Search: “models”

TMAS: Scaling Test-Time Compute via Multi-Agent Synergy

arXiv:2605.10344v1 Announce Type: new Abstract: Test-time scaling has become an effective paradigm for improving the reasoning ability of large language models by allocating additional computation dur

ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering

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DGX agent

arXiv:2510.20036v2 Announce Type: replace Abstract: Large language model (LLM) agents rely on external tools to solve complex tasks, but real-world toolsets often contain redundant tools with overlapp

agentsarxiv-cs-cl

12 May 2026

Applications

Toward General and Robust LLM-enhanced Text-attributed Graph Learning

arXiv:2504.02343v2 Announce Type: replace Abstract: Recent advancements in Large Language Models (LLMs) and the proliferation of Text-Attributed Graphs (TAGs) across various domains have positioned LL

applicationsarxiv-cs-lg

Toward Multi-Database Query Reasoning for Text2Cypher

arXiv:2605.10373v1 Announce Type: cross Abstract: Large language models have significantly improved natural language interfaces to databases by translating user questions into executable queries. In p

tutorialsarxiv-cs-cl

Towards Effective Theory of LLMs: A Representation Learning Approach

arXiv:2605.09294v1 Announce Type: cross Abstract: We propose Representational Effective Theory (RET), a framework for describing large language model computation in terms of learned macrostates rather

TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators

arXiv:2605.08231v1 Announce Type: cross Abstract: Reducing power consumption in AI accelerators is increasingly important. Approximate computing can reduce power consumption while keeping the accuracy

Hardware

Understand and Accelerate Memory Processing Pipeline for Disaggregated LLM Inference

arXiv:2603.29002v2 Announce Type: replace-cross Abstract: Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse atte

hardwarearxiv-cs-ai

Unified Noise Steering for Efficient Human-Guided VLA Adaptation

arXiv:2605.10821v1 Announce Type: new Abstract: Diffusion-based vision-language-action (VLA) models have emerged as strong priors for robotic manipulation, yet adapting them to real-world distribution

safetyarxiv-cs-ro

Variational Inference for Levy Process-Driven SDEs via Neural Tilting

arXiv:2605.10934v1 Announce Type: cross Abstract: Modelling extreme events and heavy-tailed phenomena is central to building reliable predictive systems in domains such as finance, climate science, an

Local Ai

Verifiable Process Rewards for Agentic Reasoning

arXiv:2605.10325v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning abilities of large language models (LLMs), but most existing approaches

local-aiarxiv-cs-ai

Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQA

arXiv:2605.10850v1 Announce Type: new Abstract: Self-verification, re-invoking the same vision language model (VLM) in a fresh context to check its own generated answer, is increasingly used as a defa

safetyarxiv-cs-cv

Visual Hand Gesture Recognition with Deep Learning: A Comprehensive Review of Methods, Datasets, Challenges and Future Research Directions

arXiv:2507.04465v4 Announce Type: replace Abstract: The rapid evolution of deep learning (DL) models and the ever-increasing size of available datasets have raised the interest of the research communi

When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection

arXiv:2605.10242v1 Announce Type: cross Abstract: Unsupervised tabular anomaly detection methods typically learn feature patterns from normal samples during training and subsequently identify samples

tutorialsarxiv-cs-ai

Where Do Flow Semantics Reside? A Protocol-Native Tabular Pretraining Paradigm for Encrypted Traffic Classification

arXiv:2603.10051v2 Announce Type: replace-cross Abstract: Self-supervised masked modeling shows promise for encrypted traffic classification by masking and reconstructing raw bytes. Yet recent work re

Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It

arXiv:2604.02564v2 Announce Type: replace-cross Abstract: We present MaskGen, a theoretically grounded and deliberately simple approach for domain generalization in 3D biomedical image segmentation. M

tutorialsarxiv-cs-cv

Your Recourse, My Loss? Algorithmic Recourse under Shared Constraints

arXiv:2508.11070v2 Announce Type: replace Abstract: Decision makers are increasingly relying on machine learning in sensitive situations. Algorithmic recourse aims to provide individuals with actionab

