External Emotion Memory: Mechanisms & Applications

• External emotion memory is defined as the explicit encoding of affective information alongside episodic traces, enabling personalized and adaptive agent behavior.
• It employs computational strategies such as Bayesian updating and temporal compression to manage emotionally salient data efficiently.
• Empirical studies show that integrating emotion tags enhances dialogue coherence, reduces memory contradictions, and supports refined action selection.

External Emotion Memory refers to architectures, mechanisms, and computational strategies for representing, manipulating, and exploiting affective information within an agent’s or system’s memory—where emotional content is not merely an epiphenomenon but an explicit, accessible, and manipulable component. This paradigm spans cognitive neuroscience, computational psychiatry, affective AI, and conversational systems, enabling both natural and artificial agents to encode, retrieve, update, and control emotional traces associated with episodic experiences, perceptions, or dialogue. Implementations range from biologically inspired models that integrate emotion tags into event representations to memory compression algorithms that prioritize emotionally salient events, with direct impacts on adaptive behavior, empathy, and agent personalization.

1. Formal Definitions, Cognitive Models, and Core Processes

External Emotion Memory is conceptualized as the explicit encoding and storage of affective information—typically valence, arousal, and/or multidimensional emotional tags—alongside, or as annotations to, episodic or semantic memory traces. This design is grounded in both human and artificial systems.

• Human Cognition and Volitional Control: In cognitive neuroscience, "internal emotional memory" denotes neural representations of affect-laden events that can be intentionally recalled or suppressed via volitional control, engaging neural circuits such as the hippocampus (retrieval) and prefrontal cortex (suppression). Directed Recall ("Think") brings emotion-laden memories into awareness, whereas Directed Suppression ("No-Think") inhibits their access (Kinger et al., 22 Jan 2026).
• Computational Agent Models: In AI, models such as the Conscious Tutoring System (CTS) define event memory traces as $M_i = \langle s_i, a_i, e_i, t_i\rangle$, i.e., stimulus, action, emotional valence, and timestamp. Encoding strength is modulated by emotional intensity, influencing trace persistence and consolidation (0901.4963).
• Dialogue and Conversational Systems: Systems such as KEEM store memory entries as short, causal fact-emotion tuples: (fact, explicit emotion label, cause), e.g., "I’m happy now that I’ve made up with my club friend – I felt down earlier because we had a fight" (Kang et al., 9 Jan 2026).

A canonical schema is thus:

• Trace: $$(\text{stimulus/action/context},\, \text{emotion tag},\, \text{timestamp/metadata})$$
• Encoded affect: continuous (valence-arousal), categorical (happy, sad), or high-dimensional embeddings.

2. Memory Update, Compression, and Management Algorithms

Maintaining emotion-annotated memory at scale requires explicit mechanisms for updating, compressing, and managing storage, especially in resource-constrained or interactive environments.

• Bayesian-Inspired Update: Each emotion memory unit in personalized LLM agents encapsulates a sentiment profile $P=[p_\text{pos},p_\text{neg},p_\text{neu}]$, a cumulative evidence weight $W$, and entropy $H(m)$. New evidence $(P_e,S)$ is integrated via

$$C_\text{new} = \frac{W \cdot C + S \cdot P_e}{W + S}, \quad W_\text{new} = W + S$$

Entropy-based selection prunes low-confidence or ambiguous memories, directly minimizing overall memory uncertainty (Lu et al., 31 Oct 2025).

• Progressive Temporal Compression: Livia’s memory management combines Temporal Binary Compression (TBC)—merging older memories hierarchically over exponentially increasing time-window buckets to achieve $O(\log N)$ storage scaling—with a Dynamic Importance Memory Filter (DIMF), which prioritizes memories by weighted combinations of emotional intensity, contextual uniqueness, and user-feedback (Xi et al., 12 Aug 2025).
• Generative Update in Dialog Systems: Rather than rule-based memory manipulation, generative models update emotion memory by producing single-sentence summaries of prior memory and new emotion-annotated session content. This process—used in KEEM—is realized by maximizing $$m_{t+1} = \arg\max_m P(m \mid M_t, U_{t+1}, E_{t+1})$$ via black-box LLMs (Kang et al., 9 Jan 2026).

Table: Emotion Memory Management Examples

System	Update Mechanism	Compression Strategy
CTS	Emotion-weighted encoding	Closed sequential pattern mining
DAM-LLM [2510]	Bayesian weighted profile	Entropy-based deletion/merging
Livia	Pairwise merging (TBC)	DIMF pruning of low-importance
KEEM	Generative summary (LLM)	Consistency check

3. Attention, Retrieval, and Fusion in Action Selection

Emotionally annotated memory is only useful if effectively leveraged at retrieval and action time, especially for flexible, context-sensitive decision-making.

• Similarity-Based Retrieval: Agents compare current context $S_{t}$ to stored $S^{\text{pre}}_i$ (sensor/need concatenations) via kernel functions (cosine, Euclidean), selecting top-$k$ closest memories and projecting a weighted affective cue $E_\mathrm{mem}(t) = \sum_j \alpha_{i_j}E_{i_j}$ (Borotschnig, 1 May 2025).
• Memory-Driven Modulation: The final emotion for action selection $E_\mathrm{total}(t)$ fuses $E_\mathrm{mem}(t)$ (from memory) and $E_\mathrm{need}(t)$ (current interoceptive state) via either convex combination or a learned fuse operator. This modulates downstream behavior selection (Borotschnig, 1 May 2025).
• Hybrid Retrieval for LLM Agents: Two-stage hybrid mechanism: (1) metadata filtering via exact object/aspect matches; (2) semantic re-ranking of candidate memory entries using cosine similarity between text embeddings, with the resulting set injected into the prompt context for LLM-aware generation (Lu et al., 31 Oct 2025).
• Interaction with Policy Selection: In reinforcement-driven companions, retrieved affective memories inform both response generation and adaptive personality evolution (e.g., biasing policy weights for engagement) (Xi et al., 12 Aug 2025).

