Search: “models”

Estimating Mixture Distributions via Stochastic Mirror Descent

arXiv:2605.24929v1 Announce Type: cross Abstract: We revisit the classical problem of estimating an unknown distribution from its samples by fitting a mixture model that minimizes cross-entropy loss.

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EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions

DGX agent

arXiv:2605.24110v1 Announce Type: new Abstract: Coding agents are increasingly used as iterative development partners, but most benchmarks still evaluate one specification followed by one final assess

model-releasesarxiv-cs-ai

26 May 2026

Agents

EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery

arXiv:2605.24018v1 Announce Type: new Abstract: Large language models (LLMs), have shown strong potential in scientific discovery, yet existing methods still face substantial challenges in the design

agentsarxiv-cs-ai

Explainable Retinal Imaging for Prediction of Multi-Organ Dysfunction in Type 2 Diabetes

arXiv:2605.24912v1 Announce Type: cross Abstract: Background: Type 2 diabetes mellitus (T2DM) is increasingly recognised as a systemic disease characterised by coordinated dysfunction across metabolic

Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care

arXiv:2605.24678v1 Announce Type: new Abstract: Speech and language technologies offer valuable opportunities for supporting mental health assessment through objective and interpretable cues. We prese

Extreme Region Policy Distillation

arXiv:2605.25582v1 Announce Type: cross Abstract: Reinforcement learning for large language models faces a fundamental trade-off between sample efficiency and asymptotic performance: strictly on-polic

Faithful or Fabricated? A Causal Framework for Rationalization Bias in LLM Judges

arXiv:2605.23970v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as automatic judges for summarization and dialogue evaluation. Prior work has documented biases such

safetyarxiv-cs-cl

Feature Learning in Wide Neural Networks under muP: Identifiability and Sparse-Dictionary Decomposition of the Mean-Field Limit

arXiv:2605.24710v1 Announce Type: new Abstract: We establish four structural results for feature learning in wide two-layer neural networks under the Maximal Update Parametrization (muP). First, we pr

Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs

arXiv:2501.19389v4 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) on resource-constrained clients remains a challenging problem. Recent works have fused low-rank adaptation

From DPPs to k-DPPs: identifiability analysis via spectral decomposition

arXiv:2605.25526v1 Announce Type: cross Abstract: We study the geometry of determinantal point processes (DPPs) through the spectral decomposition L=ULambda U^{op}. The spectrum Lambda governs the car

From Reasoning to Code: GRPO Optimization for Underrepresented Languages

arXiv:2506.11027v3 Announce Type: replace-cross Abstract: Generating accurate and executable code using Large Language Models (LLMs) remains a significant challenge for underrepresented programming la

GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection

arXiv:2501.18196v3 Announce Type: replace Abstract: Unsupervised anomaly detection of multivariate time series is a challenging task, given the requirements of deriving a compact detection criterion w

Generative Neural Operators through Diffusion Last Layer

arXiv:2602.04139v2 Announce Type: replace Abstract: Neural operators provide a powerful framework for learning discretization invariant mappings between function spaces, but standard deterministic mod

GreenSeg: Ground Segmentation Algorithm for Agricultural Robots in Mediterranean Greenhouses using RGB-D Point Clouds

model-releasesarxiv-cs-ro

arXiv:2605.25279v1 Announce Type: new Abstract: Greenhouse agriculture in the Mediterranean region faces significant automation challenges due to its unique structural and environmental constraints. T

Agents

Harnessing AtomisticSkills for Agentic Atomistic Research

arXiv:2605.24002v1 Announce Type: cross Abstract: Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) hav

agentsarxiv-cs-ai

Hide to Guide: Learning via Semantic Masking

arXiv:2605.25198v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a powerful paradigm for improving language models on reasoning-intensive tasks, but i

