model releases

arXiv:2511.02360v4 Announce Type: replace-cross Abstract: Chain of Thought (CoT) reasoning enhances logical performance by decomposing complex tasks, yet its multimodal extension faces a trade-off. Th

arXiv:2605.27066v1 Announce Type: new Abstract: Understanding how events evolve over time is essential for search engines handling queries about trending news. We present QDET (Query-Driven Event Time

This newsletter episode covers major AI developments including Elon Musk's legal loss against OpenAI, updates announced at Google's I/O conference, and OpenAI's achievement in solving the Erdős discre

arXiv:2605.26797v1 Announce Type: cross Abstract: We study Latent Recurrent Transformer (LRT), a lightweight augmentation of autoregressive transformers that reuses a high-level source-layer hidden st

arXiv:2505.21285v4 Announce Type: replace Abstract: This work proposes a framework LGKDE that learns kernel density estimation for graphs. The key challenge in graph density estimation lies in effecti

arXiv:2603.27146v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to assist ideation in research, but evaluating the quality of LLM-generated research proposals re

arXiv:2605.27190v1 Announce Type: cross Abstract: Recent advances in Large Audio-Language Models (LALMs) have made real-time, streaming spoken interaction increasingly practical. In this setting, reas

LiteParse 🤝 Rust🦀 We refactored the LiteParse library and CLI porting it to Rust, and here's what that means for you: ⚡ Parsing up to 100X faster, with sub-second processing for documents as large as

LiteParse v2.0 is out now, and it is blazing fast + runs everywhere! We rewrote everything from scratch in Rust, and now: - up to 100x faster parsing - install natively in Rust, JS/TS, and Python - a

arXiv:2605.26781v1 Announce Type: new Abstract: Advanced Large Multimodal Models (LMMs) have demonstrated impressive performance in K-12 reasoning tasks, exhibiting great promise as intelligent tutors

arXiv:2510.13217v2 Announce Type: replace-cross Abstract: Search systems are increasingly used for reasoning-intensive queries, where what makes a document relevant requires understanding or reasoning

arXiv:2605.27088v1 Announce Type: new Abstract: Aligning LLMs for math tutoring typically requires RL-based training with multi-GPU infrastructure. We investigate whether training-free prompt optimiza

arXiv:2601.18987v5 Announce Type: replace-cross Abstract: Determining whether a program terminates is a central problem in computer science. Turing's Halting Problem established termination as undecid

arXiv:2605.26244v1 Announce Type: new Abstract: Audio-visual generation is rapidly advancing from short clips to minute-long content, while existing evaluation protocols remain largely confined to sho

arXiv:2605.26486v1 Announce Type: new Abstract: Despite advances in audio-driven video generation, achieving commercial-grade stability remains challenging. We present LongCat-Video-Avatar 1.5, an upg

Look back at last week’s I/O announcements with @NotebookLM. You can listen to an audio overview, watch the video recap, and even check out our detailed slide deck summarizing all of the biggest news

arXiv:2605.26438v1 Announce Type: cross Abstract: Large language models can recognize when they are being evaluated (evaluation awareness) and behave differently because of that, which undermines the

arXiv:2605.27331v1 Announce Type: new Abstract: Competition law experts conducting legal research must review extensive volumes of cases, decisions, and judicial reports to identify precedents and ass

arXiv:2510.08932v2 Announce Type: replace Abstract: Recently, a growing body of research has focused on either optimizing CTR model architectures to better model feature interactions or refining train

arXiv:2601.08267v3 Announce Type: replace Abstract: While reasoning-enhanced large language models perform strongly on English medical tasks, a persistent multilingual gap remains, with substantially

arXiv:2605.26621v1 Announce Type: cross Abstract: Volumetric Reasoning Segmentation (VRS) aims to segment a target region in a 3D medical scan from a free-form clinical query, where the referent is of

arXiv:2605.26667v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external memory systems to remain consistent across long-horizon interactions, but little empiric

arXiv:2605.26394v1 Announce Type: new Abstract: Multi-turn Text-to-SQL is central to enterprise analytics yet remains predominantly evaluated in single-turn settings. We introduce EnterpriseMem-Bench,

arXiv:2605.26959v1 Announce Type: cross Abstract: MerLean-Prover is an end-to-end Lean4 theorem prover that replaces sorry declarations with kernel-checkable proofs. It is built from three agent types

