Search: “models”

Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling

arXiv:2606.02004v1 Announce Type: new Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data. A recurring obstacle is

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Measurement Geometry and Design for Trustworthy Generative Inverse Problems

DGX agent

arXiv:2606.02309v1 Announce Type: cross Abstract: Generative models are increasingly used as priors for inverse problems, but their ability to produce realistic images creates a basic trust problem: a

safetyarxiv-cs-cv

2 Jun 2026

MESA: Improving MoE Safety Alignment via Decentralized Expertise

arXiv:2606.00651v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures scale Large Language Models (LLMs) efficiently, enabling greater capacity with reduced computational cost by dy

Minimax-Optimal Policy Regret in Partially Observable Markov Games

arXiv:2606.02363v1 Announce Type: new Abstract: We study sequential decision-making in partially observable environments against strategic, adaptive opponents, modeled as partially observable Markov g

safetyarxiv-cs-lg

MINTS: Minimalist Thompson Sampling

arXiv:2606.01655v1 Announce Type: cross Abstract: The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but its reliance on a probabilistic model for all para

Normality-Preserving Continual Industrial Anomaly Detection via Orthogonal LoRA Banks

arXiv:2606.02042v1 Announce Type: new Abstract: Continual industrial anomaly detection with diffusion models suffers from historical normality prior drift and catastrophic forgetting. Existing continu

Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate

arXiv:2606.00820v1 Announce Type: new Abstract: Multi-agent debate (MAD) is a promising strategy for improving LLM reasoning, but when agents converge on a shared answer, it is unclear whether that co

agentsarxiv-cs-cl

Tutorials

Not All Points Are Equal: Uncertainty-Aware 4D LiDAR Scene Synthesis

arXiv:2606.02510v1 Announce Type: new Abstract: Constructing faithful 4D worlds from LiDAR-acquired sequences is crucial for embodied AI, yet current generative frameworks apply uniform modeling capac

tutorialsarxiv-cs-cv

Not What, But How: A Communicative Audit of LLM Response Framing

arXiv:2606.02493v1 Announce Type: new Abstract: Large language models (LLMs) are being increasingly used to answer subjective, information-seeking questions, where users are sensitive to how responses

On Effectiveness and Efficiency of Agentic Tool-calling and RL Training

arXiv:2606.00135v1 Announce Type: cross Abstract: Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge. This pa

On the Collapse of Generative Paths: A Criterion and Correction for Diffusion Steering

arXiv:2512.10339v2 Announce Type: replace Abstract: Inference-time steering adapts pretrained diffusion and flow models to new tasks without retraining, often utilizing ratio-of-densities construction

Local Ai

One Channel to Rule Them All: Rethinking Input Representation for Visual Place Recognition

arXiv:2606.00936v1 Announce Type: new Abstract: Visual Place Recognition (VPR) is fundamental to long-term robot localization and SLAM, yet current systems overwhelmingly rely on RGB input, implicitly

local-aiarxiv-cs-cv

OP-LoRA: The Blessing of Dimensionality

arXiv:2412.10362v2 Announce Type: replace-cross Abstract: Low-rank adapters (LoRA) enable finetuning of large models with only a small number of parameters. However, they often suffer from an ill-cond

OpenHospital: A Thing-in-itself Arena for Evolving and Benchmarking LLM-based Collective Intelligence

arXiv:2603.14771v3 Announce Type: replace Abstract: Large Language Model (LLM)-based Collective Intelligence (CI) presents a promising approach to overcoming the data wall and continuously boosting th

agentsarxiv-cs-ai

PaCo-VLA: Passivity-Shielded Compliance Prior for Contact-Rich Vision-Language-Action Manipulation

arXiv:2606.00515v1 Announce Type: cross Abstract: Contact-rich manipulation demands both high-level semantic reasoning and the safe regulation of high-frequency contact dynamics. While Vision-Language

applicationsarxiv-cs-ai

PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder

arXiv:2606.01537v1 Announce Type: new Abstract: Clinical diagnosis often requires combining imaging with physiological measurements, yet deployed models typically operate on unimodal data. We present

Paradoxical noise preference in RNNs

arXiv:2601.04539v2 Announce Type: replace-cross Abstract: In recurrent neural networks (RNNs) used to model biological neural networks, noise is typically introduced during training to emulate biologi

