Search: “models”

FrameDiT: Diffusion Transformer with Matrix Attention for Efficient Video Generation

arXiv:2603.09721v2 Announce Type: replace Abstract: High-fidelity video generation remains challenging for diffusion models due to the difficulty of modeling complex spatio-temporal dynamics efficient

researcharxiv-cs-cv

Frequency-guided Multi-level Reasoning for Scene Graph Generation in Video

DGX agent

arXiv:2604.17298v1 Announce Type: new Abstract: Video Scene Graph Generation aims to obtain structured semantic representations of objects and their relationships in videos for high-level understandin

researcharxiv-cs-cv

21 Apr 2026

From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction

arXiv:2507.03052v2 Announce Type: replace Abstract: As large language models (LLMs) grow in size, efficient compression techniques like quantization and sparsification are critical. While quantization

researcharxiv-cs-lg

GeoRC: A Benchmark for Geolocation Reasoning Chains

arXiv:2601.21278v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) are good at recognizing the global location of a photograph -- their geolocation prediction accuracy rivals the

Agents

Harness Engineering Without the Hype: 🦄 AI That Works #54 https://x.com/i/broadcasts/1DxLdvQrDwkxm

This episode of 'AI That Works' discusses practical approaches to harness engineering in AI systems, likely focusing on techniques for effectively prompting and controlling AI model behavior beyond ma

agentsharrison-chase--x

Safety

High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning

arXiv:2507.05920v2 Announce Type: replace Abstract: State-of-the-art large multi-modal models (LMMs) face challenges when processing high-resolution images, as these inputs are converted into enormous

safetyarxiv-cs-cv

HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions

arXiv:2505.15087v3 Announce Type: replace Abstract: Multi-Hop Question Answering (MHQA) is crucial for evaluating the model's capability to integrate information from diverse sources. However, creatin

Agents

How to Ground a Korean AI Agent in Real Demographics with Synthetic Personas

This article explains how to ground Korean AI agents in realistic demographic data by utilizing synthetic personas, likely leveraging NVIDIA's Nemotron models available on Hugging Face. The approach e

agentshugging-face

HUGGING FACE JUST AUTOMATED THEIR ENTIRE POST-TRAINING TEAM WITH AN AGENT. It reads papers, runs GPU experiments, iterates, and builds resea…

model-releasesclem-delangue--x

HUGGING FACE JUST AUTOMATED THEIR ENTIRE POST-TRAINING TEAM WITH AN AGENT. It reads papers, runs GPU experiments, iterates, and builds research-backed models autonomously. Pushed a benchmark from 10%

IMA-MoE: An Interpretable Modality-Aware Mixture-of-Experts Framework for Characterizing the Neurobiological Signatures of Binge Eating Disorder

arXiv:2604.17028v1 Announce Type: new Abstract: Binge eating disorder (BED) is the most prevalent eating disorder. However, current diagnostic frameworks remain largely grounded in symptom-based crite

researcharxiv-cs-cv

Safety

Implicit neural representations as a coordinate-based framework for continuous environmental field reconstruction from sparse ecological observations

arXiv:2604.18083v1 Announce Type: new Abstract: Reconstructing continuous environmental fields from sparse and irregular observations remains a central challenge in environmental modelling and biodive

safetyarxiv-cs-lg

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation

arXiv:2510.09275v2 Announce Type: replace Abstract: Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLM

Kimi K2.6 is now live inside Anything!

model-releaseskimi-moonshot--x

Kimi K2.6, an AI model from Moonshot, has been integrated into the Anything platform. This update likely enables users to access Kimi's capabilities directly within the Anything application interface.

