model releases

CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning

arXiv:2605.20247v1 Announce Type: cross Abstract: Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs). Although Mi

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Cursor's new Composer 2.5 takes third on the Artificial Analysis Coding Agent Index and is ~10-60x lower cost than the higher-effort Opus 4.…

DGX agent

Cursor's new Composer 2.5 takes third on the Artificial Analysis Coding Agent Index and is ~10-60x lower cost than the higher-effort Opus 4.7 and GPT-5.5 variants above it. This release puts Composer

model-releasesfireworks-ai--x

21 May 2026

DarkShake-DVS: Event-based Human Action Recognition under Low-light andShaking Camera Conditions

arXiv:2605.20680v1 Announce Type: new Abstract: Human Action Recognition (HAR) is a fundamental computer vision task with diverse real-world applications. Practical deployments often involve low-light

DASH: Fast Differentiable Architecture Search for Hybrid Attention in Minutes on a Single GPU

arXiv:2605.20936v1 Announce Type: cross Abstract: Hybrid attention architectures are becoming an increasingly important paradigm for improving LLM inference efficiency while preserving model quality,

Datasette Agent

model-releasessimon-willison

We just announced the first release of Datasette Agent, a new extensible AI assistant for Datasette. I've been working on my LLM Python library for just over three years now, and Datasette Agent repre

Decomposing Subject-Driven Image Generation via Intermediate Structural Prediction

arXiv:2605.20807v1 Announce Type: new Abstract: Subject-driven text-to-image generation still struggles to preserve high-frequency identity details such as logos, patterns, and text. Existing methods

Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning

arXiv:2601.00473v3 Announce Type: replace Abstract: We revisit the analogy between feed-forward deep neural networks (DNNs) and discrete dynamical systems derived from neural integral equations and th

DEL: Digit Entropy Loss for Numerical Learning of Large Language Models

arXiv:2605.20369v1 Announce Type: new Abstract: Number prediction stands as a fundamental capability of large language models (LLMs) in mathematical problem-solving and code generation. The widely ado

Deltaynamics: Language-Based Representation for Inferring Rigid-Body Dynamics From Videos

arXiv:2605.20576v1 Announce Type: new Abstract: Inferring rigid-body physical states and properties from monocular videos is a fundamental step toward physics-based perception and simulation. Existing

Diagnosing Overhead in Dispatch Operations: Cross-architecture Observatory

arXiv:2605.20982v1 Announce Type: cross Abstract: AlltoAll dispatch is the dominant bottleneck of MoE expert parallelism, and the interconnect community has responded with four families of mitigations

DiMextsuperscript{3}: Bridging Multilingual and Multimodal Models via Direction- and Magnitude-Aware Merging

arXiv:2605.12960v2 Announce Type: replace Abstract: Towards more general and human-like intelligence, large language models should seamlessly integrate both multilingual and multimodal capabilities; h

DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation

arXiv:2605.20856v1 Announce Type: cross Abstract: Language-conditioned manipulation policies typically process instructions and observations through shared network parameters. This task-state entangle

DIVE: Embedding Compression via Self-Limiting Gradient Updates

arXiv:2605.20689v1 Announce Type: new Abstract: High-dimensional embeddings from large language models impose significant storage and computational costs on vector search systems. Recent embedding com

Divide et Calibra: Multiclass Local Calibration via Vector Quantization

arXiv:2605.21060v1 Announce Type: new Abstract: Accurate and well-calibrated Machine Learning (ML) models are mandatory in high-stakes settings, yet effective multiclass calibration remains challengin

Do LLMs Know What Luxembourgish Borrows? Probing Lexical Neology in Low-Resource Multilingual Models

arXiv:2605.21227v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for writing assistance in small contact languages, yet it is unclear whether they respect community n

Do Vision--Language Models Understand 3D Scenes or Just Catalogue Objects?

arXiv:2605.20448v1 Announce Type: new Abstract: Vision--language models reliably name objects in a scene, but do they represent the 3D layout those objects inhabit? We introduce a 3,034-sample human-c

