PersMem: Internalizing Personality into Dual-Pathway Memory for LLM Agents

Hanzhong Zhang; Ziwei Xiang; Weicheng Xie; Shizhe Liu; Siyang Song

cs.AISep 28, 2026

PersMem: Internalizing Personality into Dual-Pathway Memory for LLM Agents

Authors: Hanzhong Zhang, Ziwei Xiang, Weicheng Xie, Shizhe Liu, Siyang Song

Organizations: Department of Computer Science, University of Exeter, United Kingdom · School of Marxism, Nanjing University, China · Shenzhen University, China · Department of Computer Science, University of Oxford, United Kingdom

Abstract

The profile of a role-playing agent usually depends on the pre-defined personality in a system prompt, whereas its memory processing pipeline, including prioritisation of stored memories and subsequent retrieval, remains independent of this personality. This separation causes the agent's memory processing to be inconsistent with the pre-defined personality, and makes it difficult to validate whether agent behaviours follow this personality. In this paper, we propose Personality-Integrated Memory (PersMem), which integrates personality into the agent's memory processing pipeline, making it consistently personality-dependent. PersMem processes memory using four steps, where the personality is mapped to operation-specific parameters controlling: (i) affective appraisal annotating emotion states of the user input; (ii) retention of previously stored memories along with the current input; (iii) passive affect-driven memory retrieval exploring memories similar to user input in semantics and personality-guided emotions; and (iv) active goal-driven memory retrieval that refines and selects passively retrieved memories for the reply. Consequently, consistency with the pre-defined personality can be examined by inspecting memory-processing traces during human-agent interactions. We evaluate these personality-dependent differences in attachment and Big Five settings. PersMem exceeds the chance baseline for four-way attachment classification by 23.1 percentage points. In Big Five dialogue comparisons, PersMem achieves 67.5% accuracy, 6.7 percentage points above a baseline using uniformly sampled memories. On CoSER, PersMem achieves an average score of 66.13, with scores of 69.33 for Character Fidelity and 84.33 for Storyline Quality. Together, these results show that PersMem produces distinguishable personality-related memory-processing patterns.

Figures & tables

Figure 1: Design Principle and Architectural Comparison of PersMem. (a) A simplified view of PersMem. In the attachment instantiation, a fixed two-dimensional trait vector parameterises affective appraisal, memory retention, passive affect-driven memory retrieval, and the redirection of active goal-driven memory retrieval, while remaining hidden from the active controller and response model. (b) A common design specifies the assigned profile through a pre-defined personality in the system prompt while retrieving memories through a separate process. (c) PersMem instead uses one personality representation to control multiple memory operations before response generation. Fig. 2 presents the complete processing and control flow.

Figure 2: Pipeline of PersMem. (a) PersMem uses the pre-defined personality to annotate the affective state of the current user input, updates the agent’s current state, and stores the completed exchange after reply generation. (b) Memory retention controls the accessibility of stored memories, while passive affect-driven memory retrieval selects an initial memory set and proposes one candidate during each active retrieval step. (c) Active goal-driven memory retrieval either adopts the candidate proposed by passive retrieval or continues semantic retrieval and query refinement. The initial passive output and active recall set are finally merged and deduplicated.

Condition	Accuracy	95% CI / $p$ -value
Held-out classification
Full PersMem	48.1%	[40.0%, 56.3%]; $p=0.0002$
Neutral gate	40.0%	–
Gate off	25.0%	–
Text embedding only	25.0%	–
Linear trait-aware reranker	30.0%	$p=0.055$

Table 1: Attachment profile classification. Held-out evaluation excludes internal personality-dependent scores; complete-trace diagnostics include resonance and retention scores. Primary and matched complete-trace results use different splits.

Analysis	Estimate	CI or statistic	$p$ -value
Affective-appraisal effect	$+0.005$	–	–
Memory-retention effect	$+0.013$	–	–
Passive-retrieval effect	$+0.130$	–	–
Active-retrieval redirection effect	$+0.085$	–	–
Primary VAD classification	48.1%	[40.0%, 56.3%]	$0.0002$
NRC-VAD classification	45.0%	[38.8%, 51.2%]	$0.0002$

Table 2: Component effects and annotation checks.

