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AISafetyBenchExplorer: A Metric-Aware Catalogue of AI Safety Benchmarks Reveals Fragmented Measurement and Weak Benchmark Governance

arXiv:2604.12875v1 Announce Type: new Abstract: The rapid expansion of large language model (LLM) safety evaluation has produced a substantial benchmark ecosystem, but not a correspondingly coherent m

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Algorithmic Analysis of Dense Associative Memory: Finite-Size Guarantees and Adversarial Robustness

DGX agent

arXiv:2604.12811v1 Announce Type: cross Abstract: Dense Associative Memory (DAM) generalizes Hopfield networks through higher-order interactions and achieves storage capacity that scales as O(N^{n-1})

researcharxiv-cs-ai

15 Apr 2026

ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods

arXiv:2603.21011v2 Announce Type: replace-cross Abstract: Finite element (FE) analysis guides the design and verification of nearly all manufactured objects. It is at the core of computational enginee

agentsarxiv-cs-ai

All in One: A Unified Synthetic Data Pipeline for Multimodal Video Understanding

arXiv:2604.12335v1 Announce Type: new Abstract: Training multimodal large language models (MLLMs) for video understanding requires large-scale annotated data spanning diverse tasks such as object coun

applicationsarxiv-cs-cv

AlphaEval: Evaluating Agents in Production

model-releasesarxiv-cs-cl

arXiv:2604.12162v1 Announce Type: new Abstract: The rapid deployment of AI agents in commercial settings has outpaced the development of evaluation methodologies that reflect production realities. Exi

An Optimal Sauer Lemma Over k-ary Alphabets

arXiv:2604.12952v1 Announce Type: new Abstract: The Sauer-Shelah-Perles Lemma is a cornerstone of combinatorics and learning theory, bounding the size of a binary hypothesis class in terms of its Vapn

researcharxiv-cs-lg

Analyzing the Effect of Noise in LLM Fine-tuning

model-releasesarxiv-cs-lg

arXiv:2604.12469v1 Announce Type: new Abstract: Fine-tuning is the dominant paradigm for adapting pretrained large language models (LLMs) to downstream NLP tasks. In practice, fine-tuning datasets may

animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics

arXiv:2406.01253v3 Announce Type: replace-cross Abstract: Bioacoustic research, vital for understanding animal behavior, conservation, and ecology, faces a monumental challenge: analyzing vast dataset

AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection

arXiv:2604.11950v1 Announce Type: cross Abstract: While recent LLM-based agents can identify many candidate bugs in source code, their reports remain static hypotheses that require manual validation,

Are Video Reasoning Models Ready to Go Outside?

arXiv:2603.10652v2 Announce Type: replace-cross Abstract: In real-world deployment, vision-language models often encounter disturbances such as weather, occlusion, and camera motion. Under such condit

ARGen: Affect-Reinforced Generative Augmentation towards Vision-based Dynamic Emotion Perception

arXiv:2604.12255v1 Announce Type: cross Abstract: Dynamic facial expression recognition in the wild remains challenging due to data scarcity and long-tail distributions, which hinder models from effec

ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search

arXiv:2604.12762v1 Announce Type: cross Abstract: We introduce ARGOS, the first benchmark and framework that reformulates multi-camera person search as an interactive reasoning problem requiring an ag

ART-VITON: Measurement-Guided Latent Diffusion for Artifact-Free Virtual Try-On

arXiv:2509.25749v2 Announce Type: cross Abstract: Virtual try-on (VITON) aims to generate realistic images of a person wearing a target garment, requiring precise garment alignment in try-on regions a

ArtifactWorld: Scaling 3D Gaussian Splatting Artifact Restoration via Video Generation Models

arXiv:2604.12251v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) delivers high-fidelity real-time rendering but suffers from geometric and photometric degradations under sparse-view constr

applicationsarxiv-cs-cv

Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic

arXiv:2604.12857v1 Announce Type: new Abstract: Autonomous vehicles (AVs) are now operating on public roads, which makes their testing and validation more critical than ever. Simulation offers a safe

agentsarxiv-cs-ai

ASGuard: Activation-Scaling Guard to Mitigate Targeted Jailbreaking Attack

arXiv:2509.25843v2 Announce Type: replace Abstract: Large language models (LLMs), despite being safety-aligned, exhibit brittle refusal behaviors that can be circumvented by simple linguistic changes.

