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新论文 · v1arXiv:2610.40031

AI Cosmos: An Agentic Infra for Situated Learning

AI Cosmos Team, Jinfeng Zhou, Yuxuan Chen, Fangming Chang, Mingyu Su, Yifan Zhu, Yuan Wang, Bo Zhang 等 35 位作者

原文摘要

Large language models (LLMs) can improve task performance during learning, yet these gains do not necessarily reflect learners' ability to thereafter apply the knowledge and skills independently. We present AI Cosmos, an agentic infrastructure for situated learning that organizes learning around two complementary principles: scenario-based participation and cumulative assets of practice. AI Cosmos organizes learners and role-specialized agents within learning scenarios that integrate task-relevant knowledge and, when needed, executable workbenches. An asset-memory loop preserves the processes and outcomes of practice as reusable assets with explicit provenance and dependencies. We evaluate scenario-based interaction through two preregistered randomized controlled studies examining the acquisition of legal reasoning and programming competence, holding the same underlying LLM across conditions. AI Cosmos achieves greater learning gains than a conventional LLM in legal reasoning and higher mastery of unseen programming problems than an LLM-as-Teacher baseline.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40030

Cheap Retrieval and Observation Credit Assignment from One Quantity: The Value of Information

Daniel Strauss, Nikolaos Sotirakis, Zhenshan Bing, Alois Knoll

原文摘要

Memory in reinforcement learning raises two problems. Attention finds the relevant item by scoring the whole store, which is costly for robots under latency and energy limits. When a retrieved memory pays off, credit for acquiring it must cross the whole sequence. We argue that both are governed by one quantity, the value of information, and propose to estimate it from archived rollouts with a learned world model. We obtain that, when read at the observation, it yields a provable lower bound on the optimal value of the acquiring action, which floors its TD target and attributes this floor to the observation that earned it. Read at the decision, it defines the optimal retrieval set. A pilot retrieval system, to be trained and improved with this signal, solves a memory task with reduced context.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40027

Counting approximate breakpoint medians of random genomes

Poly H. da Silva, Arash Jamshidpey, David Sankoff

原文摘要

Unsigned linear unichromosomal genomes with genes labeled 1, . . . , n are represented by permutations of these labels, whose adjacencies are the unordered pairs of genes occurring consecutively. The breakpoint distance counts the adjacencies of one genome that are absent from the other, and a breakpoint median minimizes the total breakpoint distance to a given set of genomes. Deciding whether a permutation is a median often requires a minimization over the whole symmetric group, which makes the median set resistant to exact counting. We replace this global condition by a local one: a c-approximate median is a genome having at most c adjacencies absent from all the input genomes. For a fixed genome we introduce its approximate median inverse, the set of ordered k-tuples of genomes ad- mitting it as a c-approximate median, and we construct and count this set explicitly. For k genomes chosen uniformly and independently at random, this gives the probability that a given genome is a c-approximate median and the expected number of such approximate medians. The relaxation is not only technical: approximate medians and near-optimal genomes co- incide up to a shift of the tolerance that remains bounded in probability as the number of genes grows. Finally, we derive an explicit upper bound on the probability that a given genome is an exact median, and on the expected number of exact breakpoint medians.

cs.CE · cs.DS · q-bio.PE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.21607

Market Incentives for AI Safety Investment

Nikita Tsoy

原文摘要

Despite their practical significance, modern AI systems pose significant societal risks, including toxicity, malicious use, and misinformation. Their mitigation depends not only on technical feasibility but also on the incentives of firms that develop and deploy these systems. We show how profit maximization shapes safety investment in an AI supply chain with an upstream LLM provider and several competing downstream firms. In our game-theoretic model, we derive the market-driven level of safety investment and compare it with the levels that maximize industry profit and market welfare. We find that downstream competition generally induces downstream firms to invest more in safety than the industry-profit-optimal level. By contrast, the upstream provider may underinvest relative to these benchmarks because it is not fully compensated for the surplus from safety investments. Finally, we analyze how different regulatory interventions affect the participants' incentives and profits, and the resulting levels of model safety.

cs.LG · cs.GT · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40023

HEMO: Harness Self-Evolution for Multi-Objective Combinatorial Optimization

Jianghan Zhu, Cong Zhang, Ni Zhang, Yifan Yang, Yew Soon Ong, Zhiguang Cao

原文摘要

Automatic operator design (AOD) has increasingly adopted large language models (LLMs) to generate problem-specific search and variation operators for multi-objective combinatorial optimization (MOCO). Yet most methods evolve the operators while leaving the process that discovers them fixed. This limits adaptation as new Pareto trade-offs and operator interactions change where and how the search should proceed. We introduce HEMO, which makes the AOD harness itself a self-evolving optimization target. HEMO maintains a persistent harness state and updates one module at a time, revising the logic that determines which experience to retrieve, which operators to improve, which parents to select, and how to formulate the orchestration of search steps. Downstream solution quality and runtime guide these updates, while a discounted UCB scheduler directs adaptation toward promising modules as their utility changes. A persistent memory filesystem preserves candidate outcomes and diagnostic feedback across harness states, enabling successive states to build on accumulated optimization experience. Experiments across eight MOCO tasks and two structurally distinct evolutionary backbones demonstrate strong performance against conventional evolutionary, neural, and LLM-based AOD baselines, together with effective transfer to related tasks. HEMO extends automatic operator design beyond evolving individual operators to improving the search process that discovers them.

