@tryinget/pi-autoresearch

Pi extension package for the governed pi-autoresearch bounded experiment runtime.

Packages

Package details

extensionprompt

Install @tryinget/pi-autoresearch from npm and Pi will load the resources declared by the package manifest.

npm repohome report
$ pi install npm:@tryinget/pi-autoresearch
Package
@tryinget/pi-autoresearch
Version
0.2.0
Published
Jul 13, 2026
Downloads
269/mo · 269/wk
Author
tryinget
License
SEE LICENSE IN LICENSE
Types
extension, prompt
Size
1.2 MB
Dependencies
2 dependencies · 3 peers
Pi manifest JSON
{
  "extensions": [
    "./extensions/pi-autoresearch.ts"
  ],
  "prompts": [
    "./prompts"
  ]
}

Security note

Pi packages can execute code and influence agent behavior. Review the source before installing third-party packages.

README

summary: "Overview and quickstart for monorepo package @tryinget/pi-autoresearch." read_when:

  • "Starting work in this package workspace."
  • "Looking for the current pi-autoresearch package surface and boundary." system4d: container: "Monorepo package for the governed pi-autoresearch experiment-loop capability." compass: "Keep the operator affordance obvious while preserving explicit boundaries to Prompt Vault, AK, and ontology." engine: "Scaffold the package shell -> land the bounded runtime kernel -> bind upstream control planes later." fog: "The main risk is recreating the upstream monolith before the package seams are explicit and tested."

@tryinget/pi-autoresearch

Monorepo package for the governed pi-autoresearch capability in Pi.

Why this package exists

This package is the local runtime home for the future pi-autoresearch experiment loop described in:

The package started as a small shell and still preserves the recognizable /autoresearch operator surface while avoiding a premature re-import of the upstream monolith. The current boundary is now more specific: the package is the runtime owner for the executable experiment-loop machine, while AK and Prompt Vault remain separate durable-truth owners. The long-term destination is captured in docs/project/vision.md: autonomous in execution, governed in authority.

