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arxiv: 2605.24060 · v1 · pith:Y3UZONHInew · submitted 2026-05-22 · 💻 cs.IR

Same Ranking, Different Winner: How Scoring Targets Shape LLM Memory Benchmarks

Sugam Panthi , Rabab Abdelfattah This is my paper

Pith reviewed 2026-06-30 15:16 UTC · model grok-4.3

classification 💻 cs.IR
keywords scoring targetsmemory benchmarksnDCG evaluationconversational memoryretrieval credittarget noninvarianceLLM evaluation
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The pith

Switching the scoring target in LLM memory benchmarks changes nDCG on most queries and can reverse which system ranks highest.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Conversational-memory systems often store multiple derived forms from one dialogue turn, so evaluation must decide which form receives retrieval credit. The paper introduces TIAP to rescore the same ranked lists under three targets—Raw, Source, and Canonical—without new retrieval runs. On LoCoMo and LongMemEval-S, changing only the target alters nDCG on 83.4 to 94.0 percent of queries, flips system orderings on transfer tasks, and reverses parser-density advice. A 1,902-case audit finds relaxed source-linked credit justified only 29.2 percent of the time. The central finding is target noninvariance: benchmark conclusions about memory architectures can flip with one often-unstated design choice, so papers must define and report the target explicitly.

Core claim

Switching only the credited target changes nDCG on 83.4--94.0 percent of shared queries, flips target orderings on Mem0 and MemoryOS transfer runs, and reverses parser-density recommendations, revealing target noninvariance where conclusions about memory architectures can silently flip with a single benchmark-design choice.

What carries the argument

TIAP, a fixed-output audit that rescores saved ranked outputs under three targets—Raw, Source, and Canonical—without rerunning retrieval.

If this is right

  • nDCG changes on 83.4-94.0 percent of shared queries when the target switches
  • Target orderings flip on Mem0 and MemoryOS transfer runs
  • Parser-density recommendations reverse with the target change
  • Conclusions about memory architectures can flip from one benchmark-design choice

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Papers should report results under all three targets to show robustness
  • The noninvariance finding may apply to other retrieval settings that store derived content from the same source
  • Standardizing a default target across the field could reduce hidden variability in comparisons

Load-bearing premise

The 1,902-case semantic audit accurately measures when relaxed source-linked credit is justified.

What would settle it

Re-scoring the same ranked outputs under different targets and finding no material change in nDCG or system orderings on the majority of queries.

Figures

Figures reproduced from arXiv: 2605.24060 by Rabab Abdelfattah, Sugam Panthi.

Figure 1
Figure 1. Figure 1: Overview of target noninvariance in conver [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the TIAP audit framework. Stage I constructs Raw, Source, and Canonical scoring targets [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: shows the per-query magnitude of nDCG change when switching from Raw to Canonical targets. The distribution is not concentrated near zero: 65% of LoCoMo and 71% of LongMemEval￾S query-provider pairs shift by at least 0.1 nDCG, with mean |∆| of 0.25 and 0.33, respectively. Ta￾ble 15 expands the compact instability summary in [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
read the original abstract

Conversational-memory systems increasingly transform dialogue history into facts, summaries, timelines, and other source-linked descendants, so a single source turn can coexist with several derived memories in the same retrieval index. This raises an underspecified evaluation question: which stored form should receive retrieval credit? We show that this scoring-target choice is often left implicit and can materially change benchmark conclusions. We present TIAP, a fixed-output audit that rescores saved ranked outputs under three targets -- Raw, Source, and Canonical -- without rerunning retrieval. On LoCoMo and LongMemEval-S, switching only the credited target changes nDCG on 83.4--94.0 percent of shared queries, flips target orderings on Mem0 and MemoryOS transfer runs, and reverses parser-density recommendations. A 1,902-case semantic audit further shows that relaxed source-linked credit is fully justified only 29.2 percent of the time, despite high rubric reliability in a validation subset. These results reveal target noninvariance: conclusions about memory architectures can silently flip with a single benchmark-design choice. Conversational-memory papers should therefore define and report the scoring target explicitly.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that scoring-target choice (Raw, Source, Canonical) in conversational-memory retrieval benchmarks is often implicit yet materially affects outcomes: on LoCoMo and LongMemEval-S, switching targets alters nDCG on 83.4–94.0% of shared queries, reverses system orderings for Mem0 and MemoryOS, and flips parser-density recommendations. It introduces TIAP, a fixed-output rescoring procedure, and reports a 1,902-case semantic audit finding that relaxed source-linked credit is fully justified only 29.2% of the time (with high rubric reliability noted only on a validation subset). The central conclusion is that memory-architecture papers must explicitly define and report the scoring target.

