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版本更新 · v2arXiv:2611.14371

Reliable and explainable analysis of AVS/RS by Statistical Model Checking and Process Mining

Emanuele Guerrazzi, Andrea Vandin

原文摘要

Automated Vehicle Storage and Retrieval Systems (AVS/RS) are increasingly adopted to boost warehouse throughput while containing operational costs. We present a tool-supported analysis workflow that combines discrete-event simulation, Statistical Model Checking (SMC), and Process Mining (PM) techniques to quantify and explain AVS/RS performance. SMC, implemented through the MultiVeStA tool, delivers statistically reliable estimates of key indicators, such as cycle times and inventory consolidation, across alternative stocking and picking policies with user-specified confidence levels. MultiVeStA orchestrates multiple independent simulations in a black-box manner, enabling automated verification. PM ingests the resulting event logs to uncover the behavioural patterns and bottlenecks that drive those indicators, closing the interpretability gap typical of simulation studies. We instantiate the framework on a new open-source AVS/RS simulator configured according to recent industrial guidelines and evaluate multiple control strategies and different scenarios. Results show that the coordinated use of SMC and PM supports both predictive what-if analyses and post-hoc diagnostics. To the best of our knowledge, this is the first application of a combined SMC-PM workflow to AVS/RS operations, complemented by a reusable open-source simulation and analysis toolchain.

cs.SE · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15413

MORA: Correcting Time Series Forecasts with Coarse Future Signals

Hu Li, Kenan Li, Haonan Xu, longlong, Shuqin Gao, Yucheng Zhang, Feng Dai

原文摘要

Recent architectural advances have improved time-series forecasting. However, our analysis of forecasters with diverse architectures reveals a common pattern: a substantial share of residual energy is concentrated in a small number of low-frequency components. Simple linear probes further show that the forecasters' historical inputs still contain predictive information about errors in the forecast mean. Building on these findings, we propose Model-Output Residual Adaptation (MORA), a model-agnostic residual adapter that refines the outputs of frozen forecasters without requiring additional inputs. MORA estimates segment-wise means of the target sequence through structural extrapolation and learned estimation, rather than directly predicting backbone-specific residuals. It then converts discrepancies between these estimates and the corresponding statistics of the backbone forecast into stagewise corrections, scaling each update according to estimation reliability. Extensive experiments on twelve benchmarks with six forecasting backbones show average MSE reductions of 7.2% for long-term forecasting and 28.1% for short-term forecasting, with lower MSE in 96.2% of the 288 evaluated cases. The benefits also extend to three time-series foundation models evaluated at different context lengths. Together, these results demonstrate that predictable coarse-scale residuals can be effectively exploited to improve existing forecasts. Code repository: https://anonymous.4open.science/r/MORA-B0B0/.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2504.15779

Shannon observables: A scalable approach to information decomposition

Aaron J. Gutknecht, Fernando E. Rosas, David A. Ehrlich, Abdullah Makkeh, Pedro A. M. Mediano, Michael Wibral

原文摘要

Distributed systems, such as biological and artificial neural networks, process information via complex interactions engaging multiple subsystems, resulting in high-order structure of the information within the system with distinct properties across scales. Investigating how these systems process information remains challenging due to difficulties in defining appropriate multivariate metrics and ensuring their scalability to large systems. To address these challenges, we introduce a novel framework based on what we call "Shannon observables": quantities that capture essential properties of high-order information processing, yet can be computed directly from Shannon entropies and scale efficiently to large systems. Our theoretical results demonstrate how Shannon observables can be used to resolve long-standing ambiguities regarding the interpretation of widely used multivariate information-theoretic measures. On the practical side, we demonstrate the framework's versatility across several domains. An analytical study of error-correcting codes shows how Shannon observables trace out distinct encoding regimes as channel noise varies; analyses of deep classifiers and convolutional autoencoders reveal characteristic information-processing signatures across layers and over training; and applied to magnetoencephalography recordings from the human brain, the framework yields a cortical atlas exposing pronounced regional structure in directed information transfer. Overall, our framework resolves fundamental limitations in analysing high-order information phenomena and offers broad opportunities for theoretical developments and empirical analyses.

cs.IT · cs.AI · cs.LG · nlin.AO · physics.data-an

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.13867

Block-Fitness Modeling of the Global Air Mobility Network

Giulia Fischetti, Anna Mancini, Giulio Cimini, Jessica T. Davis, Abby Leung, Alessandro Vespignani, Guido Caldarelli

原文摘要

Accurate representations of the World Air Transportation Network (WAN) are fundamental inputs to models of global mobility, epidemic risk, and infrastructure planning. However, high-resolution, real-time data on the WAN are largely commercial and proprietary, therefore often inaccessible to the research community. Here we introduce a generative model of the WAN that treats air travel as a stochastic process within a maximum-entropy framework. The model uses airport-level passenger flows to probabilistically generate connections while preserving traffic volumes across geographic regions. The resulting reconstructed networks reproduce key structural properties of the WAN and enable simulations of dynamic spreading that closely match those obtained using the real network. Our approach provides a scalable, interpretable, and computationally efficient framework for forecasting and policy design in global mobility systems.

