Search: “models”

arXiv:2605.18603v1 Announce Type: new Abstract: Vision-Language Models (VLMs) deployed as situated agents in high-resolution visual environments require active perception -- the ability to dynamically

tutorialsarxiv-cs-cv

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning

DGX agent

arXiv:2605.18656v1 Announce Type: cross Abstract: Federated Learning is a leading framework for training ML and AI models collaboratively across numerous user devices or databases. We study the trade-

model-releasesarxiv-cs-ai

19 May 2026

Strategic Over-Parameterization for Generalizable Low-Rank Adaptation

arXiv:2605.16470v1 Announce Type: cross Abstract: Adapting large language models (LLMs) to downstream tasks via full fine-tuning is increasingly impractical due to its computational and memory demands

Structure-Aware Masking for Protein Representation Learning

arXiv:2605.16581v1 Announce Type: new Abstract: Masked language modeling (MLM) is the standard objective for training protein language models, typically implemented by randomly masking individual resi

safetyarxiv-cs-lg

The Scaling Laws of Skills in LLM Agent Systems

arXiv:2605.16508v1 Announce Type: cross Abstract: As agent systems scale, skills accumulate into large reusable libraries, yet their scaling laws remain poorly understood. Across 15 frontier LLMs, 1,1

TIER: Trajectory-Invariant Execution Rewards for Multi-Step Tool Composition

arXiv:2605.16790v1 Announce Type: cross Abstract: Tool use enables large language models to solve complex tasks through sequences of API calls, yet existing reinforcement learning approaches fail to s

Universal Adversarial Triggers

arXiv:2605.17936v1 Announce Type: new Abstract: Recent works have illustrated that modern NLP models trained for diverse tasks ranging from sentiment analysis to language generation succumb to univers

researcharxiv-cs-cl

Universal Dynamics of Punctuated Progress

arXiv:2605.16719v1 Announce Type: cross Abstract: Scientific and technological frontiers advance through punctuated dynamics, yet the principles governing these dynamics remain poorly understood. Here

Verifier-Guided Code Translation via Meta-Step Decoding

arXiv:2605.17626v1 Announce Type: new Abstract: Test-time scaling is an important mechanism for improving large language models, especially on tasks with deterministic verifiers. Code translation is a

Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning

arXiv:2601.06943v2 Announce Type: replace-cross Abstract: In real-world video question answering scenarios, videos often provide only localized visual cues, while verifiable answers are distributed ac

We Think, Therefore We Align LLMs to Helpful, Harmless and Honest Before They Go Wrong

model-releasesarxiv-cs-cl

arXiv:2509.22510v3 Announce Type: replace Abstract: Alignment of Large Language Models (LLMs) is the ability to satisfy desired objectives during generation, which is critical for trustworthy deployme

Weighted Flow Matching and Physics-Informed Nonlinear Filtering for Parameter Estimation in Digital Twins

arXiv:2605.17146v1 Announce Type: cross Abstract: Digital twins (DTs) rely on continuous synchronization between physical systems and their virtual counterparts through online parameter estimation und

WELD: The First Naturalistic Long-Period Small-Team Workplace Emotion Dataset for Ubiquitous Affective Computing

arXiv:2510.15221v2 Announce Type: replace Abstract: Affective computing has matured rapidly in laboratory settings, yet no prior dataset combines (i) months-to-years of duration, (ii) a naturalistic w

What is Holding Back Latent Visual Reasoning?

arXiv:2605.18445v1 Announce Type: cross Abstract: Humans can approach complex visual problems by mentally simulating intermediate visual steps, rather than reasoning through language alone. Inspired b

researcharxiv-cs-ai

When Vision Speaks for Sound

arXiv:2605.16403v1 Announce Type: new Abstract: Despite rapid progress in video-capable MLLMs, we find that their apparent audio understanding in videos is often vision-driven: models rely on visual c

safetyarxiv-cs-cv

Whispers in the Noise: Surrogate-Guided Concept Awakening via a Multi-Agent Framework

arXiv:2605.18150v1 Announce Type: new Abstract: Diffusion models (DMs) are widely used for text-to-image generation, but their strong generative capabilities also raise concerns about unsafe or undesi

safetyarxiv-cs-ai

WinDeskGround: A Benchmark for Robust GUI Grounding in Complex Multi-Window Desktop Environments

arXiv:2605.16402v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have revolutionized GUI automation, yet their efficacy is largely established on idealized, single-layer interf