Sarah Perez / TechCrunch: Meta rolls out Plus plans for Instagram, Facebook, and WhatsApp globally, will test 7.99/mo. and 19.99/mo. Meta AI plans, a $49.99/mo. creator plan, and more — Meta is doubli

arXiv:2605.26712v1 Announce Type: new Abstract: Benchmarks that reflect the diversity and complexity of real-world documents are essential for accurately evaluating Automatic Text Recognition (ATR) sy

arXiv:2605.26546v1 Announce Type: new Abstract: Mobile graphical user interface (GUI) agents enable AI models to autonomously operate smartphones on behalf of users. However, most existing systems foc

arXiv:2605.27358v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) has become the de facto architecture for hundred-billion-parameter language models, yet its advantages at sub-billion scales

arXiv:2605.27158v1 Announce Type: new Abstract: Discovering the governing equations of a dynamical system from observed trajectories provides deeper insight into its structure than mere prediction of

Model-Harness-Task fit! it’s clear that RL post-training produces a model-harness fit via tool shapes and prompting as models are trained with the harness in the loop. Mentioned this in a previous Lan

arXiv:2605.26191v1 Announce Type: cross Abstract: This research addresses the problem of adaptive modeling in time-series data streams with clear input-output relationships. This problem is challengin

arXiv:2507.13762v4 Announce Type: replace Abstract: Motivation: Structure-based drug design (SBDD) has advanced with deep generative models, but bridging the gap between continuous atomic coordinates

arXiv:2605.26647v1 Announce Type: cross Abstract: Feedforward network (FFN) layers account for a large fraction of parameters and nonlinear expressivity in Transformer-based large language models (LLM

arXiv:2605.27235v1 Announce Type: new Abstract: Layered image generation and editing is a fundamental capability that enables layer-wise reuse, editing, and composition of generated visual content, an

arXiv:2605.26718v1 Announce Type: new Abstract: Efficient onboard multi-field sparse reconstruction is essential for the autonomous operation of aerospace vehicles. While existing deep learning models

arXiv:2407.15073v4 Announce Type: replace Abstract: Causal discovery aims to identify causal relationships between variables and is a fundamental problem across the sciences. Traditional statistical c

arXiv:2605.26320v1 Announce Type: cross Abstract: The application of generalist multimodal models (GMMs) to specialized scientific domains remains limited due to the scarcity of comprehensive domain-s

arXiv:2605.26585v1 Announce Type: new Abstract: We study the adversarial kernel bandit problem, in which the loss at each round is induced by an arbitrary bounded element of a reproducing kernel Hilbe

arXiv:2605.26895v1 Announce Type: cross Abstract: Normalization layers in modern large language models (LLMs) consist of a deterministic normalization operation and a learnable scale vector. While the

arXiv:2605.26678v1 Announce Type: new Abstract: Long-context language models are limited by the memory footprint of the key-value (KV) cache. Existing training-free KV compression methods usually rank

arXiv:2605.26814v1 Announce Type: cross Abstract: We train a pair of autoregressive models to construct zero-mean control variates to mitigate the sign problem in quantum Monte Carlo simulations. The

arXiv:2605.26844v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own rollouts with token-level teacher supervision. Recent selective OPD methods exploit the non-uni

arXiv:2605.26584v1 Announce Type: new Abstract: Omnimodal large language models enable unified audio video understanding, but long joint token sequences make inference costly, and existing benchmarks

arXiv:2505.17163v2 Announce Type: replace-cross Abstract: Recent advancements in multimodal slow-thinking systems have demonstrated remarkable performance across various visual reasoning tasks. Howeve

arXiv:2508.01253v2 Announce Type: replace Abstract: Existing studies typically investigate domain shift and category shift as independent problems, however, in real-world scenarios, the two types of s

arXiv:2603.16654v2 Announce Type: replace-cross Abstract: Evaluating the reasoning abilities of large language models (LLMs) solely from final answers can obscure failures in intermediate steps, espec