fix(fase1): spike B fan-out measures real conversation bleed, not a counter

/trekreview flagged the Spike B(b) fan-out experiment as BROKEN_SUCCESS_CRITERION
(BLOCKER): it asserted a per-client call_count reached 3 on a reused instance vs
1 on a fresh one — a tautology true for any un-reset mutable counter, independent
of MAF, that never exercised the real G2/B7 shared-Workflow state-corruption
footgun. It was a false-confirm of a de-risk assumption.

Rebuilt to observe genuine MAF thread state via the messages each participant
RECEIVES (new FakeChatClient.received_texts seam):
- shared_instance_conversation_bleed: a reused built ConcurrentBuilder Workflow
  accumulates the conversation across .run() calls — run N's participants receive
  runs 0..N-1's prompts/replies (measured [[p0],[p0,p1],[p0,p1,p2]], strictly
  monotonic) => genuine cross-run contamination.
- fresh_instance_conversation_isolation: a fresh instance per run gives each a
  clean thread => each participant sees only its own project ([[p0],[p1],[p2]]).

Assumption now CONFIRMED with a meaningful observable. findings-b.md gains a
Method note recording why it was rebuilt; README rows updated.

Also fixes the MINOR: a_groupchat.run_live now mkdirs the findings dir before
write_text so a post-disposal run does not lose the measured result.

Gate green: ruff check + format, mypy src, pytest 48 passed / 1 skipped.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Fif1r1En5W542HbZV88yMH

docs/fase1-spikes/README.md

@@ -13,7 +13,7 @@ the four assumptions that — if wrong — force a redesign:
 | Spike | Assumption (register ref) | What it measures |
 |-------|---------------------------|------------------|
 | **A** | Group Chat maker-checker beats a single-agent baseline by enough to justify its multiplicative token cost (U3 / G7) | convergence rounds, stall frequency, token use — maker-checker vs single-agent, with a cheaper/better verdict |
-| **B** | The known MAF footguns behave as predicted and our guards hold: Magentic unbounded termination when `limits=None` (G1/B4); shared-builder / fan-out state corruption (G2/B7) | guard fires on unbounded Magentic; zero state-bleed with the fresh-instance helper |
+| **B** | The known MAF footguns behave as predicted and our guards hold: Magentic unbounded termination when `limits=None` (G1/B4); shared-builder / fan-out state corruption (G2/B7) | guard fires on unbounded Magentic; a reused `Workflow` accumulates conversation across runs (project N contaminates N+1) while the fresh-instance helper gives each run a clean thread — measured by received-message content, not a call counter |
 | **C** | A blocking deterministic hybrid-validator (B1) can *structurally* block an out-of-range proposal | structural rejection of an out-of-range proposal; P10/P50/P90 for a valid one; capped self-repair |
 | **D** | ExpeL retrieval (B2) surfaces a relevant prior verdict for a similar new proposal | top-K retrieval returns the structurally-similar verdict over surface-text decoys |
 
@@ -50,7 +50,7 @@ The gate stays green from the logic layer alone.
 | Spike | Assumption | Result | Verdict | Token use | Implication for Fase 2 |
 |-------|-----------|--------|---------|-----------|------------------------|
 | **A** | maker-checker > single-agent (U3/G7) | verdict logic green; `GroupChatBuilder` drivable; cheaper/better is endpoint-dependent | **CONFIRMED (logic)** — empirical arm gated | word-count proxy; live arm not run (no endpoint) | Keep the codified `verdict` (better ∧ ≤3× tokens); measure the empirical cost/benefit on a LOCAL endpoint before locking the debate default |
-| **B** | Magentic unbounded + fan-out bleed (G1/G2) | unbounded `max_round_count=None` needs an external guard; shared fan-out instance bleeds, fresh does not | **CONFIRMED** | 0 — no live LLM | Require explicit round/stop caps for any Magentic loop (fail-fast); use a fresh-instance-per-run factory for fan-out (B7) |
+| **B** | Magentic unbounded + fan-out bleed (G1/G2) | unbounded `max_round_count=None` needs an external guard; a reused `Workflow` accumulates the conversation thread across runs (project N's prompts/replies leak into N+1), a fresh instance per run does not — measured by received-message content | **CONFIRMED** | 0 — no live LLM | Require explicit round/stop caps for any Magentic loop (fail-fast); use a fresh-instance-per-run factory for fan-out (B7) |
 | **C** | blocking hybrid-validator (B1) | typed IR + real CBC solve + Monte-Carlo P10/P50/P90; out-of-range → `Rejection` (distinct type, no percentiles) | **CONFIRMED** | 0 — deterministic; live gen gated | Keep the `Rejection`/`ValidatedProposal` type split (structural block) + CBC-absent escalate; migrate to `pulp[cbc]`/`COIN_CMD` for PuLP 4.0 |
 | **D** | ExpeL retrieval (B2) | structural similarity (codes + measure + magnitude) returns the true match as top-1 over surface-text decoys; deterministic | **CONFIRMED** | 0 — deterministic retrieval | Keep structured similarity as the baseline; add embeddings only if it proves insufficient on real data |
 

