跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

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

部分采集异常
正在读取账号与研究记录…

全站更新概况

+0新增论文
+0其中 AI 相关
+1已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-09 05:07 · 全站最近入库：2026-11-08 06:02各信源进度 →

统计口径与数据新鲜度

统计 2026-11-08 05:38 — 2026-11-09 05:38（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 05:07最近发现新增版本：2026-11-08 06:02库内论文最新更新时间：2026-11-06 02:59

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

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

浏览论文库

研究日报 / 周报 →按信源浏览 →

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

找到 17,100 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-09 05:05（北京时间）
版本更新 · v2arXiv:2608.28665

Understanding Temporal Semantic Stability in Open-Vocabulary UAV Perception through Metric 3D Fusion

Saurbh Singh Jamwal

原文摘要

Recent open-vocabulary segmentation models have significantly advanced semantic perception for UAVs, for aerial and remote-sensing imagery, yet predictions from moving platforms can remain temporally inconsistent across repeated observations of the same physical scene. While semantic segmentation is typically evaluated on single images, temporal reliability remains less explored despite its importance for long-horizon robotic perception. In this work, we investigate temporal semantic stability by associating frame-wise predictions with persistent world-space locations through metric 3D fusion. We introduce a voxel-level evaluation framework that jointly characterises final semantic agreement, Semantic Belief Drift (SBD), Observation Persistence (OP), and semantic uncertainty from accumulated evidence. Experiments on UAVid-3D-Scenes reveal substantial frame-wise semantic variation and show that high aggregate world-space agreement can overstate temporal stability when many locations have limited repeated-observation support. Persistence-stratified analysis shows that recurrent voxels expose substantially greater semantic disagreement, while higher-persistence voxels exhibit lower direct consecutive semantic-evidence divergence. This behaviour is observed across two segmentation backbones and remains consistent under variations in voxel resolution, geometric association, and temporal sampling density. Overall, conditions that reduce world-space recurrence can increase apparent aggregate stability, demonstrating that semantic consistency must be interpreted together with observation support. Our findings establish observation persistence as an essential conditioning variable for evaluating long-horizon semantic reliability and provide a framework for analysing temporal semantic behaviour beyond image-level prediction.

cs.CV · cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2504.05515

How Do Solidity Versions Affect Vulnerability Detection Tools? An Empirical Study

Gerardo Iuliano, Davide Corradini, Michele Pasqua, Matteo Cicalese, Mariano Ceccato, Dario Di Nucci

原文摘要

Smart contract vulnerabilities pose significant security risks to the Ethereum ecosystem, driving the development of automated tools for detection and mitigation. Smart contracts are written in Solidity, a rapidly evolving programming language that continues to add features and improvements to enhance smart contract security. New versions of Solidity change the compilation process, potentially affecting how tools interpret and analyze smart contract code. We use Solidity as a case study to investigate a broader problem in software analysis: whether tool effectiveness remains stable as the underlying programming language and execution environment evolve. In this continuously evolving landscape, we investigated the compatibility of detection tools with different Solidity versions. More specifically, we analyzed detection tools by empirically assessing (i) their crash resilience, (ii) their detection effectiveness, and (iii) their execution time across different versions of Solidity. We collected 2,500 real-world smart contracts to construct a balanced wild dataset and created a separate injected-vulnerability dataset comprising 270 contracts. We extended SmartBugs, a framework that enables the execution of vulnerability detection tools for Solidity, adding 11 new tools. Results indicate that most tools are crash-resilient to version 0.4. However, 20% of the evaluated tools have been updated to remain compatible. Tool performance varies across Solidity versions and is affected by Solidity changes; execution time is higher in version 0.4.x, which was used for evaluation. We present a list of Solidity changes that affect tool performance and offer actionable takeaways for both researchers and practitioners.

cs.SE

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.09255

Who Is the Agent Talking To? Participant-Aware Role Switching for Embodied Student-Support Dialogue

Iro Samuel, Filip Janik, Ewa Olton, Robert Smales, Harris Spratt, Shea Tait, Md Zia Ullah, Yanchao Yu

原文摘要

We present RPO-PDT, an embodied conversational agent that adapts its conversational role to the current participant. It acts as a Per- sonal Development Tutor (PDT) when supporting students and as a simulated student when faculty rehearse tutoring strategies. Voluntary closed-set participant verification informs a confidence- controlled role manager: stable enrolled-faculty matches activate the Student role, while enrolled students and unmatched partici- pants use the default PDT role and uncertain observations cause no transition. The system combines Furhat interaction, Rasa or- chestration, institutional retrieval, safeguarding, and LLM-based generation. User control is preserved through manual role override, consent-based facial enrolment, temporary interaction identifiers, and explicit retention or deletion of enrolled profiles. The demon- stration exposes both role-switching behaviour and these privacy controls in interaction.

cs.RO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38613

TRIM: Temporal Run-Time Monitoring of 3D Object Detection via Activation Statistics

