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Amortized Energy-Based Bayesian Inference

arXiv:2605.15407v1 Announce Type: cross Abstract: We consider amortized Bayesian inference for nonlinear inverse problems in settings where only samples from the joint distribution of parameters and o

An Algebraic Exposition of the Theory of Dyadic Morality

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arXiv:2605.16153v1 Announce Type: new Abstract: This paper provides an algebraic exposition of the theory of dyadic morality (TDM), a psychological model of moral judgment grounded in a simple two-nod

safetyarxiv-cs-ai

18 May 2026

An Introduction to Deep Reinforcement and Imitation Learning

arXiv:2512.08052v3 Announce Type: replace-cross Abstract: Embodied agents, such as robots and virtual characters, must continuously select actions to execute tasks effectively, solving complex sequent

safetyarxiv-cs-lg

An LLM-RAG Approach for Healthy Eating Index-Informed Personalized Food Recommendations

arXiv:2605.15213v1 Announce Type: cross Abstract: Diet quality is a leading determinant of chronic disease risk. Advances in artificial intelligence (AI) have enabled food recommendation systems to ad

Antidistillation Fingerprinting

arXiv:2602.03812v2 Announce Type: replace-cross Abstract: Model distillation enables efficient emulation of frontier large language models (LLMs), creating a need for robust mechanisms to detect when

AnyAct: Towards Human Reenactment of Character Motion From Video

model-releasesarxiv-cs-cv

arXiv:2605.15497v1 Announce Type: new Abstract: We study the problem of directly deriving an initial human reenactment from a monocular video of a non-human character. Our goal is not to reconstruct t

AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training

arXiv:2605.15793v1 Announce Type: new Abstract: Pre-training neural operators on diverse partial differential equation (PDE) datasets has emerged as a promising direction for building general-purpose

Local Ai

Approximate and Weighted Data Reconstruction Attack in Federated Learning

arXiv:2308.06822v3 Announce Type: replace-cross Abstract: Federated Learning (FL) is a distributed learning paradigm that enables multiple clients to collaborate on building a machine learning model w

local-aiarxiv-cs-ai

Approximating Global Contact-Implicit MPC via Sampling and Local Complementarity

arXiv:2505.13350v2 Announce Type: replace Abstract: To achieve general-purpose dexterous manipulation, robots must rapidly devise and execute contact-rich behaviors. Existing model-based controllers a

researcharxiv-cs-ro

Are VLMs Seeing or Just Saying? Uncovering the Illusion of Visual Re-examination

arXiv:2605.15864v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) often produce self-reflective statements like 'let me check the figure again' during reasoning. Do such statements trigg

researcharxiv-cs-cl

Argus: Evidence Assembly for Scalable Deep Research Agents

arXiv:2605.16217v1 Announce Type: cross Abstract: Deep research agents have achieved remarkable progress on complex information seeking tasks. Even long ReAct style rollouts explore only a single traj

ART: Articulated Reconstruction Transformer

arXiv:2512.14671v3 Announce Type: replace Abstract: We introduce ART, Articulated Reconstruction Transformer -- a category-agnostic, feed-forward model that reconstructs complete 3D articulated object

researcharxiv-cs-cv

Artificial Aphasias in Lesioned Language Models

arXiv:2605.16222v1 Announce Type: new Abstract: Aphasias, selective language impairments which can arise from brain damage, reveal the functional organization of human language by providing causal lin

researcharxiv-cs-cl

Asking the Right Questions: Improving Reasoning with Generated Stepping Stones

arXiv:2602.19069v2 Announce Type: replace Abstract: Recent years have witnessed tremendous progress in enabling LLMs to solve complex reasoning tasks such as math and coding. As we start to apply LLMs

ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models

arXiv:2605.15687v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) may memorize sensitive cross-modal information during pretraining, making machine unlearning (MU) crucial. Ex

safetyarxiv-cs-ai

AstraFlow: Dataflow-Oriented Reinforcement Learning for Agentic LLMs

arXiv:2605.15565v1 Announce Type: cross Abstract: Reinforcement learning (RL) is increasingly used to improve the reasoning, coding, and tool-use capabilities of large language models, but agentic RL

safetyarxiv-cs-ai

Attention Dispersion in Dynamic Graph Transformers: Diagnosis and a Transferable Fix

arXiv:2605.16112v1 Announce Type: cross Abstract: Transformer-based architectures have become the dominant paradigm for Continuous-Time Dynamic Graph (CTDG) learning, yet their performance remains lim

Attribute-Grounded Selective Reasoning for Artwork Emotion Understanding with Multimodal Large Language Models

arXiv:2605.15755v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can produce fluent artwork emotion explanations, but they often suffer from attribute flooding: they enumerate

agentsarxiv-cs-cv

Autoguided Online Data Curation for Diffusion Model Training

arXiv:2509.15267v2 Announce Type: replace-cross Abstract: The costs of generative model compute rekindled promises and hopes for efficient data curation. In this work, we investigate whether recently

Automatic Construction of a Legal Citation Graph from 100 Million Ukrainian Court Decisions: Large-Scale Extraction, Topological Analysis, and Ontology-Driven Clustering

model-releasesarxiv-cs-cl

arXiv:2605.15362v1 Announce Type: new Abstract: Half a billion citation edges extracted from 100.7 million Ukrainian court decisions reveal that judicial citation structure encodes legal domain bounda

Autonomous Intelligent Agents for Natural-Language-Driven Web Execution with Integrated Security Assurance

arXiv:2605.15281v1 Announce Type: cross Abstract: Modern web test suites rot. A UI refactor breaks locators, a timing change causes race conditions, and within weeks developers abandon the suite entir

agentsarxiv-cs-ai

BAPR: Bayesian amnesic piecewise-robust reinforcement learning for non-stationary continuous control

model-releasesarxiv-cs-lg

arXiv:2605.16170v1 Announce Type: new Abstract: Real-world control systems frequently operate under piecewise stationary conditions, where dynamics remain stable for extended periods before undergoing

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

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

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.

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

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

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

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

Applications

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

Applications

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