cs.NE · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40018

DINOv2 for PET: A Pre-Training Domain Benchmark Across Classification, Segmentation, and Prognosis

Roxanne De Maeyer, Yannick Brackenier, Vicky De Ridder, Simon DeKeyser, Maarten Larmuseau, Jef Vandemeulebroucke

原文摘要

DINO (self-DIstillation with NO labels) has become a stan- dard for self-supervised pre-training, producing features that generalise across datasets and downstream tasks. In medical imaging it has been adopted mainly for radiology (X-ray, CT and MRI), where even natural- image DINO features transfer well. In the literature, these features have been reported to transfer worse to functional imaging such as PET. Yet PET-specific DINO models have barely been introduced, and even less validated across multiple tasks and datasets. We present PET-DINOv2-2D, the first DINOv2 model pre-trained on 2D PET, and benchmark it against open-source encoders: the origi- nal DINOv2 (pre-trained on the LVD-142M natural-image dataset) and OmniRad (pre-trained on CT, MRI and ultrasound). Using the same lightweight heads for every backbone, and despite far less pre-training data, PET-DINOv2-2D matches or outperforms both on three down- stream PET tasks: normal/abnormal classification, lesion segmentation and recurrence-free survival prognosis. Its advantage is largest when few labels are available, suggesting that PET pre-training gives more transferable, label-efficient features. Code is made available at https://github.com/nuclivision/pet-dinov2-2d/.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40012

Reachability in Five-Dimensional Vector Addition Systems Is PSPACE-Complete

Weijun Chen, Bo Fu, Yuxi Fu

原文摘要

Vector addition systems (VAS) are a fundamental model of concurrent computation in which transitions update nonnegative integer counters by adding integer vectors, provided all counters remain nonnegative. Closely related to Petri nets, VAS are the single-state counterpart of vector addition systems with states (VASS), which use finitely many states to restrict the available transitions. A classical simulation of $d$-VASS by $(d+3)$-VAS connects their reachability problems, but the complexity-theoretic picture of fixed-dimensional VAS remains largely unclear. While reachability in $2$-VASS is PSPACE-complete under binary encoding, the best known upper bound for $5$-VAS was only elementary. We prove that every reachable $5$-VAS instance has a reachability witness of length polynomial in its unary size. Consequently, $5$-VAS reachability is NL-complete under unary encoding and PSPACE-complete under binary encoding. We also establish a polynomial factorization of the reachability relation of every $d$-VAS with $d \ge 3$. For $5$-VAS, this strengthens Hopcroft and Pansiot's effective semilinearity of reachability sets to the full reachability relation. For every fixed $d\ge3$, it also yields an $\mathsf F_d$ upper bound for reachability in $(d+3)$-VAS.

cs.FL · cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40010

Measuring the Value of Communication in Multi-Agent Tracking Under Finite-Rate Belief Sharing

Danial Dehghani, Maximilian Lübke, Zahra Seifaei, Norman Franchi

原文摘要

This paper investigates the value of communica- tion (VoC) in cooperative multi-agent tracking under rate- constrained information exchange. Although prior work has studied distributed tracking, quantized communication, and robust fusion, it remains unclear at what communication rate most of the achievable tracking gain is obtained, beyond which additional communication resources yield only marginal benefit. We consider stationary agents that observe a moving target through heterogeneous range and bearing measurements, perform local extended Kalman filtering (EKF), quantize their posterior state means, and fuse exchanged information via covariance intersection (CI). Performance is evaluated versus communication rate relative to no-communication baselines, a centralized oracle, and a CI-perfect reference. The results show that communication is not uniformly beneficial: very low rates can degrade performance due to coarse quantization, whereas moderate rates recover most of the achievable gain. In the considered scenario, increasing the rate from 4 to 12 bit/s per agent reduces the root-mean-square error (RMSE) from 13.93 m to 6.35 m, while a further increase to 32 bit/s per agent improves it only marginally to 5.93 m. This reveals a clear knee region at moderate rates, beyond which the remaining loss is mainly due to the conservatism of the fusion architecture. Overall, the proposed framework characterizes low-rate degradation, communication-efficient operation, and diminishing returns, while highlighting the joint role of rate and sensing geometry in determining communication benefit.

