MemScene: Thematic Memory Clusters

• MemScene is a principal abstraction that groups atomic memory traces (MemCells) into semantically coherent clusters for long-horizon, contextually relevant memory recall.
• The approach employs cosine similarity for dynamic clustering, updating centroids and summaries to maintain thematic consistency and resolve conflicts.
• MemScenes reduce memory fragmentation and improve retrieval efficiency in systems like EverMemOS, outperforming traditional flat memory stores in accuracy and relevancy.

MemScene refers to the principal abstraction of thematically grouped, semantically consolidated memory in advanced memory operating systems such as EverMemOS. The primary function of a MemScene is to organize atomic memory traces (MemCells), enabling largescale, persistent, and contextually-relevant recall for long-horizon, structured reasoning in interactive agents—including LLMs. MemScenes represent a solution to the fragmentation of episodic memory and the challenges of stateful summation, conflict resolution, and permanent user-profile evolution in computational memory architectures (Hu et al., 5 Jan 2026).

1. Formal Definition and Core Structure

A MemScene is defined as a cluster of MemCells, themselves four-tuples encompassing a re-narrated episode $E$, a set of atomic facts $\mathcal{F}$, temporally bounded Foresight signals $P$, and auxiliary metadata $M$: $c = (E, \mathcal{F}, P, M)$ MemCells are grouped into MemScenes $S = \{ s_1, ..., s_K \}$ dynamically as new memory is formed. Each MemScene $s_j$ is represented by a centroid embedding $\mu_j$ in a dense semantic space. When a new MemCell with embedding $e_c$ is formed, assignment proceeds by highest cosine similarity: $$\operatorname{sim}(e_c, \mu_j) = \frac{e_c \cdot \mu_j}{\| e_c \| \cdot \| \mu_j \|}$$ If $\max_j \operatorname{sim}(e_c, \mu_j) > \tau$ (threshold), the cell joins scene $s_j$ and updates its centroid; otherwise, it forms a new singleton scene. The MemScene thus serves as both a context window and a latent topic cluster (Hu et al., 5 Jan 2026).

2. Role Within the Memory Lifecycle

MemScenes are central in Phase II ("Semantic Consolidation") of the EverMemOS engram-inspired lifecycle. This phase follows the initial episodic trace formation, where MemCells are extracted from unbounded dialogue or interaction streams. The semantic consolidation process provides:

• Thematic clustering of MemCells into MemScenes (e.g., all medical-experience memories, travel activities, or evolving user preferences)
• Online profile update: Each MemScene maintains an LLM-summarized semantic "scene summary," refined on each addition of a new MemCell
• Conflict and version tracking: Within a MemScene, conflicting or superseded facts are detected and flagged—enabling stateful updates across time

This structure directly enables persistent, low-redundancy, and conflict-aware memory for LLM agents (Hu et al., 5 Jan 2026).

3. Algorithmic Construction and Retrieval

The MemScene scheme supports both construction and retrieval, with tightly integrated clustering and access algorithms:

Construction:

On each MemCell arrival:

• Embed the episode summary (typically via a foundation model embedding API)
• Calculate cosine similarity to every MemScene centroid
• Assign to the closest scene (if above threshold), update centroid and summary; otherwise, instantiate a new MemScene
• Profile updating: LLM-based semantic summarization combines old summary and new episode

Retrieval:

On query $q$ at time $t_{\mathrm{now}}$:

• Score relevance of all MemCells (within all MemScenes) via rank-fused retrieval mechanisms (dense + BM25)
• Compute per-scene scores: $$\mathrm{SceneScore}(s_j) = \max_{c \in s_j} \mathrm{score}(c)$$
• Select top-$N$ scenes: $S_{\mathrm{sel}}$
• Within these, re-rank and filter for minimal, sufficient episode set (agentic sufficiency verification via LLM call)
• Include only "currently valid" foresight signals (with time-interval constraints)

This two-level organization sharply reduces lookup and bandwidth costs while ensuring the returned context is both thematically coherent and minimal (Hu et al., 5 Jan 2026).

4. Functional Significance and Comparative Role

MemScenes address the core shortcomings of previous retrieval systems, which relied on isolated fragments, flat memory stores, or short-lived session summaries. By contrast, MemScenes:

• Enable structured, theme-level recall beyond raw text retrieval
• Support profile and fact evolution, with conflict detection
• Provide efficient consolidation, reducing fragmentation
• Supply a directly human-interpretable organization: each MemScene can be introspectively summarized as a topic (e.g., "career achievements," or "health incidents") and neatly attached to user profiles

In benchmark comparisons, the MemScene-centric approach in EverMemOS produces state-of-the-art results on LoCoMo and LongMemEval (EverMemOS: 86.76% and 83.00% accuracy, respectively), outperforming flat memory stores and baselines oriented toward simple retrieval-augmented generation (Hu et al., 5 Jan 2026).

5. Integration, Use Cases, and Practical Implementation

MemScenes are employed in both chat-oriented (persona, multi-session) and document-oriented (episodic, temporal) memory scenarios. Use cases demonstrated include:

• Profile-based Planning: Linking longitudinal metrics (such as changing waist size for fitness goal tracking) to a single MemScene for goal-consistent advice
• Persistent Foresight: Consolidating foresight signals (e.g., allergy reminders or travel issues) so the correct constraints are supplied during planning episodes
• Fine-grained Episode Recall: Reconstructing specific events (e.g., not just generic “injury” but "Grade-II ankle sprain during a hike") (Hu et al., 5 Jan 2026)

Integration is realized in EverMemOS via Python-based pipelines, LLM API calls, a dense retriever (e.g., Qwen3-Embedding), BM25 search, and NumPy/Scikit-learn clustering. Updates and queries invoke incremental MemScene clustering and summarization in response to dialogue or user input.

6. Limitations and Future Considerations

Current MemScene implementations operate on text-only MemCells; planned extensions include multimodal support for images, video, and structured data. While online clustering ensures scalability, ultralarge time horizons (beyond $10^5$ tokens) are not fully benchmarked. Adaptive memory pruning, scene graph hierarchization, and end-to-end prompt optimization are projected future directions for improving MemScene consolidation and recall precision. For practical deployment, amortizing LLM-call latency and asynchronous memory update are empirically recommended (Hu et al., 5 Jan 2026).

7. Distinction from Related Constructs and Terminology

MemScene occupies a semantic tier above MemCell, enabling mid-grained, theme-cohesive memory as opposed to either atomic or monolithic session summaries. It contrasts with the MemCube abstraction from MemOS, which focuses on scheduling memory units across plaintext, activation, and parameter tiers with lifecycle and governance metadata (Li et al., 4 Jul 2025). In comparison to CREEM’s “insight stream” (Kim et al., 2024), MemScene is oriented toward persistent, cluster-based organization rather than insight-driven, prompt-fused updates. This distinction underlies EverMemOS's ability to support stateful, person-level reasoning and long-term externalization of knowledge traces.