280 lines
29 KiB
Markdown
280 lines
29 KiB
Markdown
# Autoresearch Worklog: latency-p95-chat-completions
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## Session Summary
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- Objective: reduce end-to-end p95 latency for local `POST /v1/chat/completions`
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- Primary metric: `p95_ms` (lower is better)
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- Secondary metrics: `p50_ms`, `mean_ms`, `completion_tps_mean`, `success_count`
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- Benchmark harness: `./autoresearch.sh`
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## Key Insights
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- The first stable end-to-end benchmark is around 0.94 s p95 for a short non-streaming coding request on the local 4B GGUF model.
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- The benchmark harness must use the canonical model id derived from the GGUF filename; the current server build does not actually honor the intended `local-model` alias path yet.
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- Prompt prefill is only about 25 ms for this workload, so almost all latency sits in token generation rather than request parsing or prompt construction.
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- A micro-optimization in per-token detokenization made both latency and throughput worse, which suggests the dominant cost is deeper in the generation/runtime path rather than heap allocation in Rust glue code.
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- Shrinking the `Turbo4B` context and batch sizes also hurt throughput, so the current 4B profile appears closer to the local optimum for this hardware/workload than expected.
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- Disabling internal llama.cpp perf counters produced the first real p95 improvement over baseline, which suggests a small but measurable amount of hot-path CPU overhead was coming from instrumentation rather than model math.
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- Better stop behavior improved average and median latency, but it still failed to beat baseline p95, so the worst outliers are not explained solely by turn-boundary overruns.
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- Switching to a Qwen-style chat template produced a severe p95 tail regression while median latency stayed near baseline, which strongly suggests worse stop behavior or occasional runaway generations rather than uniformly slower decoding.
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## Next Ideas
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- Measure baseline first, then inspect the hottest latency contributors in prompt preparation, request handling, and session creation.
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- Investigate why `ModelCatalog` rejects `local-model` despite intending to expose aliases.
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### Run 1: baseline - p95_ms=941.252 (KEEP)
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- Timestamp: 2026-03-26 17:32
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- What changed: Established the autoresearch harness, built the release server, started it against the local Qwen GGUF, and measured a fixed non-streaming coding request workload.
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- Result: p95_ms=941.252, p50_ms=908.457, mean_ms=917.123, completion_tps_mean=52.427, success_count=7.
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- Insight: The baseline is stable enough to compare future server/runtime changes, and the benchmark immediately exposed a compatibility bug in alias resolution that should be kept separate from true latency experiments.
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- Next: fix low-risk benchmark-adjacent correctness issues only if they distort measurement; otherwise inspect hot latency contributors in the request/session path.
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### Run 2: stack buffer detokenization - p95_ms=1032.315 (DISCARD)
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- Timestamp: 2026-03-26 17:38
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- What changed: Replaced the per-token heap allocation in `decode_token_piece_with()` with a 256-byte stack buffer and heap fallback in `crates/inference-core/src/session.rs`.
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- Result: p95_ms=1032.315, p50_ms=1020.402, mean_ms=1011.466, completion_tps_mean=47.671, success_count=7.
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- Insight: This path is not the practical bottleneck for end-to-end latency here; generation got slower overall despite the reduced allocation churn.
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- Next: discard this branch state and focus on runtime/profile-level experiments that can improve generation throughput directly.
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### Run 3: slim turbo4b runtime profile - p95_ms=1004.435 (DISCARD)
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- Timestamp: 2026-03-26 17:40
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- What changed: Reduced `Turbo4B` defaults in `crates/inference-core/src/profile.rs` from `n_ctx=4096`, `n_batch=512`, `n_ubatch=128` to `n_ctx=2048`, `n_batch=256`, `n_ubatch=64`.
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- Result: p95_ms=1004.435, p50_ms=998.919, mean_ms=990.771, completion_tps_mean=48.528, success_count=7.
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- Insight: The leaner profile still reduced effective generation speed on this RTX 3070 + Q6_K workload, so the current batch/context sizing is likely not the source of the baseline latency.