Hakan Yekta Yatbaz, Konstantinos Koufos, Mehrdad Dianati

原文摘要

Reliable perception is essential to intelligent transportation systems, yet deep neural networks can silently miss safety-critical objects while continuing to produce apparently plausible outputs. Existing run-time monitors for 3D object detection generally assess each frame independently and therefore cannot exploit the temporal evolution of occlusion, point sparsity, and scene complexity. We present TRIM, a lightweight temporal monitor that detects missed-object failures without modifying or retraining the underlying detector. TRIM summarises intermediate activations using compact channel-wise statistics and processes short sequences of these summaries with a temporal convolutional network. Experiments on NuScenes show that temporal monitoring improves AUROC from 0.686 to 0.704 and reduces the false alarm rate at 95% recall from 0.818 to 0.759 compared with single-frame monitoring. Early and intermediate backbone features provide the strongest monitoring signal, while the temporal inference head requires only 0.159 ms on a CPU.

cs.CV · cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.18866

Hamming Ideals and Grobner Bases for ISD-like Syndrome Decoding

Roberto La Scala, Marco Marchesin, Sharwan K. Tiwari

原文摘要

We investigate an algebraic approach to the Syndrome Decoding Problem, based on a reformulation of the Hamming weight constraint and its integration with the Information Set Decoding paradigm. We begin with a systematic analysis of the Hamming variety, deriving its defining equations in terms of elementary symmetric functions. Since these equations may have high degree, we exploit convolution identities for elementary symmetric functions, together with factorizations based on Lucas' identity, to derive an equivalent formulation with auxiliary variables and equations of bounded degree. Building on this modeling, we generalize the ISD paradigm through an ISD-like decoding strategy, implemented by the GBDecode algorithm, in which only a subset of an information set is fixed. This approach reduces the size of the combinatorial search space at the cost of solving the associated multivariate nonlinear systems. To handle this algebraic component, we employ the MultiSolve algorithm, which replaces a single Grobner basis computation with a collection of computations on simpler systems, obtained by exhaustively assigning a varying number of indeterminates over the finite field. This provides a tunable balance between combinatorial search and algebraic solving. We evaluate the resulting approach experimentally on instances of the Syndrome Decoding Problem for random binary linear codes, using parameters corresponding to the NIST Security Category 1 parameter set of the Classic McEliece cryptosystem. The experiments assess the feasibility of this combinatorial-algebraic approach and provide insights into the practical behavior of Grobner basis techniques within an ISD-like decoding framework.

cs.CR · cs.SC · math.AC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38611

V-CAPE: Visual Consistency Assessment for Product Image Editing

Iaroslav Melekhov, Roberto Alcover Couso, Francesco Taioli, Daniel Coelho, Artur Bekasov, Virginia Fernandez Arguedas, Jose Miguel Grande Saiz

原文摘要

Recent advances in generative image editing enable complex transformations such as background replacement, object manipulation, and pose adjustment from natural language instructions. However, verifying that these transformations preserve visual consistency, defined as the preservation of an object's intrinsic visual attributes, including geometry, texture, color, and fine-grained details, remains a largely unsolved problem. This challenge is particularly pronounced at scale, where manual inspection is infeasible and automated quality assessment (AutoQA) could become essential. In this work, we introduce Visual Consistency Assessment for Product Image Editing (V-CAPE), a large-scale dataset for evaluating visual consistency in generative image editing. The dataset consists of 40K unique source-target product image pairs, each annotated with binary accept/reject labels and structured failure modes capturing fine-grained inconsistencies such as geometric distortions, pose errors, and identity mismatches. V-CAPE combines real-world edited images with a synthetic subset constructed from 3D assets, enabling both realistic evaluation and controlled analysis. Vision-language models (VLMs) across all evaluated size categories on AutoQA achieve accuracy below 65%, indicating that the dataset poses a substantial challenge regardless of model scale. Our results highlight fundamental limitations of current models in verifying visual consistency under transformation, positioning V-CAPE as a benchmark for advancing fine-grained image editing evaluation.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38610

Repairs for Whom? Evaluator-Executor Mismatch in Selecting Repairs for Failed LLM Agents

Yijun Liao, Fanwei Liang

原文摘要

When an LLM agent fails, repair methods choose which action to replace and what to put in its place. They often score candidate repairs with greedy replay, a stronger model, an environment oracle, or a judge that reads the failed log. We ask whether these evaluators pick repairs that help the agent that will actually execute them. Holding failed trajectories and candidate single-step repairs fixed, we label every candidate with independent rollouts of the deployed policy and measure how much deployment value each evaluator's choice loses. On failures not screened for repair value, pre-specified tests show that greedy replay loses 0.08-0.13 in expected net success gain in ALFWorld and 0.06-0.13 in net reward in WebShop, relative to selection with the deployed agent's own rollouts. At an equal rollout budget, selecting repairs for Qwen2.5-7B with Qwen2.5-14B rollouts loses 0.25 in ALFWorld and 0.07 in WebShop, twenty 14B rollouts per candidate are worth no more than two of the 7B agent's own, and fresh rollouts confirm that the two models prefer opposite repairs on selected disagreement cases. Between similar-sized models from different families, the losses appear in both directions in both environments (0.07-0.17). The scoring executor also changes repair-location labels and method rankings. Repairs should be verified with the executor that will run them, and repair labels should name their executor.

