Open library
The essential reading of generative AI and agents, open in a clean reading view, with Meow and Standard summaries when available. No account needed to read.
563 papers · 561 with a Meow summary · 333 posts
The selected papers and blogs: the essential papers of generative AI and agents, and the key lab publications and essays, each with Meow and Standard summaries.
Today’s strongest video-language models (VLMs) remain proprietary. The strongest open-weight models either rely on synthetic data from proprietary VLMs, effectively distilling from them, or do not disclose their training data or recipe. As a result, the open-source community lacks the foundations needed to improve on…
Zhiliang Peng, Jianwei Yu, Yaoyao Chang et al.
This report presents VibeVoice-ASR, a general-purpose speech understanding framework built upon VibeVoice [[13](#bib.bib9)], designed to address the persistent challenges of context fragmentation and multi-speaker complexity in long-form audio (e.g., meetings, podcasts) that remain despite recent advancements in short…
Jyoti Aneja, Michael Harrison, Neel Joshi et al.
We present Phi-4-reasoning-vision-15B, a compact open-weight multimodal reasoning model, and share the motivations, design choices, experiments, and learnings that informed its development. Our goal is to contribute practical insight to the research community on building smaller, efficient multimodal reasoning models…
StepFun-Audio Team
Realtime spoken interaction demands deep reasoning, prompt responses, and fluid turn-taking. We present StepAudio 3 Realtime, an audio-language foundation model organized around a continuous listen-converse-think-act loop. Deep Perception captures rich acoustic cues to interpret user intent, while Seamless Duplex mode…
Multimodal Intelligence Team, StepFun
We present **Step3-VL-10B**, a lightweight open-source foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence. Step3-VL-10B is realized through two strategic shifts: first, a **unified, fully unfrozen pre-training strategy** on 1.2T multimodal tokens t…
Meituan LongCat Team
The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal systems remain language-centric, often treating non-linguistic modalities as external attachments, leading to fragmented architectures and subop…
Meituan LongCat Team
We introduce LongCat-Flash-Thinking-2601, a 560-billion-parameter open-source Mixture-of-Experts (MoE) reasoning model with superior agentic reasoning capability. LongCat-Flash-Thinking-2601 achieves state-of-the-art performance among open-source models on a wide range of agentic benchmarks, including agentic search,…
ERNIE Team, Baidu
Tencent Hunyuan Team
We present Hunyuan-A13B, an open-source large language model employing a Mixture-of-Experts (MoE) architecture that optimizes the trade-off between computational efficiency and model performance. The architecture leverages 80 billion total parameters while activating only 13 billion parameters during inference, enabli…
We introduce **AuK**, an open-source foundational model that unifies speech generation and editing through a common interface of natural-language instructions and audio context. To support this broad capability set, we construct approximately $3.03$ billion instruction–audio instances and $1.95$ million hours of effec…
Intern-S2-Preview Team, Shanghai AI Laboratory
Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sustain progress across long task horizons. We present Intern-S2-Preview, a series of scientific agentic foundation models designed to support…
Intern-S1-Pro Team, Shanghai AI Laboratory
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancement across both general and scientific domains. Beyond stronger reasoning and image-text understanding capabilities, its intelligence is au…
Ling Team, Inclusion AI
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy. In this report, we present Ling-2.6 and Ring-2.6, a family of models designed to address this challenge at scale. Ling-2.6 is…
Tiwei Bie, Maosong Cao, Xiang Cao et al.
While **LLaDA2.0** showcased the scaling potential of 100B-level block-diffusion models and their inherent parallelization, the delicate equilibrium between decoding speed and generation quality has remained an elusive frontier. Today, we unveil **LLaDA2.1**, a paradigm shift designed to transcend this trade-off. By s…
Venus Team, Ant Group
GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance remains challenging. In this report, we present **UI-Venus-1.5**, a unified, end-to-end GUI Agent designed for robust real-world applications.…
Yoav HaCohen Benny Brazowski Nisan Chiprut Yaki Bitterman
Recent text-to-video diffusion models can generate compelling video sequences, yet they remain silent—missing the semantic, emotional, and atmospheric cues that audio provides. We introduce LTX-2, an open-source foundational model capable of generating high-quality, temporally synchronized audiovisual content in a uni…
Recent large vision–language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as distinct problems, leading to fragmented architectures, cascaded pipelines, and misaligned representation spaces. We argue that this divide is not merely an engineering art…
Keye Team, Kuaishou Group
We introduce Kwai Keye-VL-2.0-30B-A3B, an open-source Mixture-of-Experts (MoE) multimodal foundation model designed to advance long-video understanding and agentic intelligence. To address the challenges of ultra-long contexts, information redundancy, and prohibitive computational costs inherent in hour-level videos,…
Upstage Solar Team
We introduce **Solar Open**, a 102B-parameter bilingual Mixture-of-Experts language model for underserved languages. Solar Open demonstrates a systematic methodology for building competitive LLMs by addressing three interconnected challenges. First, to train effectively despite data scarcity for underserved languages,…
name=Alejandro R. Salamanca\fa, affiliation=1, name=Diana Abagyan\fa et al.
Tiny Aya redefines what a small multilingual language model can achieve. Trained on 70 languages and refined through region-aware posttraining, it delivers state-of-the-art in translation quality, strong multilingual understanding, and high-quality target-language generation, all with just 3.35B parameters. The releas…
Qwen Team
A world model predicts environment dynamics based on current observations and actions, serving as a core cognitive mechanism for reasoning and planning. In this work, we investigate how world modeling based on language models can further push the boundaries of general agents. (i) We first focus on building foundation…
Anthropic
Build the smallest system that works: Anthropic argues that added agent complexity is justified only when evaluation shows it improves outcomes.
Anthropic
Anthropic’s central shift: agent reliability depends on curating the full, changing context at each turn—not just polishing a prompt—because longer contexts can reduce recall and focus.
Anthropic
Tools for agents should be designed around variable agent behavior and limited context, then improved through realistic evaluation—not treated as ordinary API wrappers.
Anthropic
Open-ended research is path-dependent: what you search next often depends on what you just found.
Anthropic
A multi-agent harness made long app-building runs more capable, but its lasting lesson is selective: keep only the planning, decomposition, and evaluation the current model still needs.
Anthropic
Agent Skills package instructions, scripts, and resources for agents to load as needed. An update says they have been published as an open standard for cross-platform portability.
Anthropic
Claude Code’s sandbox gives its coding agent room to run commands inside set file and network boundaries, aiming to reduce permission prompts while limiting harm from prompt injection.
Anthropic
Anthropic says three infrastructure bugs—not demand, time of day, or server load—intermittently degraded Claude responses from August into early September.
Anthropic
The kernel became parallelizable only after a GCC-based oracle helped agents isolate different failing files.
Anthropic
In practice, an agent’s autonomy is not just a model capability: the model, the user, and the product shape it together.
Anthropic
Anthropic’s main claim: agent safety depends on the whole system, not just the model. Autonomy brings useful capabilities but also more room for mistaken actions and prompt-injection attacks.
Anthropic
Claude Opus 4.5 and improved safeguards sharply reduced browser prompt-injection success, but the reported gains do not solve the problem.
Anthropic
Managed Agents separates the agent’s session, harness, and execution tools behind interfaces intended to stay stable as models and implementations change.
Anthropic
Claude Code’s auto mode aims to reduce approval fatigue without switching permissions off: it uses classifiers to allow higher autonomy while retaining safeguards.