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Robust and Efficient Communication for Multi-Agent Learning

arXiv:2609.15361v1 Announce Type: cross Abstract: Effective communication is a cornerstone of distributed intelligence in Multi-Agent Reinforcement Learning (MARL), yet ensuring that generated message

agentsarxiv-cs-ai

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Robust low-rank tensor completion via factorized weighted tensor schatten-p norm minimization

DGX agent

arXiv:2609.14307v1 Announce Type: new Abstract: Low-rank tensor factorization provides a flexible framework for completing multidimensional data from incomplete and corrupted observations. However, un

researcharxiv-cs-cv

15 Sept 2026

Local Ai

Robust Multi-Model Fitting through Learning Neighbor Regions

arXiv:2609.15348v1 Announce Type: new Abstract: Multi-model fitting involves fitting multiple models accurately in a noisy environment. It is the basis for computer vision tasks such as scene reconstr

local-aiarxiv-cs-cv

Rolling Day-Wise Mortality Prediction in Critically Ill Patients With AKI on CRRT Utilizing Machine Pressure Waveforms

arXiv:2609.13524v1 Announce Type: cross Abstract: Critically ill patients with acute kidney injury (AKI) on continuous renal replacement therapy (CRRT) face high mortality, yet current risk assessment

Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures

arXiv:2609.13463v1 Announce Type: new Abstract: The increasing deployment of AI agents in long-horizon tasks yields massive execution logs. Diagnosing failures within these records is crucial for reli

Rotation-Based Subspace Tracking for Robust Kernel PCA on Streaming Data

arXiv:2609.15488v1 Announce Type: new Abstract: Machine learning models process large amounts of data, and Principal Component Analysis (PCA) is a widely used technique to reduce the dimensionality of

safetyarxiv-cs-lg

Route, Don't Fix: Regime-Dependent Decoding Correction and a Trajectory-Gated Router for Reliable Clinical LLM Answer Selection

arXiv:2609.14825v1 Announce Type: cross Abstract: Large language models (LLMs) are often deemed unsafe for clinical question answering because of their tendency to hallucinate. Retrieval augmentation,

ROVER: Robust Loop Closure Verification with Trajectory Prior in Repetitive Environments

model-releasesarxiv-cs-cv

arXiv:2508.13488v2 Announce Type: replace-cross Abstract: Loop closure detection is important for simultaneous localization and mapping (SLAM), which associates current observations with historical ke

RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments

arXiv:2609.15364v1 Announce Type: new Abstract: Digital agents must often adapt to new environments whose interfaces, tools, and failure modes are not fully captured by pretrained models. We introduce

agentsarxiv-cs-ai

Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges

arXiv:2602.13576v2 Announce Type: replace-cross Abstract: Evaluation and alignment pipelines for large language models increasingly rely on LLM-based judges, whose behavior is guided by natural-langua

Runtime-Incremental Transformer for Reinforcement-Learning-Based Adaptive Control

arXiv:2609.13545v1 Announce Type: cross Abstract: Learning-based adaptive control of robotic manipulators with non-observable friction memory has been addressed by attention- based meta-controllers wh

safetyarxiv-cs-lg

RxScribe Bench: A Multi-Axis Benchmark for Evaluating Vision-Language Models on Indian Outpatient Prescriptions

model-releasesarxiv-cs-cv

arXiv:2609.13280v1 Announce Type: new Abstract: Prescription transcription errors are not interchangeable. A model that fabricates a drug and a model that misreads a legible dose pose very different c

S3-Tracker: Self-Supervised Surgical Tissue Tracking With Contrastive Random Walks

arXiv:2609.14313v1 Announce Type: new Abstract: Robust point tracking in endoscopic videos is essential for computer-assisted intervention and autonomous robotic surgery, enabling continuous registrat

agentsarxiv-cs-cv

Safe Meta-Reinforcement Learning via Information Space Reachability

arXiv:2609.15915v1 Announce Type: new Abstract: Meta-reinforcement learning (meta-RL) enables agents to adapt to unseen tasks with limited experience. Despite its promise, the application of meta-RL i

safetyarxiv-cs-lg

Safety as a Constraint: Fine-Tuning a LLM Recommender to Explain Itself

arXiv:2609.13657v1 Announce Type: new Abstract: Traditional recommender systems are typically trained to predict what item users will interact with next, but not why. However, offering personalized ev

safetyarxiv-cs-ai

Safety Signals to Verify NetOps Agents with Action-Level Granularity

arXiv:2609.14422v1 Announce Type: new Abstract: Agentic Network Operations (NetOps) are an emerging paradigm promising to enable workload-aware, self-adjustable, and reliable autonomous networks. Whil

safetyarxiv-cs-ai

SAILOR: Solver-Assisted Interactive LLM-based Optimization Recovery

arXiv:2609.13945v1 Announce Type: new Abstract: Natural-language descriptions of optimization problems may be incomplete or vague about numerical information that a solver requires, including costs, c

