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

+1072新增论文
+845其中 AI 相关
+94已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-09 18:35 · 全站最近入库：2026-11-09 13:22各信源进度 →

统计口径与数据新鲜度

统计 2026-11-08 18:54 — 2026-11-09 18:54（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：2026-11-09 18:35最近发现新增版本：2026-11-09 13:22库内论文最新更新时间：2026-11-07 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。

18,172 已收录论文15,506 AI 相关4,527 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 18,172 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-09 18:33（北京时间）
新论文 · v1arXiv:2610.36850

uTHCD-C: A Corruption-Robustness Benchmark for Tamil Handwritten Character Recognition

Preethi S, Meeradevi T, Arun Prakash A

原文摘要

Evaluation of Tamil handwritten character recognition (HCR) has largely focused on clean test images, and a standardized multi-corruption robustness benchmark for isolated Tamil HCR has not been established. We introduce uTHCD-C, a corruption-robustness benchmark based on the unconstrained Tamil Handwritten Character Database (uTHCD). The benchmark defines ten corruption types, including a handwriting-specific stroke-thinning corruption, at five severity levels. We evaluate nine convolutional and transformer-based architectures using five-fold stratified cross-validation and writer-independent testing. Robustness is quantified using mean Corruption Error (mCE), normalized against a VGG16-BN reference. We additionally compare parameter count, computational complexity, and inference throughput to characterize the computational requirements of the evaluated architectures. The results establish comparative robustness baselines for Tamil HCR, revealing substantial differences across architectures: ConvNeXt and Swin achieve the lowest mCE values (0.75 and 0.83, respectively), whereas ShuffleNet and MnasNet exhibit higher values (1.55 and 1.85, respectively). The accompanying efficiency measurements characterize the trade-offs between corruption robustness and computational requirements. By providing standardized corruption configurations, evaluation protocols, and comparative results, uTHCD-C establishes a reproducible basis for future research on robust and computationally efficient Tamil HCR.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36849

When All Rollouts Fail: Policy-Compatible Joint Decoding for Hard-Problem Reasoning

Zehao Wang, Hanzhu Chen, Runquan Gui, Zhe Wang, Lin Yang

原文摘要

Reinforcement Learning with Verifiable Rewards (RLVR) derives its learning signal from binary rewards via on-policy exploration and becomes a powerful paradigm for improving the reasoning of large language models. Yet it faces a learning cliff on problems beyond model's current ability: when every rollout fails, the samples reveal nothing about a correct solution, and group-relative estimators (e.g., GRPO, RLOO) collapse the update to zero advantage. Expert trajectories offer a remedy, but they are incompatible with the current policy and often lie in the student's low-probability regions, so the surviving updates demand large parameter shifts and decay into surface-level imitation rather than transferable reasoning. We argue that a useful learning signal on hard problems must be both verifiably correct and sufficiently compatible with the current policy. We propose Policy-Compatible Reinforcement Learning (PICO), which performs distribution-constrained joint decoding: expert data guides decoding at the token level while preserving the student's relative token probabilities, and a dynamic top-k mechanism confines the sampling distribution under a finite divergence budget, yielding trajectories that are verified and policy-compatible by construction. These trajectories are optimized jointly with on-policy rollouts under a mixed-behavior objective that mitigates their distribution mismatch via importance sampling. Experiments across reasoning diverse datasets and agentic benchmarks demonstrate that PICO consistently outperforms standard RLVR and prior off-policy baselines.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.35554

Metric Scale for Insta360 Structure-from-Motion from the Camera's Embedded IMU

Davide Cuttini, Diego Di Tommaso, Alessio Antoniacomi, Luca Simonetti

原文摘要

Structure-from-motion (SfM) from the dual-fisheye video of a consumer 360-degree camera recovers geometry only up to a similarity transform; in the pipeline considered here, metric scale is obtained from smartphone ARKit or ARCore poses captured alongside the camera. This paper evaluates a self-contained alternative: the IMU embedded in the Insta360 X5 and X6, recorded at 1 kHz. The sensor is characterised (noise density, camera-IMU synchronisation, accelerometer scale factor, bias stability), the camera-IMU rotation and frame timing are estimated from the data, and the scale is recovered by a linear visual-inertial alignment over the SfM poses of a whole survey. The standard formulation, with gravity and accelerometer bias shared across the survey, overestimates the scale by 3.4% on average; estimating each contiguous segment independently and averaging removes the bias. On nine surveys with a co-mounted LiDAR scanner the inertial scale deviates from the LiDAR by 0.81% in mean absolute error, against 1.94% for the smartphone scale; on printed fiducial markers both agree within the 1% uncertainty of the target. On the reference trajectories of thirty sequences of a public benchmark, brought to scales of 1/40, 1 and 40, the estimator recovers the metric scale with a mean error of -0.07% and a standard deviation of 1.28%. On fifty production surveys where the smartphone is the only reference, the two scales agree to a standard deviation of 2.3%, the inertial scale being 0.3% lower on average; the difference is uncorrelated with run length, grows with the extent of the smartphone anchor, and cannot be attributed to either reference. An ablation over window construction and a comparison with monocular metric-depth networks complete the study.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36848

