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145 lines
10 KiB
Plaintext
145 lines
10 KiB
Plaintext
Query: # Independent acceptance review of the self-update
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Act as the terminal reviewer in a proposer-reviewer self-evolution loop. You
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are inspecting a Hermes checkout that started from commit
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`85c8956ec7f2b4607509980794995e1c5e21e292` and now contains an uncommitted
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candidate self-update produced by another Hermes session after reading all of
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*AI Agents in Depth*. The proposer was not given any candidate improvement or
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alleged capability gap; it selected this change itself.
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Review the current diff and `BOOK_SELF_EVOLUTION_REPORT.md`. Inspect the actual
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production trajectory conversion and persistence paths rather than trusting
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the report. Determine whether the new evaluation metadata correctly derives
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conservative signals from the real persisted ShareGPT-format trajectory,
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preserves existing trajectory content and compatibility, and accurately
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documents its evidence boundary.
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Run these checks yourself (and any additional focused read-only checks needed):
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```bash
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uv run --with pytest pytest tests/agent/test_trajectory.py -q
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uv run --with pytest pytest tests/test_trajectory_compressor.py -q
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python3 -m py_compile agent/trajectory.py agent/agent_runtime_helpers.py
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run_agent.py
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git diff --check
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```
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Do not edit any file. Reject the candidate if you find a concrete correctness,
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production/test-parity, persistence, compatibility, safety, or material
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report-accuracy defect. Do not reject merely because this bounded candidate
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does not implement every opportunity found in the book or because no downstream
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ablation campaign has run; those are explicit evidence boundaries.
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Give concise evidence for the decision. End with exactly one machine-readable
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line:
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`VERDICT: ACCEPT`
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or
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`VERDICT: REJECT`
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If rejecting, list actionable findings above that final line.
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Initializing agent...
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⚠ tirith security scanner enabled but not available — command scanning will use pattern matching only
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────────────────────────────────────────
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┌─ Reasoning ──────────────────────────────────────────────────────────────────┐
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**Inspecting tools**
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I recognize that I need to inspect something important. It seems like I'll have to
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use various tools. I’m thinking of actions like batch git status, git diff, or maybe
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a report and search. I want to make sure I'm covering everything necessary to get
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a clear view of the situation. It feels like there’s a lot to manage, but I’m on
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it! Let's see what outcomes I can gather from these tools.
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└──────────────────────────────────────────────────────────────────────────────┘
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┊ 💻 preparing terminal…
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┊ 💻 preparing terminal…
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┊ 💻 preparing terminal…
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┊ 💻 $ git status --short + 2 commands 0.0s
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┊ 💻 $ git diff -- agent/trajectory.py agent/agent_runtime_helpers.py run_agent.py tests/agent/test_trajectory.py tests/test_trajectory_compressor.py BOOK_SELF_EVOLUTION_REPORT.md 0.0s
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┊ 💻 $ git diff --name-only + 1 command 0.0s
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┌─ Reasoning ──────────────────────────────────────────────────────────────────┐
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**Inspecting files and paths**
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I need to read through reports and actual files, but it seems untracked tests aren
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’t shown in the diff since git diff doesn’t include them. I also want to inspect the
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production trajectory creation. The tools I would typically use, like read_file or
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search tools, aren’t available in this namespace! I can only use the terminal. The
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instruction says to read files, but it’s not exposed. I’ll have to use Python scripts
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with pathlib to print what I need and search appropriately.
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└──────────────────────────────────────────────────────────────────────────────┘
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┊ 💻 preparing terminal…
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┊ 💻 preparing terminal…
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┊ 💻 preparing terminal…
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┊ 💻 $ python3 - <<'PY' from pathlib import Path for p in ['BOOK_SELF_EVOLUTION_REPORT.md','agent/trajectory.py','tests/agent/test_trajectory.py']: print(f'--- {p} ---') print(Path(p).read_text()) PY 0.0s
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┊ 💻 $ python3 - <<'PY' from pathlib import Path for p in ['run_agent.py','agent/agent_runtime_helpers.py','tests/test_trajectory_compressor.py']: text=Path(p).read_text() print(f'--- {p} matches ---') for i,line in enumerate(text.splitlines(),1): if any(x in line.lower() for x in ['trajectory','save_trajectory','scratchpad']): print(f'{i}: {line}') PY 0.0s
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┊ 💻 $ git log -p -1 -- agent/trajectory.py + 1 command 0.3s
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┌─ Reasoning ──────────────────────────────────────────────────────────────────┐
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**Inspecting output paths**
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I need to look closely at the converter's exact output and check the sample save path
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, which seems to bypass saving the trajectory. There's a report suggesting persistence
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issues. The production save function uses a helper, but it looks like the run_agent
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sample manually writes an entry that isn't being evaluated. This might be a concrete
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gap in persistence if the saved trajectory includes all paths. The user wants the
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actual production trajectory conversion and persistence paths, so I need to inspect
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those relevant lines fully.
