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Beyond Direct Access: Resource Hijacking in LLM Agents

arXiv:2608.15108v1 Announce Type: cross Abstract: Large language model agents are increasingly connected to high-value resources such as computing infrastructure, credentials, usage budgets, identitie

Beyond Effective Sample Size: Effective Number of Proposals for Adaptive Importance Sampling

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DGX agent

arXiv:2608.15154v1 Announce Type: cross Abstract: Population-based adaptive importance sampling (AIS) methods use a set of proposal densities to approximate complex target distributions. Their perform

researcharxiv-cs-lg

18 Aug 2026

Beyond Field Accuracy: Two-Axis Diagnosis of Inverse-PINN Parameter Error

model-releasesarxiv-cs-lg

arXiv:2608.15373v1 Announce Type: new Abstract: Inverse physics-informed neural networks (PINNs) can reconstruct a field accurately while returning an incorrect physical parameter. We introduce a two-

Tutorials

Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering

arXiv:2608.15757v1 Announce Type: new Abstract: Incomplete multi-view clustering (IMVC) aims to uncover shared cluster structures from data with partially observed views. Although recent imputation-fr

tutorialsarxiv-cs-cv

Beyond L_2: Generalizing Abductive Latent Explanations to Diverse Prototype-Based Architectures

arXiv:2608.16773v1 Announce Type: new Abstract: Prototype-based neural networks are hailed as interpretable-by-design architectures. Recently, Abductive Latent Explanations (ALE) were introduced to pr

safetyarxiv-cs-lg

Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysis

arXiv:2608.15195v1 Announce Type: new Abstract: Vision Foundation Models (VFMs) have emerged as a promising approach in medical imaging, producing broadly applicable systems that can be efficiently ad

safetyarxiv-cs-cv

Beyond Pass@k: Measuring Reliability and Security of Agentic Code Generation

arXiv:2608.14711v1 Announce Type: new Abstract: AI coding agent benchmarks rank agents with the Chen et al. (2021) pass@k estimator, but current implementations misapply it: they set n to the number o

Beyond Peak Backlog: Conditional Energy and Temporal Geometry in Capacity-Constrained Delayed Bandit Optimization

arXiv:2608.16216v1 Announce Type: new Abstract: What is the right delay complexity when a learner can track only C pending feedback items and discarded feedback is permanently lost? Existing one-point

researcharxiv-cs-lg

Beyond Similarity Matching: Structured Reasoning for Open-Vocabulary Referring Segmentation in 3DGS

arXiv:2608.16103v1 Announce Type: new Abstract: Open-vocabulary referring segmentation in 3D Gaussian Splatting (3DGS) requires a neural model to select Gaussian primitives according to free-form lang

safetyarxiv-cs-cv

Beyond Single Object: Learning 3D Relations with Large Language Models

arXiv:2608.15710v1 Announce Type: cross Abstract: We address a fundamental gap in 3D-LLMs: existing models focus on single-object/scene description, struggling with detailed, inter-object comparison.

Beyond the pale: Assessing prevalence and contents of extremist speech in LLM training data

arXiv:2608.14813v1 Announce Type: new Abstract: Despite a strong interest on the part of the research community in the topic of trustworthy and safe AI, the composition of the text corpora that large

researcharxiv-cs-cl

Beyond Thresholds: A Quality-Aware Decision Intelligence Framework for Cold Chain IoT Systems

arXiv:2608.15082v1 Announce Type: new Abstract: Cold chain logistics has advanced technologically, yet most deployed systems remain reactive monitors, not decision-making agents: thresholds trigger al

Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models

arXiv:2608.14797v1 Announce Type: new Abstract: Large language models (LLMs) and large vision-language models (LVLMs) have demonstrated impressive generative capabilities, yet ensuring their outputs a

researcharxiv-cs-cl

Beyond Uncertainty: Generalizable Failure Monitoring for Surgical Segmentation under Acquisition Degradation

arXiv:2608.16748v1 Announce Type: new Abstract: Surgical segmentation networks can fail silently under acquisition degradation: predicted masks may be wrong even when model confidence remains high. Ex

researcharxiv-cs-cv

Tutorials

Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning

arXiv:2608.15869v1 Announce Type: cross Abstract: Multimodal large language models increasingly use visual chain-of-thought (Visual CoT) to reason about spatial, temporal, and embodied environments. B

tutorialsarxiv-cs-ai

Bias-Corrected Ceilings of Emotion Predictability from Human Label Variation Based on Instance-Level Fano Bounds

