Search: “benchmark”

TADA! Tuning Audio Diffusion Models through Activation Steering

arXiv:2602.11910v2 Announce Type: replace-cross Abstract: Audio diffusion models can synthesize high-fidelity music from text, yet achieving fine-grained control over specific musical attributes remai

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Tail Annealing for Heavy-Tailed Flow Matching

DGX agent

arXiv:2605.20068v1 Announce Type: cross Abstract: Standard generative models struggle with heavy-tailed data: Lipschitz architectures cannot produce power-law tails from Gaussian noise, and interpolat

model-releasesarxiv-cs-lg

20 May 2026

Target-Aligned Reinforcement Learning

arXiv:2603.29501v2 Announce Type: replace-cross Abstract: Many value-based deep reinforcement learning algorithms rely on target networks - lagged copies of the online network - to stabilize training.

Targeted Downstream-Agnostic Attack

arXiv:2605.19446v1 Announce Type: cross Abstract: Recently, pre-trained encoders have gained widespread use due to their strong capability in representation extraction. However, they are vulnerable to

The Growing Pains of Frontier Models: When Leaderboards Stop Separating and What to Measure Next

arXiv:2605.18840v1 Announce Type: cross Abstract: Leaderboards rank frontier models on independent axes but do not reveal whether capabilities reinforce or trade off across releases -- and at the fron

Theory-optimal Quantization Based on Flatness

arXiv:2605.18800v1 Announce Type: cross Abstract: Post-training quantization has emerged as a widely adopted technique for compressing and accelerating the inference of Large Language Models (LLMs). T

Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?

model-releasesarxiv-cs-cl

arXiv:2605.19196v1 Announce Type: new Abstract: Deep research agents increasingly automate complex information-seeking tasks, producing evidence-grounded reports via multi-step reasoning, tool use, an

Agents

Toward Training Superintelligent Software Agents through Self-Play SWE-RL

arXiv:2512.18552v2 Announce Type: replace-cross Abstract: While current software agents powered by large language models (LLMs) and agentic reinforcement learning (RL) can boost programmer productivit

agentsarxiv-cs-ai

Trust or Abstain? A Self-Aware RAG Approach

model-releasesarxiv-cs-cl

arXiv:2605.18792v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves large language models (LLMs) by incorporating external evidence, but it also introduces knowledge confli

Unlocking the Potential of Continual Model Merging: An ODE Perspective

arXiv:2605.19409v1 Announce Type: cross Abstract: Continual Model Merging (CMM) enables rapid customization of foundation models across sequentially arriving tasks, offering a scalable alternative to

What Makes a Representation Good for Single-Cell Perturbation Prediction?

arXiv:2605.19343v1 Announce Type: new Abstract: Single-cell perturbation modeling is fundamental for understanding and predicting cellular responses to genetic perturbations. However, existing approac

3DPhysVideo: Consistency-Guided Flow SDE for Video Generation via 3D Scene Reconstruction and Physical Simulation

arXiv:2605.16795v1 Announce Type: cross Abstract: Video generative models have made remarkable progress, yet they often yield visual artifacts that violate grounding in physical dynamics. Recent works

4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2605.18074v1 Announce Type: new Abstract: We present 4DLidarOpen, a large-scale open multi-modal dataset for autonomous driving, centered on 4D frequency-modulated continuous-wave (FMCW) Lidar s

A Hybrid Gaussian Process Regression Framework for Stable Volatility-Covariance Estimation: Evidence from Global Equity Indices

arXiv:2605.17275v1 Announce Type: cross Abstract: Accurate forecasting of the Volatility-Covariance Matrix (VCV) is central to regulatory capital adequacy processes such as the Internal Capital Adequa