cs.SE · cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38608

The Artificial Personality Model: Theory, Implementation, Measurement and Experimental Validation in Conversational Agents

Alexander Dregger, Barbarella Petz, Erik Kuhn, Pia Volk, Andreas Oberweis, Stefan Hillmann, Jannik Strötgen

原文摘要

Artificial personality shapes user experience (UX) in human-machine interaction, yet coherent theoretical frameworks and validated measurement instruments remain limited. This paper refines and empirically evaluates the Artificial Personality Model (APM), a hybrid framework integrating human-centred and machine-specific personality dimensions. Across three experiments (N = 395), the model was operationalised and evaluated using the Artificial Personality Questionnaire (APQ). Factor analyses revealed that some human personality dimensions (e.g., agreeableness, conscientiousness) are partially transferable, whereas others (e.g., extraversion) did not emerge as distinct dimensions. Additional machine-specific dimensions were identified. The APQ provided encouraging initial psychometric evidence, while the human-centred BFI-2 showed poor reliability and an uninterpretable factor structure in Experiment 3. Experimental manipulations demonstrated significant effects of artificial personality on user satisfaction, continuous use intention, and social presence. The findings contribute an empirically refined framework and a novel instrument for measuring perceived artificial personality, offering practical guidance for designing conversational agents.

cs.HC · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38605

Efficient Group-Penalty k-means for Text Clustering with Noisy LLM-Generated Constraints

Feiteng Nie, Yuan Sun, Shengkun Zhu, Sheng Wang, Zhiyong Peng

原文摘要

k-means provides a simple and efficient foundation for text clustering, but embedding alone can miss task-specific semantic boundaries. Must-link (ML) and cannot-link (CL) constraints can correct such errors, yet conventional constrained clustering assumes that these relations are already supplied. LLMs can generate them without task-specific labels, but exhaustive querying is costly and their judgments can be incorrect. We present GPKmeans, an efficient Group-Penalty k-means method for text clustering with noisy LLM-generated constraints. GPKmeans uses LLM as an offline local pairwise annotator, querying selected anchor-candidate pairs for sparse ML links and constructing set-valued CL evidence without all-pairs queries. It converts connected components of the ML-link graph and the generated CL sets into groups with normalized, bounded soft penalties, limiting each group's influence independently of its size. Cached coordinate descent is proposed to efficiently solve the constraints clustering problem. We prove finite-step convergence and derive deterministic and probabilistic oracle-referenced bounds that relate objective distortion to the penalty coefficients and to the group-level error probabilities of the generated constraints. Across nine datasets, GPKmeans uses fewer LLM calls and yields more accurate constraints. It achieves the best average ACC, NMI, and ARI while running approximately 30 times faster than state-of-the-art constrained clustering methods.

cs.DB

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38602

STAR: Scalable TEE-based Anonymous Rate-limiter

Kosei Akama, Yoshimichi Nakatsuka, Koichi Moriyama, Keisuke Uehara

原文摘要

Protecting Internet services from attacks such as TLS/HTTP floods requires rate limiting, yet identifying users to enforce these limits can compromise their privacy. Anonymous rate limiting promises to reconcile these goals, but existing schemes either struggle to scale or assume that services and authorities do not collude. We present STAR, which uses server-side trusted execution environments (TEEs) to overcome these limitations. By enabling lightweight cryptographic operations, TEEs make authentication efficient, but introduce a privacy challenge: malicious services can observe memory-access patterns to infer user secrets. We address this threat through countermeasures against secret-dependent memory accesses and assess our implementation using dynamic analysis. To extend these efficiency gains across servers, we also design a secure sharding mechanism that distributes client requests. We demonstrate that strong privacy guarantees can coexist with efficient authentication at scale; STAR processes authentication requests in tens of microseconds and handles approximately 13.9K requests per second per CPU core.

cs.CR

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38600

Data Scaling Laws in Stereo Matching: From In-domain Fitting to Cross-domain Generalization