arXiv:2608.15619v1 Announce Type: new Abstract: Emotion recognition from text keeps improving on benchmarks, yet whether an accuracy ceiling has been reached is seldom asked with discipline. Our aim i

safetyarxiv-cs-ai

Binarized High-Efficiency RAW Video Restoration and Beyond

arXiv:2608.16756v1 Announce Type: new Abstract: RAW video restoration is fundamental to high-quality low-level perception and serves as the basis for a wide range of downstream vision applications. Wh

researcharxiv-cs-cv

Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability

arXiv:2608.15475v1 Announce Type: cross Abstract: Quantized Vision-Language-Action (VLA) models expose a weight-fault surface: Rowhammer-style faults can corrupt deployed INT8 bits. We present the fir

Applications

Bit-Level Triangular Content-Aware Permutation for Fragile Image Watermarking: Zero False Positive Rate, Single-Bit Sensitivity, and Arbitrary Dimension Support

arXiv:2608.14800v1 Announce Type: cross Abstract: With the growth of digital document exchange, protecting image integrity against attacks such as Vector Quantization (VQ) and collage has become criti

applicationsarxiv-cs-cv

Bitstream Action Recognition is Byte Modeling

model-releasesarxiv-cs-cv

arXiv:2608.15695v1 Announce Type: new Abstract: Conventional action recognition typically relies on successful pixel decoding of the bitstream. However, bitstream corruption during storage or transmis

Tutorials

Boundary-Aligned Contribution Routing for Robust Optical--SAR Object Detection

arXiv:2608.15261v1 Announce Type: new Abstract: Optical imagery provides rich appearance cues, whereas synthetic aperture radar (SAR) offers observations that are less sensitive to illumination and we

tutorialsarxiv-cs-cv

Bounded Agents: Delegation Security for Multi-Agent AI Systems

arXiv:2608.15888v1 Announce Type: new Abstract: LLM-based agents can act on behalf of a user to access cloud services, call tools, or invoke agents. At session start, the agent's permissions are set b

agentsarxiv-cs-ai

Bounded Semantic Planning and Deterministic Compilation for Reliable Enterprise Text-to-SQL

arXiv:2608.16663v1 Announce Type: cross Abstract: Direct text-to-SQL asks a language model to do two jobs: interpret the business question and construct the complete relational query. In enterprise sc

BRA-Audit: Budgeted Runtime Auditing for LLM Multi-Agent Systems via Cumulative-Exposure Audit-Point Placement

arXiv:2608.14668v1 Announce Type: cross Abstract: LLM-based multi-agent systems (LLM-MAS) solve complex tasks through specialized collaboration, but inter-agent dependencies can propagate hallucinated

agentsarxiv-cs-ai

BRAID: Learning Equilibrium Maps in Interdependent Security Games via Weight-Tied Iterative Graph Neural Networks

model-releasesarxiv-cs-lg

arXiv:2608.14856v1 Announce Type: cross Abstract: Computing Nash equilibria in interdependent security (IDS) games on networks is computationally expensive: best-response dynamics may need hundreds of

Braided Vision Transformer for Stroke Detection in Multi-view Retinal Fundus Imaging

arXiv:2608.14722v1 Announce Type: new Abstract: Stroke remains a leading cause of mortality and morbidity worldwide, emphasizing the importance of its accurate and immediate assessment. Retinal fundus

researcharxiv-cs-cv

Hardware

BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces

arXiv:2608.15266v1 Announce Type: cross Abstract: Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer'

hardwarearxiv-cs-lg

Breaking and Defending LLM-Powered Social Media Bot Detection Systems

arXiv:2608.15893v1 Announce Type: new Abstract: The rise of social media bots poses a persistent threat, enabling misinformation, opinion manipulation, and the erosion of trust in online platforms. To

Breaking the Compression Barrier: Cross-Architecture Compression Boundary Learning via Reverse Regrowth

model-releasesarxiv-cs-cv

arXiv:2608.16010v1 Announce Type: cross Abstract: Model compression is critical for deploying networks on resource-constrained edge devices. While pruning-based methods can significantly reduce model

Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank

arXiv:2608.16681v1 Announce Type: cross Abstract: Although semi-supervised semantic segmentation (ext{S}^4) utilizes abundant unlabeled data to reduce manual labeling burdens, independent training of

Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching

arXiv:2502.14424v3 Announce Type: replace-cross Abstract: Most self-supervised learning objectives defend against collapse but leave the target representation law unspecified. We formulate representat