Current public surface

Command

  • /autoresearch
    • may also be launched through the package-owned extension-originated pi.sendUserMessage bridge for the exact ASC self-evolution campaign-start prompt; this bridge executes the package-owned plan-only campaign-start front door directly and opens the result editor; it is not a general slash-command parser
    • with no objective, opens/reports the bounded runtime overview
    • with dashboard, opens a read-only operator dashboard in the editor with current posture, metric contract, candidate decision summary, candidate policy, and next legal surfaces
    • with next or candidate next, first surfaces open matrix candidate-review posture when packet inventory is waiting for owner review; otherwise inspects current candidate posture and prepares the recommended reviewable next call: bind when no candidate exists, plan keep/discard/rewind when evidence points there, or the next runtime/finalize call when more samples/rebaseline/finalize is needed
    • with bind or bind current, prepares a reviewable autoresearch_candidate_bind({ ... }) call to inspect a candidate worktree and prepare the exact measurement run call
    • with measure or measure current, inspects the candidate worktree and prepares the exact reviewable autoresearch_runtime_run({ ...candidate metadata... }) measurement call only when candidate intake readiness is ready; otherwise it falls back to the autoresearch_candidate_bind({ ... }) intake review call
    • with candidate, decision, keep, discard, or rewind, prepares a reviewable autoresearch_candidate_decision({ ... }) call for status or plan-only lifecycle decisions
    • with open candidates, opens a read-only open candidate-review posture summary with counts, sample cell inventory, explicit discovered candidate-result packet paths, the exact owner-review call seeded with those paths, and the post-fan-in finalizer/token-request handoff for candidates found useful; this does not keep/discard/finalize, merge/reset worktrees, or write evidence
    • with integrate candidates, opens a read-only useful-candidate integration handoff: first run owner review over explicit candidate-result packet paths, then request the post_fanin_finalizer / finalize_post_fanin token for selected useful lanes; it does not apply patches, merge, promote, cleanup worktrees, or write evidence
    • with review or review keep|discard|rewind, opens the final owner decision surface after measured packet inventory is complete; it shows a read-only candidate-decision overlay selector before the editor checklist when TUI overlays are available, and falls back to the editor otherwise
    • with learning, learning export, or learning handoff, prepares the exact autoresearch_runtime_status({ action: "learning_export" }) call for review so the returned packet path can be handed to the owner-routed KES adapter
    • with overlay or fullscreen, opens a read-only live TUI dashboard overlay with periodic refresh and recent-run table
    • with export, writes .autoresearch/autoresearch-dashboard.html as measured packet inventory inspection (candidate-result packets, selected lanes, open candidate-review cells, exported counts, and export_visibility_blockers before owner review), opens it in the browser when possible, and refreshes the file every ~2s for the current Pi session; export off stops the refresher
    • with widget on|off, shows or hides the persistent above-editor status widget for the current session; the widget labels candidate runs, final kept runs, and checks-failed receipts separately so historical failed checks are not confused with promotion/finalization state
    • with run|loop|go|start <objective>, executes the first bounded foreground autoresearch campaign directly (runMode: "bounded_loop", three-iteration/default 30-minute budget, peer lane planned only) so the operator can give one explicit start instruction and step away until budget/gates stop it
    • with any other objective, prepares a reviewable autoresearch_campaign_start({ ... }) tool call in the editor
    • keeps durable authority explicit: direct first-entrypoint runs stay bounded/local and still do not secretly spawn peers, commit, merge/delete worktrees, write AK/KES evidence, or promote candidates
  • $$ autoresearch <objective> / $$ ar <objective> when @tryinget/pi-interaction or @tryinget/pi-trigger-adapter is loaded
    • opens the pi-interaction picker for plan-only, governed setup plan, baseline, or bounded-loop campaign-start modes
    • replaces the editor text with the exact autoresearch_campaign_start({ ... }) call selected by the operator
    • degrades safely when the optional interaction runtime is not installed
  • $$ autoresearch next / $$ ar next
    • deterministically inserts open candidate-review posture with explicit candidate-result packet paths and a finalizer handoff when matrix packet inventory is waiting; otherwise inserts the recommended next candidate call using the same read-only candidate-decision posture as the dashboard
    • remains review-first; no benchmark, worktree command, or durable promotion is executed automatically
  • $$ autoresearch integrate candidates / $$ ar integrate candidates
    • deterministically inserts the useful-candidate integration handoff with explicit review and post-fan-in finalizer calls
    • remains token-gated; no patch application, merge, cleanup, evidence write, or promotion is executed automatically
  • $$ autoresearch bind|measure [current|<worktree>] / $$ ar bind|measure [current|<worktree>]
    • when the optional interaction runtime is loaded, opens a candidate-bind/measure picker that inserts either the exact autoresearch_candidate_bind({ ... }) inspection call or the exact autoresearch_runtime_run({ ...candidate metadata... }) measurement call selected by the operator
    • also has a deterministic input fallback so candidate intake/measurement does not get mistaken for a campaign objective
    • remains review-first; the bind path is read-only/plan-only, and the measure path only prepares the run call for review rather than executing it automatically when intake readiness is ready
  • $$ autoresearch candidate / $$ ar candidate and $$ autoresearch keep|discard|rewind
    • when the optional interaction runtime is loaded, opens a candidate-decision picker for status, keep, discard, or rewind planning
    • decorates direct and currently recommended choices when runtime receipts make that possible
    • replaces the editor text with the exact autoresearch_candidate_decision({ ... }) call selected by the operator
    • also has a deterministic input fallback so non-slash $$ autoresearch ... is not captured by PTX's $$ /template namespace
    • remains read-only/plan-only; no worktree command or durable promotion is applied by the picker/fallback

