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版本更新 · v2arXiv:2611.09195

InstantHMR : Real-Time Human Mesh Recovery on Your Phone

Mohamed Adjel, Kahina Chalabi, Vincent Bonnet

原文摘要

Real-time 3D human mesh recovery from a single image on mobile devices is a challenging problem with many applications, including robotics, biomechanics, AR/VR, and exergames. Current state-of-the-art models are either too slow to run efficiently on mobile devices or sacrifice accuracy for speed. We present InstantHMR, a family of open-source single-image regressors suited for mobile inference, based on the Momentum Human Rig (MHR) parametric model and trained exclusively on publicly available annotations from the SAM 3D Body dataset. Our approach achieves remarkable performance on a Samsung Galaxy S23 NPU, recovering a 6,890-vertex body mesh in 5.5 ms per person, and 0.98 ms on an NVIDIA RTX 4070 GPU. This speed surpasses all existing methods we evaluated. On mobile, it is 12x faster than the single mobile-compatible method that slightly outperforms ours in accuracy, while achieving competitive accuracy with 40.0 mm PA-MPJPE on 3DPW, 48.8 mm on EMDB and 41.7 mm on RICH. To ensure fair comparisons, we re-evaluate 11 state-of-the-art methods under a unified protocol aligned with the closest MHR-based competitors, including Meta's official MHR-to-SMPL conversion, and report their reproduction results. Additionally, we measure latency across eight different targets, ranging from desktop TensorRT to two phone NPUs. Code and models are available at https://github.com/Naturalpad-studio/InstantHMR.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16574

HALight: Heterogeneous Multi-Agent Reinforcement Learning for Traffic Signal Control under Lane-Level Missing Observations

Chenkai Hu, Yingqian Zhang, Ming Xie, Yi Fang, Chengxiang Jin, Chao Li

原文摘要

Multi-agent reinforcement learning has shown great promise for traffic signal control. However, most existing methods rely on two key assumptions: (i) agents are assumed to be homogeneous with unified observation/action spaces; (ii) full and reliable lane-level traffic observations are available to all agents. In practice, urban road networks exhibit substantial structural heterogeneity (e.g., lane configurations and phase designs), while sensor failures and incomplete detector coverage often result in missing incoming lane measurements. Such issues can severely impair coordination and destabilize learning. To address these challenges, we propose HALight, a communication-enhanced heterogeneous multi-agent reinforcement learning framework for traffic signal control under lane-level missing observations. HALight supports intersection-specific observation and action modeling to accommodate heterogeneous intersections, and incorporates a lightweight one-hop neighborhood communication module to enrich spatial context when lane-level observations are partially unavailable. Moreover, HALight adopts a sequential policy update scheme to improve coordination among heterogeneous agents. Experimental results on two real-world road networks demonstrate that HALight outperforms representative homogeneous and heterogeneous baselines in terms of traffic efficiency and convergence behavior. The code and datasets used in this work are available at https://github.com/Huchenkaii/HALight.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.24195

ZFLean: a framework for set-level mathematics in Lean

Vincent Trélat, Matteo Pouillat

原文摘要

We present ZFLean, a Lean 4 library for doing core mathematics inside a model of ZFC with the ergonomics expected of typed Mathlib developments. Building on Mathlib's ZFC model, we contribute a relational calculus for sets with rewriting hints and small predictable tactics, canonical set-theoretic constructions -- Booleans, naturals, integers, sums/option -- and bridges between ZFC objects and Lean's native types enabling mixed set-level/typed proofs. The layer reduces boilerplate for extensional reasoning while remaining compatible with vanilla Mathlib. We discuss library organization and usage patterns that lower the friction of set-theoretic formalization in a dependently typed assistant. We demonstrate typical use of the framework with a case study exercising our constructions and relational calculus through a proof of an isomorphism theorem on curried functions.

cs.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.20668

BELLS-O: Evaluating the Operational Trade-offs of LLM Supervision Systems

Leonhard Waibl, Felix Michalak, Hadrien Mariaccia

原文摘要

LLM supervision systems, namely input/output moderation filters and jailbreak detectors, are the primary safeguard against misuse in deployed AI applications, yet existing benchmarks are often vendor-biased, omit cost and latency, and rarely compare specialized guardrails against repurposed generalist LLMs. We present BELLS-O (Benchmark for the Evaluation of LLM Supervision Systems, Operational), an independent operational benchmark that evaluates 33 systems from 17 developers: specialized guardrails (e.g., Llama Guard 4, ShieldGemma-2B, Lakera Guard) and frontier generalists repurposed as supervisors (e.g., GPT-5.4, Claude Sonnet 4.6, Grok 4.1 Fast), jointly on detection rate, false-positive rate, latency, and cost. We cover input/output moderation across 11 harm categories, jailbreak detection across 13 attack techniques, and prompt-injection detection, using in-house datasets built from handcrafted prompts, curated external samples, and quality-controlled synthetic generation, with synthetic moderation samples paraphrased to suppress generator fingerprints. Content-moderation runs are repeated five times and all rates reported with 95% intervals. The Pareto frontier reveals use-case-dependent tradeoffs. On content moderation, specialized supervisors are operationally ahead: top systems match the best generalist LLMs on detection (93-96%, no significant difference) at low false-positive rates while running ~5x faster; cost depends more on serving setup than on class. On jailbreak detection, generalist LLMs achieve higher median detection on three of four datasets and lower false-positive rates, but at higher latency and, for closed frontier models, 10-100x higher cost. On prompt injection, the two best dedicated detectors lead. We release the framework, an interactive leaderboard, and four datasets as a vendor-neutral basis for selecting safeguards under deployment constraints.