Hardware

A Few GPUs, A Whole Lotta Scale: Faithful LLM Training Emulation with PrismLLM

arXiv:2605.15617v1 Announce Type: cross Abstract: Large language model (LLM) training today runs on clusters spanning thousands of GPUs. While this scale enables rapid model advances, developing, debu

hardwarearxiv-cs-ai

AGOP-IxG: A Gradient Covariance Filter for Local Feature Attribution on Tabular Data, with a Controlled Benchmark

arXiv:2605.15700v1 Announce Type: new Abstract: Automated machine learning pipelines increasingly produce models whose predictions must be explained to end users, auditors, and downstream decision sys

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

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

researcharxiv-cs-ai

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

Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most

arXiv:2605.16207v1 Announce Type: new Abstract: Effective tutoring requires distinguishing optimal, valid but suboptimal, and incorrect student solutions, a distinction central to intelligent tutoring

Continual Learning of Domain-Invariant Representations

arXiv:2605.15775v1 Announce Type: new Abstract: Continual learning (CL) aims to train models sequentially over multiple domains without forgetting previously learned knowledge. However, existing CL me

Decentralized LoRA augmented transformer with multi-scale feature learning for secured eye diagnosis

arXiv:2505.06982v3 Announce Type: replace Abstract: Accurate and privacy-preserving diagnosis of ophthalmic diseases remains a critical challenge in medical imaging, particularly given the limitations

DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection

model-releasesarxiv-cs-cl

arXiv:2605.15518v1 Announce Type: new Abstract: The effective detection and governance of Large Language Model (LLM) generated content has become increasingly critical due to the growing risk of misus

Echo-Forcing: A Scene Memory Framework for Interactive Long Video Generation

arXiv:2605.16003v1 Announce Type: new Abstract: Autoregressive video diffusion models enable open-ended generation through local attention and KV caching. However, existing training-free long-video op

End-to-end plaque counting and virus titration from laboratory plate images with deep learning

arXiv:2605.16008v1 Announce Type: new Abstract: Plaque assays remain the gold standard readout of virus infectivity; however, plaque counting from plate images is labor-intensive and prone to inter-op

Enhancing Medical Image Segmentation via Heat Conduction Equation

arXiv:2511.03260v2 Announce Type: replace Abstract: Medical image segmentation models struggle to achieve efficient global context modeling and long-range dependency reasoning under practical computat

researcharxiv-cs-cv

FormulaCode: Evaluating Agentic Optimization on Large Codebases

arXiv:2603.16011v2 Announce Type: replace-cross Abstract: Large language model (LLM) coding agents increasingly operate at the repository level, motivating benchmarks that evaluate their ability to op

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery

arXiv:2605.15412v1 Announce Type: cross Abstract: Modern quantitative trading increasingly relies on systematic models to extract predictive signals from large-scale financial data, where alpha factor

GESD: Beyond Outcome-Oriented Fairness

arXiv:2605.15295v1 Announce Type: cross Abstract: Machine learning (ML) algorithms are increasingly deployed in high-stakes decision-making domains such as loan approvals, hiring, and recidivism predi

Highly Detailed and Generalizable Broadleaf Tree Crown Instance Segmentation from UAV Imagery

arXiv:2605.15673v1 Announce Type: cross Abstract: We present a highly detailed instance segmentation model for delineating individual tree crowns in natural broadleaf forests using aerial imagery acqu

researcharxiv-cs-cv

Hybrid LLM-based Intelligent Framework for Robot Task Scheduling

arXiv:2605.15486v1 Announce Type: cross Abstract: This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The LLM is fed

