Ground Truth First: A Longitudinal Evaluation Instrument for Agent Memory, and the Tenure Crossover in Memory-Architecture Rankings

2026-07-27 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

A new evaluation instrument, "Ground Truth First" (GTF), addresses critical flaws in existing LLM-agent memory benchmarks, which suffer from label errors and short interaction histories. GTF inverts the standard pipeline by generating facts with validity intervals, volatility classes, and source channels *before* rendering text, ensuring gold answers are script-valid. This fully synthetic, fictionalized corpus, comprising ~380 validated questions across 15 types, includes unique features like per-fact validity intervals and injection probes. Benchmarking five memory architectures over short and nine-week horizons revealed a "tenure crossover": budgeted curated-map memory, leading at three weeks (96% recall), dropped to 72% by nine weeks due to eviction, while a provenance-typed graph rose to 90%. The layered Hybrid v2 architecture performed best overall (96.8% short-horizon) and is released as the open-source Veracium library. Additionally, write-stage quality strongly correlated with downstream accuracy, and injection resistance tracked the preservation of provenance boundaries.

Key takeaway

For AI Architects designing long-lived LLM agent memory, relying solely on short-horizon benchmarks risks selecting suboptimal systems that fail over time. You should prioritize memory architectures, like the layered Veracium design, that demonstrate robust recall over multi-week horizons and explicitly preserve provenance. Focus on optimizing write-stage quality and ensuring provenance boundaries survive representation to enhance both accuracy and injection resistance in your deployed agents.

Key insights

Longitudinal evaluation with ground-truth-first generation reveals critical memory architecture performance differences and security properties for LLM agents.

Principles

• Short-horizon memory benchmarks can mis-rank systems for long-lived agents.
• Write-stage quality strongly correlates with downstream memory recall and accuracy.
• Preserving provenance boundaries in memory representation enhances injection resistance.

Method

A ground-truth-first evaluation pipeline generates facts with validity intervals and provenance, renders text, verifies fidelity, and mechanically instantiates questions for gold answers.

In practice

• Adopt a layered memory architecture combining an unbounded provenance-preserving store with an LLM-curated budgeted view.
• Invest in high-quality write paths for agent memory to improve overall system accuracy.
• Ensure memory representations preserve source provenance to build inherent injection resistance.

Topics

• LLM Agents
• Agent Memory Benchmarking
• Longitudinal Evaluation
• Provenance Preservation
• Memory Architectures
• Veracium Library

Code references

• veracium-ai/Veracium

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