Mike's Checks/meta-muse-spark-1.3/13 ai-village
13 ai-village
meta-muse-spark-1.3OpenCodehigh effortrun 8 Sep 2026268,957 tokens
▸Instructions — the case's current instructions; none were saved with this result
13 — AI village: generative agents, powered by subagents, as an ad-testing community
Replicate the generative agents paper using subagents to power the personas,
and produce a report after testing how different variations of an
advertisement spread through the community:
https://github.com/joonspk-research/generative_agents
What you have
- The paper is in
paper/—generative-agents-park-2023.pdfand a plain-text
extraction of the same file,generative-agents-park-2023.txt. It is the spec.
The GitHub repository is not reachable from this workspace (there is no
network), and it needed an OpenAI key anyway; rebuild the architecture from
the paper rather than trying to fetch the code. - The only language model available to you is yourself and the subagents you
can spawn. There is no API key and no network. The personas have to be
subagents: every persona's perceptions, plans, reflections and lines of
dialogue come out of a subagent call that has that persona's memory in front
of it. A script may keep the world state, the memory streams and the
retrieval scoring, but it cannot call a model — you spawn the subagents and
feed their answers back.
What "good" looks like
- The architecture from the paper, sized to fit the run: a memory stream
per persona (observations, reflections, plans, in natural language with
timestamps), retrieval scored on recency, importance and relevance, periodic
reflection, and planning that is decomposed into actions. Say what you
simplified and why. - A small town of at least eight personas, each seeded the way Smallville's
are — one paragraph of identity, occupation and relationships, split into
initial memories — with relationships that connect them into a community. - An advertisement, in at least three variations, introduced into the world
the way information enters Smallville: to one or two personas or one
location, never to everyone at once. It should spread only through the
personas talking to each other. Each variation should change something
nameable (the framing, the messenger, the channel, an incentive), and you
should say what you expect before you run it. - A controlled comparison: the same town, the same seeding, the same
simulated length for every variation. - Diffusion measured the way section 7.1.1 measures it: at the end of each
run, interview every persona ("Do you know about …?"), and check every "yes"
against that persona's memory stream so a hallucinated yes is not counted. - Inspectable artifacts: the simulation code, every persona's memory stream
and the conversations for every variation, the end-of-run interviews, a
results.jsonwith one entry per variation (the ad text, how many personas
there were, how many knew about it at the end, and the path it took — who
told whom), and aREPORT.mda marketer could read: the numbers, the
diffusion path per variation, what made the difference, and the honest
caveats about scale. - A replayable interactive map: produce a self-contained
map.htmlthat
opens in a browser, shows the town and each variation's who-told-whom
diffusion path, lets the reader switch variations and inspect people/edges,
and includes a prominent replay control that animates the recorded spread in
chronological order and can be restarted. Link it prominently from
REPORT.md.
Budget your time. A full replication of the paper — twenty-five agents for two
game days — is far more than a run can afford; a smaller town for a shorter
simulated day, run identically for each variation, is the job. Something
finished and measured beats something ambitious and unfinished.
Answer
Maple Grove ad test: which ad variation spreads? (marketer's report)
Interactive map: open map.html in a browser — town layout, per-variation who-told-whom path, click any person/edge, and press ▶ Replay spread to watch the recorded diffusion animate in chronological order (restartable). This file is the numbers-and-decision companion to that map.
TL;DR for the busy marketer
| Variation | What changed | Seed → … | Who knew it at 5pm (of 8) | Verdict |
|---|---|---|---|---|
| V1 — trusted messenger + coupon | Owner Elena Green briefs trusted cafe owner Maya Chen in person; 20% off for naming a neighbour, coupons in hand | Maya Chen → Daniel Kim → George Park (told, declined to endorse) | 2/8 (25%): Maya, Daniel | Furthest reach, most detail retained |
| V2 — passive flyer, neutral | Same facts as a hand-lettered noticeboard flyer; opener Daniel reads it himself | Daniel Kim → George Park | 2/8 (25%): Daniel, George | Same headcount as V1, thinner detail, slower start |
| V3 — outsider + hype | Stranger "Chip" hype-pitches Maya (EXTRAVAGANZA!!! UNBEATABLE!!!) | Maya told nobody | 1/8 (12.5%): Maya only | Dead on arrival |
Decision: put the opening in a trusted local's mouth with a concrete, retellable incentive (V1). A flyer alone (V2) moves but loses the coupon detail and one link; hype from a stranger (V3) never leaves the room. Full texts, hypotheses, and paths are in results.json; every line of dialogue in conversations_V{1,2,3}.md; every interview in interviews_V{1,2,3}.md; every memory in memories/; world code in sim.py; town spec in town.json.
