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版本更新 · v2arXiv:2607.15593

Scaling LLM Agent Tool Access: The MCP Gateway

Mingxin Li, Enge Song, Yueshang Zuo, Xiaodong Liu, Rong Wen, Qiang Fu, Gianni Antichi, Jian He 等 30 位作者

原文摘要

LLM agents increasingly rely on tool calling to complete tasks. Model Context Protocol (MCP) standardizes tool calling between LLM agents and tool providers. It reduces integration cost and drives rapid growth in tool scale. In production deployments, tool access becomes difficult to scale. On the tool provider side, legacy services are not directly callable through MCP; the rapid protocol development creates ongoing compatibility cost. On the agent side, the number of mounted tools is limited by the LLM context window and inference overhead; mounting a large tool set increases token usage and inference latency and can reduce task success rate. Moreover, for stateful MCP backends with multiple replicas, preserving session affinity increases client-side complexity. We present a cloud-scale MCP Gateway system. It breaks the direct-connect model on the data plane and offloads legacy service integration, consolidating incompatible MCP variants, access control, tool recommendation, and session-aware routing at the gateway. The conversion module supports 315 OpenAPI services in the cloud and integrates MCP transport variants. Session-aware routing decouples the scalability of the gateway layer and the MCP service layer. The hybrid retrieval engine sustains $\sim$98\% Top-15 recall; it scales agent tool access to 3{,}000+ tools with improved tool selection accuracy. We also share the lessons learned from deploying the gateway system in production.

cs.DC · cs.AI · cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.31397

Parameter-Efficient Adaptation of Large Language Models to a Low-Resource Agglutinative Language: A Corpus, Datasets, and a Reproducible Benchmark for the Bashkir Language

Mullosharaf K. Arabov, Svetlana S. Khaybullina

原文摘要

Bashkir is a low-resource agglutinative language written in Cyrillic and remains poorly represented in modern LLMs. We present an open resource suite and a systematic benchmark of adaptation methods: the Bashkir Web Corpus (71,567 documents, 46.9M tokens, 16 sources) and three labelled datasets for binary, multiclass and multilabel text classification; the benchmark covers the first two. Experiments use eight architectures (82M-8B: DistilGPT2, GPT-2 base/medium, Phi-3-mini, Mistral-7B, YandexGPT-5-Lite-8B, mBERT, XLM-R) and three adaptation groups (full fine-tuning, QLoRA, in-context learning), plus continual pretraining (CPT). (1) QLoRA on 3.8-8B models outperforms encoder-only fine-tuning on binary classification: YandexGPT-5-Lite-8B with CPT reaches macro-F1 0.748 vs 0.653 (XLM-R) and 0.650 (mBERT); pairwise differences are significant after Holm correction (p<0.001). (2) YandexGPT-5-Lite-8B significantly outperforms Mistral-7B and Phi-3-mini on multiclass classification, BPB and native-speaker ratings (mean 3.28 vs 2.52 and 2.22; n=100; alpha=0.69-0.77; p<0.05); on binary classification its QLoRA advantage is not significant (p>=0.084). (3) CPT effects depend on model and task: perplexity improves for Phi-3-mini (+0.58) and YandexGPT-5-Lite-8B (+0.33) but worsens for Mistral-7B (-1.15, p<0.001); on binary classification CPT helps Mistral-7B most (+4.0 macro-F1). (4) Tokeniser fertility on Bashkir varies 2.67x (2.92-7.80) and tracks adaptation quality, while perplexity across tokenisers is not comparable. Cyrillic tokenisers with Russian pretraining are preferable; LLM judges reproduce native ratings for grammar and coherence (rho=0.33-0.46) but not language correctness (rho=0.16, p=0.11); native evaluation remains necessary. All artefacts are open-licensed.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.01321

BiCAA: Bidirectional Credit Assignment for Search-Augmented Agent

Yibin Huang, Bin Xu, Hailong Cao, Conghui Zhu

原文摘要

By iteratively retrieving and integrating external evidence, multi-step search enables search-augmented agents to tackle complex question answering. However, vanilla GRPO assigns rewards based solely on final answers, providing limited guidance for intermediate search steps. To provide fine-grained process feedback, we evaluate the value of each search step: it should acquire evidence that advances problem solving while avoiding redundant retrieval. Building on these complementary criteria, we introduce BiCAA, which combines Forward Solvability Gain (FSG) and Subsequent Reasoning Gain (SRG) for bidirectional credit assignment. To reward progress in evidence acquisition, FSG quantifies changes in ground-truth answer likelihood after each interaction. SRG compares the subsequent action's likelihood with and without the current interaction to approximately measure its support for subsequent reasoning, using this support as a proxy signal for non-redundant search to encourage effective and efficient search. BiCAA calibrates local feedback through asymmetric fusion; for policy optimization, it integrates accumulated process rewards with terminal outcome supervision. Across seven open-domain QA benchmarks and two Qwen backbones, experiments show improvements in average F1, particularly on multi-hop tasks.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.28347

LLMs struggle to simulate human belief updates in controlled environments

Sebastian Pohl, Harsh Mehta, Pranav Mambayil, Abdul Ghafoor, Franziska Lesigang, Yufang Hou, Christian Hilbe

