model releases

arXiv:2601.03938v2 Announce Type: replace-cross Abstract: Continual learning (CL) for large language models (LLMs) aims to enable sequential knowledge acquisition without catastrophic forgetting. Memo

arXiv:2505.18128v4 Announce Type: replace Abstract: We introduce Frankentexts, a long-form narrative generation paradigm that treats an LLM as a composer of existing texts rather than as an author. Gi

arXiv:2604.18328v1 Announce Type: new Abstract: We present FregeLogic, a hybrid neuro-symbolic system for SemEval-2026 Task 11 (Subtask 1), which addresses syllogistic validity prediction while reduci

arXiv:2604.16918v1 Announce Type: new Abstract: Reinforcement Learning (RL) has achieved impressive success in post-training Large Language Models (LLMs) and Vision-Language Models (VLMs), with on-pol

Friendly Machines is happening next Tuesday! We’re exploring how Hermes by @NousResearch and Claude are used to build useful agents: – live demos – Q&A with @NousResearch team – discuss your workflows

arXiv:2604.16648v1 Announce Type: new Abstract: In this work, we present FRIGID, a framework with a novel diffusion language model that generates molecular structures conditioned on mass spectra via i

Today, we're excited to announce Claude Cowork in Amazon Bedrock. You can now run Cowork and Claude Code Desktop through Amazon Bedrock, directly or using an LLM gateway. In this post, we walk through

arXiv:2604.16504v1 Announce Type: new Abstract: Manual digitisation of structured handwritten documents is slow and costly. We benchmark 17 leading frontier multi-modal large language models and open-

arXiv:2604.17941v1 Announce Type: cross Abstract: Recent work has increasingly explored neuron-level interpretation in vision-language models (VLMs) to identify neurons critical to final predictions.

arXiv:2604.16462v1 Announce Type: new Abstract: High-resolution Multimodal Large Language Models (MLLMs) face prohibitive computational costs during inference due to the explosion of visual tokens. Ex

The main stage at Google Cloud Next is where the vision is set. This year, we’re bridging the gap between those massive 'Cloud-scale' announcements and your local terminal. We are thrilled to announce

arXiv:2603.14389v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed a leap in Large Language Model (LLM) reasoning, yet its optimization dynamics re

arXiv:2604.16426v1 Announce Type: new Abstract: As modern deep learning architectures grow in complexity, representational ambiguity emerges as a critical barrier to their interpretability and reliabl

arXiv:2604.16436v1 Announce Type: cross Abstract: This paper develops an end-to-end fuzzy encoder-decoder architecture for enhancing vision-based multi-modal deep spiking Q-networks in autonomous driv

arXiv:2604.17307v1 Announce Type: new Abstract: Detecting face forgeries using CLIP has recently emerged as a promising and increasingly popular research direction. Owing to its rich visual knowledge

arXiv:2604.18092v1 Announce Type: new Abstract: Small language models fine-tuned for graph property estimation have demonstrated strong in-distribution performance, yet their generalization capabiliti

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

Everyone heading into Google Cloud Next this week is bracing for another wave of artificial intelligence announcements. More Gemini. More agents. More benchmarks. More onstage demos that look great in

Julia Love / Bloomberg: Google's Chief AI Architect Koray Kavukcuoglu is working to unite its internal AI coding tools under the Antigravity platform, to counter Claude Code and Codex — At Google, lea

arXiv:2604.17822v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continuously acquire new categories while preserving previously learned knowledge. Recently, Contrastive Langua

arXiv:2604.18556v1 Announce Type: new Abstract: Weight quantization has become a standard tool for efficient LLM deployment, especially for local inference, where models are now routinely served at 2-

arXiv:2604.17210v1 Announce Type: new Abstract: Fine-tuning well-aligned large language models (LLMs) on new domains often degrades their safety alignment, even when using benign datasets. Existing sa

arXiv:2604.16430v1 Announce Type: new Abstract: Large Language Models (LLMs) are powerful and widely adopted, but their practical impact is limited by the well-known hallucination phenomenon. While re

arXiv:2604.17025v1 Announce Type: cross Abstract: Large Language Models (LLMs) produce a controllability gap in safety-critical engineering: even low rates of undetected constraint violations render a

