Search: “models”

Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models

arXiv:2602.01969v2 Announce Type: replace Abstract: Complex tables with multi-level headers, merged cells and heterogeneous layouts pose persistent challenges for LLMs in both understanding and reason

researcharxiv-cs-cl

26 Jun 2026

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Research

Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models

DGX agent

arXiv:2504.09910v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sen

researcharxiv-cs-cl

25 Jun 2026

Limitations of SGD for Multi-Index Models Beyond Statistical Queries

arXiv:2602.05704v2 Announce Type: replace Abstract: Understanding the limitations of gradient methods, and stochastic gradient descent (SGD) in particular, is a central challenge in learning theory. T

25 Jun 2026

Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection

arXiv:2606.24985v1 Announce Type: new Abstract: Personalization in wearable-based stress detection remains challenging due to substantial inter-individual variability in physiological and behavioral r

25 Jun 2026

Adversarial observations in probabilistic State-Space Models for robust Reinforcement Learning

arXiv:2606.20880v1 Announce Type: cross Abstract: Decision-making under partial or adversarial observability requires accurate inference of the environment's latent state and its associated uncertaint

safetyarxiv-cs-lg

Bracing for Impact: Robust Humanoid Push Recovery and Locomotion with Reduced Order Models

arXiv:2505.11495v2 Announce Type: replace Abstract: Push recovery during locomotion will facilitate the deployment of humanoid robots in human-centered environments. In this paper, we present a unifie

researcharxiv-cs-ro

Tutorials

Decision-Focused Learning: When and Why Traditional Prediction Models Fail

arXiv:2606.21773v1 Announce Type: new Abstract: Plugging predictions of unknown parameters into downstream optimization problems, often referred to as the ``predict-then-optimize'' paradigm, has long

tutorialsarxiv-cs-lg

Modular Diffusion Models for Structured Visual Recognition

arXiv:2606.22702v1 Announce Type: new Abstract: Traditional supervised methods for structured visual recognition tasks -- such as object detection, segmentation, and scene graph generation -- often pr

local-aiarxiv-cs-cv

Music Playlist Captioning at Scale with Large Language Models

arXiv:2606.22460v1 Announce Type: cross Abstract: Music streaming services such as Deezer often recommend personalized playlists to users. Playlist captioning, which involves describing these playlist

StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models

arXiv:2603.07307v2 Announce Type: replace Abstract: Recent token merging techniques for Vision Transformers (ViTs) provide substantial speedups by reducing the number of tokens processed by self-atten

researcharxiv-cs-cv

Temporal Logic Guidance for Action-Only Diffusion Policies with World Models

arXiv:2606.22729v1 Announce Type: new Abstract: Diffusion policies enable multimodal robot behavior but offer limited ability to choose among behavior modes at inference time, even though such control

safetyarxiv-cs-ro

OpenCodeRAG - RAG for OpenCode via locally hosted models

OpenCodeRAG is a local embedding service using FastAPI and SentenceTransformer, paired with a Node.js plugin that integrates RAG tools with Qdrant vector database via YAML configuration. It provides a

local-air-ollama

21 Jun 2026

Act on What You See: Unlocking Safe Social Navigation in Vision-Language-Action Models

arXiv:2606.10495v1 Announce Type: new Abstract: Safe social navigation requires robots to distinguish people from ordinary obstacles and to react before danger becomes imminent. We show that pretraine

safetyarxiv-cs-ro

FG-Attn: Leveraging Fine-Grained Sparse Attention in Video Diffusion Models

arXiv:2509.16518v2 Announce Type: replace Abstract: Using diffusion transformers for media generation may require evaluating attention over extremely long sequences, with attention layers accounting f

researcharxiv-cs-cv

Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models

arXiv:2606.10046v1 Announce Type: cross Abstract: Flow-matching transformers achieve strong audio separation, yet their attention dynamics are opaque. We adapt established causal-intervention principl

Agents

Resilient Navigation for Autonomous Farm Robots by Leveraging Jerk-Augmented Models with IMU-Only Disturbance Rejection

arXiv:2606.10971v1 Announce Type: new Abstract: Precise state estimation for navigation of autonomous agricultural robots is often compromised by sensor outages (GNSS/LiDAR/Visual) and high-frequency

agentsarxiv-cs-ro

BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching

arXiv:2411.16102v2 Announce Type: replace Abstract: Offline batch inference, which leverages the flexibility of request batching to achieve higher throughput and lower costs, is becoming more popular