Tong Zhao, Di Liang, Yiming Zeng, Bo Xu, Yingzhi Tang, Chen Bai

原文摘要

In the era of large models, stereo matching is developing towards generalizable foundation models trained on million-scale synthetic images. However, how model performance scales up with synthetic data is still underexplored. In this work, we preliminarily investigate the in-domain and cross-domain data scaling laws in stereo matching. A realistic synthetic dataset, named XStereo, containing 200 thousand samples is first built to serve as a high-quality benchmark for data scaling research. In-domain training on five large-scale datasets demonstrate a two-regime scaling behavior with data, transitioning from the standard power law to saturation, where the critical transition point is approximately invariant under a fixed model. Cross-domain performance exhibits a substantially weaker and less predictable dependence on the source-domain data scale. The distribution and coverage diversity are more effective for diminishing domain gaps. More instructive suggestions are provided for high-efficiency data construction and training. The insightful findings are significant verification and extension of the existing scaling laws in both language and vision, paving the way to efficient and stronger stereo matching. The XStereo dataset is public at https://huggingface.co/datasets/Tong-Zhao/XStereo.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38598

Two-Stage Clipping: Linear-Time Approximate and Exact Mixed-Norm Projections for Transformers

Michel Barlaud, Jean-Paul Marmorat

原文摘要

Large Language Models (LLMs) based on Transformer architectures have achieved remarkable performance across artificial intelligence tasks, but their computational cost remains a major bottleneck. Structured sparsification provides a promising approach to reducing this cost. In particular, $\ell_{1,\infty}$ projections can induce column-wise sparsity in model matrices, but existing proximal methods (Prox) typically require $\mathcal{O}(nm\log(nm))$ operations for matrices in $\mathbb{R}^{n\times m}$, limiting their computational and energy efficiency. In this paper, we introduce Two-Stage Clipping (TSC), an efficient structured sparsification operator inspired by bilevel optimization. Rather than solving a bilevel optimization problem in the strict sense, TSC decomposes the computation into two successive stages. First, group-level quantities are projected to allocate the global sparsity budget and identify active groups. Second, the resulting values are used as group-wise clipping thresholds for the matrix coefficients. For square attention matrices ($n \times n$), the complexity of our TSC method is $O(n^2)$, which is linear in the number of matrix coefficients. We establish a theoretical bound on the discrepancy between TSC and the exact proximal projection and show on synthetic data that the additional Euclidean approximation cost remains small in practice. Extensive benchmarks demonstrate substantial runtime improvements over proximal baselines and validate the theoretical norm identities underlying the proposed construction.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38595

Rethinking LLM Jailbreak Evaluation: Continuous Risk Landscapes Beyond Binary Outcomes

Yukai Zhou, Wenjie Wang

原文摘要

A binary success indicator is a natural representation for discriminative tasks such as image classification under adversarial perturbations, where each input is mapped to a single prediction. However, generative tasks are fundamentally different: an LLM jailbreak prompt induces a distribution over open-ended trajectories, while a sampled response reveals only one path. Yet LLM jailbreak evaluation typically retains the binary response indicator underlying attack success rate, obscuring the surrounding structure of possible generations. We introduce a continuous, context-conditioned risk landscape over first-token branches, estimated by fixing candidate openings and repeatedly sampling their continuations. This continuous risk-landscape perspective reveals two complementary properties: current decoder exposure and safe rerouting capacity, which have been obscured by binary-indicator-based ASR. The resulting representation also provides a common basis for comparing clean prompts, attacked variants, and attack strength against a known adaptive defense. Across multiple target models and jailbreak attacks, we uncover risky branches beneath apparently safe outputs, identify qualitatively different ways attacks reshape the model's opening distribution, and show that attacks strongest before a defense do not necessarily remain the strongest after the adaptive defense reacts. These findings recast jailbreaks as deformations of the surrounding continuation-risk landscape, establishing a more informative foundation for understanding jailbreak behavior and developing safer language models.

cs.LG · cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.00728

A Survey on Open Dataset Search in the LLM Era: Retrospectives and Perspectives

Pengyue Li, Sheng Wang, Hua Dai, Zhiyu Chen, Zhifeng Bao, Brian D. Davison

原文摘要

High-quality datasets are typically required for accomplishing data-driven tasks, such as training medical diagnosis models, predicting real-time traffic conditions, or conducting experiments to validate research hypotheses. Consequently, open dataset search, which aims to ensure the efficient and accurate fulfillment of users' dataset requirements, has emerged as a critical research challenge and has attracted widespread interest. Recent studies have made notable progress in enhancing the flexibility and intelligence of open dataset search, and large language models (LLMs) have demonstrated strong potential in addressing long-standing challenges in this area. Therefore, a systematic and comprehensive review of the open dataset search problem is essential, detailing the current state of research and exploring future directions. In this survey, we focus on recent advances in open dataset search beyond traditional approaches that rely on metadata and keywords. From the perspective of dataset modalities, we place particular emphasis on example-based dataset search, advanced similarity measurement techniques based on dataset content, and efficient search acceleration techniques. In addition, we emphasize the mutually beneficial relationship between LLMs and open dataset search. On the one hand, LLMs help address complex challenges in query understanding, semantic modeling, and interactive guidance within open dataset search. In turn, advances in dataset search can support LLMs by enabling more effective integration into retrieval-augmented generation (RAG) frameworks and data selection processes, thereby enhancing downstream task performance. Finally, we summarize open research problems and outline promising directions for future work. This work aims to offer a structured reference for researchers and practitioners in the field of open dataset search.