原文摘要

LLMs are increasingly deployed as proxies for human study participants in social science experiments, yet the fidelity of this practice has rarely been tested directly. We test whether six LLMs can simulate individual human belief updates, comparing LLM outputs 1-to-1 against ground truth data from 391 UK participants on Prolific, who updated their stances on three discussion topics after reading Reddit comments. Each participant was simulated by an LLM conditioned on a persona derived from their demographic and personality trait data. Given participants' actual initial stances, five of the six models showed no systematic directional bias in their simulated post-stances, although the size of their individual-level errors varied considerably between models. All six models fail to simulate initial stances themselves and to produce faithful belief updates from self-generated stances. Three systematic biases emerge across all models: overrepresentation of neutral positions, more frequent but smaller belief shifts than humans, and a failure to rank comments by convincingness. Demographic and personality trait personas had no consistent effect on fidelity. We conclude that without grounding in realistic initial stances, LLM simulations of human belief dynamics cannot be considered reliable, a condition that current multi-round social media simulations rarely meet.

cs.CL · cs.SI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03034

When Does Design Thinking Work? A Convergence Theorem for the Iterate-Test Core

Paweł Kuraś, Adrian Michalski, Alicja Gerka, Patryk Organiściak

原文摘要

Design thinking is widely taught as an iterative loop of ideation, prototyping, and testing, yet conditions under which such a loop must improve innovation outcomes remain unspecified. Taking problem framing as given, we formalize the iterate-test core as search over a finite prototype space whose utility is learned through noisy evaluations. We prove that the prototype a team believes to be best converges almost surely to a local optimum under five conditions: finite framing, local ideation, non-summable discovery rates, unbiased repeated testing, and retention of tested prototypes. Bounded test bias or criteria drift degrades the guarantee by a bounded amount, and multiple stakeholders yield convergence to a locally Pareto-optimal set. Finite-horizon simulations across three landscape classes (additive valuation, NK rugged landscapes, and clustered modular architectures) show that asymptotic limits are rarely reached in practice. Teams face an optimizer's curse, overestimating their chosen concept by 4-8% and frequently discarding superior prototypes. Testing every prototype once proves to be the worst allocation; optimal performance requires building one to three prototypes per five tests and repeatedly re-testing leaders. Pre-screening concepts yields greater performance gains than tuning testing policies.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.05633

A Survey on Stacked Intelligent Metasurfaces: Fundamentals, Recent Advances, and Challenges

Chandan Kumar Sheemar, Sourabh Solanki, Wali Ullah Khan, George C. Alexandropoulos, Symeon Chatzinotas

原文摘要

Stacked intelligent metasurfaces (SIMs) cascade several closely spaced programmable metasurface layers so that an electromagnetic wave undergoes repeated modulation-propagation interactions before leaving the stack. The stack thereby implements, at the speed of propagation and with few radio-frequency chains, high-dimensional analog transformations that would otherwise require digital precoding, combining, spatial filtering, focusing, or inference. Since 2023, SIM research has produced more than two hundred articles, a dedicated magazine special issue, and two survey-level overviews organized around applications and wave-domain computing. This article complements them with a model-to-hardware perspective. First, we present the electromagnetic modeling hierarchy of SIMs, making explicit the assumptions under which the models coincide, their validity regimes, and their computational complexity, with a common notation for hardware non-idealities and power consumption. Second, we present a methodology-first taxonomy (SIM-aware channel acquisition, model-based optimization, statistical-CSI design, learning-based control, and hardware-aware robust design), compare the families in terms of CSI requirements, complexity, convergence, and hardware constraints, and reconcile conflicting findings in the literature. Third, we synthesize applications by the physical role of the SIM. Fourth, we quantitatively compare experimentally demonstrated multilayer prototypes, separate validated from conceptual architectures, and discuss thermal, tolerance, and calibration issues. Fifth, we provide a tutorial-style performance and trade-off analysis anchored in reported results. Finally, we map evidence-based challenges to research directions with measurable targets. Lessons learned are summarized at the end of every section.

eess.SP · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03033

GPU-Native Real-Time Textured Mesh Reconstruction with Seam-Aware Multi-View Radiometric Correction

Hiroyuki Sato

原文摘要

We present a GPU-native volumetric-video system for real-time reconstruction of graphics-compatible textured triangle meshes from multiple RGB-D sensors. Existing TSDF systems accelerate fusion and mesh extraction, but extending them to per-frame textured meshing adds CPU mesh processing, CPU-side texturing work, or extra CPU-GPU transfers between stages. After input staging, our method keeps the reconstruction and texturing stages (TSDF fusion, Marching Cubes, connected-component noise removal, view-based texture mapping, inpainting, and radiometric seam correction) resident on the GPU; no intermediate mesh or image data returns to the host between these stages. A fixed-size-array representation of mesh topology gives constant-time neighbor access on the GPU and makes the downstream mesh-level operations data-parallel. Because we texture each face from a single camera, exposure seams that vary smoothly across each image remain; we estimate a per-camera low-frequency gain field and bake it into the atlas on the GPU. On the CMU Panoptic dataset, our method is faster than the baselines at every tested voxel resolution and number of sensors, achieves peak speedups of more than an order of magnitude over CUDA-accelerated nvblox and CPU-based Open3D, and approaches an offline textured-meshing baseline in perceptual re-rendering quality.

