Memory Beyond Recall: A Dual-Process Cognitive Memory System for Self-Evolving LLM Agents

The deeper provocation in DCPM is not the memory hierarchy itself but the claim that genuine personalisation requires causal reasoning about *why* a user's beliefs changed, not just logging that they did. Most current agent memory systems treat history as a lookup table; DCPM treats it as a model of a person.

This connects directly to the graph isomorphism study covered the same day ('Detecting Differences Is Not Understanding Structure'), which demonstrated that LLMs pattern-match on surface features rather than grasping abstract relationships. DCPM is, in part, a structural response to exactly that failure mode: if an agent cannot reason about topology or causal change, retrieval alone will keep reproducing the same shallow personalisation. The Self-Harness paper from the same date adds a related pressure point, showing that agent configuration itself needs to adapt autonomously. Together, these three papers sketch a coherent problem cluster: current LLM agents are brittle because their reasoning, memory, and operating harnesses are all statically conceived.

The critical test is whether DCPM's belief-trajectory layer produces measurably different downstream responses on longitudinal user simulation benchmarks compared to flat retrieval baselines. If no such evaluation appears in a follow-up within six months, the architecture remains a theoretical proposal without empirical grounding.