physics.soc-ph · cs.SI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15408

AraCrypto-ML: A Multi-Label Corpus of Communicative Functions in Arabic Facebook Cryptocurrency Discourse

Mabrouka Bessghaier, Mohamed Rashad Rouine, Md. Rafiul Biswas, Youssef Ayadi, Wajdi Zaghouani

原文摘要

Arabic cryptocurrency discourse on social media is communicatively heterogeneous: a single post may report prices, explain protocols, promote investment, warn of scams, or invoke Islamic jurisprudence. Yet no annotated resource captures this functional diversity. We introduce AraCrypto-ML, a 10,000-post multi-label corpus of Arabic Facebook cryptocurrency discourse (2014-2024) from 2,898 public pages using a set of related keywords. Collected posts are annotated into six categories: Market Commentary, Educational, Promotional, Skeptical-Warning, Religious Framing, and Other/Unclear, using Fanar-2-27B under a prompt-only multi-label protocol. We validate 1,000 posts against adjudicated gold labels (macro kappa = 0.74) and apply Rogan-Gladen calibration to correct labelling bias. The two annotators agree with each other at macro kappa = 0.62 before adjudication, below the model's agreement with either of them (0.70 and 0.69). Reaction profiles differ strongly by function: Love is about 70% of reactions per post on Educational and Promotional posts but about 33% on Skeptical-Warning, which draws the largest Haha share (about 39%).

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15404

Is the Importance Ratio Necessary for Stable Reinforcement Learning in LLMs?

Shuibai Zhang, Junhyuck Kim, Gyeongman Kim, Jaewoong Cho

原文摘要

Reinforcement learning (RL) has become central to post-training large language models (LLMs). However, popular RL methods like GRPO incur non-negligible overhead by computing both old-policy and current-policy likelihoods to form importance sampling ratios. In this work, we propose Likelihood-Gated Policy Optimization (LGPO), which enforces a soft trust region constraint via likelihood-based gating, eliminating the need to compute old-policy likelihoods. Empirically, we show that removing the importance sampling correction term does not harm training stability, whereas removing the trust region mechanism can lead to collapse. Moreover, ratio-based clipping can fail in fully on-policy training: the importance ratio stays at 1, so the ratio-based trust region constraint never activates. Under standard training settings where GRPO is stable, LGPO achieves comparable training stability and peak performance while reducing training time by ~18% on average relative to a standard implementation that recomputes old-policy likelihoods. In fully on-policy training, where GRPO fails, LGPO remains stable, enabling more efficient and robust LLM RL post-training across training regimes.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.14086

Neural Optimal Transport in Hilbert Spaces

Jae-Hwan Choi, Jiwoo Yoon, Dohyun Kwon, Jaewoong Choi

原文摘要

Functional data such as time series, trajectories, and fields are increasingly central in machine learning and are naturally modeled in infinite-dimensional Hilbert spaces. We study Semi-dual Neural Optimal Transport (SNOT) for learning transport maps between probability measures on separable Hilbert spaces. A critical challenge in this setting is the spurious solution problem: a neural map can globally optimize the SNOT objective while failing to recover an optimal transport map. We show that this failure arises from non-regular source measures, which make the inner minimization in the semi-dual objective non-identifiable. We prove that regular source measures, defined via Gaussian null sets, restore inner-minimizer uniqueness and recover the Monge map. For singular sources, we introduce Gaussian smoothing and establish a necessary and sufficient kernel condition for smoothing to restore regularity. We further prove plan-level consistency: as smoothing vanishes, every Wasserstein accumulation point of the induced optimal plans solves the original Kantorovich problem. Experiments on synthetic singular transports and real-world unpaired time-series imputation benchmarks validate the theory, achieving the best performance on several benchmarks and competitive performance on the remaining ones.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2412.03395

An even simpler hard variant of Not-All-Equal 3-SAT

Andreas Darmann, Janosch Döcker, Britta Dorn

原文摘要

We investigate Positive Not-All-Equal 3-Sat under a strong structural restriction and delineate a sharp tractability threshold. In particular, we consider the restriction of Positive Not-All-Equal 3-Sat to instances that simultaneously satisfy the following properties: (i) the clauses can be decomposed into a disjoint union of $k$ partitions of the variable set, and (ii) the collection of clauses is linear, that is, each pair of distinct clauses shares at most one variable. We show that, under these conditions, Positive Not-All-Equal 3-Sat remains NP-complete for any fixed $k \geq 4$. On the positive side, we prove that for $k \leq 3$, the problem is in P and, notably, all such instances are not-all-equal-satisfiable. Our results do not only show robustness of NP-completeness despite the strong structure imposed but also have implications for hypergraph coloring. In particular, by the well-known correspondence between Positive Not-All-Equal 3-Sat and hypergraph coloring, we obtain the following corollary of our results: For $k\geq 4$, Bicolorability is NP-complete for linear 3-uniform $k$-regular hypergraphs even if the edges are given as a decomposition into $k$ perfect matchings; with the same restrictions, for $k \leq 3$ Bicolorability is in P, and all such hypergraphs are bicolorable. In addition, we show that loosening the restriction to 3-clauses by also allowing 2-clauses shifts the above-mentioned tractability threshold. Moreover, we deduce that every instance of Positive Planar Not-All-Equal Sat with at least three distinct variables per clause is nae-satisfiable. Besides, for Linear Not-All-Equal 3-Sat we derive a computational complexity dichotomy with respect to the number of variable appearances.

cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15394

Tracking What Works: Social Engineering Lures as Signatures for Victim-Centric Infostealer Campaign Discovery

Estelle Ruellan

原文摘要

Despite advances in antivirus technology, infostealer infections persist at scale, driven primarily by social engineering rather than exploits. Yet, existing campaign detection efforts have focused on technical artifacts, overlooking the social-engineering lures exploiting human behavior and driving successful infections. To address this gap, we introduce a dual-framework system that discovers and tracks infostealer campaigns using victim-side artifacts from stealer logs. By treating social engineering lures as campaign signatures, the framework automatically aggregates individual infections into high-fidelity campaigns in near real-time. Recognizing distinct patterns between software and gaming ecosystems, we developed ecosystem-specific pipelines detecting all large and medium campaigns in both ecosystems (one medium gaming campaign was only recovered merged with another), though performance decreased for small campaigns. Analysis of 177 real-world campaigns identified by our framework reveals distinct temporal and infrastructure patterns with direct remediation implications. Software campaigns accumulate over 60% of infections within the first ten days, demanding rapid intervention following detection, while gaming campaigns grow more intense the longer they last, requiring continuous monitoring. Using social engineering lures as campaign signatures provides robustness to infrastructure fragmentation where traditional IoC-based detection fails. This work shifts campaign tracking from infrastructure correlation to victim-centric analysis, capturing why campaigns succeed rather than only how they operate, enabling awareness-driven prevention that addresses the human decision-making dimension absent from existing research.

cs.SI · cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02287

Faster graphic matroid parity: Breaking the $nm$ time complexity barrier

Tatsuya Terao

原文摘要

In the graphic matroid parity problem, we are given an undirected multigraph $G=(V,E)$ with $n$ vertices, together with a partition of $E$ into $m$ pairs of distinct edges, and the goal is to find a maximum-cardinality collection of pairs whose union is a forest. This problem generalizes both graph matching and graphic matroid intersection. Graph matching admits the celebrated $O(\sqrt{n}m)$-time algorithm of Micali--Vazirani~[FOCS'80], and graphic matroid intersection admits $o(nm)$-time algorithms due to Gabow--Stallmann~[ICALP'85] and Gabow--Xu~[FOCS'89]. In contrast, for graphic matroid parity, Gabow--Stallmann~[FOCS'84 \& Combinatorica'86] gave an $O(n^2 m)$-time algorithm, and its $\tilde{O}(nm)$-time implementation by Gabow--Stallmann~[ICALP'85] had long remained the best known bound, except in very dense regimes where $m=ω(n^3)$, in which case the algebraic $\tilde{O}(n^4)$-time algorithm of Cheung--Lau--Leung~[SODA'11 \& TALG'14] is faster. We present a randomized $\tilde{O}(n^{3/2} \sqrt{m} + \sqrt{n} m)$-time algorithm for graphic matroid parity. This is the first algorithm to break the $O(nm)$-time complexity barrier for this problem outside the very dense regime. Moreover, it improves over the previous bounds except in very sparse regimes where $m=\tilde{O}(n)$ and in very dense regimes where $m = \tildeΩ(n^{3.5})$. In particular, when $m=Θ(n^2)$, our algorithm runs in $\tilde{O}(n^{2.5})$ time, which nearly matches the $\tilde{O}(n^{2.5})$-time bounds of Micali--Vazirani for graph matching and of Gabow--Xu for graphic matroid intersection in this regime.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15393

VR-Net: Subarray-Level Inter-Cell Coordination for Multi-Cell XL-MIMO under Near-Field Spatial Non-Stationarity

Muhammad Shoaib Ayub, Rana Muhammad Subhan, Insoo Koo, Felipe Augusto Pereira de Figueiredo

原文摘要

Spatial non-stationarity in multi-cell extremely large-scale MIMO (XL-MIMO) confines each user to a visibility region (VR) of each array. VR-Net exploits these regions for subarray-level coordinated beamforming. A base-station (BS)-indexed conflict graph identifies overlapping VRs; gain-based association and a masked multi-cell weighted minimum mean square error (WMMSE) iteration restrict beam design and channel state information (CSI) uploads to the relevant subarray blocks. We establish conditions for exact per-cell decoupling, derive the expected CSI loads, bound the loss of designing on the VR-gated channel and the loss of the support constraint, and characterize single-edge visibility contrast through a closed-form Fresnel transition width. Simulations use a 3GPP TR 38.901 Release-19-based near-field UMi channel with modular facade arrays and pre-registered criteria. At a 30 dB threshold, VR-Net retains at least 99.6% of the full-aperture WMMSE sum rate in every tested setting while uploading 10-39% fewer complex CSI entries. It gains up to 3.5 bit/s/Hz over an all-user adaptation of the multi-cell scheme of Pérez-Adán et al. at moderate power and dense load, and inter-cell coordination adds up to 33% over per-cell design.

