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ARXIV:2605.15384 · LLM EVALUATION · SUBMITTED 18 MAY · 20:34 UTC · FRESHNESS STALE
ARXIV:2605.15384LLM EVALUATIONSUBMITTED 18 MAY · 20:34 UTCFRESHNESS STALESongwei Dong · Zihan Chen · Chengshuai Shi · Peng Wang · Jundong Li · Cong Shen · arXiv
A new framework for evaluating LLM memory that goes beyond aggregate metrics to reveal critical failure modes like forgetting and negative transfer.
Opportunity summary
Pain A new framework for evaluating LLM memory that goes beyond aggregate metrics to reveal critical failure modes like forgetting and negative transfer.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A new framework for evaluating LLM memory that goes beyond aggregate metrics to reveal critical failure modes like forgetting and negative transfer. However, existing evaluations of LLM memory mostly rely on aggregate metrics such…
Memory plays a central role in enabling large language models (LLMs) to operate over sequential tasks by accumulating and reusing experience over time. However, existing evaluations of LLM memory mostly rely on aggregate metrics…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Through extensive experiments across diverse tasks and memory methods, we show that higher final or cumulative accuracy does not necessarily imply better memory quality:…
LLM Evaluation moved forward this cycle; last verified May 2026. Public score 2.0/10.
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A new framework for evaluating LLM memory that goes beyond aggregate metrics to reveal critical failure modes like forgetting and negative transfer.
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10.48550/arXiv.2605.15384A new framework for evaluating LLM memory that goes beyond aggregate metrics to reveal critical failure modes like forgetting and negative transfer.
Abstract
Memory plays a central role in enabling large language models (LLMs) to operate over sequential tasks by accumulating and reusing experience over time. However, existing evaluations of LLM memory mostly rely on aggregate metrics such as final hold-out accuracy or cumulative online performance, which can obscure critical failure modes such as forgetting and negative transfer. In this paper, we introduce SeqMem-Eval, a diagnostic evaluation framework for sequentially evolving LLM memory. Drawing inspiration from continual learning, it targets a test-time setting in which memory is external, prompt-mediated, and updated without modifying model parameters. Rather than focusing only on final performance, SeqMem-Eval evaluates how memory states evolve, generalize, consolidate experience, and retain useful information during sequential inference. Specifically, it measures online utility, hold-out generalization, backward transfer, and forgetting, providing a finer-grained view of memory quality. Through extensive experiments across diverse tasks and memory methods, we show that higher final or cumulative accuracy does not necessarily imply better memory quality: many methods exhibit strong performance gains while suffering from substantial forgetting or negative transfer. Moreover, different memory designs exhibit distinct trade-offs between adaptability and stability that remain invisible under standard evaluation metrics.
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PROBLEM
A new framework for evaluating LLM memory that goes beyond aggregate metrics to reveal critical failure modes like forgetting and negative transfer. However, existing evaluations of LLM memory mostly rely on aggregate metrics such as final hold-out accuracy or cumulative online...
METHOD
Memory plays a central role in enabling large language models (LLMs) to operate over sequential tasks by accumulating and reusing experience over time. However, existing evaluations of LLM memory mostly rely on aggregate metrics such as final hold-out accuracy or cumulative onli...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Through extensive experiments across diverse tasks and memory methods, we show that higher final or cumulative accuracy does not necessarily imply better memory quality: many methods exhibit strong perfor...
WHY NOW
LLM Evaluation moved forward this cycle; last verified May 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A new framework for evaluating LLM memory that goes beyond aggregate metrics to reveal critical failure modes like forgetting and negative transfer. However, existing evaluations of LLM memory mostly rely on aggregate metrics such as final hold-out accuracy or cumulative online performance, which can obscure critical failure modes such as forgetting and negative transfer.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Memory plays a central role in enabling large language models (LLMs) to operate over sequential tasks by accumulating and reusing experience over time. However, existing evaluations of LLM memory mostly rely on aggregate metrics such as final hold-out accuracy or cumulative online performance, which can obscure critical failure modes such as forgetting and negative transfer.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Through extensive experiments across diverse tasks and memory methods, we show that higher final or cumulative accuracy does not necessarily imply better memory quality: many methods exhibit strong performance gains while suffering from substantial forgetting or negative transfer.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Evaluation moved forward this cycle; last verified May 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A new framework for evaluating LLM memory that goes beyond aggregate metrics to reveal critical failure modes like forgetting and negative transfer.
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LLM Evaluation
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