cs.CR · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2505.04978

A Tactile-Informed Bi-Level Framework with Complementary Motion-Contact Planning and Tracking for Robust Model-Based In-Hand Manipulation

Yongpeng Jiang, Mingrui Yu, Yi Ren, Xiang Li

原文摘要

In dexterous in-hand manipulation, learning-based methods achieve strong task performance with increasing generalization to unseen objects. A complementary advantage of model-based methods, central to this work, is online adaptation to new task specifications without policy retraining, given suitable system models. However, due to the complexity of physical contacts and limited sensing modalities, existing model-based methods still struggle with modeling errors, even when using advanced contact models or optimization schemes, which limit their real-world applications. To advance the effectiveness and robustness of model-based contact-rich in-hand manipulation, this paper proposes a novel bi-level framework with tactile incorporation to mitigate these limitations. Specifically, at the high level, reference finger motions and contacts are jointly generated via contact-implicit model predictive control. The high-level module enables online reactive planning and disturbance recovery. At the low level, these references are concurrently tracked through a hand force-motion model and tactile feedback. The low-level module compensates for modeling errors and enhances manipulation robustness. The complementarity between the two levels is manifested in two dimensions: 1) subsystem decomposition, with a slow optimization-based planning layer and a fast tactile-reactive control layer; and 2) functional decomposition, combining task-level optimality with contact-level stability. Extensive experiments demonstrate that our approach outperforms existing model-based methods in accuracy and robustness, while supporting online replanning during execution. Our method successfully completes a diverse set of challenging tasks in real-world environments, even under appreciable external disturbances and modeling uncertainties.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.21426

Semantic-Aware Task Clustering for Constructive and Cooperative Multi-Tasking

Ahmad Halimi Razlighi, Maximilian H. V. Tillmann, Edgar Beck, Bho Matthiesen, Armin Dekorsy

原文摘要

Cooperative multi-task semantic communication (CMT-SemCom) improves task execution performance by leveraging shared representations. However, as we demonstrated in [1], cooperative multi-tasking can be either constructive or destructive, depending on the semantic relationships among tasks. To ensure constructive cooperation, we propose a semantic-aware task clustering method for CMT-SemCom. We have formulated a sequential multi-stage optimization problem in which semantically aligned tasks are clustered once after a short initial training phase, and then end-to-end (E2E) joint training is conducted exclusively within the discovered groups. Specifically, the problem decomposes into two stages: (i) a semantic clustering problem leveraging hierarchical density-based spatial clustering, and (ii) an intra-cluster E2E CMT-SemCom learning problem. Simulation results demonstrate that the proposed framework effectively mitigates destructive cooperation and negative transfer, yielding accuracy gains compared to unclustered multi-tasking and individual training baselines.

cs.LG · cs.IT · eess.SP · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.02282

POIROT: Agents as their own auditors for failure attribution in large language model multi-agent systems

Iñaki Dellibarda Varela, R. Sendra-Arranz, Pablo Romero-Sorozabal, J. M. Valverde-García, Annemarie F. Laudanski, Álvaro Gutiérrez, Eduardo Rocon, Manuel Cebrian

原文摘要

Large language model (LLM)-based multi-agent systems (LLM-MAS) are kept out of safety-critical domains by emergent failures that are hard to detect and to attribute. Current evaluation relies on a centralised evaluator, human or LLM judge, that must master the domain and hold the whole interaction history in one context. We propose that the agents executing a task can themselves attribute its failures. We introduce POIROT (Phased Oversight via Introspection, Review and Opinion Tallying), a modular protocol in which each agent casts a private vote within a hazard space derived from the system architecture, and the votes are aggregated into a single attribution. Before the vote, phases are added that progressively increase the interaction between agents, from independent self-assessment to sharing the self-assessments and peer interrogation. We build the protocol phase by phase, measure the contribution of each phase through an ablation, and compare every configuration with a single-LLM evaluator. We evaluate one proprietary and two open-weight LLMs on the Who&When benchmark and on BLAME (Benchmark for Localising Agent Malfunctions Effectively), a new benchmark for fault attribution in clinical rehabilitation and algorithmic trading systems. Agent-based attribution outperforms the single-LLM evaluator, but the benefit comes from independent self-assessment, not from interaction: sharing the assessments makes agents converge on the same, often wrong, culprit, and interrogation does not recover the loss. Without sharing and interrogation, the protocol outperforms the evaluator in all nine model-benchmark configurations (up to +50 percentage points). POIROT and BLAME are released as open source.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16566

Towards Automated Recommendation of Steady-State Detection Algorithms for Software Performance Benchmarks

Martin Beseda, Vittorio Cortellessa, Federico Di Menna, Daniele Di Pompeo, Luca Traini

