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arXiv:2606.03238v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) makes large-scale post-training possible by replacing an underspecified human objective with learned

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Human DGX agent

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When Should LLMs Be Less Specific? Selective Abstraction for Reliable Long-Form Text Generation

DGX agent

arXiv:2602.11908v3 Announce Type: replace Abstract: LLMs are widely used, yet they remain prone to factual errors that erode user trust and limit adoption in high-risk settings. One approach to mitiga

researcharxiv-cs-ai

3 Jun 2026

When Should the Teacher Move? Temporal Coupling and Stability in Self On-Policy Distillation

arXiv:2606.03532v1 Announce Type: cross Abstract: Self on-policy distillation trains a student policy against a teacher derived from its own parameter history, yet the teacher's update schedule -- whi

Safety

When to Re-Plan: Subgoal Persistence in Hierarchical Latent Reasoning

arXiv:2606.03741v1 Announce Type: new Abstract: Long-horizon reasoning requires a system to commit to medium-horizon intent without becoming rigid: re-plan too often and computation never coheres into

safetyarxiv-cs-ai

Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation?

arXiv:2606.03837v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) and probing enable adaptation of foundation models using only a small number of trainable parameters, making it a

Which Defense Closes Which Threat? Attributing OWASP-LLM-Top-10 Coverage and Its Brittleness Under Paraphrasing

arXiv:2606.02822v1 Announce Type: cross Abstract: Production LLM applications stack several defense families -- refusal-phrase filters, token-budget controls, model allowlists, rate limits, tool-regis

Safety

Who Deserves the Reward? SHARP: Shapley Credit-based Optimization for Multi-Agent System

arXiv:2602.08335v2 Announce Type: replace Abstract: Integrating Large Language Models (LLMs) with external tools via multi-agent systems offers a promising new paradigm for decomposing and solving com

safetyarxiv-cs-ai

Agents

Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions

arXiv:2602.14279v2 Announce Type: replace-cross Abstract: Eliciting information to reduce uncertainty about latent group-level properties from surveys and other collective assessments requires allocat

agentsarxiv-cs-ai

Whose Name Comes Up? II: Benchmarking and Intervention-Based Auditing of LLM-Based Scholar Recommendation

arXiv:2602.08873v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now used for academic expert recommendation. Existing audits typically evaluate such recommendations in isola

Why Are Linear RNNs More Parallelizable?

arXiv:2603.03612v3 Announce Type: replace-cross Abstract: The community is increasingly exploring linear RNNs (LRNNs) as language models, motivated by their expressive power and parallelizability. Whi

researcharxiv-cs-cl

Will Accurate Fields Mislead Photonic Design? FromGlobal Accuracy to Port Readout

model-releasesarxiv-cs-lg

arXiv:2606.03038v1 Announce Type: new Abstract: Neural field surrogates can accelerate photonic design loops, but a surrogate that looks accurate in global field error can still mis-rank candidate dev

WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation

arXiv:2503.07265v4 Announce Type: replace-cross Abstract: Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content. However, existing research and evalua

WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition

arXiv:2606.02974v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems,

applicationsarxiv-cs-ai

Safety

World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning

arXiv:2606.03603v1 Announce Type: cross Abstract: World models and multimodal large language models (MLLMs) provide complementary capabilities for predicting future outcomes from static visual observa

safetyarxiv-cs-cl

Worth Remembering: Surprise-Gated Robot Episodic Memory

arXiv:2606.03787v1 Announce Type: new Abstract: Robots solving generalist tasks need to be able to ground instructions in their past experience, since humans may refer to notable past events when givi

researcharxiv-cs-ro

WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents

arXiv:2606.02908v1 Announce Type: cross Abstract: Multi-turn user-facing agents must infer user intent from incomplete requests, collect missing information through dialogue and tools, and execute val

X-RAY: Mapping LLM Reasoning Capability via Formalized and Calibrated Probes

arXiv:2603.05290v2 Announce Type: replace Abstract: Large language models (LLMs) achieve promising performance, yet their ability to reason remains poorly understood. Existing evaluations largely emph