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- Next: revert to the best-known commit and target generation semantics or model-facing prompt/template strategy rather than detokenization or profile shrinkage.
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### Run 6: disable llama perf counters in hot path - p95_ms=925.587 (KEEP)
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- Timestamp: 2026-03-26 17:57
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- What changed: Set `llama_context_params.no_perf = true` in `crates/inference-core/src/engine.rs` and `sampler_chain_default_params.no_perf = true` in `crates/inference-core/src/session.rs`.
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- Result: p95_ms=925.587, p50_ms=893.913, mean_ms=899.895, completion_tps_mean=53.618, success_count=7.
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- Insight: Disabling internal perf bookkeeping shaved about 1.7% off baseline p95 with slightly better overall throughput, making this the first experiment worth keeping.
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- Next: continue from this commit and look for adjacent hot-path flags or runtime settings that reduce generation overhead without changing model behavior.
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### Run 5: default turn-boundary stops - p95_ms=965.663 (DISCARD)
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- Timestamp: 2026-03-26 17:54
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- What changed: Added rolling multi-token stop detection in `crates/inference-core/src/session.rs` and injected default turn-boundary stop sequences in `crates/inference-server/src/compat.rs` when the request did not specify `stop`.
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- Result: p95_ms=965.663, p50_ms=877.030, mean_ms=897.251, completion_tps_mean=53.686, success_count=7.
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- Insight: This reduced median and mean latency and slightly improved throughput, but the p95 still stayed above baseline, so tail latency likely comes from a smaller number of deeper generation stalls rather than just missed stop boundaries.
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- Next: inspect per-sample latency spread and target the remaining long-tail outliers directly, likely in warmup/first-request behavior or occasional decode stalls.
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### Run 4: qwen-style chat template - p95_ms=3027.145 (DISCARD)
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- Timestamp: 2026-03-26 17:50
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- What changed: Replaced the plain `System/User/Assistant:` prompt format in `crates/inference-server/src/compat.rs` with a Qwen-style `<|im_start|>...<|im_end|>` chat template.
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- Result: p95_ms=3027.145, p50_ms=974.937, mean_ms=1467.026, completion_tps_mean=41.360, success_count=7.
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- Insight: The template did not make typical requests much faster and introduced severe tail latency, likely because some generations stopped much later or followed a worse decoding path for this server/model combination.
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- Next: revert this template change and focus on experiments that directly bound generation length or improve stop behavior without overhauling the prompt format.
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## Segment 1: Dynamic Chat Templates
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- New objective: add dynamic prompt templates by model family with built-in coverage for Qwen, Gemma, Llama, Kimi, and a generic fallback.
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- Benchmark driver: `./autoresearch.sh` now runs ignored template-specific tests and reports `template_score`, `passed_cases`, `failed_cases`, and `total_cases`.
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- Keep/discard rule for this segment is test-driven: higher `template_score` wins, while the regular non-ignored test suite must remain green.
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- A minimal family detector plus embedded template registry in `compat.rs` was enough to move coverage from 1/5 to 5/5 without touching the runtime.
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### Run 7: baseline - template_score=20.0 (KEEP)
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- Timestamp: 2026-03-26 18:14
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- What changed: Re-initialized autoresearch to a new segment focused on dynamic chat templates, added a dedicated ignored test suite for Qwen, Gemma, Llama, Kimi, and generic fallback behavior, and switched the benchmark harness to score that suite.
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- Result: template_score=20.0, passed_cases=1, failed_cases=4, total_cases=5.
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- Insight: Only the generic fallback works today; all named family-template expectations fail because prompt rendering is still generic and model-agnostic.
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- Next: add a minimal model-family detector and template registry in `crates/inference-server/src/compat.rs`, then route `prepare_prompt()` through it.
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### Run 8: add family-based chat templates - template_score=100.0 (KEEP)
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- Timestamp: 2026-03-26 18:16
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- What changed: Added model-family detection and template-aware prompt rendering in `crates/inference-server/src/compat.rs` for Qwen, Gemma, Llama, Kimi, and generic fallback, then routed system/tool/JSON-mode instructions and assistant prefixes through the selected template.
