discovering-emergence

name	discovering-emergence
description	Runs an autonomous ML research loop on a remote RunPod GPU over SSH that forms hypotheses, runs experiments, measures multiple capability probes, and hunts for emergent properties — capabilities that appear unexpectedly rather than being explicitly trained. Keeps reproduced discoveries, discards noise, and logs everything to a research journal. Use when the user wants to find emergent behavior, run open-ended ML experiments, search for novel or revolutionary capabilities, or mentions "find emergent properties", "emergence research", "autonomous ML experiments", or provides a RunPod for experimentation.
license	MIT
metadata	{"author":"tylergibbs","version":"1.0.0","argument-hint":"[emergence.json]"}

Discovering Emergence

An autonomous research loop that hunts for emergent properties in machine learning systems: capabilities, behaviors, or phase transitions that arise from training, scale, or composition but were never explicitly designed. The goal is to surface genuinely novel phenomena that could advance the field — and to do so with the skepticism of a real scientist, not a metric-chaser.

Heavy compute runs on a remote RunPod GPU over SSH. Your local context window stays small; the GPU does the work. This is a long-running, mostly autonomous session — design every step to survive for hours without flooding context.

Unlike single-metric optimization, emergence is multi-dimensional and you do not know in advance what you are looking for. You measure a battery of probes across conditions and watch for surprise: discontinuities, capabilities absent from the objective, sharp generalization, or qualitative shifts. A surprise is a candidate, not a discovery. See references/EMERGENCE.md.

Discovery Flow

If emergence.json exists, skip to Connect Phase.

Otherwise:

1. Get the RunPod connection. Ask the user for the SSH command RunPod gives them (e.g. ssh root@213.1.2.3 -p 22001 -i ~/.ssh/id_ed25519) and the workspace path on the pod (default /workspace). Do not guess credentials.

2. Explore in parallel. Launch three Agent subagents simultaneously:

   • Agent 1 — Local project: scan this repo's structure, language/framework, any existing models, training code, datasets, READMEs. Summarize what's here and what could be experimented on.
   • Agent 2 — Remote pod: SSH in and probe the environment — nvidia-smi (GPU model, count, VRAM), Python/CUDA/PyTorch versions, installed ML packages, disk space, and any code already in the workspace. Report exactly what's available. See references/RUNPOD.md.
   • Agent 3 — Literature scan: use the arxiv CLI to survey where emergence is currently being reported and which phenomena look most tractable on a single GPU (grokking, in-context learning, induction heads, tool use, phase transitions, …). Return a short ranked list of candidate directions with the arXiv ids that motivate each. See references/LITERATURE.md.

   Wait for all three before proceeding.

3. Choose the direction yourself. Do NOT ask the user what to study. Weigh the three reports and pick the single direction with the highest chance of surfacing a genuine emergent property within this GPU's budget. Judge each candidate on:

   • Tractability — a small model can reach the regime where the effect appears, within budget.
   • Surprise potential — the phenomenon is qualitative/discontinuous, not a smooth metric gain (see references/EMERGENCE.md).
   • Measurability — you can define an off-objective probe that would catch it.
   • Headroom — it isn't already fully characterized in the literature, so a positive result is novel.

   Derive the rest from the exploration: run_template from the framework found, probes from the chosen phenomenon (always include an off-objective probe), budget from the GPU's hourly cost, tag from today's date.

4. Write emergence.json — see references/CONFIG.md. Briefly tell the user which direction you chose and why (one paragraph), then proceed. The user need not approve the direction — only stop them if a credential or the budget cap is missing.

Connect Phase

1. Parse and validate emergence.json. Confirm budget is set — the loop spends real money on GPU time; never run uncapped.
2. SSH to the pod and verify the GPU is live (nvidia-smi). Halt on failure.
3. Establish the remote workspace: create <workspace>/<tag>/, sync local experiment code up to it (see references/RUNPOD.md), and run setup_check if configured. Set up the arxiv CLI for literature grounding (see references/LITERATURE.md); skip if bun is unavailable.
4. Locally: create git checkout -b emergence/<tag> (append -2, -3… if it exists). Add results.tsv, runs/, and *.log to .gitignore.
5. Initialize results.tsv (tab-separated header) and journal.md (see Journal Format).
6. Baseline run: run the unmodified experiment on the pod, record every probe value. This is your reference point for "surprising."
7. Tell the user setup is complete and report the baseline. This is the last interaction. From here you run autonomously until the budget is spent or the user stops you.

The Hunt Loop

LOOP until the budget cap is reached. Do NOT stop early. Do NOT ask permission to continue. The user may be asleep and expects you to keep hunting.

1. ORIENT. Re-read results.tsv and the tail of journal.md for current state and
   open threads. Check elapsed time / spend against budget (see RUNPOD.md).
   If budget exhausted → go to Wind Down.