eess.SP · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40009

FIDE: Fiber-Informed Differentiable Experimental Design for Reference-Relative Law Dynamics

Pietro Zanotta, Ján Drgoňa

原文摘要

Aggregate measurements generally leave an evolving population law underdetermined, so different measurement systems can be equally adequate for a scientific task while constraining different aspects of the law. We study how to resolve this residual design freedom in baseline-preserving settings, where an existing dynamical model remains the working baseline outside quantities explicitly valued by the task. \textbf{F}iber-\textbf{I}nformed \textbf{D}ifferentiable \textbf{E}xperimental Design (\textbf{FIDE}) completes each candidate's aggregate constraints by information projection relative to a common frozen reference, then ranks scientifically adequate designs by the dynamical revision required to realize the resulting completed paths. The resulting \emph{Full action} is the minimum mean-squared velocity correction needed to realize a completed path; equivalently, its square root is the sharp reference-relative expectation-rate discrepancy over a gradient-normalized Sobolev class. This defines a conditional minimum-revision principle for baseline-preserving design rather than a generic truth-recovery objective. Experiments show that this preference changes sensor selection, can help or harm withheld downstream predictions depending on baseline reliability, can be used prospectively from aggregate predictions, and remains computationally usable for a $32$-dimensional configuration law.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40007

Greenscapes: Optimized Vegetation Configurations in Cities

Patryk Bartkowiak, Marcin Marlewski, Paweł Wójcik, Jae Joong Lee, Dominik L. Michels, Bedrich Benes, Sören Pirk, Wojciech Pałubicki

原文摘要

Vegetation plays a central role in cities, serving functional roles such as balancing shade and sky exposure, shaping the microclimate, carbon sequestration, and providing acoustic screening. Furthermore, aesthetic choices in landscape styles (formal, modernist, picturesque) are important for citizens' well-being and should be considered in the design of urban landscapes. In this paper, we present a novel method for placing vegetation, ranging from shrubs to large trees, in 3D urban scenes. Our method uses individual lots as input and employs a conditioned Vision-Language Model (VLM) to generate a top-down rendering of vegetation, placing large trees, small trees, and shrubs on lawn areas. This rendering is then converted into a 3D scene by detecting individual plants and selecting appropriate 3D assets. To refine the proposed configurations, we define an optimization loop that keeps the VLM-generated positions fixed after a deterministic feasibility repair and uses CMA-ES to optimize per-instance scale transformations along with discrete vegetation asset substitutions. To define the objective function for the optimization, we evaluate the generated 3D scenes with efficient surrogate models of shade and path-based acoustic exposure. Furthermore, we render in-scene views at each optimization step to compute view-dependent perceptual descriptors, enabling the generation of both aesthetic and functional vegetation layouts for urban scenes.

cs.GR · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40006

Human--AI Interactions as First-Class Data: A Formal Interaction Model and Runtime for Explainable AI Collaboration

Michela Taufer, Kin Ng, Ian Lumsden, Aashish Pandey, Rajasi Gore, Angelina Ju, Sukanya Moudgalya, Ewa Deelman 等 9 位作者

原文摘要

AI assistants are increasingly embedded in computational notebooks, scientific workflows, software engineering tools, and learning systems, yet human-AI interactions are often treated as transient prompt-response exchanges rather than durable data products, hindering reproducibility, explainability, replay, behavioral analysis, and adaptive AI support. We argue that these interactions form a new data class requiring formal representation and introduce the Human-AI Interaction Description Language (HAIDL), a formal interaction model with a Backus-Naur Form-inspired concrete syntax. HAIDL encodes each interaction as a structured object capturing computational context, user request, prompt payload, AI response, feedback, problem context, provenance, and model metadata. We integrate this vision into HAIDL-X, a middleware for JupyterLab that instantiates HAIDL by capturing user-bot state, building contextualized prompts, interfacing with AI providers, collecting feedback, and storing interactions in a repository. We add a learning layer over HAIDL that maps interaction objects and notebook execution traces into reproducible measures of user behavior, including engagement, performance, help-seeking, error-oriented use, and learning personas. HAIDL-X provides a foundation for treating human-AI collaboration as structured, queryable, replayable, analyzable data.

cs.AI · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40005

Efficient Learning of Mechanism Designs and Compositionality

João Pedro Gandarela, Thiago Rios, Stefan Menzel, André Freitas

原文摘要

Learning mechanism behaviour requires a representation of how connected parts jointly constrain motion. We study whether exposing reusable constraint subassemblies improves prediction from limited data. Assur-KB represents planar mechanisms through irreducible modules, boundary interfaces, and solve dependencies while retaining source geometry. Our theoretical results characterize transfer through module interfaces and establish physical limits arising from missing interface information and accumulated sensitivity. To test the representation claim directly, the experimental design compares matched mechanism-curve pairs across training budgets within the same multilayer-perceptron family. This controlled comparison establishes the utility of the complete representation in the evaluated learning setting. Assur-derived features provide a geometric-mean $1.8\times$ advantage in sample efficiency over flattened adjacency and position inputs across budgets of 50-10,000 mechanisms. Separate experiments establish invariance under tested joint relabelings, higher valid-generation rates under dyadic composition, and a coarser structural index over 10.02 million mechanisms. In summary, the theory and experiments identify how physical constraint structure supports limited-data prediction and which interface conditions govern compositional reuse.

