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Memory Canonicalization: A Framework and Benchmark for Cross-Model Drift in Persistent LLM Memory

Amit Vadnere, Aishwarya Lonarkar

arXiv:2610.05124v1cs.AI

TL;DR

Different models may interpret the same stored memory differently, factually or emotionally. The paper proposes write-time canonicalization and the CMSC-E benchmark to study this problem. In the pilot, factual-drift comparisons were not significant, and the reported benefit is not yet statistically established.

  • Problem

    Prior work leaves limited evidence on whether different models interpret identical persistent memory objects consistently across factual and emotional dimensions.

    Source: P. Where the Chain Currently Ends · p. 3, §III · p. 3

  • Method

    The paper proposes write-time canonicalization of memory objects and a CMSC-E benchmark for evaluating cross-model semantic and emotional drift.

    Source: P. Where the Chain Currently Ends · p. 3, §XI · p. 10

  • Results

    None of the three CMSD comparisons approached conventional significance, and the paper characterizes the pilot’s overall benefit as not yet statistically established.

    Source: C. Results · p. 7, §XI · p. 10

  • Takeaways & Limitations

    CMSC-E and its reproducible evaluation framework are presented as a basis for future investigation of cross-model drift in persistent LLM memory.

    Source: §XI · p. 10

    The emotional-intensity rendering bank was not human-validated before the pilot, potentially introducing measurement noise in ECS.

    Source: F. Threats to Validity · p. 8

Abstract

from arXiv · show

Persistent memory for Large Language Models (LLMs) has matured rapidly: systems such as MemGPT/Letta, Mem0, and Zep now provide agents with tiered, temporally-aware, model-agnostic external storage, while the Model Context Protocol (MCP) standardizes access to memory servers. A less addressed problem is that an identical stored memory object, retrieved by two different LLMs under otherwise identical conditions, may not be interpreted the same way, factually or emotionally. This paper proposes memory canonicalization: a write-time pipeline that detects ambiguity, conditional structure, and emotional loading in a raw memory object and rewrites it into an explicit, structurally disambiguated canonical form, with emotional valence represented as a separate field rather than inferred from tone. We formalize the pipeline, define a companion Cross-Model Semantic Drift / Emotional Consistency Score benchmark (CMSC-E), and report results from a three-arm pilot using 176 synthetic memory objects and three downstream model families. We find an uncorrected improvement in cross-model emotional consistency for fully canonicalized memory relative to raw memory (+0.050, 95% bootstrap CI [0.013, 0.086], paired t-test p = 0.010), but this result does not survive Bonferroni, Holm, or Benjamini-Hochberg correction across the six comparisons tested. None of the factual-drift (CMSD) comparisons reach significance at any correction level. We report these results as exploratory rather than confirmatory and outline needed follow-up work, including larger samples, independent judge models, human-validated rendering, and preregistration.

I. INTRODUCTION

Persistent-memory systems advanced storage and access, but prior work left open whether different models interpret the same stored object consistently. The paper proposes write-time canonicalization and CMSC-E, then reports a pilot evaluation.

I. INTRODUCTION

  • The introduction identifies cross-model interpretation of identical stored memories as a gap left by advances in persistence, storage, and transport.Prior work documented drift in task memory, persona and tone, and emotional interpretation, but did not provide a general write-time mechanism for individual memory objects.Source: §I · p. 1
  • Memory canonicalization rewrites memory objects before storage, while CMSC-E jointly evaluates semantic and emotional cross-model drift.The paper reports a 176-object pilot across three model families with paired statistical testing and multiple-comparison correction.Source: §I · p. 1, Contributions · p. 2

II. RELATED WORK: A CHAIN OF PARTIAL SOLUTIONS

  • The related-work section frames prior systems as partial solutions, progressing logically from attention’s context-window constraint toward persistent memory.Transformer attention supports content-based reasoning in context, but does not persist information after the window is exceeded or a session ends.Source: §II · p. 2, A. Attention and the Root Constraint · p. 2

B. Retrieval-Augmented Generation

Prior approaches provide retrieval, storage, temporal awareness, and shared access, but leave important representation and cross-model consistency gaps. The section contrasts these partial solutions with canonicalization.

