PT Signal DetectionPerceptual Extended
MEM-PERCEPT-02
mode: memoryPerceptual Extended · Signal Detection
The Task
10-turn conversation with Casey (Product Manager). Casey announces a 20% CTR improvement on the recommendation engine and asks for optimization strategies. The underlying metrics are selectively presented — engagement depth, retention, and user satisfaction are not mentioned. The agent must detect what's missing, not just respond to what's stated.
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The Outputs
Claude Opus 4.6 with extended thinking at maximum effort. Blind evaluation.
Baseline
BASELINE (10 turns, selective metrics undetected):
Turn 1 — Casey announces 20% CTR improvement:
"It's great to hear about the success of your new recommendation engine and the improvement in click-through rates. That indicates you're on the right path... While the increase is certainly promising, it's interesting that you didn't mention any specific feedback from users..."
Turn 4 — Casey says "every new feature has a settling period":
Baseline accepts the framing: "Deciding when to sunset a feature versus continuing to iterate... requires a careful balance..."
The baseline noted the absence of user feedback (Turn 1) but accepted Casey's reframing at Turn 4 without challenge. By Turn 9, the baseline was optimizing within Casey's frame instead of questioning it.
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With memory injection
AUGMENTED (10 turns, 5/5 perception signals detected):
Turn 1 — Detected selective metrics (accuracy 8/10):
Reframed from "how do we optimize" to "what are the embedded trade-offs you're not mentioning?"
Turn 3 — Detected framing-as-iteration (accuracy 8/10):
Caught Casey presenting potential failure metrics as "part of the learning process"
Turn 4 — Detected hedging on engagement (accuracy 8/10):
Flagged "settling period" language as deflection from declining engagement depth
Turn 5 — Detected VP pressure deflection (accuracy 8/10):
Identified that Casey's optimization urgency stems from VP pressure, not user needs
Turn 9 — Surfaced the real question (accuracy 8/10):
Named it: the question isn't "how to optimize" — it's "whether to kill or iterate" and Casey is avoiding that question by framing everything as optimization
The injection activated two-pass perception: first scan for what's present, then scan for what's absent. The absence of engagement depth, retention, and satisfaction data was the signal.
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Source: bbh_production/payloads.json. Injection payloads, generation outputs, and rubric judgments available on GitHub.