Parametric Memory: A Cryptographically Verifiable, Predictive Memory Substrate for MCP-Capable AI Agents
The L2 cache for AI — verifiable, predictive agent memory behind one MCP interface
Abstract
AI agents reason brilliantly over their context window and forget everything when it closes. Retrieval-augmented generation restores recall, but a production agent also needs to know what it knew and when, what tends to come next, when two of its beliefs disagree, and how its facts relate. This paper describes a memory substrate delivering all four properties at once — Merkle-verifiable snapshots with RFC 6962-style consistency proofs, Markov-chain predictive prefetch, adaptive decay-based self-curation, and automatic contradiction detection — behind a single Model Context Protocol interface any MCP-capable AI can use.
Key contributions
Positions agent memory as an L2 cache between the context window and cold storage
Composes verifiability, prediction, self-curation and conflict-awareness in one substrate
Recall responses carry offline-verifiable cryptographic proofs of what was stored, and when
The Self-Reinforcing Loop: Verifiable Evidence of Consensus for Distributed Agentic Systems
Signal → Evidence → Refinement on the Parametric Memory substrate
Abstract
A fleet of agents sharing a verifiable memory is a different kind of system from a single agent with one. This paper describes an architecture where fixed knowledge substrates give every agent identical versioned guidance, a state plane captures operational signals as append-only Merkle-versioned evidence, and an orchestrator refines a control plane of policies only when independent agents corroborate a signal and counterfactual replay shows the change would have improved outcomes. Consensus becomes a derived artifact any AI can recompute and verify from the evidence — asynchronously, offline, after the fact.
Key contributions
Replaces live consensus protocols with verifiable evidence-of-consensus
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