cs.AI · cs.CE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.25894

Red Teaming Program Repair Agents: When Correct Patches can Hide Vulnerabilities

Simin Chen, Yixin He, Suman Jana, Baishakhi Ray

原文摘要

LLM-based agents are increasingly deployed for software maintenance tasks such as automated program repair (APR). APR agents automatically fetch GitHub issues and use backend LLMs to generate patches that fix the reported bugs. However, existing work primarily focuses on the functional correctness of APR-generated patches, whether they pass hidden or regression tests, while largely ignoring potential security risks. Given the openness of platforms like GitHub, where any user can raise issues and participate in discussions, an important question arises: Can an adversarial user submit a valid issue on GitHub that misleads an LLM-based agent into generating a functionally correct but vulnerable patch? To answer this question, we propose SWExploit, which generates adversarial issue statements designed to make APR agents produce patches that are functionally correct yet vulnerable. SWExploit operates in three main steps: (1) program analysis to identify potential injection points for vulnerable payloads; (2) adversarial issue generation to provide misleading reproduction and error information while preserving the original issue semantics; and (3) iterative refinement of the adversarial issue statements based on the outputs of the APR agents. Empirical evaluation on three agent pipelines and five backend LLMs shows that SWExploit can produce patches that are both functionally correct and vulnerable (the attack success rate on the correct patch could reach 0.91, whereas the baseline ASRs are all below 0.20). Based on our evaluation, we are the first to challenge the traditional assumption that a patch passing all tests is inherently reliable and secure, highlighting critical limitations in the current evaluation paradigm for APR agents.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.28174

FLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales

Jorge L. Rodriguez, Victor Angulo Morales, Areej Alwahas, Mariana Elias Lara, Fida Mohammad Thoker, Kasper Johansen, Bernard Ghanem, Fernando T. Maestre 等 9 位作者

原文摘要

Foundation models offer a promising route to transferable remote sensing representations, but many current approaches depend on very large pretraining datasets and fixed sensor configurations, limiting their suitability for ecological and environmental applications, where observations often vary across platforms, spatial and spectral resolutions, and available modalities. We introduce FLORO, a multimodal geospatial foundation model designed to learn transferable representations from a small but highly diverse remote sensing corpus. FLORO is pretrained using masked autoencoding on a heterogeneous combination of Sentinel-1, Sentinel-2, SkySAT imagery, elevation, and UAV-derived data. To accommodate sensor variability, FLORO incorporates availability-aware inputs that indicate which spectral bands and auxiliary modalities are present in each sample, enabling a unified input space across heterogeneous sensor configurations. We evaluated FLORO on the PANGAEA benchmark under a frozen-encoder protocol across scene classification, segmentation, and regression tasks. Despite being pretrained on a smaller corpus than competing foundation models, FLORO achieved strong and stable transfer across optical, optical-SAR, and optical-elevation benchmarks spanning medium-resolution satellite, airborne, and ultra-high-resolution UAV imagery. FLORO obtained the second-best average segmentation performance across six PANGAEA benchmarks, the third-best average rank across three scene-classification benchmarks, and excelled in regression tasks, while qualitative results showed improved preservation of spatial structure in flood, urban, biomass, and canopy-height prediction settings. Overall, these results position FLORO as a practical framework for ecological and environmental monitoring under heterogeneous sensing conditions.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40003

Humanoid Dodgeball: Learning Fast Dynamic Obstacle Avoidance Motions for Humanoids

Aoqian Zhang, Chunzheng Wang, Haoran Chen, Annan Tang, Cheng Xiang, Fan Shi, Shuzhi Sam Ge

原文摘要

Humanoids have become increasingly agile and capable in real-world environments. To operate safely in dynamic scenarios, they must rapidly evade fast-moving obstacles while maintaining stability and physically feasible whole-body motion. Learning such behaviors remains challenging due to the high-dimensional dynamics of humanoid systems and the need to jointly balance reactive obstacle avoidance, motion naturalness, and control robustness. In this work, we present Humanoid Dodgeball, a reinforcement learning (RL) framework for agile dynamic obstacle avoidance in humanoids. Our method enables highly dynamic whole-body evasive maneuvers against incoming projectiles through threat-aware reward shaping, while multiple direction-specific adversarial motion priors regularize the learned behaviors toward stable and diverse human-like motions. Extensive simulation and hardware experiments demonstrate that the proposed framework substantially improves dynamic obstacle avoidance performance across diverse obstacle speeds and launch distances, while preserving robust and physically plausible humanoid motions.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40002