Broken Symmetry in LLM Refusal: Answer Release Is More Local Than Refusal Restoration

arXiv:2608.15772v1 Announce Type: new Abstract: When a language model refuses to answer a prompt, it is unclear whether the correct answer is erased from its internal representations, or merely suppre

Budget-Aware Tool Use Enables Effective Agent Scaling

arXiv:2511.17006v2 Announce Type: replace Abstract: Scaling test-time computation has been extended from language model reasoning to tool-augmented agents, where scaling involves not only thinking in

agentsarxiv-cs-ai

Bye-bye, Bluebook? Automating Legal Drudgery With AI-Augmented Rule Following

arXiv:2505.02763v2 Announce Type: replace-cross Abstract: One of the central promises of legal AI is to automate drudgery -- the formal, repetitive tasks of lawyers' work that consume time without cal

CACSurv: Concordance-Aligned Comparative Learning with Large Language Models for Cancer Survival Prediction

arXiv:2608.16594v1 Announce Type: new Abstract: Cancer survival prediction supports treatment planning, risk stratification, and follow-up management. Existing methods use structured clinical variable

CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?

arXiv:2608.16829v1 Announce Type: cross Abstract: Video world models approximate the stochastic distribution of physical outcomes through generative sampling, but existing benchmarks score individual

Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews

arXiv:2509.15035v2 Announce Type: replace Abstract: This study investigates the use of generative AI to support formative assessment through machine generated reviews of peer reviews in graduate onlin

safetyarxiv-cs-ai

Calibrated Trust, Not Sharper Prediction: An Empirical Test of Uncertainty Fusion

arXiv:2608.14617v1 Announce Type: cross Abstract: A recurring proposal in legal AI is to improve case-outcome prediction by fusing uncertainty tools (evidence graphs with belief propagation, sequentia

Calibration-Free Vehicle Speed Estimation: A Monocular Keypoint-Template Approach

arXiv:2608.16785v1 Announce Type: new Abstract: This paper proposes a calibration-free framework for reliably and effectively estimating vehicle speeds from monocular videos, without relying on roadwa

safetyarxiv-cs-cv

Camera-Agnostic Pruning of 3D Gaussian Splats via Descriptor-Based Beta Evidence

arXiv:2603.21933v2 Announce Type: replace-cross Abstract: The pruning of 3D Gaussian splats is essential for reducing their complexity to enable efficient storage, transmission, and downstream process

Can Attribution Predict Risk? From Multi-View Attribution to Planning Risk Signals in End-to-End Autonomous Driving

arXiv:2605.06264v2 Announce Type: replace Abstract: End-to-end autonomous driving models generate future trajectories from multi-view inputs, improving system integration but introducing opaque decisi

agentsarxiv-cs-lg

Can LLMs Reason Like Automated Theorem Provers for Rust Verification? VCoT-Bench: Evaluating via Verification Chain of Thought

arXiv:2603.18334v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) increasingly assist secure software development, their ability to meet the rigorous demands of Rust program ve

Can Neural Networks Learn by Experimenting on Themselves? Self-Interventional Learning from Functional Consequences to Predictive Self-Knowledge

arXiv:2608.14894v1 Announce Type: new Abstract: Machine-learning systems usually model external data, while their internal functional organization is analyzed by external observers. This work introduc

safetyarxiv-cs-lg

Applications

Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT

arXiv:2608.16855v1 Announce Type: new Abstract: Background Lung cancer remains the deadliest cancer worldwide because it is often diagnosed too late. Effective treatment depends on detection at an ear

applicationsarxiv-cs-cv

CAPO: Constraint-Aware Prompt Optimization for LLM Agents

arXiv:2608.16068v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as agents that rely on system prompts to use tools and complete tasks. Such deployments impose

safetyarxiv-cs-ai

CardiacMamba: Fair and Robust RGB-RF Fusion for Remote Heart Rate Estimation via State Space Modeling

arXiv:2608.15831v1 Announce Type: cross Abstract: Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) monitoring from facial videos, but RGB-only methods are vulnerable to illuminat

Catching Hallucinated Citations in Video-LLM Question Answering: A Self-Verification Pipeline and Verifier Ablation Study

arXiv:2608.15574v1 Announce Type: cross Abstract: Video question answering systems built on vision-language models often produce timestamped claims with high confidence even when unsupported by the ci

CBX-Bench: A Human-Aligned MLLM Council for Benchmarking Concept Bottleneck Model Explanations

model-releasesarxiv-cs-cv

arXiv:2608.15404v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) are designed to make visual classification interpretable by expressing predictions through human-understandable concept