anti
f286c84d95
Absolute distinct first_token_hash count, bucketed against TOOL_VOCAB_NARROW_MAX / TOOL_VOCAB_BROAD_MIN. v0.1; D.8 re-tunes.
246 lines
13 KiB
Python
246 lines
13 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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# ── output error-signal helper (Step D.0) ──────────────────────────────────
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# The canonical bash/sh error fingerprints live in ``_parse.py`` as
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# ``_OUTPUT_ERROR_PATTERNS`` (compiled regexes). They're not threshold
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# numbers, so they live next to the helper that uses them rather than
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# here. This v0.1 heuristic will be subsumed by Phase F.0's prompt
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# parser (PS1 echo + exit-code sniff), at which point this comment and
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# the patterns block move to ``_parse.py``'s prompt section. Until then,
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# any drift in registry value definitions for ``error_resilience.*`` or
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# ``cognitive_load`` must be reflected by editing the patterns tuple
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# (not a constant, so no boundary-band logic applies).
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# ── cognitive.cognitive_load (Step D.1) ─────────────────────────────────────
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# Composite ∈ [0, 1] over three sub-signals (each clipped to [0, 1]):
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#
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# A = chunking_load = median_intra_cmd_cv / CHUNKING_REF_CV
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# B = error_load = errored_cmds / total_cmds
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# C = pace_variability_load = (stdev / mean of inter_cmd_iats) / PACE_REF_CV
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#
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# load = mean(A, B, C); bucket:
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# load < COGNITIVE_LOAD_LOW_MAX → low
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# load < COGNITIVE_LOAD_MEDIUM_MAX → medium
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# else → high
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#
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# v0.1 thresholds — D.8 re-tunes once D.1-D.7 are stable. The reference
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# CVs (CHUNKING_REF_CV / PACE_REF_CV) are the value at which that single
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# component saturates to a load contribution of 1.0; anything past
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# saturates the term but doesn't double-count.
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COGNITIVE_LOAD_CHUNKING_REF_CV: float = 1.00
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COGNITIVE_LOAD_PACE_REF_CV: float = 1.50
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COGNITIVE_LOAD_LOW_MAX: float = 0.33
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COGNITIVE_LOAD_MEDIUM_MAX: float = 0.67
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# ── cognitive.exploration_style (Step D.2) ─────────────────────────────────
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# Two-axis classification over the first_token_hash sequence:
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#
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# repetition_rate (R) = 1 - (unique_first_tokens / total_commands)
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# backtrack_rate (J) = transitions where commands[i+1].first_token_hash
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# appeared anywhere in commands[0..i-1] but is NOT
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# equal to commands[i].first_token_hash (jumping
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# back to an older tool, not just repeating).
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#
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# J >= EXPLORATION_CHAOTIC_BACKTRACK_MIN → chaotic
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# else if R >= EXPLORATION_TARGETED_REP_MIN → targeted
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# else → methodical
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#
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# Methodical = low repetition, low backtracks (linear progression through
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# novel tools). Targeted = high repetition (drilling the same tool).
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# Chaotic = jumping between prior tools without a clear thread.
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# v0.1; D.8 re-tunes.
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EXPLORATION_TARGETED_REP_MIN: float = 0.50
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EXPLORATION_CHAOTIC_BACKTRACK_MIN: float = 0.30
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# ── cognitive.planning_depth (Step D.3) ────────────────────────────────────
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# Distribution of inter-command IATs.
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# deep_pause_fraction = (count of inter_cmd_iats > IKI_THINK_MAX_S) / N
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# reactive_pause_fraction = (count of inter_cmd_iats <= INTER_CMD_INSTANT_MAX) / N
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#
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# deep_pause_fraction >= PLANNING_DEEP_MIN → deep
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# reactive_pause_fraction >= PLANNING_REACTIVE_MIN → reactive
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# otherwise → shallow
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#
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# v0.1; D.8 re-tunes once D.1-D.7 are stable.
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PLANNING_DEEP_MIN: float = 0.40
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PLANNING_REACTIVE_MIN: float = 0.50
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# ── cognitive.tool_vocabulary (Step D.4) ───────────────────────────────────
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# Absolute count of distinct first_token_hashes across the session.
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#
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# distinct <= TOOL_VOCAB_NARROW_MAX → narrow
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# distinct >= TOOL_VOCAB_BROAD_MIN → broad
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# otherwise → moderate
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#
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# Absolute, not normalised. A 3-command session with 3 unique tools is
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# ``narrow`` not ``broad`` — the operator simply hasn't shown range yet.
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# Sample-size honesty drops confidence below MIN_COMMANDS_FOR_FULL_CONFIDENCE.
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# v0.1; D.8 re-tunes.
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TOOL_VOCAB_NARROW_MAX: int = 3
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TOOL_VOCAB_BROAD_MIN: int = 10
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# ── motor.keystroke_cadence (Step B.1) ──────────────────────────────────────
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# Typing bursts split at gaps > IKI_THINK_MAX_S so think-pauses between
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# commands don't inflate the within-burst CV. Mirrors the prototype's
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# _split_into_bursts (BEHAVE/prototype_extractors/shell/extract.py:275-286).