原文摘要

Steady-state detection is a central step in software performance benchmarking, yet different detection methods rely on different assumptions and may perform best on different execution profiles. This paper formulates steady-state detection as an algorithm-recommendation problem: given a benchmark time series, predict which of two complementary detectors is more appropriate. The first, CP-SSD, identifies steady state through changepoint-based segmentation of the execution trace, whereas the second, KB-KSSD, uses a kernel-based approach to identify stable behaviour. We propose a data-driven recommender that characterises benchmark time series using automatically extracted features and combines supervised prediction with information derived from groups of time series exhibiting similar behaviour. The approach is evaluated on 407 labelled benchmark time series for which one of the two detectors can be identified as preferable with respect to a reference steady-state index. The proposed recommender selects the better detector correctly for 72.97% of the series, compared with 62.65% when always selecting the detector that performs best most often, corresponding to an absolute improvement of 10.32 percentage points. Paired statistical testing and bootstrap confidence intervals support this improvement. A comparison with a recommender using time-series features alone shows that the cluster-derived information mainly improves interpretability rather than predictive accuracy. Recommendations also remain highly stable when the unsupervised representation is learned from either the labelled subset or the full collection of 5,860 time series. These results demonstrate the feasibility of per-series steady-state detector recommendation while highlighting the need for broader labelled data, particularly for execution profiles favouring the changepoint-based detector.

cs.PF · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16565

Look Here, Then Decide: Small Decision Models in a Harness Find Memory-Safety Vulnerabilities at Scale on Commodity GPUs

Tim De Wachter, Kyryl Volkov, Myrto Eleftheria Gkogkou, Mathieu Huysman

原文摘要

We ask whether small, open-weight decision models can support vulnerability discovery in open-source software at scale and what the surrounding system must provide. These models answer a typed question in one forward pass, returning scores for the permitted answers rather than text. Our harness selects widely packaged C and C++ projects and uses deterministic incomplete-fix miners and shape localizers to decide where to look. Intern-Decision-4B, a 4B-parameter open-weight decision model, then scores every candidate function. The scores rank the queues rather than accept or reject candidates, apart from a low reach-score floor that some queues kept. The search uses no frontier model. Alone, the model is weak: it separates vulnerable from benign functions with AUC 0.73, but a vulnerable function from its own patch with only 0.56. It is more useful for ordering candidates. On 75 public CVEs from projects without any of our findings, the fixed function reached its repository's top 25 in 21% of cases, 1.4 times as often as under random order of the same queues. Recall is bounded mainly by where the localizers look. On the campaign's own queues the model's order placed 7.5 times more confirmed findings in the top 10 than random order, an observed, selection-biased upper bound. On three GB10 systems, the harness searched more than 4,000 projects at 900-1,100 repositories per hour. The campaign confirmed 187 memory-safety findings; by worst-case CVSS v3.1, 12 are critical, 148 high, 23 medium and 4 low. The decision step runs at 4.4 decisions per second on one GB10 and 9.8 on a consumer RTX 4090 gaming PC, with matching scores. A small decision model supports vulnerability discovery through prioritization, while deterministic analysis and verification remain essential. All confirmed findings are being disclosed to maintainers under coordinated disclosure before publication.

cs.CR · cs.LG · cs.SE

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2607.12586

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function

Xiao-qiang Zhai, Zhi-feng Pang, Peng Zheng, Ze-wen Li, Yan-zhe Hou

原文摘要

Pixel-level supervision in medical image segmentation ignores the geometric priors of the target region. Chan-Vese losses restore region-level priors and boundary regularity but do not characterise local boundary geometry. We propose the Deep Active Contour and Mean Curvature (DACMC) loss, which embeds the mean curvature of the predicted boundary in the Chan-Vese region functional; a fixed fourth-order-accurate convolution kernel evaluates curvature without explicit differentiation. The loss adds no trainable parameters and no inference cost, increases training time by 4.8%, and uses one hyper-parameter lambda = 10^-3 shared by all settings. We evaluate DACMC on liver CT, spleen MRI and ISIC dermoscopy images with U-Net and CE-Net backbones, four metrics (DSC, HD95, JS, ASD) and four competing losses under an identical protocol, together with a five-point lambda sweep, a wall-clock cost measurement and a five-seed replication with paired Wilcoxon signed-rank tests. DACMC ranks first or second in Dice in five of the six dataset-backbone settings, most consistently in distance metrics. On spleen MRI it reduces HD95 and ASD to 16.28 and 1.90 mm, and on ISIC dermoscopy it reduces HD95 to 7.08 mm. Dice varies by 0.0023 across five lambda values spanning three orders of magnitude; degradation appears only when curvature dominates. Five-seed replication shows that curvature stabilises training and removes occasional severe failures, although mean differences from the closest competing loss do not reach statistical significance at five seeds. DACMC imposes second-order geometric regularity on segmentation boundaries at low overhead, with the clearest benefit for small or scale-varying targets. Limitations include dataset size and partitioning, classical backbones and losses, and restriction to 2-D single-organ images.

eess.IV · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16560

When and How SignSGD Outperforms SGD: A Functional Scaling Law Perspective

Zilin Wang, Binghui Li, Jia-Nan Wang, Lean Wang, Jinbo Wang, Lei Wu

原文摘要

We study how the sign operator interacts with gradient noise to shape the learning dynamics and scaling behavior of sign-based optimizers. In power-law kernel regression with diagonal features, we establish a functional scaling law (FSL) for SignSGD under general learning rate schedules. The FSL describes the learning dynamics through two processes: signal learning and noise forgetting. The key mechanism is noise-induced preconditioning: gradient noise smooths the sign operator, inducing an effective preconditioned SGD. Noise-curvature alignment enables this preconditioning to transform the effective Hessian from $\mathbf{H}$ to $\mathbf{H}^{1/2}$. The resulting dynamics exhibit faster signal learning but slower noise forgetting than SGD. This tradeoff yields a regime-dependent comparison with SGD: (i) SignSGD achieves improved data efficiency in the hard-task regime; (ii) both optimizers achieve the same data efficiency in the easy-task regime, but SignSGD maintains this efficiency at larger batch sizes, requiring fewer optimization steps for a fixed data budget. Power-law kernel regression experiments support the proposed mechanism and predicted scaling behavior. LLM pre-training experiments further suggest that the SignSGD FSL better captures the batch-size dependence of loss dynamics for sign-based optimizers than the original SGD FSL.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16557