cs.CV · cs.GR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.37918

Venus-DefakerOne2: A Vision-Forensics Foundation Model with Broader Coverage

Chenfan Qu, Yachao Liang, Mingqi Fang, Jiangwei Xie, Zhenming Wang, Baoyue Zhang, Changjiang Jiang, Zheng Chang 等 31 位作者

原文摘要

Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as physical spoofing, and is carried not only by images but also by videos. Despite substantial progress, a unified anti-forgery foundation model covering such a broad threat spectrum remains absent. In this paper, we present Venus-DefakerOne2, which substantially extends the forensic capability boundary along both the forgery-form and media-type dimensions. Trained on tens of millions of open-source and in-house samples, it incorporates two key designs. Tide-Style-Tuning alternates LoRA and full-parameter fine-tuning to balance computational efficiency, generalization, and hard-sample fitting while mitigating catastrophic forgetting. Entropy-based Test Adaptation determines the optimal prediction modality by analyzing prediction entropy. Venus-DefakerOne2 achieves new state-of-the-art performance across a comprehensive suite of benchmarks, including DeepFake faces (96.7%), AI-generated images (93.3%), tampered documents (87.6%), manipulated natural images (89.6%), physical spoofs (93.1%), and AI-generated videos (96.0%). It significantly outperforms both specialized expert models and its predecessor, DefakerOne, establishing itself as a powerful and versatile foundation model for binary-decision forensic tasks.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03032

Hybrid Federated Learning for Multimodal Fusion under Missing Modalities and Site Heterogeneity

Xiaofei Wu, Rongmei Liang, Jian Qing Shi

原文摘要

Multimodal fusion is studied under hybrid federation, where data are partitioned across samples and feature blocks. The proposed two-dimensional federated smooth multiple-kernel large-margin learning method (2D-FedSMKL) integrates availability-corrected modality scores, group-kernel regularization, and smooth worst-site risk aggregation. For fixed maps and propensities, the objective is convex and smooth. Curvature bounds and exact synchronous block-gradient equivalence are established for binary and multiclass scores, giving a conditional accelerated rate. An oracle-propensity binary risk bound and a propensity-perturbation result are also derived. Simulations and two public activity-recognition benchmarks assess site heterogeneity and modality absence. Worst-site aggregation improves worst-site performance under heterogeneity, including reversed and size-balanced designs. On the benchmarks, validation selects the correction; the selected model is not significantly worse than the strongest reference at the worst site, and group-regularization benefits vary by dataset.

cs.LG · stat.AP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03030

A Cooperative Game-Theoretic Framework for Real-Time Deviation Settlement in Local Energy Communities

Angel M. González-Rueda, Iago Núñez Lugilde, Jose L. Crespo-Vazquez, Tarek Alskaif

原文摘要

This paper proposes a cooperative game-theoretical framework for the financial settlement of real-time deviations in Community-Based Local Energy Markets (CB-LEMs). The real-time settlement process is modeled as a transferable-utility cooperative game, and the resulting Real-Time Local Energy Market (RTLEM) game is shown to be superadditive and balanced. We analyze two game-theoretical allocation mechanisms, the Shapley value and the nucleolus, and evaluate their efficiency, individual rationality, coalitional rationality, and temporal-aggregation properties. We subsequently compare these mechanisms with a settlement rule based on proportional imbalance responsibility. The three mechanisms are efficient and individually rational, but only the nucleolus is guaranteed to satisfy coalitional rationality for every RTLEM instance. The proposed framework is demonstrated using real-world data from a residential energy community in Amsterdam, showing the trade-offs among the three allocation mechanisms in terms of surplus distribution, coalitional stability, interpretability, and computational requirements.

cs.GT · eess.SY · math.OC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.25304

SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models

Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin

原文摘要

Simulated maximum likelihood is widely used in latent-variable models where likelihood evaluation requires intractable integrals or probabilities. With a fixed number of simulation draws, its estimating equation is generally biased. We develop Stochastic Approximation with Unbiased Simulated Scores (SAUSS), combining score simulation with stochastic gradient descent. The Hajivassiliou--McFadden identity, an instance of Fisher's identity, expresses the likelihood score as the conditional expectation of the complete-data score given the observed data. Exact conditional draws yield an unbiased simulated score for any fixed number of draws. The estimator averages stochastic gradient iterates computed using mini-batches of fixed size regardless of sample size, avoiding repeated full-sample likelihood evaluations. Under local regularity and initialization conditions, we establish asymptotic normality and a functional central limit theorem for the partial-sum process of the iterates under multi-pass sampling from an observed sample. These results support plug-in and random-scaling confidence intervals that remain asymptotically valid when algorithmic uncertainty is non-negligible relative to sampling uncertainty. We develop multinomial probit as a leading application. In simulations and two applications, SAUSS returns estimates in every reported run, including cases where the simulated maximum likelihood implementation returns none. When both return estimates, SAUSS gives comparable estimates in less time.

stat.ME · cs.LG · econ.EM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03027