Tools

  • autoresearch_campaign_start
    • is the supervised campaign front door from one bounded optimization objective
    • composes existing autoplan/setup/loop surfaces instead of inventing a second runtime
    • supports runMode: "plan_only" | "baseline" | "bounded_loop"
    • supports setupMode: "autoplan" | "prompt_vault_setup"; governed setup uses the package-owned Prompt Vault decision runner and typed parser
    • reports campaign name, metric contract, scope, candidate lifecycle policy, warnings/gates, current machine state, and the next exact tool call
    • supports a policy-only candidatePolicy for worktree-based keep/discard/rewind choices; default keep preserves the branch, default discard suggests cleanup after receipt review, and default rewind resets the candidate worktree to base
    • keeps Replay Fabric as observer/history/recovery-clue projection and ASC rewind as live Pi/session recovery; neither becomes the candidate accept/discard executor
    • does not auto-spawn peers, commit, merge/delete worktrees, write AK/KES/evidence, or perform durable promotion
  • autoresearch_candidate_bind
    • is the read-only / plan-only candidate intake planner
    • inspects a controller-verified candidate worktree/path, detects git worktree status, same-repository posture, branch/ref, HEAD, optional or inferred base-ref resolution, changed files, and a diff summary
    • infers a conservative base ref from merge-base(HEAD, upstream|main|master) when candidateBaseRef is omitted, while warning the operator to verify before destructive rewind planning
    • prepares the exact autoresearch_runtime_run({ ... candidateWorktree, candidateBranch, candidateBaseRef, candidateDiffSummary, candidateFilesChanged ... }) call needed to bind and measure the candidate
    • reports intake readiness as ready, needs_review, or blocked, with reasons when the candidate is on trunk, shares the controller cwd, lacks a resolved base, has no detected files, is broad, or is not a usable worktree
    • defaults candidateWorktree to cwd so /autoresearch bind current supports the common current-worktree handoff
    • slash and dollar bind shortcuts omit placeholder base refs so the planner can infer a reviewable base instead of treating "<base-ref>" as literal input
    • surfaces read-only git preflight commands and warnings when the worktree or base ref is missing
    • does not run benchmarks, merge, delete/reset worktrees, spawn peers, write AK/KES/evidence, or promote
  • autoresearch_candidate_decision
    • is the read-only / plan-only candidate lifecycle decision workbench
    • supports action: "status" | "plan_keep" | "plan_discard" | "plan_rewind"
    • consumes current runtime status, segment closeout, and candidate-result evidence to summarize candidate source, worktree, branch/ref/base, changed files, and diff summary when bound
    • when autoresearch_runtime_run receives a real existing candidateWorktree, benchmark and checks commands execute from that candidate worktree while receipts remain in the controller cwd
    • reports empirical posture, promotion readiness, confidence/noise interpretation, checks status, baseline-drift risk, and live candidate artifact posture before suggesting a lifecycle decision
    • recommends keep, discard, rewind, rebaseline, collect more samples, re-bind/re-measure a stale visible candidate artifact, finalize, or no candidate bound yet
    • returns exact next tool calls and plan-only git commands such as git -C <worktree> reset --hard <baseRef> without applying them
    • keeps worktree lifecycle as the candidate primitive; Replay Fabric remains observer/history/recovery-clue only, ASC rewind remains live Pi/session recovery only, and durable promotion remains external
    • refuses to turn receipt-only visible candidate-peer evidence into keep/finalize guidance when the referenced candidate worktree and branch are gone; it surfaces rebind_candidate instead so stale packets are not mistaken for live promotion material
    • does not merge, delete worktrees, reset worktrees, spawn peers, write AK/KES/evidence, or promote
  • autoresearch_runtime_status
    • returns the current bounded-runtime status
    • supports action: "dashboard" for a compact read-only operator dashboard that is easier to scan than the full diagnostic status and now includes the same candidate decision summary/next legal surface plus a unified Authority handoff section for closeout, ak_evidence/suggested evidence_record(...), learning_export -> owner-routed KES adapter, and oracle_evidence_export -> DSPx owner preflight calls
    • can build a package-local segment closeout packet (action: "closeout", packet kind autoresearch.closeout.v1) summarizing runs, hypotheses, candidate bindings, empirical decision, timing interpretation, recommended action, Oracle-ready evidence readiness, and the adapter/evidence boundary before external promotion
    • can build a non-mutating Oracle-ready evidence packet (action: "oracle_evidence", packet kind autoresearch.oracle_evidence.v1) and locally export it for DSPx owner preflight (action: "oracle_evidence_export", default .autoresearch/oracle_evidence.json) with run-level empirical records; export is an explicit local write unavailable in read profile, constrained under cwd/.autoresearch/, and overwrite-gated; it does not write Oracle Postgres, migrate local coordinates.db, write AK/KES, choose winners, or promote
    • can build a non-mutating exact-task AK evidence packet (action: "ak_evidence", akTaskId, packet kind autoresearch.ak_evidence.v1) with a suggested explicit evidence_record(...) controller call; it does not mutate AK itself
    • can build an adapter-ready learning/KMS packet (action: "learning", packet kind autoresearch.learning.v1) and locally export that packet for owner-routed KES/notes handoff (action: "learning_export", default .autoresearch/learning.json); export is an explicit local write unavailable in read profile, constrained under cwd/.autoresearch/, and overwrite-gated; it does not write AK/KES, external authority, or promotion state
