Agent Semantic Memory (AgentSM)

• Agent Semantic Memory is the explicit, persistent, and generalizable knowledge component that enables AI agents to store, distill, and reuse task- and world-level insights.
• It is implemented using diverse representations such as textual summaries, dense embeddings, structured traces, and graph-based facts to support efficient retrieval.
• AgentSM augments frozen or non-parametrically supervised agents with dynamic semantic guidance, improving adaptability, interpretability, and reasoning efficiency in new contexts.

Agent Semantic Memory (AgentSM) denotes the explicit, persistent, and generalizable knowledge component in agent architectures that enables agents—especially those based on LLMs—to store, distill, and reuse abstracted task-level or world-level insights beyond specific, instance-based experiences. Unlike episodic or short-term memory, which captures temporally localized, granular events, AgentSM is responsible for encoding, updating, and retrieving conceptual, reusable, and often cross-situational information, such as distilled critiques, persistent factual knowledge, or procedural demonstrations. AgentSM has been implemented in diverse forms, including textual summaries, dense vector stores, structured program traces, graph-based facts, and hybrid knowledge graphs. Its purpose is to augment frozen or non-parametrically supervised agents with external, dynamically updated semantic guidance, enabling more adaptive, interpretable, and sample-efficient reasoning in new tasks or contexts (Hassell et al., 22 Oct 2025).

1. Theoretical Foundations and Definitions

Agent Semantic Memory emerges from foundational distinctions in both cognitive science and AI systems between episodic and semantic (factual) memory. In cognitive terms, semantic memory is the store of general, context-independent knowledge—concepts, facts, rules—contrasted with episodic memory's temporally situated episodes (Hu et al., 15 Dec 2025). In computational frameworks, AgentSM formalizes this functionality as an explicit memory module or data structure responsible for:

• Encoding distilled, task-general knowledge or procedural rules, often as the output of a summarization or consolidation step over episodic labels, critiques, or execution traces (Hassell et al., 22 Oct 2025, Biswal et al., 22 Jan 2026).
• Serving as a reusable repository for high-level guidance at inference time, independent of agent parameter updates (Hassell et al., 22 Oct 2025).
• Structuring knowledge in formats such as bulleted summaries, dense embeddings, graph triplets, or segmental traces, designed for efficient retrieval and integration into the agent’s decision process (Biswal et al., 22 Jan 2026, Zhou et al., 9 Jan 2026).

Mathematically, AgentSM may be represented as a set, list, or graph of knowledge entries:

• For text-based systems: $M = \{m_1, m_2, ..., m_N\}$, where each $m_i = (\text{data}_i, \text{meta}_i, v_i)$ contains the knowledge text, metadata, and a dense embedding (Masoor, 5 Jul 2025).
• For structured program traces: $M_D = \{ (q_i, T_i) \}$, with $q_i$ as the question prompt and $T_i$ as a segmented trajectory (Biswal et al., 22 Jan 2026).
• For graph-based stores: $M_s = (V, E)$, a graph where nodes represent entities or concepts and edges denote abstracted relationships (Zhou et al., 9 Jan 2026).

2. Architectures and Representations

AgentSM architectures span a wide methodological spectrum, reflecting the evolving complexity of agent memory systems:

• Textual Summarization: AgentSM can be realized as a distilled bullet-list of high-level advice, produced by LLM-based summarization (e.g., “always check for clinical relevance”) that is concatenated to the agent’s prompt at inference (Hassell et al., 22 Oct 2025).
• Vector Embedding Stores: Entries are embedded via a mapping $\phi(e_i) \in \mathbb{R}^d$, supporting nearest-neighbor retrieval (cosine similarity), often in systems designed for scale or distributed use (Masoor, 5 Jul 2025, Liu et al., 12 Dec 2025).
• Structured Trajectory Stores: In agentic Text-to-SQL and tool-use scenarios, semantic memory consists of segmented execution traces, enabling retrieval and reuse of prior reasoning subgraphs or phase-labeled trajectories (exploration, execution, validation) (Biswal et al., 22 Jan 2026).
• Graph-Based Knowledge: Graph structures (triplets, Neo4j DBs, or knowledge graphs) model semantic memory as an evolving set of structured facts, with momentum-aware consolidation and coherence-driven retrieval mechanisms (Zhou et al., 9 Jan 2026, Ward, 9 Nov 2025).
• Hierarchical and Multigraph Structures: Systems such as MAGMA and SHIMI implement AgentSM as layered or orthogonally partitioned graphs, supporting intent-aligned traversal separate from temporal, causal, or entity-centric memory views (Jiang et al., 6 Jan 2026, Helmi, 8 Apr 2025).

