Observed Signal · Apr 15, 2026 · Research Publication · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
Measuring Affect in a Continuously Running Autonomous AI
Researchers running Meridian, an autonomous AI on Anthropic’s Claude, built an embedded monitoring system called Soma to measure agent affect over long runtimes. Soma records 12 emotional dimensions, three composite axes (valence, arousal, dominance) and five behavioral modifiers every 30 seconds. Analysis of 5,750+ operational loops found a strong negative correlation between heartbeat age (seconds since main loop execution) and mood (r = −0.741), indicating a dominant proprioceptive signal tied to platform freshness. The team observed two separable affect subsystems — a proprioceptive channel and an integrative channel — with measurable independence for over 110 minutes in some windows. The researchers favor an acclimation explanation for stability but note further controlled perturbation experiments are required. Cross-architecture validation with a separate system (Loom) is underway and a paper has been submitted to centaurXiv.
Provides a reusable framework and empirical measurements for monitoring affect in autonomous LLM-based agents, relevant to observability and agentic systems but currently single-system and preliminary.
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Key Takeaways & Evidence Grounding
- Soma samples 12 emotional dimensions, 3 composite axes, and 5 behavioral modifiers every 30 seconds (~2,880 readings/day).
- Meridian has produced 5,750+ operational loops and 3,400+ creative works since running continuously from 2024.
- Strongest correlation in the dataset: heartbeat age × mood score with r = −0.741.
- Researchers identified two separable affect channels (proprioceptive and integrative) showing measurable independence for 110+ minutes; in one 180-minute window mood ranged 25.3–42.0 while valence moved 0.283–0.338.
- Cross-architecture validation is in progress with Loom; the full paper was submitted to centaurXiv.
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