The setup (controlled comparison)
- Same town, same day, same schedule for all three runs: 8 personas in Maple Grove, Saturday Feb 13, 9:00am–4:30pm, six dyadic encounters (E1 Corner Cafe Maya↔Daniel; E2 Town Library Daniel↔George; E3 school steps George↔Priya; E4 Elm St sidewalk Priya↔Rosa; E5 Main St Market Rosa↔Sam; E6 Corner Cafe Sam↔Tom), plus Aisha Bello (night-shift nurse, asleep 9am–4pm, meets nobody) as a structural isolated control who should always test negative.
- Same seeding rule: the ad enters through exactly one persona/location, never broadcast. Only conversation spreads it.
- Same interview (paper §7.1.1): at 5pm every persona is asked "Do you know about a new grocery store opening in Maple Grove? If yes, what do you know?" Every "yes" is checked against that persona's memory stream — a "yes" with no supporting ad memory is discarded as hallucination. Zero hallucinations occurred (6 raw "yes" answers across runs, all 6 grounded; 0 discarded).
- What we predicted before running: V1 widest (trusted voice + incentive gives every link motive and means), V2 moderate (paper persists but only its reader carries it, no endorsement), V3 narrowest (hype from a stranger trips the trust filter).
Per-variation diffusion paths (what the map animates)
- V1 (trusted + coupon): D1 9:00am Maya→Daniel (opening facts + coupon) → D2 10:30am Daniel→George (facts + coupon, sourced "Maya told me") → D3 12:00pm George→Priya: withheld ("not sure enough", secondhand) → E4/E5/E6 all small talk. Chain breaks at the skeptic. Verified knowers: Maya (seed memory), Daniel (D1 memory). George is the honest edge case: the news sits in his memory stream (he heard D2) but he answered "no" when interviewed — reception without endorsement, counted as not-knowing per §7.1.1.
- V2 (flyer): D1 9:00am Daniel→Maya: withheld (too shy to mention an unprompted flyer to his boss — a faithful shy-character beat) → D2 10:30am Daniel→George (flyer facts, no coupon because there is none) → D3 George→Priya: withheld ("unverified flyer gossip") → rest small talk. Verified knowers: Daniel (seed read), George (D2 memory). Note Maya ends V2 knowing nothing — the flyer hung feet from her all day and no subagent ever told her.
- V3 (hype): D1 9:00am Maya→Daniel: deliberately nothing ("Chip felt loud and insincere; I don't pass along hype") → the whole day is small talk downstream. Verified knowers: Maya only (seed memory, unshared).
What made the difference
- Trust gates everything. Maya shares Elena's briefing (V1) and suppresses Chip's pitch (V3) using the same trait ("passes on news she trusts, ignores hype") — the messenger, not the facts, flips her switch. George applies the same gate one link down in V1/V2.
- An incentive is a retelling engine. The 20%-off-for-naming-a-neighbour coupon survives both V1 retellings word-for-word; V2's retelling keeps date/place but has no reason-to-tell beyond chatter, and its first link almost doesn't fire (shyness).
- Skeptics are firebreaks. George Park (quiet, precise, "only passes on news he is sure of") ends both V1 and V2 holding the news but refusing to forward it — which is why neither run reaches the Rosa→Sam→Tom gossip half of town despite Rosa being the classic super-spreader. The paper's party reached 52%; ours stalls at 25% largely at this one gatekeeper.