Salesforce Koa: An Enterprise Language Model for Agentic Tool Use

arXiv:2609.15066v1 Announce Type: cross Abstract: We present Salesforce Koa, an enterprise language model built by post-training the open-weight Nemotron-3-Super-120B foundation model with reinforceme

SALUTE: Benchmarking and Adapting LLMs for the Defense Domain

model-releasesarxiv-cs-cl

arXiv:2609.15022v1 Announce Type: new Abstract: Defense is a knowledge-intensive domain that requires precise understanding of specialized terminology, doctrinal concepts, operational procedures, and

SAM3D-Part: Interactive Part Selection and Generation from 3D Objects

arXiv:2609.15639v1 Announce Type: new Abstract: Part-level control is essential for modern 3D asset creation, where objects are frequently edited, reused, animated, or fabricated through their individ

safetyarxiv-cs-cv

Same path, different: a mechanistic comparison of looped and stacked transformer encoders on 12-lead ECG

model-releasesarxiv-cs-lg

arXiv:2609.15498v1 Announce Type: new Abstract: Recurrent Transformers reusing their weights rather than stacking L distinct layers are becoming widely adopted due to their parameter efficiency [1,2,3

Same Patient, Different Order: Action-Level Reliability of Clinical LLM Agents Under Repeated Runs

arXiv:2609.13582v1 Announce Type: cross Abstract: A clinical agent benchmark can report the same verdict on identical inputs while the agent files a materially different order on each run. Such agents

Sampling headroom is not selection gain: a compute-value audit of test-time scaling for video world models

arXiv:2609.13257v1 Announce Type: cross Abstract: Test-time scaling (TTS) can improve generation only when additional compute produces better candidates and the system can reliably identify them. This

Applications

SAMReg: SAM-enabled Image Registration with ROI-based Correspondence

arXiv:2410.14083v2 Announce Type: replace Abstract: This paper describes a new spatial correspondence representation based on paired regions-of-interest (ROIs), for medical image registration. The dis

applicationsarxiv-cs-cv

Sanity Checking Causal Representation Learning on a Simple Real-World System

arXiv:2502.20099v3 Announce Type: replace-cross Abstract: We evaluate methods for causal representation learning (CRL) on a simple, real-world system that satisfies the basic problem setup of CRL. The

SAR-FAH: A Frequency-Adaptive Hybrid Network based on Neural ODEs for Structural-Preserving SAR Despeckling

arXiv:2511.05890v2 Announce Type: replace Abstract: Synthetic Aperture Radar (SAR) images are inherently degraded by speckle noise that severely limits their reliability in high-precision applications

researcharxiv-cs-cv

Applications

Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis

arXiv:2609.15681v1 Announce Type: cross Abstract: This paper presents a systematic examination and experimental comparison of the prominent Federated Learning (FL) frameworks FedML, Flower, Substra, a

applicationsarxiv-cs-ai

Scalable partial information decomposition for symptom networks via supervised embeddings

model-releasesarxiv-cs-lg

arXiv:2609.13203v1 Announce Type: new Abstract: Pairwise relationships among mental-health symptoms are routinely summarised asscalar edge weights, which cannot express whether two symptoms carry over

Scaling Hindi Quantum Natural Language Processing through Automatic Pregroup Supertagging

arXiv:2609.13721v1 Announce Type: new Abstract: Quantum Natural Language Processing (QNLP) uses pregroup grammars to translate grammatical structure into diagrammatic representations and quantum circu

researcharxiv-cs-cl

Schizophrenia Detection from EEG Signals: A Transformer Framework with Spectrogram Representation

arXiv:2609.14015v1 Announce Type: new Abstract: Schizophrenia is a serious psychiatric disorder that affects millions of people worldwide, and its diagnosis remains primarily dependent on clinical ass

ScorePrompts: Natural-Language Exploration of Symbolic Music Scores through Analysis

arXiv:2609.13291v1 Announce Type: cross Abstract: We present ScorePrompts, an interactive system in which users upload a score, receive natural-language descriptions of its musical structure, ask ques

researcharxiv-cs-cl

Local Ai

SCOUT-SLAM: Structurally-Coupled Dual Uncertainty-Aware 3DGS SLAM in the Wild

arXiv:2609.14634v1 Announce Type: new Abstract: Recently, 3D Gaussian Splatting SLAM (3DGS-SLAM) has gained significant momentum in simultaneous localization and 3DGS scene reconstruction. In real-wor

local-aiarxiv-cs-cv

SDUs DAISY: A Benchmark for Danish Culture

arXiv:2601.19930v2 Announce Type: replace-cross Abstract: We introduce Daisy, a factual knowledge benchmark for Danish cultural heritage, based on curated topics from the Danish Culture Canon 2006. Fo

