Academic

Beyond the Context Window: A Cost-Performance Analysis of Fact-Based Memory vs. Long-Context LLMs for Persistent Agents

arXiv:2603.04814v1 Announce Type: new Abstract: Persistent conversational AI systems face a choice between passing full conversation histories to a long-context large language model (LLM) and maintaining a dedicated memory system that extracts and retrieves structured facts. We compare a fact-based memory system built on the Mem0 framework against long-context LLM inference on three memory-centric benchmarks - LongMemEval, LoCoMo, and PersonaMemv2 - and evaluate both architectures on accuracy and cumulative API cost. Long-context GPT-5-mini achieves higher factual recall on LongMemEval and LoCoMo, while the memory system is competitive on PersonaMemv2, where persona consistency depends on stable, factual attributes suited to flat-typed extraction. We construct a cost model that incorporates prompt caching and show that the two architectures have structurally different cost profiles: long-context inference incurs a per-turn charge that grows with context length even under caching, whil

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Natchanon Pollertlam, Witchayut Kornsuwannawit · · 1 min read · 7 views
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arXiv:2603.04814v1 Announce Type: new Abstract: Persistent conversational AI systems face a choice between passing full conversation histories to a long-context large language model (LLM) and maintaining a dedicated memory system that extracts and retrieves structured facts. We compare a fact-based memory system built on the Mem0 framework against long-context LLM inference on three memory-centric benchmarks - LongMemEval, LoCoMo, and PersonaMemv2 - and evaluate both architectures on accuracy and cumulative API cost. Long-context GPT-5-mini achieves higher factual recall on LongMemEval and LoCoMo, while the memory system is competitive on PersonaMemv2, where persona consistency depends on stable, factual attributes suited to flat-typed extraction. We construct a cost model that incorporates prompt caching and show that the two architectures have structurally different cost profiles: long-context inference incurs a per-turn charge that grows with context length even under caching, while the memory system's per-turn read cost remains roughly fixed after a one-time write phase. At a context length of 100k tokens, the memory system becomes cheaper after approximately ten interaction turns, with the break-even point decreasing as context length grows. These results characterize the accuracy-cost trade-off between the two approaches and provide a concrete criterion for selecting between them in production deployments.

Executive Summary

This article compares the performance of fact-based memory systems and long-context large language models (LLMs) for persistent conversational AI systems. The study evaluates both architectures on accuracy and cumulative API cost using three memory-centric benchmarks. The results show that long-context LLMs achieve higher factual recall, but the memory system is competitive in certain scenarios. A cost model is constructed to analyze the cost profiles of both approaches, revealing that the memory system becomes cheaper after a certain number of interaction turns, especially with longer context lengths.

Key Points

  • ▸ Comparison of fact-based memory systems and long-context LLMs for persistent conversational AI
  • ▸ Evaluation of accuracy and cumulative API cost using three memory-centric benchmarks
  • ▸ Cost model analysis revealing different cost profiles for both approaches

Merits

Comprehensive Evaluation

The study provides a thorough comparison of both architectures using multiple benchmarks and a cost model

Practical Insights

The results offer practical guidance for selecting between fact-based memory systems and long-context LLMs in production deployments

Demerits

Limited Context

The study focuses on a specific context length of 100k tokens, which may not be representative of all use cases

Simplistic Cost Model

The cost model may not account for all relevant factors, such as infrastructure and maintenance costs

Expert Commentary

The article provides a nuanced analysis of the trade-offs between fact-based memory systems and long-context LLMs. The study's findings have significant implications for the development of conversational AI systems, particularly in scenarios where context length is a critical factor. However, the research also highlights the need for further investigation into the complexities of NLP architectures and their applications. As the field continues to evolve, it is essential to consider the interplay between accuracy, cost, and context length in the design of conversational AI systems.

Recommendations

  • ✓ Developers should consider the specific requirements of their conversational AI systems when choosing between fact-based memory systems and long-context LLMs
  • ✓ Further research is needed to explore the complexities of NLP architectures and their applications, particularly in scenarios with varying context lengths

Sources

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