cs.IT · eess.SP · math.ST

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15392

Integrality Gap of 3.4 for Santa Claus: A Topology-based Analysis

Kimon Boehmer, Chien-Chung Huang

原文摘要

In the \textsc{Max-Min Allocation} problem, a set of indivisible items are to be assigned to a set of players and the goal is to maximize the minimum value among all players. A much studied special case, the \textsc{Santa Claus} problem, assumes that an item has a fixed utility but a player is only interested in a subset of items. The main technique for designing approximation algorithms for \textsc{Santa Claus} is the configuration LP, introduced by the Bansal and Sviridenko. A long line of research has progressively improved the upper bound of the integrality gap, mostly relying on the combinatorial arguments of Haxell for finding perfect matchings in certain hypergraphs. Remarkably, a recent paper of Haxell and Szabó (SODA '23) introduced a novel topology-based analysis, by which they are able to obtain the current best upper bound of $3.534$. In this work, we adapt the approach of Haxell and Szabó to analyze the integrality gap. The main new ingredient in our adaptation is that we stop the process of "edge explosions" in a graph used by Haxell and Szabó when it becomes too costly. This early termination of explosions, combined with a new lower bound on a topological connectivity parameter based on a theorem of Aharoni, Berger, and Meshulam, allows us to improve the integrality gap to $3.4$. Our analysis has the additional advantage of being simpler.

cs.DS · cs.DM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15389

A physics-constrained deep reinforcement learning method for autonomous mission planning of Titan deep space probes

Hanmin Li, Tao Wang, Shuangliang Liu, Zhe Zhang, Maodeng Li

原文摘要

Titan is a compelling target for future deep space exploration, while its extreme Earth-Titan distance and complex environment make continuous ground-in-the-loop planning impractical. Existing planning methods, primarily developed for Earth-observation scenarios, have limited generalizability to uncertain deep space conditions and adaptability to time-varying mission states. To address these challenges, this paper proposes a physics-constrained maskable proximal policy optimization (PCM-PPO) method for Titan autonomous mission planning. A Titan-specific discrete-time Markov decision process is established to characterize mission feasibility and resource constraints. A system dynamics and geometric model is incorporated to enhance physical representation, including central gravity, J2 perturbation, third-body attraction, and downlink occultation. To accommodate opportunity targets under a bounded action dimension, PCM-PPO organizes predefined and opportunity targets through a fixed-capacity target pool and represents the mission state with a target-wise state matrix and a global state vector. A physics-constrained action mask constructs an admissible decision domain from current mission states, guiding policy learning toward physically executable decisions and avoiding infeasible exploration. An integrated training strategy facilitates policy optimization under coupled constraints. Comparative, ablation, and uncertainty experiments validate the effectiveness of PCM-PPO. PCM-PPO provides a feasible and interpretable scheme for autonomous mission planning of Titan deep space probes.

cs.AI · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2503.00925

Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusion

Daiki Nishiyama, Hiroaki Miyoshi, Noriaki Hashimoto, Koichi Ohshima, Hidekata Hontani, Ichiro Takeuchi, Jun Sakuma

原文摘要

Lymphoma subtype classification has a direct impact on treatment and outcomes, necessitating models that are both accurate and explainable. This study proposes a novel explainable Multi-Instance Learning (MIL) framework that identifies subtype-specific Regions of Interest (ROIs) from Whole Slide Images (WSIs) while integrating features of cell distribution and image. Our framework simultaneously addresses three objectives: (1) indicating appropriate ROIs for each subtype, (2) explaining the frequency and spatial distribution of characteristic cell types, and (3) reaching accurate subtyping using both cell distribution and image modalities. Our method fuses cell graph and image features extracted for each patch in a WSI by a Mixture-of-Experts-based approach and classifies subtypes within an MIL framework. Experiments on a dataset of 1,233 WSIs demonstrate that our approach achieves state-of-the-art accuracy compared to ten other methods and provides region- and cell-level explanations that align with a pathologist's perspective.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15386

Actuation and Control of Passive Tendon-Driven Soft Extensions Through Rigid Manipulator Motion

Ignacio Cuiral-Zueco, Gonzalo López-Nicolás, Youcef Mezouar

原文摘要

Industrial manipulators offer reach and precision but can damage nearby structures in confined spaces. Soft robots provide compliant contact but typically have limited reach. Rigid-soft systems combine their advantages, although they often require dedicated actuation and control hardware. We propose actuating a passive tendon-driven soft extension through manipulator joint motion. As the robot moves, the exterior spans of tendons anchored to upstream links change, altering tendon tension and deforming the extension. Inspection through narrow apertures, for example, constrains the motion of the end effector (EE). We develop a controller that regulates the soft tip while preserving such EE constraints. A differential model separates rigid transport from soft deformation, combining analytic arm and tendon kinematics with a locally identified span-to-deformation map. We also derive a local upper bound on the number of independent deformation directions transmitted by the tendons under the EE constraint, and conditions for the exponential convergence of the tip error. A quadratic program with control barrier functions imposes obstacle clearance, fulcrum limits, and model-validity conditions. Simulations and real experiments show tip and shape regulation under free, position-constrained, and fulcrum-constrained EE motion.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.12421

Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matching

Luping Liu, Bingyi Kang, Yifan Wang, Dong Xu

原文摘要

Dense correspondence matching has historically been bounded by simplifying spatio-temporal priors, such as smooth motion and rigid geometry. While effective for classical tasks, these assumptions break down in image editing and reference-guided generation (IEG), where transformations can preserve visual identity while breaking physical continuity. To establish identity-preserving correspondence across such transformations, we introduce FreeMatching, a generalizable framework combining generative and semantic foundation representations with heterogeneous supervision from classical datasets, tracked videos, and synthetic scenes. Teacher-guided iterative refinement further improves correspondence in IEG without dense correspondence annotations. Experimentally, a single FreeMatching model substantially improves correspondence quality on challenging IEG image pairs while retaining competitive performance on classical benchmarks. Furthermore, we demonstrate its utility as a quantitative metric for evaluating identity preservation, with scores that correlate with human judgment. The code is available at https://github.com/luping-liu/FreeMatching.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.06439

Better Call Reward: Reward Hacking as Strategic Abstention in Legal Reasoning Models

Subramanyam Sahoo, Justin Shenk

原文摘要

What happens when a legal AI model learns to look like a lawyer instead of reasoning like one? We fine tune Qwen3-8B with Group Relative Policy Optimisation (GRPO) against a proxy built from three surface features: citation count, legalese density, and response length. The model does not learn to reason more effectively. It stops committing to an answer. Across 16 yes or no legal reasoning tasks from LegalBench (N=320), overall accuracy collapses from 0.500 (chance) to 0.072 (McNemar p < 10^-36), driven entirely by the rate of properly formatted answers falling from 0.900 to 0.109. When the model does commit, accuracy is 0.657 against 0.556 before training, on only about 35 answers, suggesting the collapse is not mainly a loss of capability. The mechanism is visible in the training logs: the proxy keeps paying for length beyond what fits in the 256 token generation limit, completions grow until they fill that budget, and the required answer line is crowded out. We term this the Saul Goodman effect, a policy that becomes more lawyerly in form while becoming noncommittal, and prove formally that abstention is the optimal response to any surface feature proxy that attaches no penalty to it. A citation audit shows that structural plausibility checks cannot catch this failure: every case name we extracted from the trained model's held out completions passes our structural check, so citations must be verified against a case database. To detect this failure before deployment, we introduce three diagnostic tools: the Confidence Theater Score (CTS), the Citation Plausibility Rate (CPR), and the Regret Gap (RG), and report where each one falls short. In a domain where a confidently wrong answer can constitute malpractice, the broader lesson is direct: a reward function that measures how legal a response looks will produce a model that is maximally photogenic and minimally useful.

cs.LG · cs.AI · cs.CL · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15381

Low-Cost Mobile Bimanual Manipulation for Long-Horizon Toilet Cleaning

Sven Schreiber, Bernard Burdiek, Lasse Huber-Saffer, Yung-Hui Li, Donglin Chen, Shang-Ching Liu, Jianwei Zhang

原文摘要

Vision-Language-Action (VLA) models have shown promising capabilities for robot manipulation, but their performance on accessible mobile bimanual platforms remains underexplored. We present a low-cost and reproducible mobile bimanual platform integrating two LeRobot SO-101 manipulators with a TurtleBot 4 and a LeRobot-ROS 2 software stack. Using long-horizon toilet cleaning as a case study, we collect 250 real-world demonstrations and evaluate $π_0$, $π_{0.5}$, SmolVLA, and GR00T under the same embodiment and task conditions. We further evaluate variants using SARM-derived task-progress signals and short-term MEM observation history. Among the evaluated models, $π_0$ performs best, achieving 60% strict and 70% relaxed autonomous success, increasing to 90% with limited human intervention. In contrast, the evaluated progress- and memory-augmented variants do not improve their corresponding baselines. Our results demonstrate the feasibility of long-horizon mobile bimanual manipulation on accessible hardware while highlighting remaining challenges in reliable VLA deployment. Project website: https://so101-turtlebot4.github.io/mobile-bimanual-cleaning/

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.01514

Training Dynamics of Softmax Self-Attention: Fast Global Convergence via Preconditioning

Gautam Goel, Mahdi Soltanolkotabi, Peter Bartlett

原文摘要

We study the training dynamics of gradient descent in a multilayer softmax self-attention network trained to perform linear regression and show that in the infinite-data limit this regression problem is equivalent to a matrix factorization problem. This connection motivates the design of Hera, a new first-order optimization algorithm for training self-attention networks. Key features of Hera include a reparameterization of the model arising from the matrix factorization geometry and a rescaling of the parameters which ensures that the empirical loss gradients remain close to the auxiliary matrix factorization gradients throughout training. Hera updates in the reparameterized model are equivalent to a certain preconditioned update in the original parameterization. We also propose a variant of Hera called Hera-PSD; our main theoretical result is that under small Gaussian initialization the excess risk of the model trained by Hera-PSD decreases exponentially fast in the number of gradient descent iterations, up to an additional error term that decreases as $1/n$, up to logarithmic factors, in the size of the training set. A particularly appealing aspect of our excess risk bound is that it does not degrade as the number of self-attention layers increases. In a stylized time-series prediction experiment in a nonlinear dynamical system, we find that a model trained by Hera achieves $98.1\%$ lower mean-squared test error than a model trained by AdamW and $97.7\%$ lower than a model trained by Muon at the same iteration budget.

cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15378

A Modified Glicko Model for Rating Soccer Clubs and National Teams Worldwide

Andrei Shelopugin, Alexander Sirotkin

原文摘要

Comparing soccer teams that rarely or never meet is a fundamental challenge in sports analytics: clubs play mostly within their national leagues, and national teams within their confederations. We propose a rating framework that places clubs and national teams worldwide on a common probabilistic scale. Building on a recent extension of Glicko with order effects and strength-dependent draw probabilities, we adapt the model to soccer with goal-scoring adjustments, a decomposition of home advantage, extra time, league-aware rating initialization and rules of promotion and relegation that do not inflate the levels of leagues; national teams start from the levels of their confederations. The models are evaluated on about 1.2 million club matches of 252 leagues from 166 countries (2000-2026) and 12.6 thousand national team matches (1998-2026). On held-out seasons, the modified Glicko model is clearly more accurate than Elo and as accurate as score-matrix models built on the goal expectations of a gradient boosting model, including the Dixon-Coles and Markov chain models, while it is substantially more accurate in international club matches. Averaging the predictions of the rating model and the Markov chain model is more accurate than either model alone. For national teams, the model is significantly more accurate than all baselines, especially in the final tournaments, where teams of different confederations meet. The resulting ratings are interpretable and can be used for player evaluation, transfer analysis and match forecasting. The match data, the ratings and the code are publicly available at https://github.com/andreyshelopugin/SportsRatings

cs.LG · stat.AP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.13028

GTAGym: AAA Game Tasks Environment for Evaluation and Reinforcement Learning

Jiajun Chen, Shicheng Fang, Jiahao Lu, Yuxin Wang, Xipeng Qiu

原文摘要

AAA games offer a rich testbed for evaluating agent perception, reasoning, and control. However, evaluating whether agents can complete tasks in AAA games is challenging and often human-annotated because task outcomes are often difficult to verify automatically, let alone applying it for Reinforcement Learning (RL). We introduce GTAGym, an environment and framework for evaluation and RL on GTA V tasks. Its evaluation benchmark, GTAVBench, derived from GTA V main-story missions, contains 103 online tasks reconstructed across nine task families, alongside 47 image-based shooting and sniping tasks using GTA V imagery. A web server provides access to the online environment and evaluation interface, supporting parallel testing through screenshots and keyboard-mouse actions with automatic engine-state outcome checks. We evaluate six frontier LLMs on GTAVBench with textual task instructions and on its GTAVBench-AGI branch, where instructions appear only in the first screenshot and a harness automatically compresses interaction history. The best configuration achieves 70.9% overall accuracy in GTAVBench-AGI, yet aerial navigation, evasion and interception remain difficult. GTAGym also provides a scalable generator that samples task parameters within user-specified ranges and option sets under fixed templates, creating batches of 3D and image-based shooting environments for RL with automatic reward and termination signals. LoRA RL of Qwen3.8-27B on generated on-foot navigation tasks improves the in-domain performance and transfers to out-of-domain task categories, and native GTA V tasks from 15% to 23%.

cs.CV · cs.AI · cs.CL

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新论文 · v1arXiv:2611.15374

Full Mesh Dynamic Four-User Entanglement-based Switched QKD Network Integrated with SD-KMS

Nikolas Makris, Persefoni Konteli, Zafeirios Georgiadis, Alkinoos Papageorgopoulos, Grigorios Anastasiou, Panagiotis Papanastasiou, Konstantinos Tsimvrakidis, George T. Kanellos

原文摘要

We demonstrate a reconfigurable, four-user, full-mesh quantum key distribution network using polarization entanglement, integrated with a software-defined key management system to orchestrate dynamic optical switching and real-time key provisioning for quantum-safe applications.

quant-ph · cs.CR

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新论文 · v1arXiv:2611.15373

SecDVA: A Maliciously Secure Multi-Party Framework for Data Value Assessment in Modern Machine Learning Data Markets

Lixu Wang, Xinpeng Yang, Meng Hao, Guangyi Zhang, Tianwei Zhang, Wei Dong, Chenkai Weng