docs/fase1-spikes/findings-b.md

@@ -29,16 +29,41 @@ in-flight fake response surfaces a `RuntimeWarning: coroutine was never awaited`
 cosmetic (no functional effect); the spike silences the OTel context logger and filters the
 warning. Worth knowing if Fase 2 observes streamed orchestrations.
 
-## (b) Fan-out state isolation — CONFIRMED
+## (b) Fan-out state isolation — CONFIRMED (conversation-history bleed)
 
-- **Shared instance:** one `ConcurrentBuilder` workflow reused across the three reference
+The observable is the **message history each participant receives** (captured via
-  projects → the shared fake clients accumulate calls (max call count == 3) → **state
+`FakeChatClient.received_texts`), NOT a call counter. A counter rises for any reused
-  bleed**.
+mutable object and proves nothing about MAF; message content proves the workflow carried
-- **Fresh instance:** `fresh_workflow()` builds a new workflow + fresh clients per run →
+state across runs. (An earlier version of this spike measured only a call counter and was
-  each run's clients see exactly one call (`[1, 1, 1]`) → **zero bleed** across all 3
+a tautology — caught and rebuilt; see the Method note below.)
-  projects (B7).
+
+- **Shared instance:** one built `ConcurrentBuilder` workflow reused across the three
+  reference projects. MAF **accumulates the shared conversation thread across `.run()`
+  calls**: each run's participants receive the PRIOR projects' prompts and replies too.
+  Measured (project ids visible per run, in order):
+  `[[FV42-GSV-E1], [FV42-GSV-E1, RV13-RAS-TP], [FV42-GSV-E1, RV13-RAS-TP, BRU-LAKS-REHAB]]`
+  — strictly monotonic growth → run N is contaminated by runs 0..N-1 → **genuine state
+  bleed (G2/B7)**.
+- **Fresh instance:** `fresh_workflow()` builds a new workflow + clean thread per run →
+  each participant sees ONLY its own project: `[[FV42-GSV-E1], [RV13-RAS-TP],
+  [BRU-LAKS-REHAB]]` → **zero cross-run contamination** (B7 mitigation works).
 - **Implication:** Fase 2 fan-out must build a fresh workflow/executor instance per
-  project run (a factory like `fresh_workflow()`), never reuse a shared instance.
+  project run (a factory like `fresh_workflow()`), never reuse a shared instance — a
+  reused `Workflow` leaks one project's context into the next, which would corrupt
+  per-project cost analyses. This is a property of MAF's `Workflow` thread state, not of
+  the participants.
+
+### Method note (why this was rebuilt)
+
+The first cut of this experiment asserted that a reused instance's per-client `call_count`
+reached 3 while a fresh instance's stayed at 1. That is a tautology: any un-reset mutable
+counter rises across reuse, independent of MAF, so it never demonstrated the footgun.
+`/trekreview` flagged it as a `BROKEN_SUCCESS_CRITERION` (false-confirm of a de-risk
+assumption). The rebuilt experiment observes real MAF thread state via the messages each
+participant receives, so a passing test now genuinely means "the reused workflow carried
+project N's conversation into project N+1." Lesson carried into Fase 2: **a de-risk
+assertion must observe the mechanism it claims to test, not a proxy that moves for unrelated
+reasons.**
 
 ## Token use
 

spikes/_harness.py

@@ -99,6 +99,26 @@ def _word_tokens(text: str) -> int:
     return len(text.split())
 