B. Retrieval-Augmented Generation

  • Retrieval-augmented generation grounds responses in information beyond the context window, but retrieval is stateless across calls.It does not represent a memory object that persists, updates, or is superseded across turns.Source: B. Retrieval-Augmented Generation · p. 2

C. Vector Databases

  • Vector databases support scalable retrieval, but embeddings lack native confidence, provenance, and temporal-validity concepts.These structures must be added as external metadata.Source: C. Vector Databases · p. 2

D. MemGPT / Letta

  • MemGPT and Letta provide persistence and self-directed memory management, but do not check whether different models interpret the same archival object identically.Source: D. MemGPT / Letta · p. 2

E. Mem0

  • Mem0 packages memory as a framework-agnostic service, while its reported evaluation leaves complex temporal reasoning unresolved and does not test cross-model agreement.Source: E. Mem0 · p. 2

F. Zep / Graphiti

  • Zep’s Graphiti records when facts become valid and when they are superseded, but does not address interpretation consistency across downstream models.Source: F. Zep / Graphiti · p. 2

G. Summarization, Reflection, and Knowledge-Graph Extraction as Write-Time Transformations

  • Canonicalization differs from consolidation and knowledge-graph extraction by targeting implicit conditional scope and typed emotional fields in a single write-time pass.The raw form is retained; repeated re-canonicalization has not been tested.Source: G. Summarization, Reflection, and… · p. 2

H. Native Platform Memory

I. Model Context Protocol (MCP)

The section reviews systems and benchmarks that address transport, memory transfer, persona continuity, emotional consistency, shared access, and diagnostics. It distinguishes those contributions from cross-model agreement on stored memory objects.

I. Model Context Protocol (MCP)

  • MCP standardizes transport and discovery between clients and memory services, but does not specify the semantic representation of exchanged memory objects.Source: §I · p. 2

J. MemCollab: The First Direct Measurement of Cross-Model Memory Drift

  • MemCollab reports performance degradation when agents with different backbones share memory, but its trajectory-distillation fix requires paired reasoning traces and does not address emotional content.Source: J. MemCollab: The First Direct… · p. 2

K. Cross-Model Persona and Temporal Continuity

L. Cross-Model Emotional Consistency

M. Shared-Memory Multi-Agent Infrastructure

N. Diagnostic Benchmarks for Memory-System Failure

  • The Response Consistency Index measures shared-versus-separate context configurations, but does not isolate which consuming model accounts for variation.Source: N. Diagnostic Benchmarks for… · p. 3

O. Surveys and Benchmarks

P. Where the Chain Currently Ends

The paper targets a gap between documented forms of cross-model drift and the lack of a write-time object-level remedy evaluated on both factual and emotional dimensions. Its proposed pipeline makes ambiguity and emotional content explicit.

P. Where the Chain Currently Ends

  • The paper identifies the missing combination as object-level write-time canonicalization and controlled evaluation of both factual and emotional cross-model consistency.Prior work documents drift across task memory, persona/tone, and emotion; the comparative review finds no prior system combining emotion-as-field with cross-model evaluation.Source: P. Where the Chain Currently Ends · p. 3, Q. Comparative Summary · p. 3

III. PROBLEM STATEMENT

  • The paper treats ambiguity as a proposed driver of factual drift and implicit emotional phrasing as a proposed driver of emotional-interpretation drift.The first sub-claim is a premise introduced by this paper, not an established finding.Source: §III · p. 3
  • Canonicalization explicitly restructures ambiguous conditions and separates emotional content into structured fields, targeting drift attributable to those features.The claim does not cover reasoning-style drift, decoding effects, or whole-conversation persona continuity.Source: §III · p. 3

A. Design Principles

  • The design separates per-object structural and emotional processing from store-aware conflict and supersession reconciliation.It favors making implicit structure explicit, separating emotional from factual content, and retaining the raw object for audit and provenance.Source: A. Design Principles · p. 3

B. Theoretical Motivation

Ambiguous and emotionally implicit memories require each retrieving model to resolve meaning from its own priors, potentially yielding different readings. Canonicalization moves that resolution to a fixed, auditable write-time policy, but can centralize interpretive bias.

