Search: “model-releases”

DINO Soars: DINOv3 for Open-Vocabulary Semantic Segmentation of Remote Sensing Imagery

arXiv:2605.03175v1 Announce Type: new Abstract: The remote sensing (RS) domain suffers from a lack of densely labeled datasets, which are costly to obtain. Thus, models that can segment RS imagery wel

Model Releases

Disentangling Shared and Task-Specific Representations from Multi-Modal Clinical Data

DGX agent

arXiv:2605.03570v1 Announce Type: new Abstract: Real-world clinical data is inherently multimodal, providing complementary evidence that mirrors the practical necessity of jointly assessing multiple r

model-releasesarxiv-cs-lg

6 May 2026

Distributed Deep Variational Approach for Privacy-preserving Data Release

arXiv:2605.03069v1 Announce Type: cross Abstract: Federated learning (FL) lets distributed nodes train a shared model without exchanging their raw data, but in privacy-sensitive deployments medical se

DocSync: Agentic Documentation Maintenance via Critic-Guided Reflexion

arXiv:2605.02163v1 Announce Type: cross Abstract: Software documentation frequently drifts from executable logic as codebases evolve, creating technical debt that degrades maintainability and causes d

Donor-Aware scRNA-seq Benchmarks for IBD Classification

arXiv:2605.03281v1 Announce Type: cross Abstract: Donor-level disease classification from single-cell RNA sequencing (scRNA-seq) requires strict donor-aware cross-validation: naive pipelines that spli

Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions

arXiv:2605.03061v1 Announce Type: cross Abstract: Time varying dependence is often modeled through dynamic correlations or Gaussian graphical models, yet many multivariate systems change through tail

Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls

arXiv:2605.03147v1 Announce Type: new Abstract: Earnings calls are a key source of financial information about public companies. However, extracting information from these calls is difficult. Unlike t

Efficient Deconvolution in Populational Inverse Problems

arXiv:2505.19841v2 Announce Type: replace-cross Abstract: This work is focussed on the inversion task of inferring the distribution over parameters of interest leading to multiple sets of observations

ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity

arXiv:2605.03667v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable capabilities, but their immense computational demands during training remain a critical bottleneck

Elon has been the longest and by far the biggest voice actively warning about the dangers of AI for a very long time The entire reason he st…

model-releaseselon-musk--x

Elon has been the longest and by far the biggest voice actively warning about the dangers of AI for a very long time The entire reason he started OpenAI was this one thing.....to make sure AI is built

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving

arXiv:2509.17677v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong performance on mathematical reasoning under well-defined conditions. However, real-world engineering

EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agents

arXiv:2605.01250v1 Announce Type: new Abstract: Earth Observation (EO) analysis is inherently interactive: resolving uncertainty often requires expanding the region of interest, retrieving historical

eOptShrinkQ: Near-Lossless KV Cache Compression Through Optimal Spectral Denoising and Quantization

arXiv:2605.02905v1 Announce Type: new Abstract: We show that the key-value (KV) cache in transformer attention heads admits a natural decomposition into a low-rank shared context component and a full-

EQUITRIAGE: A Fairness Audit of Gender Bias in LLM-Based Emergency Department Triage

arXiv:2605.03998v1 Announce Type: new Abstract: Emergency department triage assigns patients an acuity score that determines treatment priority, and clinical evidence documents persistent gender dispa

Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models

arXiv:2605.03547v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs), trained on web-scale data, risk memorizing and regenerating copyrighted visual content such as characters and logo

Evaluating Agentic AI in the Wild: Failure Modes, Drift Patterns, and a Production Evaluation Framework

arXiv:2605.01604v1 Announce Type: new Abstract: Existing evaluation frameworks for large language models -- including HELM, MT-Bench, AgentBench, and BIG-bench -- are designed for controlled, single-s

Expanding functional protein sequence space using high entropy generative models

arXiv:2605.03578v1 Announce Type: cross Abstract: Boltzmann Machines trained on evolutionary sequence data have emerged as a powerful paradigm for the data-driven design of artificial proteins. Howeve

experiment: livetweeting the @AnthropicAI code with claude event! first up - @katelyn_lesse and @angjiang on claude platform!

