations, which are precisely the cues that AIGV detection most reliably depends on. Based on this, we propose Velocity Gated Patch Velocity Profiling (V-PVP), a lightweight readout that replaces only the aggregation layer with two parallel streams over the patch velocity field, adding only about $0.5$M trainable parameters. V-PVP serves as a general plug-and-play module that consistently improves performance across diverse video backbones under both end-to-end fine-tuning and linear probing settings. Our method reaches 95.28 AUC on AIGVDBench while keeping the backbone fully frozen. The results show that simply replacing the aggregation layer reactivates the temporal potential of frozen video backbones, restoring their advantage on AIGV detection. Code is available at https://github.com/manic-cui/Rethinking-the-readout.

cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33843

Real-Time Recurrent Qubit-State Discrimination on a Heterogeneous AI-Engine-FPGA Accelerator

Vu Le, Yilun Xu, Neel Vora, Gang Huang, Deepak Ganesan, Phuc Nguyen

原文摘要

Qubit readout, or qubit state discrimination, remains one of the slowest and most error-prone operations in superconducting quantum processors, making fast, accurate, and hardware-efficient classification essential for scalable quantum computing. Conventional discriminators integrate the I/Q record into a single point, discarding evidence a relaxing qubit leaves behind; full-resolution feed-forward classifiers recover it but are too costly to deploy under this constraint. Recurrent classifiers retain full resolution far more cheaply, but their loop-carried hidden state cannot fit a single AI Engine (AIE) tile's register capacity and spills to data memory, adding latency that grows with sequence length. We present a hardware-aware LSTM accelerator for an AMD Versal device that resolves this by partitioning at the dependency boundary - independent input projections on the AIE array, the loop-carried update in programmable logic (PL) - keeping pace with a 64 ns input cadence under a 1,000 ns budget. On the qubit affected by mid-readout state transitions, our design matches a full-resolution feed-forward network's accuracy (96.59% vs. 96.37%) using 403x fewer parameters and roughly 103x fewer DSPs while cutting relative error by 68.2% over GMM, 43.6% over QubiCML, and 16.82% over MF-NN. Using 4 out of 400 AIE tiles and 17 DSP58, it needs 2.2-8.2x fewer DSP58 blocks than QubiCML and MF-NN, and the AIE-PL partition cuts exposed tail latency by 60.3% versus a pure-AIE mapping and DSP58 usage 31.1x versus an all-PL implementation. Across eight qubits, a compact variant reaches 96.78% mean accuracy, the strongest hardware-deployed operating point. Temporal resolution and recurrent-state placement must then be treated jointly when designing low-latency quantum readout accelerators.

cs.AR

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2507.01652

Autoregressive Image Generation with Linear Complexity: A Spatial-Aware Decay Perspective

Yuxin Mao, Zhen Qin, Jinxing Zhou, Hui Deng, Xuyang Shen, Bin Fan, Jing Zhang, Yiran Zhong 等 9 位作者

原文摘要

Autoregressive (AR) models have recently emerged as a promising paradigm for image generation, as they can model visual tokens through the same next-token prediction principle that underlies large language models. However, most competitive AR image generators still rely on Transformer attention, whose quadratic token interaction cost and growing key-value cache become increasingly expensive for high-resolution visual sequences. Linear attention provides an attractive route to efficient AR generation, but our study shows that directly importing linear or decay-based attention from language modeling leads to clear quality degradation. We identify a key cause: after a 2D token map is flattened by raster scan, 1D sequence locality no longer faithfully reflects spatial locality, and conventional decay mechanisms may introduce a false-locality bias at row transitions. To address this problem, we propose Linear Attention with Spatial-Aware Decay (LASAD), a simple recurrent attention mechanism that modulates decay according to the original 2D token geometry while preserving linear complexity. Built upon this layer, LASADGen replaces standard attention in an AR image generator with spatial-aware linear attention. Experiments on ImageNet demonstrate that LASADGen achieves a favorable quality-efficiency tradeoff, improving generation quality over linear-attention baselines while reducing inference cost and memory usage compared with Transformer-based AR models.

cs.CV · cs.AI · cs.MM

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.07337

Elastoplastic inherent strain-based topology optimization for residual stress reduction in metal additive manufacturing