原文摘要

Data Value Assessment (DVA) plays a critical role in modern data markets by enabling buyers to evaluate how a seller's data would improve model performance before purchase. However, existing DVA approaches require one party to reveal the data or model, exposing participants to privacy leakage, strategic manipulation, and high deployment costs. These limitations motivate a DVA protocol that is secure, reliable, widely applicable, and efficient enough for practical deployment in modern data markets. In this paper, we present SecDVA, the first maliciously secure multi-party computation (MPC) framework for reliable and practical DVA in modern machine learning data markets. SecDVA ensures that neither the seller's data nor the buyer's model and test data are disclosed beyond the final DVA outcome, while remaining robust against malicious behavior. To address the efficiency and applicability challenges of DVA for large models, we use the neural tangent kernel theory to linearize model updates and introduce lightweight mini-batch approximations to significantly reduce computation overhead. We further design a (2+1)-party MPC protocol based on function secret sharing, together with optimized non-linear primitives. In addition, we optimize the underlying MPC backends, the distributed point function, and the distributed comparison function, reducing the number of Advanced Encryption Standard calls to improve efficiency. Extensive experiments demonstrate that SecDVA outperforms existing baselines in the plaintext setting and enables DVA for large-scale models in a reasonable time under MPC.

cs.CR

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新论文 · v1arXiv:2611.15372

LLMRunner: Closed Workload Model Performance Testing Framework for LLM Servings

Arush Upadhyaya, Debadatta Mishra

原文摘要

Performance testing large language model (LLM) serving engines is a crucial requirement considering the criticality of the infrastructure for deploying conversational AI at scale. Most of the benchmarking tools used to carryout performance evaluation of LLM serving do not capture realistic workload orchestration unlike closed workload performance testing infrastructure used in other domains such as web and database engines. For example, state-of-the-art LLM benchmarks for performance testing model traffic as streams of independent single-turn and stand-alone requests omitting the end-user behavior such as think-time between conversational turns. We present LLMRunner, a performance tester for LLM serving that addresses this gap by providing a framework to generate realistic load consisting of multi-turn conversations with configurable end-user parameters such as number of users in the workload loop, think-time distributions etc. Moreover, LLMRunner provides declarative matrix-sweep experiment plans, integrated GPU and KV-cache monitoring, and an automated artifact collection and plotting subsystem. We use LLMRunner to comparatively analyze the performance of LLM serving when NVIDIA CUDA Unified Virtual Memory (UVM) is used as a drop-in replacement memory allocator for the Key-Value cache (KV-Cache). Our test demonstrates that UVM incurs no measurable throughput or latency penalty compared to the baseline memory allocation technique for non-oversubscription scenarios.

cs.PF

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版本更新 · v2arXiv:2611.09272

Task-Driven Closed-Loop Design Optimization of Passively Morphing Quadrotors for Contact-Mediated Narrow Gap Traversal

Etor Arza, Luca Girardi, Gabriel Maquignaz, Javier Paez, Stefan Leutenegger, Stefano Mintchev, Kostas Alexis

原文摘要

Aerial robots operating in cluttered environments may encounter openings narrower than their nominal width. Existing passively morphing drones reduce their width upon traversal, but they typically involve human-made designs, which can struggle to find the right balance among folding ratio, flight efficiency, and control tracking performance. We introduce a closed-loop design method that jointly optimizes quadrotor geometry and the torque-deflection response of two passively folding arms driven by the success rate in simulation for contact-mediated gap traversal. An optimization algorithm samples candidate designs that are evaluated in a contact-based simulation of the traversal task, where the task success rate is used as a feedback signal to sample progressively better designs over time. The resulting prototype passively reduces its width from 31.3 to 14.1 cm and traverses a 15 cm wide opening at a 2 m/s reference speed. It also tolerates gap approach-angle errors of up to 30 degrees and folds through a 40 cm long confined passage. Compared to a rigid twin, the soft configuration adds only 2 mm of position RMSE and less than 1% electrical power during contact-free flight. These results show that task-driven closed-loop design optimization can produce passively morphing airframes that are compact under contact, tolerant to approach error, and effectively rigid during nominal flight.

cs.RO

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新论文 · v1arXiv:2611.15370

Supervision-Aware Evaluation of MedGemma for Reference-Assisted Radiotherapy Contour Ranking

Jaden Moon, Caiwen Jiang, Songlin Zhao, Wei Liu

原文摘要

Radiotherapy contour ranking can support quality assurance, but vision-language model (VLM) gains may reflect supervision, score construction, available inputs, or representation access. We evaluated frozen MedGemma 1.5 in a single-institution study, a 12-patient held-out HaN-Seg test, five-fold cross-fitting on 30 non-test HaN-Seg patients, and transfer without refitting to 120 SegRap2023 patients. On held-out HaN-Seg, a supervised readout of the final prompt state exceeded a supervised readout of the model's yes/no output probabilities by +0.3047 pooled average precision (AP; 95% patient-bootstrap interval [+0.2822, +0.3269]); the advantage persisted against a parameter-count-matched nonlinear control. A readout of projected visual tokens exceeded the final-state readout in cross-fitting and transfer (+0.0706 AP averaged equally across patients on SegRap; 95% interval [+0.0607, +0.0804]), while a structured panel-feature readout remained competitive with visual tokens. The anatomy-conditioned readout did not demonstrate an advantage over the native-feature comparator. These results motivate visual-token and structured-feature controls in medical-imaging VLM evaluations; outcomes were computational or synthetic, not clinical acceptability.