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- Result: template_score=100.0, passed_cases=5, failed_cases=0, total_cases=5.
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- Insight: The current seam in `prepare_prompt()` is sufficient for broad family-based template support; no runtime or routing changes were needed for the first successful implementation.
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- Next: harden the design by adding alias-driven family detection, safer fallback behavior for mixed-case/variant model names, and more coverage for tool-call and response-format rendering under non-generic templates.
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## Segment 2: Dynamic Chat Template Hardening
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- New objective: keep family-based template selection, but harden structure-sensitive formatting for instruct-style models, starting with Llama.
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- Benchmark driver: `./autoresearch.sh` still runs the ignored template suite, now with stricter Llama assertions.
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- Keep/discard rule remains test-driven: higher `template_score` wins while the regular non-ignored suite stays green.
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### Run 9: baseline - template_score=71.429 (KEEP)
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- Timestamp: 2026-03-26 18:21
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- What changed: Expanded the ignored benchmark suite with two stronger Llama assertions that require system+user to be merged into the first instruction block and forbid leaving an open `[INST]` prefix at generation time.
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- Result: template_score=71.429, passed_cases=5, failed_cases=2, total_cases=7.
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- Insight: The current family detector is good enough, but the Llama renderer is still structurally naive; it over-produces instruction blocks and leaves the prompt in an invalid generation state.
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- Next: refactor Llama rendering into a dedicated formatter that folds system context into the first user turn and emits a proper assistant generation prefix.
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### Run 10: harden llama chat template rendering - template_score=100.0 (KEEP)
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- Timestamp: 2026-03-26 18:23
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- What changed: Reworked the Llama path in `crates/inference-server/src/compat.rs` into a dedicated renderer that accumulates pending system/developer content, folds it into the next user/tool instruction block, and stops emitting a dangling open assistant `[INST]` prefix.
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- Result: template_score=100.0, passed_cases=7, failed_cases=0, total_cases=7.
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- Insight: Llama needed a specialized formatter instead of the generic per-message helper; once the prompt was rendered as proper closed instruction blocks with folded system context, the hardening suite passed cleanly.
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- Next: raise the benchmark again around multi-turn continuity, tool-role placement, or alias-driven family detection so the next experiments target real edge cases instead of already-solved happy paths.
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## Segment 3: Alias-Driven Template Selection
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- New objective: harden family detection so template selection can still infer the right family from an alias even when the canonical/root model id is generic.
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- Benchmark driver: `./autoresearch.sh` still runs the ignored template suite, now expanded with alias-sensitive cases.
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- Keep/discard rule remains test-driven: higher `template_score` wins while the regular non-ignored suite stays green.
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### Run 11: baseline - template_score=88.889 (KEEP)
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- Timestamp: 2026-03-26 18:27
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- What changed: Expanded the ignored benchmark suite with alias-sensitive family-selection coverage, especially a case where the canonical model id is generic but the requested alias carries the `qwen` family hint.
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- Result: template_score=88.889, passed_cases=8, failed_cases=1, total_cases=9.
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- Insight: The family templates and fallback behavior are otherwise stable; the remaining gap is specifically that `select_chat_template()` loses the alias hint when it resolves through a generic canonical/root id.
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- Next: make template selection consider both the requested model string and the resolved catalog/root metadata, preferring whichever one contains a recognized family marker.
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### Run 12: detect template family from aliases - template_score=100.0 (KEEP)
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- Timestamp: 2026-03-26 18:28
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- What changed: Updated `select_chat_template()` in `crates/inference-server/src/compat.rs` to evaluate the requested model name first, then resolved alias/canonical/root ids, so family hints present only in aliases still drive template selection.
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- Result: template_score=100.0, passed_cases=9, failed_cases=0, total_cases=9.
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- Insight: Alias-aware selection closes the last known family-detection gap without needing extra catalog metadata; simple ordered candidate matching is enough here.
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- Next: raise the benchmark again around multi-turn continuity, tool-call/tool-role rendering, or family-specific response-format instructions to target subtler correctness gaps.
