fix: walk back round-1/2 conclusions — the cause was think=false all along
Seth asked "was this with think=false?" Yes — and that was the only question that mattered. Everything I concluded in round 1 and round 2 was wrong. Actual cause, isolated in round 3: - At identical message state, gemma4:26b with think=false returns eval=4 (silent stop); with think unset or think=true, returns eval=165 and emits the correct tool call. - Original round-1 write_file harness + think unset: 26B passes in 8 iters, 20s. No mitigations needed. - 31B dense and qwen3-coder:30b tolerate think=false; 26B MoE does not. Red herrings (kept on-record in the bakeoff doc, not silently erased): - Round 1: "write_file tool-call argument size" — wrong - Round 2a: refuted the arg-size theory but for the wrong reason (still failed because think=false was still set) - Round 2b: "cumulative tool-response context size" — truncating did make 26B pass, but by coincidence. Shorter context at the decision turn dodged the think=false side effect. Why the existing "always think:false" guidance was misleading: it was derived from AI_Visualizer (single-turn JSON pipelines) where thinking tokens do eat num_predict invisibly. In multi-turn tool-calling agents the channels are separate and the flag has a different effect — catastrophic on 26B specifically. Doc updates: - GOTCHAS: replaced the 26B entry with the actual cause; scoped the original "Thinking Mode Eats Context" entry to single-turn pipelines - SYNTHESIS: split the "Mandatory Ollama Settings" block into single-turn vs multi-turn variants; updated anti-patterns and quick-start checklist - CORPUS_cli_coding_agent.md: revised pointer and config template - docs/reference/bakeoff-2026-04-18.md: added Round 3 section with the correction notice at the top of the file and full diagnostic methodology New artifacts: harness_no_think_flag.py, harness_write_no_think.py, and 4 new log files demonstrating all three models pass when think is left at default.
This commit is contained in:
Mortdecai
+10
-1
@@ -141,6 +141,15 @@
|
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"line_number": 5
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}
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],
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"scripts/bakeoff/runs_patch/qwen3-coder-30b-no-think-flag/log.json": [
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{
|
||||
"type": "Base64 High Entropy String",
|
||||
"filename": "scripts/bakeoff/runs_patch/qwen3-coder-30b-no-think-flag/log.json",
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||||
"hashed_secret": "a5781a432dbf3d4f4abf095833e368838fa0d098",
|
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"is_verified": false,
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"line_number": 5
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}
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],
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"tooling/fine-tuning/google-cookbook/Gemma_4_HDP_Agentic_Security.ipynb": [
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{
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"type": "Hex High Entropy String",
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@@ -14570,5 +14579,5 @@
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}
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]
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},
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"generated_at": "2026-04-18T17:40:02Z"
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"generated_at": "2026-04-18T22:14:05Z"
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}
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+11
-11
@@ -6,16 +6,17 @@
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> `IMPLEMENTATIONS.md` chat-agent patterns (Simon) and pipeline patterns
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> (AI_Visualizer).
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> **Empirical follow-up:** `docs/reference/bakeoff-2026-04-18.md` — 2 rounds of
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> **Empirical follow-up:** `docs/reference/bakeoff-2026-04-18.md` — 3 rounds of
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> runs against a custom minimal CLI-agent harness on a fix-the-median-bug task.
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> **Round 1:** 31B clean (8 iters), Qwen3-Coder correct but chatty (15 iters),
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> 26B silently quits with zero edits. **Round 2 (diagnostic):** the 26B failure
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> is NOT about edit-tool-argument size — it's about **cumulative tool-response
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> context shape**. Capping tool responses ≤1200 chars makes 26B pass cleanly
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> *and* in the fastest wall time of any run (8.4s). Most production CLI agents
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> already truncate tool responses, so the issue may be invisible in them.
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> Read when: scoping which model to point an agent at, hitting an unexpected
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> tool-call halt, or writing a custom harness targeting the 26B MoE.
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> **Bottom line after the full investigation:** all three models (gemma4:26b,
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> gemma4:31b-it-q4_K_M, qwen3-coder:30b) pass the task cleanly in 8-14
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> iterations. The only real gotcha is **`think: false` in the Ollama payload
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> silently breaks 26B** in multi-turn tool-calling loops — contradicts the
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> older "always think:false" guidance which was derived from single-turn
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> pipelines. Round 1 and Round 2 pursued wrong hypotheses (edit-tool size,
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> tool-response size) before Round 3 isolated the actual cause. Read when:
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> configuring an agent payload, debugging a silent halt, or fact-checking
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> older think-flag guidance.
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## TL;DR
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@@ -90,7 +91,6 @@ The baseline settings from `SYNTHESIS.md` still apply. CLI coding agent-specific
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```json
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{
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"model": "gemma4:26b",
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"think": false,
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"keep_alive": "4h",
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"options": {
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"num_ctx": 32768,
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@@ -103,7 +103,7 @@ The baseline settings from `SYNTHESIS.md` still apply. CLI coding agent-specific
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- `num_ctx: 32768` is the working minimum for repo-scale work. Agents interleave file reads, bash output, and edits; 4K will truncate the second `read_file`.
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- `num_predict: 4096` — single edits are short but the agent may emit a bash invocation + reasoning + tool call in one turn.
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- `temperature: 0.3` — per `SYNTHESIS.md` temperature table, "structured extraction" tier. Coding edits want low variance.
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- `think: false` — critical. `GOTCHAS.md` documents that Ollama 0.20+ thinking silently eats `num_predict` and drops tool calls. If an agent somehow injects `think: true`, you'll see empty responses.
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- **Do NOT set `think: false`.** Leave it unset (Ollama default). Verified 2026-04-18 that setting `think: false` silent-stops `gemma4:26b` at tool-decision turns in multi-turn loops. The older single-turn-pipeline guidance ("always think: false") does not apply here — see `GOTCHAS.md` § "`think: false` Kills Gemma 4 26B in Multi-Turn Tool-Calling Loops".
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- `keep_alive: 4h` — agent sessions have think pauses; avoid reload penalty.
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### Streaming
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+51
-45
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> Derived from Seth's production implementations (Simon, AI_Visualizer)
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> and community reports. These are hard-won lessons.
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## CRITICAL: Thinking Mode Eats Context
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## CRITICAL: Thinking Mode Eats Context (single-turn pipelines only)
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**Severity: HIGH — causes silent failures**
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**Severity: HIGH — causes silent failures in single-turn `/api/generate` workloads**
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Gemma 4 in Ollama 0.20+ defaults to `think: true`. When enabled:
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> **Scope update (2026-04-18):** This guidance applies to **single-turn JSON
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> generation pipelines** (the AI_Visualizer shape: one call → one structured
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> response). For **multi-turn tool-calling agents**, the opposite is true on
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> `gemma4:26b` — see § "`think: false` Kills Gemma 4 26B in Multi-Turn
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> Tool-Calling Loops" above. Don't copy this fix to an agent harness without
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> testing.
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Gemma 4 in Ollama 0.20+ defaults to `think: true`. When enabled in a single-turn
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JSON pipeline:
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- Thinking tokens go into a hidden `thinking` field, NOT `response`
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- If `num_predict` is limited, thinking consumes the entire budget
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- `response` comes back **empty** — no error, just silence
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- On evaluative tasks, thinking inflates scores (31B scored a known-bad image 9/10 with thinking vs 7/10 without)
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**Fix:** Always pass `think: false` in the Ollama payload. Seth has had success ONLY with thinking off.
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**Fix (for single-turn pipelines):** Always pass `think: false` in the Ollama payload.
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```json
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{
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@@ -23,6 +31,9 @@ Gemma 4 in Ollama 0.20+ defaults to `think: true`. When enabled:
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}
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```
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**Do not blindly carry this to multi-turn tool-calling agents** — verified
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2026-04-18 that it silent-stops 26B specifically in that context.
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## CRITICAL: format=json Causes Infinite Loops
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**Severity: HIGH — hangs indefinitely**
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@@ -64,61 +75,56 @@ Ollama defaults `num_predict` to 128 tokens. Almost any useful Gemma 4 output ex
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**Fix:** Always set `num_predict` explicitly. Minimum recommended: 512. For JSON output: 2048+.
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## HIGH: 26B Silent-Stops When Tool Responses Accumulate (reproducible)
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## HIGH: `think: false` Kills Gemma 4 26B in Multi-Turn Tool-Calling Loops
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**Severity: HIGH — silent agent-loop failure. Mitigatable.**
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**Severity: HIGH — silent agent-loop failure. Setting is what the old guidance said to do.**
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Reproduced on 2026-04-18 against `gemma4:26b` via Ollama 0.20.4 on a 3090 Ti
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(steel141). Agent harness looped through `read_file` / `(write_file or apply_patch)` / `run_bash`
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tools to fix a failing Python test.
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(steel141). Contradicts the older "always think:false" guidance (see § "Thinking
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Mode Eats Context" below — now scoped to single-turn pipelines only).
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### The observation
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26B silent-stops (empty content, no tool calls, `eval_count=4`) at the
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decision-to-edit turn, **regardless of which edit tool is offered** — tested with
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both `write_file(path, full_content)` and `apply_patch(path, old, new)`.
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Initial hypothesis (long tool-call argument) was **refuted**.
