Phase 2 Smoke Evidence (Sesija C Task C1+C2)

Date: 2026-04-30 (executed 2026-04-29 21:55–21:58 local timestamp inside ARE logs; date discrepancy is local-clock drift, branch + commit time stamps are authoritative) Branch: feature/gaia2-are-setup @ Phase 1 commit a72b724 Wall-clock: ~3 minutes total (sync 1.5min + Smoke A 1s + Smoke B-retry 31.8s) LLM cost: $0.00 (oracle mode + mock provider — no real model invocations)

§1 — ARE platform clone (Task C1.a)

Field	Value
Repo	`https://github.com/facebookresearch/meta-agents-research-environments`
Cloned to	`D:/Projects/waggle-os/external/meta-agents-research-environments/` (gitignored)
Clone strategy	`git clone --depth 1` (shallow; full history not required for setup verification)
Pinned SHA	`0330191ffef8581e3c0620b78df9c7408bcb98b0` (2026-04-20 11:53:48 +0200, "Format run-config HF split selection fix (#55)")
License	MIT
Repo size	95+ Python deps + are/ source tree (~2,000+ Python files)
Last upstream commit at clone	2026-04-20 (10 days before Sesija C kickoff)

Note on SHA discipline (per branch architecture LOCKED §4.1 binding): The pinned SHA above was captured from git rev-parse HEAD after clone, not from memory. Cite this SHA going forward, not "latest main".

§2 — Install verification (Task C1.b)

Method: uv sync --frozen from external/meta-agents-research-environments/.

Field	Value
uv version	0.8.17
Python version	3.10.18 (uv-managed; auto-selected per `requires-python = ">=3.10"` in `pyproject.toml`; system Python 3.11.9 not used)
Dependency count	95 packages
Editable install	meta-agents-research-environments 1.2.0
Key dependencies	`litellm 1.71.1` (LiteLLM-compatible — aligns with our existing routing layer); `huggingface-hub 0.33.4`; `mcp 1.11.0`; `datasets 4.0.0`; `aiohttp 3.13.2`; `httpx 0.28.1`
Install duration	~1.5 min wall-clock
Exit code	0 (clean install)

Binary verification: uv run are-run --help and uv run are-benchmark gaia2-run --help both return clean usage output.

§3 — Smoke A: Oracle mode, built-in scenario (Task C2.a)

Command:

cd external/meta-agents-research-environments && \
  uv run are-run -o -s scenario_find_image_file \
    --output_dir benchmarks/gaia2/runs/smoke-c2-2026-04-30/smoke-A-oracle

Result: PASS.

ScenarioValidationResult(success=True, exception=None, ...)
Success=100.0%, 1 scenario completed in <1 second
Output files: output.jsonl, initial_state.jsonl, final_state.jsonl
Output format (output.jsonl): {"task_id": "scenario_find_image_file", "trace_id": null, "score": 1.0, "metadata": {"scenario_id": "scenario_find_image_file", "status": "success", "has_exception": false}}

Cosmetic warning (non-blocking): Windows cp1252 codec cannot encode ✅ (✅) emoji from ARE's logger. UnicodeEncodeError raised inside logging_config.py:61, scenario completes normally. Acceptable for headless CI/CD if stdout encoding is set to utf-8.

§4 — Smoke B: Gaia2 mini config × 1 scenario, mock provider (Task C2.b)

First attempt — gaia2-run with default executor: ❌ FAILED on Windows. Error: cannot find context for 'fork'. ARE's gaia2-run orchestrates 7 phase/configs (standard/{ambiguity, adaptability, execution, search, time}, agent2agent/mini, noise/mini) using multiprocessing with fork context — Windows only supports spawn. All 7 phases skipped.

Second attempt — are-benchmark run with thread executor: ✅ PASS.

cd external/meta-agents-research-environments && \
  uv run are-benchmark run \
    --hf-dataset meta-agents-research-environments/gaia2 \
    --hf-config mini \
    --hf-split validation \
    -l 1 \
    --provider mock \
    --agent default \
    --executor_type thread \
    --max_concurrent_scenarios 1 \
    --output_dir benchmarks/gaia2/runs/smoke-c2-2026-04-30/smoke-B-gaia2-mock-thread \
    --trace_dump_format lite

Metric	Value
Dataset loaded	160 examples in `mini/validation` split
Scenarios run	1 unique × 3 runs (Pass@3 standard)
Wall-clock	31.8 seconds
Provider	`mock` (default model alias `meta-llama/llama3-70b-instruct`, no real inference)
Success rate	0.0% (mock provider returns fake responses; expected)
Exit code	0 (1 config attempted, 1 successful)
Output files	`output.jsonl`, `benchmark_stats.json`

Per-run failure mode (3/3 runs): module 'signal' has no attribute 'SIGALRM' — Windows lacks the Unix SIGALRM signal used by ARE's per-scenario timeout enforcement (scenario_runner.py raises AttributeError). Caught and recorded as exception_runs in stats, but blocks ANY scenario from completing on Windows even with mock provider.

