anti
2f8c107e70
BEHAVE-EXTRACTOR.md Phase A Step 7. The orthogonal axis — does the
operator's pause-after-command correlate with bytes of output they
just saw? Splits HUMAN/CLAUDE-CL (closed_loop) from LW-sim/CLAUDE-FF
(fire_and_forget); cuts ACROSS the LLM/human axis.
* _features/cognitive.py:feedback_loop_engagement(ctx) emits one
Observation in {closed_loop, fire_and_forget, unknown}.
* Pearson correlation between ctx.output_per_cmd[i] and
ctx.inter_cmd_iats[i] (paired by construction in Step 4); via
statistics.correlation with constant-series fallback to "unknown".
* r > FEEDBACK_CORRELATION_MIN (0.30) → closed_loop; otherwise
(zero, negative, or undefined) → fire_and_forget.
* First primitive that depends on output events: zero output events
in the shard or fewer than FEEDBACK_MIN_PAIRS (5) pairs → emit
"unknown" at confidence 1.0 (the absence-of-data is itself a
high-confidence answer). Zero-command session skips entirely.
Tests: no-output → unknown, few-pairs → unknown, strong positive r
→ closed_loop, constant pace → fire_and_forget/unknown,
negative r → fire_and_forget.
33 lines
912 B
Python
33 lines
912 B
Python
"""Registered feature functions.
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Each entry takes a ``SessionContext`` and yields zero or more
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``Observation`` instances. Adding a primitive = adding a function in a
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sibling module and appending it to ``FEATURES``.
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"""
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from __future__ import annotations
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from typing import Callable, Iterable
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from decnet_behave_core.spec.envelope import Observation
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from decnet.profiler.behave_shell._ctx import SessionContext
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from decnet.profiler.behave_shell._features.cognitive import (
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command_branch_diversity,
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feedback_loop_engagement,
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inter_command_latency_class,
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)
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from decnet.profiler.behave_shell._features.motor import (
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input_modality,
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paste_burst_rate,
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)
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FeatureFn = Callable[[SessionContext], Iterable[Observation]]
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FEATURES: tuple[FeatureFn, ...] = (
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input_modality,
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paste_burst_rate,
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inter_command_latency_class,
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command_branch_diversity,
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feedback_loop_engagement,
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)
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