Research Pipeline

Full Research Pipeline: Idea → Experiments → Submission

⏱ External cadence: non-judgmental heartbeat only. An overnight /loop / CronCreate heartbeat may wake, detect a stalled phase (no progress, dead process, blocked on a freed resource) and nudge it forward — it may NEVER decide the work is good (paper good enough, proof holds, claim supported). Every such verdict stays on its own skill's internal cadence and terminates in the cross-model jury. A heartbeat may say "keep going," never "good enough." See shared-references/external-cadence.md (overnight-pipeline rule + stall detection & forced structural pivot). At heartbeat startup, touch the run state first each tick and register this run with the watchdog loop type (so a silent death surfaces as STALE); unregister on completion. The watchdog only detects — it never acquits. Each tick also record the new-finding count via the iteration_log.py helper (resolve through the canonical .aris/tools → tools → $ARIS_REPO/tools → $ARIS_REPO/tools via ~/.aris/repo chain, integration-contract §2; warn-and-skip if unresolved): python3 "$ITER_LOG" note <root> <run_id> <phase> <n>. On the returned pivot=structural (stale ≥ 2) the nudge must change a STRUCTURAL constraint and pick an untried direction; on pivot=human (stale ≥ 4) flag for attention. Counting only — never a quality verdict.

End-to-end autonomous research workflow for: $ARGUMENTS

Optional formal proof route

Before creating an experimental run, route theorem-centered requests for Lean to /lean-formalize and report its actual proof result. Run the experimental stages below when the task includes empirical research. In a mixed project, use Lean for a user-requested formal proof or a specific mathematical obligation whose formal verification would materially help; return its checked scope and remaining obligations to the research summary and applicable proof audit. Ordinary mathematics stays in the existing theory skills. Difficulty alone does not make Lean mandatory, and a formal sublemma does not establish an empirical claim.

Constants

• AUTO_PROCEED = true — When true, every selection checkpoint is informational: report the choice and continue in the same turn. When false, ask for explicit user confirmation and end the turn at the checkpoint.

• ARXIV_DOWNLOAD = false — When true, /research-lit downloads the top relevant arXiv PDFs during literature survey. When false (default), only fetches metadata via arXiv API. Passed through to /idea-discovery → /research-lit.

• HUMAN_CHECKPOINT = false — When true, the auto-review loops (Stage 3) pause after each round's review to let you see the score and provide custom modification instructions before fixes are implemented. When false (default), loops run fully autonomously. Passed through to /auto-review-loop.

• REVIEWER_DIFFICULTY = medium — How adversarial the reviewer is. medium (default): standard MCP review. hard: adds reviewer memory + debate protocol. nightmare: GPT reads repo directly via codex exec + memory + debate. Passed through to /auto-review-loop.

• CODE_REVIEW = true — GPT-6-Astra xhigh reviews experiment code before deployment. Catches logic bugs before wasting GPU hours. Set false to skip. Passed through to /experiment-bridge.

• BASE_REPO = false — GitHub repo URL to use as base codebase. When set, /experiment-bridge clones the repo first and implements experiments on top of it. When false (default), writes code from scratch or reuses existing project files. Passed through to /experiment-bridge.

• COMPACT = false — When true, generates compact summary files for short-context models and session recovery. Passed through to /idea-discovery and /experiment-bridge.

• AUTO_WRITE = false — When true, automatically invoke Workflow 3 (/paper-writing) after Stage 4. VENUE is needed only when Stage 5 begins — a missing venue defers paper writing; it never blocks Stages 1-4. When false (default), Stage 4 generates NARRATIVE_REPORT.md and stops — user invokes /paper-writing manually.

• VENUE = (unset) — Target venue for paper writing; bound only when Stage 5 begins. Options: ICLR, NeurIPS, ICML, CVPR, ACL, AAAI, ACM, IEEE_CONF, IEEE_JOURNAL. No default: a missing venue defers paper writing — it never blocks Stages 1-4 and is never guessed.

• RENDER_HTML = true — When true (default), auto-render NARRATIVE_REPORT.md to HTML at Stage 4 completion via /render-html. Uses --no-review (this is an internal handoff doc to /paper-writing, not a reviewer-facing final artifact — the upstream Stage 3 auto-review loop already cross-model-reviewed the claims). Set false to skip, or pass — render html: false. Non-blocking: if /render-html fails or Codex MCP is unavailable, log the failure and continue — the HTML view is a nice-to-have, not a Stage 4 prerequisite.

• RESUMABLE = true — When true (default), the pipeline records per-stage state to .aris/runs/<run_id>.json so a crashed/interrupted run can resume via /research-pipeline — resume <run_id> instead of restarting. Stage status splits done (executor finished writing) from accepted (the stage's cross-model gate / deterministic verifier passed); resume re-validates any done-but-unaccepted stage. See shared-references/resumable-runs.md.

💡 Override via argument, e.g., /research-pipeline "topic" — AUTO_PROCEED: false, human checkpoint: true, difficulty: nightmare, code review: false, base repo: https://github.com/org/project, auto_write: true, venue: NeurIPS.

Checkpoint execution rule

Resolve AUTO_PROCEED once from $ARGUMENTS before Stage 1 and pass that resolved value to nested workflows.

• AUTO_PROCEED=true is non-blocking. A checkpoint is a progress update, not a question. State the result and the automatically selected next action, then continue executing in the same turn. Do not ask for confirmation, request user input, sleep, wait for silence, or end the turn at a checkpoint.
• AUTO_PROCEED=false is blocking. Present the options, ask the user, and end the turn. Resume only after an explicit reply.

