Search: “models”

Inference-Time Code Selection via Symbolic Equivalence Partitioning

arXiv:2604.06485v1 Announce Type: cross Abstract: 'Best-of-N' selection is a popular inference-time scaling method for code generation using Large Language Models (LLMs). However, to reliably identify

researcharxiv-cs-ai

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LAMP: Lift Image-Editing as General 3D Priors for Open-world Manipulation

DGX agent

arXiv:2604.08475v1 Announce Type: new Abstract: Human-like generalization in open-world remains a fundamental challenge for robotic manipulation. Existing learning-based methods, including reinforceme

researcharxiv-cs-cv

10 Apr 2026

Learning to Negotiate: Multi-Agent Deliberation for Collective Value Alignment in LLMs

arXiv:2603.10476v2 Announce Type: replace Abstract: LLM alignment has progressed in single-agent settings through paradigms such as RL with human feedback (RLHF), while recent work explores scalable a

Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation

arXiv:2511.17844v4 Announce Type: replace-cross Abstract: Fine-tuning large-scale text-to-video diffusion models to add new generative controls, such as those over physical camera parameters (e.g., sh

researcharxiv-cs-ai

Lexical Tone is Hard to Quantize: Probing Discrete Speech Units in Mandarin and Yoruba

arXiv:2604.07467v1 Announce Type: new Abstract: Discrete speech units (DSUs) are derived by quantising representations from models trained using self-supervised learning (SSL). They are a popular repr

Matrix Profile for Anomaly Detection on Multidimensional Time Series

model-releasesarxiv-cs-ai

arXiv:2409.09298v2 Announce Type: replace-cross Abstract: The Matrix Profile (MP), a versatile tool for time series data mining, has been shown effective in time series anomaly detection (TSAD). This

MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping

arXiv:2604.08364v1 Announce Type: new Abstract: In this paper, we introduce MegaStyle, a novel and scalable data curation pipeline that constructs an intra-style consistent, inter-style diverse and hi

researcharxiv-cs-cv

MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization

arXiv:2604.06798v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) based large language models (LLMs) offer strong performance but suffer from high memory and computation costs. Weight binariz

local-aiarxiv-cs-ai

Tutorials

MoRight: Motion Control Done Right

arXiv:2604.07348v1 Announce Type: cross Abstract: Generating motion-controlled videos--where user-specified actions drive physically plausible scene dynamics under freely chosen viewpoints--demands tw

tutorialsarxiv-cs-ai

NativeTernary: A Self-Delimiting Binary Encoding with Unary Run-Length Hierarchy Markers for Ternary Neural Network Weights, Structured Data, and General Computing Infrastructure

arXiv:2604.03336v2 Announce Type: replace Abstract: BitNet b1.58 (Ma et al., 2024) demonstrates that large language models can operate entirely on ternary weights {-1, 0, +1}, yet no native binary wir

local-aiarxiv-cs-lg

Applications

Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection

arXiv:2405.11619v2 Announce Type: replace-cross Abstract: Phishing emails continue to pose a significant threat, causing financial losses and security breaches. This study addresses limitations in exi

applicationsarxiv-cs-ai

NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results

model-releasesarxiv-cs-cv

arXiv:2604.06945v2 Announce Type: replace Abstract: This paper reports on the NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration (BSCVR). The challenge aims to advance research on recoverin

Optimal Decay Spectra for Linear Recurrences

arXiv:2604.07658v1 Announce Type: cross Abstract: Linear recurrent models offer linear-time sequence processing but often suffer from suboptimal long-range memory. We trace this to the decay spectrum:

Optimal Rates for Pure {arepsilon}-Differentially Private Stochastic Convex Optimization with Heavy Tails

arXiv:2604.06492v1 Announce Type: new Abstract: We study stochastic convex optimization (SCO) with heavy-tailed gradients under pure epsilon-differential privacy (DP). Instead of assuming a bound on t

PanoSAM2: Lightweight Distortion- and Memory-aware Adaptions of SAM2 for 360 Video Object Segmentation

arXiv:2604.07901v1 Announce Type: new Abstract: 360 video object segmentation (360VOS) aims to predict temporally-consistent masks in 360 videos, offering full-scene coverage, benefiting applications,

researcharxiv-cs-cv

PEER: Unified Process-Outcome Reinforcement Learning for Structured Empathetic Reasoning

arXiv:2508.09521v2 Announce Type: replace Abstract: Emotional support conversations require more than fluent responses. Supporters need to understand the seeker's situation and emotions, adopt an appr

