I've been building agents that recursively reflect on their own reasoning (Reflexion-style), and kept hitting this problem: after ~50 reflection cycles, belief embeddings drift significantly from their initial values.
It's not catastrophic forgetting (task-level) or hallucinations. More like the agent gradually "forgets" its original principles through accumulated micro-drifts in meta-reasoning.
Tried solving it with harmonic stabilization - treating belief updates as a damped oscillator rather than pure accumulation (inspired by MIT's LinOSS work on neural oscillations). The update rule looks like:
Beliefs oscillate around a stable point instead of drifting monotonically. Got ~9x improvement in stability (mean drift: 0.009 vs 0.085 baseline) on sentence-transformer embeddings over 50 cycles.
Open sourced with interactive Colab demo. Parameters are hand-tuned and I've only tested on small scales, so curious if others have seen this problem or have better approaches.
Main questions: Is "recursive belief drift" already documented somewhere? Any theoretical reasons this wouldn't scale to larger systems?
Harmonic_Logos•2h ago
It's not catastrophic forgetting (task-level) or hallucinations. More like the agent gradually "forgets" its original principles through accumulated micro-drifts in meta-reasoning.
Tried solving it with harmonic stabilization - treating belief updates as a damped oscillator rather than pure accumulation (inspired by MIT's LinOSS work on neural oscillations). The update rule looks like:
Beliefs oscillate around a stable point instead of drifting monotonically. Got ~9x improvement in stability (mean drift: 0.009 vs 0.085 baseline) on sentence-transformer embeddings over 50 cycles.Open sourced with interactive Colab demo. Parameters are hand-tuned and I've only tested on small scales, so curious if others have seen this problem or have better approaches.
Main questions: Is "recursive belief drift" already documented somewhere? Any theoretical reasons this wouldn't scale to larger systems?