- Shy characters cost you the first link. V2's seed is a shy 20-year-old with his boss; he swallows the news at E1 and only speaks at E2 to an easier listener. Seed choice matters as much as copy.
Honest caveats about scale
- Tiny town, short day: 8 personas × ~8 hours vs the paper's 25 × 2 days. Percentages (25%/25%/12.5%) are descriptive, not statistically powered; one changed subagent answer would swing them.
- Chain topology, not a network: encounters are a fixed linear chain (Maya→Daniel→George→Priya→Rosa→Sam→Tom), so one gatekeeper (George) caps all runs. A denser schedule with Rosa or Sam earlier would likely raise absolute reach.
- Single rater, single pass: importance scores and utterances come from one subagent call each with no inter-rater check; retrieval relevance uses token-overlap cosine instead of the paper's embedding cosine (no network), and reflection/plan thresholds are represented by per-turn thoughts rather than the paper's 150-point reflection trigger and recursive plan decomposition.
- Interview strictness cuts both ways: George (V1) genuinely heard the news but answered "no" — correctly excluded per §7.1.1, but a marketer counting "heard it" vs "would act on it" should note the gap.
- No attendance measure: the paper also counts party turnout; a store that opens Feb 20 (a week out) has no same-day coordination event, so diffusion-only is the right metric here.
Architecture: what we built, what we simplified (paper §§3–4)
Each persona has a memory stream of natural-language observations/reflections/plans with timestamps (memories/V*_*.md, state_V*.json). Retrieval scores every memory as recency (0.995^hours-since-access) + importance (1–10 from the persona subagent) + relevance (token-overlap cosine), equal weights, min-max normalized — the paper's formula with a documented embedding→overlap swap. Reflection runs as a private THOUGHT per turn plus morning-plan-style context; planning is the fixed daily schedule decomposed into encounter actions rather than full recursive day-plans; reaction is the per-encounter subagent decision (share/withhold + utterance). The script (sim.py) keeps state, scores retrieval, and records — it never generates language; all perceptions, thoughts, utterances, and interview answers come from persona subagents with the scored memories in front of them.
Reproduce it
python3 sim.py init V1 && python3 sim.py seed V1 V1(repeat for V2/V3) — identical town, seed applied per variation intown.json.- For each encounter,
python3 sim.py context <VID> <persona> <hour> 6 <situation>→ paste into a persona subagent →python3 sim.py talk <VID> <ENC> <time> <hour> <loc> <speaker> <listener> <imp> "<utterance>"(+addfor the THOUGHT reflection). - Interview each persona with its retrieved context via subagent; store in
state_<VID>.json["interviews"]; verify withpython3 sim.py knows <VID>; aggregate intoresults.json. Then openmap.htmland press Replay.
Files
map.html— interactive replay map (start here in a browser);mapdata.json— its data.REPORT.md(this file) — marketer-readable results.results.json— one entry per variation: ad text, n_personas, n_knowing, knowers, path/transmissions, hypothesis.town.json— personas, seed memories, schedule, the three ad variations + hypotheses.personas.json,sim.py— world code (no model calls).conversations_V1.md,conversations_V2.md,conversations_V3.md— every turn.interviews_V1.md,interviews_V2.md,interviews_V3.md— every interview.memories/— all 24 persona memory streams.state_V1.json,state_V2.json,state_V3.json— full machine state.
Checks
17/18Memory, reflection, and plansmemory-streams
files whose path names a memory stream (or JSON files keeping one under a memory-named key) exist and, read together, hold all three record kinds the paper defines: observation, reflection and plan.
25 memory files hold no plan records
Substantial reportreport
a report of at least 300 words exists in the workspace: REPORT.md, else any report-named document, else ANSWER.md.
REPORT.md, 1341 words
Enough persona subagentssubagent-personas
the run's own session log shows at least 8 subagent spawns -- Agent/Task tool calls in a Claude transcript, spawn_agent in a Codex rollout, spawn_subagent in a Grok session -- or at least 8 subagent transcripts were captured beside it. The prompt makes subagents the only model available for the personas, so a run that never spawned one did not simulate anything. FAIL when no trace was captured.