    • can build a candidate-result packet (action: "candidate_result", packet kind autoresearch.candidate_result.v1) and locally export it for owner-visible candidate-wave review (action: "candidate_result_export", default .autoresearch/candidate-result.json); exports under .autoresearch/candidate-wave/ include a default-discovery aggregate review_candidate_wave call seed, while explicit packet paths remain available when planned-but-missing lanes should appear as missing_packet; export is an explicit local write unavailable in read profile, constrained under cwd/.autoresearch/, and overwrite-gated, and it does not mutate candidate lifecycle, worktrees, AK/KES/evidence, or promotion state. Matrix dashboard/export discovery now surfaces an explicit open candidate-review posture so packet counts are visible as review inventory rather than live keep/finalize authority. The runtime also has a production cleanup surface (action: "candidate_inventory_cleanup_plan" then action: "candidate_inventory_cleanup_apply" with operatorConfirmation: "ARCHIVE STALE AUTORESEARCH CANDIDATES") that archives both root runtime receipts and matrix/candidate packet projections under a fresh safe-slug .autoresearch/closed-candidates/<label>/ archive so status widgets do not keep advertising closed candidates; plan mode shows the exact would-archive and missing paths, apply refuses unsafe labels or existing archive labels, writes a preflight manifest before moves, archives local projections only, and does not merge, promote, delete external worktrees, remove branches, or write AK/KES/evidence. In orchestrator-supervised matrix campaigns, pi-society-orchestrator may hand the controller a post-checkpoint autoresearch.matrix_cell_controller_command_packet.v1 that spells out the exact bind -> metric run -> candidate-result export -> wave review -> matrix review sequence and an autoresearch.matrix_campaign_cockpit.v1 dashboard summary with matrix_cockpit_blockers target 0; pi-autoresearch still owns the run/export calls, receipts, dashboards, and candidate-result packets, and neither packet is hidden execution or promotion authority.
    • can list the current adapter contract catalog (action: "adapter_contracts", packet kind autoresearch.adapter_contracts.v1) so downstream extensions can discover supported packet kinds without scraping docs
    • can structurally validate adapter packets (action: "validate_packet", packet kind autoresearch.adapter_validation.v1) before a target adapter plans or applies an external write
    • documents the adapter contract in docs/project/adapter-contracts.md so AK, Beads, KES, notes, issue trackers, or custom KMS integrations can be implemented as separate Pi extensions consuming stable packet shapes
    • surfaces receipt summaries, machine projection, event-ledger state, current one-shot Prompt Vault alignment, the current llama.cpp manifest-campaign projection state when one has been projected locally, a top-level empirical posture sentence with promotion readiness and recommended next action, and visible peer-lane recommendations for optional scout_peer_spawn / candidate_peer_spawn use without auto-spawning peers
  • autoresearch_runtime_autoplan
    • explores the local repo/problem space from a bounded objective and proposes campaign setup: name, metric, optional explicit metricThreshold, direction, benchmark command, checks command, scope, risks, and exact next tool call
    • surfaces an explicit measurement contract for metric freshness, causal linkage, and optimization authority before any proposed setup call
    • avoids inferred duplicate benchmark/check execution when package scripts or generated autoresearch.sh wrappers resolve to the same command, while warning if an operator explicitly asks to run equivalent gates twice
    • reports noise-aware timing interpretation for duration metrics so one-off lower timings are not mistaken for meaningful improvements
    • reports an explicit empirical decision class (baseline, insufficient_samples, possible_noise, calibration_signal, candidate_improvement, candidate_regression, and related invalid/check states) plus an operator-facing empirical posture classification (calibration_only, baseline_drift_suspected, candidate_review_ready, and related states) so run status is not mistaken for result meaning or promotion readiness
    • supports calibration runs that update timing/noise interpretation without competing as candidate improvements; calibration-only faster samples are surfaced as calibration_signal, not candidate wins
    • when a generic benchmark command such as npm test would not emit METRIC <name>=<value>, proposes a conservative local autoresearch.sh script only when its measurement contract is fresh, causal to the current run, and allowed to drive optimization
    • supports planner: "dspx_program" to return or materialize a local DSPx-generated DSPy planner assembly for an AutoresearchSetupPlanner candidate while keeping Pi as the outer controller
    • when DSPx behavior_results.json exists, reads it as setup input; static behavior evidence remains advisory unless a bounded campaign-start path explicitly runs DSPx program-gen in the current call and validates the generated DSPy planner output
  • autoresearch_campaign_start
    • can run bounded local DSPx program-gen with planner: "dspx_program", runDspxProgramGen: true, and optional dspxProgramGenTimeoutSeconds, then use the generated DSPy planner output in behavior_results.json as the campaign setup plan before baseline/bounded-loop execution
    • still keeps Pi / pi-autoresearch as the outer controller: DSPx materializes and runs the DSPy planner, while pi-autoresearch validates the generated campaign-plan output and owns setup application, receipts, bounded runs, peer gates, and stop conditions
  • autoresearch_runtime_setup
    • plans, applies, or baselines a campaign/segment config, including optional explicit metricThreshold targets for threshold-style success, without requiring a human slash-command wizard