Zero-Shot Sim-to-Real Robot Learning: A Dexterous Manipulation Study on Reactive Catching

arXiv:2605.09789v1 Announce Type: new Abstract: Dexterous manipulation is physics-intensive and highly sensitive to modeling errors and perception noise, making sim-to-real transfer prohibitively chal

tutorialsarxiv-cs-ro

6D Pose Estimation via Keypoint Heatmap Regression with RGB-D Residual Neural Networks

arXiv:2605.08059v1 Announce Type: new Abstract: In this paper, we propose a modular framework for 6D pose estimation based on keypoint heatmap regression. Our approach combines YOLOv10m for object det

A Comparative Analysis of Classical Machine Learning and Deep Learning Approaches for Sentiment Classification on IMDb Movie Reviews

arXiv:2605.07811v1 Announce Type: new Abstract: This paper presents a comparative study of classical machine learning and deep learning methods for sentiment classification on the IMDb movie reviews d

researcharxiv-cs-cl

A Linear-Transformer Hybrid for SNP-Based Genotype-to-Phenotype Prediction in Grapevine

arXiv:2605.06762v1 Announce Type: cross Abstract: Robust genotype-to-phenotype (G2P) prediction is essential for accelerating breeding decisions and genetic gain. However, it remains challenging to me

Accelerated and data-efficient flow prediction in stirred tanks via physics-informed learning

arXiv:2605.07444v1 Announce Type: cross Abstract: The simulation of fluid flows is computationally expensive due to the complexity of its governing partial differential equations. Machine learning mod

Actor-Critic Algorithm for Dynamic Expectile and CVaR

arXiv:2605.07857v1 Announce Type: new Abstract: Optimizing dynamic risk with stochastic policies is challenging in both policy updates and value learning. The former typically requires transition pert

safetyarxiv-cs-lg

AGILE: Hand-Object Interaction Reconstruction from Video via Agentic Generation

arXiv:2602.04672v3 Announce Type: replace Abstract: Reconstructing dynamic hand-object interactions from monocular videos is critical for dexterous manipulation data collection and creating realistic

agentsarxiv-cs-cv

ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule

arXiv:2601.18681v2 Announce Type: replace-cross Abstract: We consider time discretization for score-based diffusion models to generate samples from a learned reverse-time dynamic on a finite grid. Uni

BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning

arXiv:2605.07394v1 Announce Type: cross Abstract: Image captioning is one of the most fundamental tasks in computer vision. Owing to its open-ended nature, it has received significant attention in the

Bilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gain

arXiv:2605.07577v1 Announce Type: new Abstract: Bilevel graph structure learning is widely understood to improve graph neural networks by jointly optimizing model parameters and a learned graph struct

researcharxiv-cs-lg

Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding

arXiv:2605.06679v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reli

researcharxiv-cs-lg

Comprehensiveness Metrics for Automatic Evaluation of Factual Recall in Text Generation

arXiv:2510.07926v2 Announce Type: replace Abstract: Despite demonstrating remarkable performance across a wide range of tasks, large language models (LLMs) have also been found to frequently produce o

researcharxiv-cs-cl

Local Ai

DeCo-DETR: Decoupled Cognition DETR for efficient Open-Vocabulary Object Detection

arXiv:2604.02753v2 Announce Type: replace Abstract: Open-vocabulary Object Detection (OVOD) enables models to recognize objects beyond predefined categories, but existing approaches remain limited in

local-aiarxiv-cs-cv

Delta-Adapter: Scalable Exemplar-Based Image Editing with Single-Pair Supervision

arXiv:2605.07940v1 Announce Type: new Abstract: Exemplar-based image editing applies a transformation defined by a source-target image pair to a new query image. Existing methods rely on a pair-of-pai

tutorialsarxiv-cs-cv

Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation

arXiv:2605.07694v1 Announce Type: cross Abstract: Single-channel speaker distance estimation has recently achieved centimeter-level accuracy in simulated environments, yet it remains unclear which com

