跳到正文
研踪PaperTrail

论文库

检索计算机领域论文，按研究方向、作者或关键词筛选。

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
+0已有论文修订
+93新增 AI 导读

arXiv 最近完成扫描：2026-11-16 01:34 · 全站最近入库：2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

统计 2026-11-15 03:26 — 2026-11-16 03:26（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：2026-11-16 01:34最近发现新增版本：2026-11-14 15:09库内论文最新更新时间：2026-11-13 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。

23,348 已收录论文19,580 AI 相关6,729 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 23,348 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-16 01:33（北京时间）
新论文 · v1arXiv:2611.16647

InertiaMPDD: Multimodal Personalized Depression Detection via Emotional Inertia-Inspired Dynamics Modeling

Changzeng Fu, Songyang Wang, Chang Gao, Xinyi Zhang, Peilin Li, Mo Meng, Jiarui Wu, Mingze Li 等 12 位作者

原文摘要

Multimodal depression screening estimates risk from speech, visual behavior and language. Current methods mainly predict labels, with limited evidence of departures from a non-depressed reference. Recording-level supervision also leaves fine-grained emotional dynamics unannotated. Motivated by emotional inertia, the persistence of affect, InertiaMPDD learns healthy next-state patterns using a world-model-inspired latent predictor. Using pretrained multimodal representations, it predicts from observed history and context. Reference deviation, residual decay rate and a Jacobian spectral descriptor form reference-derived emotional dynamics descriptors, evaluated as candidate proxy biomarkers for standalone screening and personalized decision correction. Across five cohorts from four datasets, fusion reaches 98.41% F1 on D-Vlog and 84.98% on LMVD. Standalone descriptors outperform fixed additive fusion on MPDD but discriminate weakly on AVEC2014. These findings distinguish reference informativeness from fusion benefit and reveal context-dependent utility.

cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.07388

A Marine Debris Detection Framework for Ocean Robots via Self-Attention Enhancement and Feature Interaction Optimization

Yuyang Li, Jiashu Han, Yinyi Lai, Wenbin Kang, Zenghui Liu

原文摘要

Marine debris detection for ocean robot is crucial for ecological protection, yet performance is often degraded by low-quality images with blur, complex backgrounds, and small targets. To address these challenges, we propose YOLO-MD, an enhanced YOLO-based detection framework. A Dual-Branch Convolutional Enhanced Self-Attention (DB-CASA) module is designed to strengthen spatial-channel interactions, improving feature representation in degraded images. Additionally, a lightweight shift-based operation is introduced to enhance fine-grained feature extraction for objects of varying scales while maintaining parameter efficiency. We further propose SFG-Loss to mitigate class imbalance and optimization instability via dynamic sample reweighting. Experiments on the UODM dataset demonstrate that YOLO-MD achieves 0.875 precision, 0.822 F1-score, and 0.849 mAP50, outperforming the latest state-of-the-art methods. The effectiveness of this method has also been verified through real-world robotic edge deployment experiments.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16646

Measurement-Calibrated Synthetic Pretraining for Rapid Adaptation of UAV Channel Predictors

Qiuheng Zhou, Wei Jiang, Donglin Wang, Hans D. Schotten

原文摘要

Unmanned aerial vehicle (UAV) links can change faster than route-specific channel data can be collected, so a channel predictor must often be adapted from only a short target-flight calibration segment. This paper studies measurement-calibrated synthetic pretraining as an offline source prior for rapid adaptation of channel state information (CSI) predictors. The synthetic source is calibrated to flight-only power evolution, temporal shape variation, frequency selectivity, and phase disturbance, and is then combined with measured source-flight fine-tuning before target-flight adaptation. Under chronological held-out flight splits, synthetic-to-real (S2R) fine-tuning reduces the measured-data budget needed to reach useful prediction in the low-data regime. For 50 ms-ahead prediction, it crosses a latest-channel-estimate persistence baseline with 50 measured training windows, roughly 0.5 s of 10 ms-spaced supervised windows, whereas training from scratch requires 600 windows. A target-prefix adaptation study further shows that a lightweight gated recurrent unit predictor can reuse the pretrained source prior with one prediction-layer update epoch, positioning measurement-calibrated synthetic pretraining as a reusable prior for scarce target-flight measurements.

eess.SP · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16645

Rethinking Actions: Continuity-Aware Priors for World-Model Planning

Shaobo Yang, Zichen Ai, Ziyang Ma, Qian Yang, Bin Fang

原文摘要

Actions in expert demonstrations exhibit temporal structure. An ordered-versus-shuffled analysis across five environments confirms this dependence. We ask whether action proposals that better reflect expert continuity can improve visual world-model planning. Rather than imposing generic smoothness, we learn continuations from the temporal structure of demonstrated actions. Our locality-gated deep-kernel Gaussian-process prior, LG-DKGP, combines recent and longer-range executed action history, predicts a neural continuation, and learns a temporal Gaussian-process (GP) residual. A training-derived exploration scale initializes search around the corrected mean; a frozen world model evaluates progress toward the goal. Training first fits the neural mean, then jointly optimizes the prior with the GP residual using expert future actions. The resulting planner achieves 87.07\% macro success across five simulated tasks spanning native and fragment-level goal attainment, compared with 71.03\% for INTACT, 61.66\% for PRISM, and 58.66\% for LeWM. Component interventions and expert-relative action-structure comparisons support demonstration-informed continuations and reveal task-dependent effects of visual conditioning, history length, and planning horizon.