Takao Miki, Jike Han, Kazuhiro Izui, Shinji Nishiwaki

原文摘要

This paper proposes a topology optimization method for reducing the residual stress arising in the building process of metal additive manufacturing. First, a layer-by-layer process analysis model based on an elastoplastic inherent strain method is introduced. In this model, the incremental displacement is solved anew at each layer step, and the stress history is explicitly incorporated into the constitutive equation as the stress accumulated up to the previous step, which guarantees the stress continuity across layer interfaces without introducing activation strains. Next, the design sensitivity of this analysis model is derived based on the adjoint method. Taking the pair of the stress and the equivalent plastic strain as the state variables reduces the dependency between layer steps to a one-step recurrence, and the adjoint fields are constructed as a layer-by-layer reverse sweep that reuses the coefficient tensors obtained in the forward analysis. Consequently, the cost of the sensitivity analysis scales linearly with the number of layers and remains of the same order as that of the forward analysis. An optimization problem is then formulated based on the density method to minimize the P-norm of the residual stress at the completion of the building process under the volume and final-use compliance constraints, and the derived sensitivities are verified by comparison with central finite differences. Finally, the proposed method is demonstrated through two- and three-dimensional examples of residual stress minimization under a compliance constraint. The results clarify that, under the elastoplastic analysis, the maximum residual stress is bounded by the yield surface, and the optimization therefore reduces the extent of the yielded and plastic strain accumulating regions rather than the peak stress value.

cs.CE

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02440

Harvesting SWE Benchmarks from Wild Vibe Coding Sessions

Ao Lu, Yuanhao Zeng, Shaocheng Lan, Sijie Xu, Jiacheng Bao, Kuang Yan, Heng Lin, Kan Ren

原文摘要

Development teams need reproducible tasks from their own repositories and workflows to evaluate coding agents. Conversational developer-agent collaboration ("vibe coding") produces abundant coding sessions and repository checkpoints, but requirements evolve across turns, unrelated conversations co-occur with commits, and engineering efforts can span multiple sessions. We present SessionFarmer, an automated framework that turns these records into self-contained, executable software engineering tasks. It analyzes dialogue and commit history to filter unrelated sessions and group multi-turn, potentially multi-session interactions into work episodes centered on one engineering goal. For each episode, it reconstructs a hermetic execution container, synthesizes a task statement from the full context, and authors repository-grounded engineering-quality rubrics and unit tests. Closed-loop test refinement uses requirement-breaking mutants and a blind solver to reduce false rejections of valid alternatives and false acceptances of incomplete solutions. We construct CropChat, an open benchmark of 242 tasks across 61 public repositories and 14 programming languages, and CropEnterprise from proprietary enterprise logs. In a CropEnterprise audit, the original developers endorse the vast majority of reconstructed episode boundaries, task statements, and quality rubrics. Full-episode context covers substantially more user requirements than single-turn or single-session inputs. Test refinement cuts false-rejection rates and raises balanced accuracy against reference patch labels by 7-9 percentage points across solvers. Across 12 model-harness configurations on CropChat, the strongest setup achieves 39.3% Pass@1, while harness choice shifts Pass@1 by up to 8.4 percentage points for the same model.

cs.SE · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02438

As Expected? Efficient LLM Planning via Expectation-Observation Consistency Verification

Qingfu Zhu, Huanyang Zheng, Ting Liu, Wanxiang Che

原文摘要

LLM planning often relies on reactive executors (e.g., ReAct) that generate a single action at each step to remain grounded in the latest observations, incurring substantial inference cost. Batched executors reduce this cost by predicting multiple actions in a single model invocation, yet they are prone to compounding errors, as intermediate observations are ignored. We propose an expectation-observation consistency verification (EOCV) mechanism for batched executors, preserving grounding while improving efficiency. The mechanism generates expectations of action outcomes and compares them with the resulting observations. Consistency permits continued execution, whereas mismatches trigger early termination and replanning from the current observation. Experiments on text-based and multimodal benchmarks across multiple backbones show broadly comparable average task performance between EOCV and reactive executors, with a 29-35% reduction in average weighted token usage. Moreover, the mechanism is model-agnostic and remains effective when integrated into state-of-the-art planners.

cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02437

Evaluating Synthetic Skin Lesion Images for Classifier Training: A Safety-Aware Algorithm

