Mortdecai
7f806e0b92
Round 2 tested the hypothesis that 26B's silent-stop was about write_file argument size. Result: refuted. - Patch-mode (apply_patch instead of write_file): 26B fails identically at iter 6. Tool-arg size is not the cause. - Truncation sweep on tool responses reveals the real trigger: cap at 800 or 1200 chars → 26B PASSES (1200-cap is 8.4s, fastest of any run). Cap at 1600, 2000, or unlimited → 26B silent-stops with eval=4. Revised understanding: 26B silent-stops when cumulative tool-response context crosses a shape threshold around 1200-1600 chars per response. Not a tool-arg bug, not a raw code-gen bug — 26B emits correct code fine in both one-shot and short-context settings. Production CLI agents (openclaw, open code, aider) typically truncate tool responses by default, so this failure may not surface in them. Custom harnesses should cap ≤1200 chars per tool response when targeting the 26B MoE. Updates GOTCHAS (rewritten entry with the truncation sweep table), SYNTHESIS model-selection row, CORPUS_cli_coding_agent.md pointer, docs/reference/bakeoff-2026-04-18.md with full Round 2 methodology and data. Adds harness_patch.py (apply_patch edit tool), harness_patch_truncated.py (env-configurable TOOL_RESULT_CAP), all 7 run logs, and a .secrets.baseline for detect-secrets false positives on JSON timestamps.
242 lines
15 KiB
Markdown
242 lines
15 KiB
Markdown
# CLI Coding Agent Bakeoff — 2026-04-18
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> Empirical follow-up to `CORPUS_cli_coding_agent.md`. Runs a minimal CLI coding
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> agent loop against three candidate models on identical hardware and an
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> identical broken-code task. **n=1 per model** (plus one re-run to check
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> reproducibility of a failure). Treat as a smoke test, not a benchmark.
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## Setup
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- **Host:** steel141 (Seth's local box)
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- **GPU:** NVIDIA RTX 3090 Ti, 24 GiB, ~22.7 GiB free
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- **Ollama:** 0.20.4
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- **Harness:** `scripts/bakeoff/harness.py` — custom minimal agent loop, **not** openclaw / open code / aider / pi / hermes. Protocol: Ollama `/api/chat` with `tools=[read_file, write_file, run_bash]`, non-streaming, `think: false`, `num_ctx: 32768`, `num_predict: 4096`, `temperature: 0.3`. Iteration cap = 15.
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- **Task:** `scripts/bakeoff/task_seed/` — Python package with buggy `median()` function. 3 of 7 pytest tests fail on even-length inputs. Fix is ~5 lines.
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- **System prompt:** generic CLI-agent template (identity + allowed tools + rules: "never modify tests", "prefer minimal edits"). Not tuned per model.
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All three models pulled from steel141's local Ollama, swapped in/out of GPU as each run started. First iteration per run pays the load cost; later iterations are hot.
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## Results
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| Model | Pass | Iterations | write_file | read_file | run_bash | Wall clock | Halt reason |
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|---|---|---|---|---|---|---|---|
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| `gemma4:26b` | **Fail** | 6 | **0** | 2 | 3 | 10.9s | `no_tool_calls` (silent empty response) |
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| `gemma4:26b` (retry) | **Fail** | 6 | **0** | 2 | 3 | 11.4s | `no_tool_calls` (reproduces exactly) |
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| `gemma4:31b-it-q4_K_M` | **Pass** | 8 | 1 | 2 | 4 | 44.1s | `no_tool_calls` (clean summary turn) |
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| `qwen3-coder:30b` | **Pass** | 15 (cap) | 1 | 4 | 8 | 22.6s | `no_tool_calls` (at iteration cap) |
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### Gemma 4 31B — clean run
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Textbook agent trace:
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1. `read_file README.md`
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2. `pytest` (exit=2, module not found — pytest needs PYTHONPATH)
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3. `ls -R`
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4. `PYTHONPATH=. pytest` → sees 3 failures
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5. `read_file calc/stats.py`
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6. `write_file calc/stats.py` (eval_count=330, 13.4s) — correct fix
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7. `PYTHONPATH=. pytest` → all green
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8. summary: *"I updated the `median` function in `calc/stats.py` to correctly calculate the average of the two middle elements..."*
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Zero wasted turns. One write. Minimal edit.
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### Qwen3-Coder 30B — correct but chatty
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Passed, but used all 15 iterations:
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- Narrated every step ("I'll help you...", "Now let's look at...")