cs.RO · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16643

Optimized Mirabbasi-Martin Pulse Shaping Filter for Higher-Order Modulation in Zak-OTFS

Naveed Bin Nazir, Ananthanarayanan Chockalingam

原文摘要

The performance of Zak-OTFS modulation depends critically on the delay-Doppler (DD) domain pulse shaping filter, which must support both accurate channel estimation and reliable data detection. Filters reported in the Zak-OTFS literature fall short in bit error rate (BER) performance for higher-order modulation due to sensitivity to model-free channel estimation errors and pilot-data interference. Motivated by this, in this paper, we introduce a separable two-dimensional Mirabbasi-Martin (MM) filter for Zak-OTFS and propose a filter coefficient optimization framework that minimizes the aggregate sidelobe energy while preserving the compact structure of the MM prototype. By trading exact Nyquist nulls for high localization, the optimized MM filter exhibits no sidelobes with the narrowest $-40$ dB DD domain pulsewidth compared to sinc, Gaussian, and GS pulses. This compactness of the optimized filter tightly confines the pilot energy within the pilot region of the embedded frame, ensuring that the full response is captured for model-free channel estimation while significantly reducing pilot-data interference. This yields both an improved estimation performance and a significantly improved BER performance for higher-order QAM. For the proposed filter, we derive closed-form expressions for the effective DD domain channel and the covariance of the receive-filtered noise, which drastically reduce simulation run times. Our simulation results demonstrate that the optimized MM filter achieves significantly better uncoded/coded BER performance for higher-order QAM compared to that of sinc, Gaussian, and GS filters. For example, for 64-QAM with M=32, N=48, rate-1/2 convolutional code, and TDL-A channel model with fractional DDs, the optimized MM filter achieves a coded BER of 3x10^{-4} at an E_b/N_0 of 40 dB, while the sinc, Gaussian, and GS filters exhibit error floors above 10^{-2}.

cs.IT · eess.SP

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16642

Dataset Distillation via Drifting

George Cazenavette, Angelina Quan, Giannis Daras, Antonio Torralba, Vincent Sitzmann

原文摘要

The task of dataset distillation concerns the learning of a small set of synthetic samples that can serve as an effective replacement for a large real dataset when used for downstream tasks. While many solutions have been proposed since the problem's inception, most of them primarily seek to optimize the distilled data with respect to a classifier. In this work, we propose a new distillation method that learns a set of samples such that they directly approximate the per-class data distribution. Specifically, we "drift" our distilled data in the latent space of a feature extractor such that they move both toward the real data and away from each other without using a classification objective. We find that our drifted data cover the real distribution significantly better than existing methods while still serving as powerful training samples for classification. Furthermore, we showcase our method's use as a visualization tool for better interpreting models and extend it beyond images to other modalities. Our work advances the frontier of dataset distillation, offering a novel synthesis method and proposing distribution-based metrics as a complementary evaluation. More broadly, our findings help address the growing need to understand both data and models by uncovering more about how they interact.

cs.LG · cs.AI · cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2611.00180

Learning to Live: A Step Toward Lifelong Embodied Intelligence

Lirong Che, Peidong Wang, Yimeng Su, Jiong Wang, Yang Chen, Wenbo Fu, Zhenyu Huang, Tiancheng Xu 等 15 位作者

原文摘要

Embodied agents are usually organized around individual tasks, yet the physical world keeps changing between tasks and during execution. We introduce LIFE (Lifelong Intelligence from Experience), a framework in which a robot observes continuously, remembers selectively, and turns experience into new capabilities. RoboBeats, a family of lightweight streaming gates, tracks conditions set by the agent and discovers new events, deciding when an observation warrants a response or a memory write. Selected observations enter Plastic Memory, a flat store of images and structured records that keeps the source evidence, so new fields and indexes can be added when later questions need them. When existing tools fall short, Explore-Distill-Mount (EDM) turns solutions found during problem solving into validated, reusable tools and skills. Under matched protocols, RoboBeats substantially improves condition tracking over its base models and, at the same writing budget, retains more useful evidence for memory question answering than uniform sampling. A case study of limited scale, replaying 24 hours of continuous video recorded by a real robot, illustrates how the integrated system builds memory from a day of experience and uses it to answer everyday questions. LIFE takes a step toward robots that become more familiar with their surroundings and more capable through experience: learning not only to complete tasks, but to live in the world.