Amindu Kumarasinghe, Vinh Bui, Reza Ghanbarzadeh

原文摘要

Skin cancer classifiers need large sets of annotated images, which are scarce, especially for rare lesion types. AI-generated images can fill the gap, but a generator produces images of widely varying quality, so selecting which images to retain is important. That choice usually rests on a single quality score, most often the Fréchet Inception Distance (FID), which has not been tested against what a classifier needs and cannot tell whether the generator memorizes its training photographs. We test that practice with a tournament of 16 quality metrics. Each metric alone selects a training subset from a pool of 275,472 images generated by a diffusion model (EDM2), and a classifier trained on each subset scores the metric per class. The tournament shows that no single metric serves every class: FID computed on Inception features gives the best classifier for one lesion type and no measurable advantage over random sampling for another, and the generation setting it prefers is also the one closest to the training images. We turn these per-class measurements into a safety-aware evaluation algorithm that weights each metric by its measured value in each class and removes images too close to a real training image. On the held-out ISIC-2018 test set, the algorithm improves Macro-$F_2$, a recall-weighted measure of classification performance, over the FID baseline for a Swin-B classifier by +0.0136 to +0.0164 across four safety thresholds (Bonferroni-corrected $p < 0.02$) and over random sampling by about +0.03, though not for ResNet-50. Tightening the threshold improves a memorization-safety score with no measurable change in utility, but every curated set remains less safe than random sampling. The results show a modest but statistically significant gain on the primary classifier, and offer a practical, safety-aware procedure for evaluating and using synthetic data.

cs.CV · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02436

M3Former: Fine-Grained Cross-Layer Mixing for Transformers

Jiasong Han, Yunkai Chai, Tong Zhu, Hengyu Zhang, Wei Fang, Jintao Wang, Guoqi Li, Yu Cheng

原文摘要

Residual connections give each Transformer block access to all earlier layers only through one fixed sum, broadcast to all of the block's inputs. This broadcast bottleneck imposes three constraints: the query, key, value and residual inputs of a block, which we call its roles, read the same vector; all hidden dimensions within a role share one set of weights over earlier layers; and every earlier layer has the same fixed weight for every token. We propose the Multi-Role Multi-Group Mixing Residual (M3), which removes all three: every role, and every group of hidden dimensions within a role, reads its own token-dependent mixture of all earlier layers. M3 starts as an exact residual connection and adds as little as 1.4% parameters and 1.8% FLOPs. From 100M to 1.4B parameters, M3Former outperforms the Transformer and coarse-grained cross-layer connections at every size, matching a Transformer 1.19-1.34x the size of the one it modifies, trained with 1.4-1.8x the compute. Relaxing only some of the constraints recovers only part of this gain. M3Former also improves sparse mixture-of-experts models of up to 4.6B parameters and trains more stably in deeper networks. Code: https://github.com/Kingchet10/m3former

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02432

Diagnosing Model--Item Interactions in Language Model Benchmarks with LSIRM

Jinwen Luo, Han Jiang, Ziang Xiao, Susu Zhang

原文摘要

Language models with similar benchmark accuracy can succeed on different items. Overall scores do not show where their performance differs or which item properties might explain those differences. We demonstrate how the latent space item response model (LSIRM) can help evaluators investigate these patterns and their consequences for model comparison. We analyze 1,348 language models on 5,761 BBH items and compare ways of presenting and scoring answers across BBH, GPQA, MMLU-Pro, and MuSR. We examine LSIRM maps, which summarize differences in performance across models and items, alongside item properties and records of correct and incorrect responses. In BBH, nearby items on these maps often share the same correct answer, such as "Yes" or "No." Across the four benchmarks, this pattern is stronger when answers are scored using option labels, such as A, B, C, and D, than when using the text of the options. We then estimate each model's tendency to perform better or worse on items with particular correct answers and quantify how this tendency contributes to its score. This separates an estimated contribution associated with correct answers from the remaining differences in item performance. Two models' total numbers of correct answers differed by only 14 out of 5,761 items. Excluding the estimated answer-category contribution from their scores moved their ranks hundreds of places in opposite directions, while keeping all other estimates unchanged. The approach helps evaluators identify differences hidden by overall accuracy and understand how the choice of what to include in a score affects model rankings.

cs.CL · cs.LG · stat.ME

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02431

A Parameter-Free Best-of-Both-Worlds Policy for Queueing Bandits

Jongyeong Lee, Chansoo Kim

原文摘要

We study online channel scheduling for a single queue served by $K$ channels under bandit feedback. We propose a simple best-of-both-worlds policy that explores when the queue is empty. During busy periods, it selects the empirical best channel while the prescribed conditions hold and, if any condition is violated, switches to a robust bandit policy to clear the queue. For arbitrary arrivals and services determined by an oblivious adversary, our policy achieves $\tilde{\mathcal{O}}(\sqrt{K L_T})$ worst-case queue length regret with high probability, where $L_T$ is the longest busy-period length. This result improves the previous $T^{3/4}$ dependence and matches the worst-case lower bound up to logarithmic factors. Under bounded i.i.d. stochastic models with a unique best stabilizing channel, we show that the cumulative queue length regret converges to a finite problem-dependent constant, and further establish the logarithmic expected worst-case queue length regret bound.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02430