Significance. If the audit and rescoring results hold, the work identifies a previously under-examined source of non-invariance in LLM memory evaluation that can silently invert benchmark conclusions. The fixed-output TIAP design is a methodological strength, enabling direct target comparison without re-execution or additional model calls. The concrete percentages and use of standard nDCG provide clear, falsifiable evidence of the effect size.

major comments (2)
  1. [description of the 1,902-case semantic audit] The central claim that relaxed source-linked credit is 'fully justified only 29.2 percent of the time' rests on the 1,902-case semantic audit. The manuscript states high rubric reliability only for a validation subset and supplies no information on sampling procedure for the 1,902 cases, labeling protocol (single annotator vs. multiple, adjudication rules), or overlap between the audit set and the validation subset. Without these details the 29.2% figure cannot be treated as a stable population estimate and the supporting argument for target non-invariance remains unanchored.
  2. [results on Mem0 and MemoryOS transfer runs] The abstract reports that target switching 'flips target orderings on Mem0 and MemoryOS transfer runs' and 'reverses parser-density recommendations,' yet the manuscript provides no table or section that lists the exact nDCG values or rank positions under each target for these specific systems. This omission makes it impossible to verify the magnitude and direction of the reported flips.
minor comments (2)
  1. [TIAP method] The three targets (Raw, Source, Canonical) are introduced without an explicit formal definition or pseudocode; a short table or equation block would improve reproducibility.
  2. [experimental setup] The manuscript does not state the total number of queries in the original LoCoMo and LongMemEval-S sets or the exact overlap size used for the 'shared queries' percentage, which would help readers assess selection bias.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thorough review and valuable comments. Below we respond to each major comment and indicate the planned revisions to the manuscript.

read point-by-point responses

  1. Referee: [description of the 1,902-case semantic audit] The central claim that relaxed source-linked credit is 'fully justified only 29.2 percent of the time' rests on the 1,902-case semantic audit. The manuscript states high rubric reliability only for a validation subset and supplies no information on sampling procedure for the 1,902 cases, labeling protocol (single annotator vs. multiple, adjudication rules), or overlap between the audit set and the validation subset. Without these details the 29.2% figure cannot be treated as a stable population estimate and the supporting argument for target non-invariance remains unanchored.

    Authors: We agree that the manuscript should have included more details on the semantic audit to support the 29.2% claim. The referee is correct that without information on sampling, labeling protocol, and overlap, the figure's stability cannot be fully assessed from the text alone. We will revise the manuscript by adding a new subsection under the audit description that specifies the sampling procedure, labeling protocol (including number of annotators and any adjudication), and the overlap between the 1,902-case set and the validation subset. This will allow readers to better evaluate the result. revision: yes

  2. Referee: [results on Mem0 and MemoryOS transfer runs] The abstract reports that target switching 'flips target orderings on Mem0 and MemoryOS transfer runs' and 'reverses parser-density recommendations,' yet the manuscript provides no table or section that lists the exact nDCG values or rank positions under each target for these specific systems. This omission makes it impossible to verify the magnitude and direction of the reported flips.

    Authors: We acknowledge the omission noted by the referee. While the abstract and main text describe the flips in system orderings for Mem0 and MemoryOS, we did not include a table with the precise nDCG values and rank positions under each scoring target. We will add such a table to the results section in the revised manuscript to enable verification of the reported effects. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical rescoring of fixed outputs under standard nDCG

full rationale

The paper applies three fixed scoring targets (Raw, Source, Canonical) to pre-existing ranked retrieval outputs via TIAP and reports nDCG changes plus a separate 1,902-case semantic audit yielding the 29.2% justification rate. These are direct, non-parametric measurements on held-fixed data; no equations, predictions, or central claims reduce by construction to fitted parameters, self-citations, or ansatzes imported from prior work. The audit is presented as an independent count rather than a derived or self-defined quantity. No load-bearing self-citation chains or uniqueness theorems appear in the abstract or described method.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper introduces no free parameters or invented entities. It relies on the standard assumption that nDCG is a suitable ranking metric and on the internal validity of the semantic audit.

axioms (1)
  • standard math nDCG is an appropriate metric for comparing ranked retrieval outputs
    Used to measure changes when rescoring under different targets.

pith-pipeline@v0.9.1-grok · 5731 in / 1240 out tokens · 35717 ms · 2026-06-30T15:16:34.063872+00:00 · methodology

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Reference graph

Works this paper leans on

10 extracted references · 4 canonical work pages · 2 internal anchors

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    Transforms memory?Does the system or benchmark produce non-raw representa- tions, such as extracted facts, summaries, or knowledge-graph entries, from raw conversa- tion turns?

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    Reports retrieval unit?Is the scored unit formally defined?

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    Reports target mapping?Is there a docu- mented mapping from scored items to source conversation evidence?

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    Retrieval units are described at the architectural level in most papers, but only LoCoMo explicitly labels and separately evaluates distinct unit types

    Specifies descendant credit?When one source turn produces multiple stored items, does the paper specify which descendants re- ceive credit? Findings.All 11 papers create transformed mem- ory representations. Retrieval units are described at the architectural level in most papers, but only LoCoMo explicitly labels and separately evaluates distinct unit typ...

    2024