Game-Theoretic Analysis of RIS-Assisted Challenge-Response PLA - Part I: Perfect Channel Knowledge

Anna V. Guglielmi, Linda Senigagliesi, Marco Baldi, Stefano Tomasin

原文摘要

We consider a physical layer authentication scenario in which a legitimate user (Alice) communicates with an authen- ticator (Bob) through a reconfigurable intelligent surface (RIS), controlled by Bob. An attacker (Trudy) aims to impersonate Alice by suitably precoding her messages and still transmitting them through the RIS. This two-part study analyzes this attack-defense interaction between Bob and Trudy under perfect (Part I) and partial (Part II) channel knowledge by all the agents. Part I formulates the joint RIS selection and precoder design under perfect channel knowledge as a finite zero-sum game, establishing exact channel subspace conditions for attack indistinguishability alongside equilibrium mixed strategies. Part II extends the defense to incomplete channel information, where each agent observes only its own channel, by formulating a finite-action Bayesian game and characterizing the resulting Bayesian Nash equilibrium. Simulations in geometric and 3GPP TR 38.901 UMi-NLOS channels reveal that authentication performance is governed by available spatial degrees of freedom, channel correlation, and operating signal-to-noise ratio. The proposed equilibrium strategies effectively balance communication rate and security, outperforming conventional benchmark policies.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16555

Similarity-Guided Optimization Strategies for Approximate Network Symmetry Detection

David Hartman, Jaroslav Hlinka, Dominik Klepl, Anna Pidnebesna, Petra Vidnerová

原文摘要

Symmetry in complex networks is a structural property with broad implications for network function, dynamics, and robustness. While classical approaches rely on exact automorphisms and thus remain highly sensitive to small perturbations, recent work of Liu has introduced approximate formulations that relax the strict invariance condition and recast symmetry detection as an optimization problem over permutations. We investigate how node-level similarity information can be used to bias the search toward structurally plausible vertex correspondences, with network centralities serving as one interpretable instance. Building on this perspective, we adapt simulated annealing, genetic algorithms, tabu search, particle swarm optimization, differential evolution and the quadratic symmetry approximator to this problem, add similarity guidance to each, and introduce Bilinear Approximate Symmetry Inference (BASIN), which learns a soft assignment matrix via bilinear embeddings. Using a unified evaluation pipeline, we compare their guided and unguided variants, BASIN and a random baseline on four synthetic graph families, structural brain connectomes, and three empirical networks with known automorphism groups. Node-similarity guidance is decisive where a planted orbit exists that the unguided search misses, and algorithm-dependent elsewhere, on synthetic and empirical networks alike. Overall, tabu search achieves the strongest performance; its guided variant (SGTS) has the lowest median score among the guided variants in all 44 synthetic conditions, while BASIN is the better of the continuous relaxations on rewired lateral graphs. The effect of node-similarity guidance is therefore regime-dependent rather than uniformly positive.

cs.NE · cs.LG · q-bio.NC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16551

Hardness, Approximation, and Parameterized Algorithms for Asynchronous Border Minimization

Giulian Buzatu, Adrian Miclăuş, Alexandru Popa

原文摘要

In the Asynchronous Border Minimization Problem (BMP), a set of DNA probe strings is placed on a grid and synthesized using a common sequence of nucleotide-deposition steps. At each step, every cell may accept or skip the offered nucleotide. The cost is the total number of disagreements between neighboring cells over all steps. We study both free placement, where the probes may be rearranged, and fixed placement, where their positions are given. We show that free-placement BMP on a square grid is NP-complete over three symbols. For one-dimensional BMP, we prove APX-hardness over a binary alphabet, and NP-completeness even when every probe has length two. We also obtain an $8/7$-approximation for the latter restriction. For square grids, we give a randomized $O(n^{1/4}\log n)$-approximation, improving the earlier $O(n^{1/4}\log^2 n)$ guarantee. Both free- and fixed-placement BMP on rectangular grids are fixed-parameter tractable when parameterized only by the allowed border cost.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16541

A Finite Markov Chain Analysis of the Simplified Swarm Optimisation Update Rule

Wei-Chang Yeh

原文摘要

Simplified swarm optimisation (SSO) updates each variable by one draw that copies the global or personal best, keeps the current value or redraws uniformly, with probabilities $c_g$, $c_p$, $c_w$ and $c_r$. As a finite Markov chain under frozen memory, its coordinate kernel $c_w I + (1 - c_w)Π$ has an explicit stationary law and exact convergence rate $c_w^t$. The elitist chain has a geometric hitting-time bound, from a non-optimal start a fixed $c_r$ needs at least $\exp(nc_r/2)$ expected evaluations to reach a unique optimum, and a coupling under changing memories gives $O(n\log n)$ and $O(n^2)$ expected generations on OneMax and LeadingOnes for every population size with redraw share of order $1/n$ and no inertia, and within a $\log n$ factor for fixed inertia. With a frozen memory, inertia cannot speed up first entry into any set from stationary positions, though it can from others. Where the three reference values agree, only $c_r$ acts; where they disagree, copy branches recombine the memories, forcing a redraw only where no active branch holds the target value. Branch logging and checkpoint replay show inertia slowed the tested OneMax and LeadingOnes runs and suggest memory-mediated gains on wider gaps. On Jump functions SSO needed 3.8--8.9 times fewer evaluations than the (1+1) evolutionary algorithm with rate $1/n$, but no consistent advantage over rate-adjusted mutation or a $(μ+1)$ genetic algorithm with uniform crossover was established. The analytical redraw rule improved six of eight benchmark--setting pairs and redundancy allocation, but was slower than $c_r = 0.1$ on Jump$_2$.