cs.IR · cs.DB

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38587

RoSAMDepth: Robust Self-supervised Depth Estimation Leveraging Segment Anything Model

Xuanang Gao, Zhiwei Ning, Gengming Zhang, Jiaxi Cao, Runze Yang, Zhonglong Zheng, Jie Yang, Rong Xiao 等 9 位作者

原文摘要

Robust depth estimation aims to maintain high-quality depths across diverse conditions. However, most existing methods estimate depth without taking into account the object-level information. As a result, the predicted depth may easily deviate within objects and become blurred under adverse conditions. To overcome this weakness, we propose RoSAMDepth, a novel framework that can assist robust self-supervised depth estimation in leveraging rich and diverse object-level priors from the Segment Anything Model (SAM). We focus on incorporating object-level information across three key aspects: a segment-guided representation contrasting method that injects object-level awareness into the feature representation space; an adaptive regional outlier masking strategy combined with a regional Gaussian likelihood loss that enforces regional depth smoothness; and an object-level reliability estimation strategy that mitigates the influence of unreliable supervision. Extensive experiments across multiple datasets and diverse weather conditions demonstrate that our method produces sharper, more accurate depth predictions, consistently outperforming state-of-the-art methods. Our code is available at https://github.com/xagao/RoSAMDepth.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38586

Estimating the number of nodes and paths in a DAG by following a random path

Günter Rote

原文摘要

We investigate a simple method to compute an unbiased estimate of the number of nodes of a directed acyclic graph (DAG) by following a random directed path. This method is due to Leonard Pitt from 1987, and it generalizes a method of D. E. Knuth from the 1960s for estimating the size of a rooted tree. A slight variation makes it possible to estimate the number of source-sink paths. We extend the algorithm to use many "agents" that walk cooperatively through the graph. This extension unifies and generalizes several methods that have been proposed for approximately counting the nodes of a tree.

cs.DS · math.CO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38585

Integrated data, metadata, and paradata management system for 3D Digital Cultural Heritage objects: Workflow automation, federated authentication, and publication

Michal Orzechowski, Lukasz Opiola, Ignacio Lamata Martinez, Marinos Ioannides, Panayiotis N. Panayiotou, Lukasz Dutka, Renata G. Slota, Jacek Kitowski

原文摘要

The complexity of high-quality 3D digitised cultural heritage objects creates challenges for existing data management systems as they need to develop metadata management and processing capabilities to provide semantic insight into the interconnectivity of data that constitutes cultural heritage objects. To address these challenges, we propose a global federated authentication and authorisation mechanism, a data and metadata management system, and an integrated engine for designing and executing automated workflows that facilitate the processing of both data and metadata. The solution is evaluated with three distinct 3D digitised cultural objects and presents the complete process from data upload to cultural heritage object publication

cs.DL · cs.CY · cs.DC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38582

Robust Active Learning for More Efficient and Reliable Paths to Optimal Design

Hayriye Pehlivan Solak, Francesco Di Fiore, Jeroen Wackers, Riccardo Pellegrini, Andrea Serani, Matteo Diez, Laura Mainini

原文摘要

Simulation-driven design optimization requires active learning strategies that efficiently combine models with different computational costs and levels of fidelity. We introduce Multifidelity Cost-Aware Screening (MF-CAS), which enables the joint selection of sampling location and fidelity using a surrogate that provides decomposed uncertainty intervals rather than a complete predictive probability distribution. MF-CAS exploits the contribution of each fidelity to the high-fidelity uncertainty together with its computational cost to determine whether a candidate should first be evaluated at low fidelity or directly progressed towards high fidelity. The method is assessed on the constrained 14-dimensional hydrodynamic optimization of the DTMB 5415 hull under a deliberately limited computational budget. MF-CAS achieves an approximately 9.0% resistance reduction, compared with 6.7% for the best baseline optimisation method, demonstrating that incorporating fidelity-resolved uncertainty directly into the joint location-fidelity decision can substantially improve multifidelity active learning efficiency.

cs.CE · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38579

Session-Bound Behavioral Watermarking for Autonomous LLM Agents

Md Facklasur Rahaman, Yong Xiang, Md Palash Uddin, Shang Gao, Guang Hua, Leo Yu Zhang

原文摘要

Large Language Model (LLM) agents are increasingly acting through Web interfaces and external tools. They produce trajectories. Those trajectories may pass through several intermediaries. They can store, modify, or dispute those trajectories. In Web-based agent deployments, trajectories may pass through APIs, marketplaces, and tool servers. As such, a copied or disputed log may not by itself reveal which agent owner produced it. Behavioral watermarking can embed ownership evidence in agent action choices. However, existing methods mainly address detection, removal, rewriting, or imitation. They do not directly test whether repeated observation of watermarked trajectories enables cross-session forgery by exploiting learned action preferences. We formulate this learned-forgery threat and propose AgentAnchor, a session-keyed behavioral watermark for owner attribution from trajectory logs. We derive each step key from a secret key, a fresh public session identifier, and semantic states of recent observations, while a guarded digest of the action-policy state diversifies linear coding rows. It embeds parity evidence through a KL-bounded exponential tilt, and a trusted verifier checks a disputed log without rerunning the agent, using wrong-key calibration and abstention.

