Search: “models”

Preferred, Not Safer: Pairwise Preference Is a Poor Proxy for Clinical Safety

arXiv:2608.02617v1 Announce Type: cross Abstract: We evaluate whether clinician pairwise preferences provide a reliable signal of clinical safety in large language model (LLM) evaluation using expert

safetyarxiv-cs-ai

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Research

PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks

DGX agent

arXiv:2512.12840v2 Announce Type: replace-cross Abstract: Vertical Federated Learning (VFL) enables collaborative model training across organizations that share common user samples but hold disjoint f

researcharxiv-cs-ai

5 Aug 2026

ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning

arXiv:2608.03972v1 Announce Type: new Abstract: On-policy training has emerged as a powerful post-training paradigm for improving the reasoning capabilities of large language models, and is often enha

safetyarxiv-cs-ai

Relational Priors as Convergence Pressure in LLM-Based Multi-Agent Systems

arXiv:2608.03239v1 Announce Type: new Abstract: Large language model-based multi-agent systems (LLM-MAS) are designed through roles, debate protocols, and aggregation rules. These choices create impli

safetyarxiv-cs-cl

Rethinking Uncertainty Quantification and Entanglement in Image Segmentation

arXiv:2603.18792v2 Announce Type: replace Abstract: Uncertainty quantification (UQ) is crucial in safety-critical applications such as medical image segmentation. Total uncertainty is typically decomp

safetyarxiv-cs-cv

SAMSEM -- A Generic and Scalable Approach for IC Metal Line Segmentation

arXiv:2603.16548v2 Announce Type: replace-cross Abstract: In light of globalized hardware supply chains, the assurance of hardware components has gained significant interest, particularly in cryptogra

Scaling an Autoregressive Transformer for Single-Cell Generation

arXiv:2608.02961v1 Announce Type: cross Abstract: We study a self-supervised generation task for single-cell gene expression vectors: given a set of vectors from a cell type, we aim to generate additi

researcharxiv-cs-ai

SKILL-KD: Contrastive Skill Distillation for LLM Agents

arXiv:2607.28048v2 Announce Type: replace Abstract: Skill-based prompting has become a practical mechanism for improving large language model (LLM) agents, yet existing skill acquisition methods often

local-aiarxiv-cs-ai

SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents

arXiv:2608.02356v2 Announce Type: replace Abstract: Large language model agents increasingly solve complex tasks by composing reusable skills from a library. To address this, the key challenge is not

researcharxiv-cs-ai

SMOPD: Multi-Reward Reinforcement Learning via Specialize-and-Merge Online Policy Distillation

arXiv:2608.03092v1 Announce Type: cross Abstract: We aim to improve model performance in multi-reward reinforcement learning training process. Existing Group reward-Decoupled Normalization Policy Opti

safetyarxiv-cs-ai

SparSEEty: Extracting Tokens from Sparsity-Exploiting LLM Serving Systems via Deterministic Side Channels

arXiv:2608.02995v1 Announce Type: cross Abstract: Modern large language models (LLMs) exhibit activation sparsity, wherein only a subset of their neurons is activated for given input tokens. Researche

researcharxiv-cs-ai

Spatial proteomics guided by H&E-based AI reveals recurrence-risk niches in triple-negative breast cancer

arXiv:2608.03145v1 Announce Type: new Abstract: Deep learning models can predict cancer recurrence from H&E stained slides, but the localized molecular states underlying these predictions remain large

local-aiarxiv-cs-ai

Sphere Retraction Normalizations

arXiv:2608.02668v1 Announce Type: cross Abstract: Residual connections are the de facto mechanism for training deep neural networks stably. Geodesic Normalization (GeoNorm) recasts them on a Riemannia

Agents

Steganalysis of Adaptive Covert Collusion in Tool-Using Agent Populations: A Black-Box, Cross-Principal Approach

arXiv:2608.02698v1 Announce Type: cross Abstract: Tool-using agents built on large language models (LLMs) are increasingly deployed not by a single operator but by many, side by side on shared infrast

agentsarxiv-cs-ai

Stop Replacing Noise with Noise: Two-Source Reliability Assessment for Label Correction and Sample Reweighting in Label-Noise Learning

arXiv:2608.03432v1 Announce Type: cross Abstract: Refurbishment-based noisy-label learning mixes an observed label with a model-derived pseudo target, typically using one sample-wise cleanliness score

