HaluMem: Evaluating Hallucinations in Memory Systems of Agents
Memory systems are key components that enable AI systems such as LLMs and AI
agents to achieve long-term learning and sustained interaction. However, during
memory storage and retrieval, these systems frequently exhibit memory
hallucinations, including fabrication, errors, conflicts, and omissions.
Existing evaluations of memory hallucinations are primarily end-to-end question
answering, which makes it difficult to localize the operational stage within
the memory system where hallucinations arise. To address this, we introduce the
Hallucination in Memory Benchmark (HaluMem), the first operation level
hallucination evaluation benchmark tailored to memory systems. HaluMem defines
three evaluation tasks (memory extraction, memory updating, and memory question
answering) to comprehensively reveal hallucination behaviors across different
operational stages of interaction. To support evaluation, we construct
user-centric, multi-turn human-AI interaction datasets, HaluMem-Medium and
HaluMem-Long. Both include about 15k memory points and 3.5k multi-type
questions. The average dialogue length per user reaches 1.5k and 2.6k turns,
with context lengths exceeding 1M tokens, enabling evaluation of hallucinations
across different context scales and task complexities. Empirical studies based
on HaluMem show that existing memory systems tend to generate and accumulate
hallucinations during the extraction and updating stages, which subsequently
propagate errors to the question answering stage. Future research should focus
on developing interpretable and constrained memory operation mechanisms that
systematically suppress hallucinations and improve memory reliability.
690c081d60494e4fa7675618
Ding Chen
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2025-11-11T19:51:36.105Z
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Ding Chen
Hush-cd
user
Hush-cd
690c081d60494e4fa7675619
Simin Niu
690c081d60494e4fa767561a
Kehang Li
690c081d60494e4fa767561b
Peng Liu
690c081d60494e4fa767561c
Xiangping Zheng
690c081d60494e4fa767561d
Bo Tang
690c081d60494e4fa767561e
Xinchi Li
690c081d60494e4fa767561f
Feiyu Xiong
690c081d60494e4fa7675620
Zhiyu Li
HaluMem: Evaluating Hallucinations in Memory Systems of Agents
Memory systems are key components that enable AI systems such as LLMs and AI
agents to achieve long-term learning and sustained interaction. However, during
memory storage and retrieval, these systems frequently exhibit memory
hallucinations, including fabrication, errors, conflicts, and omissions.
Existing evaluations of memory hallucinations are primarily end-to-end question
answering, which makes it difficult to localize the operational stage within
the memory system where hallucinations arise. To address this, we introduce the
Hallucination in Memory Benchmark (HaluMem), the first operation level
hallucination evaluation benchmark tailored to memory systems. HaluMem defines
three evaluation tasks (memory extraction, memory updating, and memory question
answering) to comprehensively reveal hallucination behaviors across different
operational stages of interaction. To support evaluation, we construct
user-centric, multi-turn human-AI interaction datasets, HaluMem-Medium and
HaluMem-Long. Both include about 15k memory points and 3.5k multi-type
questions. The average dialogue length per user reaches 1.5k and 2.6k turns,
with context lengths exceeding 1M tokens, enabling evaluation of hallucinations
across different context scales and task complexities. Empirical studies based
on HaluMem show that existing memory systems tend to generate and accumulate
hallucinations during the extraction and updating stages, which subsequently
propagate errors to the question answering stage. Future research should focus
on developing interpretable and constrained memory operation mechanisms that
systematically suppress hallucinations and improve memory reliability.
HaluMem, a benchmark for evaluating memory hallucinations in AI systems, identifies and analyzes hallucinations across memory extraction, updating, and question answering stages using large-scale human-AI interaction datasets.
memory systems LLMs AI agents memory hallucinations fabrication errors conflicts omissions HaluMem operation level hallucination evaluation memory extraction memory updating memory question answering user-centric multi-turn human-AI interaction datasets HaluMem-Medium HaluMem-Long dialogue length context lengths token hallucinations memory reliability