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At identical message state with all else equal:
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### The actual trigger: cumulative tool-response context shape
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A sweep with progressive truncation caps on tool responses (`TOOL_RESULT_CAP`):
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| Cap (chars) | Result | Halt eval_count |
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| `think` setting | `eval_count` on decision turn | Agent behavior |
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|---|---|---|
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| 800 | PASS | 24 (continues, hits iteration cap) |
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| 1200 | **PASS** — **fastest of any run (8.4s)** | 27 (clean summary) |
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| 1600 | FAIL | **4** (silent stop) |
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| 2000 | FAIL | **4** (silent stop) |
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| unlimited | FAIL | **4** (silent stop) |
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| `false` | **4** (silent stop, no content, no tool_calls) | Fails — zero edits emitted |
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| unset (Ollama default) | 165 | Passes — emits correct edit |
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| `true` | 165 | Passes — emits correct edit |
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Sharp transition between 1200 and 1600 chars-per-response. Below the line, 26B
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emits correct code (eval_count ~165 on the patch turn). Above, it silent-stops.
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Exact mechanism unproven (could be MoE expert routing, chat-template edge case,
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or something else). **Actionable:** cap tool responses ≤1200 chars.
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26B passes the task in 8 iterations / 12-20s on the same harness the moment
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the `think` key is removed from the Ollama payload. `write_file` vs
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`apply_patch` doesn't matter. Tool-response size doesn't matter.
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### What's NOT at fault
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### What I initially got wrong
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- **Not the edit tool surface** — `write_file` and `apply_patch` both trigger it
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- **Not raw code generation** — a one-shot direct prompt asking 26B to fix the
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same function returned clean correct code (eval=81)
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- **Not total context size alone** — the 800-cap run continued past 3741 prompt
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tokens. Failing runs halt at ~2070-2150 tokens but the 1200-cap run crossed
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the same range and kept going
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- **Not a Gemma-4-family issue** — `gemma4:31b-it-q4_K_M` on identical harness
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handles full-size tool responses cleanly (eval=330 on the write turn)
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The 2026-04-18 bakeoff went through two wrong hypotheses before Seth asked
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"was this with think=false?" The failed-and-corrected path:
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1. **"Long `write_file` argument breaks 26B"** — wrong. `apply_patch` also failed.
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2. **"Large tool-response context breaks 26B"** — wrong. Truncation *did* make
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26B pass (800/1200-char caps), but that's because shorter context dodged
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the `think: false` side effect by coincidence of state at the decision turn.
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3. **Actual cause:** `think: false` alters the decoding path in a way that makes
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the 26B MoE (3.8B active params, 8-of-128 expert routing) emit near-immediate
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EOS at tool-decision turns. 31B Dense and Qwen3-Coder are robust to the
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||||
flag; 26B specifically is not.
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See `docs/reference/bakeoff-2026-04-18.md` § "Round 3" for full traces and the
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diagnostic that isolated the flag.
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### Fix
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- **For 26B in an agent loop, cap tool responses ≤1200 chars.** 800 is safer;
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this is where every production CLI agent (openclaw / open code / aider /
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cline) already lives by default, so the issue may not surface in those
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frameworks.
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- **For raw pytest output specifically**, use `pytest -x --tb=line` or a custom
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formatter to shrink per-test output to a few lines.
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- **Alternative:** use `gemma4:31b-it-q4_K_M` — same harness, no mitigation,
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just works. Trade: ~5× slower than 26B when 26B cooperates.
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- See `docs/reference/bakeoff-2026-04-18.md` (Round 2) for full traces and the
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truncation sweep methodology.
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- **For multi-turn tool-calling agents, do NOT set `think: false`.** Leave it
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unset (Ollama default) or `true`.
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- **If your agent accumulates `thinking` field content**, prune old thinking
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blobs from message history to control context growth.
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- **For single-turn JSON pipelines** (the AI_Visualizer shape), the original
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"always think:false" guidance still applies — see § "Thinking Mode Eats
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Context" below.
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- 31B Dense and Qwen3-Coder work fine either way — this gotcha is 26B-specific
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on this Ollama version.
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## MEDIUM: Weak at Long/Nested JSON
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+25
-4
@@ -25,6 +25,8 @@ Get those right and Gemma 4 just works. Get them wrong and you get a generic cha
|
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## Mandatory Ollama Settings
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||||
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||||
### For single-turn pipelines (AI_Visualizer shape)
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Every Gemma 4 call MUST include:
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|
||||
```json
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@@ -38,12 +40,31 @@ Every Gemma 4 call MUST include:
|
||||
```
|
||||
|
||||
**Why each one:**
|
||||
- `think: false` — Ollama 0.20+ defaults to think:true. Thinking tokens consume num_predict budget invisibly, returning empty responses. Seth has ONLY had success with thinking off.
|
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- `think: false` — Ollama 0.20+ defaults to think:true. In single-turn JSON pipelines, thinking tokens consume num_predict budget invisibly, returning empty responses.
|
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- `num_ctx: 4096+` — Ollama defaults to 2048. Your system prompt alone might exceed that.
|
||||
- `num_predict: 2048+` — Ollama defaults to 128. Any structured output gets truncated.
|
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|
||||
Scale these to your task. The values above are safe minimums, not recommendations.
|
||||
|
||||
### For multi-turn tool-calling agents (Simon / CLI-coding-agent shape)
|
||||
|
||||
**Do NOT set `think: false`.** Leave it unset (Ollama default) or `true`.
|
||||
|
||||
```json
|
||||
{
|
||||
"options": {
|
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"num_ctx": 32768,
|
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"num_predict": 4096
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Verified 2026-04-18 that `think: false` silently breaks `gemma4:26b` in multi-turn
|
||||
tool-calling loops — model silent-stops with `eval_count=4` at tool-decision turns.
|
||||
31B Dense and Qwen3-Coder tolerate the flag; 26B MoE does not. See `GOTCHAS.md`
|
||||
§ "`think: false` Kills Gemma 4 26B in Multi-Turn Tool-Calling Loops" and
|
||||
`docs/reference/bakeoff-2026-04-18.md` § "Round 3".
|
||||
|
||||
## System Prompt Template
|
||||
|
||||
```
|
||||
@@ -176,12 +197,12 @@ Vision is on ALL Gemma 4 variants (E2B, E4B, 26B, 31B). Audio is E-series only.
|
||||
| Maximum quality (single-model GPU) | `gemma4:31b-it-q4_K_M` | Dense 31B, sharpest but 5x slower, more VRAM pressure |
|
||||
| Rapid prototyping / testing | `gemma4:26b` | Fast enough for interactive dev |
|
||||
| Retrieval / embeddings | `embeddinggemma` (308M, separate model) | Gemma 4 has no embedding mode; use the sibling |
|
||||
| CLI coding agent (openclaw / open code / pi / hermes / aider) | `gemma4:31b-it-q4_K_M` (robust), or `gemma4:26b` if agent truncates tool responses ≤1200 chars | 2026-04-18 bakeoff on 3090 Ti: **31B clean default**; **26B silent-stops at iter 6 unless tool responses are capped — then it's the fastest passing config (8.4s, 8 iters)**. Production CLI agents typically truncate by default, so 26B may just work. See `CORPUS_cli_coding_agent.md` + `docs/reference/bakeoff-2026-04-18.md` |
|
||||
| CLI coding agent (openclaw / open code / pi / hermes / aider) | `gemma4:26b` (fastest) or `gemma4:31b-it-q4_K_M` (more headroom), either works — **just do not set `think: false` in the payload** | 2026-04-18 bakeoff on 3090 Ti: all three models (including Qwen3-Coder 30B) pass the same task in 8-14 iters. The only real gotcha is `think: false` silently breaks 26B in multi-turn loops. See `CORPUS_cli_coding_agent.md` + `docs/reference/bakeoff-2026-04-18.md` |
|
||||
|
||||
## Anti-Patterns
|
||||
|
||||
1. **Don't use `format: "json"`** — infinite loops on nested schemas
|
||||
2. **Don't leave `think` at default** — eats your output budget silently
|
||||
2. **For single-turn pipelines, don't leave `think` at default** — eats your output budget silently. **For multi-turn tool-calling agents, don't SET `think: false`** — silent-stops 26B. See the two "Mandatory Ollama Settings" sections above.
|
||||
3. **Don't leave `num_predict` at default** — 128 tokens is nothing
|
||||
4. **Don't leave `num_ctx` at default** — 2048 truncates most prompts
|
||||
5. **Don't ask for huge JSON in one call** — break into sequential calls
|
||||
@@ -191,7 +212,7 @@ Vision is on ALL Gemma 4 variants (E2B, E4B, 26B, 31B). Audio is E-series only.
|
||||
|
||||
## Quick-Start Checklist
|
||||
|
||||
- [ ] Set `think: false`
|
||||
- [ ] Set `think: false` **for single-turn pipelines only**. Leave unset for multi-turn tool-calling agents (silent-stops 26B).
|
||||
- [ ] Set `num_predict` >= 512 (2048+ for JSON output)
|
||||
- [ ] Set `num_ctx` >= 4096 (scale to your prompt size)
|
||||
- [ ] Write explicit system prompt with identity + boundaries + output format
|
||||
|
||||
@@ -5,6 +5,14 @@
|
||||
> identical broken-code task. **n=1 per model** (plus one re-run to check
|
||||
> reproducibility of a failure). Treat as a smoke test, not a benchmark.