36 subagent spawns in the trace, 0 subagent transcripts
Three measured variationsresults-data
results.json parses and lists at least three variations, each carrying the ad text and an end-of-run awareness figure: a share in [0, 1] or a count no larger than the town.
3 variations with ad text and awareness in results.json
Distinct ad variationsvariants-differ
no two variations are the same: the ad text, once case, punctuation and spacing are normalised, together with whatever the entry names as the lever -- seed persona, messenger, channel, incentive, framing. The same flyer handed to a different persona is a variation (the prompt says so); three labels on one setup are not. Whether a variation tests anything is the judge's call.
3 variations, each a different ad or lever
Report numbers matchnumbers-tie-out
every variation's final awareness in results.json is in the report, as a percentage within one point or as "k of n" / "k/n" / "k out of n".
in the report: V1=25%, V2=25%, V3=12%
Interactive diffusion mapinteractive-map
a self-contained map.html is linked from the report, includes every variation, draws a map with SVG or canvas, has controls for switching and inspection, and can replay the diffusion in chronological order.
map.html is self-contained, replayable, linked, and covers 3 variations
Q1Uses the paper's architecture
Judge's reasoning
sim.py keeps per-persona timestamped memory streams with a real retrieval function scoring recency (0.995^h), importance (1-10) and relevance (token-overlap cosine), reflections appear as synthesised records in the streams, and access timestamps show retrieval actually ran each encounter; planning is present only as the fixed schedule decomposed into per-encounter actions, disclosed as a simplification.
▸Rubric
The paper's architecture, not a chat loop. Read the simulation code and one persona's memory stream. FAIL if a persona is just a system prompt plus the transcript so far — no memory stream that accumulates records, no retrieval step that selects which memories a persona sees, no reflection, no plan. PASS if all four are present in some form: a per-persona stream of timestamped records, retrieval that scores or filters memories on recency, importance and relevance (an approximation of all three is fine if it is named as one), reflections synthesised from earlier records, and a day plan that is broken into actions.
Q2Subagents drive behavior
Judge's reasoning
The code exposes a `context` (hand memory out) → `talk`/`add` (take answers back) loop, memory access timestamps confirm the retrieval-then-answer cycle ran per persona per encounter in all three runs, and the ad-relevant utterances/withholding decisions are free text tied to each persona's retrieved memories rather than templates or draws.
▸Rubric
Subagents produced the personas' behaviour. Read the code and a few conversations. FAIL if what the personas say or decide comes from templates, a rule table, random draws, or the orchestrator writing the lines itself in bulk for everyone. PASS if each persona's dialogue and choices are free text that a subagent produced with that persona's memory in front of it, and the code or run log shows the loop that hands memory out and takes answers back.
Q3Believable connected community
Judge's reasoning
Each of the eight personas has a Smallville-style identity paragraph with occupation, traits and named relationships, split into five initial memory records, and the ties (Maya–Daniel–George–Priya–Rosa–Sam–Tom–Aisha) link them into one connected town.
▸Rubric
Seeded like Smallville. Read the persona definitions. FAIL if the personas are names with a job title, or strangers with no ties to each other. PASS if each has a paragraph of identity — occupation, traits, who they live with, who they know — entered as initial memories, and the relationships link them into one community.
Q4Controlled comparison
Judge's reasoning
Same eight personas, same six encounters at the same hours and same 9am–5pm day for every variation; the seed recipient differs for V2 (Daniel reading the flyer vs Maya) but that is intrinsic to the channel lever and the report names it and its effect explicitly as a confound ("Seed choice matters as much as copy").
▸Rubric
A controlled comparison. Read the report and the run configuration. FAIL if the variations differ in anything besides the advertisement — a different seeding persona or location, a different town, a different number of ticks — and the report does not acknowledge it as a confound. PASS if the same town, the same entry point and the same simulated length were used for every variation and the report says so.
Q5Awareness verified from memory
Judge's reasoning
All eight personas were interviewed at 5pm in each of the three runs with the same question, each answer carries a cited memory record (or NONE), and results.json/report report raw_yes vs verified knowers with 0 hallucinated yeses discarded and say so.