Discovering Expert-Level Nash Equilibrium Algorithms with Large Language Models

arXiv:2508.11874v2 Announce Type: replace-cross Abstract: Designing polynomial-time algorithms for approximate Nash equilibria (ANE) with provable worst-case guarantees is a fundamental open problem i

Graph-GRPO: Training Graph Flow Models with Reinforcement Learning

arXiv:2603.10395v2 Announce Type: replace Abstract: Graph generation is a fundamental task with broad applications, such as drug discovery. Recently, discrete flow matching-based graph generation, aka

HARP: Efficient Data Selection for Finetuning Large Language Models

arXiv:2606.07690v1 Announce Type: cross Abstract: Finetuning data selection requires balancing two competing goals: selecting examples that improve the downstream objective, and doing so without repea

Large Language Models for Imbalanced Classification: Diversity makes the difference

arXiv:2510.09783v2 Announce Type: replace-cross Abstract: Oversampling is one of the most widely used approaches for addressing imbalanced classification. The core idea is to generate additional minor

Learning to Attack and Defend: Adaptive Red Teaming of Language Models via GRPO

arXiv:2606.09701v1 Announce Type: cross Abstract: AI red teaming must continually adapt to evolving attackers and defenders. Reinforcement learning offers a promising approach to discovering novel att

Optimality of Sequential Filtering Under Independent Cost and Selectivity Models

arXiv:2606.07589v1 Announce Type: new Abstract: Sequential filtering pipelines are a common design pattern in large-scale systems, where a large population of items is progressively reduced by a seque

Single-Cell Cross-Modal Transfer by Adversarial Fine-Tuning of Foundation Models

arXiv:2606.07676v1 Announce Type: cross Abstract: Spatial transcriptomics (ST) is a powerful tool for exploring biological properties dependent on structure, proximity, and interaction in tissue. The

When Vision Misleads, Let Location Speak: A Worldwide Image Geo-Localization Method via Location Attention Mechanism and Large Multimodal Models

arXiv:2606.08918v1 Announce Type: new Abstract: Worldwide image geo-localization aims to determine the capture location of an image on a global scale. Existing methods often mislocalize images by matc

local-aiarxiv-cs-cv

Trace Reconstruction with Language Models

arXiv:2507.12927v2 Announce Type: replace Abstract: The general trace reconstruction problem seeks to recover an original sequence from its noisy copies independently corrupted by insertions, deletion

applicationsarxiv-cs-lg

8 Jun 2026

A Conversational Framework for Human-Robot Collaborative Manipulation with Distributed Generative AI models

arXiv:2606.06061v1 Announce Type: new Abstract: This paper presents a distributed conversational framework for human-robot collaborative manipulation that integrates local language and vision-language

local-aiarxiv-cs-ro

5 Jun 2026

Trusted healthcare AI hinges on data foundations, not models alone

Healthcare AI is moving out of the pilot phase and into production environments, where the gap between a compelling demo and a clinically trustworthy output has never been more consequential. As AI ag

applicationssiliconangle

4 Jun 2026

AirDreamer: Generalist Drone Navigation with World Models

arXiv:2606.03252v1 Announce Type: cross Abstract: Navigating a drone in unseen and cluttered environments requires reliable generalization to unseen scene layouts and understanding of environmental st

Are we really tilting? The mechanics of reward guidance in flow and diffusion models

arXiv:2606.02884v1 Announce Type: cross Abstract: Reward guidance algorithms steer a learned generative process toward the reward-tilted measure at inference time. While empirically powerful, these me

Benchmarking Speech-to-Speech Translation Models

arXiv:2606.03241v1 Announce Type: new Abstract: Speech-to-speech translation (S2ST) has advanced rapidly, but offline evaluation lacks a unified protocol: studies report non-overlapping metric subsets

applicationsarxiv-cs-cl

Inverting the Generation Process of Denoising Diffusion Implicit Models: Empirical Evaluation and a Novel Method

arXiv:2606.03111v1 Announce Type: new Abstract: This paper studies the problem of inverting the DDIM image generation process to recover latent variables, particularly the initial noise map, from a ge