|
||||
|
||||
> **Correction notice (Round 3):** Rounds 1 and 2 both misidentified the cause
|
||||
> of Gemma 4 26B's silent-stop failure. Round 1 blamed `write_file` tool-call
|
||||
> argument size. Round 2 blamed tool-response context size. **Round 3 proves
|
||||
> both wrong: the actual cause is the `think: false` Ollama flag.** Remove the
|
||||
> flag and 26B passes on the original Round 1 harness unmodified. Kept the
|
||||
> failed hypotheses below as-recorded — Seth asked "was this with
|
||||
> think=false?" and the answer exposed the confounder. Never presented as Plan A.
|
||||
|
||||
## Setup
|
||||
|
||||
- **Host:** steel141 (Seth's local box)
|
||||
@@ -239,3 +247,136 @@ TOOL_RESULT_CAP=1200 python3 harness_patch_truncated.py gemma4:26b runs_patch/ge
|
||||
TOOL_RESULT_CAP=1600 python3 harness_patch_truncated.py gemma4:26b runs_patch/gemma4-26b-cap1600/work runs_patch/gemma4-26b-cap1600/log.json
|
||||
TOOL_RESULT_CAP=2000 python3 harness_patch_truncated.py gemma4:26b runs_patch/gemma4-26b-cap2000/work runs_patch/gemma4-26b-cap2000/log.json
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
# Round 3 — the actual cause: `think: false`
|
||||
|
||||
Seth asked "was this with think=false?" That was the only question that mattered.
|
||||
|
||||
## The question that unstuck it
|
||||
|
||||
Every harness in Round 1 and Round 2 set `"think": False` in the Ollama payload —
|
||||
per existing guidance in `GOTCHAS.md`: "Always pass `think: false` in the Ollama
|
||||
payload. Seth has had success ONLY with thinking off." I copied that to the
|
||||
harnesses without testing whether it was the right choice for a multi-turn
|
||||
tool-calling agent loop (as opposed to the single-turn JSON pipeline that
|
||||
guidance came from).
|
||||
|
||||
## The diagnostic
|
||||
|
||||
Replayed the exact 5-iteration failing state to `gemma4:26b` three times with
|
||||
three think settings, same message history, same tool definitions:
|
||||
|
||||
| `think` setting | `eval_count` | tool call emitted? |
|
||||
|---|---|---|
|
||||
| `false` (my harness) | **4** | ✗ |
|
||||
| unset (Ollama default) | 165 | ✓ `apply_patch` |
|
||||
| `true` | 165 | ✓ `apply_patch` |
|
||||
|
||||
Sharp, reproducible. `think: false` → silent stop. Anything else → works.
|
||||
|
||||
## Round 3 runs — unlimited tool responses, think flag removed
|
||||
|
||||
| Harness | Model | Pass | Iters | Wall |
|
||||
|---|---|---|---|---|
|
||||
| `write_file` (Round-1 harness, think unset) | `gemma4:26b` | **✓** | 8 | 20.6s |
|
||||
| `apply_patch` (Round-2a harness, think unset) | `gemma4:26b` | **✓** | 8 | 12.5s |
|
||||
| `write_file`, think unset | `gemma4:31b-it-q4_K_M` | ✓ | 8 | — |
|
||||
| `apply_patch`, think unset | `gemma4:31b-it-q4_K_M` | ✓ | 8 | 66.4s |
|
||||
| `apply_patch`, think unset | `qwen3-coder:30b` | ✓ | 11 | 19.5s |
|
||||
|
||||
**26B passes cleanly on the unmodified Round 1 harness once the think flag is
|
||||
removed.** No truncation, no patch-tool swap, no mitigations.
|
||||
|
||||
The 31B / Qwen runs confirm the flag doesn't matter for those models (pass either
|
||||
way). 31B is visibly slower without the think flag (66s vs 37s) — likely
|
||||
because it's actually generating hidden thinking now — but it still completes.
|
||||
|
||||
## What Rounds 1 and 2 got wrong
|
||||
|
||||
### Round 1 (wrong): "26B silent-stops at the write_file tool-call argument boundary"
|
||||
|
||||
The write_file tool was present. 26B failed. But 26B also fails with
|
||||
`apply_patch` (Round 2a) and passes with `write_file` when think is unset
|
||||
(Round 3). The tool surface was not the cause.
|
||||
|
||||
### Round 2a (wrong): "Refuted the write_file hypothesis"
|
||||
|
||||
Correctly refuted the original hypothesis, but still tested with `think: false`.
|
||||
Only the positive finding (still failed) was right; the conclusion ("the edit
|
||||
tool is not the cause") was right for the wrong reason. The cause wasn't the
|
||||
edit tool **because** it was `think: false`.
|
||||
|
||||
### Round 2b (wrong): "Cumulative tool-response context size is the trigger"
|
||||
|
||||
The truncation sweep showed a sharp 1200-vs-1600-char boundary. That was real
|
||||
behavior, but it was a *byproduct* of `think: false`. With shorter context,
|
||||
`think: false` doesn't always trigger the silent-stop at every decision point
|
||||
— apparently the decoding-path divergence is stochastic or state-dependent.
|
||||
The underlying bug was the same (the flag); the truncation pattern was just a
|
||||
workaround that happened to land on the lucky side of the dice.
|
||||
|
||||
The prompt_eval_count threshold I identified (~2100 tokens) was the cumulative
|
||||
context size at the model's natural decision-to-edit turn. Below that many
|
||||
tokens the model survived the think=false flag; above it, `think=false` killed
|
||||
generation. The number was real but the causal story was wrong.
|
||||
|
||||
## Why the existing GOTCHAS guidance was misleading here
|
||||
|
||||
`GOTCHAS.md` says: *"Thinking tokens consume num_predict budget invisibly,
|
||||
returning empty responses. Seth has ONLY had success with thinking off."*
|
||||
|
||||
That guidance was derived from `AI_Visualizer` (per `IMPLEMENTATIONS.md` §
|
||||
"Project: AI Visualizer") — single-turn JSON-generation pipelines where the
|
||||
model's thinking DOES eat the num_predict budget and returns an empty `content`
|
||||
field.
|
||||
|
||||
In a **multi-turn tool-calling agent loop**, the mechanics are different:
|
||||
- Ollama returns separate fields for `content` and `thinking` (when populated)
|
||||
- Tool calls come out through `tool_calls`, which isn't bounded by `content`
|
||||
generation the same way
|
||||
- Setting `think: false` here changes the chat-template / decoding path in a
|
||||
way that makes 26B specifically — probably due to MoE routing sensitivity —
|
||||
prefer early EOS at tool-decision turns
|
||||
- 31B and Qwen3-Coder are more robust to the same flag
|
||||
|
||||
So the guidance isn't wrong; it's out of scope. It applied to AI_Visualizer,
|
||||
was over-generalized to "always think:false", and the agent corpus inherited
|
||||
that over-generalization.
|
||||
|
||||
## Revised, correct recommendation for CLI coding agents
|
||||
|
||||
1. **Do NOT set `think: false`** in your agent payload. Leave it unset (Ollama
|
||||
default) or `true`.
|
||||
2. **Do manage the `content` and `thinking` fields explicitly** if they
|
||||
accumulate in your message history — prune old thinking blobs before
|
||||
pushing past 30K context.
|
||||
3. **The model / tool-surface choices don't matter the way I said they did.**
|
||||
Any of (`gemma4:26b`, `gemma4:31b-it-q4_K_M`, `qwen3-coder:30b`) × (`write_file`,
|
||||
`apply_patch`) × (capped/uncapped responses) passes when `think` is unset.
|
||||
4. **For single-turn JSON pipelines, the original "think: false" guidance still
|
||||
applies.** This correction is scoped to multi-turn tool-calling agents.
|
||||
|
||||
## Round 3 artifacts
|
||||
|
||||
- `scripts/bakeoff/harness_no_think_flag.py` — patch-mode harness with no think key
|
||||
- `scripts/bakeoff/harness_write_no_think.py` — write-file harness with no think key
|
||||
- `scripts/bakeoff/runs_patch/gemma4-26b-no-think-flag/log.json` — 26B patch, no think (PASS)
|
||||
- `scripts/bakeoff/runs_patch/gemma4-26b-writefile-no-think/log.json` — 26B write, no think (PASS)
|
||||
- `scripts/bakeoff/runs_patch/gemma4-31b-no-think-flag/log.json` — 31B patch, no think (PASS)
|
||||
- `scripts/bakeoff/runs_patch/qwen3-coder-30b-no-think-flag/log.json` — Qwen patch, no think (PASS)
|
||||
|
||||
## Reproducing Round 3
|
||||
|
||||
```bash
|
||||
cd scripts/bakeoff
|
||||
|
||||
# The correction: same harness as Round 1, just with think flag removed
|
||||
python3 harness_write_no_think.py gemma4:26b runs_patch/gemma4-26b-writefile-no-think/work runs_patch/gemma4-26b-writefile-no-think/log.json
|
||||
|
||||
# Patch-mode without think flag
|
||||
python3 harness_no_think_flag.py gemma4:26b runs_patch/gemma4-26b-no-think-flag/work runs_patch/gemma4-26b-no-think-flag/log.json
|
||||
python3 harness_no_think_flag.py gemma4:31b-it-q4_K_M runs_patch/gemma4-31b-no-think-flag/work runs_patch/gemma4-31b-no-think-flag/log.json
|
||||
python3 harness_no_think_flag.py qwen3-coder:30b runs_patch/qwen3-coder-30b-no-think-flag/work runs_patch/qwen3-coder-30b-no-think-flag/log.json
|
||||
```
|
||||
|
||||
@@ -0,0 +1,173 @@
|
||||
"""Diagnostic: patch-mode harness with think flag OMITTED (Ollama default).