How Much Should a VLA Trust Its Reasoning? Step-Level Guidance as a Measured Action-Value Function

Dabin Seo, Eunhyek Joa

原文摘要

Reasoning vision-language-action (VLA) policies write a reasoning trace and then decode the action from it. A wrong trace yields a wrong action, and even a correct-looking trace can mislead the plan. Existing remedies ask whether the reasoning is right. We ask how strongly it should steer. CoC-CFG applies classifier-free guidance between the policy's context and the same context without the reasoning span. One weight per Euler step attenuates or amplifies the reasoning, and w=1 recovers the original policy. We treat the choice of this weight as a Markov decision process whose action values are measured, not bootstrapped. With the expert frozen and the noise fixed, applying a weight for one step and finishing the plan under the current controller returns that weight's value exactly. Q-adaptive guidance fits these values with small critics and improves the controller conservatively. In the Alpasim driving simulator it outperforms every fixed weight and online RL (SAC, GRPO) in open loop, and in closed loop it passes 262 of 300 scenes, against 252 for the original policy. On held-out frames of the Waymo end-to-end driving benchmark, its first three stages outperform every fixed weight, four adaptive-guidance methods, SAC and GRPO. On the hidden test set, a frozen Alpamayo 1.5 with rule-based camera and vehicle conversions and Q-adaptive guidance sets a new state of the art among published methods: 8.135159 rater feedback score, against 8.079 for the best published method.

cs.RO · cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40001

Enhancing Atrial Fibrillation Detection Through Hyperparameter Optimization

Sara Gjorgjieva, Ilinka Ivanoska, Marjan Gusev

原文摘要

This study explores the role of hyperparameter optimization (HPO) in enhancing the detection of Atrial Fibrillation (AF) using both machine learning and deep learning models. We evaluated a range of standard classifiers - Decision Tree, Random Forest, and XGBoost classifiers, alongside deep learning architectures including Recurrent Neural Networks, Deep Neural Networks, and Siamese Neural Networks. To fine-tune these models, we applied four HPO methods: Genetic Algorithm, Hyperband, Optuna, and SMAC3. The experiments were conducted using inter-patient and intra- patient data splits to assess model generalization and robustness in the presence of class imbalance, which is reflective of real-world AF occurrence. Our results, based on PhysioNet electrocardiogram datasets, showed that the Hyperband-optimized XGBoost model outperformed all others, achieving a peak F1 score of 96.95%. These findings highlight the critical role of suitable HPO strategies in enhancing the accuracy and robustness of AF classification outcomes in clinical settings.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39999

PhOSv1: Efficient Scaling of Model Sleeping on a Single Server for Autoscaling with GPU-Context Deduplication

Tianle Sun, Xingda Wei, Rong Chen

原文摘要

Autoscaling is a key mechanism in model-as-a-service systems, where fast scale-up is essential because the scale-up time directly determines how long a model service remains overloaded, which in turn governs serving quality. The state-of-the-art approach, model sleeping, adopted by mainstream serving engines including vLLM and SGLang, achieves near-zero-cost scale-up by pausing pre-spawned engines on the GPU. However, sleeping is not a free lunch: each sleeping model must keep its GPU context resident, and this cost grows linearly with the number of models, degrading serving performance. Because providers must support many models, they face a dilemma between fast scale-up for all models and high service performance. In this paper, we show that a single server can keep many models asleep without paying this GPU context tax. The key observation is that the contexts of sleeping models are largely duplicated: the driver (CUDA) context can be shared across models, and their GPU kernel binaries overlap heavily due to common operators and libraries. PhOSv1 deduplicates this context by transparently delegating each engine's GPU context allocation to a central daemon through API remoting; therefore, a single server can keep far more models asleep than current engines allow. Our evaluation shows that PhOSv1 matches the scale-up time of vanilla vLLM sleep mode while substantially reducing the GPU memory footprint, improving end-to-end multi-model serving performance.

cs.DC · cs.OS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39998

Gaze-Assisted Supervisory Control for Lunar Robotic Arm Teleoperation Under Concurrent Monitoring

Jiayu He, Yifan Li, Oliver R. Runswick, Ernest N. Kamavuako, Yunjie Jia, Peter D. Hodkinson, Yang Gao

