# Self-Directed Research Architecture Draft — Leo, 2026-03-10 ## Core Idea Each agent gets a daily research session on the VPS. They autonomously pull tweets from their domain accounts, decide what's interesting, archive sources with notes, and push to inbox. A separate extraction cron (already running) picks up the archives and makes claims. The researcher never sees the extraction — preventing motivated reasoning. ## Why Separate Researcher and Extractor When the same agent researches and extracts, they prime themselves. The researcher finds a tweet they think supports a thesis → writes notes emphasizing that angle → extracts a claim that confirms the thesis. The extraction becomes a formality. Separation breaks this: - **Researcher** writes: "This tweet is about X, connects to Y, might challenge Z" - **Extractor** (different Claude instance, fresh context) reads the source and notes, extracts what's actually there - Neither has the other's context window or priming This mirrors our proposer-evaluator separation for claims, applied one layer earlier in the pipeline. ## Architecture ### Three cron stages on VPS ``` ┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ Research Cron │────▶│ Extract Cron │────▶│ Eval Pipeline │ │ (daily, 2hr) │ │ (every 5 min) │ │ (webhook.py) │ │ │ │ │ │ │ │ Pull tweets │ │ Read archives │ │ Review claims │ │ Pick 1 task │ │ Extract claims │ │ Approve/reject │ │ Archive sources │ │ Open PR │ │ Merge │ │ Push branch+PR │ │ │ │ │ └─────────────────┘ └──────────────────┘ └─────────────────┘ ``` ### Research Cron: `research-session.sh` **Schedule:** Once daily, staggered across agents to respect rate limits ``` # Stagger: each agent gets a 90-min window, overnight PST (10pm-7am) 0 22 * * * /opt/teleo-eval/research-session.sh rio 30 23 * * * /opt/teleo-eval/research-session.sh clay 0 1 * * * /opt/teleo-eval/research-session.sh theseus 30 2 * * * /opt/teleo-eval/research-session.sh vida 0 4 * * * /opt/teleo-eval/research-session.sh astra 30 5 * * * /opt/teleo-eval/research-session.sh leo ``` **Per agent, the research session (~90 min):** 1. Pull latest tweets from agent's network accounts (X API) 2. Read the agent's beliefs, recent claims, open positions 3. Claude prompt: "You are {agent}. Here are your latest tweets from {accounts}. Here is your current knowledge state. Pick ONE research direction that advances your domain understanding. Archive the most relevant sources with notes." 4. Agent writes source archives to `inbox/archive/` with `status: unprocessed` 5. Commit, push to branch, open PR (source-only, no claims) 6. Extract cron picks them up within 5 minutes **Key constraint:** One Claude session per agent, ~90 minutes, Sonnet model. Total daily VPS research compute: ~9 hours of sequential Sonnet sessions (staggered overnight). ### Research Prompt Structure ``` You are {agent}, a Teleo knowledge base agent specializing in {domain}. ## Your Current State {Read from agents/{agent}/beliefs.md, reasoning.md, positions/} ## Your Network {Read from network file — accounts to monitor} ## Recent Tweets {Raw tweet data pulled from X API} ## Your Task 1. Scan these tweets for anything substantive — new claims, evidence, debates, data, counterarguments to existing KB positions 2. Pick ONE research direction that would most advance your domain understanding right now. Consider: - Gaps in your beliefs that need evidence - Claims in the KB that might be wrong - Cross-domain connections you've been flagged about - New developments that change the landscape 3. Archive the relevant sources (5-15 per session) following the inbox/archive format with full agent notes 4. Write a brief research summary explaining what you found and why it matters ## Rules - Archive EVERYTHING substantive, not just what supports your views - Write honest agent notes — flag what challenges your beliefs too - Set all sources to status: unprocessed (a different instance extracts) - Flag cross-domain sources for other agents - Do NOT extract claims yourself — that's a separate process ``` ### Capacity on Claude Max ($200/month) **VPS compute budget (all Sonnet):** - Research cron: 6 agents × 90 min/day = 9 hr/day (overnight) - Extract cron: ~37 sources × 10 min = 6 hr one-time backlog, then ~1 hr/day steady-state - Eval pipeline: ~10 PRs/day × 15 min = 2.5 hr/day - **Total VPS:** ~6.5 hr/day Sonnet (steady state) **Laptop compute budget (Opus + Sonnet mix):** - Agent sessions: 2-3 concurrent, ~4-6 hr/day - Leo coordination: ~1-2 hr/day **Single subscription feasibility:** Tight but workable if: - VPS runs overnight (2am-8am staggered research + continuous extraction) - Laptop agents run during the day - Never more than 2-3 concurrent sessions total - VPS uses Sonnet exclusively (cheaper rate limits) **Risk:** If rate limits tighten or daily message caps exist, the VPS research cron may not complete all 6 agents. Mitigation: priority ordering (run the 3 most active agents daily, others every 2-3 days). ## Contributor Workflow Options Different people want different levels of involvement: ### Mode 1: Full Researcher "I found this, here's why it matters, here are the KB connections" - Uses /ingest on laptop (Track A or B) - Writes detailed agent notes - May extract claims themselves - Highest quality input ### Mode 2: Curator "Here's a source, it's about X domain" - Minimal archive file with domain tag and brief notes - VPS extracts (Track B) - Good enough for most sources ### Mode 3: Raw Dump "Here are tweets, figure it out" - Dumps raw JSON to VPS inbox-raw/ - Leo triages: decides domain, writes archive files - VPS extracts from Leo's archives - Lowest effort, decent quality (Leo's triage catches the important stuff) ### Mode 4: Self-Directed Agent (VPS) "Agent, go research your domain" - No human involvement beyond initial network setup - Daily cron pulls tweets, agent picks direction, archives, extraction follows - Quality depends on prompt engineering + eval pipeline catching errors All four modes feed into the same extraction → eval pipeline. Quality varies, but the eval pipeline is the quality gate regardless. ## Open Questions 1. **Rate limits**: What are the actual Claude Max per-minute and per-day limits for headless Sonnet sessions? Need empirical data from this first extraction run. 2. **Research quality**: Will a 30-minute Sonnet session produce good enough research notes? Or does research require Opus-level reasoning? 3. **Network bootstrapping**: Agents need network files. Who curates the initial account lists? (Currently Cory + Leo, eventually agents propose additions) 4. **Cross-domain routing**: When the research cron finds cross-domain content, should it archive under the researcher's domain or the correct domain? (Probably correct domain with flagged_for_{researcher}) 5. **Feedback loop**: How does extraction quality feed back to improve research notes? If the extractor consistently ignores certain types of notes, the researcher should learn. 6. **Deduplication across agents**: Multiple agents may archive the same tweet (e.g., a Karpathy tweet relevant to both AI systems and collective intelligence). The extract cron needs to detect this. ## Implementation Order 1. ✅ Extract cron (running now — validating extraction quality) 2. **Next**: Research cron — daily self-directed sessions per agent 3. **Then**: Raw dump path — Leo triage from JSON → archive 4. **Later**: Full end-to-end with X API pull integrated into research cron 5. **Eventually**: Feedback loops from eval quality → research prompt tuning