Tutorials

Hypothesis-driven construction of mesoscopic dynamics

arXiv:2605.16211v1 Announce Type: new Abstract: Traditional scientific modeling typically begins with fixed, instance-wise effective equations and then carries out equation-specific analysis and compu

tutorialsarxiv-cs-lg

Agents

ICRL: Learning to Internalize Self-Critique with Reinforcement Learning

arXiv:2605.15224v1 Announce Type: new Abstract: Large language model-based agents make mistakes, yet critique can often guide the same model toward correct behavior. However, when critique is removed,

agentsarxiv-cs-ai

Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection

arXiv:2510.05676v2 Announce Type: replace Abstract: Graph-based methods are becoming increasingly popular in machine learning due to their ability to model complex data and relations. Insurance fraud

Mask-Morph Graph U-Net: A Generalisable Mesh-Based Surrogate for Crashworthiness Field Prediction under Large Geometric Variation

arXiv:2605.15231v1 Announce Type: cross Abstract: Nonlinear finite element crash simulations are accurate but computationally expensive, limiting their use in iterative design optimisation. Machine-le

MyoChallenge 2025: A New Benchmark for Human Athletic Intelligence

model-releasesarxiv-cs-ro

arXiv:2605.15650v1 Announce Type: new Abstract: Athletic performance represents the pinnacle of human motor intelligence, demanding rapid choices, precise control, agility, and coordinated physical ex

PDRNN: Modular Data-driven Pedestrian Dead Reckoning on Loosely Coupled Radio- and Inertial-Signalstreams

arXiv:2605.15252v1 Announce Type: cross Abstract: Modern pedestrian dead reckoning (PDR) systems rely on fusing noisy and biased estimates of position, velocity, and calibrated orientation derived fro

RTL-BenchMT: Dynamic Maintenance of RTL Generation Benchmark Through Agent-Assisted Analysis and Revision

arXiv:2605.15537v1 Announce Type: new Abstract: This paper introduces RTL-BenchMT, an agentic framework for dynamically maintaining RTL generation benchmarks. Large Language Models (LLMs) assisted aut

Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning

arXiv:2601.12894v2 Announce Type: replace-cross Abstract: Diffusion Policy has dominated action generation due to its strong capabilities for modeling multi-modal action distributions, but its multi-s

VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing

model-releasesarxiv-cs-cl

arXiv:2605.15677v1 Announce Type: new Abstract: Despite the rapid advancements in Vision-Language Models (VLMs), a critical gap remains in their ability to handle structured, controllable diagrammatic

A Deterministic Agentic Workflow for HS Tariff Classification: Multi-Dimensional Rule Reasoning with Interpretable Decisions

arXiv:2605.14857v1 Announce Type: new Abstract: Harmonized System (HS) tariff classification is a high-stakes, expert-level task in which a free-form product description must be mapped to a specific s

A Systematic Evaluation of Imbalance Handling Methods in Biomedical Binary Classification

arXiv:2605.14147v1 Announce Type: new Abstract: Objective: The primary goal of this study was to systematically examine the impact of commonly used imbalance handling methods (IHMs) on predictive perf

researcharxiv-cs-lg

Tutorials

AaSP: Aliasing-aware Self-Supervised Pre-Training for Audio Spectrogram Transformers

arXiv:2512.03637v2 Announce Type: replace-cross Abstract: Transformer-based audio self-supervised learning (SSL) models commonly use spectrograms, vision-style Transformers, and masked modeling object

tutorialsarxiv-cs-lg

ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation

arXiv:2506.04646v4 Announce Type: replace-cross Abstract: Planning with learned dynamics models offers a promising approach toward versatile real-world manipulation, particularly in nonprehensile sett

safetyarxiv-cs-lg

AgentTrap: Measuring Runtime Trust Failures in Third-Party Agent Skills

arXiv:2605.13940v1 Announce Type: cross Abstract: Third-party skills are becoming the package ecosystem for LLM agents. They package natural-language instructions, helper scripts, templates, documents