cs.NE · math.OC · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16538

Pl@ntNet machine-to-machine identification service with Pl@ntNet API: enabling automated biodiversity monitoring through large-scale plant identification

Benjamin Bourel, Jean-Christophe Lombardo, Alexis Joly, Mathias Chouet, Thomas Paillot, Antoine Affouard, Vanessa Hequet, Herv{é} Go{ë}au 等 9 位作者

原文摘要

1. Monitoring plant biodiversity requires identifying large numbers of plants across space and time. Although artificial intelligence (AI) can now identify plant species from images with high accuracy, most existing systems rely on users manually submitting images. As ecological monitoring increasingly generates large image collections from cameras, drones, robots and online platforms, there is a growing need for plant identification services that can be directly integrated into automated data-processing workflows. 2. Here, we present Pl@ntNet's machine-to-machine AI plant identification service. This service uses an application programming interface (API) to provide programmatic access to Pl@ntNet's plant recognition models and the associated taxonomic processing. This service enables the implementation of automated identification workflows without human intervention and can thus directly link large image collections and automated observation systems to an AI-based plant identification system. 3. We establish that this service, which covers 85,000 species from around the world, (1) has a species prediction accuracy of 0.89, (2) process images up to 666 times faster than workflows requiring human intervention, (3) and has processed over 84 million identification requests since its launch, including more than 40 million in 2025 alone. We demonstrate how it has already enabled the implementation of a new generation of ecological workflows: identifying plants from images of insects to reconstruct the plant-pollinator interactions, analysing geolocated images from social media to study plant distribution, detecting invasive plants using images taken at the roadside, and identifying weeds using agricultural robots. Finally, we illustrate its application using a reproducible example: identifying plants from insect photos on iNaturalist. In approximately 30 min on a standard laptop, 2,424 images were processed to generate a dataset of 708 plant-pollinator interactions, with a weighted precision of 0.98 for flower detection and an accuracy of 0.88 for genus plant identification. 4. In conclusion, this machine-to-machine species identification is already operating on a considerable scale and meets a genuine need. All of this paves the way for a new paradigm in biodiversity monitoring, in which automated sensors and image collections can be directly connected to AI-based identification services.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16534

Context-Predictive Low-Rank Adaptation for Face Presentation Attack Detection

Aminu Yusuf, Shakeel Muhammad Ibrahim, Sulaimon Oyeniyi Adebayo, Muhammad Sohail Ibrahim, Muhammad Shahid Jabbar, Shujaat Khan

原文摘要

Face presentation attack detection requires representations that distinguish physical spoofing cues across changes in acquisition devices, subjects, and attack media. Large visual encoders offer transferable representations, but conventional binary fine-tuning supervises their final decisions without explicitly constraining how local facial evidence is represented under partial observation. We present CP-JEPA, a context-predictive adaptation framework that combines low-rank tuning of a pretrained CLIP image encoder with a joint-embedding predictive objective. A student predicts complete-image teacher features at masked spatial locations, while unmasked classification, masked classification, and prediction consistency retain the connection to the live/spoof task. An exponential-moving-average teacher supplies evolving latent targets. The auxiliary components are used only during training; inference requires only the LoRA-adapted CLIP visual encoder and binary classifier. {We evaluate CP-JEPA under single-source and multi-source OCIM transfer, additional CelebA-Spoof training, transfer to CelebA-Spoof, two-source training, and unseen-attack evaluation on SiW-Mv2.} On the standard OCIM benchmark, CP-JEPA achieves 3.74\% mean HTER and 98.73\% mean AUC. {Single-source evaluation achieves 11.36\% mean HTER across twelve source--target combinations.} With CelebA-Spoof added to the three OCIM training sources, CP-JEPA reduces mean HTER from 3.45\% to 2.35\% and increases mean AUC from 98.68\% to 99.32\%. On SiW-Mv2, it achieves 3.36\% mean HTER across the held-out attack categories. Controlled loss ablations examine the contributions of latent prediction, masked classification, and prediction consistency. The results show that context-predictive adaptation can improve cross-dataset PAD performance, although the gains vary with the training sources and evaluation setting.

cs.CV · eess.IV · eess.SP · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.16733

TheoPhysEval: Assessing AI agents at autonomous theoretical physics problem solving

A. Ramesh Chandra, Aaghaz Mahajan, Ainesh Sanyal, Mianqi Wang, Shubhadeep Chakraborty, Sudip Chakraborty, Shailesh Chandrasekharan, Rama Govindarajan 等 35 位作者

