Reverie: A Biological Memory Architecture for AI Agents
The second architecture, written up: conclusions as units, two brains, review, confidence, contradictions, grounding, and retrieval on request.
Reverie comes out of Engineering Cognition, a research program that asks whether the understanding built during engineering work can be extracted, kept and revised by a system. We publish what we find, including when it doesn’t work.
Working paper Engineering Cognition (30 Jun).
Repository memory loaded into each session; EC-Bench Technical Report 001 (15 Jul) and Short Report (18 Jul). The finding that mattered: loaded memory anchored the agent.
Notebook: Everything till now (29 Jul).
Conclusions as units, two brains, review, confidence, contradictions, grounding, retrieval on request. Benchmark run (13–15 Aug): the gap narrowed; the baseline is still ahead.
Reverie: A Biological Memory Architecture for AI Agents (5 Sep).
The continuity experiment, a dedicated extraction model, a larger benchmark.
The second architecture, written up: conclusions as units, two brains, review, confidence, contradictions, grounding, and retrieval on request.
Method, results and limits.
A controlled comparison. Not yet run.
A research log of the thinking behind the first version. Kept as written, with an editor’s note.
Earlier documents call the first version the Engineering Memory System (EMS).
The working paper that posed the question. See the paper for where the research went.
Describes the first version, which loaded repository memory into each session. Superseded by the second architecture.
First benchmark run. Its aggregate gain came from the cognition-reuse metric; four of five metrics were lower with memory. See EC-Bench.
A short report on the first benchmark run. See EC-Bench for the current picture.
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