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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.
560 papers · 558 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.
Tong Zheng, Xidong Wu, Zheng Zhang et al.
Recursive self-improvement is becoming essential for autonomous AI agents, whose progress depends on discovering high-value solutions across complex domains. Effective exploration drives this process, yet managing and improving exploration strategies remains a major bottleneck. Current systems face a fundamental dilem…
Recent work extends Recursive Self-Improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve their execution mechanisms from experience. However, achieving **generalizable harness RSI** faces two challenges: evolution on downstream benchmarks makes transferable improve…
NeoHorse Team https://hf.co/collections/TokenRhythm/neohorse-1 https://github.com/TokenRhythm/NeoHorse
Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its own capabilities and converts that evidence into the next round of learning. We argue that a deployed routing harness already contains such a mechanism: beyond task outputs, agentic interaction leaves execution traje…
As agents move from research prototypes to deployed tools, their capability increasingly depends on model-external execution infrastructure, commonly termed the agent harness. Changing this harness while holding model weights fixed can substantially alter task performance. Current agent evaluations typically report do…
Ziyu Ma, Hailang Huang, Shun Zou et al.
Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interdependent steps. However, existing agent harnesses maintain task execution, task state, and completion assessment within a growing context, making the state difficult to…
Yifan Zhang, Yutong Dai, Juntao Tan et al.
Horizon Research, Frontis.AI, Tsinghua University
Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a concrete, executable testbed for studying this capability. We introduce **OpenMLE**, an open full-stack system for RSI research in MLE, spanning verifiable task enviro…
Ruhan Wang, Yucheng Shi, Zongxia Li et al.
The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execution. As models, APIs, execution environments, and application requirements change, the harness must be continually modified to add capabili…
Jiaru Zou, Rui Pan, Ruizhong Qiu et al.
Project Page: [https://recursivemas.github.io](https://recursivemas.github.io) Recursive or looped language models have recently emerged as a new scaling axis by iteratively refining the same model computation over latent states to deepen reasoning. We extend such scaling principle from a single model to multi-agent s…
Guanyu Jiang, Zhaochen Su, Xiaoye Qu et al.
Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestration in open-ended settings. A central challenge is enabling such agents to continually improve without parameter updates by learning from past trajectories. We identify…
Orca Team, Beijing Academy of Artificial Intelligence
We introduce Orca, an initial instantiation of a general world foundation model. Orca learns a unified world latent space from multimodal world signals and exposes it through multimodal readout interfaces. Rather than optimizing isolated next-token, next-frame, or next-action prediction, we are centered on Next-State-…
Han Luo, Bingbing Wen, Lucy Lu Wang
LLM agents are expected to act over multiple turns, using search, browsing interfaces, and terminal tools to complete user goals. Yet not every goal is well specified or achievable in the available environment. In such cases, a reliable agent should recognize that further interaction is unlikely to help and abstain fr…
Jiajie Jin, Yuyang Hu, Kai Qiu et al.
Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction. Researchers test candidate directions, interpret the evidence, and carry the resulting lessons into later attempts. We study how an AI agent can run this loop autonomously over long horizons. We introduce Arbor, a general…
Jiaqi Liu, Shi Qiu, Mairui Li et al.
Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail and inform the next attempt, and lessons accumulate across cycles. Existing autonomous research systems often model this process as a linea…
Kangning Zhang, Shuai Shao, Qingyao Li et al.
Reusable skills have become a core substrate for improving agent capabilities, yet most existing skill packages encode reusable behavior primarily as textual prompts, executable code, or learned routines. For visual agents, however, procedural knowledge is inherently multimodal: reuse depends not only on what operatio…
Siru Ouyang, Jun Yan, Yanfei Chen et al.
LLM-based agents are increasingly deployed to handle streaming tasks, yet they often remain one-off problem solvers that fail to learn from past interactions. Reusable skills distilled from experience provide a natural substrate for self-evolution, where high-quality skill curation serves as the key bottleneck. Existi…
Deep search has become a crucial capability for frontier multimodal agents, enabling models to solve complex questions through active search, evidence verification, and multi-step reasoning. Despite rapid progress, top-tier multimodal search agents remain difficult to reproduce, largely due to the absence of open high…
Ruofeng Yang, Yongcan Li, Shuai Li
This report describes Aris(**A**utonomous **R**esearch via Adversarial Multi-Agent Collaboration), an open-source research harness for autonomous ML research, including its architecture, assurance mechanisms, and early deployment experience.The performance of agent systems built on large language models depends on bot…
Shuzheng Si, Haozhe Zhao, Yu Lei et al.
Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learning, where LMs directly learn relevant knowledge from the given context. An intuitive solution is inference-time skill augmentation: extracting the corresponding rules…
Ziyu Ma, Shidong Yang, Yuxiang Ji et al.
Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment. As a result, similar workflows, tool usage patterns, and failure modes are repeatedly rediscovered across users, preventing the system from improving with experi…
Chenxi Wang, Zhuoyun Yu, Xin Xie et al.
Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover similar behaviors from limited experience, resulting in redundant exploration and poor generalization. To address this…
Ao Qu, Han Zheng, Zijian ZhouYihao Yan et al.
