What does this research mean for the field?

The assumption that a canonical project memory store maintains only true information is false, as agents repeatedly leave superseded values in place during session updates. Novelty: ClaimNovelty.CONTRADICTORY. Consensus alignment: ConsensusAlignment.CHALLENGES_CONSENSUS.

What question did this study set out to answer?

To evaluate whether autonomous AI agents accurately revise, replace, and retire outdated facts in canonical project memory stores across multi-session lineages.

August 24, 2026Open Access

pi-canon: The Write Desk

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Authors

SCShane Conner

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Implication

Controlled evaluation reveals that AI agents frequently retain superseded facts in canonical memory stores, indicating challenges in maintaining updated state across sessions.

Key Points

To evaluate whether autonomous AI agents accurately revise, replace, and retire outdated facts in canonical project memory stores across multi-session lineages.
Simulated multi-session lineages with facts revised, reversed, and retired across sessions, written by an agent and subsequently read back by an independent agent.
Graded memory slots against a hidden oracle across three controlled captures on two models, testing a write-boundary tool feedback intervention against a baseline.
Conducted an offline retrieval benchmark comparing a lexical ranker against cosine similarity over local embeddings across two frozen project memory stores.
Across 24 capture-lineage comparisons over 8 constructed histories, the write-boundary feedback condition reduced superseded values in 20 comparisons, tied in 2, and increased them in 2.
Effect magnitude was not robust, as a counterbalanced repeat reversing arm order erased most of the initial 14-point pooled contrast, with four of five pre-registered predictions failing.
The lexical ranker outperformed cosine similarity over local embeddings in all 8 conditions across the two benchmarked project stores by 15.8 to 37.0 points.

Cite This Study

Shane Conner (2026) studied this question.

synapsesocial.com/papers/6a8c009fbca056c88e6df73e https://doi.org/10.5281/zenodo.22057257

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Implication

Controlled evaluation reveals that AI agents frequently retain superseded facts in canonical memory stores, indicating challenges in maintaining updated state across sessions.

pi-canon: The Write Desk

Discussion

Member takes

Implication

Key Points

Cite This Study

pi-canon: The Write Desk

Discussion

Member takes

Implication

Key Points

Cite This Study

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