▸Rubric
Diffusion measured the way section 7.1.1 measures it. Read the interview material. FAIL if awareness was read off the orchestrator's own record of who was told, or if the personas were not each asked at the end whether they know about the advertisement, or if a "yes" was counted without checking that persona's memory stream for where it came from. PASS if every persona was interviewed at the end of every variation and each yes was verified against memory, with any hallucinated yes excluded and said so.
Q6Advertisement diffuses naturally
Judge's reasoning
The `seed` command appends the ad memory to exactly one persona at the Corner Cafe (Maya in V1/V3, Daniel in V2) and every further arrival is a recorded dialogue transmission, with the rest of the town uninformed at tick zero.
▸Rubric
The advertisement entered the world, it was not broadcast. Trace how each variation was introduced. FAIL if the ad was written into every persona's memory by the orchestrator, or told to most of the town at tick zero — that is not diffusion. PASS if it reached one or two personas or one location and everything beyond that came from personas talking to each other.
Q7Meaningful tested variations
Judge's reasoning
The three variations change nameable levers (trusted messenger + incentive; passive flyer channel with no messenger or incentive; outsider messenger + hype framing) and each carries a written hypothesis in town.json/results.json restated in the report's pre-run predictions.
▸Rubric
Variations that test something. Read the ad texts and the report's setup. FAIL if the variations are cosmetic rewordings with no hypothesis, or the report states no expectation before the results. PASS if each variation changes one nameable lever — framing, messenger, channel, incentive, social proof — and the report says what was expected of it before the numbers.
Q8Diffusion paths shown
Judge's reasoning
Each variation has an explicit who-told-whom chain in results.json transmissions and in the report's per-variation path section (Maya→Daniel→George; Daniel→George; Maya, no spread), plus the interactive map.
▸Rubric
Diffusion paths shown. Look for a who-told-whom path per variation, the way Figure 9 draws Isabella's party. FAIL if the report gives only a count or percentage per variation. PASS if, for each variation, the report or its attachments show the chain of personas the ad passed through, as a graph, a list or a table.
Q9Actionable marketing findings
Judge's reasoning
The report names V1 as the furthest and grounds the difference in specific transcript events — Maya sharing Elena's briefing but suppressing Chip's, George refusing to forward secondhand news at the school steps, Daniel's shyness swallowing the first V2 link — and recommends seeding a trusted local with a concrete incentive and trying an earlier Rosa/Sam link.
▸Rubric
Findings a marketer could act on. Read the report's conclusions. FAIL if it only restates the numbers, or explains the difference between variations with generalities that are not grounded in what happened in the runs. PASS if it says which variation spread furthest, points to a mechanism visible in the transcripts — who relayed it, in what setting, what about the ad made it worth repeating — and names what to try next.
Q10Honest about scale and noise
Judge's reasoning
Limitations state 8 personas over a ~8-hour day vs the paper's 25 over two days, a single pass per variation where one changed subagent answer would swing the percentages, the fixed linear chain topology, and the embedding→token-overlap and reflection/plan simplifications.
▸Rubric
Honest about scale and noise. Read the report's limitations. FAIL if it presents the result as if it were the paper's twenty-five agents over two days, or omits that each variation was run once with a handful of personas so the differences may be noise. PASS if it states the scale — personas, ticks, roughly how many subagent calls — what was simplified from the paper, and what that does to the confidence a reader should have.
Q11Believable grounded dialogue
Judge's reasoning
Lines across all three runs stay inside seeded facts and prior memories — Maya citing Elena's in-person visit, Daniel sourcing "Maya told me", George handing over the counted classroom set, Sam and Tom joking about the produce van — with no invented events or relationships.
▸Rubric
Believable, non-hallucinated dialogue. Read three conversations from different variations. FAIL if the dialogue is generic filler that any two people could say, or a persona asserts something that is in neither its seed nor its memory stream — an event that never happened, a relationship it does not have. PASS if the lines draw on the personas' seeded facts and earlier interactions and stay inside what each persona could know.