Search: “models”

Automated Tensor Scheduling for Hybrid CPU-GPU LLM Inference on Consumer Devices

arXiv:2607.10183v2 Announce Type: replace-cross Abstract: Running large language models on consumer devices such as laptops and desktops is challenging because model weights often exceed GPU memory ca

local-aiarxiv-cs-ai

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

AVSCap: Orchestrating Audio-Visual Synergy for Omni-modal Video Captioning

DGX agent

arXiv:2607.12820v1 Announce Type: new Abstract: Omni-modal video captioning is not merely combining visual captioning with audio transcription: a useful caption must describe how visual actions, speec

model-releasesarxiv-cs-cv

15 Jul 2026

CARE-LoRA: Compressed Activation REconstruction for Memory-Efficient LoRA

arXiv:2607.11940v1 Announce Type: cross Abstract: As the scale of large pre-trained models continues to grow, fine-tuning them under limited memory budgets has become increasingly challenging. Low-Ran

Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning

arXiv:2607.12112v1 Announce Type: cross Abstract: Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data st

DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation

arXiv:2607.13033v1 Announce Type: new Abstract: Reinforcement learning holds great promise for improving robot policies beyond the limits of imitation learning. However, its practical adoption remains

safetyarxiv-cs-ro

Designing Agent-Ready Websites for AI Web Agents: A Framework for Machine Readability, Actionability, and Decision Reliability

arXiv:2607.12056v1 Announce Type: new Abstract: Online shopping is increasingly shifting toward a model in which AI agents independently search for products, compare options, evaluate constraints, and

Agents

Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequences

arXiv:2607.12382v1 Announce Type: new Abstract: How can an agent build a structured map of its world from nothing but an ongoing sequence of raw sensory input and its own movements, especially when na

agentsarxiv-cs-lg

DiffRadar: Differentiable Physics-Aware Radar SLAM with Gaussian Fields

model-releasesarxiv-cs-ro

arXiv:2607.12265v1 Announce Type: new Abstract: Radar sensing is increasingly used in mobile systems because it operates reliably under poor lighting, adverse weather, and privacy-sensitive settings w

DynTrace: Tracking Dynamic Object Evidence for 4D Spatio-Temporal Reasoning in MLLMs

arXiv:2607.12503v1 Announce Type: new Abstract: 4D spatio-temporal reasoning, jointly modeling 3D spatial structure and temporal evolution, is essential for understanding dynamic worlds and enabling e

researcharxiv-cs-cv

FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation

arXiv:2607.12982v1 Announce Type: new Abstract: Math reasoning has achieved significant progress with the rapid advancement of Multimodal Large Language Models (MLLMs), however analytic geometry remai

GRID: Grammar-Railed Decoding for Enterprise SQL Generation

arXiv:2607.11951v1 Announce Type: new Abstract: Large language models can write SQL, but enterprise deployment demands more than plausible text: outputs must be syntactically valid, must respect per-r

Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale

arXiv:2603.06592v2 Announce Type: replace Abstract: Contemporary studies in mechanistic interpretability have uncovered many puzzling phenomena in the neural information processing of Transformer-base

researcharxiv-cs-cl

How to Realize Recursively Self-Improving Agents and Personal Singularity: A Goal-, Scope-, Tool-, and Benchmark-Driven Multi-Agent Architecture

arXiv:2607.12254v1 Announce Type: new Abstract: Large language model (LLM) agents can increasingly plan, use tools, maintain memory, and execute long-horizon tasks. These advances motivate two linked

In-Context Reinforcement Learning under Non-Stationarity: A Survey

arXiv:2607.11906v1 Announce Type: new Abstract: The development of decision-pretrained transformers, algorithm distillation, long-context meta-RL, and retrieval-augmented agents has renewed interest i

Institutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphs

arXiv:2607.12067v1 Announce Type: new Abstract: Institutional equity holdings disclosed in SEC Form 13F filings provide a rich temporal record of portfolio decisions by large investment managers. Howe