cs.CR

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38580

Structural Pattern Mining in Inka Khipus: A KFG Re-Analysis

Maria Contreras

原文摘要

This paper reports a methodological pivot from the Open Khipu Repository to the Khipu Field Guide (KFG) database, prompted by stalled progress on the OKR, and a full re-analysis of the resulting 650-khipu sample. Three findings recur across every analysis attempted. First, khipu size confounds nearly every derived structural count, explaining 42-70% of its variance; all subsequent models control for it. Second, the two-letter name prefix (AS, UR, HP, QU, JC, MM, KH) identifies the original cataloger, not a site or period, which turns documentation completeness into a second confound that must be modeled explicitly. Controlling for both, khipus cataloged by Hugo Pereyra's Proyecto Quipu (HP) show significantly fewer Ascher sum-relation types than Gary Urton's (UR) khipus (p = 0.044), while the Ascher-cataloged (AS) khipus are too collinear with their own near-total lack of side/direction documentation to assess (VIF = 5.0). Third, generic structural clustering (UMAP+HDBSCAN) does not separate the pooled 650-khipu sample from two independent null models (permutation and Gaussian), corroborating an independent conclusion already reached by the KFG itself; the same test restricted to a single cataloger (UR) proved not reproducible across runtime environments and is reported here as a methodological caveat rather than evidence of structure. A targeted replication of the KFG's own S/Z-knot spatial-block heuristic for calendar/astronomical khipus recovers 8 of 10 published candidates and proposes 34 additional ones under the same criterion.

cs.CL · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38578

Dual DeltaNet: Value-Side Memory Reorganization for Linear Attention

Yixiao Qian, Yaxin Zhang, Song Chen, Jiaxu Liu, Shengze Cai, Chao Xu

原文摘要

Linear attention and state space models (SSMs) offer efficient alternatives to softmax attention by maintaining a fixed-size recurrent state, but this design requires careful management to preserve useful historical associations and limit interference. The delta rule helps reduce interference through correction of the value associated with each key, but it does not explicitly account for how related value features are distributed across the state. We observe that a state $\boldsymbol{S}\in\mathbb{R}^{d_k\times d_v}$ can be viewed as a bank of $d_k$ addresses storing $d_v$-dimensional values. While a key-side read $\boldsymbol{S}^\top\boldsymbol{k}$ retrieves the value associated with $\boldsymbol{k}$, a value-side read $\boldsymbol{S}\boldsymbol{e}$ aggregates addresses whose stored value features match the selector $\boldsymbol{e}$. Based on this observation, we propose Dual DeltaNet (DDN), which extends Kimi Delta Attention (KDA) with a two-sided delta rule. The key-side delta rule reads with the current key to correct its associated value, while the complementary value-side delta rule reads with selected features of the current value to reorganize the addresses it occupies. We derive a chunkwise parallel training algorithm and a rank-2 recurrence that increases arithmetic intensity during inference. Compared with KDA, GDN-2, and Mamba-3, DDN achieves the strongest overall performance across language modeling, zero-shot downstream tasks, and retrieval, while incurring modest training and inference overhead relative to KDA. Code: https://github.com/ReichtumQian/DualDeltaNet

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38577

Episodic looking - using reinforcement learning to bias perception enhances few-shot and continual learning with protection from forgetting

David Rawlinson, Gideon Kowadlo

原文摘要

There is a fundamental contradiction between the desire for models that can generalize, and models that can continuously adapt to recent input with changing statistics. These conditions, captured in few-shot and continual learning settings, can bias or disrupt learned representations. This harms generalization to a broader context, leaving the model unable to overcome input variance. This paper proposes a solution based on the Complementary Learning System architecture (CLS), in which a Short-Term Memory (STM) delegates generalization to a slow, statistical Long-Term Memory (LTM). Delegation is achieved by working in a space of robust, generalized perceptions produced by the LTM. With this separation, the STM can focus on memorizing recent input and avoiding interference between memories. We show that on an image classification task, the resulting CLS model is more resistant to forgetting in a domain-incremental continual learning setting than a baseline ResNet LTM, and generalizes more effectively in a few-shot setting than other continual learners. In the continual setting, overall accuracy is slightly worse than existing continual learning models, but with a different mix of performance characteristics. Results analysis suggests that the model is limited by the representations and representational plasticity of the LTM. In future work, we will seek to address this by replaying STM memories to the LTM.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.26232