Tutorials

Style-Aware Gloss Control for Generative Non-Photorealistic Rendering

arXiv:2602.16611v3 Announce Type: replace-cross Abstract: Humans can infer material characteristics of objects from their visual appearance, and this ability extends to artistic depictions, where simi

tutorialsarxiv-cs-cv

Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks

arXiv:2608.03319v1 Announce Type: cross Abstract: Future integrated sensing and communication (ISAC) architectures separate the sensing entity (SE) that acquires measurements from the sensing function

Agents

TraceCAD: Trace-Guided Repair for Agentic CAD Generation

arXiv:2608.03062v1 Announce Type: new Abstract: LLM-based CAD agents produce executable parametric programs, but their correction loops may lose evidence about satisfied requirements, faulty operation

agentsarxiv-cs-ai

Trajectory inference via Acceleration Matching

arXiv:2608.03916v1 Announce Type: new Abstract: Trajectory inference is a fundamental problem in many scientific domains: given a collection of unpaired snapshots of observations at discrete time poin

TumorBoard: Evidence-Grounded Multi-Agent Decision Support for Longitudinal Neuro-Oncology

arXiv:2608.03190v1 Announce Type: new Abstract: Neuro-oncology decisions require coordinated interpretation of serial MRI, pathology, molecular markers, treatment history, performance status, and evol

Agents

Verified Tool Calls Improve LLM Agent Reliability Under Non-Atomic Failures

arXiv:2608.02645v1 Announce Type: cross Abstract: Large Language Model (LLM) agents rely on external tools to perform multistage tasks. Existing agent frameworks typically assume that tool calls are a

agentsarxiv-cs-ai

Vulnerabilities, Secrets and Misconfiguration in the Highest-Exposure Docker Hub Images

arXiv:2608.02669v1 Announce Type: cross Abstract: Docker Hub is the registry underneath most container deployments, and a flaw in a widely reused base image is inherited by every image built on it. Pr

WeClawArena: An Auditable Sandbox and Benchmark for Cross-User Agents Collaboration and Security in Human-Centered Agent Networks

arXiv:2608.03499v1 Announce Type: new Abstract: Recent advances in persistent personal-agent frameworks are making human-centered agent networks realistic deployment targets: each user can be served b

When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills

model-releasesarxiv-cs-cl

arXiv:2608.03700v1 Announce Type: cross Abstract: Persona skills distill personal interaction histories into portable and executable artifacts for downstream agents. While enabling flexible personaliz

When Do Fewer Visual Tokens Accelerate Multimodal Inference? A Break-Even Study Across Decision Locations and Hardware

model-releasesarxiv-cs-cv

arXiv:2608.03649v1 Announce Type: new Abstract: Fewer visual tokens do not guarantee lower end-to-end latency. We evaluate break-even with a reproducible protocol that accounts for decision overhead,

When Memory Becomes Authority: Benchmarking Authority Collapse at the Memory Consolidation Boundary

arXiv:2608.01679v2 Announce Type: replace Abstract: Persistent memory allows (self-evolving) LLM agents to adapt across tasks by consolidating heterogeneous interaction histories into reusable facts,

Where Did It Go Wrong? Process-Level Evaluation of Web Agents with Semantic State Tracking

arXiv:2606.15673v2 Announce Type: replace Abstract: Web agents act through long interaction sequences, yet existing benchmarks evaluate only terminal success, discarding all process information and of

XiDepth: a Lightweight and Efficient Network for Self-supervised Monocular Depth Estimation

arXiv:2608.03666v1 Announce Type: new Abstract: Self-supervised monocular depth estimation has emerged as an appealing solution to design lightweight and effective models for deployment on computation

A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks

arXiv:2608.02274v1 Announce Type: cross Abstract: Electric Vehicle Charging Systems (EVCSs) are increasingly connected with Internet of Things (IoT) devices, which improves charging intelligence but a

researcharxiv-cs-lg

A Triple-Robustness Analysis of Retrieval-Augmented Generation for Multi-Hop Requirements Traceability

model-releasesarxiv-cs-cl

arXiv:2608.00705v1 Announce Type: cross Abstract: Reported verdicts on GraphRAG versus vector RAG disagree, and the evidence is typically tied to a single corpus, embedder, and judge -- and, we show,

Action Chunk Scheduling for Batched Robot Policy Serving

arXiv:2608.00337v1 Announce Type: new Abstract: Deploying robot foundation models at scale is the next step towards realizing the potential of general-purpose robots. However, Vision-Language-Action (

local-aiarxiv-cs-ro

Action-grounded tissue affordance enables anticipatory auto-framing that lowers surgeon cognitive workload during laparoscopic surgery

arXiv:2608.02471v1 Announce Type: new Abstract: Computational attention models could help surgeons manage the visual demands of laparoscopy, but they require dense spatial labels that are difficult to

