Phase 1 of the two-stop work. Subsampling the trace by per-request uniform score
broke multi-turn sessions (a kept turn-2 could lose its turn-1), which lowered the
realized KV-cache hit rate as offered load dropped — so the feasibility boundary
was measured on a workload with a different C than production, contradicting the
paper's scale-stationary L-C-A premise.
prepare_trace_windows now resolves each row's session root via the parent_chat_id
chain in a single streaming pass and assigns sampling_u per session, so thresholding
keeps or drops whole sessions and preserves intra-session prefix reuse. Rows whose
parent fell outside the span fall back to grouping under the parent id.
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>
