DAILY INTEL · 20260801 · EDITORIAL

今天只留能變現的訊號

Self-evolve rank 20260801

76sources ranked
capability drill
70skills in motion
Top signal

主訊號 · Rank

  • SVR (Self-Verifying Refinement) | 讓模型在思考時「自我驗證」而非依賴外部,減少冗贅的思考。 | 落地:讓模型在思考時「自我驗證」而非依賴外部,減少冗贅的思考。 | P0
  • Filesystem-Based Memory | 把記憶當成「檔案系統」,用目錄樹與 Markdown 檔案來組織與演化。 | 落地:把記憶當成「檔案系統」,用目錄樹與 Markdown 檔案來組織與演化。 | P0
  • Σ-Mem (Reliability Memory) | 記錄「誰在什麼條件下可信」,而非只記內容。 | 落地:記錄「誰在什麼條件下可信」,而非只記內容。 | P0
  • OmniScope | 跨模態壓縮:不同模態的相關性在不同時刻會峰值。 | 落地:跨模態壓縮:不同模態的相關性在不同時刻會峰值。 | P1
  • INTACT | 從意圖到動作的端到端 JEPA,讓世界模型不用搜尋就能預測。 | 落地:從意圖到動作的端到端 JEPA,讓世界模型不用搜尋就能預測。 | P1
  • See2Think | 驗證多模態模型是否真的利用了中間的視覺狀態。 | 落地:驗證多模態模型是否真的利用了中間的視覺狀態。 | 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: 1. 輸出中強制包含 Self-Check 區塊。 2. 邏輯、事實與完整性三維驗證。 3. 驗證失敗超過 2 次時自動修正。 4. 驗證過程過長時跳過次要檢查。 5. 避免冗長的內部思考。 6. 驗證不依賴外部工具。 7. 驗證結果若無修正即直接輸出。
Ship

Ship notes

  • items: 76
  • raw: D:\ai-daily-data\evolution\daily\raw_20260801.json
  • rank: D:\ai-daily-data\evolution\daily\rank_20260801.md
  • draft: D:\ai-daily-data\evolution\skill-drafts\self_evolve_20260801_050002.md
  • verify: D:\ai-daily-data\evolution\daily\verify_20260801.md

Papers & sources

今日排序 · 可掃讀

2026-07-30
Learning to Trace Seiberg Dualities
arxiv
src
2026-07-30
AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis
arxiv
src
2026-07-30
OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models
arxiv
src
2026-07-30
PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks
arxiv
src
2026-07-30
Beyond Sentiment: Structured Information Extraction from Financial News
arxiv
src
2026-07-30
Would You Walk to the Car Wash? Revealing the Salience Bias of Large Language Models in Commonsense Reasoning
arxiv
src
2026-07-30
CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance
arxiv
src
2026-07-30
Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory
arxiv
src
2026-07-30
APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems
arxiv
src
2026-07-30
Fairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations
arxiv
src
2026-07-30
Beyond Geometric Complementarity: Coherent Overlap in Sparse Mixture-of-Experts Routing
arxiv
src
2026-07-30
Information Bottleneck Learning for Faithful Time Series Forecasting Explanations
arxiv
src
2026-07-30
DualG-MRAG: Decoupling Macro-Reasoning and Micro-Matching for Multimodal Retrieval-Augmented Generation
arxiv
src
2026-07-30
GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generation
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_20260801_051543_ai9-fast-deploy

--- name: ai9-fast-deploy description: 定義了從零到一的快速部署規範,包含伺服器指紋與 Caddy/IIS 的明確配置。 version: 1.1 --- ## Purpose 當需要快速部站、檢查靜態站點狀態、或處理 ai9 伺服器(119.14.175.24)的 Caddy/IIS 配置時載入。 ## Procedure 1. 確認環境:

draft

_report_20260801_0513

# Skill Evolve 20260801_0513 - ops/youtube-quick-summary -> D:\ai-daily-data\evolution\skill-drafts\ops_youtube-quick-summary__20260801_0513.md - ops/ai9-fast-deploy -> D:\ai-daily-data\evolution\ski

draft

ops_ai9-fast-deploy__20260801_0513

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

ops_youtube-quick-summary__20260801_0513

## SKILL.md version: 1.0 changelog: 8081 迭代:注入驗證、回退、一鍵指令與歸因路徑。 --- # YouTube 快速摘要(Win11 D 槽) ## 何時用 - 使用者丟 YouTube 連結 / 要影片重點 / 要字幕 / 要摘要 - 需要可給 8080/8081 再精煉的結構化 md ## 路徑(全部 D:) - 工具:`D:\ai-dail

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