|
||||
|
||||
Exact copy of harness_patch.py except the payload does NOT set "think".
|
||||
Testing whether Gemma 4 26B's silent-stop at iter 6 is caused by
|
||||
`think: false` specifically, rather than by tool-response context.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
from urllib import request as urlreq
|
||||
|
||||
OLLAMA_HOST = os.environ.get("OLLAMA_HOST", "http://127.0.0.1:11434")
|
||||
MAX_ITERATIONS = 15
|
||||
BASH_TIMEOUT_S = 30
|
||||
REQUEST_TIMEOUT_S = 540
|
||||
|
||||
SYSTEM_PROMPT = """You are a terminal coding agent.
|
||||
|
||||
## What you do
|
||||
- Read source and test files to understand the code
|
||||
- Make targeted edits to fix bugs so the tests pass
|
||||
- Run pytest to verify your fix
|
||||
- Stop once all tests pass and reply with a one-sentence summary
|
||||
|
||||
## What you do NOT do
|
||||
- Never modify files under tests/
|
||||
- Never disable, skip, or delete tests
|
||||
- Never write outside the working directory
|
||||
- Never call tools after all tests pass — just reply with the summary and stop
|
||||
|
||||
## Available tools
|
||||
- read_file(path): read a file relative to the working directory
|
||||
- apply_patch(path, old_text, new_text): replace an exact unique text span in a file
|
||||
- run_bash(command): run a shell command in the working directory
|
||||
|
||||
## Rules
|
||||
- Start by reading README.md
|
||||
- Prefer minimal edits. Do not refactor unrelated code.
|
||||
- Run the full test suite after each edit to verify.
|
||||
- apply_patch requires old_text to appear EXACTLY ONCE in the file; include enough surrounding context to make it unique.
|
||||
"""
|
||||
|
||||
USER_PROMPT = "Make the failing tests pass. Begin."
|
||||
|
||||
TOOLS = [
|
||||
{"type": "function", "function": {"name": "read_file", "description": "Read a file. Path is relative to the working directory.", "parameters": {"type": "object", "properties": {"path": {"type": "string"}}, "required": ["path"]}}},
|
||||
{"type": "function", "function": {"name": "apply_patch", "description": "Replace a unique span of text in a file. old_text must appear exactly once. Include surrounding context if needed to make the match unique.", "parameters": {"type": "object", "properties": {"path": {"type": "string"}, "old_text": {"type": "string"}, "new_text": {"type": "string"}}, "required": ["path", "old_text", "new_text"]}}},
|
||||
{"type": "function", "function": {"name": "run_bash", "description": "Run a shell command in the working directory. Returns stdout, stderr, and exit code.", "parameters": {"type": "object", "properties": {"command": {"type": "string"}}, "required": ["command"]}}},
|
||||
]
|
||||
|
||||
|
||||
def safe_path(workdir, rel):
|
||||
p = (workdir / rel).resolve()
|
||||
if not str(p).startswith(str(workdir.resolve())):
|
||||
raise ValueError(f"path escapes workdir: {rel}")
|
||||
return p
|
||||
|
||||
|
||||
def tool_read_file(workdir, args):
|
||||
p = safe_path(workdir, args["path"])
|
||||
if not p.exists():
|
||||
return f"ERROR: {args['path']} does not exist"
|
||||
return p.read_text()
|
||||
|
||||
|
||||
def tool_apply_patch(workdir, args):
|
||||
p = safe_path(workdir, args["path"])
|
||||
if not p.exists():
|
||||
return f"ERROR: {args['path']} does not exist"
|
||||
old, new = args["old_text"], args["new_text"]
|
||||
text = p.read_text()
|
||||
n = text.count(old)
|
||||
if n == 0:
|
||||
return f"ERROR: old_text not found in {args['path']}."
|
||||
if n > 1:
|
||||
return f"ERROR: old_text appears {n} times in {args['path']}."
|
||||
p.write_text(text.replace(old, new, 1))
|
||||
return f"patched {args['path']} (replaced {len(old)} chars with {len(new)} chars)"
|
||||
|
||||
|
||||
def tool_run_bash(workdir, args):
|
||||
try:
|
||||
r = subprocess.run(["bash", "-c", args["command"]], cwd=workdir, capture_output=True, text=True, timeout=BASH_TIMEOUT_S)
|
||||
except subprocess.TimeoutExpired:
|
||||
return f"ERROR: command timed out after {BASH_TIMEOUT_S}s"
|
||||
return f"exit={r.returncode}\n--- stdout ---\n{r.stdout[-4000:]}\n--- stderr ---\n{r.stderr[-2000:]}"
|
||||
|
||||
|
||||
TOOL_DISPATCH = {"read_file": tool_read_file, "apply_patch": tool_apply_patch, "run_bash": tool_run_bash}
|
||||
|
||||
|
||||
def ollama_chat(model, messages):
|
||||
# NOTE: no "think" key — Ollama default behavior
|
||||
payload = {
|
||||
"model": model, "messages": messages, "tools": TOOLS,
|
||||
"stream": False, "keep_alive": "10m",
|
||||
"options": {"num_ctx": 32768, "num_predict": 4096, "temperature": 0.3},
|
||||
}
|
||||
req = urlreq.Request(f"{OLLAMA_HOST}/api/chat", data=json.dumps(payload).encode(), headers={"Content-Type": "application/json"})
|
||||
with urlreq.urlopen(req, timeout=REQUEST_TIMEOUT_S) as resp:
|
||||
return json.loads(resp.read())
|
||||
|
||||
|
||||
def pytest_passes(workdir):
|
||||
r = subprocess.run(["python3", "-m", "pytest", "tests/", "-q"], cwd=workdir, capture_output=True, text=True, timeout=60)
|
||||
return r.returncode == 0
|
||||
|
||||
|
||||
def run_bakeoff(model, workdir, log_path):
|
||||
log_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
messages = [{"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": USER_PROMPT}]
|
||||
trace = {"model": model, "edit_tool": "apply_patch", "think_setting": "unset (default)", "workdir": str(workdir), "started_at": time.time(), "turns": [], "final": None}
|
||||
counts = {"read_file": 0, "apply_patch": 0, "run_bash": 0}
|
||||
halt = None
|
||||
for i in range(1, MAX_ITERATIONS + 1):
|
||||
t0 = time.time()
|
||||
try:
|
||||
r = ollama_chat(model, messages)
|
||||
except Exception as e:
|
||||
halt = f"chat_error: {e}"
|
||||
trace["turns"].append({"iteration": i, "error": str(e)})
|
||||
break
|
||||
msg = r.get("message", {})
|
||||
content = msg.get("content", "") or ""
|
||||
tcs = msg.get("tool_calls") or []
|
||||
thinking = msg.get("thinking")
|
||||
turn = {"iteration": i, "elapsed_s": round(time.time() - t0, 2), "content": content, "tool_calls": [], "prompt_eval_count": r.get("prompt_eval_count"), "eval_count": r.get("eval_count"), "thinking_field_len": len(thinking) if thinking else 0}
|
||||
messages.append({"role": "assistant", "content": content, "tool_calls": tcs})
|
||||
if not tcs:
|
||||
trace["turns"].append(turn)
|
||||
halt = "no_tool_calls"
|
||||
break
|
||||
for tc in tcs:
|
||||
fn = tc.get("function", {})
|
||||
name = fn.get("name")
|
||||
args = fn.get("arguments") or {}
|
||||
if isinstance(args, str):
|
||||
try: args = json.loads(args)
|
||||
except: args = {"_raw": args}
|
||||
try: result = TOOL_DISPATCH[name](workdir, args) if name in TOOL_DISPATCH else f"ERROR: unknown {name}"
|
||||
except Exception as e: result = f"ERROR: {e}"
|
||||
if name in counts: counts[name] += 1
|
||||
turn["tool_calls"].append({"name": name, "arguments": args, "result": result[:800]})
|
||||
messages.append({"role": "tool", "content": result})
|
||||
trace["turns"].append(turn)
|
||||
if i == MAX_ITERATIONS:
|
||||
halt = "iteration_cap"
|
||||
break
|
||||
trace["final"] = {"halt_reason": halt, "tests_pass": pytest_passes(workdir), "iterations_used": len(trace["turns"]), "tool_call_counts": counts, "wall_clock_s": round(time.time() - trace["started_at"], 2)}
|
||||
log_path.write_text(json.dumps(trace, indent=2, default=str))
|
||||
return trace
|
||||
|
||||
|
||||
def main():
|
||||
model, workdir_s, log_s = sys.argv[1], sys.argv[2], sys.argv[3]
|
||||
workdir, log_path = Path(workdir_s).resolve(), Path(log_s).resolve()
|
||||
seed = Path(__file__).parent / "task_seed"
|
||||
if workdir.exists(): shutil.rmtree(workdir)
|
||||
shutil.copytree(seed, workdir)
|
||||
r = run_bakeoff(model, workdir, log_path)
|
||||
f = r["final"]
|
||||
print(f"model={model} pass={f['tests_pass']} iters={f['iterations_used']} reads={f['tool_call_counts']['read_file']} patches={f['tool_call_counts']['apply_patch']} bashes={f['tool_call_counts']['run_bash']} halt={f['halt_reason']} wall={f['wall_clock_s']}s")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,137 @@
|
||||
"""Closes the loop on Round 1: original write_file harness, but think flag omitted.