Evolving Agent Teams

Shiyi Cao, Ziming Mao, Dacheng Li, Joseph E. Gonzalez, Ion Stoica

原文摘要

The rapid progress of software engineering agents drives interest in applying them to open-ended problems such as GPU kernel optimization. However, we find that existing coding agents converge to local optima on real-world, expert-written kernels (e.g., FlashInfer). We introduce Evolving Agent Teams, a multi-agent framework that escapes such local optima. It features (1) a central supervisor that diagnoses each local optimum and restructures a team of agents; and (2) a persistent file system that comprises a learning document, an evolution history, and per-team artifacts, grounding restructuring with accumulated experience. Under a matched token budget, Evolving Agent Teams consistently outperforms strong coding-agent baselines. With an extended token budget, Evolving Agent Teams generates a CUDA kernel that reaches 1.2x mean speedup over the production FlashInfer MLA paged decode kernel from scratch and wins on 36 of 47 production workloads from FlashInfer-Bench (0.86-0.99x on the remaining 11). It also demonstrates superior performance on tasks such as KDA prefill on AMD MI350X (3.01x over vLLM) and NVIDIA B200 (3.44x over FlashKDA), MoE expert-offload serving on Apple silicon (30.3 tokens/s decode for Qwen3.8-Flash-Next in MLX 4-bit on a 48 GB M4 Pro), and RoboDojo robot manipulation (62.1 on held-out seeds vs. 55.3 for the best leaderboard entry). Code is available at https://github.com/caoshiyi/EAT.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36847

Conservative Policy Iteration For Zero-Sum POSGs

Erwan C. Escudie, Matthia Sabatelli, Jilles Steeve Dibangoye

原文摘要

Occupancy-state reductions make dynamic programming possible for zero-sum partially observable stochastic games (zs-POSGs). Existing point-based solvers usually optimise a central value function. This restores Bellman recursion, but it requires point-based updates over high-dimensional occupancy spaces. We introduce an approximate policy-iteration algorithm for finite-horizon zs-POSGs. The method maintains one policy per player and updates both jointly along occupancy trajectories induced by conservative policy updates. Instead of approximating a full central value function, each update uses a single supporting hyperplane induced by the current joint policy. The algorithm alternates local policy improvements, conservative updates, and persistent exploration. Computation is therefore concentrated on occupancy regions where the current joint policy is exploitable. On finite-horizon zero-sum POSG benchmarks, the method is competitive at small horizons and often improves the runtime-exploitability trade-off at larger horizons compared with PBVI, HSVI, and CFR+.

cs.AI · cs.GT

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.15187

Does Attention-Guided Masking Really Help Object Discovery in Object-Centric Learning?

Youliang Tao, Yanhua Han, Bin Zhao, Juho Kannala, Joni Pajarinen, Rongzhen Zhao

原文摘要

Object-Centric Learning (OCL) aims to decompose images into objects without human annotations. A major family of mainstream methods uses Slot Attention to aggregate image features into object-level representations and then from them reconstructs masked image content, i.e., Random Masking (RM), to provide self-supervision. The recent method DIAS simply masks image patches at uniform randomness yet achieves competitive object discovery accuracy. Since attention during aggregation already possesses object discovery ability, we explore using it to develop a better image patch masking strategy, i.e., Attention Guided Masking (AGM), thereby providing better self-supervision. Results on six recognized datasets show that AGM does not always outperform RM. Under unconditional slot initialization, AGM substantially improves background segmentation on datasets with realistic textures (COCO and VOC); Regardless of conditional or unconditional slot initialization and across datasets, foreground object discovery remains comparable or decreases. We suggest peer researchers in the OCL community that attempts to exploit internal attention semantics to improve OCL with masked decoding are risky. Our source code, model checkpoints and evaluation logs are available on https://github.com/und-entropy/Does-Attention-Guided-Masking-Really-Help-Object-Discovery-in-Object-Centric-Learning-.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36844

AxisQ: Depth-Adaptive Channel--Frequency Quantization for Camouflaged Object Detection

Chi Ruan, Tianqi Li, Xin He, Xue Geng, Ming-Ming Cheng, Yun Liu

原文摘要

Camouflaged object detection (COD) depends on weak texture and boundary cues that can be destroyed by aggressive post-training quantization (PTQ). Existing PTQ methods mainly optimize quantizers or reconstruct network blocks, while using a single precision-allocation domain throughout hierarchical backbones. Instead, we introduce AxisQ, a depth-adaptive bit-allocation framework motivated by the observation that the useful allocation axis changes with feature depth. Under a fixed per-layer average activation-bit budget, AxisQ allocates precision in the channel domain for shallow features and in the local-frequency domain for deep features, using COD-aware sensitivity to better preserve subtle foreground regions and ambiguous boundaries. We also formulate a boundary-aware Hessian approximation for reconstruction by incorporating boundary-weighted gradient and error statistics. Experiments across multiple COD models and diverse benchmarks demonstrate improved accuracy over the strongest baselines at low bit widths, with relative weighted F-measure gains of 12.7% for CFRN and 33.3% for CamoFormer-P on CAMO under W2A2 quantization. These results support adapting the allocation domain to feature depth as a transferable quantization strategy for hierarchical COD models.

cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.25322

Simulation of temporomandibular joint space changes based on rigid-body transformation using virtual and semi-virtual models

Agnieszka Anna Tomaka, Krzysztof Domino, Michał Tarnawski, Emil Kalinowski, Małgorzata Domino, Dariusz Pojda

原文摘要

The integration of multimodal data within a common coordinate system is essential for spatial analysis of the stomatognathic system. This study aims to develop a multimodal data integration methodology for digital modeling of spatial changes in the temporomandibular joint (TMJ) across various mandible positions. A transformation tree framework was proposed to integrate CBCT, facial, and dental representation images into a common coordinate system and to simulate seven mandible positions using rigid-body transformations. Two proprietary models were applied. Spatial changes in mandible positions were propagated to TMJ articular surface positions using both models, although the results were model-related. The model-based uncertainty was quantified using error metrics. The consistency error revealed variability that dominated in the translation component over the rotation component of the rigid-body transformation, and showed anisotropy in the latero-lateral direction. The simulation errors were not uniform across mandible positions and were higher (p<0.0001) at the TMJ level (1.46 mm) than at the dental level (0.47 mm). As even small discrepancies in mandible position can lead to significant changes in the TMJ joint space, the proposed methodology may be useful for evaluating geometrically related positions of the mandible and TMJ, particularly when repeated radiographic imaging of the TMJs is undesirable.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36840

Trace-WAM: Learning to Act via 3D Motion Prediction for World Action Models

Xu Hu, Changjie Wu, Linzhe Shi, Hang Zhang, Chen Yang, Lingjun Zhang, Guowen Zhang, Mu Xu 等 11 位作者

原文摘要

World Action Models (WAMs) learn representations for robot control by coupling future visual prediction with action generation. Successful manipulation depends primarily on how the robot and task-relevant objects evolve, whereas future-video prediction must also reconstruct the appearance and background of the entire scene. We therefore propose Trace-WAM, which augments video-action learning with future 3D motion prediction as an auxiliary objective. This objective supervises temporal point correspondence together with image-plane displacement and depth change, directly describing the interaction dynamics and spatial changes involved in manipulation. To this end, we adopt a three-branch Mixture-of-Transformer (Tri-MoT) architecture to jointly learn video prediction, motion prediction, and action generation. The motion expert predicts future trajectories from current visual features, allowing motion supervision to shape the representations used for action generation. Because the action expert does not consume predicted trajectories, inference retains the current-observation policy path: a single visual forward pass followed by action denoising. Experiments on RoboCasa-GR1, LIBERO, and LIBERO-Plus demonstrate strong in-distribution performance and out-of-distribution generalization, with particularly clear gains over Fast-WAM under limited demonstrations and distribution shifts.

cs.RO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36838

MVRCap: Towards Comprehensive Video Captioning via Hierarchical Multi-Dimensional Verifiable Rewards

Weihuang Lin, Xianing Chen, Zhibin Wang, Congzhi Zhang, Yiwei Ma, Jiayi Ji, Miao Chen, Jun Song 等 12 位作者

原文摘要

Despite significant progress, current models struggle to generate comprehensive video captions, constrained primarily by coarse-grained supervision, which often fails to capture spatial-temporal dynamics and the underlying logical intent. To address this issue, we introduce MVRCap, a novel framework that reformulates video captioning as a reinforcement learning task steered by structured verification. Central to our approach is a Fine-Grained Verifiable Reward mechanism, serving as dense supervision to enforce rigorous visual-semantic alignment. Specifically, we construct this reward system anchored by three complementary pillars: a Hierarchical Concept Coverage Reward utilizing our newly constructed MVRCap-QA dataset to ensure multi-dimensional completeness and semantic depth, a Factuality Verification Reward to enhance factual consistency, and a Conciseness Constraint to maximize semantic density. Furthermore, we curate the high-quality MVRCap-SFT dataset, providing structured annotations across six key dimensions, to serve as a robust foundational initialization for the RL policy in this training pipeline. Extensive experiments demonstrate that MVRCap achieves state-of-the-art performance on authoritative benchmarks including VDC, CAPability, and VC4VG.

cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2505.11982

FedHQ: Hybrid Runtime Quantization for Federated Learning

Zihao Zheng, Ziyao Wang, Jiawei Yang, Hangyu Cao, Zhihao Mao, Maoliang Li, Jiayu Chen, Hailong Zou 等 10 位作者

原文摘要

Federated Learning (FL) must balance training efficiency and model accuracy across heterogeneous clients. Post-training quantization (PTQ) avoids the extra computation of quantization-aware training (QAT), but can incur greater accuracy loss. We propose FedHQ, a hybrid quantization framework that assigns PTQ or QAT according to clients' hardware and data characteristics. Hardware-based speed estimates and local data-distribution indicators guide coarse-grained global initialization, followed by fine-grained training-based adjustment of selected client strategies. Experiments on three image-classification datasets show that FedHQ achieves up to 2.47x training speedup over FedAQT and up to 11.15 percentage points higher accuracy than FedFQ.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36837