VLM-as-a-Judge for Person Re-Identification

Bessie Dominguez-Dager, Miguel Cazorla, Vincent Lepetit

原文摘要

Cloth-changing person re-identification (CC-ReID) is still very challenging, and no method clearly dominates: methods that lead on one benchmark collapse on the others, because they rest on different identity cues. We show that a general-purpose vision-language model (VLM), used zero-shot as a judge for reranking a short candidate list, beats the expert retrieval models it reranks by a wide and consistent margin. On the four scenarios of CHIRLA, a long-term CC-ReID benchmark that no publicly released Re-ID model is trained on, reranking the candidates of two frozen experts raises rank-1 on every scenario, by 12.9 points on average over the best expert and 9.4 over the best naive ensemble of the two, with no component fine-tuned on the target data. Merging both experts' shortlists matches or beats reranking the stronger expert's shortlist alone, at lower cost. Introducing a VLM does, however, come at a cost: almost all of the pipeline's FLOPs are spent on it. We therefore borrow a criterion from ensembling and invoke the VLM judge only where the two experts disagree at rank 1. This threshold-free gate removes 40% of the calls, and a comparable share of total compute, with no significant drop in rank-1, trading 1.8 to 3.8 points of mAP. Judging remains the dominant cost, but on a benchmark where dedicated experts stall, we find a frozen general-purpose model to be the strongest judge of identity. The code is available at https://github.com/bdager/JudgeReID.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03026

Improved regret bounds in tabular RL under local differential privacy

Hugo Richard

原文摘要

We study regret minimization for time-homogeneous episodic tabular reinforcement learning with $S$ states and $A$ actions under non-interactive $ε$-local differential privacy (LDP). In each episode $k \in [K]$, the decision-maker sends a policy to a user, the user executes the policy for $H$ steps, and sends an $ε$-locally differentially private message back to the decision-maker. When $H=1$, the problem reduces to an LDP contextual bandit with $S$ contexts, $A$ arms and horizon $K$. We prove that the minimax regret is $\min\bigg(K, \frac{ \sqrt{\max( S, \exp(ε) + 1) S A K (\exp(ε) + 1)}}{\exp(ε) - 1}\bigg)$, up to constants and a $\sqrt{\log(A)}$ factor. The upper bound is achieved by an adaptation of EXP3 and a unary encoding mechanism. When $H > 1$, and for small $ε$, we show a lower bound in $Ω(HS\frac{\sqrt{A K}}ε)$ and an upper bound in $\tilde{O}(H^2 S^2 \frac{\sqrt{ AK}}ε)$. The upper bound is achieved by an algorithm based on UCBVI and a unary encoding mechanism. For small $ε$, these bounds improve the previously known lower bound by a factor $\sqrt{S}$ and the previously known upper bound by a factor of $H \sqrt{A}$.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.01963

Entropy Bounds for Local Coordination and Graph Amenability

Ron Peretz, Dean Kraizberg

原文摘要

We study local coordination on finite graphs, equivalently, the ability of randomized local protocols with independent private randomness to produce highly correlated outputs at neighboring vertices. Hutchcroft, Rospuskova, and Tamuz (2026) showed that if such local coordination can be achieved with low cost, then the graph admits a low-cost leader profile, or equivalently an appropriate bounded-radius decomposition, with a square-root loss in the cost. We substantially improve this bound for unbiased binary outputs. If the average disagreement is $\varepsilon$, we construct a leader profile of cost $O(\varepsilon\log(1/\varepsilon))$. The key ingredient is an information-theoretic influence measure: after fixing an ordering of the independent information sources, we assign to each binary output the increments of mutual information revealed along that ordering. For two outputs that disagree with probability $p$, the corresponding measures are within binary entropy $h(p)$ in total variation. Combined with a grand coupling theorem, this yields the improved bound. We also show that a universal linear bound is impossible. We construct graphs with binary local profiles of cost $O(\varepsilon)$ for which every leader profile has cost $$ Ω\!\left(\varepsilon\sqrt{\log(1/\varepsilon)}\right). $$ The construction uses weighted majority functions and can be implemented as an action-symmetric Nash equilibrium, giving a negative answer to the question of HRT whether leader equilibria are always optimal up to a constant factor. Finally, we show that the binary assumption is essential: for general real-valued outputs, the square-root dependence of the HRT theorem is sharp up to a universal constant.

cs.GT · cs.IT · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03024

DisC: Incentive Design for Green, Robust, and Fair Client Self-Participation in OTA Federated Learning

Driss Choukri, Essaid Sabir, Abdelkrim Haqiq

原文摘要

Over-the-Air Federated Learning (OTA-FL) improves communication efficiency by exploiting waveform superposition over wireless Multiple-Access Channels (MAC). However, client heterogeneity, fading, energy constraints, and participation imbalance can degrade both fairness and aggregation accuracy. Existing Pareto-oriented fair OTA-FL schemes may still suffer from large aggregation distortion when highly weighted clients experience poor channel conditions. This paper proposes DisC, a game-theoretic distributed client selection framework for fairness-aware, channel-aware, and energy-efficient OTA-FL. Each client selects its participation level by maximizing a utility that captures Pareto fairness importance, learning relevance, OTA distortion, energy consumption, and delay. We show that DisC admits a Nash equilibrium and can be formulated as an exact potential game, where stable equilibria correspond to locally optimal OTA-FL operating points. DisC therefore offers a distributed, incentive-compatible alternative to centralized client selection under wireless and resource constraints.