AdvPlan-Bench: Adversarial Evaluation of Structured Plan-Generation Agents

arXiv:2608.00832v1 Announce Type: new Abstract: Structured plan-generation agents are often evaluated as if a plan has quality in isolation, yet many realistic planning tasks require asking how a cand

AeroLLE: Constrained Pseudo-Supervision for Nighttime Aerial Image Enhancement with the AeroNight-1.5K Benchmark

model-releasesarxiv-cs-cv

arXiv:2608.00702v1 Announce Type: new Abstract: Nighttime aerial image enhancement is challenged by spatially nonuniform exposure, mixed illumination, and weak structural evidence, while registered no

AgentMemBench: A Systematic Benchmark for Evaluating Long-Term Memory Management Strategies in Conversational AI Agents

model-releasesarxiv-cs-cl

arXiv:2608.00009v1 Announce Type: new Abstract: Long-term memory remains a critical bottleneck for conversational AI agents, whose finite context windows cannot support coherent recall across thousand

AirSplat: Alignment and Rating for Robust Feed-Forward 3D Gaussian Splatting

arXiv:2603.25129v2 Announce Type: replace Abstract: While 3D Vision Foundation Models (3DVFMs) have demonstrated remarkable zero-shot capabilities in visual geometry estimation, their direct applicati

local-aiarxiv-cs-cv

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery

arXiv:2608.01906v1 Announce Type: new Abstract: Rapid and accurate post-disaster building damage assessment is essential, yet remains a challenging task. Unmanned Aerial Vehicle (UAV) imagery offers a

Averaging Bias: Human Faithfulness Annotations are not Locally Faithful

arXiv:2608.00205v1 Announce Type: new Abstract: Evaluation of faithfulness of text summarization treats a model generated summary as faithful only if every of its sentences is supported by the source

safetyarxiv-cs-cl

Benchmarking Sheaf Neural Networks for Inductive Tasks

arXiv:2608.02558v1 Announce Type: new Abstract: Sheaf Neural Networks (SNNs) generalize message passing by replacing scalar edge weights of standard Graph Neural Networks (GNNs) with learnable, edge-d

Beyond Modern Asymptotics for Log-Likelihood Ratios in Logistic Regression

arXiv:2608.02507v1 Announce Type: cross Abstract: We characterize the finite sample behavior of the log-likelihood ratio statistic in binary logistic regression, uniformly over both the design and the

BiKAN: Restoring Collapsed Basis of Binary Kolmogorov--Arnold Networks

arXiv:2608.01490v1 Announce Type: new Abstract: Binarizing a polynomial Kolmogorov--Arnold Network (KAN) not only changes parameter precision, but also alters the function space available to each laye

BRiG-AFA: Bellman Risk-to-Go Learning for Non-Myopic Active Feature Acquisition

arXiv:2608.02305v1 Announce Type: new Abstract: Active feature acquisition (AFA) asks which unobserved feature to measure next for each test instance under a budget. Greedy rules are easy to train but

CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

arXiv:2509.06419v2 Announce Type: replace Abstract: Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability. In production, streams of Key Performance Indicat

CAVE: Competence-Aware Visual Boundary Evidence Alignment for Video Temporal Grounding

arXiv:2608.02078v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have achieved substantial performance gains in Video Temporal Grounding (VTG) through reinforcement learning (RL).

safetyarxiv-cs-cl

Celty: SpMspV GPU Kernel and SIMT Co-Design for Efficient Dual-Sparse LLM Inference

arXiv:2608.01536v1 Announce Type: cross Abstract: Large Language Models (LLMs) increasingly rely on sparsity to reduce inference cost, but most prior work targets a single sparsity source-either weigh

local-aiarxiv-cs-lg

ChainVLA: Chaining Vision-Language-Action Queries through a Unified Execution State for Long-Horizon Manipulation

model-releasesarxiv-cs-ro

arXiv:2608.02326v1 Announce Type: new Abstract: Humans perform long-horizon manipulation by retaining knowledge of what earlier actions have established while continuously adapting the motion underway

Chart Specification: Structural Representations for Incentivizing VLM Reasoning in Chart-to-Code Generation

arXiv:2602.10880v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have shown promise in generating plotting code from chart images, yet achieving structural fidelity remains challengin

researcharxiv-cs-cv

ChordVideo: One-Step, Training-Free, Temporally Consistent Video Editing via Low-Energy Transport

arXiv:2608.00769v1 Announce Type: new Abstract: One-step text-to-image models enable training-free, inversion-free editing with only 1--2 network function evaluations (NFE), while ChordEdit stabilizes

safetyarxiv-cs-cv