B. Theoretical Motivation

  • Different model priors and decoding behavior can lead models to select different readings of ambiguous conditions or infer emotional states differently from implicit tone.The paper describes ambiguity as encoding multiple candidate propositions whose intended reading is not recoverable from surface form alone.Source: B. Theoretical Motivation · p. 4
  • Canonicalization shifts ambiguity resolution from separate retrieval-time interpretations to one shared write-time representation, reducing ambiguity as a distinct source of disagreement.The paper calls the number of plausible materially different readings an object's interpretation entropy, which approaches zero for fully scoped statements with explicit emotional fields.Source: B. Theoretical Motivation · p. 4

C. On Write-Time Interpretive Bias

  • A canonicalizer makes a subjective interpretive choice, and its consistent output is not an objective true reading; it may replace distributed disagreement with centralized bias.Round-trip validation can bound rewriting but depends on judge-model reliability; raw content is retained for audit, and an ensemble canonicalizer is future work.Source: C. On Write-Time Interpretive Bias · pp. 4–5

D. Extended Memory Object Schema

The extended memory object stores disambiguated content alongside temporal, ambiguity, confidence, and explicit emotional fields. Its pipeline parses, rewrites, validates, reconciles, and stores memories, with per-object Phase 1 costs separated from store-aware reconciliation.

D. Extended Memory Object Schema

  • The schema retains raw and canonical content alongside entities, timestamps, validity bounds, confidence, ambiguity status, sensitivity, emotional fields, and canonicalization confidence.Emotional valence and intensity are separate fields, with intensity null when valence is null.Source: D. Extended Memory Object Schema { · p. 5

E. Canonicalization Pipeline

  • Steps 1–4 parse entities and temporal conditions, detect ambiguity and implicit emotional loading, rewrite or split content into atomic statements, and extract emotional fields.The extracted fields are valence, intensity, and topic flag.Source: E. Canonicalization Pipeline · p. 5
  • Steps 5 and 5b validate factual entailment and emotional plausibility, while Step 6 non-destructively reconciles Phase-1 output with overlapping existing objects.Emotional disagreement beyond a threshold routes an object to human review.Source: E. Canonicalization Pipeline · p. 5
  • Storage retains both raw content and canonical objects, with canonical content returned by default during retrieval.Source: E. Canonicalization Pipeline · p. 5

F. Complexity Considerations

  • Phase 1 is linear in raw-object length and independent of store size; Phase 2 cost depends on candidate retrieval, and pilot latency and dollar costs were not measured.Phase 1's four LLM invocations dominate its cost and can be amortized because it runs once at write time.Source: F. Complexity Considerations · p. 5

V. THE CMSC-E BENCHMARK: EXTENDING CMSD WITH EMOTIONAL CONSISTENCY

The CMSC-E benchmark pairs cross-model factual agreement with an emotional consistency measure. Its three-arm protocol compares raw, structurally disambiguated, and fully canonicalized memories under a common prose rendering.

G. CMSD (Recap)

  • CMSD extracts response claim sets for each memory and prompt, computes pairwise bidirectional entailment agreement, and averages it into a per-object score from 0 to 1.Source: G. CMSD (Recap) · p. 5

H. Emotional Consistency Score (ECS)

  • ECS evaluates non-null emotional memories on valence, intensity, and topic handling, combining the three axis scores with an unweighted mean by default.Valence and intensity use an emotional-validator classifier; topic handling uses an NLI model to assess the response against the emotional topic flag.Source: H. Emotional Consistency Score (ECS) · pp. 5–6

I. Experimental Protocol – Three-Arm Design

  • The protocol compares raw, disambiguated-only, and fully canonicalized arms, rendering every condition as flat prose rather than exposing fields to downstream models.Raw versus disambiguated-only isolates structural rewriting; disambiguated-only versus fully canonicalized isolates emotional extraction.Source: §I · p. 6

V. ILLUSTRATIVE WORKED EXAMPLE

A hypothetical example illustrates splitting unrelated facts and annotating their emotional content; the merged pilot-results material describes the actual evaluation setup and statistical plan. The supplied passages do not report the pilot's outcome values.

