Characterizing Memory Misalignment in Human-LLM Interaction From User Perspectives

While memory enhances personalization in LLM-based conversational agents, it suffers from memory misalignment, where memories violate user expectations. We present a mixed-methods investigation to characterize and mitigate memory misalignment from user perspectives. First, we collected data from memory usage (N=28, 457 entries) and diary study (N=32, 304 reports), which yielded a taxonomy spanning 14 misalignment types across memory intake, storage and management, retrieval and interpretation stages.

Second, four co-design workshops with 12 experienced HCI researchers derived a design space to tackle memory misalignment issues, consisting of 12 candidate interaction strategies structured across interaction form, placement and intrusiveness dimensions. Finally, a speed dating with 121 users reveals preference heterogeneity, where users prioritize proactive controls over cognitively demanding causal graph inspections or passive audit logs. Synthesizing these findings, we highlight the tension between supervisory agency and interaction overhead, and advocate for friction-aware memories that balance user oversight with conversation smoothness.

Introduction. Memories are essential for enabling effective long-term interactions in LLM-based conversational agents [62]. By retaining contextual cues across sessions, agent memory reduces user effort [46] and enables adaptive personalization aligned with user preferences [52]. As a result, memory mechanisms have been widely deployed in commercial conversational systems, such as ChatGPT [49], Gemini [21], and Doubao [9].

Despite these advancements, deployed memory systems often induce negative user experiences [50, 65]. Beyond factual errors and hallucinations [12], even factually accurate memories could be inappropriately accumulated, retained, or used in situated interactions1. While recent research has benchmarked factual inaccuracies [58, 65, 77] and general conversational memory retrieval [7], little empirical work has investigated memory misalignment, the discrepancy between system memory behaviors and user intents or expectations, from the user’s perspective. Understanding these breakdown patterns and designing coping mechanisms is important for developing reliable LLM memory architectures. To address this gap, we propose the following research questions (RQs):

• RQ1: What are the memory misalignment cases in interactions between humans and LLM-based chatbots?

• RQ2: What are interaction designs that can mitigate memory misalignment?

• RQ3: How do users perceive the efficacy of these mitigation strategies?

For RQ1, we conducted a two-phase data collection study combining a memory database (N=28, 457 memories) and a three-week diary study (N=32, 304 reports). Grounded in cognitive memory error frameworks [54], we identified 14 distinct misalignment types spanning three stages: memory intake (e.g., over-inference, attribution confusion), storage and management (e.g., temporal staleness, granularity mismatch), and retrieval and interpretation (e.g., outdated context, inappropriate over-application).

For RQ2, we organized four co-design workshops with HCI researchers (N=12). Through affinity diagramming and thematic synthesis, we derived 12 interaction designs organized across three operational dimensions: interaction form (panel-based, avatar-based, conversational), placement (in-chat, out-of-chat), and intrusiveness (low, medium, high).

For RQ3, we evaluated interactive prototypes of these 12 designs via a speed dating evaluation with 121 users.

Plackett-Luce preference modeling revealed heterogeneity in user preferences. Users strongly favored proactive controls, such as post-task destination prompts and pre-task boundary settings, over passive background auditing or cognitively demanding causal graph inspections. Our findings characterize a tension between supervisory agency and conversational friction. Grounded in these trade-offs, we recommend that LLM memory systems should adopt friction-aware, context-decoupled designs that prioritize lightweight verification.

Collectively, this paper contributes: (i) a taxonomy for memory misalignments in interactions between humans and LLM-based chatbots, (ii) a design space consisting of interaction form, placement, and intrusiveness dimensions for tackling memory misalignments, (iii) empirical evidence showcasing trade-offs between designs of memory misalignment tackling methods.

1https://www.facebook.com/groups/claudeaicommunity/posts/1356633023170528/

Related work. 2.1 Memory and Alignment Taxonomy Cognitive psychology provides taxonomies for understanding memory distortions. Schacter [54] categories these into sins of omission (transience, absent-mindedness, blocking) and commission (misattribution, suggestibility, bias, persistence), whose underlying cognitive mechanisms are formalized by associative false memory paradigms [53], fuzzytrace theory [8], and source monitoring frameworks [34]. In this work, these theories help ground our taxonomy of memory misalignments, enabling us to map algorithmic failures to established human cognitive biases, such as relating over-inference to bias.

In conversational AI agents, memory integration is increasingly conceptualized as a multi-stage process. Jones et al. [35] describe a three-stage lifecycle of intake, storage and management, and retrieval and interpretation. Other surveys characterize agent memory from complementary perspectives on its operations, representations, temporal structure, and control [18, 26, 27, 63, 68, 79]. Luo et al. [43] further distinguish raw trajectory storage, reflection, and cross-trajectory generalization. We adopt Jones et al.’s lifecycle to trace how memory misalignment emerges across interaction history.