DriveMA: Rethinking Language Interfaces in Driving VLAs with One-Step Meta-Actions

arXiv:2605.21273v1 Announce Type: new Abstract: Driving Vision-Language-Action Models (Driving VLAs) commonly introduce natural-language reasoning as an intermediate interface for end-to-end planning,

DrugRAG: Enhancing Pharmacy LLM Performance Through A Novel Retrieval-Augmented Generation Pipeline

arXiv:2512.14896v2 Announce Type: replace Abstract: In our study, we evaluated large language model (LLM) performance on pharmacy licensure-style question-answering tasks and developed an external kno

Dynamic Video Generation: Shaping Video Generation Across Time and Space

arXiv:2605.21042v1 Announce Type: new Abstract: Diffusion models have achieved impressive performance in video generation, but their iterative denoising process remains computationally expensive due t

DySink: Dynamic Frame Sinks for Autoregressive Long Video Generation

arXiv:2605.21028v1 Announce Type: new Abstract: Autoregressive long video generation often adopts bounded-memory streaming for efficiency, typically combining local windows for short-term continuity w

ECUAS{n}: A family of metrics for principled evaluation of uncertainty-augmented systems

arXiv:2605.20490v1 Announce Type: cross Abstract: In high-stakes automated decision-making, access to predictive uncertainty is essential for enabling users -- human or downstream systems -- to accept

Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computing

arXiv:2605.21213v1 Announce Type: cross Abstract: In this work, we present quantum reinforcement learning (RL) as a solution strategy for process synthesis problems. Building on our prior work, we dev

Evolutionary Generation of Multi-Agent Systems

arXiv:2602.06511v3 Announce Type: replace Abstract: Large language model (LLM)-based multi-agent systems (MAS) show strong promise for complex reasoning, planning, and tool-augmented tasks, but design

Explainability Methods for Hardware Trojan Detection: A Systematic Comparison

arXiv:2601.18696v4 Announce Type: replace Abstract: Hardware trojans are malicious circuits which compromise the functionality and security of an integrated circuit (IC). These circuits are manufactur

Exploring Deep Learning and Ultra-Widefield Imaging for Diabetic Retinopathy and Macular Edema

arXiv:2603.08235v2 Announce Type: replace Abstract: Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of preventable blindness among working-age adults. Traditional approac

FAIR-Pruner: A Flexible Framework for Automatic Layer-Wise Pruning via Tolerance of Difference

arXiv:2508.02291v3 Announce Type: replace Abstract: Structured pruning is a standard tool for compressing deep neural networks, but its practical performance depends on how sparsity is allocated acros

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning

arXiv:2605.20256v1 Announce Type: new Abstract: Reinforcement learning has become a cornerstone for aligning and unlocking the reasoning capabilities of large-scale models. At its core, the training l

FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs

arXiv:2605.21264v1 Announce Type: new Abstract: Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving distributed learning. However, existing FL methods face a fundamental

FedCritic: Serverless Federated Critic Learning-based Resource Allocation for Multi-Cell OFDMA in 6G

arXiv:2605.21418v1 Announce Type: cross Abstract: In sixth-generation (6G) ultra-dense networks, aggressive frequency reuse amplifies inter-cell interference (ICI), making multi-cell orthogonal freque

Federal records: Grok was utilized in only 3 of 400+ publicly identified federal AI use cases in 2025, behind 234 for ChatGPT, 33 for Gemini, 26 for Claude (Reuters)

Reuters: Federal records: Grok was utilized in only 3 of 400+ publicly identified federal AI use cases in 2025, behind 234 for ChatGPT, 33 for Gemini, 26 for Claude — SpaceX's initial public offering

model-releasestechmeme

Federated LoRA Fine-Tuning for LLMs via Collaborative Alignment

arXiv:2605.21217v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) has emerged as a powerful tool for parameter-efficient fine-tuning of large language models (LLMs). This paper studies LoRA

Finding the Correct Visual Evidence Without Forgetting: Mitigating Hallucination in LVLMs via Inter-Layer Visual Attention Discrepancy

arXiv:2605.20965v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have shown remarkable performance on a wide range of vision-language tasks. Despite this progress, they are still p