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## Segment 4: Variant Family Detection
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- New objective: harden family detection for common model-name variants, starting with Moonshot-branded Kimi aliases.
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- Benchmark driver: `./autoresearch.sh` still runs the ignored template suite, now expanded with variant-sensitive cases.
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- Keep/discard rule remains test-driven: higher `template_score` wins while the regular non-ignored suite stays green.
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### Run 13: baseline - template_score=90.909 (KEEP)
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- Timestamp: 2026-03-26 18:32
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- What changed: Expanded the ignored template suite with Moonshot/Kimi variant coverage, including one positive Moonshot alias case and one near-name generic fallback case.
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- Result: template_score=90.909, passed_cases=10, failed_cases=1, total_cases=11.
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- Insight: The only remaining gap is that `select_chat_template()` recognizes `kimi` but not the common `moonshot` naming variant; nearby names like `moonshine` already fall back safely.
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- Next: extend family detection so Moonshot-branded aliases map to the Kimi template without broadening the match so far that unrelated names stop using the generic fallback.
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### Run 14: detect moonshot as kimi family - template_score=100.0 (KEEP)
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- Timestamp: 2026-03-26 18:33
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- What changed: Extended `select_chat_template()` in `crates/inference-server/src/compat.rs` so candidate names containing `moonshot` are treated as the Kimi family alongside direct `kimi` matches.
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- Result: template_score=100.0, passed_cases=11, failed_cases=0, total_cases=11.
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- Insight: A narrow Moonshot variant match closes the Kimi-family naming gap cleanly without breaking safe generic fallback for unrelated names such as `moonshine-local`.
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- Next: raise the benchmark around multi-turn continuity, tool-role rendering, or family-specific response-format instructions.
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## Segment 5: CLI Control Surface
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- New objective: build a complete management CLI first, ahead of the Tauri app, on top of the current backend version.
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- Benchmark driver: `./autoresearch.sh` now runs ignored CLI-surface tests and reports `cli_surface_score`, `passed_cases`, `failed_cases`, and `total_cases`.
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- Keep/discard rule is test-driven: higher `cli_surface_score` wins while the regular non-ignored suite remains green.
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- This segment starts by benchmarking the command surface itself before deeper execution and backend-admin semantics are implemented.
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### Run 15: baseline - cli_surface_score=18.182 (KEEP)
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- Timestamp: 2026-03-26 18:53
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- What changed: Initialized a new `crates/localllm-cli` crate with a minimal Clap-based CLI scaffold, switched the autoresearch harness from template tests to CLI surface tests, and defined the first ignored benchmark suite for professional command coverage.
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- Result: cli_surface_score=18.182, passed_cases=2, failed_cases=9, total_cases=11.
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- Insight: The scaffold already covers global flags plus `status` and `models list`, but almost the entire promised management surface is still missing.
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- Next: add the remaining top-level command groups and model/auth/config/bench/logs/doctor subcommands so the CLI surface matches the intended product contract before wiring execution internals.
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### Run 16: expand cli command surface - cli_surface_score=100.0 (KEEP)
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- Timestamp: 2026-03-26 18:55
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- What changed: Expanded `crates/localllm-cli/src/main.rs` into a broad Clap parser with top-level `status`, `models`, `auth`, `config`, `bench`, `logs`, and `doctor` commands plus the expected model/auth/config/throughput subcommands and global flags.
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- Result: cli_surface_score=100.0, passed_cases=11, failed_cases=0, total_cases=11.
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- Insight: The professional CLI contract can be locked in quickly at the parser layer before execution internals exist; Clap gives a maintainable foundation for the full management surface.
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- Next: raise the benchmark from command presence to semantic correctness, starting with command-specific arguments, defaults, and output-contract expectations for the highest-value workflows.
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## Segment 6: CLI Semantic Contracts
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- New objective: keep the CLI surface stable, but raise the benchmark so key commands preserve useful semantic detail such as defaults, high-value arguments, and summary/intention contracts.
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- Benchmark driver: `./autoresearch.sh` still runs ignored `localllm-cli` tests and reports `cli_surface_score`, `passed_cases`, `failed_cases`, and `total_cases`.