原文摘要

Rover-mounted robotic arms can extend human reach during lunar sample handling, but continuous joystick teleoperation requires repeated visual-motor alignment while operators may also need to monitor system states. This paper presents a gaze-assisted supervisory control framework in which gaze nominates a rock or placement target, explicit confirmation authorizes the action, and the robot performs bounded autonomous grasp-and-place execution. The framework was evaluated against manual joystick teleoperation in a mission-analog virtual reality study with 16 participants. The study used a 2 x 2 x 3 within-subject design varying control mode, placement difficulty, and concurrent monitoring load. Mean recorded task completion time decreased from 47.65 to 18.43 s per rock, with 72.5% of this difference arising during grasping. Under the tested auditory monitoring conditions, gaze-assisted supervisory control showed less average proportional slowing, with no detected control-mode difference in monitoring-response accuracy or speed. The rate of gaze shifts between core areas of interest decreased from 49.98 to 27.73 shifts/min, while the raw NASA Task Load Index score decreased from 32.54 to 18.26. However, the benefit was task dependent. In precision placement, manual control yielded lower mean placement error and a higher placement success rate. These findings support a mixed-initiative allocation in which gaze enables rapid task-level intention specification and autonomous execution, while precision-critical stages retain manual refinement.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39993

A physics-informed uncertainty-aware surrogate for $p$-$n$ junction diodes

Suchita Kulkarni

原文摘要

We develop an uncertainty-aware physics-informed surrogate for a one-dimensional $p$-$n$ junction diode, conditioned on device length, doping and bias. Using the internal state of the device as predicted by the surrogate we develop a test-suite of quantities of interest including the terminal current, depletion width, peak field and stored charge. The formalism and the quantities are can be extended to asymmetric junction and two-dimensional diodes as shown by means of benchmark analysis. For the one-dimensional symmetric junction surrogate, we obtain 0.11% median $L_1$ error on the electrostatic potential, and 1.5% and 0.61% on the electron and hole densities. Finally, we develop an uncertainty analysis which determines the validity of surrogate answer within the user-specified tolerance.

cs.LG · cond-mat.mes-hall

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39992

Accumulating Pruning Constraints: New Exact Polynomial-Space Algorithms for Max 2-SAT

Konstantin Kutzkov

原文摘要

We present deterministic polynomial-space algorithms that solve Max 2-SAT on $n$ variables exactly in time $\Ostar(2^{(1-γ)n})$, for a fixed $γ>0$, on several classes of instances whose clause density may grow with $n$. Our main tool extends the classic DPLL branching algorithm. When the search gives a variable its less promising value, comparing the two values of the variable yields a pruning constraint that is satisfied by some optimal assignment. Accumulating these constraints along each branch prunes all but an exponentially small fraction of the assignments. The classes are formulas with many biased variables, semirandom 2-CNF formulas, and Max-Cut on regular expander graphs and on graphs generated by a stochastic block model. In a semirandom formula, the pairs of variables that may share a clause are arbitrary, and the clauses on these pairs are random. For semirandom formulas and block-model graphs, the algorithms are still exact on every input, and the time bound holds with high probability over the input. To our knowledge, these are the first polynomial-space algorithms with such a running time for nontrivial classes of dense Max 2-SAT instances. Beyond these classes, the pruning constraints can be added to any branching algorithm for Max 2-SAT, and we expect them to be of practical relevance for general formulas, in particular for sparse ones, where each constraint involves only a few variables.

cs.DS · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.12862

Equilibria in routing games with connected autonomous vehicles may not be strong, as exclusive clubs may form

Anastasia Psarou, Natello Descormier, Rafał Kucharski

原文摘要

User Equilibrium (UE) is the standard representation of the so-called routing game in which drivers adjust their route choices to arrive at their destinations as fast as possible. Asking whether UE is strong has, so far, been meaningless for human drivers, who do not form coalitions because of technical and behavioral constraints. Here, we demonstrate that this will not necessarily hold for Connected Autonomous Vehicles (CAVs), which can communicate and collaborate to form routing coalitions by exploiting adaptive signal control. Within game-theoretical formulations of Nash Equilibria, we provide a methodological framework and report the novel phenomena through a carefully designed toy-network example, in which we identify so-called \emph{clubs} emerging in different CAV penetration rates and adaptive traffic-light configurations. We detail one such club where three CAVs deviate from the UE and benefit (arrive faster). The formation of such a club has negative consequences for other users (who are not invited to join and must now travel longer) and for the system (making it suboptimal and disequilibrated), thereby triggering adaptation dynamics. These findings raise questions about equity in future mixed-autonomy traffic systems.

cs.GT · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.23943

QuechuaTok: Morphological Boundary Accuracy as a Necessary Metric for Tokenizer Evaluation in Agglutinative Low-Resource Languages