Image Forgery Detection under Generative and Composited Background Replacement: A Benchmark with Processing-Matched Controls

Bugra Alperen Uluirmak, Rifat Kurban

原文摘要

Consumer tools can now keep the subject of a photograph and replace, relight or edit everything behind it. Such edits place genuine people and objects in scenes they were never in, yet involve processing--resampling, harmonization, diffusion autoencoders--that also occurs in benign workflows. We introduce BG-REAL, a benchmark of 25,624 images built from 2,485 Open Images photographs with a dominant subject and real donor scenes. Each recipient is edited by donor compositing, learned harmonization, generative background inpainting, foreground-conditioned relighting and local background edits, and is paired with authentic controls that share the processing but not the semantic edit; all images of a recipient share file format and JPEG quality. Three generators (including FLUX.1 Fill), scene categories and subject classes are held out. Seven detectors published between 2022 and 2026 reach zero-shot AUROCs of at most 0.745 and have no useful operating point: manipulation localisers mark the preserved subject instead of the replaced background, and a detector of AI-generated images flags most authentic images passed through a diffusion autoencoder. After fine-tuning, the strongest detectors reach an AUROC of about 0.95, while re-implemented background-forgery detectors remain below 0.80. Controls show that full-background replacements by the training generators are detected from the edit itself, whereas local edits and SDXL and FLUX edits remain hard, and that full-background localisation largely reduces to subject segmentation. A model-agnostic training objective that uses the controls improves detection on unseen subjects for one detector and on local edits for another without changing in-distribution accuracy. The data are released.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38576

Extending Higher-Order Critical Pairs

Johannes Niederhauser, Aart Middeldorp

原文摘要

Confluence is a fundamental property of computation which implies that the nondeterminism of a system does not affect its outcomes. A key concept of confluence research in rewriting theory is the notion of critical pairs which captures the potential local sources of non-confluence. In this paper, we lift the definition of higher-order critical pairs to a more expressive class of higher-order rewrite systems. Thereby, we facilitate confluence analysis of rewrite systems which can express complex one-step computations as well as program transformations from functional programming.

cs.LO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38575

Exploring and optimizing the Vivado Flow on large FPGA designs: A Practical Analysis of Strategies and Their Impact on Timing

Kachkachi Nor Eddine

原文摘要

Achieving timing closure in FPGA designs is crucial, especially for high-performance applications. The Vivado Design Suite offers several strategies for optimization, but finding the best configuration for a given project remains challenging. This paper explores the various Vivado's timing directives and strategies to improve performance across timing, area, and runtime. Through two test-case designs, an NQR spectrometer SoC-FPGA and a digital readout ASIC prototype for high energy physics particle detector's, different strategies and configurations are compared to identify the most efficient flow.

cs.AR · eess.SP · hep-ex

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38573

CALM: Conflict-Aware Lifecycle Memory Governance for Long-Horizon Language Agents

Chang Li, Xianhao Ou, Xinyan Ma, Hao Li, Haoyuan Xu, Jiafeng Liang, Xiaohan Wang, Jiajun Chai 等 10 位作者

原文摘要

A memory can be outdated for present-day reasoning yet indispensable for reconstructing the past. Relevance-based retrieval overlooks this tension: semantic relevance, evidence-grounded validity, and query-specific eligibility are distinct. We propose CALM, a conflict-aware lifecycle memory governance framework that decouples persistent memory preservation from query-time admission. CALM converts heterogeneous records into evidence-grounded memory cells, updates validity and lifecycle states using support, conflict, and dependency signals, and admits query-appropriate evidence through training-free or learned gating informed by temporal applicability. Across LongMemEval-S and LoCoMo with Qwen and GPT backbones, CALM outperforms state-of-the-art baselines, achieving the best overall accuracy. Compared with simple filtering and reranking, query-conditioned admission further improves evidence selection. Conflict-blocking and robustness analyses show CALM suppresses erroneous retrieved memories while retaining useful evidence. Historical-reconstruction experiments demonstrate that preserving superseded records supports accurate earlier-state reasoning without compromising current-state answers.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38570

Do Audio LLMs HEAR Pain? Tracing Acoustic Evidence from Speech to Language Reasoning

Wenqi Li, Xiangjue Dong, Sulaiman Vesal

原文摘要

Large audio language models (LALMs) can process speech directly, but it remains unclear how well they use the non-lexical acoustic information that speech carries. We study this question through pain, using TAME-Pain, which contains pain-neutral Harvard sentences and spoken pain ratings recorded at different self-reported pain levels. We introduce HEAR, a four-stage diagnostic framework that traces pain-related information from the speech signal to the model's prediction, and apply it to seven closed-source and open-weight LALMs. On the Harvard sentences, where pain can only be inferred from the voice, all models perform at chance level, and same-speaker references or few-shot examples improve balanced accuracy by no more than six points. On the spoken ratings, the same models reach 87.5-99.7% balanced accuracy. Layer-wise probing of open-weight LALMs shows that pain can be decoded from early audio representations (74.1% balanced accuracy for binary prediction and 45.0% for three-class severity), but that this information, along with most pain-associated acoustic cues, decreases in deeper layers. In an acoustic perception test, weaker models cannot distinguish even large changes in these cues, whereas stronger models can but still fail to infer pain. Finally, we recover pain-related acoustic cues and directly reinject them as structured text into the prompt, which improves pairwise pain prediction by up to 32 points. These results indicate that current LALMs encode paralinguistic information in early layers that is largely unavailable when the model produces its answer.