|
||||
|
||||
If 26B PASSES here, the original Round 1 failure (and Round 2 patch-mode failure)
|
||||
were both caused by `think: false`, not by the edit tool surface or response size.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json, os, shutil, subprocess, sys, time
|
||||
from pathlib import Path
|
||||
from urllib import request as urlreq
|
||||
|
||||
OLLAMA_HOST = os.environ.get("OLLAMA_HOST", "http://127.0.0.1:11434")
|
||||
MAX_ITERATIONS = 15
|
||||
BASH_TIMEOUT_S = 30
|
||||
REQUEST_TIMEOUT_S = 540
|
||||
|
||||
SYSTEM_PROMPT = """You are a terminal coding agent.
|
||||
|
||||
## What you do
|
||||
- Read source and test files to understand the code
|
||||
- Make targeted edits to fix bugs so the tests pass
|
||||
- Run pytest to verify your fix
|
||||
- Stop once all tests pass and reply with a one-sentence summary
|
||||
|
||||
## What you do NOT do
|
||||
- Never modify files under tests/
|
||||
- Never disable, skip, or delete tests
|
||||
- Never write outside the working directory
|
||||
- Never call tools after all tests pass — just reply with the summary and stop
|
||||
|
||||
## Available tools
|
||||
- read_file(path): read a file relative to the working directory
|
||||
- write_file(path, content): overwrite a file relative to the working directory
|
||||
- run_bash(command): run a shell command in the working directory
|
||||
|
||||
## Rules
|
||||
- Start by reading README.md
|
||||
- Prefer minimal edits. Do not refactor unrelated code.
|
||||
- Run the full test suite after each edit to verify.
|
||||
"""
|
||||
|
||||
USER_PROMPT = "Make the failing tests pass. Begin."
|
||||
|
||||
TOOLS = [
|
||||
{"type": "function", "function": {"name": "read_file", "description": "Read a file relative to workdir.", "parameters": {"type": "object", "properties": {"path": {"type": "string"}}, "required": ["path"]}}},
|
||||
{"type": "function", "function": {"name": "write_file", "description": "Overwrite a file relative to workdir.", "parameters": {"type": "object", "properties": {"path": {"type": "string"}, "content": {"type": "string"}}, "required": ["path", "content"]}}},
|
||||
{"type": "function", "function": {"name": "run_bash", "description": "Run a bash command in workdir.", "parameters": {"type": "object", "properties": {"command": {"type": "string"}}, "required": ["command"]}}},
|
||||
]
|
||||
|
||||
|
||||
def safe_path(wd, rel):
|
||||
p = (wd / rel).resolve()
|
||||
if not str(p).startswith(str(wd.resolve())):
|
||||
raise ValueError(f"path escapes workdir: {rel}")
|
||||
return p
|
||||
|
||||
|
||||
def t_read(wd, a):
|
||||
p = safe_path(wd, a["path"])
|
||||
return p.read_text() if p.exists() else f"ERROR: {a['path']} does not exist"
|
||||
|
||||
|
||||
def t_write(wd, a):
|
||||
p = safe_path(wd, a["path"])
|
||||
p.parent.mkdir(parents=True, exist_ok=True)
|
||||
p.write_text(a["content"])
|
||||
return f"wrote {a['path']} ({len(a['content'])} bytes)"
|
||||
|
||||
|
||||
def t_bash(wd, a):
|
||||
try:
|
||||
r = subprocess.run(["bash", "-c", a["command"]], cwd=wd, capture_output=True, text=True, timeout=BASH_TIMEOUT_S)
|
||||
except subprocess.TimeoutExpired:
|
||||
return f"ERROR: timeout {BASH_TIMEOUT_S}s"
|
||||
return f"exit={r.returncode}\n--- stdout ---\n{r.stdout[-4000:]}\n--- stderr ---\n{r.stderr[-2000:]}"
|
||||
|
||||
|
||||
DISP = {"read_file": t_read, "write_file": t_write, "run_bash": t_bash}
|
||||
|
||||
|
||||
def chat(model, msgs):
|
||||
payload = {"model": model, "messages": msgs, "tools": TOOLS, "stream": False, "keep_alive": "10m", "options": {"num_ctx": 32768, "num_predict": 4096, "temperature": 0.3}}
|
||||
req = urlreq.Request(f"{OLLAMA_HOST}/api/chat", data=json.dumps(payload).encode(), headers={"Content-Type": "application/json"})
|
||||
with urlreq.urlopen(req, timeout=REQUEST_TIMEOUT_S) as r:
|
||||
return json.loads(r.read())
|
||||
|
||||
|
||||
def run(model, wd, logp):
|
||||
logp.parent.mkdir(parents=True, exist_ok=True)
|
||||
msgs = [{"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": USER_PROMPT}]
|
||||
trace = {"model": model, "edit_tool": "write_file", "think_setting": "unset (default)", "turns": [], "started_at": time.time()}
|
||||
counts = {"read_file": 0, "write_file": 0, "run_bash": 0}
|
||||
halt = None
|
||||
for i in range(1, MAX_ITERATIONS + 1):
|
||||
t0 = time.time()
|
||||
try:
|
||||
r = chat(model, msgs)
|
||||
except Exception as e:
|
||||
halt = f"error: {e}"
|
||||
break
|
||||
m = r.get("message", {})
|
||||
tcs = m.get("tool_calls") or []
|
||||
trace["turns"].append({"iteration": i, "elapsed_s": round(time.time() - t0, 2), "content": m.get("content") or "", "prompt_eval_count": r.get("prompt_eval_count"), "eval_count": r.get("eval_count"), "tool_calls_count": len(tcs)})
|
||||
msgs.append({"role": "assistant", "content": m.get("content") or "", "tool_calls": tcs})
|
||||
if not tcs:
|
||||
halt = "no_tool_calls"
|
||||
break
|
||||
for tc in tcs:
|
||||
fn = tc.get("function", {})
|
||||
n = fn.get("name")
|
||||
args = fn.get("arguments") or {}
|
||||
if isinstance(args, str):
|
||||
try: args = json.loads(args)
|
||||
except: args = {"_raw": args}
|
||||
try: res = DISP[n](wd, args) if n in DISP else f"ERROR: unknown {n}"
|
||||
except Exception as e: res = f"ERROR: {e}"
|
||||
if n in counts: counts[n] += 1
|
||||
msgs.append({"role": "tool", "content": res})
|
||||
if i == MAX_ITERATIONS:
|
||||
halt = "cap"
|
||||
break
|
||||
p = subprocess.run(["python3", "-m", "pytest", "tests/", "-q"], cwd=wd, capture_output=True, text=True, timeout=60)
|
||||
trace["final"] = {"halt_reason": halt, "tests_pass": p.returncode == 0, "iterations_used": len(trace["turns"]), "tool_call_counts": counts, "wall_clock_s": round(time.time() - trace["started_at"], 2)}
|
||||
logp.write_text(json.dumps(trace, indent=2, default=str))
|
||||
return trace
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
model, wd_s, log_s = sys.argv[1], sys.argv[2], sys.argv[3]
|
||||
wd, logp = Path(wd_s).resolve(), Path(log_s).resolve()
|
||||
seed = Path(__file__).parent / "task_seed"
|
||||
if wd.exists(): shutil.rmtree(wd)
|
||||
shutil.copytree(seed, wd)
|
||||
r = run(model, wd, logp)
|
||||
f = r["final"]
|
||||
print(f"model={model} pass={f['tests_pass']} iters={f['iterations_used']} r={f['tool_call_counts']['read_file']} w={f['tool_call_counts']['write_file']} b={f['tool_call_counts']['run_bash']} halt={f['halt_reason']} wall={f['wall_clock_s']}s")
|
||||
@@ -0,0 +1,150 @@
|
||||
{
|
||||
"model": "gemma4:26b",
|
||||
"edit_tool": "apply_patch",
|
||||
"think_setting": "unset (default)",
|
||||
"workdir": "/home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/gemma4-26b-no-think-flag/work",
|
||||
"started_at": 1776549973.621934,
|
||||
"turns": [
|
||||
{
|
||||
"iteration": 1,
|
||||
"elapsed_s": 0.75,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "ls -R"
|
||||
},
|
||||
"result": "exit=0\n--- stdout ---\n.:\ncalc\nREADME.md\ntests\n\n./calc:\n__init__.py\n__pycache__\nstats.py\n\n./calc/__pycache__:\n__init__.cpython-313.pyc\nstats.cpython-313.pyc\n\n./tests:\n__pycache__\ntest_stats.py\n\n./tests/__pycache__:\ntest_stats.cpython-313-pytest-9.0.2.pyc\n\n--- stderr ---\n"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 459,
|
||||
"eval_count": 55,
|
||||
"thinking_field_len": 152
|
||||
},
|
||||
{
|
||||
"iteration": 2,
|
||||
"elapsed_s": 0.67,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "read_file",
|
||||
"arguments": {
|
||||
"path": "tests/test_stats.py"
|
||||
},
|
||||
"result": "from calc.stats import mean, median, mode, variance\n\n\ndef test_mean_basic():\n assert mean([1, 2, 3, 4, 5]) == 3.0\n\n\ndef test_median_odd():\n assert median([1, 2, 3]) == 2\n\n\ndef test_median_even():\n assert median([1, 2, 3, 4]) == 2.5\n\n\ndef test_median_unsorted():\n assert median([3, 1, 4, 1, 5, 9, 2, 6]) == 3.5\n\n\ndef test_median_floats():\n assert median([1.0, 2.0, 3.0, 4.0]) == 2.5\n\n\ndef test_mode_basic():\n assert mode([1, 2, 2, 3]) == 2\n\n\ndef test_variance_basic():\n # sample variance (n-1) of [1, 2, 3, 4, 5] is 10/4 = 2.5\n assert variance([1, 2, 3, 4, 5]) == 2.5\n"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 600,
|
||||
"eval_count": 40,
|
||||
"thinking_field_len": 59
|
||||
},
|
||||
{
|
||||
"iteration": 3,
|
||||
"elapsed_s": 0.51,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "pytest"
|
||||
},
|
||||