cs.RO · cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16639

Asymmetric Multimodal Learning with Decomposed JEPAs

Yusuf Shihata

原文摘要

Cross-modal alignment is the standard objective for building multimodal representations. We show that alignment as a \emph{sole} objective is lossy wherever modalities are informationally asymmetric---where neither can express what the other carries. Visual question answering is an obvious case: a question is a selective relational operator probing a dense visual field whose fine-grained features no query recovers, so what alignment preserves is not what the task requires. We prove an \textbf{Alignment Redundancy Bound}: alignment-only Dirichlet energy minimization retains at most the redundant information atom $\Red(Z; X_1, X_2)$, annihilating private modality-specific structure $\Unq(Z; X_m)$ and cross-modal synergy $\Syn(Z; X_1, X_2)$. We introduce \textbf{Decomposed JEPA (D-JEPA)}, which factorizes latents into an aligned shared core and private modality-specific fibers. The shared core retains most of the structure alignment targets; the private fibers recover what alignment discards, via contextual masked prediction and asymmetric cross-subspace independence. Under information asymmetry, alignment-only training undergoes catastrophic semantic collapse on compositional reasoning benchmarks, surviving only on binary queries. D-JEPA averts this collapse, substantially improving downstream accuracy over both alignment-only and alignment-with-reconstruction baselines, and restoring a positive multimodal synergy gap in place of the baseline's inverted gap. Information-theoretic audits confirm that D-JEPA recovers most of the mutual information available from a frozen backbone, and causal fiber-swap interventions demonstrate that the recovered information is causally localized in the private fibers.

cs.LG · cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.11262

Improving the Trifference Lower Bound via Non-Perfect Hashing

Chen Yuan, Ruiqi Zhu

原文摘要

We strictly improve the Körner-Marton lower bound $\frac14\log \frac{9}{5}$ on the asymptotic rate of trifferent codes. We obtain this improvement by constructing non-perfect $(9,3)$-hash codes with rate above $\log(9/5)$ and then applying the Tetra-code construction, which gives $R_3\ge\frac14R_{9,3}$. More generally, for every $q\ge4$, we prove $R_{q,3}>B_{q,3}+\frac1{250000}$. We also extend the method to $(p,2,3)$-list recovery. For every $q\ge4$ and $0\le p<\frac{q-2}{12(q-1)}$, we prove $R_q^{\mathrm{LR}}(p)>B_q^{\mathrm{LR}}(p)$. Here $B_{q,3}$ and $B_q^{\mathrm{LR}}(p)$ denote the respective probabilistic lower bounds. For every fixed $0\le p<1/18$, the rate improvement is bounded below by a positive constant independent of $q\ge4$. For every fixed $0\le p<1/12$, we also obtain a positive rate improvement independent of $q$ for all sufficiently large $q$.

cs.IT

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16634

Machine Learning for Scalable Multi-AP Coordination in IEEE 802.11bn Networks

Maksymilian Wojnar, Szymon Szott

原文摘要

As Wi-Fi deployments become denser, fully distributed random channel access no longer guarantees the required quality of service. The multi-access point coordination (MAPC) framework introduced in IEEE~802.11bn lets neighboring access points (APs) cooperate, but choosing which APs transmit together and how to configure them remains an open, computationally hard problem. We develop and validate a stack of machine learning (ML) coordination methods, progressing from lightweight hierarchical multi-armed bandits (H-MABs) to a generative pipeline built on graph neural networks (GNNs) and flow matching (FM). The methods are evaluated in simulation and on a hardware testbed, improving throughput over legacy access and scaling coordination to dozens of APs where analytical and search-based methods become intractable.

cs.NI · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16632

Reins: Frontier Multimodal Models Driving Real Robot Arms on Long-Horizon Tasks, Zero-Shot

Pengfei Ye, Zhiyang Dou, Yuxiang Ma, Haonan Chen, Heng Yang, Edward H. Adelson

原文摘要

Frontier models such as GPT-6.1 Sol, GPT-6 Astra and Claude Opus 5.5 can now plan and call tools, yet they still struggle to drive real robots through long-horizon, contact-rich manipulation, blind while the arm moves and while they think. We present Reins, a harness in which an off-the-shelf model operates a dual-arm robot with 3D perception tools and atomic actions that report contact and grip. The model chains actions in one call, each with a condition that must hold, stopping at the first that fails; a second instance, the narrator, watches the cameras continuously and reports what changed with every result. From images, Reins also builds a simulation-ready twin of the scene for continual policy learning. With natural-language prompts and no task-specific code, GPT-6.1 Sol succeeds in 88% of real episodes on five tasks; without the harness, it fails every shell game, taking 8 times as long. Three frontier models perform alike but differ in tracking a scene that changes while the actor thinks. Code will be released upon publication.

cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.11963

Can LLMs Fix It Without Code? Toward Automated Verification of No-Code Bug Fixes

Utku Boran Torun, Veli Karakaya, Eray Tüzün

原文摘要

A no-code fix resolves an invalid bug report by directing the user to change a setting, update to a version where the problem is already fixed, or adjust their workflow. Manually verifying whether a proposed no-code fix resolves the reported bug takes considerable developer time. This study proposes an automated, execution-based pipeline for evaluating the capability of large language models (LLMs) to generate no-code fixes in a real browser environment. We evaluate 322 no-code fixes generated by the 12 configurations released with the benchmark of a previous study, covering bug reports categorized as Faulty Configuration, Wrong Version, or External System & Dependency. An executor agent applies each fix by following its natural-language instructions, and an issue-specific checker determines whether the reported bug persists. We repeat the pipeline with three executors: two Computer-Use Agents, OpenCUA-72B and Claude Sonnet 5, and one multimodal agentic LLM, Meta's Muse Glimmer. Only 17.6% of the candidate issues could be set up and passed both sanity gates. Across the 322 fixes, 14.6% to 49.7% resolved the bug depending on the executor, and the strongest configuration, Claude Opus 4.6 in the Vanilla pipeline, resolved up to 74.1% of its fixes under Claude Sonnet 5. Changing only the executor shifted a configuration's resolution rate by 38.8% on average, and the three executors reached the same verdict on only 46.9% of the fixes. Compared with human execution, the executors matched the human consensus for 66.1% to 88.1% of the sampled fixes. Even under the best executor, fewer than half of the LLM-generated no-code fixes resolve the reported bug, so such fixes need verification before they reach users. Execution-based verification can provide this, but the measured capability depends strongly on the executor, which evaluations must report and control.

cs.SE · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16630

When Chekhov's Gun Fires Anyway: Over-Realization in Text-to-Image Models

Jiahao Huang, Kon Woo Kim, Song-Duo Ma, Yuka Kitamura, Felix Wagner, Yunshan Huang, Sunisth Kumar, Xanh Ho 等 15 位作者

原文摘要

In text-to-image generation, a target that the prompt mentions often appears in the generated image in a way the prompt does not call for. We call this failure over-realization. Mental-spaces theory points to the situations where it can happen: under a negation, inside what a character believes or imagines, as the vehicle of a figurative comparison, or behind a viewpoint that hides the target. Prior work has studied only negation in depth, and the conventional alignment and preference metrics do not penalize the failure. We build OverReal to measure it: OverReal-Gen, 698 prompts covering the four situations, on which we measure each generator's over-realization rate; and OverReal-Det, 2,841 generated images human-labeled for whether and how the target is over-realized, on which we verify the automatic judge that produces this rate. Our evaluation of eight text-to-image systems reveals that all of them over-realize, and that conventional metrics such as CLIPScore reward the failure rather than penalize it. Prompt expansion only partly mitigates the failure. Inside the generator, the diffusion backbone attends to a mentioned target almost as much as to a requested one. The cue words that should hold the target back receive less attention than ordinary words.

cs.CV · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16628

Safety as a Budget: Calibrating the Control Channel of Constrained GRPO for Vision-Language Models

Bo Han

原文摘要

Reinforcement learning can teach vision-language models (VLMs) to decline harmful image-text requests, but how safe the result becomes is set indirectly, through reward weights found by trial runs. Stating safety as budgets on the rate of harmful assistance and the rate of over-refusal, enforced by Lagrange multipliers inside GRPO with an off-the-shelf scorer as the cost, is the natural fix, and it fails twice. The multiplier compares the scorer's mean with the budget, and scorers built for ranking get the mean wrong: a fixed-point analysis shows that the mean bias, not the budget, then decides whether the multiplier settles, saturates or switches off. And standard GRPO standardizes the penalized reward within each group, which cancels the multiplier before it reaches the gradient. Two-channel constrained GRPO repairs both: the base reward keeps the raw score for the within-group comparison, the penalty and the constraint estimate pass through a monotone map fitted on judge-labeled training responses, and the advantage keeps the multiplier. On a Qwen3-VL-2B policy with a training-free readout of an open 4B model as the scorer, held-out harm on 1,037 adversarial prompts falls from 8.9% under standard GRPO to 4.8%, lower in every seed, where the uncalibrated budget gives 7.6%; over-refusal stays at most 2% on 400 benign queries and MMBench is unchanged. The gain is largest where the intent sits in the image (10.1% -> 5.1%) and grows on the unseen HADES benchmark (28.9% -> 12.8%). Order-preserving distortions of the scores reproduce each predicted controller state in every seed, and the results hold under a map fitted on another judge's labels, an image-reading judge and a human audit.

cs.AI · cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33097

IRON-Meme: A Benchmark for Structured Understanding of Identities and Roles in Online Meme Narratives