Large language model (LLM)-based evolution is a promising approach for open-ended discovery, where progress requires sustained search and knowledge accumulation. Existing methods still rely heavily on fixed heuristics and hard-coded exploration rules, which limit the autonomy of LLM agents. We present **CORAL**, the f…
Jingwei Ni, Yihao Liu, Xinpeng Liu et al.
Large Language Model (LLM) agents increasingly rely on domain-specific skills, yet manually authoring such skills does not scale, and skills generated purely from parametric knowledge often miss critical operational pitfalls. We introduce Trace2Skill, a framework that consolidates broad execution trajectories in paral…
Peng Xia, Jianwen Chen, Xinyu Yang et al.
Large language model (LLM) agents have rapidly emerged as powerful assistants for complex, multi-step tasks, yet agents deployed in the wild remain largely static, trained once and served unchanged regardless of how user needs evolve. This creates a fundamental tension: they must serve users continuously without inter…
MiroMind Team
We present MiroThinker-1.7, a new research agent designed for complex long-horizon reasoning tasks. Building on this foundation, we further introduce MiroThinker-H1, which extends the agent with heavy-duty reasoning capabilities for more reliable multi-step problem solving. In particular, MiroThinker-1.7 improves the…
Yuan Liang, Ruobin Zhong, Haoming Xu et al.
Current AI agents can flexibly invoke tools and execute complex tasks, yet their long-term advancement is hindered by the lack of systematic accumulation and transfer of skills. Without a unified mechanism for skill consolidation, agents frequently “reinvent the wheel”, rediscovering solutions in isolated contexts wit…
REDSearcher Team
Large language models are transitioning from general-purpose knowledge engines to real-world problem solvers, yet optimizing them for deep search tasks remains challenging. The central bottleneck lies in the extreme sparsity of high-quality search trajectories and reward signals, arising from the difficulty of scalabl…
InternScience Team, Shanghai Artificial Intelligence Laboratory
Artificial intelligence is rapidly emerging as a powerful engine for scientific discovery. Modern machine learning and large language models support literature analysis, hypothesis generation, experimental planning, and data interpretation across biology, chemistry, and earth science. These advances have inspired AI S…
Jun Han, Shuo Zhang, Wei Li et al.
Financial markets are noisy and non-stationary, making alpha mining highly sensitive to backtest noise and regime shifts. While recent agentic frameworks improve automation, they often lack controllable multi-round search and reliable reuse of validated experience. To address these challenges, we propose **QuantaAlpha…
Jianhao Ruan, Zhihao Xu, Yiran Peng et al.
Language agents have shown strong promise for task automation. Realizing this promise for increasingly complex, long-horizon tasks has driven the rise of a sub-agent-as-tools paradigm for multi-turn task solving. However, existing designs still lack a dynamic abstraction view of sub-agents, thereby hurting adaptabilit…
Jiefeng Chen, Bhavana Dalvi Mishra, Jaehyun Nam et al.
A critical bottleneck in automating AI research is the execution of complex machine learning engineering (MLE) tasks. MLE differs from general software engineering due to computationally expensive evaluation (e.g., model training) and opaque performance attribution. Current LLM-based agents struggle here, often genera…
Multimodal large language models (MLLMs) have achieved remarkable success across a broad range of vision tasks. However, constrained by the capacity of their internal world knowledge, prior work has proposed augmenting MLLMs by “reasoning-then-tool-call” for visual and textual search engines to obtain substantial gain…
Taofeng Xue, Chong Peng, Mianqiu Huang et al.
The development of native computer-use agents (CUA) represents a significant leap in multimodal AI. However, their potential is currently bottlenecked by the constraints of static data scaling. Existing paradigms relying primarily on passive imitation of static datasets struggle to capture the intricate causal dynamic…
Tianxin Wei, Ting-Wei Li, Zhining Liu et al.
**Abstract:** Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, exemplified by standard benchmarks in mathematics and code, they struggle in open-ended and dynam…
David Silver, Demis Hassabis · Google DeepMind
AlphaGo Zero started from self-play instead of human game records, quickly surpassed human-level play, and beat the previously published champion-defeating AlphaGo 100 games to 0.
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, David Silver · Google DeepMind
MuZero learned to plan in Go, chess, shogi, and Atari without being given their rules, by modeling only what helps its decisions.
Google DeepMind
Gato uses one set of weights across games, image captioning, chat, and robot control, with context guiding what it outputs.
Daniel J. Mankowitz and Andrea Michi · Google DeepMind
AlphaDev searches CPU instructions for faster algorithms instead of merely refining familiar code—a promising approach when small routines are called again and again.
Alhussein Fawzi, Matej Balog, Bernardino Romera-Paredes, Demis Hassabis, Pushmeet Kohli · Google DeepMind
AlphaTensor turns algorithm discovery into a game: zeroing its tensor yields a provably correct matrix-multiplication algorithm.
Alhussein Fawzi and Bernardino Romera Paredes · Google DeepMind
FunSearch turns LLM proposals into evaluated code search: an automatic evaluator, not the model’s fluency, determines which ideas advance.