Measure	Sec.	Anx.	Avo.	Fear.
Neutral OGM	–	–	0.28	0.47
Distress OGM	–	–	0.37	0.73
OGM lift over no-trait	–	–	$+0.07$	$+0.27$
Independent-label OGM difference from secure, distress	0	$+0.027$	$+0.039$	$+0.067$
Neutral mean recalled valence	–	–	$-0.04$	–
Negative-redirection proportion	0.59	0.63	0.51	–

Table 3: Profile-conditioned retrieval and response outcomes. Dashes indicate profiles excluded from an analysis.

Trait	Outcome	Full system	No-trait control
Neuroticism	Valence	$-0.703$	–
Extraversion	Valence	$+0.776$	–
Agreeableness	Valence	$+0.607$	–
Conscientiousness	OGM	$-0.710$	$-0.180$

Table 4: Descriptive Big Five recall associations over 50 profiles. Dashes indicate undefined valence correlations because the no-trait control produces zero variance in recalled valence.

Condition	Correct calls	Accuracy
Full system	81/120	67.5%
Random-memory control	73/120	60.8%
No-trait control	65/120	54.2%
No-memory control	64/120	53.3%

Table 5: Blind Big Five dialogue comparison. Each of 60 high–low pairs is evaluated in two presentation orders.

Method	SC	AN	CF	SQ	Avg.
CoSER Wang et al. (2025)
GPT-4o	61.59	48.93	48.95	80.33	59.95
CoSER-70B + Conv.	64.59	53.79	54.86	77.28	62.63
CogDual Liu et al. (2025)
LLaMA3.1-8B + CogDual-RL	60.10	45.89	48.82	73.08	56.97
HER Du et al. (2026)

Table 6: Role-playing results on CoSER. Published reference scores retain their source studies’ evaluation settings; our PersMem results use DeepSeek. SC, AN, CF, and SQ denote Storyline Consistency, Anthropomorphism, Character Fidelity, and Storyline Quality, respectively. Conv. denotes conversation retrieval.

Evaluation	Condition	Result
Calibration error	Fitted gain map	0.084
	Fixed-gain reference	0.482
Adversarial retention	PersMem	89%
	Prompt-persona baseline	58%

Table 7: Calibration and adversarial-framing results.

Method	Evidence recall@5	F1 given evidence hit
PersMem	0.387	0.547
Generative-Agents-style scoring	0.341	0.540
Semantic RAG	0.444	0.542

Table 8: LoCoMo retrieval and answer results.

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Symbol	Role	Value
$\kappa_{v}$	valence adjustment	$0.30$
$\kappa_{a}$	negative-event arousal increase	$0.20$
$\kappa_{\mathrm{damp}}$	avoidance-related arousal damping	$0.15$
$\kappa_{p}$	dominance increase	$0.10$
$\kappa_{q}$	dominance decrease	$0.10$
$r_{0}$	base memory decay rate	$10^{-4}\,\mathrm{s}^{-1}$

Appendix

Table 9: Fixed coefficients used by PersMem. Calibrated retrieval gains and the Big Five sampling temperature are defined separately in Appendices C and D.

Trait	Memory operation	Implemented relationship
$N$	Affective appraisal and memory retention	Stronger negative appraisal and slower forgetting of negative memories
$N$	Passive affect-driven memory retrieval	Stronger negative-memory term and greater weight on the complete affective relation score
$N$	Active-retrieval redirection	Lower gate-firing threshold
$E$ and $A$	Affective appraisal and passive retrieval	Stronger positive appraisal and a stronger positive-memory term
Low $A$	Affective appraisal and passive retrieval	Higher dominance appraisal and a stronger preference for high-dominance memories
Low $C$ and high $N$	Passive affect-driven memory retrieval	Stronger overgeneral-memory term

Appendix

Table 10: Big Five relationships used in the implemented memory operations and calibration targets.