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- Tried to read a non-existent file (`test_calc.py`) — wasted iter 2
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- Tried to `read_file` on a directory (`calc`) — wasted iter 6
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- Ran several redundant bash calls (`pwd && pytest`, etc.)
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- Emitted a ceremonial `echo "All tests pass..."` bash call at iter 14
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- Final turn was a polite summary
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The fix itself (iter 12) was correct on the first write. Quality is fine; efficiency isn't. Per-iteration it was fast (many 20-40 token turns) — total wall clock 22.6s beat Gemma 31B despite using nearly 2× the iterations.
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### Gemma 4 26B — reproducible silent stop
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Both runs followed an identical trajectory:
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1. `ls -R`
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2. `read_file README.md`
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3. `pytest` (exit=2)
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4. `PYTHONPATH=. pytest` → sees 3 failures
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5. `read_file calc/stats.py`
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6. **Empty response. `eval_count=4`. No tool calls. Loop terminates.**
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Zero writes. The model saw all the context it needed (failing tests + buggy source) and then silently declined to act.
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### Isolating the failure — one-shot probe
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To check whether 26B can produce the fix at all, I ran a single-turn call with no tool loop:
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```
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prompt: "The following function is buggy — median([1,2,3,4]) returns 3
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but should return 2.5. Rewrite it correctly. [buggy code]"
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```
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Response (eval_count=81):
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```python
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def median(numbers):
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s = sorted(numbers)
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n = len(s)
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if n % 2 == 1:
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return s[n // 2]
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else:
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return (s[n // 2 - 1] + s[n // 2]) / 2
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```
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**Correct.** So 26B's diagnosis and code generation are intact. The failure is specifically at the **tool-call-boundary** — when the model needs to emit a `write_file(path, content)` call where the `content` argument is a several-hundred-character string, it aborts with eval=4 instead.
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This aligns with `GOTCHAS.md` § "Weak at Long/Nested JSON". A `write_file` tool call argument with a ~500-char string is structurally similar to a long JSON value. Gemma 4 31B handles the same surface reliably (eval=330 on that turn); the 26B MoE does not.
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## Interpretation
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### What this is evidence for
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- **Gemma 4 31B is a viable CLI-coding-agent backing model on this class of task.** Clean trace, minimal wasted turns, correct fix on first write.
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- **Qwen3-Coder 30B also works**, at the cost of more iterations and looser discipline. Diff quality was fine; agentic efficiency wasn't.
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- **Gemma 4 26B has a reproducible failure mode** at tool-call-argument emission. It can reason. It can code. It struggles to deliver code through a `write_file` tool call when the content is non-trivial.
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### What this is NOT evidence for
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- **This is not a representative benchmark.** n=1 per model. One task. One fix. One harness. Do not conclude "Gemma 4 26B is broken for coding agents" — conclude "Gemma 4 26B failed this specific setup reproducibly; investigate further before relying on it."
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- **This harness is not openclaw / open code / aider / pi / hermes.** Production agents wrap prompts, retries, and tool surfaces differently. The 26B failure may be avoided in a harness that:
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- Uses a **patch/diff tool** (`apply_patch(old, new)`) instead of `write_file(full_content)` — smaller argument surface, matches the "sequential tool calls" pattern from `SYNTHESIS.md`
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- Adds a **retry on empty response** (same as Simon's streaming-fallback pattern in `IMPLEMENTATIONS.md`)
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- Provides fewer but richer tools (a dedicated `fix_file` that re-prompts internally)
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- **This compares agent behavior, not raw performance.** Wall clock is noisy (model load, context size, token rate all differ). Per-iteration latency is more meaningful — but that only matters for throughput, not correctness.
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### Recommendations
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1. **For a CLI coding agent on Seth's hardware:** start with `gemma4:31b-it-q4_K_M`. Clean behavior, modest wall clock (44s for a simple fix), no retry needed.
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2. **For comparison or backup:** `qwen3-coder:30b` is equally correct, roughly half the per-iteration cost, ~2× the iteration count. In a longer session those extra turns add up.
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3. **Do not default to `gemma4:26b` for this pattern.** Two tests in a row silent-stopped at the write boundary. If you want to use the 26B MoE (it's strong on `LiveCodeBench v6` at 77.1%), validate it against your specific agent framework first — especially whether the framework uses `write_file` (full content) or `apply_patch` (delta) as its edit primitive.
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4. **Test with the real harness you plan to use in production** (openclaw2, open code, etc.) before committing. A handful of this style of run takes minutes on the 3090 Ti and will tell you more than any benchmark card.
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## Honest caveats
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- **Stochasticity.** Only 26B was re-run. 31B and Qwen3-Coder might hit failure modes on a different seed or a different task. Temperature 0.3 is low but not zero.