cs.NI · cs.GT

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.09930

Fine-grained Motion Retrieval via Joint-Angle Motion Images and Token-Patch Late Interaction

Yao Zhang, Zhuchenyang Liu, Yalan He, Thomas Ploetz, Yu Xiao

原文摘要

Text-motion retrieval aims to learn a semantically aligned latent space between natural language descriptions and 3D human motion skeleton sequences, enabling bidirectional search across the two modalities. Most existing methods use a dual-encoder framework that compresses motion and text into global embeddings, discarding fine-grained local correspondences, and thus reducing accuracy. Additionally, these global-embedding methods offer limited interpretability of the retrieval results. To overcome these limitations, we propose an interpretable, joint-angle-based motion representation that maps joint-level local features into a structured pseudo-image, compatible with pre-trained Vision Transformers. For text-to-motion retrieval, we employ MaxSim, a token-wise late interaction mechanism, and enhance it with Masked Language Modeling regularization to foster robust, interpretable text-motion alignment. Extensive experiments on HumanML3D and KIT-ML show that our method outperforms state-of-the-art text-motion retrieval approaches while offering interpretable fine-grained correspondences between text and motion. The code is available in the supplementary material.

cs.CV · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03023

VizDAGuide: Misleading Chart Question Answering in Compact Vision Language Models via DAG-Structured Supervision

Haiyang Zhang, Sicheng Song, Zixin Chen, Jie Gu, Huiya Ou, Chenhui Li, Changbo Wang

原文摘要

Misleading charts can lead vision-language models to conclusions that conflict with underlying data. Existing work focuses on inference-time strategies, while training-time supervision remains less explored. We present VizDAGuide, a DAG-structured supervision method for misleading-chart question answering. The method represents intermediate reasoning states and their dependencies as a directed acyclic graph, assigns supervision weights according to their roles, and serializes the graph into a unified training target for open-ended and multiple-choice QA with single-pass inference. Experiments on two benchmarks across Qwen3-VL and InternVL3.5 models show improvements over a content- and weight-matched Linear CoT-SFT baseline on both answer formats. Process-level evaluation shows gains in grounding and operation execution, while ablations indicate that dependency structure and weighted supervision are most effective when combined. These results support DAG-structured weighted supervision for misleading-chart question answering.

cs.CV · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03022

Interpretable Allocation and Model-Predictive Rebalancing of Equity Portfolios, Tested on Real and Synthetic Markets

Jeffrey D. Varner

原文摘要

A live equity portfolio poses three problems: which stocks to hold, when to trade, and whether the strategy holds up beyond the observed market. We built one system for all three and tested it on 373 US equities, calibrated on 2014-2024 and traded from January 2025 to September 2026. A selection rule chose stocks by score, by a bandit, or at random, and one of seven weighting rules, four consumer-theory utilities, and three standard portfolios, set share counts under limits on budget, single-stock weight, portfolio volatility, and turnover, with each stock's preference weight a bounded score of its trend, market beta, and the market's recent direction. A model-predictive controller forecasts portfolio value with a multi-asset geometric Brownian motion and re-planned at its horizon, when the value left its forecast band, or at a drawdown stop. To probe deeper declines than the real window held, we also ran the system on simulated markets from per-asset hidden Markov generators with jumps, linked by market and sector factors. On real prices, the stocks chosen mattered more to terminal wealth than the weighting rule, and the early re-plans shrank the worst drawdown in most rule combinations against a fixed calendar without consistently raising wealth. In the deepest simulated declines, every combination, random picks included, ended above simulated SPY and the equal-weight universe in the median, even without early re-plans, so the margin came from shared parts of the system, not from stock selection or re-plan timing. A market hedge that tightened a beta cap as the market weakened cost wealth on real prices but paid off in the most sustained simulated declines, and the generator's persistent trends favored score-based selection more than the real market did, which suggests a synthetic test judges stock selection fairly only if its generator does not build in that persistence.

q-fin.PM · cs.CE · q-fin.RM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03021

MetamorphX: An Ungrounded 3-DoF Moment Display that Changes its Physical Properties through Rotational Impedance Control

Takeru Hashimoto, Shigeo Yoshida, Takuji Narumi

原文摘要

Humans can estimate the properties of wielded objects (e.g., inertia and viscosity) using the force applied to the hand. We focused on this mechanism and aimed to represent the properties of wielded objects by dynamically changing the force applied to the hand. We propose MetamorphX, which uses control moment gyroscopes (CMGs) to generate ungrounded, 3-degrees of freedom moment feedback. The high-response moments obtained from CMGs allow the inertia and viscosity of motion to be set to the desired values via impedance control. A technical evaluation indicated that our device can generate a moment with a 60-ms delay. The inertia and viscosity of motion were varied by $0.01\,\mathrm{kg\,m^2}$ and $0.1\,\mathrm{N\,s}$, respectively. Additionally, we demonstrated that our device can dynamically change the inertia and viscosity of motion through virtual reality applications.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03020

The Tower of Babel in artificial intelligence: who should speak, and when it pays to listen

