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BARRIER: Bounded Activation Regions for Robust Information Erasure

arXiv:2605.15737v1 Announce Type: new Abstract: Machine unlearning has reached a critical bottleneck. As traditional weight-space interventions focus primarily on erasing targeted concepts, they often

researcharxiv-cs-cv

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Human DGX agent

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Belief Engine: Configurable and Inspectable Stance Dynamics in Multi-Agent LLM Deliberation

DGX agent

arXiv:2605.15343v1 Announce Type: new Abstract: LLM-based agents are increasingly used to simulate deliberative interactions such as negotiation, conflict resolution, and multi-turn opinion exchange.

model-releasesarxiv-cs-ai

18 May 2026

Benchmark of Benchmarks: Unpacking Influence and Code Repository Quality in LLM Safety Benchmarks

arXiv:2603.04459v3 Announce Type: replace-cross Abstract: The rapid expansion of research in LLM safety presents challenges in tracking advancements, making benchmarks important evaluation infrastruct

Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processes

arXiv:2602.01295v3 Announce Type: replace Abstract: We investigate episodic Markov Decision Processes with heavy-tailed losses (HTMDPs). Existing approaches for HTMDPs are conservative in stochastic e

safetyarxiv-cs-lg

Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty

arXiv:2507.16806v2 Announce Type: replace-cross Abstract: When language models (LMs) are trained via reinforcement learning (RL) to generate natural language 'reasoning chains', their performance impr

Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise

model-releasesarxiv-cs-lg

arXiv:2605.15314v1 Announce Type: new Abstract: We study nonconvex stochastic optimization under the Blum-Gladyshev (mathsf{BG}-0) noise model, where the stochastic gradient variance grows quadratical

Beyond Collision Avoidance: Multi-Robot Yielding and Spatial Affordance in Emergency Evacuations

arXiv:2605.16115v1 Announce Type: new Abstract: As mobile service robots increasingly coexist with pedestrians, ensuring passively safe behaviour during confined emergency evacuations is critical. Exi

researcharxiv-cs-ro

Beyond Content: A Comprehensive Speech Toxicity Dataset and Detection Framework Incorporating Paralinguistic Cues

arXiv:2605.15984v1 Announce Type: cross Abstract: Toxic speech detection has become a crucial challenge in maintaining safe online communication environments. However, existing approaches to toxic spe

Beyond First-Order: Learning Riemannian Geometries for Invariant Visual Place Recognition

model-releasesarxiv-cs-cv

arXiv:2602.00841v4 Announce Type: replace Abstract: Visual Place Recognition (VPR) demands representations robust to drastic environmental and viewpoint shifts. Existing aggregation paradigms either d

Beyond Forgetting: Machine Unlearning Elicits Controllable Side Behaviors and Capabilities

arXiv:2601.21702v3 Announce Type: replace-cross Abstract: We consider Representation Misdirection (RM), a class of large language model (LLM) unlearning methods that achieve forgetting by redirecting

researcharxiv-cs-cl

Beyond Objective-Based Improvement: Stationarity-Aware Expected Improvement for Bayesian Optimization

arXiv:2601.21357v2 Announce Type: replace Abstract: Bayesian Optimization (BO) is a principled framework for optimizing expensive black-box functions, with Expected Improvement (EI) among its most wid

safetyarxiv-cs-lg

Beyond Partner Diversity: An Influence-Based Team Steering Framework for Zero-Shot Human-Machine Teaming

arXiv:2605.15400v1 Announce Type: new Abstract: While AI agents are rapidly advancing from isolated tools to interactive collaborators, data-driven human-machine teaming (HMT) methods remain costly in

agentsarxiv-cs-ai

Beyond Performance Disparities: A Three-Level Audit of Representational Harm in CelebA

arXiv:2605.15312v1 Announce Type: cross Abstract: Large-scale facial datasets like CelebA are widely used in computer vision, yet the cultural biases embedded in their labels remain underexplored. Fai

safetyarxiv-cs-cv

Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts

arXiv:2510.08008v2 Announce Type: replace Abstract: As the computational demands for pre-training Large Language Models (LLMs) continue to surge, the need for efficient training paradigms becomes crit

researcharxiv-cs-lg

Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation

arXiv:2605.15722v1 Announce Type: cross Abstract: Accurate delineation of electrocardiogram (ECG), the segmentation of meaningful waveform features, is crucial for cardiovascular diagnostics. However,