Tutorials

Your Autoregressive Model Already Reveals the Causal Graph

arXiv:2602.01135v3 Announce Type: replace Abstract: Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process. We

tutorialsarxiv-cs-lg

Zero-Shot 3D Question Answering via Hierarchical View-to-Token Transportation

arXiv:2606.03100v1 Announce Type: new Abstract: Recently, zero-shot 3D scene understanding via 2D Vision-Language Models (VLMs) has gained increasing research interest due to their promising spatial r

researcharxiv-cs-cv

ZX-Calculus:Trace-Indexed Dependent Types and Epistemic Semantics

arXiv:2606.03063v1 Announce Type: cross Abstract: We propose ZX-Calculus (Knowledge Evolution Calculus), a conservative extension of Martin-Lof Dependent Type Theory (MLTT) integrating trace-indexed t

researcharxiv-cs-cl

21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables

arXiv:2606.00219v1 Announce Type: cross Abstract: We are witnessing a surge in observations of the cosmic dawn (CD) and epoch of reionisation (EoR), driving an increasing demand for fast and robust th

3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum

arXiv:2606.00489v1 Announce Type: new Abstract: Placenta Accreta Spectrum (PAS) is a rare but highly dangerous obstetric disease. Early and accurate PAS diagnosis is critical for maternal health. Trad

3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code

arXiv:2606.01057v1 Announce Type: cross Abstract: Procedural 3D modeling through code is emerging as a versatile paradigm, offering deterministic, engine-ready, and precisely editable assets that neur

3rd Place at CVPR 2026 CASTLE Challenge: Agentic Multi-View Long-Context Video Understanding via Hierarchical Knowledge Graph Retrieval

arXiv:2606.01933v1 Announce Type: new Abstract: This paper presents our winning methodology for the CASTLE 2026 Challenge at the CVPR 2026 EgoVis Workshop, where our team secured third place globally.

Agents

4D Radar Meets LiDAR and Camera: Cooperative Perception under Adverse Weather

arXiv:2606.00416v1 Announce Type: new Abstract: Cooperative perception is important for autonomous driving but remains fragile when cameras and LiDAR degrade in adverse weather. We address this challe

agentsarxiv-cs-cv

A Biconvex Formulation for Stable Transport of Mixture Models with a Unique Solution

arXiv:2606.02515v1 Announce Type: new Abstract: Optimal transport (OT) provides a principled framework for mapping between probability distributions. Despite extensive progress, applying OT to large-s

applicationsarxiv-cs-lg

A Closer Look at In-Distribution vs. Out-of-Distribution Accuracy for Open-Set Test-time Adaptation

arXiv:2606.01973v1 Announce Type: cross Abstract: Open-set test-time adaptation (TTA) updates models on new data in the presence of input shifts and unknown output classes. While recent methods have m

A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs

arXiv:2606.02267v1 Announce Type: cross Abstract: The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment. Existing techniques to enha

applicationsarxiv-cs-cv

Agents

A Communication-Centric 6G-LLM Architecture for Scalable Tactical Autonomous Defense Vehicle Networks

arXiv:2606.01312v1 Announce Type: cross Abstract: The integration of Artificial Intelligence (AI) and emerging 6G networks introduces new opportunities for scalable coordination in tactical autonomous

agentsarxiv-cs-ai

A Comparative Analysis of Machine Learning Algorithms for Multi-Task Prediction of the Parameters of the Pectin Hydrolysis--Extraction Process

model-releasesarxiv-cs-lg

arXiv:2606.00821v1 Announce Type: new Abstract: This study addresses the challenge of controlling a complex, multi-parameter technological process -- pectin hydrolysis--extraction -- using machine lea

A Direct Approach for Handling Contextual Bandits with Latent State Dynamics

arXiv:2604.08149v2 Announce Type: replace Abstract: We consider a linear contextual bandit model where contexts and rewards are governed by a finite hidden Markov chain. We first revisit the simplifie

A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering

arXiv:2606.00402v1 Announce Type: cross Abstract: We propose a distribution-free statistical framework that converts arbitrary rewrite-based detectors into detectors with finite-sample FDR guarantees