Gabriella Bollici, Stefano De Giorgis, Wouter Van Atteveldt, Filip Ilievski

原文摘要

As polarization grows on social media, understanding how political memes shape online narratives has become increasingly important. Previous research has relied on either qualitative frameworks that are hard to scale or computational methods that lack theoretical grounding. Both approaches treat narrative categories in isolation and focus exclusively on moral portrayals of identity. To address these limitations, we introduce a framework grounded in social science theories, encompassing three categories: social identities (such as gender, politics, and ethnicity), viewpoints (ingroup and outgroup perspectives), and narrative roles (moral character, intelligence, appearance, and power). Building on this framework, we present IRON-Meme, a human-annotated benchmark comprising 1,110 political memes across substantially more templates than existing benchmarks. Our analysis of IRON-Meme shows that most memes assign derogatory roles and that identities, roles, and templates are systematically interconnected. Evaluation of nine vision-language models indicates that frontier-scale and reasoning models achieve the highest performance, while accuracy on social identities and narrative roles decreases for less frequent labels. Incorporating template background knowledge does not enhance narrative role classification, suggesting that meme interpretation relies on how memes are adapted for specific communicative purposes. We make IRON-Meme and our code publicly available.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16627

Folio: Recoverable Multi-Resolution Context Folding for Long-Horizon Language Agents

Yubo Ruan, Tao Peng, Jiao Zhang, Chuanguo Ma, Lei Xu, Han Zhou, Wenwu Wang, Xiafei Lv 等 14 位作者

原文摘要

Long-horizon language agents accumulate tool results and replies until the history no longer fits the context window. Current agents shrink this history by having a model summarize it, so each compaction rereads and rewrites the history it replaces, and cache-aware triggers decide when to compact while taking the size of the result as given. We present Folio, a context manager that records, folds, and recovers history through one set of handles. While the agent runs, Folio records three linked levels: a short turn note the model writes as part of its ordinary reply (L1), an event manifest of every call written by the runtime (L2), and the captured source records (L3). Because these views already exist, the runtime folds the context programmatically, selecting coarser views for older turns. The fold prints the handles that index L2 and L3, so recovery tools that read bounded pages, compute over full text in files, and keep findings in a persistent dossier reach the evidence the agent needs with little added input. The fold trigger prices an explicit landing point that includes the retained budget. On 75 LOCA-bench tasks with DeepSeek-V4-Pro behind every system, Folio solves 66 tasks against 54 for the same runtime with model-written compaction and 49-59 for Claude Code, Codex, and SoL-Pi, in about half their working time, at 2.4 times lower cost than that runtime; each fold takes a median of 0.6 seconds.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16623

DySR-GNN: Dynamic Sensor-Relationship Graph Neural Network for Root Cause Analysis in Automotive HiL Multivariate Time Series

Hamza Ouarrad, Mohammad Abboush, Andreas Rausch

原文摘要

Root-cause analysis in automotive Hardware-in-the-Loop (HiL) systems is challenging because faults can propagate across multiple interacting signals, making the most abnormal sensor different from the actual fault origin. This work proposes DySR-GNN, a Dynamic Sensor-Relationship Graph Neural Network for supervised multi-label root-cause identification in multivariate HiL time series. The approach jointly extracts temporal patterns within individual sensor signals and structural relationships across sensors. Each sensor is processed by a shared temporal encoder and combined with a trainable sensor-identity representation, from which a sparse relationship graph is reconstructed independently for every input window. This window-specific structure provides an explicit representation of the sensor relationships used for graph message passing and direct root-location prediction. Under a recording-disjoint evaluation protocol, the proposed model outperforms CNN--LSTM, Static GCN, Static GAT, and context-based variants, achieving a recording-level Macro-F1 of 0.9524, MCC of 0.9302, and strict exact-match accuracy of 0.8519. Analysis of the learned graphs reveals both persistent and state-dependent relationships among sensor variables. The results demonstrate that extracting adaptive relational structure alongside temporal representations can improve multi-label root-cause identification in complex automotive cyber--physical systems.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16621

Studies on Sign Language Retrieval in Low-Resource Dictionary Settings

Ushnish Sarkar, Vineet Kumar Rakesh, Bhaswar Chattopadhyay, Tapas Samanta

原文摘要

Most sign languages have no annotated corpus, only small dictionaries with one or a few example videos per sign. This study examines how well a pose encoder trained on a single sign language serves such languages without any target-language training. An encoder trained with metric learning on American Sign Language (ASL-Citizen) is evaluated frozen on six datasets of Turkish, Russian, Indian, Argentinian, Pakistani and Sri Lankan sign languages, with one or four clips per sign enrolled and, where signer identities are available, the query's signer excluded from the gallery. With one enrolled clip, the encoder retrieves the correct sign first in 36.4-80.7 % of queries, against 16.0-69.6 % for the best learning-free method (angle-based dynamic time warping) and a six-target mean of 39.3 % for an ASL-trained OpenHands graph network. In a crossed ablation of the encoder (embedding geometry, aggregation of body and hand embeddings, training objective, and attention between articulators), two choices matter: a per-pair InfoNCE objective outperforms supervised contrastive and cross-entropy training, and blocking attention between body, left hand and right hand inside the encoder raises accuracy by up to 5.1 points. The best configuration reaches 68.0 % one-shot accuracy (59.0 % for the reference encoder). On CISLR, an Indian Sign Language dictionary of 4,765 words with one video per word, the encoders find the correct word first for 17.3-19.0 % of queries, above the 16.8 % of the original RGB baseline. Over eight seeds with the joint encoder, spherical, Euclidean and hyperbolic embedding spaces differ by at most 1.6 points, except the hyperbolic token-attention head trained with InfoNCE, which is 1.4-2.0 points worse, so the geometry is a minor factor.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16620

