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

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 cache 與 summary - 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.