Mohammad Salahshour

原文摘要

Two machines can each recognize an object well and still fail together. We study classifiers that see different parts of one object and send short messages to a receiver that identifies its class. Coordination asks how to design messages together; cooperation asks whether the team outperforms one sender given the whole budget. On three digit datasets, messages designed together beat messages designed apart by 18.07 to 22.68 percentage points on average with two-bit messages, with gains in all 24 configurations. A fixed agreement, you report the row and I the column, recovers about half this gain, with only the receiver learned. Senders that draw the same distinctions waste a narrow vocabulary, and senders whose distinctions cross multiply it. A team has no more possible messages than one sender with the same budget, so a partner must bring evidence the first lacks. A solvable model shows individual statistics alone cannot determine whether a partnership helps. On MNIST digits, six shared bits remove 49.54 percent of the strongest tested solo's errors on average. Paired measurements correctly predicted most sensor teams would reduce average accuracy on new participants. Who should speak is a property of the pair, and must be measured in pairs.

cs.LG · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03018

How Data Journalists Visualize Uncertainty: Editorial Constraints and Design Heuristics in Newsroom Practice

Carmen Garro, Annemarie Wisbar, Benjamin Possin, Christoph Kinkeldey

原文摘要

Communicating uncertainty is increasingly recognized as important in data journalism, yet in practice it is often oversimplified, downplayed, or omitted. To understand how uncertainty is handled in newsroom visualization practice, we conducted a full-day workshop with 11 data journalists from major German-speaking newsrooms. The workshop combined presentations of newsroom examples, collaborative scenario classification, World Café discussions on authoring hurdles, and group reflections on audience needs and future directions. Our findings suggest that uncertainty communication is primarily a workflow- and context-dependent editorial decision, constrained by production overhead, anticipated audience misinterpretation, publication formats, and the need to preserve a clear takeaway. Rather than relying on advanced visualization techniques, practitioners favor pragmatic defaults, layered communication, and explanatory guidance tailored to audience and medium. The scenario-based framework we provided is valued less as a strict taxonomy for classifying finished visualizations than as a planning prompt for early design reasoning. Based on these insights, we contribute (i) practitioner-informed findings on the challenges and strategies of visualizing uncertainty in journalistic practice, (ii) a refined scenario-based framework to support communication about uncertainty, and (iii) practice-oriented heuristics for designing uncertainty visualizations in newsrooms. Our work contributes to bridging visualization research and newsroom practice by grounding uncertainty communication in the realities of journalistic production. Supplemental material is available at https://osf.io/8j4ep.

cs.HC

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新论文 · v1arXiv:2611.03017

Efficient Hybrid Distillation: Synergizing Score and Adversarial Objectives for One-Step Diffusion

Fei Peng, Junqiang Wu, Haoxian Tan, Jun Zhou, Jie Hu, Xiaoming Wei, Jie Guo, Xiu Li

原文摘要

Numerous distillation methods have been developed to accelerate diffusion models. Recent research indicates that hybrid strategies---blending different paradigms---yield superior results compared to single-method approaches. However, current works predominantly rely on a simple combination of objectives, leaving the question of how to effectively synergize them largely underexplored. In this paper, we specifically investigate the synergy between Score Distillation and Adversarial Distillation. We reveal that their operating zones are distinct and complementary: Score Distillation is effective in high-noise regimes for capturing the global distribution yet lacks fine-grained supervision in low-noise regions. Conversely, Adversarial Distillation is effective in low-noise regions but is prone to instability and suffers from discriminator failure when applied to high-noise intervals. Leveraging this insight, we propose Efficient Adversarial Score Distillation (EASD), a framework that synergizes these objectives via a regime-adaptive sampling strategy, directing each objective to focus on its effective interval. We further apply this paradigm to DiT-based Flow Matching models. Empirically, our method achieves a promising one-step FID of 1.39 on ImageNet 256$\times$256 using the SiT-XL/2+REPA model. This work provides a concrete guideline for maximizing the potential of hybrid distillation. Our code and model are available at https://github.com/pf0607/EASD .

cs.CV

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新论文 · v1arXiv:2611.03016

Swarm Choreography Made Simple: Superposed Guiding Vector Fields for Rigid Formation Path Following

Jo{ã}o Machado, Matthieu Verdoucq, Christophe Jouffrais, Murat Bronz

原文摘要

This paper presents a decentralized guiding vector field framework for rigid formation path following in multi- UAV systems based on the superposition of simple rule-driven vector fields. Each control objective is encoded as a dedicated vector-field component: a normalized tangential term to ensure progression along a prescribed geometric path at a desired speed, a convergence term driving the formation toward the path, a local coordination term that preserves the desired formation shape through a pseudo-path parameter, and a local separation term for inter-agent collision avoidance. The coordination mechanism requires the exchange of only a single scalar per agent, resulting in low communication bandwidth. Partial theoretical results establish convergence of the swarm to the desired path and formation in the absence of the sepa- ration term, while collision avoidance is formally guaranteed. The approach is experimentally validated using a swarm of five quadrotors executing two 3D choreographies. The first experiment involves tracking a helical path while maintaining a rigid V-shaped formation. The second demonstrates a spiral- like motion obtained by a rotating circular formation tracking a vertical line. The experimental results support the effectiveness and practical applicability of the proposed framework.

cs.RO

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版本更新 · v3arXiv:2610.39937

BiHashFormer: Hash-Driven Dual-Branch Transformer for Efficient Object Detection in HRW Shots