cs.CL · cs.AI · cs.SD

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.24501

HiPPO Functional Memory: Coordinate-Queryable Attention for Irregular Time Series

Zhaodong Guo, Haoyun Feng

原文摘要

Irregular time series require models to answer temporal queries at arbitrary coordinates, yet existing approaches typically retain observation-dependent representations that must be repeatedly resolved for each query. We introduce HiPPO Functional Memory (HiPPO-FM), a functional-memory attention operator that compiles an irregular history into a reusable coordinate-addressable state. Instead of storing observation-indexed tokens or latent trajectories, HiPPO-FM writes observations into a fixed-rank functional space and performs direct coordinate evaluation through matched functional geometry. Kernel-smoothed HiPPO coordinates and Gram-based readout define a shared write/read mechanism, while observed-support self-attention enables content-dependent representations before compression. We characterize the induced finite-dimensional query space and derive an error decomposition separating functional geometry approximation from learned-memory mismatch. Across continuous-coordinate reconstruction, irregular representation learning, real-world extrapolation, and event-sequence prediction, HiPPO-FM consistently improves strong irregular time-series baselines, achieving strong reconstruction accuracy on Spiral, competitive performance across 20 UEA datasets, improved real-world extrapolation, and superior results on several irregular event benchmarks. After encoding, the compact functional state can be reused across different query sets without reprocessing the observation history.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.09540

A Co-Design Approach to Physically-Informed Molecular Docking for Quantum Annealers

Francesco Micucci, Matteo Barbieri, Gabriella Bettonte, Domenico Bonanni, Anita Camillini, Anna Fava, Daniele Gregori, Andrea R. Beccari 等 9 位作者

原文摘要

Molecular docking is a crucial step in computer- aided drug design, involving the identification of ligand poses that are physically plausible within a protein pocket. Recent studies have shown that this problem can be formulated as a geometric weighted subgraph isomorphism, which can then be encoded as a QUBO and thus solved on D-Wave quantum annealers. In this work, we adopt a co-design approach, addressing molecular docking at the formulation and encoding levels, and examining how hardware topology affects solution quality on current quantum annealers. We propose new QUBO formulations that extend the state-of-the-art ones by including van der Waals, Coulomb, hydrogen bonding, and hydrophobic interactions. Experimental results show that incorporating physicochemical terms improves the average docking quality by more than 18% compared to the purely geometric baseline on D-Wave quantum annealers with the Pegasus topology, and by up to 29% on those with the Zephyr topology. The choice of topology, along with an improved encoding design, yields significant gains in embedding efficiency and the number of valid solutions. Our designs reduce the number of physical qubits required by up to 14.5% and decrease the chain break fraction by an order of magnitude, thereby enhancing confidence in the resulting solutions.

cs.ET · q-bio.BM

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38569

A Multi-Layer Navigation System for Smart Wheelchairs with a Limited Input Interface

Piero Simonetto, Anna Polato, Francesco Pasti, Muhammad Rameez ur Rahman, Sebastiano Vascon, Nicola Bellotto, Emanuele Menegatti, Stefano Tortora 等 9 位作者

原文摘要

Smart wheelchairs are robotic devices that gather information from the surroundings and allow people to safely drive through various environments. They usually rely on the interpretation of high-level user's intentions, typically decoded through human-robot interface (HRI) or brain-machine interface (BMI). However, navigating in an unknown environment and accurately understanding user commands remain significant challenges. Misinterpretation of commands or the environment can lead to user frustration, distrust of the system, or even unsafe situations. In this paper, we present a novel Multi-Layer Navigation System (MLNS) for a smart wheelchair that enables navigation in unknown environments while interpreting and safely contextualizing user's intentions even in the case of limited and faulty commands. The stack relies on a Virtual Sensors-Driven Potential Fields (ViSPF) method for obstacle detection that accounts for the wheelchair's actual size, a Model Predictive Control (MPC) module for trajectory generation, and an open-vocabulary computer vision algorithm for target identification. Finally, a system based on Priority Queue (PQ) was implemented for target selection. We validated our system on a real smart wheelchair with experiments involving 10 participants without disabilities, including scenarios with faulty input interfaces. The results demonstrate the robustness and effectiveness of our system in real-world environments, proving its potential for practical applications in assistive technologies.

cs.RO

查看论文
arXiv ↗PDF ↗