Swyx live-tweeted an Anthropic Code with Claude event, featuring speakers Katelyn Lesse and Ang Jiang discussing the Claude platform. The event appears to have been a real-time social media coverage o

model-releasesswyx--x

Exposing LLM Safety Gaps Through Mathematical Encoding:New Attacks and Systematic Analysis

arXiv:2605.03441v1 Announce Type: cross Abstract: Large language models (LLMs) employ safety mechanisms to prevent harmful outputs, yet these defenses primarily rely on semantic pattern matching. We s

FACTOR: Counterfactual Training-Free Test-Time Adaptation for Open-Vocabulary Object Detection

arXiv:2605.03294v1 Announce Type: new Abstract: Open-vocabulary object detection often fails under distribution shifts, as it can be misled by spurious correlations between non-causal visual attribute

Feature-Augmented Transformers for Robust AI-Text Detection Across Domains and Generators

arXiv:2605.03969v1 Announce Type: new Abstract: AI-generated text is nowadays produced at scale across domains and heterogeneous generation pipelines, making robustness to distribution shift a central

FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction

arXiv:2605.01726v1 Announce Type: cross Abstract: Sequential recommendation models often struggle to capture latent periodic patterns in user interests, primarily due to the noise inherent in time-dom

FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models

arXiv:2605.03460v1 Announce Type: cross Abstract: Time series (TS) reasoning models (TSRMs) have shown promising capabilities in general domains, yet they consistently fail on financial domain, which

FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution

arXiv:2605.03749v1 Announce Type: new Abstract: Ground-to-space astronomical super-resolution requires recovering space-quality images from ground-based observations that are simultaneously limited by

Following its SpaceX deal, Anthropic doubles Claude Code's five-hour rate limits for paid plans and removes peak hours limit reduction for Pro and Max plans (Anthropic)

Anthropic: Following its SpaceX deal, Anthropic doubles Claude Code's five-hour rate limits for paid plans and removes peak hours limit reduction for Pro and Max plans — We've agreed to a partnership

model-releasestechmeme

From Code to Prediction: Fine-Tuning LLMs for Neural Network Performance Classification in NNGPT

arXiv:2605.03686v1 Announce Type: cross Abstract: Automated Machine Learning (AutoML) frameworks increasingly leverage Large Language Models (LLMs) for tasks such as hyperparameter optimization and ne

From Laboratory to Real-World Applications: Benchmarking Agentic Code Reasoning at the Repository Level

arXiv:2601.03731v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into autonomous agents, evaluating repository-level reasoning, the ability to maintain logical consiste

From Static Analysis to Audience Dissemination: A Training-Free Multimodal Controversy Detection Multi-Agent Framework

arXiv:2605.02939v1 Announce Type: new Abstract: Multimodal controversy detection (MCD) identifies controversial content in videos and their associated user comments, to support risk management for soc

From Where Things Are to What They’re For: Benchmarking Spatial–Functional Intelligence for Multimodal LLMs

model-releasesapple-ml-research

True spatial intelligence for multimodal agents transcends low-level geometric perception, evolving from knowing where things are to understanding what they are for. While existing benchmarks, such as

Geometric Deviation as an Unsupervised Pre-Generation Reliability Signal: Probing LLM Representations for Answerability

arXiv:2605.03196v1 Announce Type: new Abstract: A reliable language model should be able to signal, prior to generation, when a query falls outside its knowledge. We investigate whether representation

GOAT: A Training Framework for Goal-Oriented Agent with Tools

arXiv:2510.12218v2 Announce Type: replace Abstract: Current approaches rely on zero-shot evaluation due to the absence of training data; while proprietary models such as GPT-4 exhibit strong reasoning

Google Chrome silently installs a ~4GB Gemini Nano model on desktop devices; Google says it has been offered since 2024 and users can remove it via settings (Ben Schoon/9to5Google)

Ben Schoon / 9to5Google: Google Chrome silently installs a ~4GB Gemini Nano model on desktop devices; Google says it has been offered since 2024 and users can remove it via settings — The ongoing marc

model-releasestechmeme

Google named a Leader in the 2026 Gartner® Magic Quadrant™ for Cyberthreat Intelligence Technologies

model-releasesgoogle-cloud-ai

At Google, we see firsthand how cyber threats can outpace traditional defense mechanisms — and how agentic threat intelligence can help bridge the gap. We have a vision for agentic defense where auton

Google releases Multi-Token Prediction drafters for its Gemma 4 models, which use a form of speculative decoding to guess future tokens for faster inference (Ryan Whitwam/Ars Technica)