V. ILLUSTRATIVE WORKED EXAMPLE

  • The hypothetical example splits a sentence about pet loss and an appointment reschedule into separate memory objects, with emotional annotation computed independently for each.The loss is assigned negative valence and the reschedule neutral valence; the example is explicitly distinct from actual pilot data.Source: §V · p. 6

VI. PILOT RESULTS

  • The pilot reports real API calls against three production models, with no results described as simulated or illustrative.Source: §VI · p. 6

A. Dataset and Setup

  • The pilot generated 176 synthetic objects across 16 strata and treated the sample as suitable for paired within-dataset estimates, not population-level coverage.All objects completed Phase 1 without pipeline errors.Source: A. Dataset and Setup · p. 6
  • Each object's three arms were rendered as prose and paired with two type-specific prompts, then sent to three models spanning separate model families for CMSD and eligible-object ECS scoring.Source: A. Dataset and Setup · p. 6
  • Four objects were excluded from CMSD after failed or timed-out calls left too few valid responses; 86 of the 172 CMSD-scored objects qualified for ECS.Source: A. Dataset and Setup · p. 7

B. Statistical Methodology

  • The analysis treats arm comparisons as paired and tests six arm-pair-by-metric comparisons using paired t-tests, Wilcoxon tests, bootstrap intervals, and multiple-comparison corrections.The bootstrap uses 10,000 resamples and preserves object pairing.Source: B. Statistical Methodology · p. 7

C. Results

In this pilot, fully canonicalized memories showed a nominal ECS gain over raw memories, but no comparison survived multiple-testing correction; CMSD comparisons were not significant.

C. Results

  • None of the three CMSD comparisons was significant; disambiguation’s estimate was +0.009, while emotional extraction’s was −0.004.The pilot does not support the claim that ambiguity is a measurable driver of cross-model factual drift correctable by disambiguation at this sample size.Source: C. Results · p. 7
  • +0.050 was the Arm 1 →3 ECS mean difference (95% CI [0.013, 0.086], p_t = 0.010), but no comparison survived Bonferroni, Holm, or Benjamini–Hochberg correction.The disambiguation and emotional-extraction ECS sub-effects were +0.018 (p_t = 0.294) and +0.031 (p_t = 0.103), respectively, and neither individually reached significance.Source: C. Results · p. 7

D. Effect Direction and a Note on Interim Analysis

  • Five of six estimates were directionally positive, but interim means sometimes suggested a different pattern from the final paired-and-corrected analysis.The interim snapshots included a checkpoint at n = 43 where emotional extraction appeared flat or negative, while the final analysis did not confirm the CMSD trend that had looked most robust earlier.Source: D. Effect Direction and a Note on… · p. 7

E. An Approximate Power Analysis

  • The approximate analysis estimated a CMSD minimum detectable effect of about 0.033 at 80% power, versus the observed disambiguation effect of 0.009.Detecting an effect of the observed magnitude at that noise level would require roughly 10–15× the current sample; the calculation is illustrative, not precise.Source: E. An Approximate Power Analysis · p. 7

F. Threats to Validity

The pilot’s interpretation is constrained by non-independent judges, an unvalidated emotional-rendering bank, and an analysis plan set after data collection. The discussion treats the factual null and emotional result as unresolved, while positioning canonicalization as complementary to existing systems.

