Prism: An Evolutionary Memory Substrate for Multi-Agent Open-Ended Discovery

2026-04-24 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, long

Summary

Prism (Probabilistic Retrieval with Information-Stratified Memory) is an evolutionary memory substrate designed for multi-agent AI systems engaged in open-ended discovery. It integrates four paradigms: layered file-based persistence, vector-augmented semantic memory, graph-structured relational memory, and multi-agent evolutionary search, all within a decision-theoretic framework comprising eight interconnected subsystems. Prism introduces an entropy-gated stratification mechanism for memory assignment, a causal memory graph with agent-attributed provenance, a Value-of-Information retrieval policy with self-evolving strategy selection, a heartbeat-driven consolidation controller, and a replicator-decay dynamics framework that interprets memory confidence as evolutionary fitness. On the LOCOMO benchmark, Prism achieved an 88.1 LLM-as-a-Judge score, a 31.2% improvement over Mem0. For CORAL-style evolutionary optimization tasks, a 4-agent Prism system demonstrated a 2.8x higher improvement rate compared to single-agent baselines.

Key takeaway

For research scientists developing advanced multi-agent AI systems, Prism offers a robust, formally grounded memory architecture. You should consider integrating its principles, particularly the replicator-decay dynamics and entropy-gated stratification, to build systems that can achieve superior performance in open-ended discovery tasks and converge to an Evolutionary Stable Memory Set. This approach can enhance knowledge reuse and exploration diversity, critical for complex problem-solving.

Key insights

Prism unifies memory paradigms under an evolutionary framework, treating memory confidence as fitness for open-ended AI discovery.

Principles

• Memory confidence can be modeled as evolutionary fitness.
• Information-theoretic stratification improves memory organization.
• Multi-agent exploration diversity drives higher improvement rates.

Method

Prism stratifies memories by Shannon entropy, uses a causal graph for provenance, retrieves via evolutionary VoI, consolidates with a heartbeat controller, and evolves memory confidence using replicator-decay dynamics.

In practice

• Use entropy-gated stratification for memory tiering.
• Implement causal graphs with agent provenance for multi-agent tracing.
• Employ replicator-decay dynamics for adaptive memory management.

Topics

• Prism Memory Substrate
• Replicator-Decay Dynamics
• Evolutionary Stable Memory Set
• Causal Memory Graph
• Entropy-Gated Stratification

Code references

• bytedance/deer-flow

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.