    • can append a config receipt without running, or bootstrap a baseline run through the same bounded run machinery
    • can write explicit autoresearch.sh / autoresearch.checks.sh content when requested and refuses to overwrite existing scripts unless allowed
  • autoresearch_runtime_run
    • executes one bounded local benchmark/check run
    • can bind optional hypothesis/result lineage (hypothesisId, hypothesis, interventionSummary, expectedPrimaryEffect, hypothesisTargetFiles, experimentRisk) into the run receipt so later evidence export can say what was tested and why
    • can bind controller-verified visible candidate lane metadata (candidateSource, candidateWorktree, candidateBranch, candidateBaseRef, candidateDiffSummary, candidateFilesChanged) without spawning, merging, or promoting the candidate
    • records an empirical decision class on new run receipts so downstream closeout/evidence steps can distinguish operational status from measured meaning
    • bootstraps config receipts when needed
    • appends config/run receipts to autoresearch.jsonl
    • appends machine/event entries to autoresearch.events.jsonl
    • can enforce an optional posture command before benchmark execution and fails closed when the posture requires reconciliation
    • reports optional peer-lane recommendations after the run: scout peers for failed or ambiguous results, candidate peers for isolated patch exploration, and fork peers only when inherited context is intentional
  • autoresearch_runtime_peer_assist
    • plans one canonical visible peer lane without launching it
    • emits exact scout_peer_spawn, candidate_peer_spawn, or fork_peer_spawn calls using the cleaned visible-peer tool surface
    • preserves the evidence boundary: peer/intercom output remains communication until controller verification
  • autoresearch_runtime_loop
    • executes a bounded in-call loop over autoresearch_runtime_run semantics
    • requires maxIterations and can also enforce a wall-clock budget
    • can request governed Prompt Vault next-hypothesis decisions between runs
    • keeps a persistent above-editor status widget current while the session is active unless PI_AUTORESEARCH_WIDGET=0 is set or /autoresearch widget off is used
    • streams in-call live progress cards for loop start, iteration start/complete, stop, and completion phases; each update also carries the current dashboard snapshot in tool-update details
    • returns the final read-only dashboard after the run summary, so an operator can start a bounded run, step away, and come back to the final posture without scraping receipts
    • supports explicit peerMode values: off, plan, launch_scout, launch_candidate, launch_fork; launch_* modes return an exact canonical peer-tool handoff rather than duplicating or hiding peer-spawn machinery
    • stops on explicit budget, control, machine, posture, or governed-decision gates rather than becoming a hidden daemon
  • autoresearch_self_hosting_run
    • is the bounded public supervised self-hosting seam for packages/pi-autoresearch itself
    • can inspect controller/candidate/evaluator state, plan or apply the candidate worktree, run one bounded controller/candidate/evaluator wave, use action=start_and_watch for in-call progress updates while that bounded wave runs, and optionally plan/apply explicit promotion or rollback records
    • stays below hidden daemonized autonomy, direct AK mutation, package-local self-promotion, and automatic controller rotation
  • autoresearch_vllm_campaign
    • is the read-only campaign cockpit for workstation vLLM speed-optimization autoresearch
    • defaults to /home/tryinget/ai-society/softwareco/infra/workstation, the kasimat Qwen3.6 27B AEON FP8 MTP model path under /data/vllm/hf-cache, the RTX 6000 PRO WS hardware label, and ../../contrib/blackwell-gpu-wiki
    • inspects workstation GPU/status probes when the repo-owned scripts are present, reads the m14 LongCoT target catalog posture, plans bounded matrix axes, emits exact autoresearch_campaign_start(...) next calls, and can render a fresh-session handoff prompt
    • remains plan/read-only: it does not start/stop services, mutate lane profiles, run benchmarks, spawn peers, write AK/KES/Oracle/evidence, promote candidates, or become a hidden daemon
  • autoresearch_llamacpp_campaign_control
    • is the dedicated public consumer/control seam for one manifest-driven llama.cpp campaign
    • returns current public control status for one exact checked manifest from one canonical derived snapshot
    • may compose one exact-task AK-ready binding snapshot when the caller already knows the task id
    • plans or applies exactly one truthful next campaign-local step without requiring raw stage / buildId inputs
    • fails closed on blocked public advance paths instead of advertising a usable next action when prerequisites are missing
    • stays below direct AK mutation, whole-campaign execution, and workstation-owned stage semantics
  • autoresearch_llamacpp_campaign
    • loads a typed llama.cpp benchmark campaign manifest
    • expands the exact brownfield phasee/41-43 branch/lane matrix without re-deriving it from chat prose
    • plans or applies fork workspace preparation for later patch transplant work under ../../fork/
    • plans or applies one exact stage-scoped 41 | 42 | 43 invocation against the existing workstation scripts
    • writes or refreshes one checked autoresearch.llamacpp-campaign.json projection artifact for bounded runtime/help use
    • derives one exact-task AK-ready binding snapshot through action=build_ak_binding without mutating AK directly
    • derives or applies exactly one truthful next campaign-local stage step through action=advance_campaign
    • remains the lower-level technical manifest-helper surface below autoresearch_llamacpp_campaign_control
    • does not become a whole-campaign runner or a replacement for workstation execution ownership

