# From Lossy to Verified: A Provenance-Aware Tiered Memory for Agents

arXiv: https://arxiv.org/abs/2602.17913 · HTML: https://mlsanity.com/paper/2602.17913/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 20 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://mlsanity.com/paper/2504.19413/) (2025)
- [Extending Context Window of Large Language Models via Positional Interpolation](https://mlsanity.com/paper/2306.15595/) (2023)
- [LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory](https://mlsanity.com/paper/2410.10813/) (2024)
- [Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions](https://mlsanity.com/paper/2507.05257/) (2025)
- [SCM: Enhancing Large Language Model with Self-Controlled Memory Framework](https://mlsanity.com/paper/2304.13343/) (2023)
- [O-Mem: Omni Memory System for Personalized, Long Horizon, Self-Evolving Agents](https://mlsanity.com/paper/2511.13593/) (2025)
- [MemR3: Memory Retrieval via Reflective Reasoning for LLM Agents](https://mlsanity.com/paper/2512.20237/) (2025)
- [Verifiable Generation with Subsentence-Level Fine-Grained Citations](https://mlsanity.com/paper/2406.06125/) (2024)
- [Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Control](https://mlsanity.com/paper/2502.12145/) (2025)
- [Goal-Directed Search Outperforms Goal-Agnostic Memory Compression in Long-Context Memory Tasks](https://mlsanity.com/paper/2511.21726/) (2025)

## Built on by (most cited first, 17 of 17)

- [MemMark: State-Evolution Attribution Watermarking for Agent Long-Term Memory Systems](https://mlsanity.com/paper/2605.25002/) (2026)
- [Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems](https://mlsanity.com/paper/2605.26302/) (2026)
- [Retrieval-Augmented LLM Agents: Learning to Learn from Experience](https://mlsanity.com/paper/2603.18272/) (2026)
- [EMBER: Efficient Memory via Budgeted Evidence Retention for Long-Horizon Agents](https://mlsanity.com/paper/2606.05894/) (2026)
- [TRUSTMEM: Learning Trustworthy Memory Consolidation for LLM Agents with Long-Term Memory](https://mlsanity.com/paper/2606.25161/) (2026)
- [D-Mem: A Dual-Process Memory System for LLM Agents](https://mlsanity.com/paper/2603.18631/) (2026)
- [When Stale Constraints Go Unchecked: Budgeted Verification Failures in Inherited Agent Memory](https://mlsanity.com/paper/2608.25553/) (2026)
- [MEMOREPAIR: Barrier-First Cascade Repair in Agentic Memory](https://mlsanity.com/paper/2605.07242/) (2026)
- [A Frozen 12B Beats Frontier Models on Verified Work: 100% Accuracy, 0 Tokens, Bit-Exact, Forever](https://mlsanity.com/paper/2607.23806/) (2026)
- [Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents](https://mlsanity.com/paper/2609.16053/) (2026)
- [EA-Graph: Artifact-Anchored Verification Memory for Coding Agents under Upstream Drift](https://mlsanity.com/paper/2608.04278/) (2026)
- [Silence Is Endorsement: Verification-Status Laundering in LLM Agent Pipelines](https://mlsanity.com/paper/2609.20211/) (2026)
- [Plan Pointers and Record-Directive Form in Budgeted Verification of Inherited Agent Memory](https://mlsanity.com/paper/2609.03450/) (2026)
- [Self-Cleaning and Captured Anyway: One Measured Primitive for Error in a Store an Agent Writes to Itself, and What a Falling Score Actually Measures](https://mlsanity.com/paper/2609.25052/) (2026)
- [Memory as Middleware for Self-Improving AI Agents](https://mlsanity.com/paper/2609.32091/) (2026)
- [Vestrum: Improving Agent Harnesses by Adapting Their Verification, Structure and Memory](https://mlsanity.com/paper/2609.33822/) (2026)
- [FORGE: Form-Optimal Routing of Grounded Evidence for Frozen LLM Agents](https://mlsanity.com/paper/2609.34358/) (2026)

## Abstract (arXiv)

Long-horizon agents often compress interaction histories into write-time summaries. This creates a fundamental write-before-query barrier: compression decisions are made before the system knows what a future query will hinge on. As a result, summaries can cause unverifiable omissions -- decisive constraints (e.g., allergies) may be dropped, leaving the agent unable to justify an answer with traceable evidence. Retaining raw logs restores an authoritative source of truth, but grounding on raw logs by default is expensive: many queries are answerable from summaries, yet raw grounding still requires processing far longer contexts, inflating token consumption and latency. We propose TierMem, a provenance-linked framework that casts retrieval as an inference-time evidence allocation problem. TierMem uses a two-tier memory hierarchy to answer with the cheapest sufficient evidence: it queries a fast summary index by default, and a runtime sufficiency router Escalates to an immutable raw-log store only when summary evidence is insufficient. TierMem then writes back verified findings as new summary units linked to their raw sources. On LoCoMo, TierMem achieves 0.851 accuracy (vs.0.873 raw-only) while reducing input tokens by 54.1% and latency by 60.7%.