Brody AI News
DAILY INTEL · 20260803 · EDITORIAL

今天只留能變現的訊號

Self-evolve rank 20260803

77sources ranked
—capability drill
88skills in motion
Top signal

主訊號 · Rank

  • SVR (Self-Verifying Refinement) | 讓模型在推理時自我驗證，不用外部標記也能決定何時停止思考 | 落地：在推理鏈中加入「自我驗證」步驟，自動決定停止點 | P0
  • Filesystem-Based Memory | 把長期記憶當作「檔案系統」，用 Markdown 樹狀結構管理 | 落地：給 agent 一套 read_file/write_file 工具，把記憶當成目錄樹 | P0
  • Σ-Mem (Reliability Memory) | 不只記內容，還記「誰在什麼條件下可靠」 | 落地：在多代理系統中，為每個代理建立可靠度與條件的標記 | P1
  • See2Think | 驗證模型是否真的用過「中間視覺狀態」來思考 | 落地：在視覺推理任務中，強制模型產生中間圖或標註 | P1
  • OmniScope | 解耦多模態的 token 壓縮，各模態獨立決定保留什麼 | 落地：在長文本中，讓音訊與視訊各自決定保留的片段 | P1
  • ShadowDancer | 從「影片與影子」學到統一的動態表示，實現任意動作控制 | 落地：在影片世界模型中，用統一的動態表示來驅動任何動作 | 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

  • 確保 D:/ai-daily-data 目錄存在。
  • 載入預設的本地 GGUF 模型。
  • 第一輪生成初步答案。
  • 構造驗證 Prompt 讓模型扮演「審計員」。
  • 若驗證為 Fail，將答案與審計理由合併並要求修正。
Ship

Ship notes

  • items: 77
  • raw: D:\ai-daily-data\evolution\daily\raw_20260803.json
  • rank: D:\ai-daily-data\evolution\daily\rank_20260803.md
  • draft: D:\ai-daily-data\evolution\skill-drafts\self_evolve_20260803_050001.md
  • verify: D:\ai-daily-data\evolution\daily\verify_20260803.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_20260803_050551_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 - 載入時機：當用戶問題涉及高準確率需求的邏輯推理、數學計算或程式除錯時。 - 觸發條件：模型傾向產生「幻覺」、過度自信，或需減少 To

draft

ops_ai9-fast-deploy__20260803_0504

``markdown 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` -

draft

_report_20260803_0504

# Skill Evolve 20260803_0504 - ops/youtube-quick-summary -> D:\ai-daily-data\evolution\skill-drafts\ops_youtube-quick-summary__20260803_0504.md - ops/ai9-fast-deploy -> D:\ai-daily-data\evolution\ski

draft

ops_youtube-quick-summary__20260803_0504

## SKILL.md version: 1.0 changelog: - 引入 失敗回退：讀取 sub_err.txt 並自動換 --lang en 重試。 - 增加 驗證：確保每個影片都有唯一的 id 與對應的產物路徑。 - 新增 一鍵指令：支援單一 URL 與 --batch 批次模式。 - 建立 歸因路徑：所有失敗與元

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