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arXiv:2609.05736v2 Announce Type: new Abstract: LLM tool agents can be improved without retraining by modifying the runtime harness around a fixed model: prompts, tool interfaces, middleware, state ha

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Beyond Top-k Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents

DGX agent

arXiv:2609.05824v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many

model-releasesarxiv-cs-ai

10 Sept 2026

Characterizing Contention-Induced Reliability Collapse in KV-Cache Timing Side Channels for Multi-Tenant LLM Serving

arXiv:2609.06853v1 Announce Type: cross Abstract: Shared key--value (KV) cache reuse improves large language model (LLM) serving, but it can also create a timing side channel that reveals whether a pr

CIVI: A Framework for Diagnosing Search Agent Failures in Civic Information

arXiv:2609.08094v1 Announce Type: new Abstract: Large Language Models are increasingly deployed in public-sector settings, where incorrect guidance can cause irreversible harm. We introduce CIVI, the

ConvMem: Convolutional Memory for Long-Context Reasoning

arXiv:2609.10441v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have demonstrated impressive capabilities, they often struggle with extremely long contexts due to fixed context li

researcharxiv-cs-cl

Development and Validation of a Physics-Guided Machine Learning Extrapolation Framework Using a Classical Transient Diffusion Benchmark

arXiv:2609.09912v1 Announce Type: new Abstract: Machine learning models used in engineering are typically trained within limited operating ranges, yet reliable predictions are often required beyond th

Local Ai

Do LLMs Make More Mistakes If They Do Not Believe the Input Data?

arXiv:2609.09363v1 Announce Type: new Abstract: Large language models (LLMs) are prone to hallucinating or misinterpreting facts, which impairs their usability in retrieval-augmented generation or dat

local-aiarxiv-cs-cl

ESSA: Evolutionary Strategies for Scalable Alignment

arXiv:2507.04453v4 Announce Type: replace Abstract: Online alignment of large language models (LLMs) is dominated by reinforcement learning from human feedback (RLHF) with gradient-based optimizers su

Evaluating Steering Techniques using Human Similarity Judgments

arXiv:2505.19333v2 Announce Type: replace Abstract: Current evaluations of Large Language Model (LLM) steering techniques focus on task-specific performance, overlooking how well steered representatio

safetyarxiv-cs-ai

Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Tasks

arXiv:2508.04125v2 Announce Type: replace-cross Abstract: The application of Large Language Models (LLMs) is growing in the productive completion of Software Engineering tasks. Yet, studies investigat

Forward-Free LLM Depth Pruning via Weight Redundancy

arXiv:2609.09883v1 Announce Type: new Abstract: Depth pruning reduces large language model (LLM) inference cost by removing complete Transformer blocks. Activation-based methods collect hidden states

researcharxiv-cs-lg

From Representation to Enactment: The ABC Framework of the Translating Mind

arXiv:2511.16811v2 Announce Type: replace Abstract: Building on the third-wave Extended Mind (EM) theory and radical enactivism, this article suggests an alternative to representation-based models of

researcharxiv-cs-cl

GANDR: Claim Auditing for Verifiable Legal Answer Generation

arXiv:2609.10293v1 Announce Type: new Abstract: In high-stakes domains such as legal practice, a language-model answer is only useful to the extent that a reader can verify each claim against the sour

Local Ai

GLOSS: Geometric Local Self-Similarity Learning for Faithful Reference-Guided Texture Fill

arXiv:2608.25461v1 Announce Type: cross Abstract: Using conditional image generators, texture artists can explore many single-view looks for an existing 3D shape. Despite impressive progress, state-of

local-aiarxiv-cs-cv

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

arXiv:2609.10539v1 Announce Type: new Abstract: A research idea may be novel, coherent, and scientifically plausible, yet its proposed method may remain insufficiently specified for faithful implement

