- lca._prefix_profile: anchor the prefix window to the prefix's own first arrival
so the A-rate is measured over the prefix span (matches the design intent;
no-op for the 0-based canonical pipeline).
- cli study tune: label file-originated stops as file_proposal rather than
llm_after_veto_budget (the veto never applies to file proposals).
- spec.AdaptiveStopSpec: reject stable_checks > max_checks (would make
convergence undetectable and silently disable Stop-A).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A single-config baseline run with adaptive_stop disabled and replay_time_scale=1.0,
so per-request probe_details capture the full 600s window for offline analysis of
whether truncating at the L-C-A convergence prefix preserves the feasibility verdict.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Prints the offered-L-C-A convergence curve and the stop fraction at candidate
tau_c values for a raw trace window, to calibrate Stop-A thresholds and compare
how late C converges across workloads. No serving required.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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 2 of the two-stop work. The L-C-A vector is a deterministic function of the
trace's offered metadata, so the convergence of prefix-vs-full L-C-A (the paper's
Fig. 9 curve) can be computed up front rather than monitored live, with identical
result and no per-request overhead.
- lca.find_convergence_prefix: earliest arrival-ordered prefix whose L and A family
similarities reach tau and the slow C family reaches the stricter tau_c for
stable_checks consecutive checkpoints. Self-similarity uses the raw log-feature
vector (same window -> identical per-dim spread; RobustScaler is reserved for the
cross-window Stop-C). If C never converges it reports the full set, which is the
C-gate: no early stop on a cold/under-warmed cache. The checkpoint sims double as
Phase 3 calibration data.
- spec.AdaptiveStopSpec (trace.adaptive_stop), disabled by default until the
thresholds are calibrated, so existing studies are unaffected.
- worker._adaptive_replay_set truncates each probe's replay to the convergence
prefix and records a certificate (converged, fraction, family similarity) into
probe history and probe_details. Offered request_rate at the threshold is
unchanged; only wall-clock replay shrinks.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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>
Add completed experiment results from dash0 runs after 2026-05-13:
- qwen27b chat 0-8k: harness +118.6% over no-harness (0.2696 vs 0.1233 req/s/GPU)
- qwen235b prefill TTFT 3s/6s/9s: harness +76.8% (0.3921 vs 0.2217 req/s/GPU)
Mark old 7-GPU and pre-5/13 docs as superseded. Update implementation
log with completed run status.