"result": "exit=2\n--- stdout ---\n============================= test session starts ==============================\nplatform linux -- Python 3.13.5, pytest-9.0.2, pluggy-1.6.0\nrootdir: /home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/gemma4-26b-no-think-flag/work\nplugins: asyncio-1.3.0, anyio-4.12.1\nasyncio: mode=Mode.STRICT, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function\ncollected 0 items / 1 error\n\n==================================== ERRORS ====================================\n_____________________ ERROR collecting tests/test_stats.py _____________________\nImportError while importing test module '/home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/gemma4-26b-no-think-flag/work/tests/test_stats.py'.\nHint: make sure your test modules/p"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 903,
|
||||
"eval_count": 19,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 4,
|
||||
"elapsed_s": 1.37,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "PYTHONPATH=. pytest"
|
||||
},
|
||||
"result": "exit=1\n--- stdout ---\n============================= test session starts ==============================\nplatform linux -- Python 3.13.5, pytest-9.0.2, pluggy-1.6.0\nrootdir: /home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/gemma4-26b-no-think-flag/work\nplugins: asyncio-1.3.0, anyio-4.12.1\nasyncio: mode=Mode.STRICT, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function\ncollected 7 items\n\ntests/test_stats.py ..FFF.. [100%]\n\n=================================== FAILURES ===================================\n_______________________________ test_median_even _______________________________\n\n def test_median_even():\n> assert median([1, 2, 3, 4]) == 2.5\nE assert 3 == 2.5\nE + where 3 ="
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 1345,
|
||||
"eval_count": 103,
|
||||
"thinking_field_len": 302
|
||||
},
|
||||
{
|
||||
"iteration": 5,
|
||||
"elapsed_s": 0.66,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "read_file",
|
||||
"arguments": {
|
||||
"path": "calc/stats.py"
|
||||
},
|
||||
"result": "\"\"\"Basic statistics helpers.\"\"\"\n\n\ndef mean(numbers):\n \"\"\"Arithmetic mean of a non-empty list.\"\"\"\n return sum(numbers) / len(numbers)\n\n\ndef median(numbers):\n \"\"\"Return the median of a list of numbers.\"\"\"\n s = sorted(numbers)\n n = len(s)\n return s[n // 2]\n\n\ndef mode(numbers):\n \"\"\"Return the most common value. Ties broken by first occurrence.\"\"\"\n counts = {}\n for x in numbers:\n counts[x] = counts.get(x, 0) + 1\n best = None\n best_count = -1\n for x in numbers:\n if counts[x] > best_count:\n best = x\n best_count = counts[x]\n return best\n\n\ndef variance(numbers):\n \"\"\"Sample variance (divides by n-1).\"\"\"\n if len(numbers) < 2:\n raise ValueError(\"variance requires at least 2 values\")\n m = mean(numbers)\n return"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 1986,
|
||||
"eval_count": 23,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 6,
|
||||
"elapsed_s": 6.2,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "apply_patch",
|
||||
"arguments": {
|
||||
"new_text": "def median(numbers):\n \"\"\"Return the median of a list of numbers.\"\"\"\n s = sorted(numbers)\n n = len(s)\n if n % 2 == 1:\n return s[n // 2]\n else:\n return (s[n // 2 - 1] + s[n // 2]) / 2",
|
||||
"old_text": "def median(numbers):\n \"\"\"Return the median of a list of numbers.\"\"\"\n s = sorted(numbers)\n n = len(s)\n return s[n // 2]",
|
||||
"path": "calc/stats.py"
|
||||
},
|
||||
"result": "patched calc/stats.py (replaced 130 chars with 210 chars)"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 2275,
|
||||
"eval_count": 671,
|
||||
"thinking_field_len": 1173
|
||||
},
|
||||
{
|
||||
"iteration": 7,
|
||||
"elapsed_s": 0.52,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "PYTHONPATH=. pytest"
|
||||
},
|
||||
"result": "exit=0\n--- stdout ---\n============================= test session starts ==============================\nplatform linux -- Python 3.13.5, pytest-9.0.2, pluggy-1.6.0\nrootdir: /home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/gemma4-26b-no-think-flag/work\nplugins: asyncio-1.3.0, anyio-4.12.1\nasyncio: mode=Mode.STRICT, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function\ncollected 7 items\n\ntests/test_stats.py ....... [100%]\n\n============================== 7 passed in 0.01s ===============================\n\n--- stderr ---\n"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 2463,
|
||||
"eval_count": 22,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 8,
|
||||
"elapsed_s": 0.74,
|
||||
"content": "The median function was incorrectly returning the middle element for even-sized lists without averaging the two middle elements; I updated it to correctly calculate the average of the two middle elements when the list length is even.",
|
||||
"tool_calls": [],
|
||||
"prompt_eval_count": 2677,
|
||||
"eval_count": 46,
|
||||
"thinking_field_len": 0
|
||||
}
|
||||
],
|
||||
"final": {
|
||||
"halt_reason": "no_tool_calls",
|
||||
"tests_pass": true,
|
||||
"iterations_used": 8,
|
||||
"tool_call_counts": {
|
||||
"read_file": 2,
|
||||
"apply_patch": 1,
|
||||
"run_bash": 4
|
||||
},
|
||||
"wall_clock_s": 12.53
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,83 @@
|
||||
{
|
||||
"model": "gemma4:26b",
|
||||
"edit_tool": "write_file",
|
||||
"think_setting": "unset (default)",
|
||||
"turns": [
|
||||
{
|
||||
"iteration": 1,
|
||||
"elapsed_s": 8.52,
|
||||
"content": "",
|
||||
"prompt_eval_count": 369,
|
||||
"eval_count": 55,
|
||||
"tool_calls_count": 1
|
||||
},
|
||||
{
|
||||
"iteration": 2,
|
||||
"elapsed_s": 1.07,
|
||||
"content": "",
|
||||
"prompt_eval_count": 510,
|
||||
"eval_count": 46,
|
||||
"tool_calls_count": 1
|
||||
},
|
||||
{
|
||||
"iteration": 3,
|
||||
"elapsed_s": 0.75,
|
||||
"content": "",
|
||||
"prompt_eval_count": 691,
|
||||
"eval_count": 19,
|
||||
"tool_calls_count": 1
|
||||
},
|
||||
{
|
||||
"iteration": 4,
|
||||
"elapsed_s": 1.05,
|
||||
"content": "",
|
||||
"prompt_eval_count": 1140,
|
||||
"eval_count": 27,
|
||||
"tool_calls_count": 1
|
||||
},
|
||||
{
|
||||
"iteration": 5,
|
||||
"elapsed_s": 1.19,
|
||||
"content": "",
|
||||
"prompt_eval_count": 1786,
|
||||
"eval_count": 23,
|
||||
"tool_calls_count": 1
|
||||
},
|
||||
{
|
||||
"iteration": 6,
|
||||
"elapsed_s": 4.87,
|
||||
"content": "",
|
||||
"prompt_eval_count": 2075,
|
||||
"eval_count": 330,
|
||||
"tool_calls_count": 1
|
||||
},
|
||||
{
|
||||
"iteration": 7,
|
||||
"elapsed_s": 0.99,
|
||||
"content": "",
|
||||
"prompt_eval_count": 2422,
|
||||
"eval_count": 26,
|
||||
"tool_calls_count": 1
|
||||
},
|
||||
{
|
||||
"iteration": 8,
|
||||
"elapsed_s": 0.96,
|
||||
"content": "The `median` function was updated to correctly average the two middle elements when the input list has an even number of elements.",
|
||||
"prompt_eval_count": 2641,
|
||||
"eval_count": 30,
|
||||
"tool_calls_count": 0
|
||||
}
|
||||
],
|
||||
"started_at": 1776550159.4912357,
|
||||
"final": {
|
||||
"halt_reason": "no_tool_calls",
|
||||
"tests_pass": true,
|
||||
"iterations_used": 8,
|
||||
"tool_call_counts": {
|
||||
"read_file": 2,
|
||||
"write_file": 1,
|
||||
"run_bash": 4
|
||||
},
|
||||
"wall_clock_s": 20.58
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,150 @@
|
||||
{
|
||||
"model": "gemma4:31b-it-q4_K_M",
|
||||
"edit_tool": "apply_patch",
|
||||
"think_setting": "unset (default)",
|
||||
"workdir": "/home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/gemma4-31b-no-think-flag/work",
|
||||
"started_at": 1776550017.9478421,
|
||||
"turns": [
|
||||
{
|
||||
"iteration": 1,
|
||||
"elapsed_s": 17.62,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "read_file",
|
||||
"arguments": {
|
||||
"path": "README.md"
|
||||
},
|
||||
"result": "# Bakeoff Task\n\nA tiny Python package (`calc/`) with a statistics module. Run `pytest` from this\ndirectory \u2014 two tests currently fail because `median` returns the upper-middle\nelement instead of averaging the two middle elements on even-length inputs.\n\nYour job: make all tests pass. Do not disable or modify the tests.\n\nAllowed tools:\n- `read_file(path)` \u2014 read a file (relative to this directory)\n- `write_file(path, content)` \u2014 overwrite a file (relative to this directory)\n- `run_bash(command)` \u2014 run a shell command (cwd is this directory)\n\nWhen all tests pass, reply with a short summary of the fix and stop calling tools.\n"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 459,
|
||||
"eval_count": 38,
|
||||
"thinking_field_len": 67
|
||||
},
|
||||
{
|
||||
"iteration": 2,
|
||||
"elapsed_s": 3.6,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "ls -R"
|
||||
},
|
||||
