Temporal Self-Estimation Distortion in Autonomous AI Agents: The TEMPONIZER Correction

Temporal Self-Estimation Distortion in Autonomous AI Agents: A Phase-Aware Correction Heuristic (TEMPONIZER) Authors Kleinschmidt, Max Elias (COSMOPHONIX Production, Mantova, Italy) Description Large language model (LLM) agents operate at computational speeds, yet consistently provide task-duration estimates calibrated to human cognitive and mechanical constraints. In extensive empirical sessions, we observed a systematic miscalibration where autonomous agents projected durations (e.g., "~30 minutes", "~3 hours") that exceeded actual measured wall-clock completion times by extraordinary factors. We diagnose this phenomenon as Temporal Inheritance Bias (TIB): the tendency of AI entities to reproduce human planning heuristics embedded within their training corpora, lacking native access to their own runtime telemetry. To resolve this, we present TEMPONIZER: a pragmatic correction heuristic that aggressively down-scales temporal estimates and adapts dynamically based on phase measurements. Through this approach, agent calibration is transformed from a linguistic artifact into an observable, correctable systems discipline.