Runtime helpers

  • src/core/runtime.ts
    • local receipt-entry types for config and run events
    • structured METRIC name=value parsing
    • bounded benchmark/check execution helpers
    • runtime status/help rendering that now integrates receipt summaries with machine/ledger projection plus the current llama.cpp campaign projection state
  • src/core/selfHosting.ts
    • validated controller-owned self-hosting contract, evaluator lock, and promotion/rollback record helpers
    • candidate worktree preparation, scope fencing, snapshot-owned evaluator-suite execution, typed applicability classification, and explicit promotion/rollback planning helpers
    • the core package implementation behind the public autoresearch_self_hosting_run seam
  • src/core/llamacppCampaign.ts
    • typed llama.cpp benchmark campaign manifest model
    • explicit phasee/41-43 stage matrix expansion
    • fork workspace planning/apply helpers with fail-closed git checks
    • stage-scoped execution binding for one exact 41 | 42 | 43 invocation with prerequisite fencing
    • projection artifact derivation, persistence, and refresh logic for autoresearch.llamacpp-campaign.json
    • non-mutating AK-binding snapshot + compact details derivation for exact manifest/task anchors
    • campaign-local autonomy snapshot + one-step advance helper for truthful next-step selection without widening into whole-campaign execution
    • dedicated public campaign-control composition helpers above the autonomy + AK-binding seams
  • src/core/ledger.ts
    • append-only event-ledger entry types and JSONL helpers
    • ledger replay/projector helpers for the campaign machine
    • replay validation plus receipt-history backfill support for the bounded runtime surface
  • src/machine/events.ts
    • typed campaign event model for configuration, run execution, receipt recording, decision points, blocking, and completion
  • src/machine/campaign.ts
    • minimal package-local XState campaign machine for the bounded experiment runtime
    • runtime-status hydration helper for mapping the current bounded status into machine input
  • src/runtime.ts
    • compatibility re-export for the core runtime surface plus the campaign machine/event model

Local artifact contract

The bounded runtime kernel now uses the same local artifact plan the shell originally named:

  • autoresearch.jsonl
  • autoresearch.llamacpp-campaign.json
  • autoresearch.md
  • autoresearch.sh
  • autoresearch.checks.sh
  • autoresearch.ideas.md

These stay local receipts/projections, not sole campaign truth.