 
+def message_texts(messages: Sequence[Message]) -> list[str]:
+    """Extract the text payloads from a sequence of MAF ``Message`` objects.
+
+    MAF content items vary (objects with ``.text``, bare strings, or ``{"text": ...}``
+    dicts); this normalizes them to a flat list of strings. Used by ``FakeChatClient``
+    to record exactly what an agent *received* per call — the observable Spike B uses to
+    detect cross-run conversation bleed (G2/B7)."""
+    out: list[str] = []
+    for m in messages:
+        for c in getattr(m, "contents", []) or []:
+            text = getattr(c, "text", None)
+            if text is None and isinstance(c, str):
+                text = c
+            if text is None and isinstance(c, dict):
+                text = c.get("text")
+            if text is not None:
+                out.append(str(text))
+    return out
+
+
 class FakeChatClient(BaseChatClient):
     """A deterministic, network-free ``BaseChatClient`` for driving MAF agents in tests.
 
@@ -116,6 +136,9 @@ class FakeChatClient(BaseChatClient):
         self._default = default_reply
         self.total_tokens = 0
         self.call_count = 0
+        # One entry per call: the text payloads this client RECEIVED that call. Lets a
+        # spike observe whether a reused workflow feeds run N+1 the prior runs' history.
+        self.received_texts: list[list[str]] = []
 
     def _next_reply(self) -> str:
         reply = self._scripted[self._idx] if self._idx < len(self._scripted) else self._default
@@ -134,6 +157,7 @@ class FakeChatClient(BaseChatClient):
     ) -> Any:
         # Matches the GA BaseChatClient contract: return a ResponseStream when
         # streaming, otherwise an awaitable resolving to a ChatResponse.
+        self.received_texts.append(message_texts(messages))
         reply = self._next_reply()
 
         if stream:

spikes/a_groupchat.py

@@ -176,6 +176,10 @@ async def run_live(
 
     table = render_comparison(mc, single)
     if write_findings:
+        # Guard the parent dir: the spike may run after the documented disposal
+        # (`rm -rf docs/fase1-spikes`), and a missing dir must not discard the
+        # just-measured live result with a bare FileNotFoundError.
+        _FINDINGS.parent.mkdir(parents=True, exist_ok=True)
         _FINDINGS.write_text(_findings_doc(table), encoding="utf-8")
     return {"mc": mc, "single": single, "verdict": verdict(mc, single), "table": table}
 

spikes/b_footguns.py

@@ -10,9 +10,14 @@ the experiment here — no live LLM, so this whole spike runs in the quality gat
     round/iteration guard and confirm the guard is what stops it (it does NOT
     self-terminate). With an explicit `max_round_count` it terminates cleanly.
 
-(b) **Fan-out state isolation (G2/B7).** Reusing ONE workflow instance across the three
+(b) **Fan-out state isolation (G2/B7).** Reusing ONE built `ConcurrentBuilder` workflow
-    reference projects bleeds state (the shared fake clients accumulate calls); a FRESH
+    across the three reference projects bleeds *conversation state*: MAF accumulates the
-    instance per run — via `fresh_workflow()` — shows zero bleed.
+    shared thread across `.run()` calls, so each run's participants receive the PRIOR
+    projects' prompts and replies (project N contaminates N+1's context). A FRESH instance
+    per run — via `fresh_workflow()` — gives each run a clean thread (zero contamination).
+    The observable is the message history each participant *receives*, captured via
+    `FakeChatClient.received_texts` — NOT a call counter (a counter would rise for any
+    reused mutable object and prove nothing about MAF state).
 
 Token use: 0 — no live LLM (the fake client's "tokens" are word-counts of canned replies).
 """
@@ -101,22 +106,41 @@ def fresh_workflow() -> tuple[object, tuple[FakeChatClient, FakeChatClient]]:
     return workflow, (c1, c2)
 
