PRISM: Pareto-Efficient Retrieval over Intent-Aware Structured Memory for Long-Horizon Agents

TL;DR

PRISM is a retrieval framework for long-horizon language agents that efficiently manages memory by combining graph-structured retrieval, query-sensitive traversal, and compression, improving accuracy and cost-effectiveness.

Contribution

PRISM introduces a training-free, retrieval-side approach that treats memory management as a joint retrieval and compression problem over a graph structure, enhancing long-horizon agent performance.

Findings

PRISM outperforms baselines in accuracy on the LoCoMo benchmark.

PRISM achieves higher accuracy with significantly smaller context budgets.

PRISM demonstrates a superior balance between answer quality and retrieval efficiency.

Abstract

Long-horizon language agents accumulate conversation history far faster than any fixed context window can hold, making memory management critical to both answer accuracy and serving cost. Existing approaches either expand the context window without addressing what is retrieved, perform heavy ingestion-time fact extraction at substantial token cost, or rely on heuristic graph traversal that leaves both accuracy and efficiency on the table. We present PRISM, a training-free retrieval-side framework that treats long-horizon memory as a joint retrieval-and-compression problem over a graph-structured memory. PRISM combines four orthogonal inference-time components: Hierarchical Bundle Search over typed relation paths, Query-Sensitive Edge Costing that aligns traversal with detected query intent, Evidence Compression that compresses the candidate bundle into a compact answer-side context, and Adaptive Intent Routing that routes most queries through zero-LLM tiers. By formulating retrieval as min-cost selection over typed path templates and pairing it with an LLM-side compression step, PRISM surfaces the right evidence under a strict context budget without any fine-tuning or modification to the upstream ingestion pipeline. Experiments on the LoCoMo benchmark show that PRISM delivers substantially higher LLM-judge accuracy than every same-protocol baseline at an order-of-magnitude smaller context budget, occupying a previously empty corner of the accuracy-context-cost frontier and demonstrating a superior balance between answer quality and retrieval efficiency.