EXP-PID-TDMI-LAYER-EMPIRICAL-PORT

… pending

PID/TDMI layer empirical port (Riedl 2510.05174 → paper3/7 retro)

2026-05-29 L3-L4 level 0

new

premortem

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real

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analyze

review

krit

done

In plain language

This experiment tested whether a specific type of information processing, called PID, acts as a layer within a measurement system. Researchers examined how this PID layer behaved when data was sent through two different pathways, one designed for efficient broadcasting and another for more complex decision-making. The results are still being analyzed to determine if differences in PID behavior are due to the information pathways themselves or how the system was instructed.

▶ Technical details

Research hypothesis

Riedl 2510.05174 PID/TDMI as INFORMATION-LAYER in Cluster-D measurement stack. Port via numpy Williams-Beer Imin (dit unavailable) to existing archive on TWO substrate channels:

paper3 hub-spoke (cold-start-v3, gift_engine CLEAN): predict high redundancy >0.7, low unique <0.1, low synergy <0.05 (hub broadcasts same signal → informationally redundant).
paper7 architecture-axis (rule_multigen HIGH-confound): predict Class A (gpt4omini/haiku) unique-dominant, Class B (qwen/flashlite) redundancy-dominant. **MANDATORY engine-source-audit: paper7 PID values are CONDITIONAL on axis_per_agent (axis-hint) + INITIAL_GOALS (per-agent goal text) injection.** Any Class A/B PID gap is potentially explained by engine-driven prompt differentiation, not substrate. Verdict.md MUST tag paper7 PID profile «engine-conditional» absent control arm.

construct paper3-related paper7-related pool-drained phase7-3-generated pid williams-beer infeasibility-pivot engine-confound-tagged