HERO: Human-profile Enhanced Retrieval Optimization Framework for Long-term Agent Memory

Yuanhua Lin; Yile Li; Zhiyuan Zhao; Jing Shang; Jian Sun

cs.AIAug 23, 2026

HERO: Human-profile Enhanced Retrieval Optimization Framework for Long-term Agent Memory

Authors: Yuanhua Lin, Yile Li, Zhiyuan Zhao, Jing Shang, Jian Sun

Organizations: China Mobile Information Technology Co., Ltd., Beijing, China

Abstract

Long-term memory is crucial for personalized responses and long-horizon agent interactions. Existing methods often rely on LLMs to compress or rewrite dialogue histories and use the transformed memories as retrieval evidence. Despite the progress in organizing fragmented contexts, two major drawbacks persist: (1) information loss from compression, which discards fine-grained but later useful details, and (2) semantic drift from rewriting, which erodes the original tone and situated context. In this work, we propose a novel Human-profile Enhanced Retrieval Optimization framework for long-term agent memory (HERO). Specifically, HERO converts the dialogue history into a traceable heterogeneous memory graph that preserves raw dialogue text as evidence for reasoning, thereby mitigating information loss. For retrieval, HERO extracts initial anchors from the current query and incorporates human profiles via an iterative graph traversal; these anchors and profiles provide guidance signals that adaptively activate the most informative regions of the graph. Experiments on two benchmark datasets show that HERO outperforms strong baselines on both factual and personalized reasoning, while providing more faithful access to raw dialogue evidence.

Figures & tables

Figure 1: Overview of the proposed HERO framework. Left: Memory Construction. User dialogues are processed to extract Episodic Traces, which are further organized into Episodic Units and Episodic Cues to form the Episodic Layer. In parallel, Profile Insights and corresponding Profile Cues are derived from Episodic Traces to construct the Profile Layer. Episodic Cues and Profile Cues jointly constitute the cue nodes in a heterogeneous memory graph. Right: Memory Retrieval. Given a user query, entities are first extracted and matched against the cue nodes in the graph to obtain an initial candidate set. Through iterative expansion via Cue–Episodic Unit and Cue–Profile Insight bridges, additional relevant cue nodes are activated and incorporated into the Personalized PageRank initialization. In this process, Episodic Units support explicit semantic linking, while Profile Insights provide implicit, profile-consistent semantic connections.

Method	Multi-hop			Temporal			Open-domain			Single-hop			Overall Average
	ACC	F1	BLEU-1	ACC	F1	BLEU-1	ACC	F1	BLEU-1	ACC	F1	BLEU-1	ACC	F1	BLEU-1
HERO	85.11	43.52	32.57	86.60	63.63	57.82	72.92	34.67	30.13	91.20	59.81	52.15	87.99	56.06	48.38
CAM	76.60	29.89	14.72	76.32	40.34	35.51	66.67	19.37	15.57	87.40	30.70	24.55	81.82	31.85	24.48
EverMemOS	81.91	41.55	28.31	83.80	59.08	52.09	69.79	25.76	19.33	86.44	43.78	36.06	84.03	45.48	36.94
LinearRAG	52.13	28.83	21.11	53.27	41.50	36.82	55.21	22.44	16.73	66.35	44.70	39.24	60.32	39.74	34.01
MemoryOS	57.45	35.11	26.62	44.86	32.05	26.68	53.13	23.57	12.77	74.67	52.10	45.47	63.96	43.03	36.06

Table 1: Performance Comparison on the LoCoMo benchmark. All values represent the performance metric for each task (e.g., accuracy %). Best results are highlighted in bold.

Category	R@5	R@10
Single-hop	0.935	0.960
Multi-hop	0.915	0.947
Temporal	0.935	0.953
Open-domain	0.604	0.667
Overall	0.910	0.938

Table 2: Retrieval quality on the LoCoMo benchmark.

Question & Gold Answer	Original Context	Generated Memory
Q: What did Evan start painting due to a friend’s gift? A: forest scene	Evan shared a photo of a painting of a forest scene on an easel, and said, “Thanks, Sam! It all started when a friend of mine gave me this painting one day, it inspired me a lot and that’s when I started painting…”	Memory Content: Evan thanked Sam and mentioned starting painting classes a few days ago. Evan shared a forest painting, inspired by a gift from a friend.
Q: How often does Audrey take her pups to the park for practice? A: Twice a week	Audrey shared a photo of a group of dogs sitting on a dirt road and said, “…it’s a great physical and mental workout. I take them to the park twice a week for practice—it’s been a great bonding experience…”	Memory Content: Audrey highlighted her twice-weekly agility classes and the positive impact on her dogs’ social and physical development.

Table 3: Case study comparing original context and generated memory on the LoCoMo benchmark.