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└──────────────────────────────────────────────────────────────────────────────┘
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┊ 💻 preparing terminal…
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┊ 💻 preparing terminal…
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┊ 💻 $ python3 - <<'PY' from pathlib import Path for p,a,b in [('agent/agent_runtime_helpers.py',76,245),('run_agent.py',2270,2300),('run_agent.py',7508,7545)]: print(f'--- {p}:{a}-{b} ---') lines=Path(p).read_text().splitlines() for i in range(a-1,min(b,len(lines))): print(f'{i+1}: {lines[i]}') PY 0.0s
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┊ 💻 $ uv run --with pytest pytest tests/agent/test_trajectory.py -q + 3 commands 0.8s
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┊ 💻 preparing terminal…
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┊ 💻 preparing terminal…
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┊ 💻 preparing terminal…
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┊ 💻 $ search_files --help 0.0s [exit 127]
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┊ 💻 $ git grep -n -E 'trajectory_samples|failed_trajectories|sample_.*json|"conversations"' -- ':!BOOK_SELF_EVOLUTION_REPORT.md' 0.1s
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┊ 💻 $ git grep -n -E 'save_trajectory\(|_save_trajectory\(' -- ':!agent/trajectory.py' ':!run_agent.py' 0.1s
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┌─ Reasoning ──────────────────────────────────────────────────────────────────┐
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**Identifying defects in paths**
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I need to pinpoint the defects. I'm looking into the sample trajectory path in run
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_agent, which seems to bypass save_trajectory. The report mentions that evaluation
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metadata has been broadly persisted. However, the actual sample JSON doesn't show
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this evaluation. The user specifically asked for the production trajectory conversion
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and persistence paths. It seems like the candidate defect might be in the save_sample
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path rather than the metadata. I must inspect the batch path around 450 for accuracy
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.
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└──────────────────────────────────────────────────────────────────────────────┘
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┊ 💻 preparing terminal…
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┊ 💻 preparing terminal…
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┊ 💻 preparing terminal…
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┊ 💻 $ python3 - <<'PY' from pathlib import Path p=Path('batch_runner.py') + 1 command 0.0s
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┊ 💻 $ python3 - <<'PY' from pathlib import Path p=Path('website/docs/developer-guide/trajectory-format.md') print(p.read_text()) PY 0.0s
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┊ 💻 $ python3 - <<'PY' from agent.trajectory import derive_trajectory_signals samples=[ [{'from':'tool','value':'<tool_response>\n{"content": {"success": false}}\n</tool_response>'}], [{'from':'tool','value':'<tool_response>\n{"content": "{\\"success\\": false}"}\n</tool_response>'}], ] for s in samples: print(derive_trajectory_signals(s,True)) PY 0.0s
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╭─ ⚕ Hermes ───────────────────────────────────────────────────────────────────╮
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The focused checks passed:
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- tests/agent/test_trajectory.py: 3 passed
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- tests/test_trajectory_compressor.py: 21 passed
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- py_compile for the requested production files: passed
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- git diff --check: passed
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The main AIAgent._save_trajectory() path correctly converts internal messages through convert_to_trajectory_format(), preserves the conversations list, and writes conservative metadata without altering existing fields. The wrapped production <tool_response> format is parsed correctly, including nested JSON content.
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Actionable finding:
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- The metadata is not added to all production trajectory persistence paths. batch_runner.py writes ShareGPT-format entries directly at batch_runner.py:473-487, and the save_sample path in run_agent.py:7530-7541 also writes entries directly. Neither includes the new evaluation object. The report says evaluation metadata is persisted for saved JSONL entries and presents the change as covering trajectory persistence generally, but these documented/real persistence paths remain uncovered. This creates inconsistent metadata across trajectory datasets and means the candidate does not satisfy its stated persistence scope. Either route all persistence through a shared writer or add equivalent metadata to those paths, with focused tests.
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VERDICT: REJECT
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╰──────────────────────────────────────────────────────────────────────────────╯
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Resume this session with:
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hermes --resume 20260802_043834_a43bbc
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Session: 20260802_043834_a43bbc
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Duration: 32s
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Messages: 21 (1 user, 19 tool calls)
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