Search: “models”

Predicting cognitive load in immersive driving scenarios with a hybrid CNN-RNN model

arXiv:2408.06350v2 Announce Type: replace-cross Abstract: One debatable issue in traffic safety research is that cognitive load from sec-ondary tasks reduces primary task performance, such as driving.

safetyarxiv-cs-lg

Reference-Free Assessment of Physical Consistency in World Model-based Video Generation

DGX agent

arXiv:2606.22363v1 Announce Type: cross Abstract: We introduce reference-free measures for evaluating the physical consistency of generated videos, combining relative and absolute approaches to assess

applicationsarxiv-cs-lg

23 Jun 2026

Render-FM: Feedforward Model for Real-time Photorealistic Volumetric Rendering

arXiv:2505.17338v2 Announce Type: replace Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and

researcharxiv-cs-cv

Rethinking Object-Centric Representations for Video Dynamics Modeling

arXiv:2606.23436v1 Announce Type: new Abstract: Unsupervised video object tracking aims to decompose dynamic scenes into persistent, object-centric entities without manual annotations. Many recent app

Tutorials

Sparse Point-Guided Fusion of Supervised and Self-Supervised Learning Model for Seaweed Segmentation

arXiv:2606.21026v1 Announce Type: new Abstract: The ocean plays a critical role in sustainable development, particularly in climate change mitigation. Among marine ecosystems, blue carbon ecosystems a

tutorialsarxiv-cs-cv

Stabilizing Consistency Training: A Flow Map Analysis and Self-Distillation

arXiv:2601.22679v2 Announce Type: replace-cross Abstract: Consistency models have been proposed for fast generative modeling, achieving results competitive with diffusion and flow models. However, the

Strengthening LLMs for Tabular Prediction with Structural Priors

arXiv:2510.17385v5 Announce Type: replace Abstract: Tabular prediction has long been dominated by gradient-boosted decision trees and specialized deep tabular models, while large language models (LLMs

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

UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning

arXiv:2606.19328v2 Announce Type: replace Abstract: Preference-based RL provides an approach to learning reward models from pairwise comparisons of behaviors, bypassing the need for explicit reward de

Unlimited OCR Works

arXiv:2606.23050v1 Announce Type: new Abstract: Recently, end-to-end OCR models, exemplified by DeepSeek OCR, have once again thrust OCR into the spotlight. A widely held view is that employing a larg

Local Ai

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

AsFT: Anchoring Safety During LLM Fine-Tuning Within Narrow Safety Basin

arXiv:2506.08473v4 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) improves performance but introduces critical safety vulnerabilities: even minimal harmful data can severely

Cross-Modal Benchmarking for Robotic Perception in Natural Environments

arXiv:2606.11563v1 Announce Type: new Abstract: Natural environments present a complex challenge to robotics perception systems. Current models, particularly vision foundation models, are largely trai

Tutorials

How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit

arXiv:2606.12182v1 Announce Type: new Abstract: Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering. While early approaches

tutorialsarxiv-cs-lg

Market Design for AI: Beyond the Copyright Binary

arXiv:2606.12260v1 Announce Type: cross Abstract: How can we design a market of human-generated content for use in training AI models that both enables technological progress and preserves individual

researcharxiv-cs-ai

MobileFineTuner: A Mobile-Native Framework for On-Device LLM Fine-Tuning in Real-World Embedded AI Applications

arXiv:2512.08211v2 Announce Type: replace Abstract: Large language models (LLMs) are moving from cloud-centric services toward on-device embedded AI, where models interact with private, longitudinal s

Semantic Grading of Written Answers in Low-Resource Language Bangla Using a Fine-Tuned Lightweight Language Model

arXiv:2606.11931v1 Announce Type: new Abstract: Bangla is among the world's most widely spoken languages, yet it remains underserved in educational NLP research. In many remote and rural regions, acce

researcharxiv-cs-cl

The Language You Ask In: Language-Conditioned Ideological Divergence in LLM Analysis of Contested Political Documents

model-releasesarxiv-cs-cl

arXiv:2601.12164v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as analytical tools across multilingual contexts, yet their outputs may carry systemati

A fine-grained attention and geometric correspondence model for musculoskeletal risk classification in athletes using multimodal visual and skeletal features

arXiv:2509.05913v3 Announce Type: replace Abstract: Musculoskeletal disorders pose significant risks to athletes, and early risk assessment is essential for prevention. However, most existing methods

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

Advancing the State-of-the-Art in Empirical Privacy Auditing

arXiv:2606.10481v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization of individual training examples. Empirical privac

