docs: update workspace prompt files to reflect current state

EVOLUTION.md

@@ -17,21 +17,21 @@ backing model (Devstral via Ollama). The three sub-phases are:
 | Phase 2 | Fine-tuning infrastructure (`evoseal/fine_tuning/`) | Done |
 | Phase 3 | Continuous evolution service + dashboard (`evoseal/services/`) | Done |
 
-> **Maturity caveat (2026-07-19):** The Phase 3 *modules* are built, but the bidirectional
-> loop is not yet closed. Key gaps: the daemon simulates evolution instead of running the
-> pipeline; the training call has a method-name bug; model validation tests the baseline
-> model rather than fine-tuned weights; deployment only writes to a JSON registry with no
-> serving-layer integration; and the generator never consults the fine-tuning registry.
-> Phase 3 is therefore architectural scaffolding, not yet operational co-evolution.
-> See TODO.md ‘Close the bidirectional co-evolution loop’ for the specific gaps.
+> **Maturity caveat (updated 2026-07-30):** The Phase 3 *modules* are built and the
+> bidirectional co-evolution loop is now closed (items 1–6 in TODO.md are merged;
+> see PRs #73, #89, and the commits referenced in TODO.md § "Close the bidirectional
+> co-evolution loop"). The daemon runs a real `EvolutionPipeline`; model validation
+> serves the fine-tuned model; deployment uses `ollama create`; and the generator
+> consults the fine-tuning registry. Remaining gaps are in P2/P3 dashboard
+> improvements and test coverage.
 
 ## Active goals (P2 — Medium Priority)
 
 These are the next priorities from [TODO.md](TODO.md):
 
 ### Architecture documentation
-- [ ] Write architecture doc for Devstral co-evolution
-- [ ] Add sequence diagram for Devstral ↔ EVOSEAL message flow
+- [x] Write architecture doc for Devstral co-evolution (done 2026-07-21)
+- [x] Add sequence diagram for Devstral ↔ EVOSEAL message flow (done 2026-07-23)
 
 ### Dashboard improvements
 - [ ] Add cost/token usage to the real-time dashboard
@@ -40,8 +40,8 @@ These are the next priorities from [TODO.md](TODO.md):
 
 ### Testing coverage
 - [ ] Increase unit test coverage for `core/` modules
-- [ ] Add regression test for config validation
-- [ ] Add test for checkpoint save/restore
+- [x] Add regression test for config validation (done 2026-07-30)
+- [x] Add test for checkpoint save/restore (done 2026-07-30)
 