原文摘要

We present a suite of 50 unpublished research-level theoretical physics problems with verifiable answers. Without AI assistance, a typical problem in our collection could be solved by a world expert in about a day or less but would take considerably longer for experts in adjacent specialties. We test the ability of off-the-shelf AI agents to tackle these problems autonomously, without human hints. When given access to tools available in a realistic setting, including the ability to search the literature and write scripts for numerical and symbolic work, the best-performing agent, GPT-6-Astra harnessed in Codex, obtains the final answers with a near-perfect accuracy of 98% at an average cost under USD 3 per problem. On average, agents show only small changes in accuracy when denied web search and access to background research papers, suggesting significant memorization of the literature. These results suggest that state-of-the-art agents can accurately perform concrete calculations required across theoretical physics research. We comment on the broader implications of these findings and discuss possible explanations for the current lack of agent-led breakthroughs in theoretical physics despite their outstanding problem-solving abilities.

hep-th · cond-mat.stat-mech · cond-mat.str-el · cs.AI · gr-qc

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16526

Lost in Delegation: Studying the Effectiveness of Task Orchestration in Generative Search

Tamara Stojanovska, Elisabeth Kirsten, Leon Swazinna, Muhammad Bilal Zafar

原文摘要

LLMs have quickly evolved from models used in isolation to agentic systems that delegate tasks to external tools and other LLMs to solve complex assignments. This agentic augmentation has led to impressive capability gains. However, when and how delegation improves performance remains poorly understood. For example, it is unclear whether delegation helps when tasks involve post-cutoff information that the orchestrating LLM cannot reliably resolve from its own knowledge. In this work, we use purpose-built datasets with pre- and post-cutoff splits to compare various delegation modes. We find that delegation provides no consistent accuracy improvement over non-delegated setups, with effects varying across models and tasks. When tasks involve knowledge unavailable to the orchestrator, delegation can substantially degrade accuracy, while direct tool use with the same LLM performs much better. Our analysis identifies distorted delegated queries, inefficient tool use, and failures to preserve and use information between agents as recurring failure modes. These findings suggest that effective orchestration requires both well-calibrated delegation decisions and effective exchange of information between agents.

cs.AI · cs.CL

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新论文 · v1arXiv:2611.16525

ZONOS3-Mini Technical Report

Gabriel Clark, Sofian Mejjoute, George Close, Beren Millidge

原文摘要

ZONOS3-Mini is an ultra-compact, ultra-fast, zero-shot voice-cloning TTS model. The model can run in real time on a laptop or smartphone. For its latency and size, it achieves state-of-the-art accuracy and cloning fidelity. To achieve this we propose a latent MoE backbone with only 886M parameters (100M active per token) and a new audio codec, Zyphra Audio Codec 1 (ZAC-1), which represents 44.1 kHz audio at a 15 Hz frame rate. On the new ZTTS2-Eval benchmark, ZONOS3-Mini reaches a 6.71% macro-average WER over 55 language sets (35 clean and 20 in the wild), which is competitive with larger and slower state-of-the-art models. We release the model weights, inference, and training code.

cs.SD · cs.AI

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新论文 · v1arXiv:2611.16524

A Joint LLM and Optimization Agent Solution for Drone Scheduling in Victim Search Missions

Yinghao Zhang, Yalin Liu, Hong-Ning Dai, Yan Zhang, Qubeijian Wang, Bishenghui Tao, Yaohan Li

原文摘要

Victim search in wilderness areas is time-critical. Drones enable rapid area search, but scheduling them is challenging because victims may continue moving, detection results are uncertain, and effective decisions must combine structured mission information with unstructured contextual clues and domain knowledge. To address this, we propose an agent-aided drone scheduling system. The system employs an agent at the ground control station (GCS) to coordinate multiple drones over successive search cycles. In each cycle, the agent applies a scheduling policy that combines a large language model (LLM) for reasoning about where to search with an optimizer for determining how to dispatch the drones under energy constraints. Simulation results show that the proposed system consistently completes the victim-search mission, whereas none of the six non-LLM baselines does so and direct LLM scheduling is considerably less reliable. The source code is publicly available at https://github.com/1643204431/llm-uav-victim-search.

eess.SY · cs.AI · cs.ET

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新论文 · v1arXiv:2611.16523

RESYNC: Residual Latent-State Synchronization for Deadline-Bound Streaming Inference

Eljas Linna, Hao Dong, Juho Kanniainen

原文摘要

Predicting every observation in a high-rate stream requires a model to finish before the next arrival. A large model may offer better predictions but miss that deadline. We propose RESYNC: the large model runs asynchronously, while a fast path updates its latest exact but stale latent state using the observations received since that state was computed and applies its unchanged prediction head. The updater learns from the frozen model's representations without task labels. Across one real and two synthetic streams and five architecture families, RESYNC beats the best compact or distilled alternative with a paired 95% interval above zero in all twelve dataset--architecture pairs with a material teacher advantage at anchor ages of 4--12 observations, and in eleven pairs at older tested ages up to 32. The share of the teacher's gain preserved remains complete on the real stream but is lower and generally declines with anchor age on the synthetic streams. In live replay with 1 ms arrivals, the synchronous large models cannot deliver any predictions on time while RESYNC serves at least 99.4% of arrivals within the interval, with 0.35--0.62 ms median latency on one CPU thread: around the same speed as or faster than the compact model, and 7.9--17.7$\times$ faster than synchronous large-model inference. RESYNC targets settings with background compute available but a strict per-observation response deadline.

cs.LG · cs.AI

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新论文 · v1arXiv:2611.16522

PISim: A Unified Photonic Inference Simulator for Hardware-Aware Cross-Layer Design-Space Exploration

