Memory that forgets: a persistent, self-revising belief-state MCP server for agentic R&D. Uses a hypothesis DAG to auto-prune dead ends and deduce truth without extra probes.
A persistent, self-revising hypothesis DAG for agentic R&D — exposed as an MCP server.
Current agent memory is passive: vector stores and scratchpads accumulate facts but never revise them. Hypotree structures the agent's working knowledge as a directed acyclic graph of hypotheses backed by SQLite-WAL. When an experiment fails, the engine walks the dependency edges and retracts what rested on it. When a premise collapses, every dependent subtree is pruned automatically.
- Write-back belief revision — an ATMS-style engine (de Kleer, 1986) that propagates evidence failures upstream through the dependency graph. - Cascading prune — invalidating a parent hypothesis instantly transitions its entire subtree to PRUNED. No tokens spent on dead branches. - Exclusion-group inference — confirming one member of a mutually exclusive group retires the rest as EXHAUSTED without probing them. - Deduction by elimination — last-man-standing: when all but one alternative in an exclusion group are refuted, the survivor is VERIFIED without a probe. - Thompson Sampling navigation — Beta-distribution sampling over the open frontier, giving bounded worst-case regret (no catastrophic lock-in). - Conflict resolution via differential ablation — when an integration test fails but every component passes alone, the engine rebuilds the failing combination one swap at a time to pinpoint the culprit. - A derivation trail, not just a state — generatelearningpath narrates what was settled, in order, separating what an experiment paid for from what the engine inferred for free, and calling out beliefs that
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