InteractionBench: Measuring Timing, Task Accuracy, Whisper and Volume, and Grounding in Real-Time Voice Systems

Richard Yucheng He, Chen Xu, Yihang Liu, Tairan Chen, Baodong Cao

原文摘要

Most benchmarks for real-time voice systems focus on one aspect, such as turn-taking, instruction following, paralinguistic control, or conversational memory, each with its own data and set of systems. We present InteractionBench, a benchmark and evaluation harness that measures four dimensions on the same systems from the same recorded user audio: conversational timing, spoken-task accuracy, whisper and volume, and groundedness in conversations of more than twenty turns. The benchmark contains 221 items per system in thirteen scenarios. Before any system runs, we fix the expected floor-control actions for spoken tasks by rule and take the expected answers from scripted dialogues. We score whispering from the audio signal. We evaluate GPT-Live, GPT-Realtime, Gemini Live, Moshi, PersonaPlex, Freeze-Omni, and a turn-based cascade in 1,519 graded runs. Among the six full-duplex systems, timing and spoken-task accuracy are rank-correlated ($ρ= 0.83$), while the cascade has the highest spoken-task score (38/50) and the second-lowest timing score. No system corrects a user's grammar within the required window. Only Gemini Live whispers on most requests (8/10). Hosted systems pass 67-88% of groundedness probes, compared with 14-35% for open full-duplex models. Timing is measured where the client hears each system, so the timing of hosted systems includes network delay: for GPT-Live, whose server reports its own clock, removing a delay of about 1.5 s lowers its timing score from 86% to 56%. We also document where the protocol differs across systems, including the text instructions.

cs.SD · cs.CL · eess.AS

查看论文
arXiv ↗PDF ↗
版本更新 · v5arXiv:2509.24773

ViSSFlow: Unifying Video-Conditioned Sound and Speech Generation via Joint Learning

Xin Cheng, Yuyue Wang, Xihua Wang, Yihan Wu, Kaisi Guan, Yijing Chen, Peng Zhang, Xiaojiang Liu 等 10 位作者

原文摘要

Video-conditioned audio generation, including Video-to-Sound (V2S) and Visual Text-to-Speech (VisualTTS), has traditionally been studied as two separate tasks despite their shared objective of generating audio conditioned on visual content. Existing methods rely on task-specific architectures and training paradigms, while the possibility of a unified generative framework remains largely unexplored due to heterogeneous conditioning signals and limited joint training data. In this paper, we propose ViSSFlow, a unified flow-matching framework for both V2S and VisualTTS. To effectively integrate diverse conditioning modalities within a Diffusion Transformer (DiT), we introduce a disentangled condition aggregation mechanism that exploits the complementary roles of attention layers, using cross-attention to model semantic conditions and self-attention to capture temporally dense visual cues. Furthermore, contrary to the common assumption that joint optimization of V2S and VisualTTS causes task interference, we show that a single model can be trained end-to-end without sacrificing task-specific performance. To address the scarcity of paired training data covering both sound and speech generation, we further propose a simple yet effective feature-level data synthesis strategy that enables robust joint learning. Extensive experiments on V2S, VisualTTS, and joint generation benchmarks demonstrate that ViSSFlow consistently outperforms state-of-the-art task-specific baselines while providing a unified solution for video-conditioned audio generation. Code and demos are provided in Supplementary Material.

eess.AS · cs.AI · cs.CL · cs.CV · cs.SD

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02428

Singularity-Induced Local Complexity for High-Dimensional Learning Models

Atsuyoshi Muta, Masaaki Imaizumi

原文摘要

Many overparameterized models that have achieved remarkable success are singular models. That is, the set of optimal parameters consists of multiple points, or the Hessian of the generalization error at such parameters is degenerate, reflecting redundancy in the model parametrization. Therefore, understanding the effective dimension and intrinsic model complexity, beyond the apparent dimension of the parameter space, is essential for studying the generalization ability of such overparameterized models. In this work, we focus on the real log canonical threshold (RLCT) and its localized counterpart, the local RLCT, which are fundamental quantities for characterizing the generalization of singular models. We clarify the theoretical relationship between the local RLCT and existing two key metrics related to generalization under some localization assumption: (I) the rank of the Hessian matrix of the generalization error and (II) the covering number of a family of loss functions. This result provides novel insights into effective dimension and intrinsic complexity of overparameterized models.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02427

