A LoCoMo score is meaningful only alongside the evaluation protocol that produced it. Published results can differ in the questions scored, answer and judge models, correctness rules, metrics, and category aggregation—not just in the memory system. One recent report gives EverMemOS 94.7% on single-hop questions and 94.5% overall, but explicitly warns that its lenient scoring is not directly comparable to strict exact-match baselines. The title’s 94.7% figure should not be attributed to EverMemOS without verifying its original citation.
What does “94.7% on LoCoMo” mean?
It means a system answered a particular set of LoCoMo questions under a particular scoring procedure and received a score of 94.7%. By itself, the percentage does not tell you which questions were included, how answers were generated, what counted as correct, or whether the number is a category score or an overall result.
A TrueMemory project report, accessed in 2026, reports EverMemOS at 94.7% for single-hop questions and 94.5% overall across its stated 1,540-question evaluation. The report describes its semantic-match rubric as lenient: answers sharing the same core topic or fact may count as correct, and equivalent date formats are accepted. Its authors caution that its absolute results are not directly comparable to published LoCoMo baselines using strict exact-match scoring: TrueMemory benchmark report.
This is a documented example of a 94.7% LoCoMo result, not proof that it is the source of the figure in this article’s title. The title’s underlying citation could not be confirmed. A matching number should not be assigned to a system until its original experiment or paper has been checked.
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Why can published LoCoMo scores differ?
The benchmark name does not guarantee that two scores measure the same task in the same way. The reviewed evaluations make several important differences visible:
- Question subset and category coverage: The TrueMemory setup scores 1,540 questions from 10 conversations across four categories and excludes the adversarial category.
- Answer model: TrueMemory uses GPT-4.1-mini to generate answers. Another result card describing the Mem0-paper protocol uses GPT-4o-mini as both answerer and judge, also scoring 1,540 questions and excluding adversarial questions.
- Judge and scoring rule: TrueMemory uses GPT-4o-mini judges and a semantic-match rule, with a majority vote across three judge runs. A different answer model or judging procedure can change which responses count as correct.
- Metric and aggregation: A percentage for one category is not the same quantity as an overall percentage. The peer-reviewed MemoryOS paper reports category-level F1 and BLEU-1, with separate results under GPT-4o-mini and Qwen2.5-3B answer-model conditions.
These are protocol differences, not a controlled experiment that isolates the effect of any one choice. The available comparisons do not establish how much of a particular score gap comes from evaluation rather than memory architecture.
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Are two scores comparable if both say 94.7%?
Not necessarily. A shared percentage and benchmark label are insufficient if the systems were evaluated on different slices, with different models or correctness criteria. Treat results from unlike protocols as contextual references, not as a ranking.
For example, the TrueMemory report evaluates its systems under a shared answer model, judge, prompt, top-k, and scoring procedure, and says only the retrieval layer differs within that comparison. That consistency makes the report’s internal comparison more controlled; it does not make its scores directly comparable to results from other papers or vendors that use different protocols. The report’s setup and caveat are described in its benchmark report. A separate Rovemark result card summarizes the Mem0-paper setup using GPT-4o-mini as answerer and judge.
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What to check before comparing LoCoMo results
Look for the following details in the paper, report, or result card. If a detail is missing, the score is harder to interpret—not automatically invalid.
- System and version: Identify the exact system, release, and configuration evaluated.
- Dataset and denominator: Check the dataset release, conversations, number of questions, and whether the score covers all questions or a subset.
- Included categories: Note which categories were scored and whether adversarial questions were omitted.
- Answer generation: Record the answer model and relevant generation settings.
- Judging: Check the judge model, judge prompt, and number of judge runs or votes.
- Correctness metric: Distinguish exact match from semantic matching, and note metrics such as F1 or BLEU-1.
- Memory and retrieval setup: Find out what memory configuration or retrieval settings were used.
- Provenance: Determine whether the result is vendor-reported, independently reproduced, or a paper baseline.
A useful comparison table should keep these fields beside each score. If two rows differ in scoring rubric, model, or question coverage, the table should label them as different evaluation setups rather than implying a head-to-head ranking.
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How to read category scores and overall scores
Category and overall results answer different questions. The TrueMemory report’s 94.7% is a single-hop result; its overall figure is 94.5%. Reporting only the higher category figure would make the result look like an overall score when it is not.
Category breakdowns can also reveal performance patterns that a single aggregate hides. MemoryOS reports category-level F1 and BLEU-1 under more than one answer-model condition, so its results should be read with the category, metric, and model attached—not reduced to one unlabeled percentage. See the peer-reviewed MemoryOS paper for its specified conditions.
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What the score gap does—and does not—show
Different published LoCoMo numbers are a reason to inspect evaluation details before concluding that one memory system is better. They do not, on their own, prove that evaluation choices explain most of a particular gap, or quantify the share caused by a memory architecture. Establishing that would require aligned datasets, answer and judge models, prompts, scoring rules, and system conditions.
The defensible takeaway is narrower: a LoCoMo score is a result of both the system and the protocol used to evaluate it. Until those conditions are aligned, compare the methods and qualifications alongside the percentages rather than treating the benchmark name as a guarantee of comparability.
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