 
-async def shared_instance_max_calls(project_ids: list[str]) -> int:
+def _projects_seen(received_texts: list[str], project_ids: list[str]) -> list[str]:
-    """Reuse ONE fan-out instance across every project — state bleeds: the shared clients
+    """Which project ids appear anywhere in the messages an agent received on one call.
-    accumulate calls across runs. Returns the max per-client call count (== len once
+
-    bled)."""
+    The observable for genuine state bleed: if a later run's agent sees an EARLIER
+    project's id, the workflow carried that project's conversation forward."""
+    blob = " ".join(received_texts)
+    return [pid for pid in project_ids if pid in blob]
+
+
+async def shared_instance_conversation_bleed(project_ids: list[str]) -> list[list[str]]:
+    """Reuse ONE built fan-out workflow across every project. MAF accumulates the shared
+    thread across `.run()` calls, so run N's participants also receive runs 0..N-1's
+    prompts/replies — genuine cross-run state corruption (G2/B7).
+
+    Returns, per run (in order), which project ids were visible to a participant on that
+    run. With a reused instance this grows monotonically: ``[[p0], [p0, p1], [p0, p1, p2]]``."""
     workflow, clients = fresh_workflow()
     for pid in project_ids:
         await workflow.run(f"Evaluate project {pid}.")
-    return max(c.call_count for c in clients)
+    # One participant is representative: concurrent fan-out feeds every participant the
+    # same accumulated thread. clients[0] was called once per run, in order.
+    rep = clients[0]
+    return [_projects_seen(call_view, project_ids) for call_view in rep.received_texts]
 
 
-async def fresh_instance_call_counts(project_ids: list[str]) -> list[int]:
+async def fresh_instance_conversation_isolation(project_ids: list[str]) -> list[list[str]]:
-    """A FRESH instance per project — zero bleed: every run's clients see exactly one
+    """A FRESH instance per project (the B7 mitigation): each run gets a clean thread, so
-    call. Returns the per-run max call count (each should be 1)."""
+    a participant sees ONLY its own project — zero cross-run contamination.
-    counts: list[int] = []
+
+    Returns, per run, the project ids visible to a participant; each should be exactly
+    its own: ``[[p0], [p1], [p2]]``."""
+    seen_per_run: list[list[str]] = []
     for pid in project_ids:
         workflow, clients = fresh_workflow()
         await workflow.run(f"Evaluate project {pid}.")
-        counts.append(max(c.call_count for c in clients))
+        # fresh client -> exactly one call this run; read its single received view.
-    return counts
+        seen_per_run.append(_projects_seen(clients[0].received_texts[0], project_ids))
+    return seen_per_run

tests/spikes/test_b_footguns.py

@@ -7,8 +7,8 @@ Step 2 builder smoke. Pattern: tests/test_backends.py.
 from portfolio_optimiser.reference_domain import load_reference_projects
 from spikes.b_footguns import (
     bounded_magentic_terminates,
-    fresh_instance_call_counts,
+    fresh_instance_conversation_isolation,
-    shared_instance_max_calls,
+    shared_instance_conversation_bleed,
     unbounded_magentic_self_terminates,
 )
 
@@ -24,16 +24,22 @@ async def test_bounded_magentic_terminates_within_limit() -> None:
     assert await bounded_magentic_terminates(max_round_count=2) is True
 
 
-async def test_shared_instance_bleeds_state_across_projects() -> None:
+async def test_shared_instance_bleeds_conversation_across_projects() -> None:
     ids = [p.id for p in load_reference_projects()]
     assert len(ids) == 3
-    bled = await shared_instance_max_calls(ids)
+    seen = await shared_instance_conversation_bleed(ids)
-    # One shared instance reused across all 3 projects -> calls accumulate -> bleed.
+    # Reusing ONE built workflow accumulates the MAF thread across runs: each run's
-    assert bled == len(ids)
+    # participant receives the EARLIER projects' prompts too (genuine G2/B7 bleed),
+    # measured by message CONTENT — not a call counter.
+    assert seen[0] == [ids[0]]  # first run is clean — nothing prior to bleed
+    assert set(seen[-1]) == set(ids)  # last run saw every project's prompt
+    # Strictly monotonic growth = conversation history accumulating, not coincidence.
+    assert all(set(seen[i]) < set(seen[i + 1]) for i in range(len(seen) - 1))
 
 
-async def test_fresh_instance_zero_bleed_across_projects() -> None:
+async def test_fresh_instance_isolates_conversation_across_projects() -> None:
     ids = [p.id for p in load_reference_projects()]
-    counts = await fresh_instance_call_counts(ids)
+    seen = await fresh_instance_conversation_isolation(ids)
-    # A fresh instance per project -> each run sees exactly one call -> zero bleed.
+    # A fresh instance per project gives each run a clean thread -> a participant sees
-    assert counts == [1, 1, 1]
+    # ONLY its own project -> zero cross-run contamination (the B7 mitigation works).
+    assert seen == [[pid] for pid in ids]