A More Word-like Image Tokenization for MLLMs

arXiv:2605.17954v1 Announce Type: cross Abstract: Modern multimodal large language models (MLLMs) typically keep the language model fixed and train a visual projector that maps the pixels into a seque

researcharxiv-cs-ai

A Systematic Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation

arXiv:2605.17131v1 Announce Type: cross Abstract: Point cloud stands as the most widely adopted format for representing 3D shapes and scenes due to its simplicity and geometric fidelity. However, its

researcharxiv-cs-ai

A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows

arXiv:2605.17808v1 Announce Type: new Abstract: We develop a unified theoretical framework for data-free one-step sampling from unnormalized target distributions based on Wasserstein gradient flows. F

researcharxiv-cs-lg

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks

arXiv:2504.14820v2 Announce Type: replace Abstract: For peg-in-hole tasks, humans rely on binocular visual perception to locate the peg above the hole surface and then proceed with insertion. This pap

safetyarxiv-cs-ro

ACE: Self-Evolving LLM Coding Framework via Adversarial Unit Test Generation and Preference Optimization

arXiv:2605.16299v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at code generation but remain heavily reliant on large-scale annotated solutions and verification-based supervision

researcharxiv-cs-ai

Agents

Action-Gradient Monte Carlo Tree Search for Non-Parametric Continuous (PO)MDPs

arXiv:2503.12181v4 Announce Type: replace Abstract: Online planning in continuous state, action, and observation spaces remains challenging for autonomous systems. While Monte Carlo Tree Search (MCTS)

agentsarxiv-cs-ai

Activation Steering with a Feedback Controller

arXiv:2510.04309v3 Announce Type: replace Abstract: Controlling the behaviors of large language models (LLM) is fundamental to their safety alignment and reliable deployment. However, existing steerin

safetyarxiv-cs-lg

Tutorials

AdaGraph: A Graph-Native Clustering Algorithm That Overcomes the Curse of Dimensionality and Enables Scientific Discovery

arXiv:2605.16320v1 Announce Type: cross Abstract: We present AdaGraph, a graph-native clustering algorithm born from the Structure-Centric Machine Learning (SC-ML) paradigm -- a new field of unsupervi

tutorialsarxiv-cs-ai

Adaptive Camera Sensor for Vision Models

arXiv:2503.02170v2 Announce Type: replace-cross Abstract: Domain shift remains a persistent challenge in deep-learning-based computer vision, often requiring extensive model modifications or large lab

Adaptive Fused Prior Transfer for Controllable Generative Image Compression

arXiv:2605.16817v1 Announce Type: cross Abstract: Learned image compression has achieved competitive rate-distortion performance, but very-low-bitrate reconstruction remains difficult because the tran

ADR: An Agentic Detection System for Enterprise Agentic AI Security

arXiv:2605.17380v1 Announce Type: new Abstract: We present the Agentic AI Detection and Response (ADR) system, the first large-scale, production-proven enterprise framework for securing AI agents oper

Advancing Narrative Long Video Generation via Training-Free Identity-Aware Memory

arXiv:2605.18733v1 Announce Type: new Abstract: Autoregressive video generation has improved rapidly in visual fidelity and interactivity, but it still suffers from long-term inconsistency and memory

Adversarial Agent Collaboration for Correctness Improvements of C to Safe Rust Translation

arXiv:2510.03879v3 Announce Type: replace-cross Abstract: Translating C to memory-safe languages, like Rust, prevents critical memory safety vulnerabilities that are prevalent in legacy C software. Ev

AgentSteerTTS: A Multi-Agent Closed-Loop Framework for Composite-Instruction Text-to-Speech

arXiv:2605.17583v1 Announce Type: new Abstract: While existing text-to-speech (TTS) models exhibit high expressiveness, fine-grained control over composite instructions remains challenging due to the

AgentWall: A Runtime Safety Layer for Local AI Agents

arXiv:2605.16265v1 Announce Type: new Abstract: The safety of autonomous AI agents is increasingly recognized as a critical open problem. As agents transition from passive text generators to active ac

AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplers

arXiv:2605.18476v1 Announce Type: cross Abstract: Coding and computation remain major bottlenecks in Markov chain Monte Carlo (MCMC) workflows, especially as modern sampling algorithms have become inc

Aligned Training: A Parameter-Free Method to Improve Feature Quality and Stability of Sparse Autoencoders (SAE)

arXiv:2605.18629v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are one of the main methods to interpret the inner workings of deep neural networks (DNNs), decomposing activations into high

AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering

arXiv:2605.17352v1 Announce Type: new Abstract: Despite substantial advances in large language models (LLMs), generating factually consistent responses for knowledge-intensive question answering remai

safetyarxiv-cs-cl

AMR-SD: Asymmetric Meta-Reflective Self-Distillation for Token-Level Credit Assignment

arXiv:2605.18529v1 Announce Type: new Abstract: The alignment of Large Language Models (LLMs) for complex reasoning heavily relies on Reinforcement Learning with Verifiable Rewards (RLVR). However, st

