PRISM: Pareto-Efficient Retrieval over Intent-Aware Structured Memory for Long-Horizon Agents
arXiv:2605.12260v2 Announce Type: replace 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 ret
The rapid scaling of large language models and the increasing complexity of agentic systems necessitate more efficient and effective memory management solutions to overcome context window limitations.
Efficient memory management is critical for the practical deployment and cost-effectiveness of long-horizon AI agents, directly impacting their commercial viability and strategic utility.
This framework offers a training-free, retrieval-side solution that improves both the accuracy and efficiency of memory usage for AI agents, potentially setting a new standard for their design.
- · AI Agent Developers
- · Cloud Computing Providers (cost reduction)
- · Enterprises deploying AI agents
- · Research institutions in AI/ML
- · Inefficient memory management techniques
- · Systems relying solely on context window expansion
Improved performance and reduced operational costs for long-horizon AI agents.
Accelerated development and adoption of more sophisticated and autonomous AI agents across various industries.
Enhanced automation of complex tasks, leading to changes in white-collar workflows and the emergence of new AI-driven business models.
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Read at arXiv cs.CL