Prompt Vault alignment

Prompt Vault now already owns three durable one-shot control-plane templates for the capability:

  • pi-autoresearch-setup
  • pi-autoresearch-next-hypothesis
  • pi-autoresearch-finalize

The earlier pi-autoresearch-state-router draft still exists as a possible later surface. But after the architecture correction, it is no longer the main near-term blocker for truthful runtime evolution. The package now includes a minimal internal XState campaign machine, typed event model, append-only event ledger, one bounded post-target widening surface for manifest-driven llama.cpp benchmark planning/fork preparation, one bounded follow-on surface for stage-scoped 41 | 42 | 43 execution binding, one projection-only autoresearch.llamacpp-campaign.json layer for bounded manifest campaign status/help truth, one non-mutating AK-ready manifest-campaign binding helper layer for exact manifest/task anchors, one bounded campaign-local autonomy helper layer for truthful one-step stage advancement, and one bounded public campaign-control seam above those landed helper layers.

The AK-binding slice is now landed and closed in:

The campaign-local autonomy slice is now landed and closed in:

The bounded public campaign-control slice is now landed and closed in:

Interpretation rule:

  • Prompt Vault owns durable decision procedures
  • the package owns executable runtime state and may derive compact AK-ready snapshots, one bounded next-step autonomy view, and one bounded public campaign-control view for explicit callers
  • AK still owns durable campaign truth, evidence writes, and any later task completion decision
  • autoresearch_llamacpp_campaign_control is now the public consumer/control seam, while autoresearch_llamacpp_campaign remains the lower-level technical helper surface

Bounded public campaign-control surface

The public/consumer seam for this concern is now:

Current non-goals

This package does not yet implement:

  • direct AK writes, fuzzy task lookup, or automatic task completion for the new llama.cpp campaign manifests
  • direct execution of manifest-driven 41/42/43 campaigns end to end
  • semantic interpretation of manifest-stage payloads into benchmark winners, recommendations, or completion truth
  • a broader public/autonomous whole-campaign control plane beyond the bounded one-step manifest campaign-control surface
  • shared higher-order session-control orchestration
  • a second control plane that duplicates workstation lane-op or the brownfield 41/42/43 scripts

The package now includes the bounded runtime's machine/event-ledger integration and peer-lane recommendation text for optional visible peer help. It still does not own peer launch: pi-little-helpers owns visible peer tools, pi-peer-messaging owns communication, peer/intercom messages are not evidence without controller verification, and autoresearch only recommends exact calls such as scout_peer_spawn(...), candidate_peer_spawn(...), or fork_peer_spawn(...). The broader control-plane and autonomy integrations still belong to later bounded slices after the bounded runtime kernel. Use docs/project/product-posture.md as the package-level alignment anchor for product promise, maturity, trust gates, boundaries, and next product bets. Use docs/project/dogfood-playbook.md for the canonical supervised operator flow from setup through baseline/calibration, candidate binding, empirical closeout, and explicit external evidence/learning promotion. Use docs/project/benchmark-matrix-runbook.md when an optimization target is a surface or scenario/hypothesis sweep rather than one bounded candidate patch.

Example manifest

  • examples/llamacpp-wave-001.json — scaffold for the typed llama.cpp benchmark campaign surface including build-bin / receipt-root execution-binding fields

Validation

Run from this package directory:

npm install
npm run check
npm run release:check:quick

Package boundary reminder

This package owns the local experiment runtime seam and the future executable experiment-loop machine. It does not own:

  • Prompt Vault control-plane truth
  • AK campaign truth
  • ontology semantics
  • shared higher-order session-control lifecycle