Findings of the Counter Turing Test: AI-Generated Text Detection

arXiv:2605.20761v1 Announce Type: new Abstract: The rapid proliferation of AI-generated text has introduced significant challenges in maintaining the integrity of digital content. Advanced generative

Fine-grained Claim-level RAG Benchmark for Law

arXiv:2605.21071v1 Announce Type: new Abstract: The rapid progress of large language models (LLMs) is shifting semantic search toward a question-answering paradigm, where users ask questions and LLMs

Free-Grained Hierarchical Visual Recognition

arXiv:2510.14737v3 Announce Type: replace Abstract: Hierarchical image recognition seeks to predict class labels along a semantic taxonomy, from broad categories to specific ones, typically under the

FT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents

arXiv:2603.01712v2 Announce Type: replace-cross Abstract: Fine-tuning large language models for vertical domains remains labor-intensive, requiring practitioners to curate data, configure training, an

FullFlow: Upgrading Text-to-Image Flow Matching Models for Bidirectional Vision--Language Generation

arXiv:2605.20316v1 Announce Type: new Abstract: Modern text-to-image diffusion models encode rich visual priors, but expose them only through one-way text-conditioned generation. Existing unified visi

Gated Normalization Removal and Scale Anchoring in Pre-Norm Transformers

arXiv:2602.10408v2 Announce Type: replace-cross Abstract: Normalization layers are standard in transformers, but it is not clear whether their sample-dependent computations are necessary throughout bo

GenAI-Driven Threat Detection with Microsoft Security Copilot

arXiv:2605.20896v1 Announce Type: cross Abstract: Defending against today's increasingly sophisticated cyberattacks requires security analysts to continuously translate evolving attacker tradecraft in

Geometry-Lite: Interpretable Safety Probing via Layer-Wise Margin Geometry

arXiv:2605.20241v1 Announce Type: cross Abstract: Prompt-level safety probes for large language models use hidden-state representations to separate safe from unsafe prompts, but strong average detecti

Google just revealed Omni, personalized cross-device intelligence, and Spark agents at I/O 2025. I sat down with CEO Sundar Pichai to figure…

model-releasesrowan-cheung--x

Google just revealed Omni, personalized cross-device intelligence, and Spark agents at I/O 2025. I sat down with CEO Sundar Pichai to figure out what comes next: 1:46 Omni: 'Nano Banana for video' 4:5

GradPower: Powering Gradients for Faster Language Model Pre-Training

arXiv:2505.24275v3 Announce Type: replace Abstract: We propose GradPower, a lightweight gradient-transformation technique for accelerating language model pre-training. Given a gradient vector g=(g_i)_

GraphRAG on Consumer Hardware: Benchmarking Local LLMs for Healthcare EHR Schema Retrieval

arXiv:2605.20815v1 Announce Type: new Abstract: Graph-based Retrieval Augmented Generation (GraphRAG) extends retrieval-augmented generation to support structured reasoning over complex corpora, but i

Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery

arXiv:2605.20440v1 Announce Type: new Abstract: We introduce the star_G tensor algebra, in which any finite group G defines the multiplication rule, making equivariance an intrinsic algebraic property

Hack-Verifiable Environments: Towards Evaluating Reward Hacking at Scale

arXiv:2605.20744v1 Announce Type: new Abstract: Aligning autonomous agents with human intent remains a central challenge in modern AI. A key manifestation of this challenge is reward hacking, whereby

HalluCXR: Benchmarking and Mitigating Hallucinations in Medical Vision-Language Models for Chest Radiograph Interpretation

arXiv:2605.20469v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used for medical image interpretation, yet they frequently hallucinate, generating clinically plausible b

How Much Online RL is Enough? Informative Rollouts for Offline Preference Optimization in RLVR

arXiv:2605.21266v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for reasoning in language models, with GRPO as its primary exam

HRM-Text: Efficient Pretraining Beyond Scaling

arXiv:2605.20613v1 Announce Type: new Abstract: The current pretraining paradigm for large language models relies on massive compute and internet-scale raw text, creating a significant barrier to foun