EverMemOS: MemScene Memory Abstraction

• EverMemOS MemScene is a high-level, semantically unified memory structure that clusters related episodic MemCells to provide a coherent context for LLMs.
• It employs a dynamic construction methodology that embeds episodes and uses online clustering with similarity thresholds to update memory profiles in real time.
• MemScene enables state-of-the-art retrieval and reasoning, achieving significant accuracy gains on long-horizon benchmarks through structured, context-aware retrieval.

MemScene is the central abstraction within EverMemOS, denoting a thematic, dynamically evolving cluster of atomic memory units (“MemCells”) that encapsulate semantically consolidated context for LLMs and memory-augmented reasoning systems. A MemScene provides the minimal, coherent context window needed for efficient, structured retrieval and downstream reasoning across long-horizon, personalized interactions. The following sections systematically examine the technical foundations, construction lifecycle, retrieval algorithms, empirical results, comparative context, and practical design considerations of MemScene within the broader EverMemOS memory operating system.

1. Formal Definition and Core Function

A MemScene is a high-level, semantically unified memory structure formed by clustering MemCells derived through episodic trace formation. Each MemCell c is a tuple $(E, \mathcal{F}, P, M)$, comprising: a third-person narrative episode $E$, a set of atomic facts $\mathcal{F}$, time-bounded Foresight signals $P$, and metadata $M$ (Hu et al., 5 Jan 2026). The MemScene $s_j$ aggregates and organizes related MemCells (episodes and facts) such that $s_j = \{c_i : \text{sim}(e_{c_i}, \mu_j) > \tau\}$, where $e_{c_i}$ is the embedding of the $i$th MemCell episode and $\mu_j$ is the cluster centroid.

The primary purpose of a MemScene is to distill and maintain stable semantic structures—bracketing temporally or thematically aligned information—thereby supporting robust profile construction, reasoning, and context reconstruction for LLM agents operating over long conversational or multimodal timelines (Hu et al., 5 Jan 2026).

2. MemScene Construction and Semantic Consolidation

MemScene formation constitutes Phase II (“Semantic Consolidation”) in the EverMemOS engram-inspired lifecycle. The process is as follows:

1. Embedding MemCells: Each episode $E_{c}$ is embedded to yield $e_c = \text{Embed}(E_c)$.
2. Online Clustering: Upon arrival of $e_c$, compute similarity scores $\text{sim}(e_c, \mu_j)$, where $\mu_j$ is the centroid of candidate scenes. If $\max_j \text{sim}(e_c, \mu_j) > \tau$ (where $\tau$ is a tunable threshold), assign $c$ to $s_j$ and update its centroid:

$$\mu_j \leftarrow \frac{n_j \mu_j + e_c}{n_j + 1},\, n_j \leftarrow n_j + 1$$

Otherwise, create a new MemScene with $e_c$ as the initial centroid (Hu et al., 5 Jan 2026).

1. Scene Summarization and Profile Update: Each MemScene maintains a compact summary (e.g., via $LLM_\text{summarize}$ combining current MemScene summary and $E_c$); these summaries are used for both user profiling and downstream context selection.

The MemScene architecture supports incremental growth and structural plasticity: as new episodes or facts arise, existing scenes are dynamically updated, or new scenes are instantiated, mirroring evolving conversational or knowledge domains.

3. Role in Retrieval and Recollection

MemScene-guided retrieval forms the backbone of Phase III (“Reconstructive Recollection”) in EverMemOS. The retrieval pipeline proceeds as follows (Hu et al., 5 Jan 2026):

1. Query Scoring: For user query $q$ at time $t_{\text{now}}$, each MemCell $c$ is scored using fused dense ($$\langle \text{Embed}(q), \text{Embed}(\mathcal{F}_c) \rangle$$) and BM25 scores, aggregated by reciprocal rank fusion (RRF).
2. Scene Ranking: For each MemScene $s_j$, compute $$\text{SceneScore}(s_j) = \max_{c \in s_j} \text{score}(c)$$; select the top $N$ MemScenes.
3. Filtered Recall: From the selected MemScenes, aggregate top $K$ MemCells by relevance; extract only those Foresight entries $p_i$ whose $[t_i^{\text{start}}, t_i^{\text{end}}]$ include $t_{\text{now}}$.
4. Agentic Sufficiency: Pass the reconstructed context (episodes, filtered foresight, profile summary) to an LLM verification step that judges contextual sufficiency—potentially triggering follow-up queries if gaps remain.