Tutorials

How Should LLMs Consume High-Quality Data? Optimal Data Scheduling via Quality-Aware Functional Scaling Laws

arXiv:2605.25698v1 Announce Type: cross Abstract: High-quality data is scarce in large language model (LLM) training, yet how to schedule its use jointly with training dynamics lacks theoretical guida

tutorialsarxiv-cs-ai

Agents

Hybrid Deep Searcher: Scalable Parallel and Sequential Search Reasoning

arXiv:2508.19113v3 Announce Type: replace Abstract: Large reasoning models (LRMs) combined with retrieval-augmented generation (RAG) have enabled deep research agents capable of multi-step reasoning w

agentsarxiv-cs-ai

Identifying and Mitigating Systemic Measurement Bias in Production LLM Inference Benchmarks

arXiv:2605.24217v1 Announce Type: new Abstract: As Large Language Models (LLMs) transition from research environments to production deployments, evaluating their performance against strict Service Lev

Intent Signal Theory: A Computational Framework for Intent-State Control in Human-AI Interaction

arXiv:2605.25058v1 Announce Type: cross Abstract: Current AI interaction models treat the prompt as the primary object of exchange, omitting a critical layer: the user's latent source intent, the goal

Interdomain Attention: Beyond Token-Level Key-Value Memory

arXiv:2605.24330v1 Announce Type: new Abstract: Transformers and deep state space models (SSMs) sit at opposite ends of a basic design choice: attention routes each query through a growing key-value (

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification

arXiv:2601.19743v3 Announce Type: replace-cross Abstract: Echocardiography is a cornerstone for managing heart failure (HF), with Left Ventricular Ejection Fraction (LVEF) being a critical metric for

Local Ai

Interpretation, Learning, and Empathy as One Constraint: A Residual-Adequacy Architecture with Accountable Abstention

arXiv:2605.24999v1 Announce Type: cross Abstract: An agent must act on the situation before it, learn what it cannot yet represent, and model other agents well enough to coordinate. These faculties ar

local-aiarxiv-cs-ai

IVR-R1: Refining Trajectories through Iterative Visual-Grounded Reasoning in Reinforcement Learning

arXiv:2605.23997v1 Announce Type: cross Abstract: Multimodal large language models via reinforcement learning (RL) have demonstrated remarkable capabilities in complex visual reasoning tasks, yet they

Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning

arXiv:2605.25916v1 Announce Type: new Abstract: Federated edge learning (FEEL) has recently emerged as a promising paradigm for achieving edge intelligence (EI) via enabling collaborative model traini

safetyarxiv-cs-lg

JudgmentBench: Comparing Rubric and Preference Evaluation for Quality Assessment

arXiv:2605.25240v1 Announce Type: cross Abstract: Two methodologies dominate current practices of benchmarking: rubric-based scoring evaluates items against predefined criteria, whereas comparative ju

Knowing but Not Showing: LLMs Recognize Ambiguity but Rarely Ask Clarifying Questions

arXiv:2605.25284v1 Announce Type: new Abstract: User queries are often underspecified and may admit multiple valid interpretations. Rather than silently making assumptions about the user's intent, a h

researcharxiv-cs-cl

Knowledge Graph-Driven Expert-Level Reasoning for Neuroscience

arXiv:2605.25183v1 Announce Type: cross Abstract: Knowledge graph (KG) is an abstraction that can be extracted from text corpora and used for in-depth reasoning. Prior work has leveraged KGs to fine-t

KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing

arXiv:2605.23933v1 Announce Type: cross Abstract: Educational Question Generation (EQG) aims to synthesize customized exercise questions that enhance student learning. An effective EQG system should i

Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning

arXiv:2605.25267v1 Announce Type: cross Abstract: Safe in-context reinforcement learning (ICRL) adapts online from interaction history without test-time parameter updates while controlling episode cos

LC-ERD: Mining Latent Logic for Self-Evolving Reasoning via Consistency-Regulated Reward Decomposition

arXiv:2605.24005v1 Announce Type: new Abstract: The evolution of Large Language Model (LLM) reasoning is bottlenecked by the scarcity of high-quality process data. While self-alignment via endogenous

Length Generalization with Log-Depth Recurrent Units

arXiv:2605.26035v1 Announce Type: new Abstract: Length generalization remains a persistent challenge for neural networks: recurrent models tend to suffer from positional biases, while transformers are