Andreas Falkenberg

原文摘要

Photonic neural accelerators offer ultra-low latency linear algebra, yet their usable numerical precision, sustainable throughput, and application accuracy remain tightly bounded by surrounding electro-optical interfaces and physical non-idealities. Pre-fabrication design-space exploration (DSE) requires a cross-layer electronic design automation (EDA) framework capable of evaluating disparate optical topologies under identical neural workloads and physical assumptions. We present \textbf{PISim}, a unified Photonic Inference Simulator that couples pretrained deep neural network execution with physics-based models of six accelerator architecture families, six electro-optic modulator technologies, four optical source organizations, and direct or coherent readout schemes. PISim embeds stage-resolved impairment operators---covering control and analog-to-digital quantization, finite extinction ratio, shot noise, correlated relative-intensity noise (RIN), thermal drift, propagation loss, and channel crosstalk---alongside an analytical cycle-level interface timing model to expose electronic-optical bottlenecks. Using simulated MNIST multilayer perceptron (MLP) benchmarks, we demonstrate how PISim identifies optimal precision operating points under multi-impairment stress (peaking at 6 bits for Mach--Zehnder and microring modulators), quantifies a 52.5 percentage-point accuracy gap between VCSEL-array and parallel-fanout laser architectures arising, under the modeled conditions, from power partitioning and correlated source noise, and models interface-limited throughput scaling across emitter-array widths from 32 to 128 channels. This comprehensive framework establishes an extensible, reproducible foundation for accuracy-constrained Pareto optimization prior to photonic tape-out.

physics.optics · cs.AI

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版本更新 · v3arXiv:2606.31399

World-Model Collapse as a Phase Transition

Xinyuan Song, Zekun Cai

原文摘要

Water looks unchanged as it warms, then at a critical point it boils. We ask whether long-horizon language agents show an analogous transition in their ability to maintain an accurate representation of the evolving world state. In some parameter settings, a small change in a task parameter, or adding a single step to the task, leaves behavior nearly unchanged; near a critical boundary, the same small change causes a sudden collapse. We study this effect in a deterministic task family where the true world state is known at every step. A large grid search over state size, state dependency, horizon, number of available actions, observation mode, and frequency of state changes reveals a phase diagram: a low-stress region where the agent consistently succeeds, a narrow region where success drops sharply, and a high-stress region where the agent consistently fails. Step-by-step traces show the mechanism: the agent loses track of the true world before its actions become invalid, so the agent is not merely choosing a bad action; it is acting from a corrupted world. Stronger models reach the critical boundary later but do not eliminate the sharp transition. These results make world-model collapse a measurable bottleneck for long-horizon agents. Code will be made publicly available soon.

cs.AI

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新论文 · v1arXiv:2611.16517

UniSpark: Exploring Hybrid Attention for Multimodal Understanding and Generation

Jiancheng Pan, Yuzheng Liu, Hengyuan Cao, Qiyuan Zhang, Yangyan Li, Biao Gong, Zheng Zhang, Ka Long Cheng 等 10 位作者

原文摘要

We present UniSpark-9B, a unified multimodal model that investigates hybrid attention for visual understanding and generation. Built on a Mixture-of-Transformer-Experts backbone with approximately 9B parameters in each branch, UniSpark combines full attention for explicit token interactions with GatedDeltaNet linear attention for compact context propagation. Autoregressive text prediction and visual flow matching jointly support visual question answering, text-to-image generation, instruction-guided editing, and interleaved text-image prediction. In interleaved generation, noisy visual targets are isolated from subsequent predictions, while clean visual representations provide context for later outputs. A blockwise protocol enforces this distinction through attention masks in full-attention layers and recurrent-state retention rules in linear-attention layers. The selected training sources contain 159.7M generation samples, 213.4M understanding samples, and 624.8K interleaved sequences. A four-stage curriculum jointly trains multimodal understanding and visual generation, refining answer accuracy and instruction following while extending image synthesis to 2K. UniSpark scores 2470.3 on MME-S and 0.83 on GenEval. Qualitative examples further illustrate its capabilities in interleaved image generation, video synthesis, and video editing. Together, these results demonstrate that hybrid attention provides an effective backbone for unified multimodal understanding and generation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16516

Authenticated Approximate Nearest Neighbor Search in Outsourced Vector Databases

Yuanhang Zhou, Xinyi Zhang, Cheng Xu, Jianliang Xu

原文摘要

Approximate Nearest Neighbor (ANN) search has become a fundamental query primitive in modern vector databases for managing large-scale embedding data. To support growing vector collections, organizations increasingly outsource database management and query processing to third-party clouds. However, untrusted service providers may return suboptimal or fabricated results, posing a serious threat to ANN search integrity. Existing authenticated ANN approaches face substantial scalability challenges. They typically require transmitting high-dimensional vectors for distance verification and rely on topology-agnostic authentication structures, resulting in excessive communication and verification overhead. In this paper, we propose VIDA, a novel and efficient scheme for authenticated graph-based ANN search. VIDA replaces raw vector transmission with compact vector commitments and employs an aggregated Inner Product Argument to efficiently verify multiple distance computations. Furthermore, we introduce MeST, a topology-aware authenticated data structure that aligns authentication paths with graph traversal, significantly reducing structural proof overhead. Additionally, we design a quantization-aware authentication strategy that exploits distance bounds for pruning, reducing the number of exact distances requiring cryptographic authentication. Extensive experiments demonstrate that the proposed schemes substantially improve the efficiency of authenticated ANN search, achieving up to a $6.6\times$ speedup in end-to-end latency and reducing data transfer time by up to 99.2% compared with state-of-the-art methods.