BioBlobs: Unsupervised Discovery of Functional Substructures for Protein Function Prediction

arXiv:2510.01632v3 Announce Type: replace-cross Abstract: Protein function is driven by cohesive substructures, such as catalytic triads, binding pockets, and structural motifs, that occupy only a sma

BiomedAP: A Vision-Informed Dual-Anchor Framework with Gated Cross-Modal Fusion for Robust Medical Vision-Language Adaptation

arXiv:2605.15736v1 Announce Type: cross Abstract: Biomedical Vision--Language Models (VLMs) have shown remarkable promise in few-shot medical diagnosis but face a critical bottleneck: extit{fragility

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling

arXiv:2507.01679v3 Announce Type: replace-cross Abstract: Existing LLMs-post-training techniques are broadly categorized into supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). Each par

safetyarxiv-cs-ai

BootstrapAgent: Distilling Repository Setup into Reusable Agent Knowledge

arXiv:2605.15815v1 Announce Type: cross Abstract: Code agents increasingly help developers work with unfamiliar repositories, but every such task depends on a costly prerequisite: bootstrapping the re

agentsarxiv-cs-cl

Bounded-Rationality, Hedging, and Generalization

arXiv:2605.15340v1 Announce Type: new Abstract: A learner does not only fit data; it also determines how strongly the training sample may shape its output and how much distortion it can hedge. We stud

researcharxiv-cs-lg

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models

arXiv:2602.04163v2 Announce Type: replace Abstract: Large language model inference is often bounded by memory footprint and bandwidth in resource-constrained deployments, making quantization fundament

researcharxiv-cs-lg

Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG

arXiv:2602.23410v3 Announce Type: replace-cross Abstract: Brain foundation models have achieved remarkable advances across a wide range of neuroscience tasks. However, most existing models are limited

Breakeven complexity: A new perspective on neural partial differential equation solvers

arXiv:2605.15399v1 Announce Type: cross Abstract: Neural surrogate solvers of partial differential equations (PDEs) promise dramatic speedups over numerical methods, especially in scenarios requiring

Hardware

Bridging Silicon and the Hippocampus: Algebro-Deterministic Memory 'VaCoAl' as a Substrate for Vector-HaSH and TEM

arXiv:2605.15652v1 Announce Type: cross Abstract: Vector-HaSH and the Tolman-Eichenbaum Machine (TEM) propose that the hippocampal-entorhinal circuit factorizes content from a prestructured grid-cell

hardwarearxiv-cs-ai

Building Specialized Software-Assistant ChatBot with Graph-Based Retrieval-Augmented Generation

arXiv:2511.05297v2 Announce Type: replace-cross Abstract: Digital Adoption Platforms (DAPs) have become essential tools for helping employees navigate complex enterprise software such as CRM, ERP, or

applicationsarxiv-cs-lg

Calibrating LLMs with Semantic-level Reward

arXiv:2605.15588v1 Announce Type: new Abstract: As large language models (LLMs) are deployed in consequential settings such as medical question answering and legal reasoning, the ability to estimate w

applicationsarxiv-cs-cl

Can Large Language Models Imitate Human Speech for Clinical Assessment? LLM-Driven Data Augmentation for Cognitive Score Prediction

model-releasesarxiv-cs-cl

arXiv:2605.16077v1 Announce Type: new Abstract: Accurate assessment of cognitive decline from spontaneous speech remains challenging due to limited dataset size and class imbalance. In this work, we p

Can Vision Language Models Be Adaptive in Mathematics Education? A Learner Model-based Rubric Study

arXiv:2605.16011v1 Announce Type: cross Abstract: Adaptive learning refers to educational technologies that track learners' learning progress and adapt the instructional process based on individual le

applicationsarxiv-cs-ai

Can We Trust AI-Inferred User States. A Psychometric Framework for Validating the Reliability of Users States Classification by LLMs in Operational Environments

arXiv:2605.15734v1 Announce Type: new Abstract: The use of large language models to assess user states in conversational and adaptive systems is based on the assumption that the metrics used for such

Capability Conditioned Scaffolding for Professional Human LLM Collaboration

arXiv:2605.15404v1 Announce Type: new Abstract: Large language model personalization typically adapts outputs to user preferences and style but does not account for differences in user evaluation capa

researcharxiv-cs-cl

CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning

arXiv:2605.15513v1 Announce Type: new Abstract: Parallel reasoning, where a generator samples many candidate solutions and an aggregator selects the best, is one of the most effective forms of test-ti