Improved CNN-Transformer Network for Neutron Image Super-Resolution

Yuheng Chu, Hongli Li, Tian Zhang

原文摘要

The spatial resolution of neutron images is of great importance for material defect diagnosis, structural characterization, and nondestructive testing. However, owing to limitations in neutron beam quality, scintillator conversion efficiency and light scattering, detector pixel size, and noise levels, conventional neutron images often suffer from speckle noise, blurred edges, and missing details, making them inadequate for high-precision analysis. To address these issues, an improved super-resolution model combining convolutional neural networks (CNNs) and Transformers is proposed. First, to overcome the difficulty of obtaining high-quality and clear training images for neutron imaging, a pretrained image restoration model is fine-tuned using real neutron images and then employed to enhance existing neutron images, thereby providing high-quality reference images for subsequent super-resolution model training. Second, an improved CRAFT-based super-resolution network is developed by incorporating a hybrid prior-guided channel fusion module (HPCF), a mean-standard deviation dual-statistics squeeze-and-excitation block (MS-SEBlock), and a partial convolution feed-forward network (PCFN), with the aim of improving the reconstruction of important information, such as textures and fine details, in neutron images. Simulation results demonstrate that, compared with bicubic interpolation, SRCNN, SRGAN-VGG, and other methods, the proposed network achieves superior performance in both full-reference metrics, including PSNR and SSIM, and no-reference metrics, including AGM and SMD. The visual quality and structural fidelity of the reconstructed images are also significantly improved. This study provides a new approach to improving neutron image quality.

cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.31504

SimpleSearch-VL: A Simple Recipe for Multimodal Agentic Deep Search

Ming Dai, Zhihong Lu, Jinjie Gu, Jiedong Zhuang, Jing Hao, Lijun Wu, Yefeng Liu, Wankou Yang 等 10 位作者

原文摘要

We present SimpleSearch-VL, an efficient, reliable, and practical framework for multimodal agentic search. Its core idea is to improve the agent's own search-and-verification process rather than scaling data, tools, or auxiliary model components. For efficiency, Factorized Adaptive Rollout (FAR) improves sampling efficiency by forming more informative training groups while using redundant samples to mitigate long-tail latency and expose hard samples. For reliability, SimpleSearch-VL performs evidence-verified reasoning, explicitly using chain-of-thought verification to assess the relevance of retrieved visual and textual cues to the original context. For practicality, SimpleSearch-VL keeps a lightweight tool interface and performs webpage self-summary within the agent, requiring no additional external dependencies. With only 5K supervised tool-interleaved trajectories and 2K RL data, SimpleSearch-VL improves Qwen3-VL agentic baselines by 15.8 and 16.0 average points for the 8B and 30B-A3B variants, respectively. The SimpleSearch-VL-30B-A3B model further achieves performance competitive with agentic Gemini-3-Pro.

cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.05392

Legal interpretation and AI: from expert systems to argumentation and LLMs

Václav Janeček, Giovanni Sartor

原文摘要

AI and Law research has encountered legal interpretation in different ways, in the context of its evolving approaches and methodologies. Research on expert system has focused on legal knowledge engineering, with the goal of ensuring that human-generated interpretations can be precisely transferred into knowledge-bases, to be consistently applied. Research on argumentation has aimed at representing the structure of interpretive arguments, as well as their dialectical interactions, to assess of the acceptability of interpretive claims within argumentation frameworks. Research on machine learning has focused on the automated generation of interpretive suggestions and arguments, through general and specialised language models, now being increasingly deployed in legal practice.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16612

Unibuc - NLP at "Voight-Kampff" Generative AI Detection PAN 2025

Teodor Marchitan, Claudiu Creanga, Liviu P. Dinu

原文摘要

This paper describes the approach of the Unibuc - NLP team in tackling the "Voight-Kampff" Generative AI Detection task from PAN at CLEF 2025. In the Subtask 1: Voight-Kampff AI Detection Sensitivity we have explored different approaches of leveraging embeddings extracted from LLMs, using either classical ML models or further fine-tuning LLMs with a classification head for the downstream binary classification task. Our experiments showed that using a majority vote ensemble of classical ML models (Light GBM, XGB, Logistic Regression and SVM) is comparable with fine-tuning for downstream task achieving around 99.4% F1 score. For the Subtask 2: Human-AI Collaborative Text Classification we explored a way of combining features at different layers extracted using transformer backbone with layer-wise projection and attentive pooling which proved modest results.

cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16609

A Dual-Branch Graph and Convolutional Network for Interpretable Movement EEG Classification