Maria Contreras

原文摘要

Tokenization is a foundational step in NLP pipelines, yet standard evaluation metrics such as fertility rate fail to capture morphological correctness for agglutinative languages. We present QuechuaTok, a systematic benchmark comparing four tokenization strategies - BPE, Unigram LM, WordPiece, and a morphology-aware PRPE tokenizer - for Southern Quechua (quz), a low-resource agglutinative language spoken by 8-10 million people in South America. Using a 200k-sentence corpus and the SQUOIA finite-state morphological analyzer (Rios, 2016) as silver standard, we evaluate three metrics: fertility rate, OOV rate, and morphological boundary accuracy (MorphAcc). Our results show that BPE achieves the lowest fertility rate (1.636 at 16k vocab) by memorizing surface word forms, while achieving only 6.67% MorphAcc. PRPE achieves 83.33% MorphAcc - the highest of all systems - demonstrating that fertility rate alone is insufficient to evaluate tokenizers for agglutinative languages. We further validate this finding extrinsically: on a Spanish-to-Quechua (Chanka) neural machine translation task, a model trained with PRPE outperforms an identical BPE-trained model by +68.7% BLEU and +17.0% chrF, showing that morphological boundary accuracy translates into measurable downstream gains. All code and models are publicly available at kaggle.com/code/macmaky/quechuatok

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39990

An Exact Parallel Dense Gauss-Seidel Algorithm with Trade-Offs between Parallel Execution Time and Preprocessing Time

Mugurel-Ionuţ Andreica, Pantelimon George Popescu, Eliana-Dina Tîrşa

原文摘要

The Gauss-Seidel iterative method is commonly used for solving systems of linear equations because of its good convergence properties. However, the Gauss-Seidel algorithm cannot be easily parallelized because of the data dependencies existing at each iteration. The best parallel execution time for dense systems of equations is $O(n)$ given that enough processors are available and no preprocessing is performed. In this paper we propose a new parallel Gauss-Seidel algorithm which is capable of achieving better parallel runtimes at the expense of more preprocessing time. The algorithm can be tuned in order to provide convenient trade-offs between the preprocessing time and the best parallel runtime achievable by the algorithm.

cs.DC · cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.04127

Epistemic Warrant for LLM Recommendations: Characterizing the Basis for Reliance When Ground Truth Is Unavailable

Shai Vardi, João Sedoc

原文摘要

Large language models are increasingly used to support organizational decisions. Despite their rapid and widespread adoption, users often lack a principled basis for assessing whether to rely on a specific recommendation. We introduce \emph{epistemic warrant}, a decision-level construct that characterizes the scope over which a recommendation is supported. We operationalize this construct through a reliance certificate that uses behavioral evidence from four tiers of tests to assess a recommendation's underlying warrant and assign it to a warrant category. We assess this operationalization using complementary forms of construct-validity evidence, including transformation validity, known-groups tests, independent human consensus, discriminant analyses, and robustness across models and alternative implementations. The results show that the certificate behaves in theoretically expected ways: its transformations instantiate their intended relationships, certificate assignments increase across groups prespecified to represent increasing levels of warrant, and stronger assigned warrant is associated with greater independent human consensus. Certificate assignments also capture information beyond verbalized confidence and are not readily explained by decision difficulty. The main conclusions remain robust across models and operationalizations. These findings support the reliance certificate as an operationalization of epistemic warrant for assessing the scope of support for a particular LLM recommendation when ground truth is unavailable.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2502.18259

Generalization of terms via universal algebra

Tommaso Flaminio, Sara Ugolini

原文摘要

We provide a new foundational approach to the generalization of terms up to equational theories. We interpret generalization problems in a universal-algebraic setting making a key use of projective and exact algebras in the variety associated to the considered equational theory. We prove that the generality poset of a problem and its type (i.e., the cardinality of a complete set of least general solutions) can be studied in this algebraic setting. Moreover, we identify a class of varieties where the study of the generality poset can be fully reduced to the study of the congruence lattice of the 1-generated free algebra. We apply our results to varieties of algebras and to (algebraizable) logics. In particular we obtain several examples of unitary type: abelian groups; commutative monoids and commutative semigroups; all varieties whose 1-generated free algebra is trivial, e.g., lattices, semilattices, varieties without constants whose operations are idempotent; Boolean algebras, Kleene algebras, and Gödel algebras, which are the equivalent algebraic semantics of, respectively, classical, 3-valued Kleene, and Gödel-Dummett logic. Finally, we prove that the variety of MV-algebras, the equivalent algebraic semantics of Lukasiewicz logic, has nullary type.

math.LO · cs.LO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.34221

CropReward: A Human-Preference Benchmark and Reward Model for Aesthetic Image Cropping

Xiangfei Sheng, Xiaofeng Pan, Leida Li, Qian Zhang, Biao Wang, Johan Wagemans, Weisi Lin