Architecture-Aware Mixed Precision for Replica-Exchange Training of Geometry-Aware Neural Operators with Uncertainty Quantification

Ujin Jeon, Michael Penwarden, Wenqian Chen, Guang Lin

原文摘要

Geometry-aware neural operators face substantial computational and memory demands when learning across complex physical domains. We introduce Architecture-Aware Mixed Precision (AA-MP), a replica-exchange training strategy that allocates numerical precision according to replica roles and architecture-specific operations. By retaining FP32 for refinement while selectively employing BF16 for exploration, AA-MP preserves predictive accuracy while improving training efficiency. Across fluid dynamics and three-dimensional structural mechanics benchmarks, AA-MP achieves comparable accuracy to full-precision training over an ensemble of runs, while reducing hot-replica peak GPU memory by 46.9% and training time by up to 25.8%. It also outperforms naive Automatic Mixed Precision (AMP) in memory efficiency and computational cost, with increasing memory advantages at larger model and geometry scales. Ablation studies show that retaining FP32 in the cold-replica is important for predictive accuracy, while additional FP32 operations in the exploratory replica provide limited benefits relative to their computational cost. Finally, conformal prediction for uncertainty calibration shows that AA-MP achieves near-nominal coverage with narrower prediction intervals than the alternatives. These results advance the scalability of geometry-aware scientific machine learning by reducing computational costs without compromising predictive accuracy or uncertainty quantification.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2509.19610

Look as You Leap: Planning Simultaneous Motion and Perception for High-DOF Robots

Qingxi Meng, Emiliano Flores, Carlos Quintero-Peña, Peizhu Qian, Zachary Kingston, Shannan K. Hamlin, Vaibhav Unhelkar, Lydia E. Kavraki

原文摘要

Most common tasks for robots in dynamic spaces require that the environment is regularly and actively perceived. The perception task considered in this work can represent a broad range of robot perception objectives, including object detection, human activity recognition, and human face detection. For example, a service robot may need to continuously localize an object during manipulation, while an assistive robot may need to reliably perceive a human face or activity for interaction and safety. These tasks impose perception constraints on robot motion. However, solving motion and perception tasks simultaneously is challenging, as their requirements often conflict. Furthermore, robots must react quickly to environmental changes, while directly evaluating perception quality (e.g., object detection confidence) is often expensive or infeasible at runtime. This problem is especially important in human-centered environments, such as homes and hospitals, where effective perception is essential for safe operation. In this work, we address motion planning for high-degree-of-freedom (DoF) robots from a start to a goal configuration with continuous perception constraints in static and dynamic environments. Our solution is a GPU-parallelized perception-score-guided probabilistic roadmap planner with a neural surrogate model (PS-PRM). Unlike existing active perception-, visibility-aware, or learning-based planners, our work jointly considers perception tasks and constraints when searching for a motion-planning solution. Our method uses a neural surrogate model to approximate perception scores, incorporates them into roadmap planning, and leverages GPU parallelism for efficient online replanning. We demonstrate that our planner outperforms RL- and trajectory-optimization-based baselines in static and dynamic environments in simulation and real-robot experiments.

cs.RO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02425

Finite attainment and efficient computation of regularized quantum channel relative entropy

Yiwei Xu, Kun Fang

原文摘要

Quantum channel relative entropy can be nonadditive, making its regularization essential for asymptotic analysis. We study when the regularized value is attained at finite blocklength and how efficiently it can be approximated. For the first question, we establish a sharp dichotomy: the regularized value is either attained already at the single-copy level, implying additivity at every blocklength, or is never attained at any finite blocklength. Notably, a simple two-copy additivity test distinguishes between these alternatives for qubit channels and for channel pairs in arbitrary dimensions admitting an interior optimizer. We further show that both the interiority condition and finite nonattainment are generic, holding for almost every channel pair. For the second question, we establish explicit convergence rates for approximating the regularized channel relative entropy. For fixed channel dimensions, $\widetilde O(\varepsilon^{-3/2})$ channel copies suffice to approximate the regularized value to additive accuracy $\varepsilon$. Alternatively, a semidefinite hierarchy achieves the same accuracy at level $\widetilde O(\varepsilon^{-2})$, yielding a certified polynomial-time approximation. Together, these results reveal the intriguing structure and generic behavior of regularized channel relative entropy, sharpening our understanding of both its analytical and computational aspects.