Isotropic Embedding Perturbations for Robust Vision Language Encoders

arXiv:2609.10292v1 Announce Type: new Abstract: Data augmentation is fundamental to training modern deep vision and multimodal models. While individual methods, such as RandAug, CutMix, Mixup, RandEra

safetyarxiv-cs-cv

Learning Terrain-Adaptive Humanoid Locomotion on Granular Terrain

arXiv:2609.10286v1 Announce Type: new Abstract: Humanoid locomotion on granular terrain remains a significant challenge due to its complex foot-terrain interaction dynamics that are difficult to model

safetyarxiv-cs-ro

Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs

arXiv:2609.10333v1 Announce Type: new Abstract: Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-fre

safetyarxiv-cs-cv

Learning transferable human physiology from two million hours of sleep with SleepFM-2

arXiv:2609.06849v1 Announce Type: new Abstract: Sleep provides a nightly window into health by capturing coordinated activity across the brain, heart, muscles and respiratory system. We introduce Slee

researcharxiv-cs-ai

LightMedSeg-ISLES: Stroke Lesion Segmentation with 81x Fewer Parameters than nnU-Net

arXiv:2609.09634v1 Announce Type: new Abstract: Large networks and ensembles often lead medical image segmentation challenges, but their storage and inference demands complicate deployment. We present

Multi-dimensional Preference Alignment by Conditioning Reward Itself

arXiv:2512.10237v2 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback has emerged as a standard for aligning diffusion models. However, we identify a fundamental limitation in

safetyarxiv-cs-cv

Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering

arXiv:2609.09973v1 Announce Type: new Abstract: Evaluating video captioning remains a critical challenge for Visual Large Language Models (VLLMs). Existing metrics primarily rely on matching generated

Router Prior Bias: Preserving Base Routing Structure in MoE Post-Training

arXiv:2609.08115v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) pretraining relies on an auxiliary load-balancing loss (LBL) to drive per-expert utilization toward uniformity. Post-training i

Same Values, Different Languages? From Multilingual Probing to Steering LLMs Toward Chinese Social Values

arXiv:2609.08515v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly integrated into human society, aligning them with pluralistic social values has become a critical pri

safetyarxiv-cs-ai

Scratchy: Visual-Scratchpad Multimodal Reasoning for Cryptographic Proof Generation in EasyCrypt

arXiv:2609.06226v1 Announce Type: cross Abstract: Large language models (LLMs) have recently made substantial progress in formal proof generation, yet presenting distinctive challenges in cryptographi

Staying on the Attack Path: Structured State for Long-Horizon Automated Penetration Testing

arXiv:2609.07344v1 Announce Type: cross Abstract: Large language model (LLM) based agents are increasingly applied to cybersecurity tasks such as vulnerability discovery and automated penetration test

Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks

arXiv:2609.09233v1 Announce Type: cross Abstract: How can language model agents effectively leverage libraries of reusable knowledge to solve long-horizon tasks? Recent work has increasingly focused o

SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction

arXiv:2609.06229v1 Announce Type: cross Abstract: Vulnerability discovery is becoming an important ability of large language model (LLM) agents: agents that silently miss real defects leave critical s

Towards a Resilience-Theoretic Foundation for Adversarial Robustness in Industrial Control System Anomaly Detection

arXiv:2609.07244v1 Announce Type: cross Abstract: Anomaly-based intrusion detection systems in industrial control systems (ICS) and operational technology (OT) environments are increasingly required t

Typed Federated Artifacts for the Agentic Web:Sharing Tool-Routing Knowledge Across Frozen,Heterogeneous LLM Agents

arXiv:2609.06815v1 Announce Type: cross Abstract: An open, networked web will allow agents to run frozen models from multiple vendors, keep their history private, and teach each other which tool to ca

When Does Defendant Statement Matter? A Study of Bias and Persuasion in LLM-Simulated Jurors

arXiv:2609.09887v1 Announce Type: new Abstract: LLMs have been used to simulate human decision-making in professional settings, yet their behaviors in common-law jury trials remain unexplored. We stud

Where Does the Sound Go? Tracing Acoustic Information Loss in Audio-Conditioned LLMs

arXiv:2609.05871v1 Announce Type: cross Abstract: Audio-conditioned language models often underuse acoustic cues such as prosody, emotion, and non-speech sounds, raising the question of whether ASR-su

safetyarxiv-cs-ai

XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions?

arXiv:2609.06842v1 Announce Type: cross Abstract: When non-expert users ask LLMs for assistance, their queries can often have misconceptions (e.g., 'How do I parse XML with regex?'). In such cases, of

