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.
561 papers · 559 with a Meow summary · 333 posts
Every paper prepared on Reading + AI, newest first.
Haozhen Zhang, Quanyu Long, Jianzhu Bao et al.
Most Large Language Model (LLM) agent memory systems rely on a small set of static, hand-designed operations for extracting memory. These fixed procedures hard-code human priors about what to store and how to revise memory, making them rigid under diverse interaction patterns and inefficient on long histories. To this…
Hong Liu, Jiaqi Zhang, Chao Wang et al.
While Mixture-of-Experts (MoE) architectures have become the standard for sparsity scaling in large language models, they increasingly face diminishing returns and system-level bottlenecks. In this work, we explore embedding scaling as a potent, orthogonal dimension for scaling sparsity. Through a comprehensive analys…
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…
Hanxiang Chao, Yihan Bai, Rui Sheng et al.
Large Language Model (LLM) agents are increasingly expected to maintain coherent, long-term personalized memory, yet current benchmarks primarily measure static fact retrieval, overlooking the ability to revise stored beliefs when new evidence emerges. We identify a critical and underexplored failure mode, **Implicit…
Wei Zhou, Xuanhe Zhou, Shaokun Han et al.
Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports persistent information storage, retrieval, update, consolidation, and dynamic lifecycle governance throughout agent execution. Despite this evolution, existing evaluat…
Jingdi Lei, Di Zhang, Junxian Li et al.
Large language models increasingly need to accumulate and reuse historical information in long-term assistants and agent systems. Simply expanding the context window is costly and often fails to ensure effective context utilization. We propose $\delta$-mem, a lightweight memory mechanism that augments a frozen full-at…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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-…
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…
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…
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…
Yifan Zhang, Yutong Dai, Juntao Tan et al.
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…
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…
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…
Yubo Li, Yidi Miao, Ramayya Krishnan et al.
LLM-as-a-judge scales evaluation, but reasoning judges are slow and costly. We study **JEV-as-a-Judge**: evaluation with JEV, a decision-only judge that returns label probabilities instead of text, and whose confidence decides whether to accept its verdict or escalate to a reasoning judge. Against sixteen generative a…
LimiX Team
We introduce LimiX-2, a new model in the LimiX family, developed through model and data scaling guided by our previously established scaling laws. LimiX-2 adopts the Contextual Mechanism Networks (CMNs) paradigm and is pretrained with Context-Conditional Masked Modeling (CCMM). CMNs shifts the organizing principle of…
Muxi Diao, Lele Yang, Wuxuan Gong, Yutong Zhang,
Supervised Fine-Tuning (SFT) is the standard paradigm for domain adaptation, yet it frequently incurs the cost of catastrophic forgetting. In sharp contrast, on-policy Reinforcement Learning (RL) effectively preserves general capabilities. We investigate this discrepancy and identify a fundamental distributional gap:…