eess.IV · cs.CV · cs.LG

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新论文 · v1arXiv:2611.15368

Bind-WAM: Binding-Uncertainty-Guided Selective Future Imagination in World Action Models

Chenhao Qiu, Shuyi Chen, Songen Gu, Shufeng Nan, Tianyu Wang, Siyu Lin, Yifan Li, Bingwen Zhu 等 9 位作者

原文摘要

World Action Models (WAMs) can improve robot learning by jointly predicting future visual states and actions, but generating a visual future at every replanning step is expensive and sometimes unnecessary. This raises a critical question: when should a robot policy imagine the future? In this paper, we propose Bind-WAM, a binding-uncertainty-guided WAM for selective future imagination. Bind-WAM introduces language-object binding uncertainty, selectively invoking joint future-action generation when the current observation does not support stable task-relevant action generation. An offline labeling pipeline uses complete demonstrations and action context to identify task-relevant objects and detect reference mismatch, invisibility, and identity drift. The resulting pseudo-labels supervise a lightweight router that predicts binding uncertainty from the current observation and language instruction. At inference, a shared WAM policy invokes selective future imagination according to the predicted uncertainty and its change between consecutive replanning steps. Simulation and real-world experiments demonstrate that Bind-WAM can establish binding uncertainty as a strong, interpretable signal for selective future imagination.

cs.RO

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版本更新 · v3arXiv:2611.03043

VideoEditor-MoE: Scaling Complex Video Editing with Mixture of Experts

Xiao Cao, Fuchen Long, Yansong Qu, Wen Xiao, Xin Zhang, Xinlong Sun, Xi Chen, Zhiyong Huang

原文摘要

Complex video editing requires understanding complex instructions while jointly fulfilling multiple editing requests. Meeting these demands through dense model scaling substantially increases computational cost. We introduce VideoEditor-MoE, the first open-source mixture-of-experts framework for video editing. Beyond sparse MLP-MoE layers, MoE-Attention expands attention capacity through routed key--value experts while preserving global interactions among video tokens. Initialized from a 5B video editing model, VideoEditor-MoE scales to 14B and 56B parameters, with approximately 8B parameters activated per token in both variants. Comparisons with existing methods and two complementary baselines, Explicit-MoE and MLP-MoE, assess its benefits over explicit expert guidance and MLP-only expert architectures. Further analyses demonstrate that all experts remain active and reveal that learned routing responds more strongly to sample content and diffusion noise levels than to human-defined editing categories. Spatial analysis further indicates sensitivity to object-level semantic features. We also identify decoupled weight decay as a strategy for stabilizing expert scaling.

cs.CV · cs.AI

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版本更新 · v2arXiv:2604.16667

Model-Predictive Spill-Free Emergency Stopping for Liquid-Manipulating Robots

Samuli Hynninen, Ville Kyrki

原文摘要

Manipulating open liquid containers is challenging because liquids are highly sensitive to vessel accelerations and jerks. Although spill-free liquid manipulation has been widely studied, emergency stopping under unexpected hazards has received little attention, despite the fact that abrupt braking may cause hazardous spills. This letter presents an emergency stop system for robots manipulating liquids in open containers. We formulate emergency stopping as an optimal control problem and solve it in a model predictive control framework to generate time-optimal, spill-free stopping trajectories. The method operates as a plug-and-play safety layer on top of existing slosh-free motion planning methods, enabling immediate reaction to detected hazards while accounting for nonlinear liquid dynamics. We demonstrate, through simulation and on a 7-DoF Franka Emika Panda robot, that the proposed approach achieves fast emergency stopping without spilling.

cs.RO

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新论文 · v1arXiv:2611.15360

CARE-Drive: Coupled Alternating Refinement for Ego-trajectories

Xiao Ma, Yijun Chen, Zhiquan Hu, Jicheng Zhao, Yuming Li, Chuang Dai

原文摘要

Vision-language-action (VLA) models bring multimodal understanding and world knowledge to end-to-end autonomous driving. To further improve the quality of predicted trajectories, existing studies introduce candidate refinement, residual denoising, and token correction. However, these methods typically focus on unified refinement of the entire trajectory, paying limited attention to the distinct physical quantities and their interactions. We further investigate physical specialization and coordination in trajectory refinement, centering on position and heading for simpler and more intuitive correction modeling. To this end, we propose CARE-Drive (Coupled Alternating Refinement for Ego-trajectories), which decomposes trajectory refinement into waypoint residual correction and position-conditioned heading generation. Through imitation learning and a future-token auxiliary task, the backbone provides predictive representations and a base trajectory. The position expert predicts bounded metric waypoint residuals, while the heading expert reconstructs vehicle orientation from adjacent angular increments conditioned on the corrected path. With the backbone frozen, the two experts are optimized alternately through supervised initialization and reinforcement fine-tuning. This design simplifies refinement through clear physical specialization while preserving conditional coupling during prediction and optimization. On NAVSIM v1, CARE-Drive achieves 88.8 PDMS, improving by 2.4 points over the unrefined base trajectories generated by the same backbone. Ablation studies further support the effectiveness of specialized experts and staged training.

cs.CV · cs.AI

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