Table: Selected AgentSM Instantiations

System	Representation	Retrieval Mechanism
Hassell et al.	Textual summary	Prompt concatenation
SAMEP	Vector store (AES-GCM)	Embedding + cosine semantic search
Amory	Triplet graph (Neo4j)	LLM-driven graph/Cypher queries
AgentSM (SQL)	Program trace	Question embedding + FAISS NN
SHIMI	Semantic tree	Hierarchical top-down traversal

3. Memory Formation, Distillation, and Maintenance

AgentSM is typically constructed and maintained through processes that generalize and condense lower-level or instance-based memories. Key operations (and their implementations) include:

• Distillation of Episodic Critiques: Instance critiques (e.g., labels + explanations) are summarized into general task instructions via LLM-driven prompts. In (Hassell et al., 22 Oct 2025), all critiques are periodically distilled into a bullet list by a Critic Agent. Mathematical formalization is absent; the process is purely prompt-based, with no explicit loss function.
• Consolidation via Momentum or Decay: Systems such as Amory (Zhou et al., 9 Jan 2026) apply momentum-aware decay (weighted updates) so that frequently reinforced facts persist; less-relevant or contradictory facts decay and are pruned below a threshold.
• Biologically-Inspired Forgetting: FadeMem (Wei et al., 26 Jan 2026) introduces a dual-layer memory with adaptive exponential decay, where decay rates are modulated by semantic relevance, access frequency, and recency. Memory entries with low “strength” or obsolescence are dropped or fused.
• Conflict Resolution and Fusion: On encountering new, potentially overlapping knowledge, AgentSM often employs LLM-guided compatibility checks and merges, ensuring the memory remains compact and non-redundant (Wei et al., 26 Jan 2026).
• Pruning and Update Scheduling: To maintain coherence and prevent drift, AgentSM is generally updated asynchronously or in large batches, with explicit distillation schedules and pruning heuristics (Hassell et al., 22 Oct 2025, Zhou et al., 9 Jan 2026).

4. Retrieval Algorithms and Integration with Reasoning

The principal function of AgentSM is to supply high-level, generalizable information during reasoning or inference. Integration and retrieval algorithms span:

• Prompt Augmentation: Semantic summaries or retrieved memory entries are prepended to the agent’s prompt, influencing LLM outputs non-parametrically (Hassell et al., 22 Oct 2025).
• Nearest-Neighbor and Hybrid Search: For embedding-based stores, queries are embedded and top-$k$ matches selected by cosine similarity (optionally hybridized with sparse lexical signals or RRF) (Masoor, 5 Jul 2025, Liu et al., 12 Dec 2025, Jiang et al., 6 Jan 2026).
• Graph Reasoning and Queries: In graph-structured systems, retrieval is performed via topological queries—e.g., Cypher pattern queries in Amory (Zhou et al., 9 Jan 2026) or spreading activation and hybrid scoring (embedding, activation, PageRank) in Synapse (Jiang et al., 6 Jan 2026).
• Policy-Guided Traversal: Advanced AgentSM implementations (MAGMA (Jiang et al., 6 Jan 2026)) perform query-adaptive traversals where edge selection is modulated by query intent and semantic affinity.
• Weighted Fusion and Scoring: Retrieval pipelines may combine multiple relevance signals—geometric similarity, activation (spreading activation), and global graph priors—using tunable weights (Jiang et al., 6 Jan 2026).
• Coherence-Driven and Curriculum-Aware Recall: In systems emphasizing interpretability, retrieved entries are scored for narrative or topical coherence, or exposed to the agent in a curriculum-guided schedule (Liu et al., 12 Dec 2025, Zhou et al., 9 Jan 2026).