AnchorDiff: Topology-Aware Masked Diffusion with Confidence-based Rewriting for Radiology Report Generation

arXiv:2605.17071v1 Announce Type: new Abstract: Radiology report generation (RRG) aims to automatically produce clinically accurate textual reports from medical images. Existing methods predominantly

Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild

arXiv:2603.04727v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have demonstrated impressive general competence in video understanding, yet their reliability for rea

Applications

Articulation in Prime: Primitive-Based Articulated Object Understanding from a Single Casual Video

arXiv:2605.18645v1 Announce Type: new Abstract: Retrieving the 3D kinematics of articulated objects from monocular video is a fundamental challenge in computer vision. Existing methods rely on complex

applicationsarxiv-cs-cv

ArtifactLinker: Linking Scientific Artifacts for Automatic State-of-the-Art Discovery

arXiv:2605.16902v1 Announce Type: new Abstract: Scientific artifacts such as models and datasets are foundations for research. With the rapid growth of platforms like HuggingFace, researchers now have

AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization

arXiv:2603.23566v2 Announce Type: replace-cross Abstract: Optimizing AscendC (Ascend C) operators for Ascend NPUs is difficult for two reasons. First, unlike CUDA, the ecosystem offers few public kern

Assessing Localization Technologies for Pedestrian Collision Avoidance

arXiv:2605.18295v1 Announce Type: new Abstract: Robust pedestrian safety is crucial to the next-generation of intelligent transportation systems. Such systems rely on active pedestrian localization an

safetyarxiv-cs-ro

AtlasVA: Self-Evolving Visual Skill Memory for Teacher-Free VLM Agents

arXiv:2605.17933v1 Announce Type: new Abstract: Vision-language model (VLM) agents increasingly rely on memory-augmented reinforcement learning to reuse experience across long-horizon tasks, yet most

researcharxiv-cs-cv

Attention-Guided Fusion of 1D and 2D CNNs for Robust ECG-Based Biometric Recognition

arXiv:2605.17685v1 Announce Type: cross Abstract: Electrocardiogram (ECG)-based biometric recognition has emerged as a promising solution for secure authentication and liveness detection. However, mos

Auditing Multimodal LLM Raters: Central Tendency Bias in Clinical Ordinal Scoring

arXiv:2605.16386v1 Announce Type: new Abstract: Multimodal large language models (LLMs) are increasingly explored as automated evaluators in clinical settings, yet their scoring behavior on ordinal cl

Augmenting Human Evaluation with LLM Judges: How Many Human Reviews Do You Need?

arXiv:2605.16354v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as automated evaluators of AI systems, including in high-stakes applications. In this role, LLMs ar

Local Ai

Aurora: Unified Video Editing with a Tool-Using Agent

arXiv:2605.18748v1 Announce Type: new Abstract: Recent video editing models have converged on a unified conditioning design: a single diffusion transformer jointly consumes text, source video, and ref

local-aiarxiv-cs-cv

AutoRubric-T2I: Robust Rule-Based Reward Model for Text-to-Image Alignment

arXiv:2605.17602v1 Announce Type: new Abstract: Aligning Text-to-Image (T2I) generation models with human preferences increasingly relies on image reward models that score or rank generated images acc

Avoiding Structural Failure Modes in Tabular Fair SSL: Online Primal-Dual Allocation under Confidence Gating

arXiv:2605.16446v1 Announce Type: cross Abstract: Semi-supervised learning (SSL) enables prediction with limited labels, but high-stakes tabular applications (medical, credit, recidivism) require stat

Bayesian-Monte Carlo Schedule Updating for Construction Digital Twins: A Probabilistic Framework for Dynamic Project Forecasting

arXiv:2605.17608v1 Announce Type: cross Abstract: Construction projects frequently experience schedule delays and forecasting uncertainty due to variability in labor productivity, material availabilit

Beacon: Single-Turn Diagnosis and Mitigation of Latent Sycophancy in Large Language Models

arXiv:2510.16727v2 Announce Type: replace-cross Abstract: Large language models internalize a structural trade-off between truthfulness and obsequious flattery, emerging from reward optimization that