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IKI_THINK_MAX_S: float = 1.50
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# Sub-human floor for the "machine" classification — only paired with a
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# pathologically uniform CV, since real humans never produce sub-5ms IATs
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# in a sustained burst.
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IKI_MACHINE_MAX_S: float = 0.005
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CV_MACHINE_MAX: float = 0.05
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CV_STEADY_MAX: float = 0.50
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CV_BURSTY_MAX: float = 1.50
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# Need this many input events before we'll claim a cadence at all.
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MIN_INPUTS_FOR_CADENCE: int = 5
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# ── motor.motor_stability (Step B.2) ────────────────────────────────────────
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# Tremor proxy: fraction of within-burst IATs below TREMOR_FAST_FLOOR_S
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# (30 ms — physiologically implausible double-press floor; humans can't
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# reliably produce IATs below ~50 ms in sustained typing). High rate
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# of sub-floor IATs flags double-press / motor twitch / stuck-key.
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TREMOR_FAST_FLOOR_S: float = 0.030
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TREMOR_RATE_MIN: float = 0.10 # ≥10% sub-floor → tremor
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# ── motor.error_correction (Step B.3) ───────────────────────────────────────
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# Backspace within this many seconds of the preceding key = "caught the
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# typo mid-keystroke" (immediate). Beyond this = the operator paused,
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# noticed, then went back (deferred).
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BACKSPACE_IMMEDIATE_MAX_S: float = 0.50
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# ── motor.command_chunking (Step B.4) ───────────────────────────────────────
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# Median CV of within-command IATs. Below this → fluent (steady within
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# each command); above → fragmented (operator pauses mid-command).
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CMD_CHUNKING_FLUENT_CV_MAX: float = 0.50
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# ── motor.shell_mastery.* (Phase C) ─────────────────────────────────────────
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# Readline control bytes counted toward ``shortcut_usage``. The seven
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# pinned by BEHAVE-EXTRACTOR.md §Phase C (line 472):
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# ^A start-of-line ^E end-of-line ^W kill-prev-word
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# ^U kill-line ^R reverse-i-search ^B back-char ^F forward-char
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# v0.2 may extend to ^K/^Y/^L/^D/^P/^N once corpus calibration justifies it.
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# Note: ^U / ^W also feed ``motor.error_correction`` (Step B.3) via the
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# ``kill_line_count`` channel — these are independent measurements over
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# the same byte stream, not double-counting.
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SHORTCUT_CTRL_BYTES: frozenset[str] = frozenset({
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"\x01", "\x05", "\x17", "\x15", "\x12", "\x02", "\x06",
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})
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# motor.shell_mastery.tab_completion — fraction of commands containing
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# at least one ``\t`` keystroke. Registry buckets per BEHAVE-EXTRACTOR.md
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# line 471: ``none`` (0%), ``occasional`` (<30%), ``habitual`` (≥50%).
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# The 30%-50% gap rounds down to ``occasional`` — the registry's own gap.
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TAB_COMPLETION_OCCASIONAL_MAX: float = 0.30
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TAB_COMPLETION_HABITUAL_MIN: float = 0.50
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# motor.shell_mastery.shortcut_usage — total readline ctrl-byte
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# keystrokes per command. Registry buckets are qualitative
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# (``none / moderate / heavy``); v0.1 thresholds are best-guesses
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# pinned for five-class corpus calibration. Re-tune once HUMAN /
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# YOU-sim / LW-sim / CLAUDE-FF / CLAUDE-CL data lands.
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# 0/cmd → none
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# <0.05/cmd → none (counted shortcuts but rare; rounds down)
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# 0.05-0.30 → moderate
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# ≥0.30/cmd → heavy
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SHORTCUT_USAGE_MODERATE_MIN: float = 0.05
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SHORTCUT_USAGE_HEAVY_MIN: float = 0.30
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# motor.shell_mastery.pipe_chaining_depth — median ``|`` count across
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# commands. Pipes are counted on every byte (typed AND pasted) — a
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# pasted pipeline still indicates pipeline fluency the operator chose
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# to execute. Registry buckets per BEHAVE-EXTRACTOR.md line 473:
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# median ≤ 1 → shallow (no pipeline at all, or one stage)
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# median == 2 → moderate
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# median ≥ 3 → deep
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# Median is integer-valued (sum of ints over commands), so the
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# boundaries here are integer step boundaries; the proximity-band
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# logic uses integer equality.
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PIPE_CHAINING_MODERATE_MEDIAN: int = 2
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PIPE_CHAINING_DEEP_MEDIAN: int = 3
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# Sample-size floor below which Phase C primitives drop confidence to
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# 0.40 (sample-size honesty). Mirrors MIN_COMMANDS_FOR_FULL_CONFIDENCE
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# but is named separately so a future tune can move them independently.
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SHELL_MASTERY_MIN_COMMANDS: int = 5
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# Width of the "near a bucket boundary" band (relative to the boundary)
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# used by Phase C primitives. ±10% of the boundary value drops
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# confidence by 0.20 per BEHAVE-EXTRACTOR.md §"Threshold proximity".
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SHELL_MASTERY_BOUNDARY_BAND: float = 0.10
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