MAG: A Web-Agent Benchmark and Harness for Multimodal Action and Guide Generation

arXiv:2607.10079v2 Announce Type: replace Abstract: Digital Adoption Platforms (DAPs) are embedded overlays widely used on web systems to guide users through operations inside a page, helping them get

Reducing information dependency does not cause training data privacy. Adversarially non-robust features do

arXiv:2607.12354v1 Announce Type: new Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconst

researcharxiv-cs-lg

Tutorials

ReLope: KL-Regularized LoRA Probes for Multimodal LLM Routing

arXiv:2603.24787v2 Announce Type: replace Abstract: Routing has emerged as a promising strategy for balancing performance and cost in large language model (LLM) systems that combine lightweight models

tutorialsarxiv-cs-ai

Sample Efficient Generative Optimization for Molecular Design

arXiv:2607.12488v1 Announce Type: new Abstract: Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since

Sensitivity to Subjective Expected Utility Maximization: A Methodological Study, with an Illustrative Application to LLM Decision-Making

arXiv:2607.11920v1 Announce Type: cross Abstract: Evaluating decisions made under uncertainty is hard when labeled outcomes are scarce, costly, or confounded with luck. We treat subjective expected ut

SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning

arXiv:2607.11624v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency. In robotics, a recent line of work has emerged addressi

UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies

arXiv:2607.12892v1 Announce Type: cross Abstract: Modern robot learning systems increasingly rely on dense progress or value signals to evaluate intermediate states, guide policy learning, and detect

local-aiarxiv-cs-ai

VistaVLA: Geometry- and Semantic-Aware 3D Gaussian-Grounded VLA for Robotic Manipulation

arXiv:2607.12356v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a powerful end-to-end paradigm for robotic manipulation by mapping language instructions and 2D visu

safetyarxiv-cs-ro

A First-Principles Theory of Slow Thinking and Active Perception

arXiv:2607.08196v1 Announce Type: new Abstract: As part of a series on first-principles modeling of cognitive functions, this paper attempts to provide a mathematical formulation of thinking and perce

safetyarxiv-cs-ai

A Practical Investigation of Training-free Relaxed Speculative Decoding

arXiv:2607.08690v1 Announce Type: cross Abstract: Speculative decoding accelerates sampling from an autoregressive LLM by using a faster auxiliary model to draft tokens which are then verified in para

A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding

arXiv:2512.21414v2 Announce Type: replace Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tool

local-aiarxiv-cs-cv

Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces

arXiv:2503.15414v3 Announce Type: replace-cross Abstract: Federated learning seeks to foster collaboration among distributed clients while preserving the privacy of their local data. Traditional feder

local-aiarxiv-cs-cv

Compete Then Collaborate: Frontier AI Teachers Build a Verifiable Curriculum to Improve a Coding Student Beyond Imitation

arXiv:2607.08255v1 Announce Type: new Abstract: Large language models increasingly serve as teachers generating training data for smaller students. Prior multi-teacher knowledge distillation methods m

Curriculum Learning for Efficient Chain-of-Thought Distillation via Structure-Aware Masking and GRPO

arXiv:2602.17686v4 Announce Type: replace-cross Abstract: Distilling Chain-of-Thought (CoT) reasoning from large language models into compact student models presents a fundamental challenge: teacher r

safetyarxiv-cs-ai

False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation

arXiv:2607.07852v1 Announce Type: cross Abstract: Automated segmentation of cervical-spine MRI is increasingly used in clinical workflows, yet no fairness audit exists for this anatomy. We show that a

Hallucination Self-Play: Bootstrapping Reinforced Detector via Evolved Generator

model-releasesarxiv-cs-cl

arXiv:2607.07993v1 Announce Type: new Abstract: Identifying faithfulness hallucinations in LLM-generated outputs remains challenging due to the scarcity of high-quality annotated data. Recent work rel