Output format (sample run, output.jsonl line):

{
  "task_id": "scenario_universe_21_xvc7uo",
  "trace_id": null,
  "score": 0.0,
  "metadata": {
    "scenario_id": "scenario_universe_21_xvc7uo",
    "run_number": 1,
    "status": "failed",
    "has_exception": true,
    "exception_type": "AttributeError",
    "exception_message": "module 'signal' has no attribute 'SIGALRM'"
  }
}

Stats schema (benchmark_stats.json): metadata{model, model_provider, timestamp, report_version} + statistics{per_capability{<config>{success_rate, pass_at_k, pass_k, total_runs, ...}}, global{macro_success_rate, micro_success_rate, pass_at_k, pass_k, job_duration, ...}}. Pass@k + Pass^k are first-class metrics (k=3 by default for Gaia2 standard).

§5 — Windows compat findings + Phase 3+4 implications

Finding	Where it surfaces	Workaround for Phase 3 (adapter)	Workaround for Phase 4 (dry run)
`multiprocessing.get_context('fork')` fails on Windows	`gaia2-run` full benchmark orchestrator	Use `are-benchmark run` directly (per-config) instead of `gaia2-run`; our adapter wraps single-config calls anyway	Use `--executor_type thread` for parallelism + run configs sequentially
`signal.SIGALRM` missing on Windows	`scenario_runner.py` per-scenario timeout enforcement (every scenario, regardless of provider/agent)	Hard blocker for Windows — adapter must either (a) patch `signal.SIGALRM` shim, (b) run inside Docker/WSL, or (c) document that dry run requires Linux	DECISION POINT for PM: Windows-host dry run blocked. Options: (i) WSL2 / Linux subsystem; (ii) Docker (Dockerfile present in ARE repo); (iii) accept Windows + monkey-patch; (iv) defer Phase 4 to Linux CI runner.
Unicode `✅` emoji in logger crashes on cp1252	`logging_config.py:61` (cosmetic only)	Set `PYTHONIOENCODING=utf-8` env var before invoking	Same env var setting

Severity ranking: SIGALRM is HIGH (blocks all scenario execution on Windows host). Fork is MEDIUM (blocks gaia2-run orchestrator only, our adapter doesn't need it). Unicode is LOW (cosmetic).

Fastest unblock for Phase 4: Docker. The ARE repo ships a Dockerfile (external/meta-agents-research-environments/Dockerfile). A containerized run sidesteps both fork + SIGALRM issues since the container runs Linux. Trade-off: Docker daemon dependency on operator machine, ~5-15min initial image build.

Decision deferred to PM: Phase 4 Windows host vs Docker vs WSL ratification before Task C5 (dry run execution).

§6 — What the smoke verified (smoke verdict)

Brief §0.1 acceptance criterion	Verified by Smoke A+B
ARE platform installable	✅ uv sync exit 0, 95 deps
Default agent runs scenarios	✅ Smoke A: scenario_find_image_file PASS oracle; Smoke B: Gaia2 mini scenario_universe_21_xvc7uo executed (failed only on SIGALRM, not on agent logic)
HF dataset accessible	✅ Smoke B downloaded mini/validation 160 examples
Output format reproducible	✅ output.jsonl + benchmark_stats.json schemas captured §3 + §4 above
Provider config supports mock + LiteLLM-compatible providers	✅ `mock` provider works; ARE shares LiteLLM 1.71.1 with our LLM routing layer (zero-friction Phase 3 wiring)

Phase 2 verdict: SMOKE PASS (with documented Windows-compat halt-and-PM trigger for Phase 4 host choice).

§7 — Audit anchors

ARE clone SHA: 0330191ffef8581e3c0620b78df9c7408bcb98b0
Smoke A output: runs/smoke-c2-2026-04-30/smoke-A-oracle/ (gitignored; reproducible from §3 command)
Smoke B output: runs/smoke-c2-2026-04-30/smoke-B-gaia2-mock-thread/ (gitignored; reproducible from §4 command)
This evidence: benchmarks/gaia2/smoke-evidence.md

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