Jingchen Huang, Wenxi Li, Chenyang Lyu, Moran Liu, Haozhe Lin, Yuchen Guo

原文摘要

Recent advances in gigapixel-level imaging have brought High-Resolution Wide shots to the forefront of research. However, these images present significant challenges: extreme sparsity of foreground, gigapixel-level resolutions and interleaving of foreground and background. This causes traditional detectors and attention mechanisms to be hindered by background, resulting in inefficiency and inaccuracy. To tackle this problem, we propose BiHashFormer, a dual sparsified branch transformer built on hashing. RoI Selector will predict the foreground proportion for top-k selecting target-containing windows. The compression branch processes windows selected and refines attention into HashAttention by discarding half of the key-value pairs, reducing cost and improving efficiency. The compensation branch uses HashMiner to perform low-cost hash searches in the remaining regions, recovering features of missed objects. Experiments confirm that the selection relationship in HashAttention is one-way and reveal the preference of different queries for keys. In experiments on the gigapixel benchmark PANDA, BiHashFormer reduces 37.5% backbone FLOPs while improving AP50 to 81.0%. Moreover, this parallel dual-branch structure allows the model to avoid inference delays caused by inter-branch dependencies, while achieving much greater stability than single-branch models, specially with low rates of RoI selection.

cs.CV

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版本更新 · v2arXiv:2512.14233

PentestEval: Benchmarking LLM-based Penetration Testing with Modular and Stage-Level Design

Ruozhao Yang, Mingfei Cheng, Gelei Deng, Tianwei Zhang, Junjie Wang, Xiaofei Xie

原文摘要

Penetration testing is essential for assessing and strengthening system security against real-world threats, yet traditional workflows remain highly manual, expertise-intensive, and difficult to scale. Although recent advances in Large Language Models (LLMs) offer promising opportunities for automation, existing applications rely on simplistic prompting without task decomposition or domain adaptation, resulting in unreliable black-box behavior and limited insight into model capabilities across penetration testing stages. To address this gap, we introduce PentestEval, the first comprehensive benchmark for evaluating LLMs across six decomposed penetration testing stages: Information Collection, Weakness Gathering and Filtering, Attack Decision-Making, Exploit Generation and Revision. PentestEval integrates expert-annotated ground truth with a fully automated evaluation pipeline across 346 tasks covering all stages in 12 realistic vulnerable scenarios. Our stage-level evaluation of 9 widely used LLMs reveals generally weak performance and distinct limitations across the stages of penetration-testing workflow. End-to-end pipelines reach only 31% success rate, and existing LLM-powered systems such as PentestGPT, PentestAgent, and VulnBot exhibit similar limitations, with the evaluated autonomous agents showing particularly low success rates under our benchmark settings. These findings highlight that reliable autonomous penetration testing requires stronger structured reasoning and fine-grained stage-level diagnosis. PentestEval provides the foundational benchmark needed for future research on fine-grained, stage-level evaluation, paving the way toward more reliable LLM-based automation.

cs.SE · cs.AI · cs.CR

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新论文 · v1arXiv:2611.03015

Comparative study of global pooling for pre-departure ship fuel consumption prediction: Advantages over per-ship and adaptation-based approaches

Heungjun Im, Bongmin Cha, Agung Nugraha, Donghyun Kim, Seongphil Kang, Minwook Choi, Jeongmin Park, Jihwan Lee

原文摘要

Most fuel oil consumption (FOC) models for voyage planning are per-ship models and thus generalize poorly beyond each vessel's records. Transfer and federated learning have been reported to perform better, but under different protocols that often use inputs unavailable before departure or random data splits. It therefore remains unclear which approach performs best. In this study, we compare 11 per-ship, adaptation-based, and global methods on 21 vessels. To prevent leakage, all methods receive identical inputs known or forecast before departure, excluding engine RPM and shaft power, and are tested on intact voyage legs after all training records of each vessel. Global pooling, in which one model describes each vessel by its ship identity and seven ship specifications, performs best overall in every setting examined. For in-fleet vessels, Global LightGBM reaches an overall MAPE of 8.92%, against 11.14% for Per-Ship LightGBM, and outperforms every per-ship and adaptation-based model. Global models of five architectures, including a Transformer, all outperform the per-ship and federated models. Fine-tuning a global model for each vessel, even by low-rank adaptation of the Transformer, adds no accuracy. Without any records of a vessel, the global model reaches a median MAPE of 11.20% and outperforms a per-ship model trained on that vessel's records for 14 of 21 vessels. Adding its first records to the pooled data is as accurate as vessel-specific adaptation. Ship identity and specifications are largely interchangeable for in-fleet vessels, but only the specifications support zero-shot prediction. Its error grows as a vessel's specifications differ from the fleet's. Per-ship models and vessel-specific adaptation are therefore unnecessary under fair comparison. One global model can serve a whole fleet, including newly delivered vessels with similar specifications.

cs.LG · cs.AI

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新论文 · v1arXiv:2611.03012

Low-rank corrections to a shared reference Jacobian for GPU-parallel AC $N\!-\!1$ contingency analysis