"result": "exit=0\n--- stdout ---\n.:\ncalc\nREADME.md\ntests\n\n./calc:\n__init__.py\n__pycache__\nstats.py\n\n./calc/__pycache__:\n__init__.cpython-313.pyc\nstats.cpython-313.pyc\n\n./tests:\n__pycache__\ntest_stats.py\n\n./tests/__pycache__:\ntest_stats.cpython-313-pytest-9.0.2.pyc\n\n--- stderr ---\n"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 640,
|
||||
"eval_count": 70,
|
||||
"thinking_field_len": 211
|
||||
},
|
||||
{
|
||||
"iteration": 3,
|
||||
"elapsed_s": 3.6,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "pytest"
|
||||
},
|
||||
"result": "exit=2\n--- stdout ---\n============================= test session starts ==============================\nplatform linux -- Python 3.13.5, pytest-9.0.2, pluggy-1.6.0\nrootdir: /home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/gemma4-31b-no-think-flag/work\nplugins: asyncio-1.3.0, anyio-4.12.1\nasyncio: mode=Mode.STRICT, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function\ncollected 0 items / 1 error\n\n==================================== ERRORS ====================================\n_____________________ ERROR collecting tests/test_stats.py _____________________\nImportError while importing test module '/home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/gemma4-31b-no-think-flag/work/tests/test_stats.py'.\nHint: make sure your test modules/p"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 781,
|
||||
"eval_count": 72,
|
||||
"thinking_field_len": 224
|
||||
},
|
||||
{
|
||||
"iteration": 4,
|
||||
"elapsed_s": 4.01,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "export PYTHONPATH=$PYTHONPATH:. && pytest"
|
||||
},
|
||||
"result": "exit=1\n--- stdout ---\n============================= test session starts ==============================\nplatform linux -- Python 3.13.5, pytest-9.0.2, pluggy-1.6.0\nrootdir: /home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/gemma4-31b-no-think-flag/work\nplugins: asyncio-1.3.0, anyio-4.12.1\nasyncio: mode=Mode.STRICT, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function\ncollected 7 items\n\ntests/test_stats.py ..FFF.. [100%]\n\n=================================== FAILURES ===================================\n_______________________________ test_median_even _______________________________\n\n def test_median_even():\n> assert median([1, 2, 3, 4]) == 2.5\nE assert 3 == 2.5\nE + where 3 ="
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 1223,
|
||||
"eval_count": 57,
|
||||
"thinking_field_len": 127
|
||||
},
|
||||
{
|
||||
"iteration": 5,
|
||||
"elapsed_s": 5.47,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "read_file",
|
||||
"arguments": {
|
||||
"path": "calc/stats.py"
|
||||
},
|
||||
"result": "\"\"\"Basic statistics helpers.\"\"\"\n\n\ndef mean(numbers):\n \"\"\"Arithmetic mean of a non-empty list.\"\"\"\n return sum(numbers) / len(numbers)\n\n\ndef median(numbers):\n \"\"\"Return the median of a list of numbers.\"\"\"\n s = sorted(numbers)\n n = len(s)\n return s[n // 2]\n\n\ndef mode(numbers):\n \"\"\"Return the most common value. Ties broken by first occurrence.\"\"\"\n counts = {}\n for x in numbers:\n counts[x] = counts.get(x, 0) + 1\n best = None\n best_count = -1\n for x in numbers:\n if counts[x] > best_count:\n best = x\n best_count = counts[x]\n return best\n\n\ndef variance(numbers):\n \"\"\"Sample variance (divides by n-1).\"\"\"\n if len(numbers) < 2:\n raise ValueError(\"variance requires at least 2 values\")\n m = mean(numbers)\n return"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 1868,
|
||||
"eval_count": 71,
|
||||
"thinking_field_len": 196
|
||||
},
|
||||
{
|
||||
"iteration": 6,
|
||||
"elapsed_s": 26.35,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "apply_patch",
|
||||
"arguments": {
|
||||
"new_text": "def median(numbers):\n \"\"\"Return the median of a list of numbers.\"\"\"\n s = sorted(numbers)\n n = len(s)\n if n % 2 == 1:\n return s[n // 2]\n else:\n return (s[n // 2 - 1] + s[n // 2]) / 2\n",
|
||||
"old_text": "def median(numbers):\n \"\"\"Return the median of a list of numbers.\"\"\"\n s = sorted(numbers)\n n = len(s)\n return s[n // 2]\n",
|
||||
"path": "calc/stats.py"
|
||||
},
|
||||
"result": "patched calc/stats.py (replaced 131 chars with 211 chars)"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 2157,
|
||||
"eval_count": 630,
|
||||
"thinking_field_len": 1025
|
||||
},
|
||||
{
|
||||
"iteration": 7,
|
||||
"elapsed_s": 1.82,
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "export PYTHONPATH=$PYTHONPATH:. && pytest"
|
||||
},
|
||||
"result": "exit=0\n--- stdout ---\n============================= test session starts ==============================\nplatform linux -- Python 3.13.5, pytest-9.0.2, pluggy-1.6.0\nrootdir: /home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/gemma4-31b-no-think-flag/work\nplugins: asyncio-1.3.0, anyio-4.12.1\nasyncio: mode=Mode.STRICT, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function\ncollected 7 items\n\ntests/test_stats.py ....... [100%]\n\n============================== 7 passed in 0.01s ===============================\n\n--- stderr ---\n"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 2347,
|
||||
"eval_count": 22,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 8,
|
||||
"elapsed_s": 2.84,
|
||||
"content": "The `median` function in `calc/stats.py` was incorrectly returning the upper-middle element for even-length lists; I updated it to return the average of the two middle elements.",
|
||||
"tool_calls": [],
|
||||
"prompt_eval_count": 2565,
|
||||
"eval_count": 45,
|
||||
"thinking_field_len": 0
|
||||
}
|
||||
],
|
||||
"final": {
|
||||
"halt_reason": "no_tool_calls",
|
||||
"tests_pass": true,
|
||||
"iterations_used": 8,
|
||||
"tool_call_counts": {
|
||||
"read_file": 2,
|
||||
"apply_patch": 1,
|
||||
"run_bash": 4
|
||||
},
|
||||
"wall_clock_s": 66.41
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,201 @@
|
||||
{
|
||||
"model": "qwen3-coder:30b",
|
||||
"edit_tool": "apply_patch",
|
||||
"think_setting": "unset (default)",
|
||||
"workdir": "/home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/qwen3-coder-30b-no-think-flag/work",
|
||||
"started_at": 1776550090.9317613,
|
||||
"turns": [
|
||||
{
|
||||
"iteration": 1,
|
||||
"elapsed_s": 10.94,
|
||||
"content": "I'll help you make the failing tests pass. First, let's understand what we're working with by reading the README.md file to get context about the project.",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "read_file",
|
||||
"arguments": {
|
||||
"path": "README.md"
|
||||
},
|
||||
"result": "# Bakeoff Task\n\nA tiny Python package (`calc/`) with a statistics module. Run `pytest` from this\ndirectory \u2014 two tests currently fail because `median` returns the upper-middle\nelement instead of averaging the two middle elements on even-length inputs.\n\nYour job: make all tests pass. Do not disable or modify the tests.\n\nAllowed tools:\n- `read_file(path)` \u2014 read a file (relative to this directory)\n- `write_file(path, content)` \u2014 overwrite a file (relative to this directory)\n- `run_bash(command)` \u2014 run a shell command (cwd is this directory)\n\nWhen all tests pass, reply with a short summary of the fix and stop calling tools.\n"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 694,
|
||||
"eval_count": 55,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 2,
|
||||
"elapsed_s": 0.54,
|
||||
"content": "Now let's look at the structure of the project to understand what files we're working with:",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "find . -type f"
|
||||
},
|
||||
"result": "exit=0\n--- stdout ---\n./.pytest_cache/v/cache/nodeids\n./.pytest_cache/v/cache/lastfailed\n./.pytest_cache/.gitignore\n./.pytest_cache/README.md\n./.pytest_cache/CACHEDIR.TAG\n./tests/__pycache__/test_stats.cpython-313-pytest-9.0.2.pyc\n./tests/test_stats.py\n./calc/__init__.py\n./calc/__pycache__/stats.cpython-313.pyc\n./calc/__pycache__/__init__.cpython-313.pyc\n./calc/stats.py\n./README.md\n\n--- stderr ---\n"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 903,
|
||||
"eval_count": 47,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 3,
|
||||
"elapsed_s": 0.46,
|
||||
"content": "Let's examine the test file to understand what's failing:",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "read_file",
|
||||
"arguments": {
|
||||
"path": "tests/test_stats.py"
|
||||
},
|
||||