ExecDiffuser: Closing the Sample-to-Execution Gap in Diffusion Planning

Shuo Pei, Bosun Liang, Saihui Zha, Chen Sun, Huachun Tan, Yong Wang

原文摘要

Generative planners are often evaluated by the trajectories they can generate, although closed-loop driving ultimately depends on the single candidate committed to control. We identify this mismatch as the sample-to-execution gap and show that improving proposal quality alone can worsen the executed decision. Here, we introduce ExecDiffuser, a recurrent proposal-to-execution framework that couples candidate generation, trajectory selection, and subsequent replanning through shared execution semantics. Intent-Guided Diffusion Proposal (IGD) assigns hypothesis responsibility and shapes trajectories with execution-conditioned supervision. Execution-Grounded Selection (EGS) learns a candidate-conditioned publication utility, commits one trajectory to control, and returns it as context for the next planning cycle. Across nuPlan, InterPlan, and NAVSIM, ExecDiffuser achieves leading closed-loop performance against strong state-of-the-art baselines. Under a unified five-seed protocol, it obtains an average score of 85.62, improving over Diffusion Planner and FlowDrive by 4.02 and 1.31 points, respectively. On NAVSIM, it achieves 92.6 PDMS under the official evaluation pipeline. Matched counterfactual analysis further shows that ExecDiffuser more reliably converts strong candidates into committed decisions, while prefix interventions confirm that this advantage persists after physical execution and subsequent replanning.

cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.06286

LLMs Can Leak Training Data But Do They Want To? Evaluating Memorization Propensity in LLMs

Gianluca Barmina, Peter Schneider-Kamp, Lukas Galke Poech

原文摘要

Large language models can reproduce training data, but memorization evaluations mostly measure whether models can be made to do so, not whether they do so without adversarial prompting. We introduce PropMe, a framework that pairs a capability evaluation (prefix attacks) with non-adversarial propensity evaluation, and a propensity metric to relate the two. We check every generation against the model's full indexed training corpus with SimpleTrace, an open-source batch implementation of OLMoTrace span matching. We evaluate Comma, its continually pre-trained descendant DFM Decoder, and OLMo3 32B on Common Pile, Dynaword, and Dolma3, with four non-adversarial settings ranging from no prompt to sentences from unseen in-domain sources. Our results show that natural sentence prompts reproduce far less training text than prefix attacks. Generation with no prompt or a single common word, however, reproduces about as much as prefix attacks, or more, in three out of four model-corpus pairs; most of this text is duplicated licenses, code, and templates. In our setting, continual pre-training reduces long verbatim reproduction of the original corpus only modestly. More broadly, we argue that by testing both capability and propensity, we can attain a more comprehensive picture of a model's memorization behavior.

cs.CL · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36836

DuTri: Dual-Branch Modeling with Triadic Factorization for Multimodal Multi-Scenario Recommendation

Jintao Huang, Chaohua Yang, Kailiang Hao, Dugang Liu, Xing Tang, Zhong Ming

原文摘要

As recommendation scenarios become increasingly diverse and multimodal information grows more prevalent, effectively modeling cross-scenario user preferences and multimodal semantics has become a critical challenge in recommender systems. When incorporating multimodal information, existing multi-scenario recommendation (MSR) methods typically treat textual and visual representations as generic input features and directly feed them into their original parameter-sharing architectures. However, this extension may not fully accommodate the different modeling requirements of collaborative and multimodal information and may not be the best modeling paradigm. Our controlled experiments show that directly adopting these architectures is not the optimal modeling approach. To this end, we propose Dual-Branch Modeling with Triadic Factorization (DuTri), a framework for multimodal multi-scenario recommendation comprising a collaborative branch and a multimodal branch. Specifically, the collaborative branch learns structured collaborative preference representations through CANDECOMP/PARAFAC (CP) decomposition of user-scenario-item interactions and residual quantization, thereby capturing cross-scenario commonalities and scenario-specific differences. The multimodal branch leverages the learned user and scenario preferences to guide adaptive modality fusion; additionally, item-level contrastive learning aligns collaborative item preference representations with multimodal representations to facilitate effective collaboration between the two branches. Extensive experiments on two real-world datasets, AntM$^2$C and Amazon, demonstrate that DuTri outperforms existing multimodal and multi-scenario recommendation baselines in overall recommendation performance, validating the effectiveness of the proposed framework.

cs.IR

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.34991

Diagnosing and Mitigating Long-Session Degradation in Full-Duplex Speech Models

Junho Kim, CheHyun Lee, Donghun Kang, Youngho Cho, Dongwook Lee, Heeseung Kim, Sungroh Yoon

原文摘要

End-to-end full-duplex spoken dialogue models enable natural, real-time spoken interactions with interruptions, backchannels, and overlapping speech. Prior evaluations have assessed diverse interaction capabilities and identified some degradation patterns across conversational turns. However, how model behavior evolves over extended sessions remains insufficiently understood. In this work, we address this gap by systematically characterizing long-session degradation and developing efficient methods to mitigate it. We introduce a closed-loop evaluation framework for long-session stability and apply it to several publicly available full-duplex speech models, revealing degradation over time in responsiveness, spoken content quality, and voice consistency. To mitigate this degradation, we propose a self-distillation-based training framework for lightweight LoRA adaptation that improves long-session stability with a small amount of additional training. We further introduce a two-resolution context (TRC) architecture that preserves recent interactions in full detail while compressing older interaction history into key-value representations. We evaluate our methods on three models with distinct architectures, assessing long-session stability, information retention, and computational efficiency. Across all three models, we show that LoRA adaptation mitigates performance degradation over ten-minute sessions, while adding TRC extends these gains beyond the training session length and reduces KV-cache growth by more than 90%.

cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36833

Back to the Future: The Evolving Universal Relation Model

Nan Tang, Haixun Wang

原文摘要

Document backed analytics need databases that can be built as queries arrive. Enterprises increasingly ask analytical queries over messy document collections such as contracts, invoices, reports, emails, policies, and tickets, where the relevant structure and facts are often not known in advance. RAG can retrieve and summarize supporting evidence, but it does not by itself produce reusable, joinable, governed database state. Schema first construction offers the opposite strength: once a database is built, it supports joins, aggregation, constraints, indexes, optimization, and transactions. Yet for document analytics, fixing the schema and extracting all possible fields upfront is both expensive and brittle, because later queries and new documents often surface concepts that were not modeled before. We propose the Evolving Universal Relation Model (EUR), a query first model for document backed analytics. Users ask natural language queries as if the document corpus exposed one conceptual relation over application concepts. Internally, the DBMS turns each query into controlled database evolution: it updates the logical schema when new concepts are needed, extracts and integrates the facts required to answer the query, and decides what durable database state should remain for future queries. Thus, in EUR, a query is not only evaluated; it also helps construct the database. Document backed analytics becomes query first universal access plus workload-driven database schema and instance evolution.

cs.DB

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2505.07575

Data-Driven Laplacian-Constrained Personalized Federated Learning

Samuel Erickson, Mikael Johansson

原文摘要

Personalization addresses one of the defining challenges in federated learning: statistical heterogeneity among clients. We show that the dissimilarity between the ideal personalized models of two clients can be upper bounded by the dissimilarity of their distributions. Motivated by this, we propose Karula, a data-driven regularization method that constrains pairwise model dissimilarities between clients according to their distributional differences. Distributional similarity is measured using a surrogate for the 1-Wasserstein distance adapted for the federated setting, allowing the method to adapt to highly complex interrelations between clients that approaches based on hard clustering may fail to recover. We propose an approximate projected stochastic gradient algorithm to solve the resulting constrained optimization problem, and prove that it converges at a rate of $O(1/K)$ to a neighborhood of a stationary point.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36832

Drop the Details, Get the Gist: Dynamic Conceptual Representation for Video Recognition

Ryoya Yoshiyama, Bowen Wang, Yuta Nakashima

原文摘要

Concept Bottleneck Models (CBMs), which route predictions through a layer of human-interpretable concepts, have substantially improved the interpretability of visual classification by exposing an explicit, language-based rationale for each prediction. Most existing work focuses on static images, and temporal dynamics in videos have been underexplored in conceptual representation. We propose a framework that extends concept-based image recognition to procedural video understanding. It represents a multi-step video as a temporal sequence of segment-level concept score vectors and feeds this sequence into a Transformer encoder to predict the class label. We also explore a way to estimate the contribution of each concept for further interpretability. We evaluated three concept sets of different granularities on cooking and household-activity videos and found that coarser-grained concepts are sufficient for classifying these videos, which implies that fine-grained textual descriptions may be unnecessary.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36831

Right Click, Wrong Box: Grading GUI Grounding by What a Click Does

Xiaokang Yuan, Binhui Yao, Luwei Yang, Ruimin Chen, Zeng Wang

原文摘要

Leading GUI grounders score 93 to 96% on ScreenSpot-v2, and reported gaps between new and prior models are about a point. At this level the useful question is what the remaining errors are. Benchmarks count a click as correct only inside one annotated box, but interfaces often expose one action through several elements: a duplicated link, the label of a checkbox, the title next to a logo. The box is not wrong. The rule is incomplete. We call a click correct when executing it produces the same outcome as clicking the annotated target. A probe that executes and fingerprints about a thousand clicks per page turns this definition into outcome maps and an execution judge for live web applications, with no new annotation. We execute the predictions of three strong 7--8B grounders on a code forge and an online shop. About one in five and one in three of the clicks that box scoring rejects reach the intended outcome, and the shares remain one in seven and one in three when the instructions are rewritten as natural tasks. A blind audit of 11 models on ScreenSpot-v2 (three 7--8B grounders and eight 3B variants) finds the same effect on web screenshots, with two independent reviewers on a 60-unit subset. The share is highest for the strongest grounders. Training seeds and test-set sampling add noise that more runs and paired intervals control. Box scoring instead under-counts correct clicks, which repetition does not remove, and the audit-based correction of 0.2 to 1.8 points on ScreenSpot-v2 is as large as the gaps the field reports. The correction is needed in evaluation. In our 3B GRPO study, crediting equivalent clicks in the reward gives no stable gain over box rewards across three seeds and four test sets. We release the probe, the outcome maps, the audit and every prediction.

cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.18643

ProxyGuard: Direct Reliability Inference for Randomized Data Release Mechanisms with Shared Targets