CAX-Agent: A Lightweight Agent Harness for Reliable APDL Automation

arXiv:2605.15218v1 Announce Type: new Abstract: Large language models deployed for MAPDL finite-element simulation face practical reliability challenges: without structured execution control, tool enc

Centralized vs Decentralized Federated Learning: A trade-off performance analysis

arXiv:2605.16089v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed edge devices while preserving data pri

CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models

arXiv:2601.21798v2 Announce Type: replace Abstract: Large Language Models(LLMs) have revolutionized text generation and multimodal perception,but their capabilities in 3D content generation remain und

researcharxiv-cs-cv

Local Ai

ChangeFlow -- Latent Rectified Flow for Change Detection in Remote Sensing

arXiv:2605.15375v1 Announce Type: cross Abstract: Remote sensing change detection (RSCD) aims to localise changes between two images of the same geographic region. In practice, change masks often foll

local-aiarxiv-cs-ai

Tutorials

Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis

arXiv:2605.15551v1 Announce Type: new Abstract: Training large-scale deep neural networks (DNNs) is resource-intensive, making model compression a practical necessity. The widely accepted ''learning a

tutorialsarxiv-cs-lg

CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts

arXiv:2605.15888v1 Announce Type: cross Abstract: Heterogeneous Graph Prompt Learning (HGPL)has emerged as a promising paradigm for bridging the gap between the objectives of pre-training foundation m

applicationsarxiv-cs-ai

ChronoEarth-492K: A Large Scale and Long Horizon Spatiotemporal Hyperspectral Earth Observation Dataset and Benchmark

model-releasesarxiv-cs-cv

arXiv:2605.15666v1 Announce Type: new Abstract: Hyperspectral imaging (HSI) provides dense spectral information for the Earth's surface, enabling material-level understanding of land cover and ecosyst

CIS-BWE: Chaos-Informed Speech Bandwidth Extension

arXiv:2507.15970v3 Announce Type: replace-cross Abstract: Recovering high-frequency components lost to bandwidth constraints is crucial for applications ranging from telecommunications to high-fidelit

Local Ai

CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity

arXiv:2605.16000v1 Announce Type: cross Abstract: Editors and reviewers are expected to ensure that manuscripts cite relevant, accurate, current, and ethically appropriate literature, yet manuscript-l

local-aiarxiv-cs-ai

CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion

model-releasesarxiv-cs-lg

arXiv:2601.09512v2 Announce Type: replace-cross Abstract: To teach robots complex manipulation tasks, a common approach is to fine-tune a pre-trained vision-language-action model (VLA) on task-specifi

Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery

arXiv:2605.15938v1 Announce Type: cross Abstract: Finding an odor source in a turbulent flow requires effectively leveraging the history of olfactory observations into a robust navigation strategy. In

agentsarxiv-cs-lg

Tutorials

CM-EVS: Sparse Panoramic RGB-D-Pose Data for Complete Scene Coverage

arXiv:2605.15597v1 Announce Type: new Abstract: Modern 3D visual learning relies on observations sampled from metric 3D assets, yet existing scans, meshes, point clouds, simulations, and reconstructio

tutorialsarxiv-cs-cv

COCO-Inpaint: A Benchmark for Detecting and Localizing Inpainting-Based Image Manipulations

arXiv:2504.18361v2 Announce Type: replace-cross Abstract: Recent advances in image manipulation have enabled highly photorealistic content generation, but also lowered the barrier to arbitrary editing

CodeDistiller: Automatically Generating Code Libraries for Scientific Coding Agents

arXiv:2512.01089v2 Announce Type: replace Abstract: Automated Scientific Discovery (ASD) systems can help automatically generate and run code-based experiments, but their capabilities are limited by t

agentsarxiv-cs-ai

ColPackAgent: Agent-Skill-Guided Hard-Particle Monte Carlo Workflows for Colloidal Packing

arXiv:2605.15625v1 Announce Type: new Abstract: We introduce ColPackAgent, an agent framework that autonomously runs Monte Carlo simulations of colloidal packing through a Model Context Protocol (MCP)

Community-aware evaluation and threshold calibration for open-set plankton image recognition

arXiv:2605.15835v1 Announce Type: new Abstract: Automated plankton image recognition is increasingly used in aquatic ecosystem monitoring, but deployed classifiers inevitably encounter unseen taxa and

researcharxiv-cs-cv

CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs

arXiv:2605.15763v1 Announce Type: cross Abstract: Current state-of-the-art Quality Estimation (QE) in machine translation relies on massive, proprietary LLMs, raising data privacy concerns. We demonst