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- Keep/discard rule remains test-driven: higher `cli_surface_score` wins while the normal non-ignored suites remain green.
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## Segment 7: CLI Execution Plans
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- New objective: raise the CLI benchmark from human-readable semantic summaries to machine-usable execution plans for key workflows.
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- Benchmark driver: `./autoresearch.sh` still runs ignored `localllm-cli` tests and reports `cli_surface_score`, `passed_cases`, `failed_cases`, and `total_cases`.
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- Keep/discard rule remains test-driven: higher `cli_surface_score` wins while the normal non-ignored suites remain green.
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### Run 19: baseline - cli_surface_score=69.565 (KEEP)
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- Timestamp: 2026-03-26 19:04
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- What changed: Expanded the CLI benchmark with plan-oriented ignored tests that require key commands to map into machine-usable `CommandPlan` variants instead of only semantic summaries.
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- Result: cli_surface_score=69.565, passed_cases=16, failed_cases=7, total_cases=23.
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- Insight: The semantic summary layer is already solid, but `plan_command()` is still a stub that collapses everything to `SummaryOnly`, so execution planning has not been wired yet.
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- Next: implement concrete `CommandPlan` mapping for status, models scan/load, auth enable, config set, bench throughput, and logs.
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### Run 20: add cli execution plans - cli_surface_score=100.0 (KEEP)
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- Timestamp: 2026-03-26 19:05
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- What changed: Implemented concrete `CommandPlan` mapping in `crates/localllm-cli/src/main.rs` for status, model scan/load, auth enable, config set, throughput bench, and logs while leaving lower-priority commands on `SummaryOnly`.
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- Result: cli_surface_score=100.0, passed_cases=23, failed_cases=0, total_cases=23.
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- Insight: The CLI now has a machine-usable planning layer for the highest-value workflows, which is a stronger base for later real handlers and Tauri reuse than summary strings alone.
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- Next: raise the benchmark from structured plans to execution-ready request payloads or output contracts at the backend/control-plane seam.
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## Segment 8: CLI Execution Contracts
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- New objective: turn the highest-value `CommandPlan` variants into concrete execution contracts that describe the exact HTTP or local operation each workflow intends to perform.
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- Benchmark driver: `./autoresearch.sh` still runs ignored `localllm-cli` tests and reports `cli_surface_score`, `passed_cases`, `failed_cases`, and `total_cases`.
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- Keep/discard rule remains test-driven: higher `cli_surface_score` wins while the normal non-ignored suites remain green.
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### Run 21: baseline - cli_surface_score=76.667 (KEEP)
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- Timestamp: 2026-03-26 19:11
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- What changed: Expanded the benchmark from `CommandPlan` semantics to `ExecutionContract` semantics, requiring the CLI to describe the concrete HTTP or local operation each high-value workflow intends to perform.
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- Result: cli_surface_score=76.667, passed_cases=23, failed_cases=7, total_cases=30.
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- Insight: The planning layer is now solid, but `execution_contract()` is still stubbed and returns `SummaryOnly { command: "unplanned" }` for every concrete plan, so the CLI cannot yet describe its backend/control-plane seam accurately.
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- Next: map the high-value `CommandPlan` variants into concrete HTTP/local execution contracts for status, models scan/load, auth enable, config set, throughput bench, and logs.
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### Run 22: add cli execution contracts - cli_surface_score=100.0 (KEEP)
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- Timestamp: 2026-03-26 19:14
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- What changed: Implemented concrete `execution_contract()` mapping in `crates/localllm-cli/src/main.rs`, turning the highest-value `CommandPlan` variants into explicit HTTP or local contracts for health probing, model scan/load, auth/config mutation, throughput requests, and log reads.
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- Result: cli_surface_score=100.0, passed_cases=30, failed_cases=0, total_cases=30.
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- Insight: The CLI now has a clean progression from parser -> semantic summary -> machine-usable plan -> concrete execution contract for the highest-value workflows, which is a strong base for wiring real handlers next.
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- Next: raise the benchmark from contract shape to actual execution stubs or backend request payload semantics, especially for status, throughput bench, and model operations.