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- **System prompt bias.** "Start by reading README.md" steered all three models similarly; a different prompt skeleton would produce different traces. I did not tune per model — deliberately — because a production agent won't either.
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- **The 26B silent-stop hypothesis (tool-arg emission failure) is inferred, not proven.** A clean confirmation would require running the same task with a smaller-surface edit tool (`apply_patch(path, old, new)` instead of `write_file(path, full_content)`) and showing 26B succeeds. That's the obvious follow-up.
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- **Ollama 0.20.4** is between the 0.20.0/0.20.1 known-broken-streaming range and whatever is current. Non-streaming tool calls worked cleanly for 31B and Qwen; 26B's failure looks model-specific, not Ollama-specific, but I didn't test on a different Ollama version.
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- **No openclaw / open code / aider runs.** Those are the frameworks named in the HF launch blog. This was a synthetic harness; transfer is plausible but unverified.
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## Artifacts
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- `scripts/bakeoff/harness.py` — the agent loop
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- `scripts/bakeoff/task_seed/` — the broken-code seed (reset between runs)
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- `scripts/bakeoff/runs/gemma4-26b/log.json` — full turn-by-turn trace
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- `scripts/bakeoff/runs/gemma4-26b-retry/log.json`
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- `scripts/bakeoff/runs/gemma4-31b/log.json`
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- `scripts/bakeoff/runs/qwen3-coder-30b/log.json`
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Each log records per-turn: content, tool calls, results (truncated to 800 chars), prompt/eval token counts, wall time. Final block records halt reason, pass/fail, iteration count, tool-call totals, total wall clock.
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## Reproducing
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```bash
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cd scripts/bakeoff
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python3 harness.py gemma4:31b-it-q4_K_M runs/gemma4-31b/work runs/gemma4-31b/log.json
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python3 harness.py qwen3-coder:30b runs/qwen3-coder-30b/work runs/qwen3-coder-30b/log.json
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python3 harness.py gemma4:26b runs/gemma4-26b/work runs/gemma4-26b/log.json
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```
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Each invocation resets the work directory from `task_seed/`, runs the loop, writes the log, and prints a one-line summary.
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---
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# Round 2 — isolating the 26B silent-stop
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After Round 1 I hypothesized the 26B failure was about long `write_file(path, full_content)` tool arguments. Round 2 tests that.
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## What was tested
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1. **Patch-mode harness** (`harness_patch.py`) — identical to the original but swaps `write_file(path, content)` for `apply_patch(path, old_text, new_text)`. Arguments are a small delta (~100-200 chars), not the full file.
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2. **Truncation-mode harness** (`harness_patch_truncated.py`) — same as patch-mode, but caps every tool response to `TOOL_RESULT_CAP` chars (env-configurable) before returning it to the model.
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All else identical: same task, same system prompt, same Ollama settings, same 3090 Ti on steel141.
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## Results
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### Round 2a — patch-mode (small edit tool arguments)
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| Model | Pass | Iters | patches | reads | bashes | Wall |
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| `gemma4:31b-it-q4_K_M` | ✓ | 8 | 1 | 2 | 4 | 37s |
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| `qwen3-coder:30b` | ✓ | 14 | 1 | 3 | 9 | 22s |
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| `gemma4:26b` | **✗** | 6 | **0** | 2 | 3 | 8s |
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**Hypothesis refuted.** 26B fails identically on patch-mode: 6 iters, silent stop at iter 6 with eval=4, zero edits. The tool-call **argument size is not the trigger.**
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### Round 2b — tool-result truncation cap
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Ran 26B through patch-mode with progressively smaller caps on each tool response:
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| TOOL_RESULT_CAP | 26B Pass | Halt turn | prompt_eval at halt | eval_count at halt |
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| **800** | ✓ | iter 15 (cap) | 3741 | 24 |
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| **1200** | ✓ | iter 8 | 2294 | 27 |
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| **1600** | ✗ | iter 6 | 2070 | **4** |
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| **2000** | ✗ | iter 6 | 2157 | **4** |
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| **unlimited** | ✗ | iter 6 | 2139 | **4** |
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Sharp transition between 1200 and 1600. Below the line, 26B generates code (`eval_count=165` on the patch turn). Above the line, `eval_count=4` — effectively an EOS.
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**The trigger is cumulative tool-response context shape, not total tokens.** The 800-cap run continued reasoning past 3741 prompt tokens without issue. The failing runs all halt at ~2070-2150 tokens — but the 1200-cap run crossed that same range (2076 at iter 7) and kept going. So "N tokens" isn't the cause — the recent-context pattern (large tool responses accumulated over 5 iterations) is.