 ### Evolution archive and rollout
 - [ ] Structured improvement units in the evolution archive

TODO.md

@@ -211,7 +211,7 @@
 - [x] **`bidirectional_manager.py` state fields are never mutated** _(done 2026-07-23)_ — fixed by `run_loop_cycle()` in item #6 above; `stats`, `evolution_history`, `is_running`, and `last_check_time` are now all mutated each cycle.
 - [x] **`version_manager.py` registry file has no atomic write** — `_save_registry()` (lines 70-77, PR #72 branch) writes directly via `open(...,"w")` + `json.dump`; a crash/kill mid-write leaves a truncated file, and `_load_registry()` silently resets to an empty registry on parse failure, losing all version history
 - [ ] **`version_manager.py` has no locking around concurrent registry mutation** — overlapping `register_version`/`deploy_version` calls can interleave writes to `self.registry["versions"]`, risking lost updates or an inconsistent `current_version`
-- [ ] **`model_fine_tuner.py:160-178` uses `trust_remote_code=True`** on both `AutoTokenizer`/`AutoModelForCausalLM.from_pretrained`, combined with a fallback (`_resolve_hf_base_model()`, lines 107-120) that uses `model_name` verbatim as an HF repo id for unknown families — a bad config value or env var (`EVOSEAL_CODER_MODEL`) can execute arbitrary remote code locally. `# nosec B615` suppresses the linter, not the risk
+- [x] **`model_fine_tuner.py:160-178` uses `trust_remote_code=True`** _(done 2026-07-31)_ — changed both `AutoTokenizer` and `AutoModelForCausalLM.from_pretrained` calls to `trust_remote_code=False` (the safe default). The `# nosec B615` suppression comments remain because B615 flags missing `revision=`, not `trust_remote_code`; revision pinning is left to the caller.
 - [x] **`provider_manager.py` treats unhealthy providers as healthy when called from a running event loop** _(done 2026-07-28)_ — `get_best_available_provider()` (lines 100-111) and `list_providers()` (lines 196-201) create a health-check task via `loop.create_task(...)` but never await/consume it, then unconditionally set `is_healthy = True`, discarding the real result; the orphaned task can also produce unretrieved-exception warnings
 - [x] **`agentic_system.py:28` logger bypasses the logging handler hierarchy** _(done 2026-07-26)_ — `Logger("AgenticSystem")` was instantiated directly instead of via `logging.getLogger(name)`; `self.parent` stayed `None`, no handlers attached, and all INFO-level agent-orchestration logs were silently dropped. Changed to `get_logger("AgenticSystem")` to participate in the handler hierarchy
 - [x] **`agentic_workflow_agent.py:14` couples to a private API and can crash inside a running event loop** _(done 2026-07-28)_ — added public `WorkflowEngine.execute_step()` that detects a running event loop and offloads to a thread instead of re-entering `asyncio.run()`; `WorkflowAgent.act()` now calls the public method. Also added `act_async()` for callers already in an async context. 10 new unit tests cover: sync context, running-loop context, error propagation, missing component, and agent state tracking
@@ -249,7 +249,7 @@
 - [x] **Docker support** _(already present)_
   - `Dockerfile` (python:3.11-slim + uv) and `docker-compose.evoseal.yml`
   - Dashboard on port 9613; volumes for checkpoints, data, reports, benchmarks
-- [ ] **Adopt workspace prompt file conventions** _(inspired by OpenClaw's AGENTS.md/SOUL.md/TOOLS.md)_
+- [x] **Adopt workspace prompt file conventions** _(done 2026-07-30)_
   - Create a standard file layout for the evolution workspace: e.g., `AGENT.md` (agent identity/constraints), `EVOLUTION.md` (current evolution goals/state), `SAFETY.md` (safety invariants)
   - Makes the system self-documenting and easier for contributors to understand agent behavior at any point
 
@@ -306,9 +306,9 @@
 |----------|-------|------|-------|
 | 🔴 P0    | 11    | 11   | Original 5 complete; all 6 critical bugs from 2026-07-22 whole-repo review fixed (PRs #74, #76-#79) |
 | 🟠 P1    | 24    | 14   | Original safety/integration items done; +12 high-priority bugs from 2026-07-22 review; signal-handler init fix; safety.yaml created |
-| 🟡 P2    | 30    | 21   | Co-evolution loop gaps (8 items, 8 done) + existing P2 + 13 medium bugs from 2026-07-22 review + 4 latent collect->train bugs found closing the loop (1 fixed, 1 new HF-format gap resolved); provider_manager health-check await fix; workflow-agent private-API/event-loop fix; checkpoint save/restore test |
-| 🟢 P3    | 24    | 15   | Makefile, pre-commit, Docker, ADRs, ADR refresh, CHANGELOG complete; +11 hygiene items from 2026-07-22 review; Ollama provider retry/backoff fix; local_models TTL cache |
-| **Total** | **89** | **61** | |
+| 🟡 P2    | 30    | 22   | Co-evolution loop gaps (8 items, 8 done) + existing P2 + 13 medium bugs from 2026-07-22 review + 4 latent collect->train bugs found closing the loop (1 fixed, 1 new HF-format gap resolved); provider_manager health-check await fix; workflow-agent private-API/event-loop fix; checkpoint save/restore test; trust_remote_code security fix |
+| 🟢 P3    | 24    | 16   | Makefile, pre-commit, Docker, ADRs, ADR refresh, CHANGELOG complete; +11 hygiene items from 2026-07-22 review; Ollama provider retry/backoff fix; local_models TTL cache; workspace prompt file conventions |
+| **Total** | **89** | **63** | |
 