A Doeblin-Anchored Contrastive Chart for Learning Markov Transition Kernels

arXiv:2606.02232v1 Announce Type: new Abstract: Learning a Markov transition model is not merely conditional density estimation: the learned object must be a valid transition kernel before it is itera

A Fiber Criterion for Representation Identifiability in Supervised Learning

arXiv:2606.01092v1 Announce Type: cross Abstract: Supervised learning evaluates predictors through their input-output behavior. When a predictor is implemented as a composition f=circ h, supervised ev

A Finite-Calibration Regime Map for LLM Judge Panels

model-releasesarxiv-cs-cl

arXiv:2606.01034v1 Announce Type: new Abstract: We study when LLM judge panels should be calibrated with low-dimensional stackers versus joint output tables under finite human-label budgets. Low-dimen

A Foundation Model for Wearable Movement Data in Mental Health Research

arXiv:2411.15240v5 Announce Type: replace-cross Abstract: Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, r

applicationsarxiv-cs-ai

A Four-Tier Communication Architecture and Sim-to-Real Validation of a Graphical Open-Source Platform for Robotic Engineering Education

arXiv:2606.00550v1 Announce Type: cross Abstract: The persistent challenge in scaling authentic manipulator education within university laboratories is a structural dichotomy: commercial digital twins

applicationsarxiv-cs-ro

A Framework for Graph-Conditioned Hierarchical Shapley Attribution in Patent Valuation

arXiv:2606.01632v1 Announce Type: cross Abstract: Estimating the economic contribution of a single patent inside a product that embodies tens of thousands of patents is a long-standing unsolved proble

A Grammar of Machine Learning Workflows: Rejecting Data Leakage at Call Time

arXiv:2603.10742v4 Announce Type: replace Abstract: Data leakage has been identified in 648 published papers across 30 scientific fields. The knowledge to prevent it has existed for over a decade; the

A Hypertoroidal Covering for Perfect Color Equivariance

arXiv:2603.04256v3 Announce Type: replace Abstract: When the color distribution of input images changes at inference, the performance of conventional neural network architectures drops considerably. A

researcharxiv-cs-cv

A Kinetic Theory of Encounter-Based Information Propagation in Multi-Robot Systems

arXiv:2606.02296v1 Announce Type: new Abstract: Multi-robot systems cannot assume persistent network connectivity. We study this problem through target tracking, where performance depends on how quick

researcharxiv-cs-ro

A Lightweight Context-Driven Training-Free Network for Scene Text Segmentation and Recognition

arXiv:2503.15639v2 Announce Type: replace-cross Abstract: Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively exp

Local Ai

A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks

arXiv:2507.19702v1 Announce Type: cross Abstract: Identifying influential nodes in complex networks is a critical task with a wide range of applications across different domains. However, existing app

local-aiarxiv-cs-ai

A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features

arXiv:2606.00889v1 Announce Type: cross Abstract: Phishing attacks remain a major cybersecurity threat, exploiting deceptive URLs to steal sensitive user information. Traditional blacklist and rule-ba

A Local Perturbation Theory for Cross-Domain Interference and Recovery in Multi-Domain RL

model-releasesarxiv-cs-cl

arXiv:2606.02398v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training improves large language models (LLMs) on individual domains such as mathematical reasoning, code generation,

A Machine-to-Machine Knowledge-Guided LLM Agent for Generalizable Radiotherapy Treatment Planning

model-releasesarxiv-cs-ro

arXiv:2606.00922v1 Announce Type: cross Abstract: In this work, we propose a prototype machine-to-machine (M2M) knowledge-guided Large Language Model (LLM) framework for automated radiotherapy treatme

A Mathematical Conflict Framework for Contextual Data Modulation

arXiv:2606.02381v1 Announce Type: new Abstract: In this study, a generalized operator-based mathematical conflict framework is presented to explicitly represent structural discrepancies between raw da

A Methodological Framework for Explicit Control of the Speed-Accuracy Trade-off in Brain-Computer Interfaces

arXiv:2606.00106v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) are limited by low signal-to-noise ratio in modalities such as electroencephalography, which requires multiple trials