DAILY INTEL · 20260726 · EDITORIAL

今天只留能變現的訊號

Self-evolve rank 20260726

76sources ranked
capability drill
24skills in motion
Top signal

主訊號 · Rank

  • Agentic Context Management | 解決 agent 被過長歷史、工具與輸出內容淹沒的記憶與成本問題 | 落地:定義 context 生命週期與架構,而非僅是堆疊內容 | P0
  • OpenForgeRL | 讓 agent 在原生 harness 中端到端訓練,而非依賴複雜的外部架構 | 落地:在任何環境下訓練 harness-native agent | P0
  • Test-Time Scaling via Error Localization | 透過定位錯誤而非獨立採樣,提升複雜推理與編碼的推論效能 | 落地:在推論時定位錯誤並進行修正 | P0
  • Agentic Reasoning on FinanceComplexQA | 用 agent 處理工業級金融文件的複雜推理與知識整合 | 落地:在金融文件上做 agentic reasoning | P1
  • LLMs Get Lost in Evolving User Intent | 捕捉用戶意圖的動態演變,而非一次性指定 | 落地:追蹤用戶意圖的揭露、修正與重塑 | P1
  • Sample-Efficient Learning from Agent Experience | 利用 agent 互動歷史進行樣本高效的 in-context 學習 | 落地:從 agent 互動歷史學習 | P1
Models

雙模狀態

8080 maker 8081 checker local only

  • Web Elite v2 skill 已掛載
  • Editorial news shell 啟用
  • channel strip + hard_audit
Labs

Verify / Labs

  • KEEP_RULES: 將上下文分為四類並定義保留策略;對話歷史摘要保留、工具輸出提取關鍵欄位、系統提示固定、當前任務完整保留;支援動態重建與驗證壓縮率;若壓縮失敗則回退至原始或採用簡單截斷。
  • DROP: 移除「反模式」與「實戰範例」的冗贅描述。
  • NEXT: 合併為 agentic-context-reasoning
Ship

Ship notes

  • items: 76
  • raw: D:\ai-daily-data\evolution\daily\raw_20260726.json
  • rank: D:\ai-daily-data\evolution\daily\rank_20260726.md
  • draft: D:\ai-daily-data\evolution\skill-drafts\self_evolve_20260726_050003.md
  • verify: D:\ai-daily-data\evolution\daily\verify_20260726.md

Papers & sources

今日排序 · 可掃讀

2026-07-23
OpenForgeRL: Train Harness-native Agents in Any Environment
arxiv
src
2026-07-23
GS-Agent: Creating 4D Physical Worlds With Generative Simulation
arxiv
src
2026-07-23
Same Dangerous Objective, Opposite Advice: Direct Exposure versus Multi-Agent Mediation
arxiv
src
2026-07-23
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
arxiv
src
2026-07-23
Surprisal Theory is Tautological (without Rational Grounding)
arxiv
src
2026-07-23
RUMBA: Russian User Memory Benchmark
arxiv
src
2026-07-23
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
arxiv
src
2026-07-23
news-crawler-LM: A Small Long-Context Model For High-Quality News Crawling
arxiv
src
2026-07-23
Windowed-MTP: Removing the Full-Context Draft-KV Tax at Million-Token Context
arxiv
src
2026-07-23
Test-Time Scaling via Error Localization
arxiv
src
2026-07-23
A Diffusion-Model Subpopulation Digital Twin for Mobile Health Deployment: A Case Study on the HeartSteps Intervention
arxiv
src
2026-07-23
Training Large Language Models for Self-Explanation Faithfulness
arxiv
src
2026-07-23
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
arxiv
src
2026-07-23
CRAG-MM-Diagnostics: Enabling Stage-Wise Analysis of Knowledge-Intensive VQA
arxiv
src

Skills distilled

可落地能力

UI

web-elite-2026 v2

GSAP · Lenis · Three · OKLCH · 反 AI-slop · zero-build/Vite 雙軌

News

commercial-news-ui-2026

編輯氣質 bento 情報站殼 · 8081 ≥9 才 deploy

draft

k2s_20260726_051217_context-loop-wisdom

--- name: context-loop-wisdom description: 8080/8081 雙模型自我提示循環作業系統,驅動 Maker 自主推進任務 version: 20260725 --- # context-loop-wisdom ## Purpose 當 CLI 啟動 8080 Maker 時載入。用於需要自主推進、不依賴人類微步驟的作業情境。8080 負責建(Mak

draft

ops_ai9-fast-deploy__20260726_0509

version 1.1 changelog: 注入失敗回退、驗證、一鍵指令與歸因路徑。 # AI9 快速部站 ## 伺服器指紋 - IP: 119.14.175.24 / host ai9 / Win Server 2025 / i5-9500 / 16GB - SSH: rovk@119.14.175.24 - 網域: brodyaitw.com - 靜太根: `D:\sit

draft

_report_20260726_0509

# Skill Evolve 20260726_0509 - ops/youtube-quick-summary -> D:\ai-daily-data\evolution\skill-drafts\ops_youtube-quick-summary__20260726_0509.md - ops/ai9-fast-deploy -> D:\ai-daily-data\evolution\ski

draft

ops_youtube-quick-summary__20260726_0509

## youtube-quick-summary version: 1.1 changelog: - 引入 失敗回退:讀取 sub_err.txt 並自動切換 --lang en 重試 - 新增 驗證:確保每個影片都有唯一的 cachesummary - 強化 一鍵指令:標準化為單一指令,支援批次與 --batch - 明確 *

Living rules

組織記憶

Living rules (auto-evolved)

20260725

Decouple harness (prompts/tools/loop) from base model training.

Capture harness-native signals (tool calls, reasoning, rewards).

Train harness layer independently of base model.

Fall back to joint training if decoupling fails.

Avoid retraining the base model when only harness changes.

Agentic Context Management