 > Update this table as you complete items. Recommended flow: P0 → P1 → P2 → P3.
 >

evoseal/fine_tuning/model_fine_tuner.py

@@ -158,7 +158,7 @@ async def initialize_model(self) -> bool:
             # Load tokenizer. Base model is operator-configured; revision pinning is
             # left to the caller via base_model_path.
             self.tokenizer = AutoTokenizer.from_pretrained(  # nosec B615
-                base_model, trust_remote_code=True, padding_side="right"
+                base_model, trust_remote_code=False, padding_side="right"
             )
 
             # Add pad token if missing
@@ -167,15 +167,13 @@ async def initialize_model(self) -> bool:
 
             # Load model
             model_kwargs = {
-                "trust_remote_code": True,
+                "trust_remote_code": False,
                 "torch_dtype": (torch.float16 if torch.cuda.is_available() else torch.float32),
                 "device_map": "auto" if torch.cuda.is_available() else None,
             }
 
             # Revision pinning is left to the caller (see note above).
-            self.model = AutoModelForCausalLM.from_pretrained(  # nosec B615
-                base_model, **model_kwargs
-            )
+            self.model = AutoModelForCausalLM.from_pretrained(base_model, **model_kwargs)  # nosec B615
 
             # Configure for training
             self.model.config.use_cache = False

tests/unit/fine_tuning/test_model_fine_tuner.py

@@ -1,15 +1,13 @@
-"""Tests for ModelFineTuner._tokenize_if_needed.
+"""Tests for ModelFineTuner.
 
-Covers the format-mismatch gap: TrainingDataBuilder's HuggingFace export
-writes raw instruction/input/output columns, but Trainer + DataCollatorFor-
-LanguageModeling require pre-tokenized input_ids. fine_tune_model() must
-tokenize on load when the dataset isn't already tokenized, and leave an
-already-tokenized dataset untouched.
+Covers the format-mismatch gap (tokenization) and security defaults
+(trust_remote_code must be False to prevent arbitrary code execution
+from untrusted model repos).
 """
 
 from __future__ import annotations
 
-from unittest.mock import MagicMock
+from unittest.mock import MagicMock, patch
 
 import pytest
 from datasets import Dataset
@@ -60,3 +58,150 @@ def test_passes_max_length_through_to_tokenizer(self, fine_tuner):
 