SEA-Nav: Efficient Policy Learning for Safe and Agile Quadruped Navigation in Cluttered Environments

arXiv:2603.09460v2 Announce Type: replace Abstract: Efficiently learning safe and agile quadruped navigation in densely cluttered environments remains difficult: existing methods often lack safety and

safetyarxiv-cs-ro

Seeing What the Vehicle Sees: Video-Augmented Virtual Reality for Physical Autonomous Vehicles

arXiv:2609.13224v1 Announce Type: new Abstract: Autonomous vehicles are expected to improve road safety and efficiency, but passengers often remain uncertain about what the vehicle perceives and why i

safetyarxiv-cs-ro

Selecting k Paths with the Minimum Longest Path Length in the Stochastic Semi-Bandit Setting

arXiv:2609.14557v1 Announce Type: new Abstract: When performing parallel data transmission through a network using multiple paths, it is practically important to minimize the maximum transmission time

researcharxiv-cs-lg

Selective Tool Use for Agentic Change Visual Question Answering in Remote Sensing

arXiv:2609.14523v1 Announce Type: new Abstract: Change visual question answering (Change VQA) requires understanding semantic changes across bi-temporal remote sensing images. Although vision language

agentsarxiv-cs-cv

Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement

model-releasesarxiv-cs-ro

arXiv:2609.13236v1 Announce Type: new Abstract: Humanoid robots are becoming an important part of embodied artificial intelligence, driven by advances in reinforcement learning for locomotion, world m

Self-Evolving Memory for Generative Recommendation

arXiv:2609.15598v1 Announce Type: cross Abstract: Generative recommendation has emerged as a promising end-to-end paradigm for personalized recommendation. However, user preferences continuously evolv

Self-Indexing Attention for Compression-Compatible Sparse Long-Context LLM Inference

arXiv:2609.13205v1 Announce Type: cross Abstract: Sparse long-context inference requires efficient token retrieval in both prefill and decode. Existing methods often use different retrieval strategies

researcharxiv-cs-cl

Self-Orchestrating Language Models: Leveraging Semantic Dependence for Efficient Inference

arXiv:2609.14850v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate impressive capabilities, but their deployment presents significant efficiency challenges. Autoregressive decodi

Semantic Fibers and Cross-Gram Interference: A Calculus of Safety Drift in Overcomplete Representations

arXiv:2609.14861v1 Announce Type: cross Abstract: A deployed language model may refuse a harmful request in English yet comply with its faithful translation, revealing a cross-lingual safety failure t

safetyarxiv-cs-ai

Semantic Knowledge Technologies: what the Semantic Web lost sight of, and what it never had

arXiv:2609.14121v1 Announce Type: new Abstract: The Semantic Web set out to give information a machine-interpretable form so that software could integrate and reason over it. Its standards became scie

agentsarxiv-cs-ai

Semantic Privacy Protection with Utility Preservation for 3D Point Clouds

model-releasesarxiv-cs-cv

arXiv:2609.13823v1 Announce Type: new Abstract: Point cloud data face serious semantic privacy risks during acquisition, transmission, and cross-institutional sharing. Existing methods mostly rely on

Semantic-TVM: Structure-Preserving Trustworthy Virtual Memory for Memory-Augmented and Tool-Using Agents

arXiv:2609.15011v1 Announce Type: new Abstract: Memory-augmented and tool-using agents expose exact private values when remote LLMs process retrieved memory, tool actions, and intermediate observation

Applications

Semiotic Relations and Proof Methods: A Cross-Genre Study of Argument Structure with Large Language Models

arXiv:2609.15194v1 Announce Type: new Abstract: When a direct proof of a statement S seems hard or even impossible to obtain, there may exist another statement (or set of statements) S^{*}, somehow re

applicationsarxiv-cs-cl

Sensory Precision Inference for Multimodal Arbitration under Uncertainty

arXiv:2609.15065v1 Announce Type: new Abstract: Autonomous agents operating on multisensory data cannot assume that all sensory modalities remain consistently informative. In real environments, sensor

agentsarxiv-cs-lg

SENTINEL: A Multi-Pathway Architecture for Detecting Living-Off-the-Land APT Attacks on Windows Command Lines

arXiv:2609.14593v1 Announce Type: cross Abstract: Living-Off-the-Land (LOTL) is the dominant evasion technique of Advanced Persistent Threat (APT) actors, exploiting legitimate Windows utilities to co