cs.DB · cs.CR

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新论文 · v1arXiv:2611.16515

PiLLVD: A Real-World Low-Light Video Dataset for Dynamic Scenes Captured with Off-the-Shelf Cameras

Yaser Abu Awwad, Paul L. Rosin, Oktay Karakuş

原文摘要

Low-light image and video enhancement has received increasing attention in recent years. However, most existing enhancement methods are developed and evaluated on still images, while progress in low-light video enhancement remains limited by the lack of real-world paired video datasets. Acquiring paired low-light and normal-light video sequences is challenging because it requires spatially and temporally aligned captures while avoiding motion blur, exposure mismatch, and correspondence errors in dynamic scenes. To address this gap, we introduce PiLLVD, the Pi-based Low-Light Video Dataset, a paired low-light video dataset captured using an accessible Raspberry Pi-based multi-camera system with off-the-shelf cameras. PiLLVD provides paired low-light and aligned normal-light reference sequences for dynamic indoor and outdoor scenes, including diverse human and object motion under varying illumination conditions. Unlike existing datasets that are often image-based, static, synthetic, or limited to simple motion patterns, PiLLVD focuses on motion-rich scenes where frame-to-frame stability and motion-related artefacts are important for evaluation. In addition to presenting the dataset and acquisition pipeline, we benchmark representative pre-trained low-light video enhancement methods on PiLLVD under a zero-shot setting. The results show that existing methods do not generalise uniformly across the proposed sequences, highlighting the challenges posed by the proposed dataset and its value for both benchmarking existing methods and supporting the development of future low-light video enhancement algorithms.

cs.CV · eess.IV

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新论文 · v1arXiv:2611.16514

Learning Temporal Dynamics with Derivative Representations for Time Series Forecasting

Shujie Duan, Jia Hong Puah, Haocheng Wu, Yifei Mao, Hongting Zhou, Chenxuan Li, Zitao Fang, Yang Lu 等 9 位作者

原文摘要

Time series forecasting aims to predict the future evolution of complex systems by learning their underlying dynamics from past observations. Despite recent advances in deep learning, Transformers in particular still struggle to capture the temporal evolution of system dynamics. Existing approaches have been largely driven by architectural innovation, with comparatively little attention paid to training strategies that explicitly exploit the temporal structure governing system dynamics. In this work, we introduce DeltaTSF, a dynamics-aware forecasting framework based on discrete derivative representations of time series. Specifically, DeltaTSF jointly learns the series and their discrete temporal derivatives across both input and forecast horizons within a unified, model-agnostic framework. By coupling these representations over time, the model is encouraged to maintain temporal consistency, a property we show to induce a form of discrete Taylor consistency during training. This formulation enables the model to better reflect the underlying system dynamics, resulting in more stable and physically coherent forecasts. Notably, DeltaTSF does not require explicit derivative computation and introduces no additional computational overhead at inference time. Extensive experiments across diverse benchmarks and backbone architectures demonstrate that DeltaTSF consistently improves forecasting performance and yields more dynamically coherent predictions than strong SOTA methods. These results highlight the effectiveness of enforcing dynamic coherence through derivative representations as a model-agnostic paradigm that operates alongside and complements architectural design. Source code will be made publicly available upon acceptance.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.13636

An Interpretable Stylometric Framework for Early Chinese Buddhist Translator Styles: A Case Study on the Attribution of Disputed Works of An Shigao

Yating Pan, Xinyue Zhang, Jun Wang

原文摘要

Attributing early Chinese Buddhist translations requires evidence that scholars can trace back to the texts. We develop an interpretable stylometric framework that uses features defined or reviewed by Buddhist philologists, including function characters, sentence formulas, sentence-final characters, sentence length, and part-of-speech n-grams. We construct stylistic profiles from 31 authenticated translations by seven translators from the Eastern Han through the Western Jin, then compare eight disputed texts with An Shigao's profile. Two texts, T1557 and T0101, show the strongest stylistic similarity to the authenticated An Shigao corpus, with scores of 0.783 and 0.687, respectively. The attributions agree with those reported by a published BERT-based approach on the same set of disputed texts. The framework requires no task-specific training and allows scholars to inspect which linguistic features support each proposed attribution.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16507

Input-output characterization of robotic grasping: Connecting design parameters to grasp behavior through the admittance

Julien Vanderheyden, Guillaume Drion, Pierre Sacré, Fulvio Forni

原文摘要

Low-impedance grippers make the dynamics of a grasp matter. Current grasp metrics, however, are static: they describe how a held object resists constant loads, not how it responds to time-varying ones. To quantify this, we rely on small-signal analysis to characterize a grasp by its admittance, the transfer matrix relating load perturbations to object displacements around an equilibrium. This transfer matrix recovers the stiffness and stability of the grasp at zero frequency and captures its natural frequencies and peak gain under time-varying loads. We apply this characterization to a planar two-finger gripper under proportional-derivative control. We show that two grasps of the same object in the same position and with the same contact forces can differ in stiffness by two orders of magnitude. Likewise, two grasps that share the same stiffness can exhibit very different resonances. Nonlinear simulations confirm the effectiveness of our small-signal analysis in capturing the dynamic behavior of grasping.

cs.RO · eess.SY

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