This pipeline ensures that only the most thematically coherent, temporally valid, and profile-consistent context is surfaced for reasoning, minimizing spurious retrieval and supporting structured, accurate, and efficient agent output.

4. Comparative Evaluation and Empirical Impact

EverMemOS demonstrates state-of-the-art performance on memory-augmented reasoning benchmarks, largely attributable to its MemScene-based consolidation and retrieval mechanism (Hu et al., 5 Jan 2026). Key empirical findings include:

• On LoCoMo (long-horizon QA benchmark): EverMemOS achieves 86.76% accuracy, outperforming all compared baselines by margins of +14.48 points on multi-hop and temporal questions.
• On LongMemEval: accuracy of 83.00%, exceeding the next-best method by 5.20 points on knowledge update tasks.
• In profile ablation studies (PersonaMem v2), “Episodic + Profile” mode yields a 9.32-point gain over Episodic-only, underscoring the complementary signal from consolidated MemScenes.
• Qualitative analyses show higher-fidelity recall (e.g., reconstructing detailed health events), consistent longitudinal planning (aggregating episodic weight-loss goals), and precise Foresight application (avoidance of recurring travel conflicts).

A plausible implication is that MemScene abstraction, by enforcing thematic consolidation and guided recall, enables superior context assembly compared to flat retrieval or isolated record recall methods.

5. Relation to Other Memory Architectures

MemScene represents a marked evolution from prior memory designs in several respects:

• In contrast to stateless vector storage (as in encode-store-retrieve pipelines for egocentric lifelogging (Shen et al., 2023)), MemScene enforces semantic aggregation and explicit profile update, enabling multi-granular retrieval and consolidation beyond mere nearest-neighbor search.
• Compared to session-based or summary-based memory (e.g., SumMem_MSC, ReBot_CC, GPT_CareCall in (Kim et al., 2024)), MemScenes dynamically blend episodic, factual, and foresight components, incorporating context-aware consolidation and minimal-loss forgetting.

The MemCube abstraction in MemOS (Li et al., 4 Jul 2025) generalizes memory management across plaintext, activation, and parameter tiers, with MemScene functionality expressed at the semantic aggregation layer for non-parameter memory. This suggests MemScene could serve as a unifying substrate for long-horizon retrieval across heterogeneous memory types.

6. Limitations, Extensions, and Practical Considerations

Known limitations of the MemScene model include:

• Text-Only Modality: Current instantiations operate over textual memory; extension to multimodal MemCells (e.g., video, structured data) is noted as future work (Hu et al., 5 Jan 2026).
• Scalability and Efficiency: LLM-mediated memory steps can incur latency and token cost; mitigation strategies include asynchronous batching and caching. Ultra-long timelines (>100k tokens) remain an open benchmarking frontier.
• Adaptive Pruning: There is ongoing research into hierarchical scene-graph organization and adaptive memory pruning to manage memory footprint and retrieval speed.
• Profile Governance: Access control, compliance, and data governance (carried over from MemCube/ACL meta-attributes (Li et al., 4 Jul 2025)) become critical as MemScene profiles are used in sensitive applications.

Practical deployment requires pipelined ingestion (segmentation, clustering), incremental clustering updates, and integration with structured retrieval APIs; the reference implementation is available at https://github.com/EverMind-AI/EverMemOS (Hu et al., 5 Jan 2026).

7. Broader Significance and Outlook

MemScene, as deployed in EverMemOS, consolidates the semantic, structural, and operational requirements for long-horizon AI memory. It bridges low-level episodic storage and high-level user modeling by supporting memory plasticity, context sufficiency, and dynamic reasoning. Its impact is manifest in enhanced personalization, temporal reasoning, and cost-efficient memory access across LLM agents. Iterative refinement and hierarchical extensions, as outlined in ongoing EverMemOS and MemOS developments, position MemScene as a foundational abstraction for scalable, adaptive, and interpretable agent memory architectures (Hu et al., 5 Jan 2026, Li et al., 4 Jul 2025, Kim et al., 2024).