Parametric Social Identity Injection and Diversification in Public Opinion Simulation

arXiv:2603.16142v2 Announce Type: replace Abstract: Large language models (LLMs) have recently been adopted as synthetic agents for public opinion simulation, offering a promising alternative to costl

applicationsarxiv-cs-cl

Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents

arXiv:2606.00826v1 Announce Type: new Abstract: Strategic machine learning investigates scenarios where agents manipulate their features to receive favorable decisions from predictive models. To addre

safetyarxiv-cs-lg

Physics-Aware Linearized ADMM and Its Unrolling

arXiv:2606.01652v1 Announce Type: cross Abstract: Recently, partial differential equations (PDEs) have been used to directly model the measurement process in signal processing, although their evaluati

PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation

arXiv:2602.01662v4 Announce Type: replace Abstract: Recent advances in vision-language models (VLMs) have enabled increasing progress in real-world robot manipulation. However, long-horizon manipulati

agentsarxiv-cs-ro

PMC-InterCPT: Rethinking Biomedical Interleaved Data for Multimodal Continued Pretraining

arXiv:2606.01049v1 Announce Type: new Abstract: Large-scale biomedical image-text datasets extracted from scientific literature provide valuable resources for medical multimodal model training. These

Hardware

Predicting Future Utility: Global Combinatorial Optimization for Task-Agnostic KV Cache Eviction

arXiv:2602.08585v2 Announce Type: replace-cross Abstract: Given the quadratic complexity of attention, KV cache eviction is vital to accelerate model inference. Current KV cache eviction methods typic

hardwarearxiv-cs-ai

Principle-Evolvable Scientific Discovery via Uncertainty Minimization

arXiv:2602.06448v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based scientific agents have accelerated scientific discovery, yet they often suffer from significant inefficiencie

ProtoAda: Prototype-Guided Adaptive Adapter Expansion and Geometric Consolidation for Multimodal Continual Instruction Tuning

arXiv:2606.02576v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, but real-world deployment requires them to continually a

applicationsarxiv-cs-cv

PSG-Nav: Probabilistic Scene Graph Navigation via Multiverse Decision Making

arXiv:2606.01313v1 Announce Type: cross Abstract: Open-vocabulary navigation requires embodied agents to manage significant perception uncertainty stemming from semantic ambiguity and model errors. Ho

RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography

arXiv:2604.15231v2 Announce Type: replace Abstract: Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (

agentsarxiv-cs-ai

Rank-Constrained Deep Matrix Completion for Group Recommendation

arXiv:2606.01948v1 Announce Type: cross Abstract: The growing popularity of group activities has increased the need for methods that provide recommendations to groups of users given their individual p

applicationsarxiv-cs-ai

Reason, Retrieve, Re-rank: A Zero-Shot Reasoning-Aware Framework for Composed Video Retrieval

arXiv:2606.00910v1 Announce Type: new Abstract: Composed Video Retrieval (CoVR) seeks the target video that results from applying a free-form textual modification to a reference video. We address the

Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief

arXiv:2606.00680v1 Announce Type: new Abstract: Offline reinforcement learning (RL) aims to optimize policies from pre-collected datasets. A bottleneck of this paradigm is managing epistemic uncertain

Repurposing Adversarial Perturbations for Continual Learning: From Defense to Active Alignment

arXiv:2606.02322v1 Announce Type: cross Abstract: In dynamic environments, large language models need to keep adapting to new tasks, but continual learning often suffers from forgetting, limited trans

Retrieve What's Missing: Coverage-Maximizing Retrieval for Consistent Long Video Generation

arXiv:2606.02479v1 Announce Type: new Abstract: Maintaining long-term geometric consistency remains challenging for long-horizon autoregressive video generation. Memory-augmented generative models add