quant-ph · cs.IT

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.22615

GazeVLA: Learning Human Intention for Robotic Manipulation

Chengyang Li, Kaiyi Xiong, Yuan Xu, Lei Qian, Yizhou Wang, Wentao Zhu

原文摘要

Embodied foundation models have significantly propelled the advancement of artificial general intelligence, yet they rely heavily on large-scale data. Recent studies identify human behavior as one of the most scalable sources for fostering robotic physical intelligence. However, the inherent embodiment gap remains a fundamental challenge for effective human-to-robot learning. In this work, we model human intention to enable efficient learning from human demonstrations. Among human behavioral cues, gaze simultaneously encodes high-level task semantics and exhibits a synergistic coupling with low-level action. By modeling intention through gaze, we derive a mid-level representation that facilitates cross-embodiment knowledge transfer. We demonstrate that robots can internalize human intentions to enhance manipulation, even without robotic intention annotations. To capture the synergy between intention and action, we adopt a Chain-of-Thought reasoning paradigm during inference. Our approach enables the execution of long-horizon and fine-grained tasks across diverse embodiments. Extensive experiments in both simulation and real-world demonstrate that our method consistently outperforms baseline approaches and generalizes to unseen settings. Project page: https://gazevla.github.io .

cs.RO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03743

EvoMemState: Evolving Memory State for Personal Agents

Zhishang Xiang, Yunbo Tang, Zhongqiao Liu, Junkai Chen, Zerui Chen, Qingguo Hu, Yuxuan Zhang, William Grant 等 10 位作者

原文摘要

Long-term memory enables large language model (LLM) agents to provide continuous personalized assistance by retaining user information across interactions. Existing systems support this process through fact extraction, organization, updating, and retrieval, but largely maintain memories as independent facts. This fact-centric paradigm may overlook how new facts update, constrain, or conflict with earlier ones, while leaving their applicability, role, and priority for the current request implicit. In this paper, we introduce EvoMemState, a state-centric framework that augments persistent facts with two complementary states: Relational States that capture how facts remain valid and relate to one another as memory evolves, and a Reasoning State that determines how these facts should be applied to the current request. Specifically, EvoMemState consists of three key components: (i) Ontology-Guided Memory State Induction extracts Persistent Facts and constructs Relational States that maintain their validity, scope, and evolving relations. (ii) RL-Based State Evolution internalizes memory-use experience into a policy through reinforcement learning to generate a request-specific Reasoning State. (iii) State-Aware Reasoning then combines the relevant facts with their Relational States and the generated Reasoning State to guide the personal agent. Extensive experiments on long-term dialogue and memory-use benchmarks demonstrate consistent improvements over existing memory systems in retrieval completeness and memory-grounded response accuracy. Our code and implementation details are available at https://github.com/XMUDeepLIT/EvoMemState.git.

cs.IR · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02419

TERA: Task-Evidence Restoration for Underwater Object Detection under Bandwidth-Constrained Transmission

Heejin Kim, Joon-Seok Song, Eunil Park

原文摘要

Underwater object detectors are typically evaluated on pre-transmission images, overlooking additional degradation from bandwidth-constrained downscaling and compression. We introduce AquaTX, a controlled benchmark built from real underwater detection datasets with seen and held-out codecs. We also propose TERA, a metadata-blind receiver-side framework combining task-guided pixel restoration and dual-state feature correction while keeping the detector frozen. Under seen compound degradation, TERA improves mean average precision (mAP50:95, averaged over IoU thresholds from 0.50 to 0.95) from 37.7 to 50.0 on DUO-TX and from 31.5 to 41.3 on RUOD-TX relative to the frozen detector without restoration. TERA also preserves clean-input performance and remains competitive with detector fine-tuning under the held-out AVIF codec. These findings support transmission-aware evaluation and task-guided restoration for underwater object detection. Code is available at https://github.com/hijinkim/TERA.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02415

Behavior-Discriminative Reward Shaping for Reward-Robust Reinforcement Learning

Zixuan Liu, Fangzheng Wu, Brian Summa, Zizhan Zheng

原文摘要

Reward-robust RL typically models misspecification by specifying an uncertainty set that constrains the discrepancy of plausible rewards from a base reward. However, such reward space discrepancies can be behaviorally irrelevant: under potential-based reward shaping (PBRS), many distinct rewards preserve policy ordering. This can introduce substantial redundancy into standard uncertainty sets and degrade optimization performance. We propose Shaping-Aware Reward-Robust RL, which constructs uncertainty sets over PBRS equivalence classes by projecting each reward to a canonical representative, ensuring that the resulting set contains only rewards that induce behaviorally distinct policy rankings. We prove that this projection preserves the optimal robust value while shrinking the uncertainty set and improving empirical performance. Using the connection between robustness and regularization, we obtain a practical algorithm to solve the shaping-aware reward-robust RL problem and enjoy convergence guarantees under standard assumptions. Experiments on benchmarks spanning diverse task domains and levels of complexity show consistent improvements over representative robust RL baselines and exhibit improved robustness to reward perturbations.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2609.25816

