All AI updates

Migration as a Steady-State Execution Primitive: A Runtime for Virtual Stationarity in Low-Earth Orbit

arXiv:2610.32720v1 Announce Type: new Abstract: Low Earth Orbit (LEO) satellite mega-constellations enable orbital edge computing for latency-critical geocentric applications, but satellite motion compresses line-of-sight communication windows to mere minutes. To maintain logical geographic proximity, a paradigm termed Virtual Stationarity (VS), applications must continuously migrate across passing satellites in the constellation. Traditional live migration mechanisms optimize individually initiated relocations or replication, but not the continuous handover between successive hosts that orbital motion requires. We reformulate live migration as a steady-state execution model and introduce a novel virtual stationarity runtime paradigm. We formalize the viability of continuous migration thro

Bounding Queue Waits for Fair Scheduling of Bursty HPC Workloads via Hierarchical Algorithms

arXiv:2610.32027v1 Announce Type: new Abstract: HPC clusters are shared among many projects, each holding an allocation of node-hours, and ensuring fairness is becoming harder. Foundation models workflows and automated pipelines have made job submissions bursty and concentrated among a few heavy users, so occasional users absorb queueing delays created by frequent ones, with no indication of how long their jobs will wait. Existing schedulers either forget past usage, protect only the job at the head of the queue, or assume that running jobs can be preempted. We propose HyDRA, a Hybrid Deadline and Ranking-Aware scheduler that enforces fairness among users while bounding the queue wait of every job. HyDRA assigns each job a deadline on arrival based on its size, the priorities of its projec

CEMIR vs. FISER: a comparative study of reasoning-based and enrichment-based approaches to pragmatic intent recognition

arXiv:2610.32022v1 Announce Type: new Abstract: Intent recognition in human-robot interaction requires agents to resolve ambiguous natural language instructions by inferring the speaker's underlying goals. Research has increasingly focused on two notable paradigms for addressing this task: structured symbolic reasoning and multimodal input enrichment. However, it remains unclear whether enrichment-based methods provide benefit in settings where the ambiguity concerns unstated human intent rather than missing observable scene information. In this paper, we present a comparative and diagnostic study benchmarking FISER - a framework utilizing structured social and embodied reasoning - against CEMIR (Context-Enriched Multimodal Intent Recognition), a method that performs multimodal enrichment

Tradeoff-Aware Territory Design: A Multi-Objective Decision System for Enterprise Customer-to-Seller Allocation

arXiv:2610.31701v1 Announce Type: new Abstract: Enterprise customer journeys are shaped not only by product experiences and marketing, but also by recurring resource-allocation decisions. In B2B go-to-market (GTM) organizations, territory carving determines which accounts receive seller coverage, which seller-account relationships are preserved, and how limited field capacity is distributed. Yet the process often remains manual, making it difficult to compare opportunities, value continuity, and evaluate tradeoffs among growth, balance, capacity, and feasibility. We present a production decision system for tradeoff-aware customer-to-seller allocation in enterprise GTM planning. It integrates three components: a multidimensional account-value layer that combines predictive signals to score

Collaborative In-Context Learning for Indoor Radio Map Estimation

arXiv:2610.31702v1 Announce Type: new Abstract: Indoor radio map estimation (RME) is essential for wireless applications such as coverage planning, localization, and network deployment. However, most existing methods follow a supervised learning paradigm that requires costly labeled data and task-specific model training. Moreover, indoor environments involve privacy-sensitive information, such as building layouts and structures, that often cannot be centralized. To address these challenges, we make the first attempt to formulate indoor RME as a collaborative in-context learning (ICL) problem. We propose Poly-Ripple, a framework in which multiple clients perform ICL using large autoregressive vision models (LAVMs) without model updates. Each client is prompted by its private demonstrations

Patronizing and Condescending Language Detection in English and Mandarin

arXiv:2610.31808v1 Announce Type: new Abstract: Patronizing and condescending language (PCL) is a form of harmful language that belittles and assumes pity for vulnerable groups, reinforcing negative societal stereotypes. However, unlike overt hate speech, it is often difficult to detect, even for human judges. This paper proposes a system for the automated detection of PCL. Our first approach uses static word embeddings and a convolutional neural network for classification, while our second approach uses pre-trained BERT embeddings fine-tuned for the PCL task. Our second approach was considerably more successful, and achieved an F1 score of 0.528 over the development set.

The study proposes a new paradigm of fuzzy-aware structured generation driven by graph reasoning.

A paper published on October 26, 2026, on arXiv (ID 2610.31681v1) points out that traditional structured generation treats the mapping from input to output as a deterministic one-to-one function, which conflicts with the inherent multiplicity of semantic validity in natural language. The authors reformulate this as a “fuzzy-perception reasoning problem,” constructing a candidate structure space represented as a graph and performing graph-based reasoning to enhance output stability and robustness while preserving semantic ambiguity.

Why it matters：This study directly addresses the long-neglected defect of ambiguity modeling in structured generation, proposing a verifiable technical approach—multi‑effective learning, graph‑structured candidate space, and semantic alignment coupled with relational consistency reasoning—and reports systematic improvements in accuracy, stability, and robustness on standard benchmarks, demonstrating both methodological innovation and practical guidance value.

