Search: “models”

Self-evolving LLM agents with in-distribution Optimization

arXiv:2606.07367v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently emerged as powerful controllers for interactive agents in complex environments, yet training them to perform

safetyarxiv-cs-lg

Skip a Layer or Loop It? Learning Program-of-Layers in LLMs

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DGX agent

arXiv:2606.06574v1 Announce Type: new Abstract: Large language models (LLMs) perform inference by following a fixed depth and order, non-recurrent execution of all layers. We reveal the wide existence

researcharxiv-cs-lg

8 Jun 2026

SleepExplain: Explainable Non-Rapid Eye Movement and Rapid Eye Movement Sleep Stage Classification from EEG Signal

arXiv:2606.07351v1 Announce Type: cross Abstract: Classification of sleep stages is one of the most important diagnostic approaches for a variety of sleep-related disorders. Electroencephalography (EE

Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills

arXiv:2606.07412v1 Announce Type: cross Abstract: LLM-driven software engineering agents have become a central testbed for real-world language-model capability, yet their training remains limited by t

The Effect of Training Task Diversity on In-Context Learning through the Lens of Low-Dimensional Subspaces

arXiv:2606.06814v1 Announce Type: cross Abstract: The transformer's emergent ability to perform in-context learning (ICL) has sparked a wide range of studies designed to understand its underlying mech

researcharxiv-cs-lg

The Utility and Complexity of in- and out-of-Distribution Machine Unlearning

arXiv:2412.09119v3 Announce Type: replace Abstract: Machine unlearning, the process of selectively removing data from trained models, is increasingly crucial for addressing privacy concerns and knowle

researcharxiv-cs-lg

Translate-R1: Cost-Aware Translation Tool Use via Reinforcement Learning

arXiv:2606.06835v1 Announce Type: new Abstract: The performance gap across languages in LLMs is well documented, and closing it natively requires pretraining or fine-tuning on corpora that, for most l

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness

arXiv:2606.06938v1 Announce Type: new Abstract: Vision-language models such as CLIP have achieved remarkable zero-shot recognition capabilities, yet their robustness against adversarial perturbations

Whisper Hallucination Detection and Mitigation via Hidden Representation Steering and Sparse AutoEncoders

arXiv:2606.07473v1 Announce Type: cross Abstract: Whisper, a widely adopted ASR model, is known to suffer from hallucinations - coherent transcriptions generated for non-speech audio entirely disconne

Workflow-to-Skill: Skill Creation via Routing-Workflow-Semantics-Attachments Decomposition

arXiv:2606.06893v1 Announce Type: new Abstract: Large language model agents increasingly rely on Skills to encode procedural knowledge, yet high-quality Skills remain costly to hand-write. This paper

An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)

arXiv:2606.05357v1 Announce Type: new Abstract: Purpose: To develop an interpretable and trustworthy AI framework that combines deep learning based MRI Osteoarthritis Knee Score (MOAKS) prediction wit

Benchmarks in Leipzig

arXiv:2606.05818v1 Announce Type: cross Abstract: Between April 1 and May 15, 2026, a group of 49 mathematicians compiled a dataset of research-level mathematics questions with known answers. Most of

Binary Gaussian Copula Synthesis: an LLM-powered data augmentation framework for early dialysis prediction in chronic kidney disease

arXiv:2403.00965v2 Announce Type: replace-cross Abstract: Only a small fraction of patients with chronic kidney disease (CKD) progress to dialysis, creating severe class imbalance that limits the perf

applicationsarxiv-cs-ai

Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation

arXiv:2606.06345v1 Announce Type: new Abstract: Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes. To address this issue, we investigate

Conformal Risk-Averse Decision Making with Action Conditional Guarantee

arXiv:2606.05551v1 Announce Type: cross Abstract: Reliable decision making pipelines powered by machine learning models require uncertainty quantification (UQ) methods that come with explicit safety g

Differentiable Efficient Operator Search

arXiv:2606.05232v1 Announce Type: cross Abstract: Efficient multimodal foundation models often rely on manually designed token-reduction operators, such as pruning, merging, pooling, and adaptive rewe