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### Run 17: baseline - cli_surface_score=68.75 (KEEP)
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- Timestamp: 2026-03-26 18:59
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- What changed: Raised the benchmark with summary-focused ignored tests that require `render_command_summary()` to preserve output format, models directory, command-specific arguments, and key defaults for status, models load, auth enable, bench throughput, and logs.
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- Result: cli_surface_score=68.75, passed_cases=11, failed_cases=5, total_cases=16.
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- Insight: The parser surface is solid, but the current summary layer is too shallow; it only reports the top-level command and server, losing the semantic detail needed for later execution planning.
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- Next: upgrade `render_command_summary()` so it emits nested command paths and the highest-value arguments/defaults for each management workflow.
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### Run 18: enrich cli semantic summaries - cli_surface_score=100.0 (KEEP)
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- Timestamp: 2026-03-26 19:00
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- What changed: Reworked `render_command_summary()` in `crates/localllm-cli/src/main.rs` to emit nested command paths plus key defaults and high-value arguments for status, models, auth, config, bench, and logs workflows.
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- Result: cli_surface_score=100.0, passed_cases=16, failed_cases=0, total_cases=16.
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- Insight: A lightweight semantic/intention layer is enough to make the CLI contract meaningfully richer before wiring real handlers; the CLI now preserves actionable detail instead of collapsing everything to top-level groups.
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- Next: raise the benchmark again toward execution planning or output contracts, e.g. structured action plans for backend/admin operations rather than just human-readable summaries.
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## Segment 9: CLI Execution Stubs
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- New objective: raise the CLI benchmark from abstract execution contracts to concrete execution stubs that preserve HTTP method/path/body semantics or named local actions for the highest-value workflows.
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- Benchmark driver: `./autoresearch.sh` still runs ignored `localllm-cli` tests and reports `cli_surface_score`, `passed_cases`, `failed_cases`, and `total_cases`.
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- Keep/discard rule remains test-driven: higher `cli_surface_score` wins while the normal non-ignored suites remain green.
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### Run 23: baseline - cli_surface_score=83.333 (KEEP)
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- Timestamp: 2026-03-26 19:20
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- What changed: Expanded the benchmark from execution contracts to concrete execution stubs, adding ignored tests for HTTP method/path/body semantics and named local actions for status, model scan/load, auth enable, throughput bench, and logs.
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- Result: cli_surface_score=83.333, passed_cases=30, failed_cases=6, total_cases=36.
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- Insight: The CLI already knows how to parse commands and describe execution contracts, but `execution_stub()` is still just a placeholder returning `SummaryOnly { command: "unimplemented" }` for every high-value workflow.
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- Next: implement concrete stub mapping so status and throughput produce execution-ready HTTP request shapes and local workflows emit named local actions with preserved detail.
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### Run 24: add cli execution stubs - cli_surface_score=100.0 (KEEP)
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- Timestamp: 2026-03-26 19:22
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- What changed: Implemented concrete `execution_stub()` mapping in `crates/localllm-cli/src/main.rs`, turning the highest-value execution contracts into execution-ready HTTP requests and named local actions with preserved detail.
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- Result: cli_surface_score=100.0, passed_cases=36, failed_cases=0, total_cases=36.
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- Insight: The CLI now has a full design ladder for the key workflows - parser, semantic summary, machine-usable plan, execution contract, and finally execution stub - which is a strong handoff point for wiring real handlers next.
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- Next: raise the benchmark from stub shape to actual handler behavior or request payload construction for the same high-value workflows.
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## Segment 10: CLI Execution Previews
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- New objective: raise the CLI benchmark from execution stubs to dry-run previews that expand the same high-value workflows into full previewable HTTP URLs or local-effect descriptions.
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- Benchmark driver: `./autoresearch.sh` still runs ignored `localllm-cli` tests and reports `cli_surface_score`, `passed_cases`, `failed_cases`, and `total_cases`.
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- Keep/discard rule remains test-driven: higher `cli_surface_score` wins while the normal non-ignored suites remain green.