$M^3$: A Multi-LLM System for Multi-Turn Jailbreaking as a Markov Decision Process

arXiv:2610.31937v1 Announce Type: new Abstract: Modern Large Language Models (LLMs) remain vulnerable to carefully orchestrated multi-turn interactions that progressively bypass safety mechanisms. However, many existing red-teaming approaches rely on heuristics or impose strong assumptions that limit their applicability in fully black box settings. We address this gap by introducing $M^3$, an automated red-teaming framework that formalizes multi-turn jailbreaking as a finite-horizon Markov Decision Process (MDP). In $M^3$, an attacker LLM interacts with a target LLM while operating over a compact internal state produced by a state LLM. Across two benchmarks and six target models, $M^3$ consistently outperforms state-of-the-art multi-turn red-teaming methods in Attack Success Rates (ASR), a

Does LeWorldModel Preserve Local Action Geometry?

arXiv:2610.31716v1 Announce Type: new Abstract: We examine how closely pretrained LeWorldModel (LeWM) reproduces the effects of small action changes. Using matched simulator and model responses, we measure action sensitivity with Gram matrices at three perturbation scales. Across nearly a thousand states from Reacher and OGBench Cube, the most sensitive action directions differ by median angles of about $36^\circ$ and $42^\circ$, respectively, at the smallest scale. Increasing the perturbation tenfold reduces median normalized Gram discrepancy by roughly 70\% on Reacher and 47\% on Cube. Geometry measured in pixels and embeddings varies more across scales than the chosen physical-state geometry. On held-out directions, environment Grams achieve median embedding response errors of 17\% and

The Path to NDE 4.0: A Review of Algorithms, Techniques, and Strategies for Autonomous Inspection

arXiv:2610.31704v1 Announce Type: new Abstract: Non-Destructive Inspection (NDI) is a cornerstone of modern quality assurance, critical for ensuring the safety and reliability of infrastructure and manufactured components without causing damage. This paper presents a comprehensive review of the current NDI landscape, analyzing the field through four distinct lenses, namely, computational algorithms, physical techniques, industrial applications, and research objectives. We synthesize a broad range of academic literature to illustrate the rapid evolution from manual, analog interpretation to a digitized, data-centric paradigm known as NDE 4.0. Our analysis highlights the dominance of ultrasound and visual inspection as primary physical techniques, while revealing a significant shift toward d

Towards Adversarially Robust Multigraph Neural Networks for Financial Crime Analysis

arXiv:2610.31718v1 Announce Type: new Abstract: Financial transaction networks are naturally represented as multigraphs, where repeated transactions between the same pair of accounts are modeled as distinct edges. Recently proposed multigraph neural network architectures leverage this structure explicitly to achieve a strong performance on anti-money laundering (AML) tasks. However, their robustness to adversaries that manipulate transaction structures to evade detection remains largely unexplored. We present the first scalable framework for structural attacks and adversarial training of multigraph neural networks using node-edge incidence matrices, adapting projected gradient descent and projected randomized block coordinate descent methods to optimize structural perturbations over such m

Autoregressive Multimodal Anomaly Detection with Large Language Models for Microservice Systems

arXiv:2610.31722v1 Announce Type: new Abstract: Microservice systems have become a dominant architecture for large-scale cloud applications, enabling rapid deployment and elastic scaling. However, ensuring the reliability and availability of microservice systems critically relies on effective anomaly detection, as failures and performance anomalies can quickly propagate across services and severely impact end-to-end system behavior. Existing multimodal anomaly detection approaches for microservice systems often struggle with two fundamental limitations: (i) the neglect of modality-specific characteristics in favor of cross-modality fusion, and (ii) the "identity-shortcut" problem that affects the feature reconstruction process. To address these issues, we propose LLMMAD, an autoregressive

PnP*: A certifiable solver for Perspective-n-Point

arXiv:2610.31797v1 Announce Type: new Abstract: Estimating a camera pose from 3D-2D correspondences - Perspective-n-Point - is one of the most fundamental problems in computer vision. Existing methods either solve a minimal problem or replace the objective with an easier one. Surprisingly, no scalable certifiable solver for the actual Maximum Likelihood Estimation (MLE) exists. We propose PnP*, the first practical solver that guarantees efficient computation and returns a certifiable globally optimal solution. We prove that the underlying convex relaxation is exact under moderate noise, which to our knowledge is the first stability guarantee for any relaxation for the MLE of PnP. Furthermore, we reduce the relaxation by exploiting chordal sparsity pattern and quotient ring reduction such t

RetiJev: Fast and Source-Grounded Ophthalmic Decision Models

arXiv:2610.30947v1 Announce Type: new Abstract: Ophthalmic clinical decision support requires fast, accurate, and privacy-preserving inference, yet autoregressive large language models (LLMs) incur substantial latency and computational overhead for decisions among predefined actions. We therefore investigate a decision-oriented alternative to generative reasoning. Specifically, we first introduce RetiJev-Data, a source-grounded ophthalmology corpus comprising 78,158 clinical-decision and diagnosis questions derived from articles in PubMed Central and a MeSH vocabulary covering 531 eye diseases, and 50,074 guideline-grading questions spanning 11 grading systems, along with a 24,662-question evaluation benchmark. Based on this, we develop RetiJev, a family of 0.8B-9B decision models that dir