Exponential Quantum Advantage in Testing Fourier Dimensionality

Kenny Chen

原文摘要

A boolean function $f$ has Fourier dimension $k$ if its strings with nonzero Fourier coefficients span a subspace of dimension $k$. We first consider the property testing task of determining whether a function has Fourier dimension at most $k$, or is $ε$-far from being so. We show that there is an $O(k/\sqrtε)$-query quantum property tester for this problem, which we show to be almost optimal. Combined with Gopalan \etal's classical lower bound of $Ω(2^{k/2})$, this demonstrates an exponential quantum advantage for this task. We complement this result with a $\tilde{O}(2^{k/2}/ε)$ classical tester, giving a quadratic improvement over the previous best tester, and essentially settling the classical query complexity. We then introduce and study the tolerant version of this problem. That is, determining whether a function is $αε$-close to having Fourier dimension at most $k$, or is $ε$-far from being so. Perhaps surprisingly, we show that this tolerant version of the problem is essentially just as hard as the standard version of the problem. That is, we show this problem has a $\tilde{O}(k/ε)$ quantum tester, and a $\tilde{O}(2^{k/2}/ε)$ classical tester.

quant-ph · cs.CC · cs.DS

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.35252

Labference: Benchmarking the Reasoning-Action Connection in Robotic Laboratories

Shitong Liu, Xu Yang, Jingwei Zhang, Sheng Lian, Xinyu Zhuang, Kaiwen Guo, Quan Liu, Chunhai Zhao 等 21 位作者

原文摘要

Realizing Embodied AI for Science requires robots to translate experimental goals into reliable physical procedures, reducing hazardous and repetitive human work while generating evidence for scientific discovery. Although language-based planning and learned manipulation provide complementary capabilities, their integration requires robots to adapt subsequent operations to actual execution outcomes. For example, an unsuccessful placement changes what must be done next, even when the experimental goal remains unchanged. Existing benchmarks assess planning, manipulation, and laboratory workflows through task completion and reasoning or execution metrics; however, aggregate performance leaves the connection between outcome-dependent choices and physical execution unresolved. To address this evaluation gap, we introduce Labference, a benchmark for evaluating how observed physical outcomes guide subsequent choices and how those choices translate into robotic laboratory actions. Its evaluation protocol is designed to test outcome-dependent behavioral adaptation through controlled evidence variations, while independently measuring operation selection, physical execution, and task completion. Matched Oracle-assisted continuations from shared checkpoints then assess which forms of support improve subsequent choices and task completion. Initial weighing evaluations reveal target-selection errors in the tested VLA policies despite successful object transfers, whereas GPT-driven control achieves correct selection but encounters execution failures and substantial computation costs. Together, these findings highlight the need to connect task-appropriate reasoning with reliable, timely action. By making this connection measurable and diagnosable, Labference supports targeted improvements toward autonomous experimentation for Embodied AI for Science.

cs.RO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02414

SGR-OPSD: Learning from Subgoal Plans with Graph-Regularized Credit Assignment for Agentic Reinforcement Learning

Shu Liu, Yang Chong, WenShi Chen, Man Lan

原文摘要

OPSD provides dense supervision for agentic reinforcement learning through privileged information feedback. However, existing privileged information often lacks explicit task-progression planning, while teacher-student log-probability gaps reflect shifts in model preference rather than true task progress. To address these limitations, we propose Subgoal-Guided Graph-Regularized OPSD (SGR-OPSD), a framework that combines subgoal supervision with interaction-derived evidence for turn-level credit assignment. Specifically, SGR-OPSD uses LLM-generated subgoal plans to provide dense action-level guidance, converting them into advantage weights via a constrained, KL-regularized objective. To ground credit in observed outcomes, we construct a Subgoal Progress Graph that estimates empirical returns from sampled transition frequencies. The graph compares empirical action returns against the state average to derive preferences that calibrate the teacher's credit weights. Evaluated on ALFWorld, WebShop, and Search-QA across two model scales (Qwen2.5-3B and 7B), SGR-OPSD consistently outperforms GRPO and strong self-distillation baselines, improving success rates over GRPO by up to 7.4 percentage points and reaching 85.8% on ALFWorld with Qwen2.5-7B. Ablation studies confirm that subgoal guidance and graph-based weight calibration are key to these performance gains.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02413