A Quantitative Evaluation Framework for Temporal Explainability in Echocardiographic Video Segmentation

arXiv:2609.08043v1 Announce Type: new Abstract: Deep learning has achieved state-of-the-art performance in echocardiographic video segmentation, with an increasing number of models incorporating tempo

researcharxiv-cs-cv

Asymptotically-informed neural networks for Black-Scholes implied volatility computation

arXiv:2609.05491v1 Announce Type: cross Abstract: The computation of Black-Scholes implied volatility is a fundamental task in quantitative finance, underpinning option valuation, model calibration an

BanglaMemeX: Advancing Cultural Metaphoric Image Interpretation in Bangla with a Multimodal Explainable Dataset

arXiv:2609.08029v1 Announce Type: cross Abstract: Vision Language Models have achieved strong performance on multimodal benchmarks, yet their ability to reason about culturally grounded and metaphor-r

Condition aware learning enables robust prediction of oligonucleotide melting behavior across diverse chemistries and assay conditions

arXiv:2609.05454v1 Announce Type: cross Abstract: Oligonucleotide melting temperature is a fundamental determinant of nucleic acid hybridization and underpins the design of molecular diagnostics, poly

CrossModalQA: A Cross-modal and Multi-hop Benchmark for Multimodal Retrieval-augmented Generation

arXiv:2609.05518v1 Announce Type: new Abstract: Despite the strong capabilities of multimodal large language models (MLLMs), their parametric knowledge remains incomplete and difficult to update, moti

Deep learning from the crowd Fundamentals of morphological galaxy classification

arXiv:2609.06316v1 Announce Type: cross Abstract: Aims. The objective of this work is to adapt a deep neural network model to perform galaxy morphological classification trained from crowd annotations

researcharxiv-cs-cv

Do Quantum AIs Dream in Paths? Path-Integral Slow Thinking through Grover Interference

arXiv:2609.05842v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards enables large language models to think slowly, but the same training can induce policy collapse: probab

Do Reasoning Representations Help Humans Evaluate LLM Outputs?

arXiv:2609.09038v1 Announce Type: new Abstract: Reasoning representations are increasingly used as explanations for large language model outputs. Yet they are typically evaluated with model-centric cr

researcharxiv-cs-lg

EgoSIS: From Factorized Visual Ego-Transitions to Motion-Canonical Spatial Evidence for UAV Reasoning

arXiv:2609.08938v1 Announce Type: new Abstract: UAV video question answering requires separating camera motion from changes in the scene, but RGB-only multimodal models receive no explicit, stable ref

Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification

arXiv:2609.07403v1 Announce Type: new Abstract: Privacy-sensitive surveillance systems could benefit from large vision-language models (VLMs), but such models typically require centralized access to r

researcharxiv-cs-cv

FedSubMuon: Communication-Efficient Federated LLM Fine-Tuning via Structured Subspace Muon

arXiv:2609.06073v1 Announce Type: new Abstract: Federated fine-tuning adapts large language models (LLMs) to decentralized client data, but its scalability in cross-device training is often limited by

FMMO: Detecting the Divergence Between Local Attribution and Global Drift

arXiv:2609.06173v1 Announce Type: new Abstract: Post-deployment drift poses a critical risk to algorithmic accountability, particularly when ground truth labels are delayed and performance degradation

From Glance to Scrutiny: Progressive Distortion Reasoning for Fine-Grained Image Quality Assessment

arXiv:2609.08316v1 Announce Type: new Abstract: Multi-modal large language models (MLLMs) have demonstrated significant potential in image quality assessment (IQA) by bridging visual perception with d

Geometry-Aware Bayesian Parameter-Efficient Fine-Tuning on the Stiefel Manifold via Stein Variational Gradient Descent

arXiv:2609.08354v1 Announce Type: new Abstract: Several geometry-aware approaches to low-rank adaptation have emerged for parameter-efficient fine-tuning of large pre-trained models. These methods aim

GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning

model-releasesarxiv-cs-ro

arXiv:2609.05927v1 Announce Type: new Abstract: Robot manipulation foundation models require scalable evaluation and data generation across diverse scenarios, with simulation providing an environment