ANCHOR: Autoregressive Non-intrusive Chunk-Ordered Refinement for Joint Multi-Resolution Speech Quality Modeling

arXiv:2606.10233v1 Announce Type: cross Abstract: While speech quality is typically assessed on complete utterances, streaming and generative systems require incremental estimation from partial audio.

researcharxiv-cs-lg

Assessing Automated Prompt Injection Attacks in Agentic Environments

arXiv:2606.10525v1 Announce Type: cross Abstract: Indirect prompt injection poses a critical threat to LLM agents that interact with untrusted external data, yet automated attack methods--proven effec

Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings

arXiv:2606.10716v1 Announce Type: cross Abstract: Pre-trained language models (PLMs) have achieved strong performance in keyphrase extraction (KPE), largely due to their ability to generate rich conte

Claude Fable 5 thinks document parsing is beneath it It is absolutely crushing on all reasoning-intensive/long horizon benchmarks: SWE-Bench…

model-releasesjerry-liu--x

Claude Fable 5 thinks document parsing is beneath it It is absolutely crushing on all reasoning-intensive/long horizon benchmarks: SWE-Bench Pro, FrontierCode, GDPval, Runescape, etc. But for document

Express Language Modeling

arXiv:2606.10944v1 Announce Type: new Abstract: We introduce a new tool, Express, for converting a non-causal attention approximation into a causal approximation with matching approximation guarantees

researcharxiv-cs-lg

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

IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts

arXiv:2606.09908v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a major challen

IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation

arXiv:2606.10818v1 Announce Type: cross Abstract: Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting o

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

researcharxiv-cs-ai

iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervision

arXiv:2606.10136v1 Announce Type: new Abstract: Semantic segmentation in remote sensing requires costly pixel-level annotations, and nearly every problem demands a new dataset since models rarely tran

ProbeLLM: Automating Principled Diagnosis of LLM Failures

model-releasesarxiv-cs-cl

arXiv:2602.12966v2 Announce Type: replace Abstract: Understanding how and why large language models (LLMs) fail is becoming a central challenge as models rapidly evolve and static evaluations fall beh

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

Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall

arXiv:2606.09959v1 Announce Type: cross Abstract: Precipitation nowcasting is increasingly being approached with deep learning models that learn directly from recent radar observations. Although such

Local Ai

Tractogram foundation model

arXiv:2606.09893v1 Announce Type: cross Abstract: Diffusion MRI (dMRI) tractography is the only noninvasive approach for mapping white-matter pathways in the living human brain. It represents each bra

local-aiarxiv-cs-ai

AliyunConsoleAgent: Training Web Agents in Real-World Cloud Environments via Distillation and Reinforcement Learning

arXiv:2606.09447v1 Announce Type: new Abstract: We present AliyunConsoleAgent, a web agent framework for automated documentation verification in real-world cloud consoles. Major cloud platforms encomp

AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library

arXiv:2510.18428v4 Announce Type: replace Abstract: Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions

Local Ai

An Alternative Trajectory for Generative AI

arXiv:2603.14147v2 Announce Type: replace Abstract: The generative artificial intelligence (AI) ecosystem is undergoing rapid transformations that threaten its sustainability. As models transition fro

local-aiarxiv-cs-ai

Beyond English benchmarks: clinical llm evaluation in Brazilian Portuguese

arXiv:2606.07853v1 Announce Type: cross Abstract: Large Language Models are transforming the support for clinical decision and their application in real scenarios. Yet, most benchmarks are conducted i

Applications

Beyond Item IDs: Scaling Short-Form-Video Recommendation via Semantic-Native Long Sequence Modeling

arXiv:2606.07546v1 Announce Type: cross Abstract: Capturing user interests across extensive watch histories is critical for short-form video recommendation, yet scaling sequence length is limited by t

applicationsarxiv-cs-ai

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

researcharxiv-cs-lg

ChronoPhyBench: Do MLLMs Truly Understand the World or Merely Exploit Language Priors?

arXiv:2606.07962v1 Announce Type: new Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in open-world reasoning and understanding. Howe

Correlation Is Not Enough: Embedding Human Metadata for Individual Causal Discovery

arXiv:2606.09672v1 Announce Type: new Abstract: Ask a pretrained biomedical language model whether 'cortisol 28 ug/dL' and 'stock-market volatility' are related, and it returns a cosine similarity of

DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination

arXiv:2606.08068v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often fail to reliably outperform a single strong model equipped with best-of-N sampling. We argue that a

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

researcharxiv-cs-ai

Applications

DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling

arXiv:2606.09086v1 Announce Type: new Abstract: Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone, without relying on historic

applicationsarxiv-cs-ai

End-to-End Context Compression at Scale

arXiv:2606.09659v1 Announce Type: cross Abstract: Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length. Recent techniques to compress the KV cache