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ARXIV:2604.26243 · AGENTS · SUBMITTED 30 APR · 15:13 UTC · FRESHNESS STALE
ARXIV:2604.26243AGENTSSUBMITTED 30 APR · 15:13 UTCFRESHNESS STALEYerong Wu · Tianxing Wu · Minghao Zhu · Hangyu Sha · Haofen Wang · arXiv
A benchmark and framework to evaluate strategic memory use in virtual character conversations, identifying limitations in current LLMs.
Opportunity summary
Pain A benchmark and framework to evaluate strategic memory use in virtual character conversations, identifying limitations in current LLMs.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A benchmark and framework to evaluate strategic memory use in virtual character conversations, identifying limitations in current LLMs. Current memory utilization relevant (e.g., memory-augmented generation, long-term dialogue, and etc.) benchmarks overlook this nuance, treating…
Achieving realistic human-like conversation for virtual characters requires not only a simple memorization and recall of past events, but also the strategic utilization of memory to meet factual needs and social engagement. Current memory…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Experiments on StratMem-Bench which leverage the state-of-the-art large language models as virtual characters show that all models perform well at distinguishing between required and…
Agents moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
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A benchmark and framework to evaluate strategic memory use in virtual character conversations, identifying limitations in current LLMs.
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10.48550/arXiv.2604.26243A benchmark and framework to evaluate strategic memory use in virtual character conversations, identifying limitations in current LLMs.
Abstract
Achieving realistic human-like conversation for virtual characters requires not only a simple memorization and recall of past events, but also the strategic utilization of memory to meet factual needs and social engagement. Current memory utilization relevant (e.g., memory-augmented generation, long-term dialogue, and etc.) benchmarks overlook this nuance, treating memory primarily as a static repository of facts rather than a dynamic resource to be strategically deployed in dialogues. To address this gap, we design StratMem-Bench, a new benchmark to evaluate strategic memory use in character-centric dialogues. This dataset comprises 657 instances where virtual characters must navigate heterogeneous memory pools containing required, supportive, and irrelevant memories. We also propose a framework with different evaluation metrics including Strict Memory Compliance, Memory Integration Quality, Proactive Enrichment Score and Conditional Irrelevance Rate, to evaluate strategic memory use capabilities of virtual characters. Experiments on StratMem-Bench which leverage the state-of-the-art large language models as virtual characters show that all models perform well at distinguishing between required and irrelevant memories, but struggle once supportive memories are introduced into the decision process.
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unverified0 refs; 3 sources; 50% coverage.
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Dimensions overall score 6.0
PROBLEM
A benchmark and framework to evaluate strategic memory use in virtual character conversations, identifying limitations in current LLMs. Current memory utilization relevant (e.g., memory-augmented generation, long-term dialogue, and etc.) benchmarks overlook this nuance, treating...
METHOD
Achieving realistic human-like conversation for virtual characters requires not only a simple memorization and recall of past events, but also the strategic utilization of memory to meet factual needs and social engagement. Current memory utilization relevant (e.g., memory-augme...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Experiments on StratMem-Bench which leverage the state-of-the-art large language models as virtual characters show that all models perform well at distinguishing between required and irrelevant memories,...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 20, "author": "Yerong Wu; Tianxing Wu; Minghao Zhu; Hangyu Sha; Haofen Wang"
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A benchmark and framework to evaluate strategic memory use in virtual character conversations, identifying limitations in current LLMs.
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6.0/10 public viability
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