Having a 20-agent system is a million times more powerful than 100 agents working in silos. Last night, I stayed up way too late fixing and improving my digital workforce for my AI Agent Mastermind pr

arXiv:2604.18349v1 Announce Type: new Abstract: Long-term conversational large language model (LLM) agents require memory systems that can recover relevant evidence from historical interactions withou

arXiv:2604.17751v1 Announce Type: cross Abstract: Adapting foundation models under resource budgets relies heavily on Parameter-Efficient Fine-Tuning (PEFT), with LoRA being a standard modular solutio

arXiv:2604.17745v1 Announce Type: new Abstract: Recent advances in large language models have highlighted their potential to automate computational research, particularly reproducing experimental resu

arXiv:2604.17259v1 Announce Type: cross Abstract: User behavior in the real world is diverse, cross-domain, and spans long time horizons. Existing user modeling benchmarks however remain narrow, focus

arXiv:2604.17283v1 Announce Type: new Abstract: User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent lif

arXiv:2604.16320v1 Announce Type: cross Abstract: LLMs demonstrate remarkable reasoning capabilities, yet whether they utilize internal world models or rely on sophisticated pattern matching remains o

arXiv:2505.15404v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have achieved remarkable success on reasoning-intensive tasks such as mathematics and programming. However, their enha

arXiv:2604.16499v1 Announce Type: new Abstract: Black-box adversarial attack on vision-language pre-trained models is a practical and challenging task, as text and image perturbations need to be consi

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%

arXiv:2510.22215v2 Announce Type: replace-cross Abstract: Retrieval over visually rich documents is essential for tasks such as legal discovery, scientific search, and enterprise knowledge management.

I agree, and I will add to this. Companies should own their memories, skills and other resources agents need. Use Claude code, codex, copilot, etc for intelligence but your execution layer should plug

I came up with a somewhat foolish new benchmark for testing image generation models, to exercise the new ChatGPT Images 2.0: 'Do a where's Waldo style image but it's where is the raccoon holding a ham

I find that open weights models over-perform on benchmarks compared to actual real-world usage, and Kimi feels like no exception. For example, a small amount of use will show that Kimi is not as good

I hope this helps. If you need more help let me know: Connecting OpenClaw to the X API is straightforward now thanks to X’s official native support... The best and most direct method uses the official

arXiv:2604.16405v1 Announce Type: cross Abstract: Video-generative world models are increasingly used as neural simulators for embodied planning and policy learning, yet their ability to predict physi

arXiv:2604.18521v1 Announce Type: new Abstract: Epidemic forecasting has become an integral part of real-time infectious disease outbreak response. While collaborative ensembles composed of statistica

arXiv:2604.16726v1 Announce Type: new Abstract: Considering the imminent massification of digital books, it has become critical to facilitate searching collections through graphical patterns. Current

arXiv:2604.17010v1 Announce Type: new Abstract: We introduce a self-play framework for semantic equivalence in Haskell, utilizing formal verification to guide adversarial training between a generator

arXiv:2603.15250v2 Announce Type: replace Abstract: Symbolic regression aims to replace black-box predictors with concise analytical expressions that can be inspected and validated in scientific machi

arXiv:2604.16747v1 Announce Type: new Abstract: Dynamic 3D Gaussian Splatting methods achieve strong training-view PSNR on monocular video but generalize poorly: on the D-NeRF benchmark we measure an

arXiv:2604.17736v1 Announce Type: new Abstract: As AI generative models evolve at unprecedented speed, image attribution has become a moving target. New diffusion, adversarial and autoregressive gener