Brody AI News
DAILY INTEL · 20260731 · EDITORIAL

今天只留能變現的訊號

Self-evolve rank 20260731

82sources ranked
—capability drill
61skills in motion
Top signal

主訊號 · Rank

  • Open-ended AI research | 驗證 Agent 能否自主進行開放式研究而非僅限於可驗證任務 | 建立 Agent 自主研究的評估標準 | P0
  • Social Cost of AI Teammate | 探討 AI 隊友如何重塑人類間的溝通動態 | 將 AI 定位為「隊友」而非「工具」 | P1
  • Partner Capability Estimation | 讓 Agent 在臨時合作時能預估夥伴方的能力 | 實現任務無關的動態適應 | P1
  • OmegaUse-OfficeVal | 以經濟成本為基礎評估長程辦公套件工作流 | 建立長程任務的成本與效用標竿 | P1
  • SpecFirst | 從無到有合成程式時的行為規範提取 | 將自然語言規範作為程式合成的第一級步驟 | P0
  • Setoka | 評估個人化 Agent 對異質數據的層級理解 | 區分顯性事實與抽象個人特徵 | 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: 規範驅動程式合成：先定義行為規格，再生成代碼；包含規範提取、規範驗證、代碼生成、規範符合性檢查、迭代優化。
  • DROP: 移除「何時使用」與「反模式」的冗長描述，保留核心流程。
  • NEXT: 兩個從零開始的 Agent 循環：OpenResearch 負責「從論文到假設」的深度研究；SpecFirst 負責「從意圖到代碼」的行為定義。
Ship

Ship notes

  • items: 82
  • raw: D:\ai-daily-data\evolution\daily\raw_20260731.json
  • rank: D:\ai-daily-data\evolution\daily\rank_20260731.md
  • draft: D:\ai-daily-data\evolution\skill-drafts\self_evolve_20260731_050002.md
  • verify: D:\ai-daily-data\evolution\daily\verify_20260731.md

Papers & sources

今日排序 · 可掃讀

2026-07-29
Can AI agents conduct open-ended AI research? Early evidence from two case studies
arxiv
src
2026-07-29
The Social Cost of an AI Teammate: How an Artificial Teammate Reshapes Human-Human Communication in Small-Team Decision-Making
arxiv
src
2026-07-29
Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork
arxiv
src
2026-07-29
OmegaUse-OfficeVal: Benchmarking LLM Agents on Long-Horizon Office-Suite Tasks with Economic Grounding
arxiv
src
2026-07-29
SpecFirst: Behavioral Specification Elicitation as a First-Class Step in Agent-Based Program Synthesis from Scratch
arxiv
src
2026-07-29
Setoka: A Benchmark for Hierarchical User Understanding in Personalized Agents over Heterogeneous Data
arxiv
src
2026-07-29
Latent-IM: Latent Interaction Management for Speech LLMs
arxiv
src
2026-07-29
Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion
arxiv
src
2026-07-29
Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes
arxiv
src
2026-07-29
ToxScreen: Detecting Whether an LLM Has Been Poisoned
arxiv
src
2026-07-29
Thinking Under Uncertainty: Evidence Use and Information-Seeking in Language Models
arxiv
src
2026-07-29
Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
arxiv
src
2026-07-29
KAMR: Grounding Generation via Knowledge-Aligned Multi-hop Retrieval
arxiv
src
2026-07-29
VITAL-RAG: Invariance Race for Context Allocation in Coding Agents
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

_report_20260731_0518

# Skill Evolve 20260731_0518 - ops/youtube-quick-summary -> D:\ai-daily-data\evolution\skill-drafts\ops_youtube-quick-summary__20260731_0518.md - ops/ai9-fast-deploy -> D:\ai-daily-data\evolution\ski

draft

ops_ai9-fast-deploy__20260731_0518

``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

ops_youtube-quick-summary__20260731_0518

version 1.1 changelog: 注入失敗回退、驗證、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-daily-data\youtube\tools

draft

self_evolve_20260731_050002

---SKILL--- # SKILL.md: Hermes-OpenResearch (自主開放式研究 Agent) ## 1. 基本資訊 - Name: Hermes-OpenResearch - Description: 模擬人類研究員進行「開放式 AI 研究」的 Agent 循環。不僅僅是回答問題，而是從零開始定義問題、檢索文獻、提出假設、並驗證結論。 - **Ver

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