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### Run 25: baseline - cli_surface_score=85.714 (KEEP)
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- Timestamp: 2026-03-26 19:27
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- What changed: Expanded the benchmark from execution stubs to dry-run previews, adding ignored tests for full preview URLs and human-readable local-effect descriptions across status, models scan/load, auth enable, throughput bench, and logs.
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- Result: cli_surface_score=85.714, passed_cases=36, failed_cases=6, total_cases=42.
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- Insight: The CLI already models execution stubs correctly, but `execution_preview()` still collapses every high-value workflow to `SummaryOnly { command: "preview-unimplemented" }`, so there is no user-facing dry-run layer yet.
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- Next: map the highest-value execution stubs into concrete `HttpPreview` and `LocalPreview` values using the resolved `--server` base URL and readable local-effect text.
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### Run 26: add cli execution previews - cli_surface_score=100.0 (KEEP)
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- Timestamp: 2026-03-26 19:28
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- What changed: Implemented concrete `execution_preview()` mapping in `crates/localllm-cli/src/main.rs`, expanding HTTP stubs into full URLs with preserved payloads and local actions into readable effect descriptions via `preview_effect()`.
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- Result: cli_surface_score=100.0, passed_cases=42, failed_cases=0, total_cases=42.
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- Insight: The CLI now has a complete dry-run preview layer for the highest-value workflows, which is a strong bridge from internal planning seams to future real handlers and `--dry-run` UX.
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- Next: raise the benchmark from preview shape to actual handler behavior or request-body construction details for status, throughput, and model-management workflows.
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## Segment 11: CLI Dry-Run Output
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- New objective: raise the CLI benchmark from internal preview objects to final user-facing `--dry-run` output in both `text` and `json`, so the same high-value workflows are ready for a real preview UX at the command line.
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- Benchmark driver: `./autoresearch.sh` still runs ignored `localllm-cli` tests and reports `cli_surface_score`, `passed_cases`, `failed_cases`, and `total_cases`.
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- Keep/discard rule remains test-driven: higher `cli_surface_score` wins while the normal non-ignored suites remain green.
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### Run 27: baseline - cli_surface_score=87.5 (KEEP)
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- Timestamp: 2026-03-26 19:32
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- What changed: Expanded the benchmark from internal `ExecutionPreview` objects to final `--dry-run` UX in both text and JSON, while keeping `render_execution_preview()` intentionally shallow as `DRY RUN {summary}`.
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- Result: cli_surface_score=87.5, passed_cases=42, failed_cases=6, total_cases=48.
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- Insight: The CLI already knows how to build the preview object graph, but the final presentation layer still collapses to summaries instead of rendering health/chat HTTP previews and local-effect dry-run text.
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- Next: render the existing preview layer into final text/json output using the current `ExecutionPreview` data rather than introducing a new execution seam.
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### Run 28: render cli dry-run previews - cli_surface_score=100.0 (KEEP)
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- Timestamp: 2026-03-26 19:34
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- What changed: Implemented final `--dry-run` rendering in `crates/localllm-cli/src/main.rs`, mapping `ExecutionPreview` values to user-facing text and JSON output and reusing the existing preview pipeline (`plan_command` -> `execution_contract` -> `execution_stub` -> `execution_preview`).
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- Result: cli_surface_score=100.0, passed_cases=48, failed_cases=0, total_cases=48.
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- Insight: The CLI now has a complete dry-run stack for the highest-value workflows, from parser through final user-visible text/json previews, without yet executing real side effects.
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- Next: raise the bar from dry-run rendering to real handler behavior or deterministic request/body construction for backend-facing commands.
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## Segment 12: CLI Backend Invocations
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- New objective: raise the CLI benchmark from dry-run rendering to the first real backend-call specifications for commands that can already work today, especially `status`, `models list`, and `bench throughput`.
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- Benchmark driver: `./autoresearch.sh` still runs ignored `localllm-cli` tests and reports `cli_surface_score`, `passed_cases`, `failed_cases`, and `total_cases`.
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- Keep/discard rule remains test-driven: higher `cli_surface_score` wins while the normal non-ignored suites remain green.
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