Robust Integrated Planning and Control for Quadrotors in Dynamic Environments via NMPC with CBF Penalties

arXiv:2606.01038v1 Announce Type: new Abstract: This paper presents a new robust integrated planning and control (IPC) strategy for multirotor uncrewed aerial vehicles. We propose a nonlinear model pr

safetyarxiv-cs-ro

Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation

arXiv:2606.00808v1 Announce Type: new Abstract: Source-free graph domain adaptation (SF-GDA) aims to adapt source-trained graph models to unlabeled target graphs when source graphs are no longer acces

applicationsarxiv-cs-lg

Scaling Agentic Capabilities via Grounded Interaction Synthesis

arXiv:2606.02001v1 Announce Type: new Abstract: General agentic intelligence hinges on the ability to interact with diverse real-world tools to complete complex tasks, a capability fundamentally tied

agentsarxiv-cs-cl

Self-Improving Small Object Grounding in LVLMs

arXiv:2606.01612v1 Announce Type: new Abstract: Can internal attention patterns in Large Vision Language Models (LVLMs) identify reliable small-object boxes without fine-tuning? In this work, we provi

Situation-Aware Interactive MPC Switching for Autonomous Driving

arXiv:2512.06182v2 Announce Type: replace Abstract: Autonomous driving in interactive traffic scenarios remains challenging because of the mutual influence among vehicles and the inherent uncertainty

agentsarxiv-cs-ro

Hardware

Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills

arXiv:2503.05641v4 Announce Type: replace-cross Abstract: Combining existing pre-trained LLMs is a promising approach for diverse reasoning tasks. However, task-level expert selection is often too coa

hardwarearxiv-cs-ai

'Skill issues'': data-centric optimization of lakehouse agents

arXiv:2606.01185v1 Announce Type: new Abstract: Coding agents are becoming users of data infrastructure, but their success depends not only on model quality: it also depends on the skills and environm

agentsarxiv-cs-ai

SN-WER: Script-Normalized WER for Multi-Script Indic ASR Evaluation

arXiv:2606.02548v1 Announce Type: new Abstract: Word Error Rate (WER) is the dominant metric for automatic speech recognition (ASR), but it can overestimate errors when references and hypotheses encod

Statistical Guarantees for Reasoning Probes on Looped Boolean Circuits

arXiv:2602.03970v3 Announce Type: replace-cross Abstract: We study the statistical behavior of reasoning probes in a stylized model of iterative computation inspired by neural algorithmic reasoning. T

researcharxiv-cs-lg

SWARD: Stochastic Window-Attention-Based Relational Distillation for Cross-Architectural Semantic Segmentation

arXiv:2606.00999v1 Announce Type: new Abstract: Large-scale vision foundation models have driven substantial gains on dense prediction tasks such as semantic segmentation, but their size makes deploym

safetyarxiv-cs-cv

T-POP: Test-Time Personalization with Online Preference Feedback

arXiv:2509.24696v2 Announce Type: replace-cross Abstract: Personalizing large language models (LLMs) to individual user preferences is a critical step beyond generating generically helpful responses.

Tutorials

Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation

arXiv:2606.01020v1 Announce Type: new Abstract: Identifying logical fallacies in everyday discourse is challenging for many people. This challenge is amplified in the era of Large Language Models (LLM

tutorialsarxiv-cs-ai

TERRA: Task-Embedded Reasoning and Representation Architecture for Cross-Domain Applications

arXiv:2606.01520v1 Announce Type: new Abstract: A single action-conditioned latent predictive architecture can in principle be trained on the structured state of a driving scene, a robot workspace, or

The Social Cost of Intelligence: Emergence, Propagation, and Amplification of Stereotypical Bias in Multi-Agent Systems

arXiv:2510.10943v2 Announce Type: replace-cross Abstract: Bias in large language models (LLMs) remains a persistent challenge, often leading to stereotyping and unfair treatment across social groups.

safetyarxiv-cs-cl

Tutorials

TIDES: Time-Derivative Event Simulation via Deformable Reconstruction

arXiv:2606.02058v1 Announce Type: new Abstract: Event cameras emit asynchronous events in response to environmental appearance changes. The scarcity of real-world event datasets makes simulation essen

tutorialsarxiv-cs-cv

Toward Responsible and Epistemically Grounded Multilingual LLMs for Computational Social Science and Humanities

arXiv:2606.00596v1 Announce Type: new Abstract: Large language models have rapidly evolved in multilingual competence and reasoning capacity, enabling their integration into Social Sciences and Humani

safetyarxiv-cs-cl