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2026-08-28:[AINews] OpenAI to reach AGI;Breaking Claude Code Opus 5 ;Warp 如何让 Agent 自我进化

日期
2026/08/28
今日条目
50

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#16

Training Agents to Evolve with Their Harness: TaoLive Digital Avatar Agent Technical Report

AI-powered digital avatar streamers must answer product questions, engage viewers, and execute marketing strategies in real time, demanding low latency, frequent strategy updates, and accurate yet effective responses. Ev

推荐理由Training Agents to Evolve with Their:AI-powered digital avatar streamers must answer product questions, engage viewers。
模型与评测HF daily papers
#18

WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution

Agent skills package specialized knowledge and workflows into reusable resources that extend AI agent capabilities. Recent work automatically discovers such skills from agent experience, which enables agents to progressi

推荐理由WikiSkill: Compiling Agent Experience:Agent skills package specialized knowledge and workflows into reusable resources that。
模型与评测HF daily papers
#24

AI 工程师笔记本:在 Colab 上免费、无需框架即可使用 RAG/智能体/评估工具

一套可运行的 Colab 笔记本,面向 AI 工程师与 FDE 技能栈,用原始 API 而非框架构建基于基础模型的系统,覆盖提示词、RAG、评估、智能体、微调与服务化。全部在免费 Groq API 上运行,无需信用卡;LoRA 微调和自托管服务提供概念讲解及可选的 Colab-GPU 附录。包含三个端到端案例研究,且全程兼容 OpenAI API,模式可直接

推荐理由一套可运行的 Colab 笔记本,面向 AI 工程师与 FDE 技能栈,用原始 API 而非框架构建基于基础模型的系统,覆盖提示词、RAG、评估、智能体、微调与服务化。
Agent与工具AIHOT feed.xml
#29

Zero-WAM: In-Context World-Action Modeling from Human Videos for Open-Ended Task Generalization

Zero-shot cross-task generalization, where a policy must execute manipulation tasks never seen during training, remains a central challenge in robot learning. In large language models, a novel task can be performed simpl

推荐理由Zero-WAM: In-Context World-Action:Zero-shot cross-task generalization, where a policy must execute manipulation tasks。
模型与评测HF daily papers
#30

Understanding Evolution Strategies for LLM Reasoning: Broader Reasoning Coverage than GRPO

Evolution Strategies (ES) have recently emerged as a memory-efficient post-training paradigm for LLM reasoning. However, the optimization behavior of ES remains understudied, making it hard to define its advantage scope

推荐理由Understanding Evolution Strategies:Evolution Strategies (ES) have recently emerged as a memory-efficient post-training。
模型与评测HF daily papers
#32

CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes

Recent advances in inference-time scaling have significantly improved the reasoning performance of large language models (LLMs). However, these methods typically rely on repeated generation or external verification. To a

推荐理由CritICL: Inference-Time Weak-to-Strong:Recent advances in inference-time scaling have significantly improved the reasoning。
模型与评测HF daily papers