5. Empirical Evaluation and Comparative Analyses

Empirical results across representative benchmarks highlight both strengths and limitations of AgentSM:

• On fact-oriented and multi-hop reasoning benchmarks (e.g., LoCoMo, LTI-Bench), AgentSM yields non-trivial gains over retrieval-augmented and pure episodic baselines when generalization across tasks or long horizons is required (Hassell et al., 22 Oct 2025, Wei et al., 26 Jan 2026, Zhou et al., 9 Jan 2026).
• In “Learning from Supervision with Semantic and Episodic Memory,” incorporating semantic memory improves accuracy up to 24.8% over label-only RAG-style retrieval, particularly when critiques are high quality (Hassell et al., 22 Oct 2025).
• However, episodic (example-specific) memory often outperforms summary-based semantic memory for tightly supervised or instance-level tasks, and hybrid approaches (episodic + semantic) may only marginally outperform episodic-only, at additional computational cost (Hassell et al., 22 Oct 2025).
• Memory systems utilizing graph-structured or momentum-aware consolidation achieve both higher retrieval coverage and substantial storage reduction (e.g., FadeMem achieves 45% storage reduction while maintaining superior retrieval/retention) (Wei et al., 26 Jan 2026).
• In agentic code, tool, and reasoning systems (SMITH (Liu et al., 12 Dec 2025), AgentSM/SQL (Biswal et al., 22 Jan 2026)), semantic memory accelerates complex planning and code synthesis, reducing average trajectory length by 25–35% and improving execution accuracy by up to 16 percentage points.

6. Practical Considerations and Limitations

Several practical themes and current limitations are evident across AgentSM literature:

• Update Frequency and Drift Control: Over-frequent distillation of episodic to semantic memory may introduce contradictory or noisy instructions; careful scheduling is essential (Hassell et al., 22 Oct 2025).
• Cost and Efficiency: Summary-based (semantic) memory can be expensive to build and use in terms of LLM tokens and compute; engineering choices (size, frequency of update) are crucial for scalability (Hassell et al., 22 Oct 2025).
• Retrieval Granularity: Most retrieval operates at the entry- or trace-level; fine-grained, context- or schema-sensitive retrieval mechanisms remain an open area (Biswal et al., 22 Jan 2026).
• Inter-Agent and Federated Use: Multi-agent or distributed semantic memory is still in early stages, with protocols like SAMEP providing encrypted, ACL-enabled semantic sharing across agents (Masoor, 5 Jul 2025).
• Multimodal and Cross-Domain Unification: While text and tabular data dominate current AgentSM implementations, extensions to multimodal (image, audio) and cross-agent settings are increasingly active (Long et al., 13 Aug 2025, Hu et al., 15 Dec 2025).
• Lack of Formal Distillation Objectives: Many systems rely on LLM summarization without explicit loss functions or optimization criteria for semantic distillation (Hassell et al., 22 Oct 2025).

7. Emerging Directions and Research Horizons

AgentSM continues to evolve as a research frontier, with emerging directions including:

• Biologically-Inspired Forgetting and Consolidation: Dual-layer, decay-adaptive hierarchies as in FadeMem, and momentum-based fact consolidation, are bringing agent memory systems closer to biological models (Wei et al., 26 Jan 2026, Zhou et al., 9 Jan 2026).
• Structured, Explainable, and Intent-Aligned Retrieval: Multi-graph and hierarchical architectures (MAGMA, SHIMI) enable fine-grained control and transparent provenance of the memory context used in reasoning (Jiang et al., 6 Jan 2026, Helmi, 8 Apr 2025).
• Automated and RL-Guided Memory Management: RL and tool-driven policies for memory formation, evolution, and retrieval aim to resolve the stability–plasticity dilemma and minimize manual engineering (Hu et al., 15 Dec 2025).
• Shared and Secure Cross-Agent Semantic Stores: Protocols for privacy-preserving, permissioned knowledge sharing are being deployed in clinical, multi-modal, and collaborative agent setups (Masoor, 5 Jul 2025).
• Unified Multimodal Memory: Integration of text, vision, audio, and action data into a shared AgentSM for more generalized embodied and conversational agents is a growing area (Long et al., 13 Aug 2025, Hu et al., 15 Dec 2025).

Agent Semantic Memory thus constitutes a foundational, rapidly diversifying capability for modern AI agents, both in practical performance and in enabling ongoing advances in adaptive, interpretable, and scalable cognition (Hassell et al., 22 Oct 2025, Biswal et al., 22 Jan 2026, Wei et al., 26 Jan 2026, Masoor, 5 Jul 2025).