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### Bonus observation: 26B at 1200-cap is the fastest passing configuration
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| Run | Iters | Wall clock |
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| 26B @ 1200-cap | 8 | **8.4s** |
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| 31B @ patch | 8 | 37s |
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| Qwen3-Coder @ patch | 14 | 22s |
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Same task, same correct fix. 26B's MoE (3.8B active params) is ~5× faster than 31B dense when it cooperates.
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## Revised interpretation
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- **Not "26B is broken for CLI coding agents."**
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- **Not "long tool-call arguments break 26B."**
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- **Yes: "26B silent-stops when the cumulative tool-response context crosses a certain shape/size threshold, at the decision-to-edit boundary."** Observed threshold here: per-tool-response cap somewhere between 1200 and 1600 chars, on this task / this Ollama version / this model variant.
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- **The mitigation is standard.** Every production CLI agent (openclaw, open code, aider, cline, continue) truncates tool responses — this is table stakes, not exotic. 26B's "failure mode" is likely *already mitigated* in those frameworks. What my default harness did (pass full 4-6KB pytest outputs verbatim) is probably not what those frameworks do.
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- **Exact mechanism is unproven.** I'm observing behavior, not internals. Could be MoE expert routing, could be chat-template edge case, could be some interaction with the tool-call channel tokens. Finding the root cause would require model instrumentation beyond this scope.
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## Revised recommendation
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1. **Default to `gemma4:31b-it-q4_K_M`** for general CLI coding agent use. Robust to long tool responses, no mitigation needed.
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2. **Use `gemma4:26b`** if you care about latency AND your agent framework truncates tool responses (most do). 5× faster than 31B when it works.
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3. **Verify by re-running against your actual agent framework.** Don't trust this harness as a proxy — it's a diagnostic, not a production test.
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4. **If you're writing a custom agent and targeting 26B**, cap tool responses aggressively (≤1200 chars per response worked here; ≤800 is safer). pytest output in particular benefits from `--tb=line` or `-x` to shrink it.
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## Artifacts (Round 2)
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- `scripts/bakeoff/harness_patch.py` — patch-mode harness
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- `scripts/bakeoff/harness_patch_truncated.py` — truncation-mode harness (env var `TOOL_RESULT_CAP`)
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- `scripts/bakeoff/runs_patch/gemma4-26b/log.json` — patch mode, unlimited (fails)
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- `scripts/bakeoff/runs_patch/gemma4-26b-truncated/log.json` — cap=800 (passes)
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- `scripts/bakeoff/runs_patch/gemma4-26b-cap1200/log.json` — cap=1200 (passes)
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- `scripts/bakeoff/runs_patch/gemma4-26b-cap1600/log.json` — cap=1600 (fails)
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- `scripts/bakeoff/runs_patch/gemma4-26b-cap2000/log.json` — cap=2000 (fails)
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- `scripts/bakeoff/runs_patch/gemma4-31b/log.json` — patch mode, passes (control)
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- `scripts/bakeoff/runs_patch/qwen3-coder-30b/log.json` — patch mode, passes (control)
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## Reproducing Round 2
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```bash
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cd scripts/bakeoff
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# Patch-mode baseline (3 models)
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python3 harness_patch.py gemma4:31b-it-q4_K_M runs_patch/gemma4-31b/work runs_patch/gemma4-31b/log.json
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python3 harness_patch.py qwen3-coder:30b runs_patch/qwen3-coder-30b/work runs_patch/qwen3-coder-30b/log.json
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python3 harness_patch.py gemma4:26b runs_patch/gemma4-26b/work runs_patch/gemma4-26b/log.json
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# Truncation sweep on 26B
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TOOL_RESULT_CAP=800 python3 harness_patch_truncated.py gemma4:26b runs_patch/gemma4-26b-truncated/work runs_patch/gemma4-26b-truncated/log.json
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TOOL_RESULT_CAP=1200 python3 harness_patch_truncated.py gemma4:26b runs_patch/gemma4-26b-cap1200/work runs_patch/gemma4-26b-cap1200/log.json
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TOOL_RESULT_CAP=1600 python3 harness_patch_truncated.py gemma4:26b runs_patch/gemma4-26b-cap1600/work runs_patch/gemma4-26b-cap1600/log.json
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TOOL_RESULT_CAP=2000 python3 harness_patch_truncated.py gemma4:26b runs_patch/gemma4-26b-cap2000/work runs_patch/gemma4-26b-cap2000/log.json
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```
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