Differentially Private Auditing Under Strategic Response

arXiv:2605.07674v1 Announce Type: cross Abstract: Regulatory audits of AI systems increasingly rely on differential privacy (DP) to protect training data and model internals. We study audit design whe

safetyarxiv-cs-lg

DINO-MVR: Multi-View Readout of Frozen DINOv3 for Annotation-Efficient Medical Segmentation

arXiv:2605.07221v1 Announce Type: new Abstract: Adapting foundation models to medical segmentation typically requires either backbone fine-tuning or high-capacity task-specific decoders, both of which

Does Your Neural Network Extrapolate? Feature Engineering as Identifiability Bias for OOD Generalization

arXiv:2605.07483v1 Announce Type: cross Abstract: Successful deep neural networks discover salient features of data. We show when and why they fail to learn out-of-distribution (OOD)-relevant represen

Drifting Field Policy: A One-Step Generative Policy via Wasserstein Gradient Flow

arXiv:2605.07727v1 Announce Type: cross Abstract: We propose Drifting Field Policy (DFP), a non-ODE one-step generative policy built on the drifting model paradigm. We frame the policy update as a rev

DVD: Discrete Voxel Diffusion for 3D Generation and Editing

arXiv:2605.07971v1 Announce Type: new Abstract: We introduce Discrete Voxel Diffusion (DVD), a discrete diffusion framework to generate, assess, and edit sparse voxels for SLat (Structured LATent) bas

Entropy-Regularized Adjoint Matching for Offline Reinforcement Learning

arXiv:2605.06156v2 Announce Type: replace-cross Abstract: Integrating expressive generative policies, such as flow-matching models, into offline reinforcement learning (RL) allows agents to capture co

Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation

arXiv:2605.07381v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models offer broad general capabilities, deploying them on specific hardware requires real-world adaptation to brid

Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity

arXiv:2605.07097v1 Announce Type: cross Abstract: We show that, in a precise sense, a broad class of feedforward neural networks learn (have finite sample complexity) in the PAC model: every fixed fin

tutorialsarxiv-cs-lg

EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle

arXiv:2510.16079v2 Announce Type: replace-cross Abstract: Current Large Language Model (LLM) agents show strong performance in tool use, but lack the crucial capability to systematically learn from th

Exposing and Mitigating Temporal Attack in Deepfake Video Detection

arXiv:2605.07398v1 Announce Type: cross Abstract: While spatiotemporal deepfake detectors achieve high AUC, our experiments reveal their susceptibility to evasion attacks. These models tend to overfit

f-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses

arXiv:2605.06977v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a cornerstone technique for post-training large language models. While most existing appr

From Canopy to Collision: A Hybrid Predictive Framework for Identifying Risk Factors in Tree-Involved Traffic Crashes

arXiv:2605.06684v1 Announce Type: new Abstract: Tree-involved crashes represent a critical subset of run-off-road (ROR) collisions, often resulting in fatal or severe injuries due to high-energy impac

researcharxiv-cs-lg

From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms

arXiv:2605.06716v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. Whi

From Time Series Analysis to Question Answering: A Survey in the LLM Era

arXiv:2506.11512v2 Announce Type: replace-cross Abstract: Recently, Large Language Models (LLMs) have introduced a novel paradigm in Time Series Analysis (TSA), leveraging strong language capabilities

Applications

Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension

arXiv:2510.01105v2 Announce Type: replace Abstract: Neural multivariate regression underpins a wide range of domains, including control, robotics, and finance, yet the geometry of its learned represen

applicationsarxiv-cs-lg

GRAPE: Let GRPO Supervise Query Rewriting by Ranking for Retrieval

arXiv:2509.23370v2 Announce Type: replace Abstract: The CLIP model has established itself as a cornerstone of large-scale retrieval systems. However, its performance often degrades under distributiona

safetyarxiv-cs-cv

GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning

arXiv:2605.06671v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong potential for many mathematical problems. However, their performance on graph algorithmic tasks is