researcharxiv-cs-cv

Industry

Trump plan to test AI models has a problem—US security teams were gutted by DOGE

Trump signed an executive order on June 2, 2026, establishing a voluntary framework for the federal government to evaluate risks posed by advanced AI systems before release and encouraging companies t

industryars-technica

Tutorials

Video-Mirai: Autoregressive Video Diffusion Models Need Foresight

arXiv:2606.03971v1 Announce Type: new Abstract: Causal video generators must predict from the past, but they need not learn only from it. In streaming autoregressive video diffusion, each emitted segm

tutorialsarxiv-cs-cv

Cross-lingual Self-Consistency for Multilingual Reasoning with Language Models

arXiv:2606.01464v1 Announce Type: new Abstract: Despite expanding their multilingual coverage, the advanced reasoning capabilities of LLMs remain largely confined to a few high-resource languages like

researcharxiv-cs-cl

Drifting Preference Optimization for One-Step Generative Models

arXiv:2606.02521v1 Announce Type: cross Abstract: One-step text-to-image generators are attractive for deployment because they generate an image with a single forward pass, but preference finetuning t

safetyarxiv-cs-cv

Efficient Weighted Sampling via Score-based Generative Models

arXiv:2502.04646v2 Announce Type: replace-cross Abstract: Weighted sampling -- sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function -- is

Safe2Drive: Evaluating Safe Driving Behaviors of E2E Autonomous Driving Models

arXiv:2606.00191v1 Announce Type: cross Abstract: Recent end-to-end (E2E) autonomous driving policies achieve high driving scores in closed-loop simulations. Yet it remains unclear whether these polic

safetyarxiv-cs-cv

Tutorials

Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models

arXiv:2606.02374v1 Announce Type: new Abstract: Earth Observation (EO) has fundamentally transformed the monitoring of environmental processes and human activities up to planetary scale. Recent advanc

tutorialsarxiv-cs-ai

A Lecture Note on Offline RL and IRL, Part II: Foundations of Inverse Reinforcement Learning and Dynamic Discrete Choice Models

arXiv:2605.30843v1 Announce Type: new Abstract: In the forward reinforcement-learning problem, the reward is fixed and known; the learner is asked to find a good policy or value function. Here we turn

safetyarxiv-cs-lg

A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models

arXiv:2605.31080v1 Announce Type: cross Abstract: Blind and low-vision (BLV) audiences remain underserved by visual art descriptions, particularly across languages and in museum settings where privacy

GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization

arXiv:2605.31464v1 Announce Type: cross Abstract: GPU kernels are the workhorse of modern deep learning, and optimizing them (via evolutionary search or coding agents) usually requires repeated measur

local-aiarxiv-cs-ai

Import AI 459: AI oversight is difficult; scaling laws for protein folding models; and pricing the extinction risk of AI systems

This newsletter issue discusses three key topics in AI development and safety: the challenges involved in overseeing and controlling advanced AI systems, empirical findings about how protein folding A

safetyimport-ai

Scaling Multi-Agent Environment Co-Design with Diffusion Models

arXiv:2511.03100v2 Announce Type: replace-cross Abstract: The agent-environment co-design paradigm jointly optimises agent policies and environment configurations in search of improved system performa

A Matter of Interest: Understanding Interestingness of Math Problems in Humans and Language Models

arXiv:2511.08548v2 Announce Type: replace Abstract: The evolution of mathematics is shaped importantly by interestingness: researchers choose which problems to pursue, and students choose which proble

applicationsarxiv-cs-ai

Do Language Models Track Entities Across State Changes?

arXiv:2605.30233v1 Announce Type: cross Abstract: Entity tracking (ET), the ability to keep track of states, is a fundamental skill that underlies complex reasoning. An increasing amount of work inves

From General Vision to Reliable Traversability Estimation: Adapting Vision Foundation Models for Unstructured Outdoor Environments

arXiv:2605.29565v1 Announce Type: new Abstract: Vision-based approaches have become the dominant paradigm for traversability estimation in unstructured outdoor environments, typically adapting vision

safetyarxiv-cs-cv

Agents

Improving Collaborative Storytelling with a Multi-Agent Framework Based on Large Language Models

arXiv:2605.29625v1 Announce Type: new Abstract: The topic of Co-creation, i.e., AI agents interacting with humans to generate outputs (e.g., art), has gained significant attention recently. However, m

agentsarxiv-cs-ai