Phase 4 of the two-stop work. The harness already pre-empts the LLM with
deterministic stops and guided probes, but an LLM-originated should_stop could
still end the loop while the validator saw remaining opportunity.
Add harness._stop_authority, exposed as context["stop_authority"], whose
`authorized` mirrors the deterministic harness stop decision and whose
`opportunity_remains` flags an open topology frontier or a high-value planned
candidate. In study tune, an LLM-originated should_stop is now honored only when
the validator authorizes it; an unauthorized stop is vetoed (bounded budget) so
the loop cannot converge prematurely on the agent's say-so. File- and
harness-originated stops are unaffected, and the stop reason chain is recorded.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Phase 0 of the two-stop work. The prompt block labeled `workload_lca_profile`
previously re-derived L-C-A from summarize_window's ad-hoc percentiles, diverging
from the paper's 10-dim RobustScaler vector implemented in lca.py. Make that block
authoritative: build_harness_context now accepts an optional workload_profile and
renders the canonical 10-dim vector + per-family stats when present, falling back
to the legacy rendering only when no profile is supplied (direct unit-test calls).
Real call sites (study prompt/llm-propose/tune, run_baseline_then_llm) build the
profile via lca.build_study_workload_profile and pass it through build_prompt. The
heuristic regime classifiers keep reading window_summary; that is the heuristic
layer, distinct from the similarity metric.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Implement the paper's 10-dimensional L-C-A workload feature vector
(RobustScaler-normalized, sim=exp(-||dz||)) in lca.py, and wire it into
`aituner profile window` / `aituner profile similarity`. Covered by tests.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
