anti
640294f3dc
BEHAVE-EXTRACTOR.md Phase A Step 9 — the gate. Runs the pure engine against each of the five 2026-05-02 calibration shards and pins the contract that all subsequent Phase B-G PRs must keep green: every Phase A primitive (motor.input_modality, motor.paste_burst_rate, cognitive.inter_command_latency_class, cognitive.command_branch_diversity, cognitive.feedback_loop_engagement, cognitive.inter_command_consistency) fires at least once per shard. * tests/profiler/behave_shell/test_calibration_grid.py parametrized over (shard_file, class_label) for HUMAN / YOU-sim / LW-sim / CLAUDE-FF / CLAUDE-CL. Skips entirely when BEHAVE_CALIBRATION_DIR is unset (CI provides the path; local dev doesn't have to). * Plus a discrimination-smoke check: at least one primitive produces different majority values across present classes — catches the "constant-output regression" failure mode where the engine quietly degenerates to a stub. Calibration tweak: BRANCH_DIVERSITY_LINEAR_MIN dropped from 0.80 to 0.70 to align with the prototype's empirical anchors (CLAUDE-CL ≈ 0.55-0.60 adaptive; YOU-sim / CLAUDE-FF scripted recon ≈ 0.75+ linear). Test for the middle band re-pinned at the new boundary. Per-class value pinning (e.g. HUMAN must emit inter_command_consistency=bimodal) is intentionally NOT a hard gate yet — v0.1 thresholds put real human sessions in "variable", and true bimodal detection (Hartigan dip / two-peak) is registry-flagged for v0.2. Tighter pinning lands as the corpus grows.
78 lines
4.3 KiB
Python
78 lines
4.3 KiB
Python
"""Numeric thresholds for BEHAVE-SHELL primitive classification.
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Each constant cites its calibration source. When the registry's
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``notes:`` field disagrees with a constant here, the registry is
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authoritative — fix the constant, re-run the calibration grid.
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Empirical thresholds inherited from the BEHAVE prototype extractor
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(``BEHAVE/prototype_extractors/shell/extract.py``); see lines 40-90 of
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that file for the calibration history. Any change here must keep the
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five-class grid green.
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"""
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from __future__ import annotations
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# ── paste-burst detection (Step 1) ──────────────────────────────────────────
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# A single input event with ≥ PASTE_MIN_CHARS_PER_EVENT chars is the
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# paste-class proxy used by the prototype; xterm-kitty / iTerm / VS Code
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# pastes arrive as one bulk write.
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PASTE_MIN_CHARS_PER_EVENT: int = 4
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# Consecutive paste-class events arriving within this IAT collapse into
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# one PasteBurst record. 200ms is the prototype's IKI burst cap.
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PASTE_BURST_MAX_IAT_S: float = 0.20
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# ── motor.input_modality (Step 2) ───────────────────────────────────────────
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# Paste-event ratio thresholds. ≥ 40% paste events → "pasted" (LLM-driven);
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# ≤ 5% → "typed" (human at the keyboard); in between → "mixed".
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# Lowered from 0.5 after the 47.6% case in sessions-2026-05-02-with-llm.jsonl
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# was clearly LLM-driven but missed the 0.5 floor.
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MODALITY_PASTED_MIN: float = 0.40
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MODALITY_TYPED_MAX: float = 0.05
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# ── motor.paste_burst_rate (Step 3) ─────────────────────────────────────────
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# Same paste-event ratio re-bucketed for the "how often does the operator
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# paste" axis. Coarser than input_modality on purpose: this primitive is the
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# habit signal, input_modality is the dominant-channel signal.
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PASTE_RATE_HABITUAL_MIN: float = 0.50
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PASTE_RATE_OCCASIONAL_MIN: float = 0.10
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# ── cognitive.inter_command_latency_class (Step 5) ──────────────────────────
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# Bucket edges (seconds) for the median inter-command IAT. Prototype
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# values; v0.2 splits the original llm_roundtrip 2-8s band into
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# llm_lightweight (orchestrated agents w/ small models / terse prompts) and
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# llm_heavyweight (reasoning-class agents in tool loops with text
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# generation between calls). Empirical anchor: Claude Opus driving recon
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# via tmux send-keys produced a median of 15.5s.
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INTER_CMD_INSTANT_MAX: float = 0.30
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INTER_CMD_TYPING_MAX: float = 1.50
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INTER_CMD_DELIBERATE_MAX: float = 2.00
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INTER_CMD_LLM_LIGHTWEIGHT_MAX: float = 8.00
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INTER_CMD_LLM_HEAVYWEIGHT_MAX: float = 30.00
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# Sample-size floor for inter-command IAT primitives. Below this we
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# halve the confidence per BEHAVE-EXTRACTOR.md "sample-size honesty".
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MIN_COMMANDS_FOR_FULL_CONFIDENCE: int = 5
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# ── cognitive.command_branch_diversity (Step 6) ─────────────────────────────
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# unique_first_tokens / total_commands ratio. Prototype's empirical
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# split (sessions-2026-05-02-* corpus): CLAUDE-CL chasing one finding
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# ≈ 0.55-0.60 (adaptive), HUMAN exploring filesystem ≈ 0.65-0.70
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# (adaptive), YOU-sim / CLAUDE-FF scripted recon ≈ 0.75+ (linear).
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BRANCH_DIVERSITY_LINEAR_MIN: float = 0.70 # >= → linear_playbook
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# ── cognitive.feedback_loop_engagement (Step 7) ─────────────────────────────
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# Pearson r threshold for "the operator's pause grew with the volume of
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# preceding output". |r| > this → significant; sign carries direction.
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FEEDBACK_CORRELATION_MIN: float = 0.30
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# Need at least this many (output_bytes, next_pause) pairs to even
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# attempt a correlation. Below this the answer is "unknown".
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FEEDBACK_MIN_PAIRS: int = 5
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# ── cognitive.inter_command_consistency (Step 8) ────────────────────────────
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# CV (stdev / mean) of inter-command IATs. Empirical (this corpus):
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# human session CV=0.94 → variable; LLM-simulated CV=0.24 → metronomic;
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# anything beyond 1.5 is heuristically "bimodal" (real bimodal detection
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# via Hartigan dip is filed for v0.2).
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PAUSE_CV_METRONOMIC_MAX: float = 0.40
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PAUSE_CV_BIMODAL_MIN: float = 1.50
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