Never implement auto-proceed as “ask, then continue if there is no response.” Once a turn ends, silence cannot resume the pipeline. The user can still interrupt a non-blocking run at any time.

This rule governs only AUTO_PROCEED-controlled selection checkpoints. If the user explicitly enables a Feishu interactive gate, that external approval or reply is an intentional blocking exception; wait for that user-controlled gate rather than treating it as a silence timeout. Feishu off/push-only modes remain non-blocking under AUTO_PROCEED=true.

Overview

This skill chains the entire research lifecycle into a single pipeline:

/idea-discovery → /experiment-bridge → /auto-review-loop → /paper-writing (optional)
├── Workflow 1 ──┤├── Workflow 1.5 ──┤├── Workflow 2 ───┤ ├── Workflow 3 ──┤

It orchestrates up to four major workflows in sequence. Workflow 3 (paper writing) is optional and controlled by AUTO_WRITE.

Resumable runs (— resume <run_id>)

This pipeline is long and can fail mid-run; it tracks per-stage state via run_state.py so you can resume instead of restarting (see shared-references/resumable-runs.md). Skip this whole section if RESUMABLE = false.

Resolve the helper via the canonical chain (integration-contract §2): .aris/tools/run_state.py → tools/run_state.py → $ARIS_REPO/tools/run_state.py → $ARIS_REPO/tools/run_state.py via ~/.aris/repo (warn-and-skip if unresolved — never block the pipeline).

Phases, in order: idea-discovery, experiment-bridge, auto-review-loop, summary, paper-writing.

• At start: if — resume <run_id> was passed, run run_state.py resume <root> <run_id> — it prints the first non-accepted phase; begin the pipeline at that stage (re-run a running/failed stage; re-audit a done-but-unaccepted stage). Otherwise derive <run_id> from the direction slug + date and run_state.py start <root> <run_id> --phases "idea-discovery,experiment-bridge,auto-review-loop,summary,paper-writing".

• Per stage: set <run_id> <phase> running on entry; set <run_id> <phase> done --artifact <path> once the stage's artifact is written.

• Mark accepted ONLY after the stage's gate passes — never on the executor's own say-so (run_state.py accept requires a recorded verdict id + reviewer):

  phase	what sets accepted	record as reviewer
  idea-discovery	Gate 1 cross-model jury / novelty-check passed	codex-gpt-6-astra + thread id
  experiment-bridge	experiments actually ran (jobs completed) — deterministic	deterministic:experiment-bridge
  auto-review-loop	the loop hit its positive STOP (score>=6 AND verdict∈{ready,almost} — codex's verdict)	codex-gpt-6-astra + final review trace id
  summary	NARRATIVE_REPORT.md written (+ rendered if RENDER_HTML) — deterministic	deterministic:summary
  paper-writing	submission audits passed (verify_paper_audits.sh exit 0) — deterministic	deterministic:verify_paper_audits.sh

If AUTO_WRITE = false (default), paper-writing is not part of this run: after summary is accepted, set <run_id> paper-writing skipped so resume reports COMPLETE instead of pointing forever at a pending stage. Record each accept verdict_id as a durable handle — the codex thread/trace id, or the path/sha of the deterministic verifier's report (e.g. the verify_paper_audits.sh output JSON) — not just the reviewer label.

A stage left done (gate failed/ambiguous, or the run crashed before the gate) is re-validated on the next resume — the acceptance obligation is never skipped.

Overnight heartbeat: stall detection → forced structural pivot

Only when an unattended heartbeat is driving this run (overnight /loop / CronCreate). Skip otherwise. Doctrine + rationale: shared-references/external-cadence.md → "Stall detection & forced structural pivot". This is a Type-A signal — it counts findings and changes direction, never judges quality.

Resolve the helper via the canonical chain (integration-contract §2), warn-and-skip if unresolved (never block the run):

ITER_LOG=".aris/tools/iteration_log.py"
[ -f "$ITER_LOG" ] || ITER_LOG="tools/iteration_log.py"
[ -f "$ITER_LOG" ] || ITER_LOG="${ARIS_REPO:-}/tools/iteration_log.py"
[ -f "$ITER_LOG" ] || { [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ] && ARIS_REPO="$(cat "$HOME/.aris/repo" 2>/dev/null)"; } || true
[ -f "$ITER_LOG" ] || ITER_LOG="${ARIS_REPO:-}/tools/iteration_log.py"
[ -f "$ITER_LOG" ] || { echo "WARN: iteration_log.py not resolved; skipping stall detection" >&2; ITER_LOG=""; }

Then, each heartbeat tick, record how many concrete new findings the current stage produced and read the returned pivot:

[ -n "$ITER_LOG" ] && python3 "$ITER_LOG" note "$ROOT" "$RUN_ID" "$STAGE" "$N_NEW_FINDINGS"
# → {"stale_count": N, "pivot": "none|structural|human"}

Act on pivot:

• none — keep going.
• structural (stale ≥ 2) — the next nudge must change a structural constraint (frame / objective / data / representation), not a tactical parameter, and pick a direction different from every one already tried. Record the chosen frame so future ticks can avoid it: python3 "$ITER_LOG" note "$ROOT" "$RUN_ID" "$STAGE" 0 --direction "<the new frame>".
• human (stale ≥ 4) — stop nudging blindly; flag for human attention (escalate, do not silently abandon).

The heartbeat may say "keep going / change direction," never "good enough" — every quality verdict still terminates in the cross-model jury (acceptance-gate.md).

Pipeline