Physics-Informed Functional Link Constrained Framework with Domain Mapping for Solving Bending Analysis of an Exponentially Loaded Perforated Beam

arXiv:2604.07025v1 Announce Type: cross Abstract: This article presents a novel and comprehensive approach for analyzing bending behavior of the tapered perforated beam under an exponential load. The

PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization

arXiv:2503.24135v3 Announce Type: replace Abstract: Weakly supervised object localization (WSOL) methods allow training models to classify images and localize ROIs. WSOL only requires low-cost image-c

local-aiarxiv-cs-cv

ProofSketcher: Hybrid LLM + Lightweight Proof Checker for Reliable Math/Logic Reasoning

arXiv:2604.06401v1 Announce Type: new Abstract: The large language models (LLMs) might produce a persuasive argument within mathematical and logical fields, although such argument often includes some

researcharxiv-cs-ai

Agents

Pseudo-Expert Regularized Offline RL for End-to-End Autonomous Driving in Photorealistic Closed-Loop Environments

arXiv:2512.18662v2 Announce Type: replace-cross Abstract: End-to-end (E2E) autonomous driving models that take only camera images as input and directly predict a future trajectory are appealing for th

agentsarxiv-cs-cv

Agents

RAGE-XY: RADAR-Aided Longitudinal and Lateral Forces Estimation For Autonomous Race Cars

arXiv:2604.07939v1 Announce Type: new Abstract: In this work, we present RAGE-XY, an extended version of RAGE, a real-time estimation framework that simultaneously infers vehicle velocity, tire slip a

agentsarxiv-cs-ro

Applications

Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs

arXiv:2604.07562v1 Announce Type: new Abstract: Unsupervised methods are widely used to induce latent semantic structure from large text collections, yet their outputs often contain incoherent, redund

applicationsarxiv-cs-cl

RoboAgent: Chaining Basic Capabilities for Embodied Task Planning

arXiv:2604.07774v1 Announce Type: cross Abstract: This paper focuses on embodied task planning, where an agent acquires visual observations from the environment and executes atomic actions to accompli

safetyarxiv-cs-cv

Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction

arXiv:2604.08542v1 Announce Type: new Abstract: This paper addresses the task of large-scale 3D scene reconstruction from long video sequences. Recent feed-forward reconstruction models have shown pro

local-aiarxiv-cs-cv

SkillSieve: A Hierarchical Triage Framework for Detecting Malicious AI Agent Skills

model-releasesarxiv-cs-ai

arXiv:2604.06550v1 Announce Type: cross Abstract: OpenClaw's ClawHub marketplace hosts over 13,000 community-contributed agent skills, and between 13% and 26% of them contain security vulnerabilities

SMT-AD: a scalable quantum-inspired anomaly detection approach

arXiv:2604.06265v1 Announce Type: new Abstract: Quantum-inspired tensor networks algorithms have shown to be effective and efficient models for machine learning tasks, including anomaly detection. Her

researcharxiv-cs-lg

Tutorials

Steering the Verifiability of Multimodal AI Hallucinations

arXiv:2604.06714v1 Announce Type: new Abstract: AI applications driven by multimodal large language models (MLLMs) are prone to hallucinations and pose considerable risks to human users. Crucially, su

tutorialsarxiv-cs-ai

Stochastic Auto-conditioned Fast Gradient Methods with Optimal Rates

arXiv:2604.06525v1 Announce Type: cross Abstract: Achieving optimal rates for stochastic composite convex optimization without prior knowledge of problem parameters remains a central challenge. In the

Tutorials

TEC: A Collection of Human Trial-and-error Trajectories for Problem Solving

arXiv:2604.06734v2 Announce Type: replace Abstract: Trial-and-error is a fundamental strategy for humans to solve complex problems and a necessary capability for Artificial Intelligence (AI) systems o

tutorialsarxiv-cs-cl

Tensor-Efficient High-Dimensional Q-learning

arXiv:2511.03595v2 Announce Type: replace Abstract: High-dimensional reinforcement learning(RL) faces challenges with complex calculations and low sample efficiency in large state-action spaces. Q-lea

Tight Convergence Rates for Online Distributed Linear Estimation with Adversarial Measurements

arXiv:2604.06282v1 Announce Type: cross Abstract: We study mean estimation of a random vector X in a distributed parameter-server-worker setup. Worker i observes samples of a_i^op X, where $a_