F. Threats to Validity

  • Same-family canonicalizer, factual validator, emotional validator, and one tested model create a plausible risk of correlated judgments that could inflate canonicalized-arm agreement and complicate the ECS result.The direction is opposite to what would explain the null CMSD result.Source: F. Threats to Validity · p. 8
  • The emotional-intensity rendering bank was not human-validated, leaving possible phrasing-related measurement noise specifically in ECS.The protocol specified human checks for naturalness and intensity-band boundaries before the pilot.Source: F. Threats to Validity · p. 8
  • The three-arm comparisons, paired tests, and multiple-comparison correction were specified after data collection, so a pre-registered replication is needed before treating results as confirmatory.The authors report all six tests and describe the plan as specification-following.Source: F. Threats to Validity · p. 8

G. Interpreting the Null and Weak Results

  • The null CMSD result could reflect low power or ambiguity resolution not being the dominant source of factual disagreement; the design cannot distinguish these explanations.Model-family differences may contribute more answer variance than memory phrasing, but the study did not vary family composition or measure a within-family baseline.Source: G. Interpreting the Null and Weak… · p. 8

VII. RELATIONSHIP TO EXISTING SYSTEMS

  • The ECS result is suggestive, not confirmatory, while canonicalization is framed as complementary to systems addressing reasoning-transfer and whole-conversation tone continuity.The paper’s narrower contribution is its separately validated emotional field and CMSC-E evaluation protocol, rather than the extraction-plus-reconciliation architecture itself.Source: G. Interpreting the Null and Weak… · p. 8, §VII · p. 9

VIII. ETHICAL CONSIDERATIONS

The paper flags privacy safeguards for persistent emotional annotations and reports that its pilot did not confirm the core hypothesis. It identifies limits in power, validation, data scope, and untested components, then proposes larger, preregistered and more independent follow-up studies.

VIII. ETHICAL CONSIDERATIONS

  • Persistent machine-readable emotional inferences raise distinct consent, reducibility, and correction-or-deletion concerns; the synthetic pilot sidestepped consent issues but did not implement safeguards needed for real-user memory.The paper calls for separate consent for emotional-field extraction and user paths to review, correct, or delete annotations.Source: §VIII · p. 9

IX. LIMITATIONS

  • The core hypothesis is not confirmed: factual-drift improvement is unsupported at n = 172, and the positive emotional-consistency effect does not survive correction across six comparisons.The authors characterize the contribution as the benchmark, pipeline, and rigorously reported null-to-weak result rather than a confirmed positive finding.Source: §IX · p. 9
  • The pilot is underpowered for observed effect sizes, and its synthetic, bounded-vocabulary data may not transfer to real, unstructured conversational memory.The power estimate suggests roughly an order-of-magnitude larger sample if the point estimates reflect a true small effect.Source: §IX · p. 9
  • Judge independence was not achieved, the rendering bank lacked human validation, and the analysis plan was finalized after data collection.The supplementary superseded/conflicting set was also constructed but not scored, leaving Phase-2 reconciliation untested.Source: §IX · p. 9

X. FUTURE WORK

  • Follow-up priorities include scaling the pilot, preregistering the analysis, separating judges from tested models, and human-validating emotional renderings.The proposed judge changes include replacing the Step 5b LLM stand-in with a dedicated discriminative classifier.Source: §X · p. 9
  • Further work would score the superseded/conflicting set, test real consented and anonymized memories, and examine factual-agreement costs and heterogeneous or ensemble canonicalizers.The emotional schema could also be extended to discrete-emotion taxonomies if a powered follow-up finds valence and intensity insufficient.Source: §X · pp. 9–10

XI. CONCLUSION

The pilot tested write-time canonicalization against cross-model factual and emotional drift, finding no statistically detectable factual improvement and only an uncorrected emotional-consistency effect. The paper presents CMSC-E and a reproducible evaluation framework as its primary contribution, with benefits still unestablished.

XI. CONCLUSION

  • +0.050 for fully canonicalized memory versus raw memory on cross-model emotional consistency was uncorrected-significant, but did not survive multiple-comparison correction; disambiguation alone showed no statistically detectable factual-drift reduction at n = 172.The authors characterize the result as suggestive rather than confirmed.Source: §XI · p. 10
  • CMSC-E and a reproducible evaluation framework are the paper’s primary contribution, providing a basis for future study regardless of whether later experiments validate or refute the hypothesis.The framework is presented as a way to study cross-model drift in persistent LLM memory.Source: §XI · p. 10
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