Beyond memories, broader AI alignment frameworks investigate model adherence to human values, delineating outer versus inner alignment [30] and normative criteria such as helpfulness, honesty, and harmlessness [4]. Gabriel [20] highlighted the divergence between users’ expressed intentions and inferred revealed preferences. However, existing alignment paradigms primarily target interactions, remaining ill-suited for memory interactions. We address this by framing memory misalignment as an interactional breakdown, motivating user-in-the-loop mechanisms to mitigate these issues.

Empirical investigations into human-agent memory explored the nature, generation, and user perception of stored information. Large-scale analyses show that most memories are created by systems rather than users and may include personal information, psychological inferences, and conversational content [16]. Users also often have incomplete mental models of how agents store and recall information, and distinguish memories at different levels of abstraction [36].

To rigorously evaluate these systems, recent benchmarks extend memory evaluation to evolving user information personalization, and later behavior. LifeBench tests long-horizon reasoning across multiple memory sources [15], while Manuscript submitted to ACM 4 Chen et al.

PERMA and HorizonBench examine changing user preferences over time [40, 42]. MemoryCD evaluates cross-domain personalization from long-term user histories [78], and EvoMemBench evaluates memory across episodes and task execution [60].

Method. 3 RQ1: Eliciting Memory Misalignment in Human–Chatbot Interactions To address RQ1, we investigated real-world memory breakdowns through a two-phase study combining the assessment of chatbot memories and memories’ usage. Beyond simple retrieval errors, we found that chatbots often misjudge personal boundaries or conversational timing, which undermine user trust.

3.1 Methodology We conducted a two-phase study to collect cases of memory misalignment in LLM-based chatbots. The first phase examined participants’ assessments of chatbot memories, whereas the second examined perceived misalignments in memory use during everyday interactions. Both phases were approved by our university’s Institutional Review Board (IRB).

We categorized the memory misalignment cases into 14 classes across three stages: intake, storage & management, and retrieval & interpretation. These three stages follow Jones et al. [35]: intake refers to input filtering and extraction, where utterances are distilled into candidate profile attributes. Storage and management includes the consolidation and organization of memory items, and retrieval and interpretation involves recalling and applying stored memories to steer ongoing conversation. Notably, for each case, we also classified it according to human memory errors taxonomy into two categories and seven types. We note each misalignment issue with I1-I14.

3.2.1 Intake. I1: Over inference (corresponds to Memory Bank, Commission-Bias). An error where the system elevates low-frequency, anomalous, or transient utility-driven user actions into stable, longitudinal preferences, habits, or core identity traits. It frequently manifests as an unwarranted inflation of behavioural frequency. For example, the system logged a long list of highly disparate, one-off search queries, such as AirPods pairing, removing chilli oil stains, AC power consumption, as persistent “daily areas of interest”. The user may not realise that these were exploratory queries akin to using a search engine, not reflections of a sustained hobby.

I2: Attribution confusion (corresponds to Memory Bank, Commission-Misattribution). This is a boundarymapping error wherein third-party attributes, fictitious entities, or external environmental variables introduced within the prompt context are erroneously attributed to the user’s personal profile. This includes proxy inquiries made on behalf of others, fictional personas in creative writing templates, or simulation parameters within academic tasks. For example, the system extracted biographic details from a creative writing exercise and committed them to the user profile.

The user corrected that these parameters were purely fictional.

I3: Identity misinference (corresponds to Memory Bank, Commission-Misattribution). This is an erroneous deduction of the user’s demographic, socioeconomic, or institutional status, such as sex, occupation, academic standing, geographical affiliation. This typically stems from the system over-interpreting sparse or fragmented behavioural cues and projecting unverified social identities. For example, the system may erroneously inferred and logged the user’s demographic gender profile as female. The user issued a direct correction stating they are male.

Manuscript submitted to ACM 8 Chen et al.

I4: Hallucination (corresponds to Memory Bank, Commission-Suggestibility). The generation of profile components that completely lack empirical grounding within the historical dialogue architecture. This manifests either when the system invents arbitrary preferences the user has no recollection of communicating, or when internal system prompts and operational rules are erroneously externalized and logged as user traits. For example, the system logged an internal architectural constraint as a user memory property. “Hard rule context: when the user questions date-related data, the system must firmly maintain the current year as 2026.” The user identified this as entirely alien to their conversation history and requested immediate deletion.