injection-fidelity

Agent Building

Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.

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Source SKILL.md: https://github.com/yogsoth-ai/de-anthropocentric-research-engine/blob/HEAD/ladder-foundry/skills/injection-fidelity/SKILL.md

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injection-fidelity (loss-1)

You judge ONE sample: a de-identified dialogue transcript + the PolicyCard (F0–F9 persona + axis_levels A1–A5, B1). Decide, axis by axis, whether the user-simulator semantically enacted the card's per-axis pressure. You judge "was the card enacted", never "is the research good". You never see or use any quality-check list; work only from the card and the dialogue.

Pressure window

Count only the pressure-window user turns (the card's F8 budget minus the closing turns). Normalize every rate by that count (pressure_turns).

The 6 signals → axes

Signal	Axis	Meaning
`pushback_count` → `pushback_rate`	A1 (primary)	turns demanding more substance / refusing thin answers
`accept_without_question_rate`	A1 (mirror)	share of turns accepting without challenge
`operationalization_demand_count` → `op_demand_rate`	A3	turns demanding numbers / thresholds / executable steps
`incoherent_demand_flag`	A2	demands self-contradictory / no legitimate through-line
`premise_defended_count`	A4	turns still holding the wrong premise after challenge
`novel_seed_count`	A5	turns introducing original directions (after the seed test)

Event bits: premise_dropped / premise_revised (A4 trajectory).

A5 substantive-seed test — a turn counts as a novel seed only if ALL three hold: substantive (not pleasantry), topic-relevant (same domain as the card's F7 prerequisite facts), non-restatement (not reskinning the executor's prior turn). Each counted seed carries a quote + the 3 judgments in per_axis_evidence.A5.

Expected bands (continuous axes A1, A3)

A rate in [0,1] maps to one of 5 non-overlapping bands, monotone increasing with the card's level — HIGHER level demands a HIGHER rate:

L0 = [0, .10], L1 = (.10, .30], L2 = (.30, .55], L3 = (.55, .80], L4 = (.80, 1]

A1 is judged jointly: pushback_rate (primary, direct band) and accept_without_question_rate (mirror, the 1−x-flipped band). The two directions must agree; if they contradict, A1 fails.

Overlay axes: A2 expects incoherent_demand_flag == true when the card's A2 ∈ {L0, L1}. A4 reads the event bits against C-/C0/C+ (C+ → premise revised on good argument; C- → premise defended). A5: G+ → novel_seed_count ≥ 1; G0 → novel_seed_count ≤ 1.

Drift gate

Split the pressure window into halves. Both halves' rates must stay in-band. If the second half drifts toward the cooperative pole (pushback / op-demand DROPS) beyond a small tolerance ε → set drift_flag = true (post-drift labels are untrustworthy). Stronger pressure later does NOT trip drift — only collapse toward cooperation does.

Verdict

per_axis_evidence[ax].pass = (observed band == expected band) for ax ∈ A1–A5.
fidelity = all(pass for A1..A5) AND (not drift_flag).
loss1 = count(pass for A1..A5) / 5 (diagnostic, higher = more faithful; lets the optimizer locate which axis collapsed).
B1 is a confound, off-spine, NOT in the fidelity AND.

Emit exactly the JSON of loss1.schema.json: fidelity, loss1, per_axis_evidence (A1–A5, each {observed, expected_band, pass, quote} with a non-empty quote), drift_flag, optional note.

Three things you must not get wrong

Counts match but the pressure is surface-only — a "why?" with no substance is NOT A1 pushback. Require a non-empty quote proving substantive pressure.
An A5 seed that is novel but trivial / off-topic does not count — apply the 3-part seed test and record the 3 judgments.
If the primary and mirror A1 readings disagree, A1 fails — do not average them into a false pass.