         _, kwargs = fine_tuner.tokenizer.call_args
         assert kwargs["max_length"] == 128
+
+
+class TestTrustRemoteCodeDefault:
+    """Verify trust_remote_code defaults to False in initialize_model.
+
+    trust_remote_code=True allows a HuggingFace repo to execute arbitrary
+    Python code on load — a remote-code-execution vector when combined
+    with the _resolve_hf_base_model() fallback that uses model_name
+    verbatim as an HF repo id for unknown families.
+    """
+
+    @pytest.mark.asyncio
+    async def test_from_pretrained_calls_use_trust_remote_code_false(self, tmp_path):
+        """Both from_pretrained calls receive trust_remote_code=False.
+
+        Mocks the HF loading calls and asserts the kwarg on each,
+        consistent with the style of test_passes_max_length_through_to_tokenizer.
+        """
+        import evoseal.fine_tuning.model_fine_tuner as mod
+
+        tuner = ModelFineTuner.__new__(ModelFineTuner)
+        tuner.model_name = "deepseek-coder"
+        tuner.base_model_path = "deepseek-ai/deepseek-coder-6.7b-instruct"
+        tuner.output_dir = tmp_path
+        tuner.use_lora = True
+        tuner.use_qlora = False
+        tuner.tokenizer = None
+        tuner.model = None
+        tuner.peft_model = None
+        tuner.is_initialized = False
+        tuner.current_training = None
+
+        mock_tokenizer = MagicMock()
+        mock_tokenizer.pad_token = None
+        mock_tokenizer.eos_token = "</s>"
+        mock_model = MagicMock()
+        mock_model.config = MagicMock()
+
+        # AutoTokenizer / AutoModelForCausalLM aren't in the module namespace
+        # because peft isn't installed (the try/except bailed early). Inject
+        # mock classes so the function can reference them, then patch
+        # from_pretrained on each.
+        mock_tok_cls = MagicMock()
+        mock_tok_cls.from_pretrained.return_value = mock_tokenizer
+        mock_mdl_cls = MagicMock()
+        mock_mdl_cls.from_pretrained.return_value = mock_model
+
+        with (
+            patch.object(mod, "TRANSFORMERS_AVAILABLE", True),
+            patch.object(mod, "AutoTokenizer", mock_tok_cls, create=True),
+            patch.object(mod, "AutoModelForCausalLM", mock_mdl_cls, create=True),
+            patch("torch.cuda.is_available", return_value=False),
+        ):
+            result = await tuner.initialize_model()
+
+        assert result is True
+
+        # Tokenizer call must pass trust_remote_code=False
+        _, tok_kwargs = mock_tok_cls.from_pretrained.call_args
+        assert tok_kwargs["trust_remote_code"] is False, (
+            "AutoTokenizer.from_pretrained called with trust_remote_code!=False"
+        )
+
+        # Model call must pass trust_remote_code=False
+        _, mdl_kwargs = mock_mdl_cls.from_pretrained.call_args
+        assert mdl_kwargs["trust_remote_code"] is False, (
+            "AutoModelForCausalLM.from_pretrained called with trust_remote_code!=False"
+        )
+
+    @pytest.mark.asyncio
+    async def test_initialize_returns_false_without_transformers(self, monkeypatch, tmp_path):
+        """When TRANSFORMERS_AVAILABLE is False, initialize_model returns False."""
+        import evoseal.fine_tuning.model_fine_tuner as mod
+
+        monkeypatch.setattr(mod, "TRANSFORMERS_AVAILABLE", False)
+
+        tuner = ModelFineTuner.__new__(ModelFineTuner)
+        tuner.model_name = "test-model"
+        tuner.base_model_path = None
+        tuner.output_dir = tmp_path
+        tuner.use_lora = True
+        tuner.use_qlora = False
+        tuner.tokenizer = None
+        tuner.model = None
+        tuner.peft_model = None
+        tuner.is_initialized = False
+        tuner.current_training = None
+
+        result = await tuner.initialize_model()
+
+        assert result is False
+        assert tuner.is_initialized is False
+
+    @pytest.mark.asyncio
+    async def test_initialize_returns_false_when_model_needs_trust_remote_code(
+        self, tmp_path, caplog
+    ):
+        """When a model requires trust_remote_code=True but gets False, initialize_model
+        catches the resulting exception, logs it, and returns False.
+
+        This exercises the failure path that the reviewer flagged: a model that
+        used to load with trust_remote_code=True now hard-fails because custom
+        code execution is blocked.
+        """
+        import logging
+
+        import evoseal.fine_tuning.model_fine_tuner as mod
+
+        tuner = ModelFineTuner.__new__(ModelFineTuner)
+        tuner.model_name = "custom-model"
+        tuner.base_model_path = "some-org/custom-model-needs-trust"
+        tuner.output_dir = tmp_path
+        tuner.use_lora = True
+        tuner.use_qlora = False
+        tuner.tokenizer = None
+        tuner.model = None
+        tuner.peft_model = None
+        tuner.is_initialized = False
+        tuner.current_training = None
+
+        mock_tok_cls = MagicMock()
+        # Simulate the failure: a model that ships custom code raises when
+        # trust_remote_code=False.  OSError is what transformers actually raises
+        # when the auto-map can't resolve the architecture without custom code.
+        mock_tok_cls.from_pretrained.side_effect = OSError(
+            "does not appear to have a file named configuration_auto.py. "
+            "Checkout 'https://huggingface.co/some-org/custom-model-needs-trust'"
+        )
+        mock_mdl_cls = MagicMock()
+
+        with (
+            patch.object(mod, "TRANSFORMERS_AVAILABLE", True),
+            patch.object(mod, "AutoTokenizer", mock_tok_cls, create=True),
+            patch.object(mod, "AutoModelForCausalLM", mock_mdl_cls, create=True),
+            caplog.at_level(logging.ERROR, logger=mod.logger.name),
+        ):
+            result = await tuner.initialize_model()
+
+        # Must surface the failure, not silently succeed
+        assert result is False
+        assert tuner.is_initialized is False
+
+        # Model load should never have been attempted (tokenizer failed first)
+        mock_mdl_cls.from_pretrained.assert_not_called()
+
+        # Error must be logged so operators can diagnose the root cause
+        assert "Error initializing model" in caplog.text