Tool-MCoT: Tool Augmented Multimodal Chain-of-Thought for Content Safety Moderation

arXiv:2604.06205v1 Announce Type: cross Abstract: The growth of online platforms and user content requires strong content moderation systems that can handle complex inputs from various media types. Wh

safetyarxiv-cs-ai

Towards Robust Content Watermarking Against Removal and Forgery Attacks

arXiv:2604.06662v1 Announce Type: cross Abstract: Generated contents have raised serious concerns about copyright protection, image provenance, and credit attribution. A potential solution for these p

researcharxiv-cs-lg

Towards the Development of an LLM-Based Methodology for Automated Security Profiling in Compliance with Ukrainian Cybersecurity Regulations

arXiv:2604.06274v1 Announce Type: cross Abstract: In recent years, the pace of development of information technology in various areas has increased drastically, forcing cybersecurity specialists to co

safetyarxiv-cs-ai

Validated Intent Compilation for Constrained Routing in LEO Mega-Constellations

model-releasesarxiv-cs-ai

arXiv:2604.07264v1 Announce Type: cross Abstract: Operating LEO mega-constellations requires translating high-level operator intents ('reroute financial traffic away from polar links under 80 ms') int

What They Saw, Not Just Where They Looked: Semantic Scanpath Similarity via VLMs and NLP metric

arXiv:2604.08494v1 Announce Type: cross Abstract: Scanpath similarity metrics are central to eye-movement research, yet existing methods predominantly evaluate spatial and temporal alignment while neg

Hardware

ZipMap: Linear-Time Stateful 3D Reconstruction via Test-Time Training

arXiv:2603.04385v3 Announce Type: replace Abstract: Feed-forward transformer models have driven rapid progress in 3D vision, but state-of-the-art methods such as VGGT and pi^3 have a computational

hardwarearxiv-cs-cv

Abstracts or full text? Evaluating input scope and computational efficiency in biomedical lay summarization

arXiv:2610.21690v1 Announce Type: new Abstract: Biomedical research articles are mainly written for experts and often contain terms that are difficult for the general public to understand. Automated l

Approve What, Exactly? Effect Binding and Trusted Rendering for Agent Step-Up Authorization

arXiv:2610.21063v1 Announce Type: cross Abstract: AI agents now pause for approval before they send money, delete files or email outsiders, and that approval is usually treated as a second factor. We

safetyarxiv-cs-ai

Asynchronous Indulgent Gathering of Opaque Robots

arXiv:2509.10711v2 Announce Type: replace-cross Abstract: We consider the benchmarking problem of gathering in an (N,f)-fault system consisting of N robots, of which at most f may fail at any executio

researcharxiv-cs-ro

Agents

Augment the Cache, Replace the Activation: A 2x2 Study of Internal-State Sharing

arXiv:2610.22026v1 Announce Type: new Abstract: Multi-agent systems built from frozen language models communicate through text, which discards the internal state the sender already computed and makes

agentsarxiv-cs-lg

Baselines and Score Centering in Policy Gradient Estimation

arXiv:2610.22263v1 Announce Type: new Abstract: Reinforcement learning has become a widely used approach to post-training large language models. In a typical implementation, learning is off-policy: Th

safetyarxiv-cs-lg

Beyond Pairwise Repulsion: Collective Diversity via Determinant Maximization in Diffusion Sampling

arXiv:2610.22267v1 Announce Type: new Abstract: Strong guidance in text-to-image (T2I) diffusion models concentrates sampling toward high-density modes of the conditional distribution, collapsing inde

researcharxiv-cs-cv

CarryOn: A Dual-Loop Harness for Personalized and Reliable Robot Assistance

arXiv:2610.21453v1 Announce Type: cross Abstract: While generalist vision-language-action (VLA) models have enabled diverse open-world manipulation, real-world robotic assistance requires two critical

safetyarxiv-cs-cv

CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades

arXiv:2610.20668v1 Announce Type: cross Abstract: We introduce CascadeLedger, a counterfactual execution-audit framework for budgeted language-model cascades. The protocol enforces call-before-read se

Applications

CoCopula: Conformal Calibration and Copula Aggregation for Reliable Renewable Energy Forecasting

arXiv:2602.02583v2 Announce Type: replace Abstract: The rapid growth of renewable energy penetration has intensified the need for reliable probabilistic forecasts to support grid operations at aggrega

applicationsarxiv-cs-lg

DINO-FIL: Multi-Layer Transformer Fusion for Generative Inpainting Forgery Localization

arXiv:2610.21780v1 Announce Type: cross Abstract: We propose DINO-FIL, a transformer-based framework for pixel-level localization of generative image forgeries. The method leverages multi-layer repres

local-aiarxiv-cs-ai

DPA-MoE: A Runtime Precision Control Framework for MoE Inference

arXiv:2610.21378v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models reduce computation by routing each token to a small set of experts, but each selected expert is still used at a fixed pr