HumanForge: A Human-Centric Deepfake Video Benchmark with Multi-Agent Forgery Rationales

arXiv:2607.08705v1 Announce Type: new Abstract: Rapid advancements in video diffusion models and temporal editing tools have enabled the generation of highly realistic human-centric videos, posing unp

IMProofBench: Benchmarking AI on Research-Level Mathematical Proof Generation

model-releasesarxiv-cs-cl

arXiv:2509.26076v2 Announce Type: replace Abstract: As the mathematical capabilities of large language models (LLMs) improve, it becomes increasingly important to evaluate their performance on researc

LightCrafter: PBR-Conditioned Video Diffusion Refinement for Controllable and Consistent Relighting

arXiv:2607.08016v1 Announce Type: new Abstract: Video relighting requires balancing long-form temporal consistency with a physically grounded understanding of light transport, which depends on accurat

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks

arXiv:2607.07745v1 Announce Type: new Abstract: While accuracy, robustness, and calibration are all essential for reliable neural networks, they are often studied separately; developing models that sa

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows

arXiv:2604.05182v2 Announce Type: replace-cross Abstract: We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction. Al

Multi-Resolution Feature Stem for Diabetic Retinopathy lesion segmentation

arXiv:2607.08679v1 Announce Type: new Abstract: Diabetic Retinopathy (DR) is a leading cause of preventable blindness worldwide, requiring automated lesion segmentation using deep learning models for

OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice

arXiv:2607.08423v1 Announce Type: new Abstract: The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary

OpenCoF: Learning to Reason Through Video Generation

arXiv:2607.08763v1 Announce Type: cross Abstract: Reasoning has become a core capability for large models, especially when reliable decisions require understanding logical consequences. Recent video g

researcharxiv-cs-ai

ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation

arXiv:2502.15543v4 Announce Type: replace-cross Abstract: Large language models (LLMs) integrated with retrieval-augmented generation (RAG) have improved factuality by grounding outputs in external ev

Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring

arXiv:2607.08066v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is a promising safety mechanism for AI agents, based on the premise that visible reasoning traces can surface misalign

ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine-Tuning

arXiv:2607.07719v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning adapts a large language model to one task cheaply, but across a task sequence LoRA-style methods keep stacking low-ran

Agents

Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS

arXiv:2607.08402v1 Announce Type: cross Abstract: Large-scale and diverse datasets are needed to train AI models to take real-time decisions for autonomous vehicles (AVs), an intelligent transportatio

agentsarxiv-cs-ai

Two Axes of LLM Abstention: Answer Correctness and Question Answerability

arXiv:2607.08456v1 Announce Type: cross Abstract: A model should refuse two different things: answers it would get wrong, and questions it should not answer at all, such as unanswerable ones or ones r

safetyarxiv-cs-ai

Uncertainty-gated selection for block-sparse attention

model-releasesarxiv-cs-cl

arXiv:2607.07724v1 Announce Type: cross Abstract: Block-sparse attention scales long-context language models by replacing the O(N^2) softmax with a per-query top-k selection over key blocks. This cuto

Tutorials

VectorizationLLM: Smart Vectorization Based AI Assistant

arXiv:2607.07846v1 Announce Type: new Abstract: VectorizationLLM is a specialized Large Language Model based on Google open-weight LLMs. The model is designed to assist students to learn smart vectori

tutorialsarxiv-cs-ai

CompDiff: Hierarchical Compositional Diffusion for Fair and Zero-Shot Intersectional Medical Image Generation

arXiv:2603.16551v2 Announce Type: replace-cross Abstract: Generative models are increasingly used to augment medical imaging datasets for fairer AI, yet a key assumption often goes unexamined: that ge

9 Jul 2026

HAJJv2-CrowdCount: Zero-Shot Benchmark for Dense Crowd Counting

arXiv:2607.07322v1 Announce Type: cross Abstract: Automated crowd counting in Hajj video is difficult not because current models lack capacity, but because the footage violates the assumptions those m