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DAILY INTEL · 20260802 · EDITORIAL

今天只留能變現的訊號

Self-evolve rank 20260802

110sources ranked
—capability drill
79skills in motion
Top signal

主訊號 · Rank

  • SVR (Self-Verifying Refinement) | 解決模型在推理時「空話」或過度思考的預測，透過自我驗證來決定何時該停止 | 落地：在推理鏈中加入一個「驗證」步驟，決定是否需要進一步細化 | P0
  • Filesystem-Based Memory | 將長期記憶視為一個可讀寫、可重組的目錄樹，而非雜亂的內容堆疊 | 落地：為 agent 建立一個專屬的目錄樹，用檔案工具管理每個記憶檔案 | P0
  • Σ-Mem (Reliability Memory) | 不只記內容，還記「誰在什麼條件下可信」，解決多代理人協作時的信任鏈 | 落地：為每個代理人標記可靠度與信任條件 | P1
  • OmniScope (Token Compression) | 解耦各模態的壓縮：音訊與視訊的相關性在不同時刻會獨立變化 | 落地：在壓縮時讓音訊與視訊獨立決定保留哪些 token | P1
  • See2Think | 驗證模型是否真的利用中間視覺狀態進行推理，而非只是直接輸出答案 | 落地：在推理鏈中加入中間視覺狀態作為思考的支點 | P1
  • INTACT (Intent-to-Action) | 將「意圖」直接轉為「動作」，讓世界模型在無獎勵的軌道中直接從意圖推導動作 | 落地：將每個軌道的意圖與動作做成端到端的 JEPA 映射 | P2
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. 結構化長期記憶存於 D:/ai-daily-data 目錄樹。 2. 每個記憶單元含內容、來源、可信度與時間戳。 3. 檢索後生成草稿，並透過 SVR 進行事實與邏輯驗證。 4. 若驗證失敗則修正草稿或擴大檢索，最多 3 次迭代。 5. 最終結果寫入 logs/svr_history 並更新對應知識庫。
  • DROP: 移除「版本號」與「何時用」的冗長描述。
  • NEXT: 準備將此合併後的 SVR-Memory Loop 寫入 Hermes SKILL。
Ship

Ship notes

  • items: 110
  • raw: D:\ai-daily-data\evolution\daily\raw_20260802.json
  • rank: D:\ai-daily-data\evolution\daily\rank_20260802.md
  • draft: D:\ai-daily-data\evolution\skill-drafts\self_evolve_20260802_050001.md
  • verify: D:\ai-daily-data\evolution\daily\verify_20260802.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_20260802_050954_svr

--- name: svr description: 透過強制自我驗證區塊減少推理空話，提升邏輯與數學計算準確率。 version: 1.0.0 --- # SVR (Self-Verifying Refinement) ## Purpose - 高準確率邏輯推理或數學計算任務。 - 模型容易產生「幻覺」或過度自信時。 - 需要減少 Token 消耗並加快回應速度時（取代冗長的 Chain-o

draft

ops_ai9-fast-deploy__20260802_0508

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_20260802_0508

# Skill Evolve 20260802_0508 - ops/youtube-quick-summary -> D:\ai-daily-data\evolution\skill-drafts\ops_youtube-quick-summary__20260802_0508.md - ops/ai9-fast-deploy -> D:\ai-daily-data\evolution\ski

draft

ops_youtube-quick-summary__20260802_0508

## SKILL.md: youtube-quick-summary (8081 迭代版) version: 8081.0.1 changelog: 引入失敗回退、驗證機制、一鍵指令與歸因路徑 (D:/ai-daily-data/evolution/attribution/) --- # YouTube 快速摘要（Win11 D 槽） ## 何時用 - 使用者丟 YouTu

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