ASTRA: Let Arbitrary Subjects Transform in Video Editing

model-releasesarxiv-cs-cv

arXiv:2510.01186v2 Announce Type: replace Abstract: While existing video editing methods excel with single subjects, they struggle in dense, multi-subject scenes, frequently suffering from attention d

Asymptotically Stable Gait Generation and Instantaneous Walkability Determination for Planar Almost Linear Biped with Knees

arXiv:2604.12274v1 Announce Type: new Abstract: A class of planar bipedal robots with unique mechanical properties has been proposed, where all links are balanced around the hip joint, preventing natu

researcharxiv-cs-ro

Audio Source Separation in Reverberant Environments using eta-divergence based Nonnegative Factorization

arXiv:2604.12480v1 Announce Type: cross Abstract: In Gaussian model-based multichannel audio source separation, the likelihood of observed mixtures of source signals is parametrized by source spectral

Automatic Road Subsurface Distress Recognition from Ground Penetrating Radar Images using Deep Learning-based Cross-verification

arXiv:2507.11081v3 Announce Type: replace-cross Abstract: Ground penetrating radar (GPR) has become a rapid and non-destructive solution for road subsurface distress (RSD) detection. However, recogniz

AutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow

arXiv:2604.11945v1 Announce Type: cross Abstract: High-fidelity numerical simulation of subsurface flow is computationally intensive, especially for many-query tasks such as uncertainty quantification

agentsarxiv-cs-ai

BarbieGait: An Identity-Consistent Synthetic Human Dataset with Versatile Cloth-Changing for Gait Recognition

arXiv:2604.12221v1 Announce Type: new Abstract: Gait recognition, as a reliable biometric technology, has seen rapid development in recent years while it faces significant challenges caused by diverse

applicationsarxiv-cs-cv

BayMOTH: Bayesian optiMizatiOn with meTa-lookahead -- a simple approacH

arXiv:2604.12005v1 Announce Type: cross Abstract: Bayesian optimization (BO) has for sequential optimization of expensive black-box functions demonstrated practicality and effectiveness in many real-w

[b]=[d]-[t]+[p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic

arXiv:2602.18899v3 Announce Type: replace-cross Abstract: Self-supervised speech models (S3Ms) are known to encode rich phonetic information, yet how this information is structured remains underexplor

researcharxiv-cs-cl

BEAM: Bi-level Memory-adaptive Algorithmic Evolution for LLM-Powered Heuristic Design

arXiv:2604.12898v1 Announce Type: new Abstract: Large Language Model-based Hyper Heuristic (LHH) has recently emerged as an efficient way for automatic heuristic design. However, most existing LHHs ju

Benchmarking Deflection and Hallucination in Large Vision-Language Models

arXiv:2604.12033v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) increasingly rely on retrieval to answer knowledge-intensive multimodal questions. Existing benchmarks overlook c

Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving

arXiv:2510.00919v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) with foundation models has achieved strong performance across diverse tasks, but their capacity for exper

Beyond Factual Grounding: The Case for Opinion-Aware Retrieval-Augmented Generation

arXiv:2604.12138v1 Announce Type: new Abstract: RAG systems have transformed how LLMs access external knowledge, but we find that current implementations exhibit a bias toward factual, objective conte

Beyond Majority Voting: Efficient Best-Of-N with Radial Consensus Score

arXiv:2604.12196v1 Announce Type: new Abstract: Large language models (LLMs) frequently generate multiple candidate responses for a given prompt, yet selecting the most reliable one remains challengin

agentsarxiv-cs-cl

Beyond Output Correctness: Benchmarking and Evaluating Large Language Model Reasoning in Coding Tasks

arXiv:2604.12379v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on explicit reasoning to solve coding tasks, yet evaluating the quality of this reasoning remains chall

Beyond Perception Errors: Semantic Fixation in Large Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2604.12119v1 Announce Type: new Abstract: Large vision-language models (VLMs) often rely on familiar semantic priors, but existing evaluations do not cleanly separate perception failures from ru

Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering

arXiv:2604.12210v1 Announce Type: new Abstract: Simulating Standardized Patients with cognitive impairment offers a scalable and ethical solution for clinical training. However, existing methods rely

Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage

arXiv:2603.08819v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks l

Beyond Scores: Diagnostic LLM Evaluation via Fine-Grained Abilities

arXiv:2604.12191v1 Announce Type: new Abstract: Current evaluations of large language models aggregate performance across diverse tasks into single scores. This obscures fine-grained ability variation

Beyond Single-Dimension Novelty: How Combinations of Theory, Method, and Results-based Novelty Shape Scientific Impact

model-releasesarxiv-cs-cl

arXiv:2604.12471v1 Announce Type: cross Abstract: Scientific novelty drives advances at the research frontier, yet it is also associated with heightened uncertainty and potential resistance from incum

Beyond Static Sandboxing: Learned Capability Governance for Autonomous AI Agents

arXiv:2604.11839v1 Announce Type: cross Abstract: Autonomous AI agents built on open-source runtimes such as OpenClaw expose every available tool to every session by default, regardless of the task. A

Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs

arXiv:2604.12506v1 Announce Type: new Abstract: Recent Audio Large Language Models (AudioLLMs) exhibit a striking performance inversion: while excelling at complex reasoning tasks, they consistently u

safetyarxiv-cs-cl

Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia

arXiv:2604.12304v1 Announce Type: new Abstract: Accurate short-term residential energy consumption forecasting at sub-hourly resolution is critical for smart grid management, demand response programme

applicationsarxiv-cs-lg

BID-LoRA: A Parameter-Efficient Framework for Continual Learning and Unlearning

arXiv:2604.12686v1 Announce Type: cross Abstract: Recent advances in deep learning underscore the need for systems that can not only acquire new knowledge through Continual Learning (CL) but also remo

Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem

arXiv:2604.13013v1 Announce Type: new Abstract: This paper tackles the Electric Capacitated Vehicle Routing Problem (E-CVRP) through a bilevel optimization framework that handles routing and charging

Local Ai

BIND-USBL: Bounding IMU Navigation Drift using USBL in Heterogeneous ASV-AUV Teams

arXiv:2604.11861v1 Announce Type: new Abstract: Accurate and continuous localization of Autonomous Underwater Vehicles (AUVs) in GPS-denied environments is a persistent challenge in marine robotics. I

local-aiarxiv-cs-ro

BINDER: Instantly Adaptive Mobile Manipulation with Open-Vocabulary Commands

arXiv:2511.22364v2 Announce Type: replace-cross Abstract: Open-vocabulary mobile manipulation (OVMM) requires robots to follow language instructions, navigate, and manipulate while updating their worl

applicationsarxiv-cs-ai

Bipedal-Walking-Dynamics Model on Granular Terrains

model-releasesarxiv-cs-ro

arXiv:2604.11981v1 Announce Type: new Abstract: Bipeds have demonstrated high agility and mobility in unstructured environments such as sand. The yielding of such granular media brings significant sin

Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks

arXiv:2604.12325v1 Announce Type: cross Abstract: We consider the problem of offline black-box optimization, where the goal is to discover optimal designs (e.g., molecules or materials) from past expe

BLOSSOM: Block-wise Federated Learning Over Shared and Sparse Observed Modalities

arXiv:2603.27552v2 Announce Type: replace Abstract: Multimodal federated learning (FL) is essential for real-world applications such as autonomous systems and healthcare, where data is distributed acr

agentsarxiv-cs-lg

Boosting Robust AIGI Detection with LoRA-based Pairwise Training

arXiv:2604.12307v1 Announce Type: new Abstract: The proliferation of highly realistic AI-Generated Image (AIGI) has necessitated the development of practical detection methods. While current AIGI dete

researcharxiv-cs-cv

Boosting Visual Instruction Tuning with Self-Supervised Guidance

arXiv:2604.12966v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) perform well on many vision-language tasks but often struggle with vision-centric problems that require fine-gr

researcharxiv-cs-cv

BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding

arXiv:2508.18187v2 Announce Type: replace-cross Abstract: Memory decay makes it harder for the human brain to recognize visual objects and retain details. Consequently, recorded brain signals become w