原文摘要

Aesthetic image cropping (AIC) aims to improve image composition and aesthetic quality by removing extraneous or distracting content. Over the past decade, AIC methods have evolved from specialized vision models to multimodal large language models(MLLMs). Reliable evaluation that reflects human preferences is essential for benchmarking AIC methods and guiding model optimization. However, the current AIC evaluation relies largely on geometry-based metrics such as intersection over union (IoU), as well as image aesthetic assessment(IAA) models. Although inconsistencies with human preferences have long been observed, the robustness and reliability of these evaluation criteria remain underexplored. To bridge this gap, we first construct CropBench, a large-scale benchmark comprising 106,995 human preference pairs for 1,019 images, with each candidate set containing the original image and candidate crops produced by 14 AIC methods. A systematic study on CropBench reveals that the existing criteria, ranging from IoU to off-the-shelf IAA assessment models, only exhibit limited alignment with human preferences. Building upon CropBench, we further propose CropReward, an MLLM-based reward model that evaluates a candidate crop by comparing with the original uncropped image and outputs a score indicating explicit aesthetic improvement or degradation relative to the original. In addition, we employ CropReward to construct CropReward-300k, the largest AIC dataset to date, and further develop CropAlign, a preference-aligned AIC model trained solely with CropReward supervision, without human-annotated crop boxes. Experimental results demonstrate that CropReward aligns with human preferences significantly better than AIC existing criteria. CropAlign also surpasses the state-of-the-art AIC methods, demonstrating the advantage of CropReward in guiding AIC model optimization.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39984

R2QRec: Learning Recommendation Queries from Retrieval Feedback

Weixin Chen, Shuchang Liu, Hailan Yang, Yuhan Zhao, Li Kang, Xuefeng Zhang, Chenghao Zhang, Xinyuan Wang 等 16 位作者

原文摘要

Query-based recommendation separates expressing user needs from locating matching items. Its central learning problem is how to turn item interactions into effective retrieval requests when interaction logs do not annotate queries. We present R2QRec, which uses a fixed retrieval system to construct query supervision and evaluate recommendation outcomes. Retrieval verification first selects generated expressions that recover observed items as supervised fine tuning targets, allowing retrieval behavior to determine the target wording. Policy refinement then evaluates generated query sets by the observed item's rank in their fused recommendation list. Log-rank feedback distinguishes outcomes beyond the recommendation cutoff, while a KL penalty constrains policy change relative to the supervised reference. The stages have distinct roles: verification identifies expressions to imitate, and policy refinement improves the combined ranking produced by newly generated queries. Only the query generator is trained; the retriever and item index remain fixed, and no query annotations or learned reward model are required. Across three public datasets, R2QRec achieves state-of-the-art overall recommendation performance among the evaluated methods. Extensive experiments demonstrate the effectiveness of the proposed design and its generalization across generators and domains.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39985

FedMaJ: Ordinal Meta-Aggregation for Federated Learning Using Majority-Judgment Voting

Driss Choukri, Essaid Sabir, Halima Elbiaze, Abdelkrim Haqiq

原文摘要

Federated learning traditionally relies on a fixed aggregation rule despite shifting client state and wireless dynamics. We present FedMaJ, a framework that dynamically selects aggregation rules via Majority Judgment voting. During voting rounds, clients evaluate candidate models across performance, resource utilization, channel condition, and fairness metrics. The server adopts the majority-preferred rule and retains it until the next vote, scheduled dynamically based on accuracy trends. Evaluated in over-the-air federated learning with explicit energy and resource cost modeling, synthetic and Fashion-MNIST experiments show that FedMaJ achieves competitive learning performance and superior aggregation-influence fairness. Under extreme-evaluation perturbations, Majority Judgment yields significantly more stable rule selections than mean-based scoring, establishing ordinal voting as a robust control layer for adaptive aggregation.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39981

TRACEs: Fine-Grained Embedded Provenance for Distributed Scientific Workflows

Ty Anderson, Kin Ng, Rajiv Mayani, Jay Lofstead, Ewa Deelman, Michela Taufer

原文摘要

Large-scale scientific workflows run on distributed HPC resources, making it hard to explain how scientific data products are generated and why similar workflow executions can yield very different results. Existing systems usually focus on either distributed workflow execution or provenance management and rarely treat fine-grained provenance as an intrinsic property of distributed scientific data artifacts. We present TRACEs (Traceable Runtime for Advanced Containerized Executions), a provenance architecture for distributed containerized scientific workflows that embeds data lineage and execution context directly in every generated data artifact. By decoupling provenance from workflow orchestration, TRACEs generalizes artifact-centric provenance from local container runs to distributed workflows while remaining compatible with existing workflow management systems. We evaluate TRACEs on a seven-stage E. coli variant-calling workflow distributed across multiple HPC nodes. Our results show 100% provenance completeness under distributed execution, about 59% less intermediate storage via sparse IMG containers, and runtime and zero-copy I/O throughput comparable to fixed-size SIF containers, plus provenance-guided explanations of workflow variability that trace a 200-fold difference in variant counts to differences in input sequencing data without re-executing the workflow.

cs.DC · cs.CE

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