Applications

LETS Forecast: Learning Embedology for Time Series Forecasting

arXiv:2506.06454v2 Announce Type: cross Abstract: Real-world time series are often governed by complex nonlinear dynamics. Understanding these underlying dynamics is crucial for precise future predict

applicationsarxiv-cs-ai

LGMT: Logic-Grounded Metamorphic Testing for Evaluating the Reasoning Reliability of LLMs

arXiv:2605.23965v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance on logical reasoning benchmarks, yet their reliability remains uncertain. Existing evaluations r

Local Ai

LLM Agent Based Renewable Energy Forecasting Using Edge and IoT Data A Review of Solar Wind Weather and Grid Aware Decision Support

arXiv:2605.25141v1 Announce Type: cross Abstract: Reliable forecasting of renewable energy generation is a foundational requirement for grid stability energy trading battery scheduling and carbon awar

local-aiarxiv-cs-ai

Local Ai

Localization then Neutralization: Gradient-guided Token Suppression against Visual Prompt Injection Attack

arXiv:2605.25194v1 Announce Type: new Abstract: Adversarial images pose a severe security threat to multimodal large language models through prompt injection. Existing defenses largely lack a principl

local-aiarxiv-cs-lg

Machine Learning Multiscale Interactions

arXiv:2605.25710v1 Announce Type: cross Abstract: Realistic physical systems are characterised by emergent interactions across multiple length and time scales, posing a significant challenge for predi

Minimax Limits of k-Fold Cross-Validation via Majority

arXiv:2605.25859v1 Announce Type: cross Abstract: We study the mean-squared error of k-fold cross-validation as a risk estimator, with particular emphasis on how its accuracy depends on the number of

Mitigating Hallucinations in Healthcare LLMs with Granular Fact-Checking and Domain-Specific Adaptation

arXiv:2512.16189v3 Announce Type: replace Abstract: In healthcare, it is essential for any LLM-generated output to be reliable and accurate, particularly in cases involving decision-making and patient

safetyarxiv-cs-cl

Mixture of Complementary Agents for Robust LLM Ensemble

arXiv:2605.24048v1 Announce Type: cross Abstract: Multi-AI collaboration, such as ensembling or debating large language models (LLMs), is a promising paradigm for aggregating information and boosting

Multi-Alignment Contrastive Learning for Enzyme--Reaction Retrieval

arXiv:2512.08508v2 Announce Type: replace-cross Abstract: Identifying enzymes that catalyze target biochemical reactions is a key step in computational enzyme discovery and biocatalyst design. Recent

safetyarxiv-cs-lg

Neural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables

arXiv:2605.25985v1 Announce Type: new Abstract: Complex Query Answering (CQA) is a fundamental knowledge representation and reasoning task over incomplete knowledge graphs (KGs). Answering existential

Neuromorphic LiDAR-based Bird's Eye View Object Detection using Energy-efficient Spiking Neural Networks

arXiv:2605.25293v1 Announce Type: cross Abstract: Autonomous driving perception demands accurate and efficient processing of three-dimensional sensor data under strict power constraints. Traditional c

Overview of the PsyDefDetect Shared Task at BioNLP 2026: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations

model-releasesarxiv-cs-cl

arXiv:2605.24907v1 Announce Type: new Abstract: We present an overview of PsyDefDetect, the shared task on detecting levels of psychological defense mechanisms in emotional support dialogues, co-locat

Parameter-Efficient CT Reconstruction via Deep Graph Laplacian Regularization

arXiv:2605.25348v1 Announce Type: cross Abstract: Low-dose computed tomography (LDCT) reconstruction faces a critical tradeoff between reconstruction quality and resource requirements. While recent de

PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs

arXiv:2603.09943v2 Announce Type: replace Abstract: Computational pathology demands both visual pattern recognition and dynamic integration of structured domain knowledge, including taxonomy, grading

PerSoMed: A Large-Scale Balanced Dataset for Persian Social Media Text Classification

arXiv:2602.19333v2 Announce Type: replace Abstract: This research introduces the first large-scale, well-balanced Persian social media text classification dataset, specifically designed to address the

researcharxiv-cs-cl

Personalized Federated Learning by Energy-Efficient UAV Communications

arXiv:2605.25212v1 Announce Type: new Abstract: Federated learning (FL) is an effective paradigm for enhancing the learning capability of edge devices while preserving data privacy. In geographically