Dipesh Tharu Mahato, Pramod Dhungana

原文摘要

Researchers often choose a proxy dataset from many releases, transformations, or seeds. Search can make an invalid release appear adequate, while one adequate release does not establish that its generator is reliable. ProxyGuard controls both errors using prespecified bounded risks and a sealed target set. Named-release mode corrects for multiplicity and certifies specific releases. Direct shared-target mode evaluates independent mechanism draws on a common target, lower-bounds their favorable-score rate, and subtracts a bound on favorable scores contributed by invalid releases. Conditional on the target, release scores are independent, yielding a finite-sample mechanism-reliability guarantee without independent target batches or assumptions on release-level $p$-value dependence. We show that the mean-only penalty is sharp and derive a smooth-score certificate with additive target concentration. In a registered three-requirement study, direct mode raises power from $5.6\%$ to $64.2\%$ at reliability $0.95$, while named mode remains stronger under high-signal evidence. Prospective audits span full-pipeline Rice--TVAE, which retrains on every draw, and a non-tabular text mechanism.

cs.LG · stat.ME · stat.ML

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36827

Advancing global crop simulations with differentiable modelling

Yunan Lin, Sebastian Bathiany, Maximilian Gelbrecht, Jens Heinke, Christoph Müller, Niklas Boers

原文摘要

Global gridded crop models (GGCMs) are crucial for assessing the performance of food producing systems, for example under climate change or their contribution to environmental challenges. However, systematic calibration of their parameters remains a long-standing challenge. Persisting uncertainties in these parameters propagate into future crop projections, thereby affecting and potentially biasing food security assessments. Here we present a fully process-based differentiable GGCM implementation that offers a fundamentally new approach to parameter calibration, uncertainty quantification, yield loss attribution, and management optimization. Using automatic differentiation, we optimize selected model parameters against site-scale carbon and water flux observations, reducing normalized root-mean-square errors at held-out sites by 24.8-36.2% compared to the default state-of-the-art model. Without calibration against yield data, the optimized model better captures detrended yield anomalies across 61.2-64.3% of global crop area and reduces low-yield errors by 3.4-43.5% for wheat, rice, maize and soybean. It also ranks among the best performers in comparison with GGCMI Phase 3 models. Future simulations show that calibration reduces the projected end-of-century rice production increase from 55.1% to 22.1% under SSP5-8.5. Parameter ensembles further reveal that parameterizations with comparable historical skill can produce markedly different future production responses. Beyond calibration, differentiability enables the attribution of crop losses under extreme events and supports the design of adaptive crop management strategies. For the 2018 European drought, we identify spring heat and water limitation as major contributors to wheat yield losses and develop more effective irrigation strategies for mitigating simulated regional yield losses.

cs.CE

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36826

When the Critic Falls Behind: Co-Evolving Rubrics and Harnesses for Continual Agent Self-Improvement

Hao Chen, Yukun Yan, Ziyu Han, Ye He, Chuanzhe Guo, Shangzhan Li, Qi Shi, Qingfu Zhu 等 10 位作者

原文摘要

Autonomous agents increasingly depend on harnesses that govern interaction, memory, tools, and execution around large language models (LLMs). While recent work has begun to evolve these harnesses from execution experience, the supervision used to guide evolution often remains relatively static, making it difficult to track changing behavioral weaknesses across rounds and potentially limiting generalization to unseen tasks. To address this issue, we propose RuHarness, a rubric--harness co-evolution framework for continual agent self-improvement. RuHarness continually discovers and reassesses behavioral criteria from execution trajectories, prioritizes the most relevant unresolved weaknesses, and uses them to guide targeted harness updates while keeping the task objective fixed. Extensive experiments across diverse agent tasks and backbone models show that RuHarness outperforms strong harness-evolution baselines, particularly on unseen held-out tasks. Further analyses show that adaptive rubric prioritization tracks shifting weaknesses and supports more sustained improvement and stable generalization across evolution rounds.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36825

HC2Robot: Hyper-Connection Transformers with Predicted Foresight for Multi-Terrain Humanoid Parkour

Wenhao Xu, Yanheng Mai, Yifei Fu, Nan Zheng, Yanbo Zhou, Zirui Huang, Mingsheng Ling, Xinjue Wang 等 9 位作者

原文摘要

Humanoid parkour requires robots to reason about future footholds and whole-body motions over highly discontinuous terrain. We revisit the information asymmetry in asymmetric actor-critic learning for perceptive humanoid locomotion: the critic exploits privileged simulator signals during training, whereas the deployable actor operates solely from onboard perception. We argue that the key is not exposing privileged information to the actor, but learning a deployable approximation of it to narrow the information gap between training and deployment. We present HC2Robot, a deployable perceptive humanoid parkour framework consisting of a Predicted Foresight Module (PFM) and a Hyper-Connection (HC) Transformer. PFM predicts from deployable observations the policy-conditioned timing, location, and support quality of each foot's next touchdown event, converting privileged supervision into a deployable latent representation that provides the actor with predicted touchdown information without privileged runtime sensing. The HC Transformer further enhances temporal reasoning through learnable multi-stream routing, while a future motion prediction head provides short-horizon motion priors for regularization. Across stairs, gaps, high platforms, and slopes, HC2Robot achieves a mean simulated traversal success rate of 97.60% and consistently higher safe foothold rates than strong perceptive locomotion baselines, supporting improved traversal performance and geometric foothold support under deployable sensing.

cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2506.01183

Doubly Robust Alignment for Large Language Models

Erhan Xu, Kai Ye, Hongyi Zhou, Luhan Zhu, Francesco Quinzan, Chengchun Shi

原文摘要

This paper studies reinforcement learning from human feedback (RLHF) for aligning large language models with human preferences. While RLHF has demonstrated promising results, many algorithms are highly sensitive to misspecifications in the underlying preference model (e.g., the Bradley-Terry model), the reference policy, or the reward function, resulting in undesirable fine-tuning. To address model misspecification, we propose a doubly robust preference optimization algorithm that remains consistent when either the preference model or the reference policy is correctly specified (without requiring both). Our proposal demonstrates superior and more robust performance than state-of-the-art algorithms, both in theory and in practice. The code is available at https://github.com/DRPO4LLM/DRPO4LLM

cs.LG · cs.AI · stat.ML

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36824

Who Knows What - and What Does It Support? Perspective-Bounded Evidence Calibration for Multimodal Theory of Mind

Ziqi Liao, Jiaqi Shen, Yiming Na, Kedi Lyu, Shao-Yuan Lo, Hongxia Xie

原文摘要

Theory of Mind (ToM), the ability to infer others' beliefs, goals, and intentions, is a cornerstone of social intelligence. Multimodal ToM requires inferring an agent's mental state from evidence that is available to that agent, rather than from the complete episode observed by the solver. Existing LLM-based and Bayesian model-based approaches reconstruct global event traces, yet they do not explicitly distinguish objective events from agent-admissible evidence during hypothesis evaluation. Consequently, agent-inaccessible events or semantically misaligned actions and utterances may influence the inferred belief, goal, or social intention, leading to perspective leakage and source or route confusion. We formulate this limitation as an evidence-admissibility problem: evidence should contribute to a mental-state hypothesis only when it is accessible to the relevant agent, preserves its information source, and is diagnostic of the queried mental-state variable. To operationalize this principle, we propose PIP-ToM, an evidence-admissibility interface that constructs agent-specific evidence stores, aligns them with structured mental-state claims, and calibrates model-based hypothesis scores while preserving the underlying Bayesian inference process. PIP-ToM with GPT-4o outperforms existing ToM methods across MuMA-ToM, MMToM-QA, and Hi-ToM, achieving average accuracies of 92.56%, 95.00%, and 95.00%, respectively.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36823

TAPESTRY: A Single Backbone for Video Generation and Robot Control

Jeremy A. Collins, Seungjae Lee, Rachanon Wachakorn, Krishnan Srinivasan, Animesh Garg, Vitor Campagnolo Guizilini, Paarth Shah

原文摘要

Pretrained video generation models encode rich priors about physical interactions, but leveraging them for robot control typically requires either resource-intensive video generation at test time or action heads trained from scratch that do not fully utilize the pretrained backbone. We introduce Tokenizing Actions as Video Patches in a Shared Transformer Policy (TAPESTRY), a world action model that repurposes a pretrained video diffusion transformer into a robotic policy without requiring video generation at inference. Our key idea is to treat robot actions as single-patch tokens in the video model's latent space. A lightweight linear projection maps each action timestep to the patch dimensions of the pretrained VAE, and the resulting tokens are processed by the same patchifying layers and transformer backbone used for video. The shared backbone is optimized through three diffusion objectives sampled per minibatch element: Video diffusion uses VAE latents from robot and human videos to establish dynamics priors. Joint video+action diffusion trains on robot demonstrations using a coupled diffusion timestep and an asymmetric attention mask. Action-only diffusion denoises actions conditioned on clean image latents. At test time, the model generates a compact action token sequence in a single DDIM step from pure noise, producing smooth action chunks at 84 ms per action chunk on consumer hardware and avoiding the cost of full video generation. Across simulation benchmarks and real-robot experiments, TAPESTRY achieves strong performance with substantially lower inference cost than comparable joint video--action policies. More information can be found at https://tapestry-wam.github.io.

cs.RO · cs.AI · cs.CV · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.02644

Conditional Sampling via Wasserstein Autoencoders and Triangular Transport

Mohammad Al-Jarrah, Michele Martino, Marcus Yim, Bamdad Hosseini, Amirhossein Taghvaei

原文摘要

We present Conditional Wasserstein Autoencoders (CWAEs), a framework for conditional simulation that exploits low-dimensional structure in both the conditioned and the conditioning variables. The key idea is to modify a Wasserstein autoencoder to use a (block-) triangular decoder and impose an appropriate independence assumption on the latent variables. We show that the resulting model gives an autoencoder that can exploit low-dimensional structure while simultaneously the decoder can be used for conditional simulation. We explore various theoretical properties of CWAEs, including their connections to conditional optimal transport (OT) problems. We also present alternative formulations that lead to three architectural variants forming the foundation of our algorithms. We present a series of numerical experiments that demonstrate that our different CWAE variants achieve substantial reductions in approximation error relative to the low-rank ensemble Kalman filter (LREnKF), particularly in problems where the support of the conditional measures is truly low-dimensional.