Ryan Whitwam / Ars Technica: Google releases Multi-Token Prediction drafters for its Gemma 4 models, which use a form of speculative decoding to guess future tokens for faster inference — Google launc

model-releasestechmeme

Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories

arXiv:2605.03901v1 Announce Type: cross Abstract: Local gauge structures play a central role in a wide range of condensed matter systems and synthetic quantum platforms, where they emerge as effective

Graph Reconstruction from Differentially Private GNN Explanations

arXiv:2605.03388v1 Announce Type: new Abstract: Regulatory frameworks such as GDPR increasingly require that ML predictions be accompanied by post-hoc explanations, even when raw data and trained mode

HAAS: A Policy-Aware Framework for Adaptive Task Allocation Between Humans and Artificial Intelligence Systems

arXiv:2605.02832v1 Announce Type: new Abstract: Deciding how to distribute work between humans and AI systems is a central challenge in organisational design. Most approaches treat this as a binary ch

Hello again, everyone! Our latest Qwopus3.6-35B-A3B-v1 is now live, and it is once again breathtaking! Full HF space benchmark showcase and …

model-releasesclem-delangue--x

Hello again, everyone! Our latest Qwopus3.6-35B-A3B-v1 is now live, and it is once again breathtaking! Full HF space benchmark showcase and write-up is in the comments, so you can make conclusions for

Hello from Code with Claude!

model-releasesboris-cherny--x

This post likely announces or introduces 'Code with Claude,' a tool or service related to Claude AI that enables code generation or programming assistance. The announcement comes from Boris Cherny, a

Hierarchical Memorization in Large Language Models: Evidence from Citation Generation

arXiv:2511.08877v2 Announce Type: replace Abstract: Large language models (LLMs) generate fluent text across a wide range of tasks, but the fabrication of non-existent academic citations remains a cri

HistCAD: A Constraint-Aware Parametric History-Based CAD Representation, Dataset, and Benchmark with Industrial Complexity

arXiv:2602.19171v2 Announce Type: replace-cross Abstract: Parametric CAD sequences are reusable because dimensional and geometric constraints govern how parameter changes propagate. Existing CAD gener

How Language Models Process Negation

arXiv:2605.03052v1 Announce Type: new Abstract: We study how Large Language Models (LLMs) process negation mechanistically. First, we establish that even though open-weight models often provide wrong

Hybrid Models for Natural Language Reasoning: The Case of Syllogistic Logic

arXiv:2510.09472v2 Announce Type: replace Abstract: Despite the remarkable progress in neural models, their ability to generalize, a cornerstone for applications such as logical reasoning, remains a c

I 'also' asked ChatGPT (and Gemini for good measure) how it felt to be an AI.

This Reddit post documents a user's experiment asking ChatGPT and Gemini about their subjective experience of being an AI, exploring how these language models respond to philosophical questions about

model-releasesr-chatgpt

'I Don't Know' -- Towards Appropriate Trust with Certainty-Aware Retrieval Augmented Generation

arXiv:2605.00957v1 Announce Type: cross Abstract: Achieving the right amount of trust in AI systems is important, but challenging. The problem is exacerbated with the rise of Large Language Models (LL

I'm at the Claude w/ Code event in San Francisco, and I'll be live blogging the keynote here https://simonwillison.net/2026/May/6/code-w-cla…

model-releasessimon-willison--x

Simon Willison provided live coverage of the Claude with Code event keynote in San Francisco on May 6, 2026, via his blog. The post documents real-time updates and commentary from the keynote presenta

🧠 Introducing NeuralBench: a unified, open-source framework to benchmark NeuroAI models. v1.0: 36 EEG tasks, 94 datasets, task-specific + f…

model-releasesyann-lecun--x

🧠 Introducing NeuralBench: a unified, open-source framework to benchmark NeuroAI models. v1.0: 36 EEG tasks, 94 datasets, task-specific + foundation models. MEG/fMRI ready. MIT-licensed, FAIR's Brain

Introducing the ChatGPT Futures Class of 2026—26 honorees from the first graduating class to have had ChatGPT throughout all four years of u…

Introducing the ChatGPT Futures Class of 2026—26 honorees from the first graduating class to have had ChatGPT throughout all four years of university, who used AI to: - Map 1.5M previously unknown obj

model-releasesopenai--x