A Scientific Claim Stability Framework for Evaluating MRI Morphometry Pipelines

Duy-Cat Can, Dang-Duat Tran, Anh-Khang Nguyen, Quoc-Bao Tran, Trung-Hieu Do, Van-Tu Nguyen, Duy-Thanh Vu, Oliver Y. Chén 等 9 位作者

原文摘要

MRI morphometry pipelines are often evaluated by measurement agreement or downstream accuracy, yet their scientific value depends on the conclusions they support. Two pipelines can produce similar feature-level results yet support different conclusions about aging, cognitive decline, or neurodegeneration. We introduce Scientific Claim Stability (SCS), a claim-centric evaluation framework that converts region-of-interest (ROI) morphometry tables into auditable claim cards and evaluates their support under changes in dataset, processing pipeline, atlas, feature family, and target. Across 10,370 MRI scans from three datasets and five cohorts, SCS generates 2,783 claim cards and evaluates 89 claim-family pairs. The evaluation distinguishes stable, transferable, and fragile claims. FreeSurfer 8-FastSurfer comparisons show high evidence concordance and low anatomical-support overlap under the primary covariate specification. Acquisition-adjusted analyses show that the magnitude of support disagreement depends on the model specification. Structured review by specialists in geriatrics, neuroscience, and radiology, alongside auxiliary ChatGPT and Gemini reviewers, assesses claim plausibility, clarity, faithfulness, usefulness, and caution. SCS provides a reproducible way to audit the evidential stability of morphometry claims without assuming a universal gold standard.

cs.LG · q-bio.QM · stat.ME

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02412

A Novel Federated Learning Framework for Defending Against Gradient Inversion Attacks

Pengxin Guo, Ziying Huang, Shuang Zeng, Xiaodan Zhang, Weihong Ren, Yuyin Zhou, Liangqiong Qu

原文摘要

Federated Learning (FL) offers a paradigm for collaborative machine learning model training among clients, preserving data privacy by avoiding direct data sharing. However, the gradient information exchanged in FL has been shown to be vulnerable to Gradient Inversion Attacks (GIA), which can reconstruct sensitive user data. To counter these threats, various privacy-enhancing technologies like Secure Multi-party Computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP) have been integrated into FL. While effective, these methods often introduce significant trade-offs between privacy and model utility. By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy-preserving FL framework that effectively ``breaks the direct connection'' between the shared parameters and the local private data to defend against GIA. Our proposed framework, HyperFL++, leverages hypernetworks to generate local model parameters, where only the hypernetwork parameters are aggregated by the server. To ensure practicality, HyperFL++ incorporates a Row-wise Kronecker Decomposition (RKD) to mitigate the memory and communication overhead from the hypernetwork. Comprehensive theoretical analysis and extensive experiments validate our method's effectiveness in protecting data privacy while maintaining high model utility. Our code is available at https://github.com/Pengxin-Guo/HyperFL/tree/hyperfl%2B%2B.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.36672

Human-inspired, Task-Dimension-Guided Exploration for Efficient Learning in High Dimensions

Fanyu Zhu, Jiahui An, Ni Ji

原文摘要

Efficient exploration in high-dimensional decision spaces remains a central challenge for decision-making systems. Humans, in contrast, can navigate large decision spaces with remarkable efficiency. Recent behavioral studies suggest that humans reduce dimensionality in large decision spaces by probing candidate feature dimensions, identifying reward-relevant ones, and restricting the effective decision space. Inspired by this mechanism, we propose TDGE (Task-Dimension-Guided Exploration), a human-inspired, model-agnostic algorithm with an automatically constructed task-dimension--feature--item hierarchy. TDGE follows a top-down exploration strategy: it first selects task-relevant feature dimensions, then identifies informative features within those dimensions, and finally recommends concrete items based on the selected features. Experiments on MovieLens-20M, LastFM, and Amazon recommendation datasets show that TDGE substantially improves exploration efficiency and cold-start adaptation over baseline algorithms. Comparisons with other structured algorithms and ablation studies attribute these gains to TDGE's hierarchical structure and semantic feature-space exploration, with robust results across clustering methods and hierarchy depths. Recommendation-trajectory visualizations also show exploration patterns similar to human dimension-guided behavior.

cs.LG

查看论文
arXiv ↗PDF ↗