Method	Accuracy	$\Delta\mathrm{PCB}$
Semantic RAG	28.3%	$0$
Generative-Agents-style scoring	25.8%	$0$
No-trait control	26.7%	$0$
PersMem	72.5%	$+1.087$

Appendix

Table 11: Complete-trace comparison on the matched analysis split. The first three methods are negative controls without personality-dependent retrieval.

Condition	Accuracy	95% CI	$p$ -value
Full A1R1P1G1, personality gate	48.1%	[40.0%, 56.3%]	$0.0002$
Neutral gate	40.0%	–	–
Gate off	25.0%	–	–
Text embedding only	25.0%	–	–
Linear trait-aware reranker	30.0%	–	$0.055$

Appendix

Table 12: Held-out attachment profile classification. The classifier is trained on development data and fixed before test evaluation. Personality parameters and internal personality-dependent scores are excluded.

Component	Average main effect
Appraisal (A)	$+0.005$
Retention (R)	$+0.013$
Passive affect-driven memory retrieval (P)	$+0.130$
Gate (G)	$+0.085$

Appendix

Table 13: Descriptive average main effects on held-out profile classification.

Profile	Mean recalled valence	Negative-cue congruence	OGM rate
Secure	$-0.12$	0.60	0.23
Anxious	$-0.25$	0.53	0.44
Avoidant	$-0.04$	0.53	0.28
Fearful	$-0.23$	0.63	0.47

Appendix

Table 14: Mean recalled valence, negative-cue congruence, and OGM rate across attachment profiles.

Comparison	State	Difference	$p$
Fearful: S on $-$ off	Neutral	$+0.0422$	$10^{-5}$
Fearful: S on $-$ off	Distress	$+0.0770$	$10^{-5}$
Avoidant: S on $-$ off	Distress	$+0.0394$	$10^{-5}$
Anxious: S on $-$ off	Distress	$+0.0370$	$10^{-5}$
Anxious $-$ secure	Distress	$+0.0272$	$\leq 4\times 10^{-5}$
Avoidant $-$ secure	Distress	$+0.0393$	$\leq 4\times 10^{-5}$

Appendix

Table 15: Cross-label specificity differences computed using the second blind model-generated label set.

	O	C	E	A	N
Mean	0.725	0.592	0.490	0.693	0.517
Standard deviation	0.158	0.184	0.228	0.182	0.215

Appendix

Table 16: Sample statistics for the 50 normalised IPIP FFM profiles.

Response Model and Evaluator	Full System	Random-Memory Control	No-Memory Control	No-trait control
Qwen3.5-9B and Qwen3.5-9B	72.5%	55.0%	57.5%	50.8%
Qwen3.5-9B and Llama-3.1-8B	67.5%	60.8%	53.3%	54.2%
Llama-3.1-8B and Llama-3.1-8B	65.8%	56.7%	54.2%	52.5%

Appendix

Table 17: Exploratory forced-choice accuracy across response model and evaluator configurations. Each cell contains 120 order-balanced calls over 60 underlying high–low profile pairs.

Method	Evidence recall@5	F1 given evidence hit
Semantic RAG	0.444	0.542
Generative-Agents-style scoring	0.341	0.540
PersMem	0.387	0.547

Appendix

Table 18: LoCoMo retrieval and answer decomposition. Evidence recall@5 measures retrieval coverage, while hit-conditional F1 is computed within each method’s own evidence-hit subset.

Method	SC	AN	CF	SQ	Avg.
CoSER Wang et al. (2025)
GPT-4o	61.59	48.93	48.95	80.33	59.95
Doubao-pro	60.95	49.72	47.02	79.28	59.24
Step-2	61.43	49.06	47.33	77.96	58.94
Gemini Pro	59.11	52.41	47.83	77.59	59.24
Claude-3.5-Sonnet	57.45	48.50	45.69	77.23	57.22

Appendix

Table 19: Extended comparison on CoSER. Published reference results are taken from the corresponding source studies. SC, AN, CF, and SQ denote Storyline Consistency, Anthropomorphism, Character Fidelity, and Storyline Quality, respectively.

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