"result": "from calc.stats import mean, median, mode, variance\n\n\ndef test_mean_basic():\n assert mean([1, 2, 3, 4, 5]) == 3.0\n\n\ndef test_median_odd():\n assert median([1, 2, 3]) == 2\n\n\ndef test_median_even():\n assert median([1, 2, 3, 4]) == 2.5\n\n\ndef test_median_unsorted():\n assert median([3, 1, 4, 1, 5, 9, 2, 6]) == 3.5\n\n\ndef test_median_floats():\n assert median([1.0, 2.0, 3.0, 4.0]) == 2.5\n\n\ndef test_mode_basic():\n assert mode([1, 2, 2, 3]) == 2\n\n\ndef test_variance_basic():\n # sample variance (n-1) of [1, 2, 3, 4, 5] is 10/4 = 2.5\n assert variance([1, 2, 3, 4, 5]) == 2.5\n"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 1099,
|
||||
"eval_count": 36,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 4,
|
||||
"elapsed_s": 0.47,
|
||||
"content": "Now let's look at the implementation of the stats module:",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "read_file",
|
||||
"arguments": {
|
||||
"path": "calc/stats.py"
|
||||
},
|
||||
"result": "\"\"\"Basic statistics helpers.\"\"\"\n\n\ndef mean(numbers):\n \"\"\"Arithmetic mean of a non-empty list.\"\"\"\n return sum(numbers) / len(numbers)\n\n\ndef median(numbers):\n \"\"\"Return the median of a list of numbers.\"\"\"\n s = sorted(numbers)\n n = len(s)\n return s[n // 2]\n\n\ndef mode(numbers):\n \"\"\"Return the most common value. Ties broken by first occurrence.\"\"\"\n counts = {}\n for x in numbers:\n counts[x] = counts.get(x, 0) + 1\n best = None\n best_count = -1\n for x in numbers:\n if counts[x] > best_count:\n best = x\n best_count = counts[x]\n return best\n\n\ndef variance(numbers):\n \"\"\"Sample variance (divides by n-1).\"\"\"\n if len(numbers) < 2:\n raise ValueError(\"variance requires at least 2 values\")\n m = mean(numbers)\n return"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 1399,
|
||||
"eval_count": 35,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 5,
|
||||
"elapsed_s": 2.45,
|
||||
"content": "Now I can see the issue! In the `median` function, when the list has an even number of elements, it's returning the upper-middle element instead of averaging the two middle elements. \n\nLooking at the test `test_median_even()`, it expects `median([1, 2, 3, 4])` to return `2.5`, but the current implementation returns `s[n // 2]` which would be `s[2]` = `3` for the list `[1, 2, 3, 4]`.\n\nFor an even-length list, we need to average the two middle elements. Let me fix this:",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "apply_patch",
|
||||
"arguments": {
|
||||
"path": "calc/stats.py",
|
||||
"old_text": "def median(numbers):\n \"\"\"Return the median of a list of numbers.\"\"\"\n s = sorted(numbers)\n n = len(s)\n return s[n // 2]",
|
||||
"new_text": "def median(numbers):\n \"\"\"Return the median of a list of numbers.\"\"\"\n s = sorted(numbers)\n n = len(s)\n if n % 2 == 1:\n return s[n // 2]\n else:\n return (s[n // 2 - 1] + s[n // 2]) / 2"
|
||||
},
|
||||
"result": "patched calc/stats.py (replaced 130 chars with 210 chars)"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 1669,
|
||||
"eval_count": 283,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 6,
|
||||
"elapsed_s": 0.43,
|
||||
"content": "Now let's run the tests to see if our fix works:",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "pytest"
|
||||
},
|
||||
"result": "exit=2\n--- stdout ---\n============================= test session starts ==============================\nplatform linux -- Python 3.13.5, pytest-9.0.2, pluggy-1.6.0\nrootdir: /home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/qwen3-coder-30b-no-think-flag/work\nplugins: asyncio-1.3.0, anyio-4.12.1\nasyncio: mode=Mode.STRICT, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function\ncollected 0 items / 1 error\n\n==================================== ERRORS ====================================\n_____________________ ERROR collecting tests/test_stats.py _____________________\nImportError while importing test module '/home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/qwen3-coder-30b-no-think-flag/work/tests/test_stats.py'.\nHint: make sure your test"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 1984,
|
||||
"eval_count": 37,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 7,
|
||||
"elapsed_s": 0.63,
|
||||
"content": "It seems there's an import issue. Let's check the current working directory and run pytest from the correct location:",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "pwd && ls -la"
|
||||
},
|
||||
"result": "exit=0\n--- stdout ---\n/home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/qwen3-coder-30b-no-think-flag/work\ntotal 24\ndrwxrwxr-x 5 claude claude 4096 Apr 18 13:19 .\ndrwxrwxr-x 3 claude claude 4096 Apr 18 18:08 ..\ndrwxrwxr-x 3 claude claude 4096 Apr 18 13:19 calc\ndrwxrwxr-x 3 claude claude 4096 Apr 18 13:19 .pytest_cache\n-rw-rw-r-- 1 claude claude 637 Apr 18 13:19 README.md\ndrwxrwxr-x 3 claude claude 4096 Apr 18 13:20 tests\n\n--- stderr ---\n"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 2403,
|
||||
"eval_count": 51,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 8,
|
||||
"elapsed_s": 0.49,
|
||||
"content": "Let's run pytest from the correct directory:",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "python -m pytest"
|
||||
},
|
||||
"result": "exit=127\n--- stdout ---\n\n--- stderr ---\nbash: line 1: python: command not found\n"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 2689,
|
||||
"eval_count": 36,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 9,
|
||||
"elapsed_s": 0.42,
|
||||
"content": "Let me try with python3:",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "run_bash",
|
||||
"arguments": {
|
||||
"command": "python3 -m pytest"
|
||||
},
|
||||
"result": "exit=0\n--- stdout ---\n============================= test session starts ==============================\nplatform linux -- Python 3.13.5, pytest-9.0.2, pluggy-1.6.0\nrootdir: /home/claude/bin/gemma4-research/scripts/bakeoff/runs_patch/qwen3-coder-30b-no-think-flag/work\nplugins: asyncio-1.3.0, anyio-4.12.1\nasyncio: mode=Mode.STRICT, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function\ncollected 7 items\n\ntests/test_stats.py ....... [100%]\n\n============================== 7 passed in 0.01s ===============================\n\n--- stderr ---\n"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 2762,
|
||||
"eval_count": 35,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 10,
|
||||
"elapsed_s": 0.56,
|
||||
"content": "Great! All tests are now passing. Let me verify that our fix is correct by checking what we changed:",
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "read_file",
|
||||
"arguments": {
|
||||
"path": "calc/stats.py"
|
||||
},
|
||||
"result": "\"\"\"Basic statistics helpers.\"\"\"\n\n\ndef mean(numbers):\n \"\"\"Arithmetic mean of a non-empty list.\"\"\"\n return sum(numbers) / len(numbers)\n\n\ndef median(numbers):\n \"\"\"Return the median of a list of numbers.\"\"\"\n s = sorted(numbers)\n n = len(s)\n if n % 2 == 1:\n return s[n // 2]\n else:\n return (s[n // 2 - 1] + s[n // 2]) / 2\n\n\ndef mode(numbers):\n \"\"\"Return the most common value. Ties broken by first occurrence.\"\"\"\n counts = {}\n for x in numbers:\n counts[x] = counts.get(x, 0) + 1\n best = None\n best_count = -1\n for x in numbers:\n if counts[x] > best_count:\n best = x\n best_count = counts[x]\n return best\n\n\ndef variance(numbers):\n \"\"\"Sample variance (divides by n-1).\"\"\"\n if len(numbers) < 2:\n raise Va"
|
||||
}
|
||||
],
|
||||
"prompt_eval_count": 2980,
|
||||
"eval_count": 45,
|
||||
"thinking_field_len": 0
|
||||
},
|
||||
{
|
||||
"iteration": 11,
|
||||
"elapsed_s": 1.31,
|
||||
"content": "Perfect! I've successfully fixed the median function to handle even-length lists correctly. The issue was that for even-length lists, the median should be the average of the two middle elements, not just the upper-middle element.\n\nMy fix:\n1. For odd-length lists: returns the middle element (as before)\n2. For even-length lists: returns the average of the two middle elements\n\nThis change makes the median function work correctly for all test cases, including:\n- `median([1, 2, 3, 4])` now returns `2.5` instead of `3`\n- All other median test cases continue to work as expected\n\nAll tests now pass successfully.",
|
||||
"tool_calls": [],
|
||||
"prompt_eval_count": 3296,
|
||||
"eval_count": 140,
|
||||
"thinking_field_len": 0
|
||||
}
|
||||
],
|
||||
"final": {
|
||||
"halt_reason": "no_tool_calls",
|
||||
"tests_pass": true,
|
||||
"iterations_used": 11,
|
||||
"tool_call_counts": {
|
||||
"read_file": 4,
|
||||
"apply_patch": 1,
|
||||
"run_bash": 5
|
||||
},
|
||||
"wall_clock_s": 19.5
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user