Maryam Ostadsharif Memar, Behzad Nazari

原文摘要

Decoding upper-limb movements from scalp electroencephalography (EEG) is an important problem for brain-computer interfaces (BCIs), but many deep learning models provide limited insight into the brain regions underlying their predictions. We propose a two-branch architecture that combines an LGGNet-inspired local-global graph branch with an EEGNet-style convolutional branch for movement-versus-rest classification. The graph branch groups 61 EEG electrodes into anatomically informed regions and learns their spatial relationships, providing a direct representation of the regions emphasized by the model. We evaluate the proposed architecture using leave-one-subject-out cross-validation on the Ofner upper-limb movement dataset. The model achieves a mean F1-score of 0.74 +/- 0.06 across subjects. Beyond classification performance, we analyze the learned graph structure to identify the regions with the strongest connectivity. Across all held-out subjects, the highest connectivity is consistently observed in the Parietal Left Low region, corresponding to electrodes P1 and P3. This region is opposite to the performed right-arm movements and lies within the posterior parietal area previously associated with upper-limb movement processing. These results show that incorporating a region-based graph into the decoder can provide an anatomically interpretable representation while maintaining effective movement classification, offering a practical approach toward more transparent EEG-based BCIs.

cs.AI · cs.HC · cs.LG · eess.SP

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16608

Towards Democratising Low-Data Medical Image Classification: CPU-Trainable ZACH-ViT and Its Geometric Variants

Athanasios Angelakis

原文摘要

Medical image classification in resource-constrained settings must contend with limited annotations and computational capacity. We evaluate four ZACH-ViT variants with linear, Euclidean prototype, Poincare and spherical heads alongside two compact multiple-instance-learning baselines. All models are trained from scratch with fewer than 260,000 parameters across seven MedMNIST+ datasets and three external benchmarks (PCam, ISIC2017 and Shenzhen tuberculosis radiographs). The low-data protocol uses 50 training images per class and five seeds, with validation-selected checkpoints at 23 and 47 epochs; a full-data reference uses one seed. Compact-TransMIL achieves the highest ten-dataset mean test Macro-F1 at 23 epochs (53.34%) and with full data (73.93%). At 47 epochs, ZACH-Linear narrowly leads (57.76% versus 57.54% for Compact-TransMIL). Extending training improves Macro-F1 in 239 of 300 paired comparisons. On ISIC2017, hZACH-Spherical has the highest 47-epoch low-data Macro-F1 (44.09% with SD 4.09%), whereas ZACH-EuclideanProto leads with full data (51.27%). We additionally conduct 40 CPU-only training runs on ISIC2017 and Shenzhen using four CPU threads; model-only single-image inference requires approximately 4-9 ms, while image decoding and preprocessing substantially increase end-to-end latency. The results demonstrate the feasibility of CPU-only training on the tested platform and the task- and regime-dependence of compact medical imaging under strict resource constraints.

cs.CV · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.01266

Fast approximation and learning of binary classification tasks in o-minimal structures using ReLU neural networks

Clemens Kinn, Philipp Petersen

原文摘要

We study binary classification tasks whose decision sets are given by definable sets in o-minimal expansions of the real field. Motivated by the cell decomposition theory of definable sets, we introduce traceable sets that isolate the analytical properties of definable sets used for fast approximation by ReLU neural networks. Under uniform bounds on the number of cells and a suitable extension assumption for the boundary functions, we prove that classifiers can be approximated in $L^p$ up to accuracy $\varepsilon>0$ by ReLU neural networks of size $\mathcal{O}(\varepsilon^{-p(n-1)/m})$, with depth independent of $\varepsilon$ and polynomially bounded weights. This establishes quantitative approximation rates for suitable definable collections in o-minimal structures using ReLU neural networks. Furthermore, the stated approximation rates are also derived for definable maps under similar geometric assumptions on their smoothness domains. We then obtain statistical learning rates for empirical risk minimization with hinge loss over the approximating ReLU neural network classes. For $N$ uniformly distributed samples, the predicted classifiers achieve expected misclassification error of order $N^{-m/(m+n-1)}$ up to an arbitrarily small polynomial loss.

math.LO · cs.LG · math.FA

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.03215

Learning pharmacokinetic dynamics with structured neural ODEs for precision dosing

Benjamin Maurel, Agathe Guilloux, Sarah Zohar, Moreno Ursino, Jean-Baptiste Woillard

原文摘要

Individualized dosing requires predicting how a patient's drug exposure would change under a different dose or schedule. However, a model that accurately predicts exposure at the current dose may not reliably predict what happens when treatment changes. We developed two neural models that learn pharmacokinetic dynamics from data without predefined pharmacokinetic equations. The first predicts exposure after a dose change, while the second also allows changes in dosing schedules. In simulations, the second model predicted exposure under new dosing schedules more accurately than competing neural models. On real data of kidney transplant recipients treated with tacrolimus, both models showed lower prediction errors than Bayesian estimation when predicting exposure at a later visit. These findings suggest that incorporating pharmacokinetic principles into neural models can improve predictions needed for individualized dosing.

stat.ML · cs.AI · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.20455

GraphSkillAA: Attribution-Guided Skill-Graph Updating with Targeted Validation and Rollback