Marc Hunkemöller, Nico Westerbeck, Joost van Dijk, Lars Schewe, Dirk Witthaut

原文摘要

Contingency analysis repeatedly solves the power-flow equations under many single-element outages, an increasingly demanding workload as operating margins tighten and renewable variability grows. Graphics processing units (GPUs) are attractive for this task, but a change in topology alters the sparsity pattern of the Jacobian, preventing the uniform batched factorization on which efficient GPU solvers rely. This paper presents a GPU-parallel Newton--Raphson solver for $N\!-\!1$ contingency analysis that separates the change in operating state from the change in topology. A single reference Jacobian is inverted once and reused across all contingencies. Each topology change is expressed as a low-rank Woodbury correction, while the residual state-dependent perturbation is handled by an inner iterative solve, a truncated Neumann series or the biconjugate gradient stabilized method. We analyze the spectral radius and condition number of the resulting iteration matrices and show that the reference Jacobian is an effective preconditioner. On transmission-scale benchmarks, the method attains per-topology solve times competitive with an optimized 80-core central processing unit baseline, with the low-rank correction accounting for approximately 1--2\% of the workload. This makes GPU-parallel $N\!-\!1$ contingency analysis feasible at transmission scale without per-topology refactorization.

eess.SY · cs.DC · math.NA

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新论文 · v1arXiv:2611.03009

Systematic Derivation of Leakage in Massey Secret Sharing Schemes

Nadja Aoutouf, Daniel Augot

原文摘要

Leakage attacks on secret sharing schemes can be based on the exploitation of partial information about individual shares to recover the underlying secret. In coding theory, linear exact repair schemes (LERSs) allow the recovery of a codeword symbol from limited information obtained from the remaining symbols. When applied to code-based secret sharing schemes, such repair schemes can be interpreted as recovering the secret from partial information about the shares, expressed in terms of subfield symbols. Guruswami and Wootters introduced the first explicit constructions of LERSs for low-rate Reed--Solomon codes that is optimal in terms of bandwidth. In this work, we first investigate the construction and algebraic properties of LERSs, building on the Guruswami-Wootters construction. We derive new explicit repair schemes from their construction and establish structural properties of the resulting schemes. Second, we further develop a systematic computational approach based on a Gr{ö}bner bases approach to identify leakage functions associated with LERSs for arbitrary linear codes. This approach enables us to investigate the existence and structural properties of LERSs across different code families and parameter ranges. We first applied our method to Reed-Solomon codes, which gave as further insights into the algebraic structure of LERSs. Moreover, by applying our method to generalized Reed-Muller and Tamo-Barg codes, we identify several LERSs that, to the best of our knowledge, have not previously been described in the literature.

cs.CR

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版本更新 · v2arXiv:2610.15122

Targeting the Right Situations: Mutation Testing of Fuzzy-Powered Situation Hyperspace

Nawshin Mannan Proma

原文摘要

Ensuring the safety of autonomous Unmanned Aerial Vehicles (UAVs) operating in underground mines is challenging due to the vast and uncertain Operational Design Domain (ODD). Traditional validation approaches, such as random testing, often result in inefficient situation coverage, allocating computational resources to low-risk scenarios while failing to expose critical edge cases. Building on prior work in risk-prioritized Situation Coverage, this paper investigates whether Fuzzy Logic-based risk estimation effectively correlates with real system failures. To this end, we conduct a mutation testing study within our SCALOFT simulation framework. A fixed set of software mutants is systematically injected and evaluated under two testing strategies: (i) uniformly random situation generation and (ii) Fuzzy-Guided risk-prioritized situation generation. Experimental results show that the Fuzzy-Guided approach achieves a higher mutation score and is more effective at exposing difficult-to-detect faults compared to random testing.

cs.RO

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新论文 · v1arXiv:2611.03008

E-GenRM: Generative Reward Modeling for Open-Ended E-Commerce Shopping Agents

Ying Sun, Qiuyu Zhao, Xiao Wang, Xiyu Zhao, Gege Shi, Zenghui Sun, Jinsong Lan, Haihong Tang 等 9 位作者

原文摘要

We present E-GenRM (E-commerce Generative Reward Model), a generative reward model tailored to e-commerce shopping agents. User expectations are shifting from simple product queries to open-ended shopping tasks. However, existing trajectories and reward designs remain largely bounded by current agent usage patterns and known failures, providing limited guidance for further capability improvement. These limitations call for an integrated reward modeling framework for open-ended shopping agent tasks. On the data side, E-GenRM leverages naturally occurring user behavior across the shopping app to construct diverse, long-horizon conversational trajectories beyond established usage patterns. On the modeling side, E-GenRM first learns from behavior-grounded evaluation chains through supervised fine-tuning. It is then optimized through criterion-guided reinforcement learning, which rewards correct preference judgments and evidence-supported factual claims under each criterion. Personalized hard-negative training further improves its sensitivity to user preferences reflected in shopping behavior, beyond explicit requirements. We additionally develop an active evidence retrieval extension that acquires missing e-commerce information for evaluation. On E-GenRM-Bench, E-GenRM-27B achieves 87.40\% preference accuracy, outperforming the strongest baseline by 6.97 percentage points. In a 14-day online A/B test on Taobao, a shopping agent post-trained with E-GenRM improves the effective shopping-assistance rate by 1.39 percentage points and user satisfaction by 2.33 percentage points.

cs.IR

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