Local Ai

Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things

arXiv:2512.20627v2 Announce Type: replace-cross Abstract: Intent-Based Networking (IBN) offers a promising paradigm for intelligent and automated network control in Industrial Internet of Things (IIoT

local-aiarxiv-cs-ai

Escaping the Verifier: Learning to Reason via Demonstrations

arXiv:2511.21667v4 Announce Type: replace-cross Abstract: Training Large Language Models (LLMs) to reason often relies on Reinforcement Learning (RL) with task-specific verifiers. However, many real-w

HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation

arXiv:2602.07739v2 Announce Type: replace-cross Abstract: Embedding geometry plays a fundamental role in retrieval quality, yet dense retrievers for retrieval-augmented generation (RAG) remain largely

Local Ai

Ontology-constrained multi-LLM scoring of hypothesis support in the predictive processing literature

arXiv:2606.05206v1 Announce Type: cross Abstract: Fragmentation is common in interdisciplinary fields with diverse methods and theoretical commitments. Predictive coding neuroscience is a clear exampl

local-aiarxiv-cs-ai

PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation

arXiv:2606.05697v1 Announce Type: new Abstract: User interface (UI) and user experience (UX) evaluation is central to product development, yet reliable feedback still relies on recruiting human partic

Tutorials

Pretraining Recurrent Networks without Recurrence

arXiv:2606.06479v1 Announce Type: cross Abstract: Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations. Standard backpropagation through time (BPTT

tutorialsarxiv-cs-ai

QCFuse: Query-Aware Cache Fusion via Compressed View for Efficient RAG Serving

arXiv:2606.05875v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves large language model (LLM) answer quality by grounding generation in external evidence, but processing ret

Soft Sequence Policy Optimization

arXiv:2602.19327v3 Announce Type: replace-cross Abstract: A significant portion of recent research on Large Language Model (LLM) alignment focuses on developing new policy optimization methods based o

Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting

arXiv:2606.06102v1 Announce Type: new Abstract: Ultra-short-term solar irradiance prediction is critical for photovoltaic system dispatch and power grid stability. Existing approaches suffer from thre

Towards AI epidemiology: a measurement standardisation framework for prospective risk detection

arXiv:2512.15783v3 Announce Type: replace Abstract: This paper proposes a measurement standardisation framework that compresses expert-AI interactions into structured, comparable fields for prospectiv

A Komi-Yazva--Russian Parallel Corpus and Evaluation Protocol for Zero- and Few-Shot LLM Translation

arXiv:2606.06420v1 Announce Type: new Abstract: We present the first Komi-Yazva--Russian parallel corpus together with an explicit evaluation protocol for studying LLM translation in an endangered, ex

Hardware

Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation

arXiv:2606.05687v1 Announce Type: new Abstract: In humanoid motion control, model predictive control (MPC) offers physically grounded prediction and constraint handling, while reinforcement learning (

hardwarearxiv-cs-ro

Beyond Absolute Scores: Relative Edit-induced Difference for Generalizable Image Aesthetic Assessment

arXiv:2606.05778v1 Announce Type: new Abstract: Traditional Image Aesthetic Assessment (IAA) methods mainly rely on regressing absolute Mean Opinion Scores (MOS). However, such a paradigm overlooks th

Beyond tokens: a unified framework for latent communication in LLM-based multi-agent systems

arXiv:2606.05711v1 Announce Type: new Abstract: Multi-agent systems built on large language models (LLMs) have become a prevailing paradigm for tackling complex reasoning, planning, and tool-use tasks

ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation

arXiv:2601.12983v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, improving analysis and reporting effic

researcharxiv-cs-cl

CHASE: Adversarial Red-Blue Teaming for Improving LLM Safety using Reinforcement Learning

arXiv:2606.05523v1 Announce Type: new Abstract: Despite advances in safety alignment, prompt-rewriting attacks such as persona modulation, fictional framing and persuasion-based reformulation, can byp

Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning

arXiv:2606.05645v1 Announce Type: new Abstract: Autonomous driving requires reasoning about how ego actions shape the evolution of the surrounding world. However, most end-to-end methods rely on direc

safetyarxiv-cs-ro

Drishti AI-Event Guardian: An Intelligent Real-Time Crowd Monitoring and Emergency Response System for Mass Gathering Events

arXiv:2606.05185v1 Announce Type: cross Abstract: Mass gathering events are associated with critical safety incidents caused by insufficient crowd monitoring and inadequate emergency response coordina

safetyarxiv-cs-cv

Dual Feature Decoupling for Fine-Grained OOD Detection

arXiv:2606.05536v1 Announce Type: new Abstract: Out-of-distribution detection (OOD) is an indispensable technique when applying machine learning models to real-world scenarios. Most existing OOD detec

applicationsarxiv-cs-cv

Emotion-Aware Image Generation from Korean Diary Text via LLM-based Prompt Translation and LoRA Fine-Tuning

arXiv:2606.05816v1 Announce Type: new Abstract: T2I models cannot effectively capture sentiment from various types of text, including diaries, as they primarily focus on visual object-related patterns

Agents

EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts

arXiv:2606.05513v1 Announce Type: cross Abstract: Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming proces

agentsarxiv-cs-cl

ExpSpeech-Net: Multimodal Fusion of Expression and Speech for Deepfake Detection

arXiv:2606.05760v1 Announce Type: new Abstract: Deepfake videos are increasingly challenging the credibility of online content. Many existing detection methodology relies on complex, resource-intensiv

Flow-based Policy Adaptation without Policy Updates

arXiv:2606.06461v1 Announce Type: new Abstract: Leveraging prior knowledge from pretrained policies, foundation models, or human operators offers an efficient alternative to learning robot skills from

safetyarxiv-cs-ro

Agents

Harnessing Generalist Agents for Contextualized Time Series

arXiv:2606.05404v1 Announce Type: cross Abstract: Time series are often embedded in rich contexts that are essential for holistic modeling. Moreover, real-world practitioners often require end-to-end

agentsarxiv-cs-cl

HomeWorld: A Unified Floorplan-to-Furnished Framework for Generating Controllable, Densely Interactive Whole-Home Scenes

arXiv:2606.06390v1 Announce Type: new Abstract: Indoor scene generation is crucial for robot simulation and modern interior design. However, complex layouts together with scarce 3D scene data make lea

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning

arXiv:2606.06100v1 Announce Type: new Abstract: Vision-Language Models (VLMs) struggle with compositional reasoning that requires understanding inter-object relationships. A natural remedy is to injec

Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction

arXiv:2606.05769v1 Announce Type: new Abstract: Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Existing video MLLMs usually verbalize inter

In-Context Multiple Instance Learning

arXiv:2606.06458v1 Announce Type: cross Abstract: Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied

applicationsarxiv-cs-cv

Learning Predictive Visuomotor Coordination

arXiv:2503.23300v2 Announce Type: replace Abstract: Understanding and predicting human visuomotor coordination is crucial for applications in robotics, human-computer interaction, and assistive techno

applicationsarxiv-cs-cv

MemoryCard: Topic-Aware Multi-Modal Clue Compression for Long-Video Question Answering

arXiv:2606.05917v1 Announce Type: cross Abstract: Long-video question answering remains challenging for Vision-Language Models (VLMs), as answer-relevant evidence is often sparse, transient, and tempo

researcharxiv-cs-cl

Agents

MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery

arXiv:2606.06473v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly applied to long-horizon tasks such as scientific discovery and machine learning engineering (MLE),

agentsarxiv-cs-cl

Next-Generation Parallel Decoder for LPDR: Architectural Optimization and Class-Balanced GAN-Augmentation

arXiv:2606.05785v1 Announce Type: new Abstract: Real-Time License Plate Detection and Recognition (LPDR) forms the backbone of modern smart cities. Although the YOLOV5-PDLPR model substantially improv