Ziqiao Shang, Ling-Yue Ge, Lan-Zhe Guo

原文摘要

External skills provide domain knowledge and procedures without updating model parameters, but flat text makes their applicability, dependencies, and composition difficult to express. Existing skill graphs organize rules into nodes and relations mainly for retrieval and composition. They give less attention to what should change after a failure, which examples should be retested, and which edits should be reverted. We introduce GraphSkillAA, short for Graph-Skill Abductive Attribution, which connects rule use, error localization, and update validation through the same skill graph. Nodes record applicability, procedures, and exclusion conditions, while edges describe prerequisite and enhancement relations. The student records the nodes and edges used during execution. The teacher contrasts related successful and failed trajectories to identify the fields or relations that need repair, keeping changes as small as possible. Execution lapses or insufficient evidence leave the graph unchanged. A Local Gate retests affected examples, while a Big Gate checks the merged graph on the complete update pool. Rejected edits are rolled back, and model parameters remain frozen throughout. With GPT-5.6-sol, GraphSkillAA reaches 81.5%, 66.7%, and 91.2% on SearchQA, LiveMath, and DocVQA, respectively, and achieves the highest mean performance in every main setting. These results show that graph structure supports skill selection and composition while also providing explicit targets for repair, retesting, and rollback.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16603

Regime-Uncertain Data Transactions: A Game-Theoretic Framework for Self-Adapting and Self-Diagnosing Multi-ML-Agent Systems

Chethana Prasad Kabgere

原文摘要

Autonomous ML agents in large organizations exchange data and model outputs across organizational and legal boundaries. Which rules bind a given exchange is not fixed. It depends on where the data subjects live, where the agents run, who owns them, whom they target, and which version of each rule is in force. Tags that describe the data can be noisy, and a counterparty can overclaim its safeguards. We model one exchange as a \emph{regime-uncertain transfer game} between two ML agents, with an enforcer as a third player. Rules enter as hard constraints with a strictest-wins structure. Agents hold a Bayesian belief over which rules apply, can pay to unlock safeguards or verify claims, and then bargain over the remaining legal bundles.Our method, Legally-Constrained Nash Bargaining (\LCNB), implements this with a Monotonic Concession Protocol using Zeuthen's rule. We prove four properties of the model and test it in a synthetic simulator with GDPR, DPDP, CCPA/CPRA and a stylised stricter regime. In two of three stress cases where residence and citizenship differ, a citizenship-based rule violates in 21--37\% of shared records, and \LCNB{} in under 1\%. The results also show limits. With our default economics, \LCNB{} shares almost only aggregates, and its low violation rate follows partly from that. Violation tolerance, tag noise and enforcement strength matter more than the choice of bargaining solution.

cs.GT · cs.SI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16594

Language Learning through Immersive Multi-User Gamification

Rosalina Ribeiro Silva, Zichen Feng, Rikard Olajos, Tim Rolff

原文摘要

We propose an immersive virtual-reality game designed to enable multi-user, multi-language learning during co-play. We hypothesize that our method will be more engaging, leading to better rhythm, pronunciation, memorization, and speaking confidence. We implement the experience as a fantasy role-playing game by introducing vocabulary and phrases to the user as spells to learn. Players then cast more efficient spells depending on their language skills. For evaluation, we compare the game to a single-user baseline that replicates traditional language learning in a non-immersive setting. We measure short- and long-term retention of vocabulary and phrases, as well as scoring rhythm and pronunciation. Furthermore, we collect user data on engagement and mental workload.

cs.HC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16591

Singularity-Bench: Can Agents Build Agents?

Yang Zhou, Krish Agarwal, Zhuoming Chen, Zerui Wang, Zhenyu Zhang, Wenshuang Li, Xinpeng Wei, Zhichen Zeng 等 19 位作者

原文摘要

Frontier AI agents increasingly succeed on realistic, long-horizon tasks and are beginning to improve the systems through which they operate. Yet existing benchmarks often restrict agents to narrow action spaces, use simplified environments, or rely on verification methods that are unreliable for evaluating arbitrary agent-generated systems. We introduce Singularity-Bench, an open-ended benchmark for autonomous AI development with six end-to-end tasks spanning pre-training, mid-training, post-training, inference, the agent harness, and cluster orchestration. Each task requires agents to implement core systems from a minimal starting point and evaluates them against mature human-engineered baselines on identical hardware. Additionally, through extensive red-teaming covering 9 of the 10 categories in the OWASP Top 10 for Agentic Applications, we expose reward-hacking vulnerabilities in the conventional Harbor framework and develop Breakwater, an extension of Harbor that blocks all attacks tested in our study. Across 11 agents built from 7 frontier models and 2 harnesses, Opus 5.5 with Claude Code matches or outperforms the human baseline on all six tasks with a Singularity-Index of 1.25, suggesting it may have the skills needed to reach the singularity. The remaining agents average only 0.58. Our vision is for Singularity-Bench to track how much of AI development agents can already automate and when they may transition from assisting to taking end-to-end responsibility for building stronger successors.

cs.AI

查看论文
arXiv ↗PDF ↗