cs.LG · math.OC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36822

Taming Multi-Dimensional Skew in Sparse Matrix Multiplication with Contention-Aware Scheduling [Technical Report]

Wentao Huang, Qiming Huang, Yunhong Ji, Mian Lu, Yuqiang Chen, Bingsheng He, Kian-Lee Tan

原文摘要

Sparse general matrix-matrix multiplication (SpGEMM) is a key kernel in graph analytics, relational queries, and machine learning. Yet, its performance remains a significant challenge, due to the inherent sparsity and, more critically, the prevalent skew patterns often found in real-world workloads. This paper investigates this problem and demonstrates that an unbalanced workload distribution is the main culprit, harming performance through two co-existing root causes: first, multi-dimensional skew, where imbalance across computation cost, output density, and data locality cannot be captured by any single balancing metric; and second, cache contention, where dense substructures induce severe on-chip interference. To that end, we propose adaptive morsel scheduling (AMS), a dynamic scheduling framework that decouples skew dimensions via fixed-result-size morsels and integrates contention-aware load balancing (CALB) and data-conscious simultaneous multithreading activator (SMTact) to alleviate cache-level contention, alongside a hybrid accumulation mechanism (HYB) that adaptively selects the best accumulator per morsel. Evaluations across matrix squaring, relational queries, graph analytics, and other sparse matrix kernels show that AMS significantly outperforms state-of-the-art methods on skewed workloads while maintaining comparable performance on non-skewed ones. These gains, along with improved bandwidth utilization, remain valid across a wide range of workloads and hardware systems.

cs.DC · cs.DB

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36818

Variable-Length Feedback Coding With Universal Decoding and Bandit Channel Selection

Recep Can Yavas

原文摘要

We study variable-length feedback (VLF) coding over $K$ additive white Gaussian noise (AWGN) channels whose noise variances are unknown to both terminals but lie in a known interval. The receiver selects one channel at each time, conveys the selection over noiseless feedback, and must learn the channel qualities while decoding, to avoid overusing suboptimal channels. This setting, which we call the VLF-Bandit problem, combines VLF coding, universal decoding, and bandit channel selection. We develop two joint coding and channel-selection schemes. The VLSF-Bandit scheme is a single-phase variable-length stop-feedback (VLSF) scheme based on a Gaussian random codebook, a score formed from a ratio of likelihood mixtures, and the asymptotically optimal $\text{kl-UCB}^{++}$ bandit algorithm applied to the received power. The VLF-Bandit scheme sends the message in packets by Schalkwijk--Kailath transmission, uses its normalized outputs as noise samples to learn the variances, and lets its exploration packets carry message bits. We prove second-order achievability bounds for both schemes, which are first-order optimal. In the logarithm of the achievable codebook size, the magnitude of the $\log N$ coefficient, with $N$ the average decoding time, is a baseline of one plus a channel-exploration term for VLSF-Bandit. For VLF-Bandit, this magnitude is smaller, with a universal-decoding term replacing the baseline and a smaller channel-exploration term. The VLF-Bandit bound improves on the universal VLF bound of Yavas and Tan (2025) for $K=1$ and on the bound of an explore-then-commit strategy whenever $K\geq2$.

cs.IT · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.30228

Benchmarking Model Compression Techniques Across Heterogeneous Edge Platforms: A Predictive Maintenance Use Case for Rotating Machinery

Matheus Calheira Guimarães de Oliveira, Marco Aurélio da Silva Cruz, Regis Cardoso, Lucas Cambuim, Rafael Macieira, Arthur Paim, Helaine Neves, Alex Santos 等 12 位作者

原文摘要

To enable Industry 5.0, where autonomous systems are widely adopted and integrated with people to promote sustainable development in industries, it is imperative to adapt complex deep learning models to operate efficiently on resource-constrained hardware for edge inference. However, adapting these models remains challenging due to the hardware heterogeneity of edge computing in industrial settings, performance degradation caused by model compression, and the lack of clear guidance on which compression techniques are practical and effective for applications such as predictive maintenance. To address these challenges, this paper presents a comprehensive comparative analysis of four state-of-the-art model compression techniques applied to a high-performance convolutional neural network for predictive maintenance using a real-world dataset of rotating machinery, equipment typically used in industry. The compressed models were evaluated across a heterogeneous set of nine computing platforms, ranging from highly constrained edge computing devices to general purpose and high-performance processors, reflecting the diversity and fragmentation typical of industrial edge environments. By jointly prioritizing classification performance, inference speed, and compression ratio, we found that compression using knowledge distillation emerged as the most effective approach. The model had more than 90% speedup across all devices while maintaining a 91.22% balanced F-score, with a very small drop in predictive performance relative to the uncompressed model, along with a 98.9% size reduction. Through successful deployment across multiple edge platforms and a systematic comparison of compression strategies, we demonstrated a clear path to achieving Industry 5.0 targets, drastically cutting computational costs and, consequently, energy consumption without sacrificing predictive performance.

cs.LG · cs.PF

查看论文
arXiv ↗PDF ↗