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feat: outer loop Phase 1 — population, mutations, similarity, serialization - #1258

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feat: outer loop Phase 1 — population, mutations, similarity, serialization#1258
akashgit wants to merge 14 commits into
feat/outer-loop-evolutionfrom
feat/outer-loop-phase1-foundation

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Closes #1257

Changes

  • factory/outer_loop/models.py — Strict Pydantic v2 models: MutationType (6 operators), MutationRecord, Individual, HyperparameterRecord, SwarmConfig (with train/holdout overlap validation), OuterLoopState, GenerationSummary
  • factory/outer_loop/mutations.py — 6 structured graph mutation operators (insert_node, remove_node, redirect_edge, parallelize, serialize, mutate_params) with frozen node support, validate_and_repair for post-mutation graph integrity (cycle detection, unreachable pruning, reads/writes chain repair), MutationStrategy protocol and WeightedRandomStrategy default
  • factory/outer_loop/population.pyPopulation (add/remove/get/best/mean_score, JSON serialization) and MAPElitesArchive (4D quality-diversity grid, tournament selection, Pareto front computation, diversity metric)
  • factory/outer_loop/similarity.pystructural_hash (SHA-256 canonical form), graph_edit_distance (approximate GED), compute_features (depth/fork_degree/agent_count/gate_count), NoveltyFilter (hash + GED dedup)
  • factory/workflow/primitives.py — Added Workflow.to_dict() / Workflow.from_dict() for round-trip serialization with _type discriminators on nodes
  • 85 tests across 5 files covering models, mutations, population/archive, similarity, and serialization round-trips (including all builtin workflows)

Test results

85 passed in 0.21s
ruff check: All checks passed
mypy: Success (no issues in 6 source files)

akashgit and others added 14 commits August 14, 2026 15:10
…ialization

Closes #1257

- Add factory/outer_loop/ module with Pydantic v2 strict models for
  evolutionary search (Individual, SwarmConfig, OuterLoopState, etc.)
- Implement 6 graph mutation operators (insert, remove, redirect,
  parallelize, serialize, param_mutate) with MutationStrategy protocol
- Add Population and MAPElitesArchive with tournament selection and
  Pareto front computation
- Add structural hashing, graph edit distance, feature extraction,
  and NoveltyFilter for deduplication
- Add Workflow.to_dict()/from_dict() for round-trip serialization
  with _type discriminators
- 85 tests covering all components

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…verfit detection

SwarmEngine: seed population from base workflow + random mutations,
evolve_generation with tournament selection + mutation via MutationStrategy,
run() with budget exhaustion / target score / plateau termination,
post-evolution holdout audit via OverfitDetector.

SwarmEvaluator: pluggable EvaluatorFn protocol, FitnessCache keyed by
(structural_hash, frozenset(instances)), multi-metric fitness composition
(0.6*benchmark + 0.2*hygiene + 0.1*(1-cost) + 0.1*(1-complexity)),
mandatory component and frozen node enforcement.

SubsetSelector protocol + FixedSubsetSelector.
OverfitDetector: flags overfit when (train-holdout)/train > 15%.
BudgetTracker: warnings at 80% and 95%.
WeightedRandomStrategy.on_plateau() increases mutation rate 0.3→0.5.

New models: EvalResult, AuditResult, OuterLoopResult.
129 tests passing, ruff clean, mypy clean.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…ation

Add DesignerAgent with dual mode:
- Design mode: design_minimal (3 nodes), design_thorough (10 nodes),
  design_custom (constrained) — all produce valid Workflow objects
- Mutation mode: propose() returns targeted MutationRecords based on
  failure telemetry (high failure rate → remove, timeout → increase,
  low diversity → insert new role)

Add extract_telemetry() for structured diagnostics from EvalResult.

Integrate with SwarmEngine.seed(): designer_count controls how many
from-scratch designs are added to the initial population alongside
random mutations of the seed workflow.

163 tests pass (34 new + 129 existing).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…graph, e2e tests

Add CLI args (--benchmark, --budget, --population, --target-score, --seed,
--training-instances, --holdout-instances) for outer-loop mode. Register
outer-loop in CEO_MODES, RUN_MODES, and workflow registry. Add filesystem
module for checkpoint/export persistence and workflow graph definition.
196 tests pass, lint and type checks clean.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…Bench

Wire the outer loop's SwarmEvaluator to run FeatureBench via Harbor by
adding a HarborEvaluator that implements the EvaluatorFn protocol. For
each instance, it invokes run-harbor.sh with the workflow's prompt slots
serialized as a YAML annotation surface via FACTORY_WORKFLOW_YAML_B64.

- harbor_evaluator.py: HarborEvaluator class, create_seed_workflow()
  (4-node: researcher → builder → health_checker → gate), and
  workflow_to_harbor_yaml() for Workflow → YAML surface conversion
- run_evolution.py: standalone CLI script for running the evolutionary
  search loop (python -m factory.outer_loop.run_evolution)
- 22 tests covering seed workflow creation, YAML serialization,
  evaluator protocol conformance, and result parsing

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…nch containers

Docker containers used by FeatureBench tasks ship with mirrors.aliyun.com
apt sources that have invalid GPG signatures, causing apt-get update to
fail. Replace these mirrors with archive.ubuntu.com before running
apt-get update.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…n FeatureBench containers

The previous mirror fix wasn't enough — the Docker containers also have
corrupted/outdated GPG keyrings. Remove them and use --allow-unauthenticated
as a fallback to unblock apt-get install.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…h containers

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Containers with curl, git, and procps pre-installed were failing because
apt-get tried to upgrade them with no free disk space in
/var/cache/apt/archives/. Now checks if curl and git exist first and
skips the entire apt block if so. When install IS needed, uses
--no-install-recommends and || true to minimize disk usage and tolerate
partial failures.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Three-step architecture that avoids installing agents in Docker containers:
1. Extract /testbed/ from Docker image to a local temp dir
2. Run factory agents directly on the host against the extracted testbed
3. Copy modified testbed back into Docker and run test.sh for verification

Updates run_evolution.py to use DirectFeatureBenchEvaluator by default.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The test.sh guardrail checks git baseline state against
/tmp/image_baseline_status.txt inside the Docker image, which fails
when the testbed is volume-mounted from the host with different git
state. Replace the test.sh approach with direct pytest execution:

1. Reverse-apply test_patch.diff on the host testbed to restore
   deleted test files (lv1 tasks delete tests during setup)
2. Parse test.sh for install command and P2P regression test args
3. Run pytest directly in Docker, checking exit code for pass/fail

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…erification

Bind mounts don't work reliably when running amd64 Docker images on
arm64 hosts. Replace `docker run -v` with create → cp → start → exec
pattern so the modified testbed is copied into the container filesystem.

Also: use /tmp/ prefix for temp dirs (more portable than /var/folders/),
add --color=no to pytest for cleaner log output, and pipe install output
through tail to reduce noise.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…conflicts

docker cp fails with "cannot overwrite directory with non-directory" when
the testbed contains symlinks (e.g. tests/pydantic_core -> ../pydantic-core/tests)
that resolve to directories already present in the container.

Instead of copying the entire testbed, use git diff --name-only HEAD and
git ls-files --others to find only agent-modified files and copy each
individually. This avoids symlink issues entirely and is faster.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…r gen

- direct_evaluator.py: add --disallowedTools WebSearch,WebFetch to agent invocations
- engine.py: add holdout evaluation at end of each generation
- models.py: add holdout_score field to GenerationSummary
- uv.lock: dependency update

These are partial/untested changes from the first experiment analysis.
See issue #1272 for the full fix plan.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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