LayerCache: Exploiting Layer-wise Velocity Heterogeneity for Efficient Flow Matching Inference

model-releasesarxiv-cs-cv

arXiv:2604.16492v1 Announce Type: new Abstract: Flow Matching models achieve state-of-the-art image generation quality but incur substantial inference cost due to iterative denoising through large Tra

Learning Stable Predictors from Weak Supervision under Distribution Shift

model-releasesarxiv-cs-lg

arXiv:2604.05002v2 Announce Type: replace Abstract: Learning from weak, proxy, or relative supervision is common when ground-truth labels are unavailable, but robustness under distribution shift remai

Learning to Control Summaries with Score Ranking

arXiv:2604.17197v1 Announce Type: new Abstract: Recent advances in summarization research focus on improving summary quality across multiple criteria, such as completeness, conciseness, and faithfulne

Learning to Correct: Calibrated Reinforcement Learning for Multi-Attempt Chain-of-Thought

arXiv:2604.17912v1 Announce Type: new Abstract: State-of-the-art reasoning models utilize long chain-of-thought (CoT) to solve increasingly complex problems using more test-time computation. In this w

researcharxiv-cs-lg

Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction

arXiv:2601.05654v3 Announce Type: replace Abstract: Estimating the persuasiveness of messages is critical in various applications, from recommender systems to safety assessment of LLMs. While it is im

Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage

arXiv:2601.03043v3 Announce Type: replace Abstract: Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing si

LIVE: Leveraging Image Manipulation Priors for Instruction-based Video Editing

model-releasesarxiv-cs-cv

arXiv:2604.17021v1 Announce Type: new Abstract: Video editing aims to modify input videos according to user intent. Recently, end-to-end training methods have garnered widespread attention, constructi

LLM Hypnosis: Exploiting User Feedback for Unauthorized Knowledge Injection to All Users

arXiv:2507.02850v3 Announce Type: replace Abstract: We describe a vulnerability in language models (LMs) trained with user feedback, whereby a single user can persistently alter LM knowledge and behav

Tutorials

Lorentz Framework for Semantic Segmentation

arXiv:2604.16836v1 Announce Type: new Abstract: Semantic segmentation in hyperbolic space enables compact modeling of hierarchical structure while providing inherent uncertainty quantification. Prior

tutorialsarxiv-cs-cv

ltzGLUE: Luxembourgish General Language Understanding Evaluation

arXiv:2604.17976v1 Announce Type: new Abstract: This paper presents ltzGLUE, the first Natural Language Understanding (NLU) benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for En

Applications

MambaKick: Early Penalty Direction Prediction from HAR Embeddings

arXiv:2604.16588v1 Announce Type: new Abstract: Penalty kicks in soccer are decided under extreme time constraints, where goalkeepers benefit from anticipating shot direction from the kickers motion b

applicationsarxiv-cs-cv

MemBuilder: Reinforcing LLMs for Long-Term Memory Construction via Attributed Dense Rewards

arXiv:2601.05488v3 Announce Type: replace Abstract: Maintaining consistency in long-term dialogues remains a fundamental challenge for LLMs, as standard retrieval mechanisms often fail to capture the

Multimodal In-context Learning for ASR of Low-resource Languages

arXiv:2601.05707v2 Announce Type: replace Abstract: Automatic speech recognition (ASR) still covers only a small fraction of the world's languages, mainly due to supervised data scarcity. In-context l

MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding

arXiv:2505.20715v2 Announce Type: replace-cross Abstract: Video temporal understanding is crucial for multimodal large language models (MLLMs) to reason over events in videos. Despite recent advances

Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approach

arXiv:2503.15845v2 Announce Type: replace Abstract: Network-wide Traffic State Estimation (TSE), which aims to infer a complete image of network traffic states with sparsely deployed sensors, plays a

local-aiarxiv-cs-lg

One-Step Diffusion with Inverse Residual Fields for Unsupervised Industrial Anomaly Detection

arXiv:2604.18393v1 Announce Type: new Abstract: Diffusion models have achieved outstanding performance in unsupervised industrial anomaly detection (uIAD) by learning a manifold of normal data under t

researcharxiv-cs-cv

OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation

arXiv:2506.05606v5 Announce Type: replace Abstract: Can large language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generat

Privacy-R1: Privacy-Aware Multi-LLM Agent Collaboration via Reinforcement Learning

arXiv:2510.16054v2 Announce Type: replace-cross Abstract: When users submit queries to Large Language Models (LLMs), their prompts can often contain sensitive data, forcing a difficult choice: Send th

local-aiarxiv-cs-cl

Progressive Online Video Understanding with Evidence-Aligned Timing and Transparent Decisions

model-releasesarxiv-cs-cv

arXiv:2604.18459v1 Announce Type: new Abstract: Visual agents operating in the wild must respond to queries precisely when sufficient evidence first appears in a video stream, a critical capability th

Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic Speech Recognition

arXiv:2510.08047v2 Announce Type: replace-cross Abstract: Robust ASR under domain shift is crucial because real-world systems encounter unseen accents and domains with limited labeled data. Although p

Applications

ReconVLA: An Uncertainty-Guided and Failure-Aware Vision-Language-Action Framework for Robotic Control

arXiv:2604.16677v1 Announce Type: new Abstract: Vision-language-action (VLA) models have emerged as generalist robotic controllers capable of mapping visual observations and natural language instructi

applicationsarxiv-cs-ro

ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition

arXiv:2503.21248v3 Announce Type: replace Abstract: Large language models (LLMs) have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses r

Revisiting Active Sequential Prediction-Powered Mean Estimation

model-releasesarxiv-cs-lg

arXiv:2604.18569v1 Announce Type: cross Abstract: In this work, we revisit the problem of active sequential prediction-powered mean estimation, where at each round one must decide the query probabilit

Sampling for Quality: Training-Free Reward-Guided LLM Decoding via Sequential Monte Carlo

arXiv:2604.16453v1 Announce Type: new Abstract: We introduce a principled probabilistic framework for reward-guided decoding in large language models, addressing the limitations of standard decoding m

researcharxiv-cs-lg

Scaling Codex to enterprises worldwide

OpenAI's Codex, a code generation model trained on publicly available code, was scaled for enterprise deployment to provide developers with AI-assisted coding capabilities across organizations worldwi

model-releasesopenai

Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration

arXiv:2505.21471v2 Announce Type: replace Abstract: With the rapid advancement of post-training techniques for reasoning and information seeking, large language models (LLMs) can incorporate a large q

Scaling Test-Time Compute for Agentic Coding

arXiv:2604.16529v1 Announce Type: cross Abstract: Test-time scaling has become a powerful way to improve large language models. However, existing methods are best suited to short, bounded outputs that

ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows

arXiv:2505.19897v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have extended their impact beyond Natural Language Processing, substantially fostering the development of interdi

SpatialStack: Layered Geometry-Language Fusion for 3D VLM Spatial Reasoning

arXiv:2603.27437v2 Announce Type: replace Abstract: Large vision-language models (VLMs) still struggle with reliable 3D spatial reasoning, a core capability for embodied and physical AI systems. This

local-aiarxiv-cs-cv

Spec-o3: A Tool-Augmented Vision-Language Agent for Rare Celestial Object Candidate Vetting via Automated Spectral Inspection

arXiv:2601.06498v2 Announce Type: replace Abstract: Due to the limited generalization and interpretability of deep learning classifiers, The final vetting of rare celestial object candidates still rel

ST-pi: Structured SpatioTemporal VLA for Robotic Manipulation

arXiv:2604.17880v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have achieved great success on general robotic tasks, but still face challenges in fine-grained spatiotemporal man

local-aiarxiv-cs-cv

STaD: Scaffolded Task Design for Identifying Compositional Skill Gaps in LLMs

arXiv:2604.18177v1 Announce Type: new Abstract: Benchmarks are often used as a standard to understand LLM capabilities in different domains. However, aggregate benchmark scores provide limited insight

Stop Tracking Me! Proactive Defense Against Attribute Inference Attack in LLMs

arXiv:2602.11528v2 Announce Type: replace-cross Abstract: Recent studies have shown that large language models (LLMs) can infer private user attributes (e.g., age, location, gender) from user-generate

Stronger Across Languages ChatGPT Images 2.0 can produce images with non-English text that’s not only rendered correctly but with language t…

Stronger Across Languages ChatGPT Images 2.0 can produce images with non-English text that’s not only rendered correctly but with language that flows coherently. This makes the model more globally use

model-releasesopenai--x

Applications

Surgical Repair of Insecure Code Generation in LLMs

arXiv:2604.16697v1 Announce Type: cross Abstract: Large language models write production code, and yet they routinely introduce well-known vulnerabilities. We show that this is not a knowledge deficit

applicationsarxiv-cs-lg