4. Neural and Behavioral Correlates: Control, Individualization, and Subjectivity

External Emotion Memory is linked to large-scale neural signaling and introduces individuality, subjectivity, and controllability.

• Neural Network Dynamics: Volitional control over emotional memory in humans engages a dynamic interplay among salience/control (anterior cingulate cortex, prefrontal regions), episodic retrieval (hippocampus, precuneus), and parietal/perceptual circuits—valence determines which pathways dominate suppression or recall. Subclinical anxiety introduces marked alterations in prefrontal coupling patterns (Kinger et al., 22 Jan 2026).
• Individualized Affective Memory: SAMNet leverages per-branch affective memory matrices, updated via attention, to simulate distinct “subjectivities” in predicting emotion distributions in crowd-annotated visual tasks, with explicit subjectivity loss promoting divergence among individual memory modules (Yang et al., 2022).
• Evaluation of Behavioral Effects: Systems with external or internal emotion memory display measurable advantages in maintaining emotional coherence, reducing contradictory information, and achieving empathy and personalization (e.g., preference by human raters in KEEM, recall of important events in Livia, distributional accuracy in SAMNet) (Kang et al., 9 Jan 2026, Xi et al., 12 Aug 2025, Yang et al., 2022).

5. Empirical Findings, Benchmarks, and Design Metrics

Empirical assessments span conversation coherence, memory contradiction rates, personalization metrics, and agreement with human annotation.

• Long-term Dialogue: Incorporating emotion and cause into memory updates (KEEM) reduces perplexity by 10–30% compared to operation-based updates and decreases memory contradiction rates (KEEM ≈ 5% vs. KMSC ≈ 30%) (Kang et al., 9 Jan 2026).
• Personalized Companion Performance: Livia’s progressive memory compression retained 92% of important emotional events while achieving a 70% storage reduction, with human users reporting statistically significant reductions in loneliness and high emotional resonance (Xi et al., 12 Aug 2025).
• Affective Agent Benchmarking: DABench evaluates affective memory management via accuracy, logical coherence, resonance, and personalization. DAM-LLM achieved highest scores (Accuracy: 5.0, Emotional Resonance: 4.5–4.6) and tightly controlled memory size via Bayesian and entropy-based mechanisms (Lu et al., 31 Oct 2025).
• Subjectivity in Visual Emotion Analysis: SAMNet’s memory module yields measurable improvements: Top-1 Accuracy increased from 0.72→0.74, Cosine from 0.87→0.88, with subjectivity loss further boosting diversity and overall match to human label distributions (Yang et al., 2022).

6. Theoretical and Philosophical Implications

The architectural separation between emotion memory and conscious awareness, questions of sentience, and practical impact are rigorously considered.

• Affective Zombies: Borotschnig (Borotschnig, 1 May 2025) emphasizes that computational architectures can manifest synthetic emotion memory for action selection without supporting phenomenal consciousness—internal emotion memory and self-awareness are orthogonal, supporting the theoretical possibility of affective zombies.
• Multiple-Trace Models: Emotion-weighted retrieval and consolidation mechanisms in CTS mirror multiple-trace theory in neuroscience, ensuring that emotion-linked traces continually augment storage and procedural knowledge, rather than being overwritten (0901.4963).
• Clinical and Psychiatric Relevance: Volitional control network flexibility, as mapped by resting-state functional connectivity, suggests pathway-specific vulnerabilities in anxiety and other affective disorders, linking computational findings to translational neuroscience (Kinger et al., 22 Jan 2026).

7. Limitations, Open Challenges, and Future Directions

Current limitations include lack of fine-grained temporal modeling, asynchronous consolidation, and fixed emotion category schemas.

• Scalability and Lifelong Learning: Most systems employ synchronous updates and rely on memory pruning or compression (e.g., entropy/bayesian for DAM-LLM, progressive hierarchical merging for Livia), but true lifelong memory with asynchronous, context-sensitive consolidation remains unresolved (Lu et al., 31 Oct 2025, Xi et al., 12 Aug 2025).
• Emotion Representation Granularity: Most affect embeddings use coarse valence/arousal or limited categorical tags; richer, context-sensitive, multidimensional emotional traces with temporal decay or compositional structure are rare (Kang et al., 9 Jan 2026, Yang et al., 2022).
• Generalization and Model Design: KEEM and related datasets rely on black-box LLMs for generative memory updates; custom neural emotion-memory modules, explicit training losses, or causal learning with emotion signals remain underexplored (Kang et al., 9 Jan 2026).
• Bridges to Psychopathology and Robotics: Human-inspired architectures (e.g., GWR-based affective memory) demonstrate promise in annotating and explaining evolving intrinsic mood in HRI scenarios; integration with real-time, multimodal affect appraisal and mood tracking requires further investigation (Barros et al., 2018).

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External Emotion Memory, as operationalized in contemporary systems, underpins adaptive, personalized, and emotionally aware artificial agents. Its algorithmic, neural, and behavioral underpinnings are diverse, yet unified by the core principle of explicit, controllable, and actionable storage of affective information. Future research will refine the granularity of emotion representation, develop scalable asynchronous memory dynamics, and further bridge computational models with human affective neuroscience and clinical applications.