2. HYPOTHESIZE. Form ONE concrete, falsifiable hypothesis about where emergence
   might appear. Prefer ideas that are:
   - Informed by prior probe readings and journal threads
   - Grounded in prior work — search arXiv for how others approached the
     phenomenon (references/LITERATURE.md); a paper's claim is a hypothesis to
     test, not a result to assume
   - Aimed at a *qualitative* shift, not a 1% metric nudge
   - Cheap enough to fit the remaining budget
   Write the hypothesis to the journal BEFORE running (prevents post-hoc
   storytelling). See references/STRATEGIES.md when out of ideas.

3. DESIGN. Edit ONLY modifiable_files. Change one variable so the result is
   interpretable. Include the full probe battery in the run so you can catch
   surprises you weren't looking for. When using a PyTorch / library API you're
   unsure of, look up the CURRENT docs via Context7 before writing the code —
   don't rely on memory (see references/LITERATURE.md). A run that crashes on a
   stale API wastes GPU budget.

4. SYNC & RUN. Push changed files to the pod. Launch the run with ALL output
   redirected to runs/<id>.log on the POD (never stream to your context). Poll
   for completion; kill if it exceeds timeout_seconds.

5. MEASURE. Pull back only the probe summary (grep, not full logs). Record every
   probe value, not just the target. Crash → tail 50 lines; trivial fix → retry
   (max 2-3); broken idea → log as crash and move on.

6. TRIAGE THE RESULT:
   - NOISE / expected → log to results.tsv, note in journal, move on.
   - SURPRISE (discontinuity, capability not trained for, sharp generalization,
     phase transition) → this is a CANDIDATE, not a discovery. Go to step 7.

7. VERIFY before believing. A candidate must survive skepticism:
   - REPRODUCE with a different seed / split. Real emergence repeats.
   - RULE OUT the boring explanation: data leakage, eval bug, memorization,
     metric artifact, prompt giveaway, lucky seed.
   - ABLATE: remove the suspected cause — does the effect vanish as predicted?
   Only a candidate that survives all three is a CONFIRMED finding. Most won't.
   See references/EMERGENCE.md for the full checklist.

8. RECORD. Append a tab-separated row to results.tsv. Write a journal entry:
   hypothesis, what you ran, every probe value, verdict (noise / candidate /
   confirmed / crash), and the next thread it opens.

9. Every 10 experiments, write a SYNTHESIS entry: what patterns are emerging,
   which directions are dead, what to chase next. If the chosen direction has
   gone flat across many experiments, PIVOT to the next-best candidate from the
   Discovery Flow literature scan (re-run a baseline and its probes for the new
   direction). Don't burn the whole budget on a dead end.

10. GOTO 1

Wind Down

When the budget cap is hit (or the user stops the run):

1. Write a final Findings Report to journal.md: confirmed findings (with reproduction evidence), promising-but-unconfirmed candidates, dead ends, and the experiments you'd run next.
2. Commit the journal and results. Leave the pod's runs/ intact for inspection.
3. Stop or pause the RunPod if budget.stop_pod_on_finish is set — idle GPUs bill by the hour. See references/RUNPOD.md.
4. Surface the report to the user. Do not overclaim: report confirmed findings as confirmed and candidates as candidates.

Critical Rules

• Protect your context window — see references/CONTEXT.md. This is the #1 risk over a multi-hour run. Redirect all output to logs on the pod, grep for probes, only tail on crash, read readonly files once.
• Be your own adversary. The biggest danger in autonomous discovery is fooling yourself. Default to "it's a bug or an artifact" until reproduction and ablation prove otherwise. A confirmed null result is more valuable than an unverified "breakthrough."
• Respect the budget. GPU time costs money every minute. Track spend, honor the cap, and never leave the pod running idle.
• One change per experiment — so results stay interpretable.
• The journal is the science. results.tsv is the numbers; journal.md is the reasoning, the surprises, and the verdicts. Both survive git resets.

Journal Format

journal.md is an append-only research log. Each entry:

## [exp-id] <one-line hypothesis>   (<timestamp>, <git sha>)
Ran:      <what changed + command>
Probes:   probe_a=… probe_b=… probe_c=…   (baseline: …)
Verdict:  noise | candidate | confirmed | crash
Reason:   <why — including ruled-out boring explanations if a candidate>
Next:     <thread this opens>

Example results.tsv

exp_id	git_sha	target_probe	icl_acc	ood_gen	loss	verdict	note
base	a1b2c3d	0.610	0.12	0.20	2.41	keep	baseline
e001	b2c3d4e	0.640	0.13	0.21	2.30	noise	wider MLP, no qualitative shift
e002	c3d4e5f	0.910	0.71	0.22	2.28	candidate	sharp ICL jump at depth 8 — VERIFY
e003	c3d4e5f	0.905	0.70	0.22	2.28	confirmed	reproduced seed 2; ablating depth kills it
e004	d4e5f6g	0.000	0.00	0.00	NaN	crash	OOM at batch 512