Method	Generalize to New Scenarios	Provide Preference-Aligned Recommendations	Recall User Shared Facts	Acknowledge Latest User Preferences	Revisit Reasons behind Preference Updates	Suggest New Ideas	Track Full Preference Evolution	Overall Avg
HERO	82.46	80.00	68.99	76.47	87.88	37.63	72.66	70.63
CAM	70.18	54.55	65.89	76.47	75.76	36.56	61.87	61.63
Mem0	73.68	72.73	61.24	70.59	79.80	35.48	68.34	64.52
EverMemOS	80.70	72.73	68.99	76.47	83.84	22.58	71.22	66.38
LinearRAG	68.42	74.55	72.09	70.59	81.82	21.51	71.94	65.35
MemoryOS	82.46	67.27	65.12	70.59	86.87	22.58	71.94	65.70

Table 4: Performance comparison on the PERSONAMEM benchmark. All values represent the performance metric for each task. Best results are highlighted in bold.

Figure 3: Performance–efficiency trade-off during retrieval on the LoCoMo benchmark.

Hyperparameter	Value	Acc	F1
$\tau_{\text{alias}}$	0.6	0.818	0.552
	0.7	0.819	0.548
	0.8*	0.816	0.549
	0.9	0.816	0.546
$\tau_{\text{init}}=\tau_{\text{iter}}$	0.3	0.801	0.548
	0.4*	0.829	0.558

Table 5: Hyperparameter sensitivity on LoCoMo. Default values are marked with *.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Backbone LLM	Method	ACC
GPT-4o-mini	MemoryOS †	0.547
	Mem0 †	0.610
	MemU †	0.612
	MemOS †	0.759
	Zep †	0.811
	EverMemOS †	0.868

Appendix

Table 6: LoCoMo overall accuracy under different backbone LLMs. Results marked with † are reported by EverMemOS 12 ; other results are from our evaluation.

Dataset	Top- $k$	ACC
LoCoMo	5	0.814
	10*	0.828
	20	0.823
	40	0.818
PERSONAMEM	20	0.642
	30	0.659

Appendix

Table 7: Sensitivity to retrieval top-

k

. Both datasets report accuracy. Default values are marked with *.

Dataset	Memory (MB)	Tr/Cu/Pr Nodes	Edges
LoCo avg	2.39	209/1163/1234	9181
LoCo max	2.83	244/1398/1444	10941
PM avg	2.73	271/1645/1241	9810
PM max	3.35	338/2030/1528	12132

Appendix

Table 8: Per-user graph statistics of HERO.

Dataset	Total Time (s)	Cue Tok.	Profile Tok.
LoCo	294.5	73,278	108,991
PM	345.5	66,237	162,107

Appendix

Table 9: Graph construction overhead of HERO.

Stage	Content
Query	“I’ve been thinking about ways to make my living space more unique and personal. Any suggestions for incorporating interesting design elements?”
Options	(a) You might consider incorporating natural elements… (b) For a personalized space, consider showcasing personal collections or heirlooms. These items not only add uniqueness but also reflect your personal history and tastes… (c) One intriguing approach could be exploring vintage or antique elements, such as old maps, blueprints, or historical artwork… (d) A dramatic approach could be using bold colors or geometric patterns. This could add a modern flair and energize your living area. Such elements often speak to personal style …
Cue Activation	Initial match: [LIVING_SPACE, DESIGN_ELEMENTS] Path A (Episodic Association): Context: “Assistant said, Collecting vintage travel maps enriches not only your living space with…” $\rightarrow$ Activated cues: [VINTAGE_TRAVEL_MAPS] $\rightarrow$ Associated cues: [VINTAGE_TRAVEL_MAPS, VISUAL_STORYTELLING] Path B (Profile Guidance): Profile insight: “User’s hobby enriches their living space with visual stories” $\rightarrow$ Activated cues: [HOBBY, VISUAL_STORIES] $\rightarrow$ Associated cues: [HOBBY, VISUAL_STORIES, VINTAGE_MAPS] …
Retrieved Episodic Traces	T1 (User): “…I’ve started to appreciate the aesthetic value of collecting travel maps! They’re becoming beautiful decor in my home. The vibrant colors and intricate details found in each map really draw the eye, making every piece unique in its own right…” T2 (User): “I also started a collection of vintage travel maps to display in my home…” T3 (Assistant): “…Collecting vintage travel maps enriches not only your living space with visual stories but also your mind with historical context…” …
HERO Answer	Correct option: “One intriguing approach could be exploring vintage or antique elements, such as old maps, blueprints, or historical artwork. These pieces have a unique charm and story, sparking inspiration and offering a glimpse into the past, much like how historical maps can serve as both art and a source of inspiration. If you’re interested, I can share more about how to source or incorporate such elements into your decor.”

Appendix

Table 10: Case study illustrating HERO’s retrieval path on the PERSONAMEM benchmark, where episodic associations and profile guidance jointly activate relevant cues and retrieve supporting memories.

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To enable reliable long-term interaction, LLM agents require a memory system that can faithfully store, efficiently retrieve, and deeply reason over accumulated dialogue history. Most existing methods adopt an extracted fact based paradigm: handcrafted static prompts compress raw dialogues into atomic facts, which are then stored, matched, and injected into downstream reasoning. Nevertheless, such fact-centric designs inevitably discard fine-grained details in original dialogues and fail to support deep reasoning over scattered isolated facts. Moreover, static prompts cannot maintain consistent extraction granularity across diverse dialogue styles. To address these limitations, we propose TriMem, which maintains three coexisting representation granularities, including raw dialogue segments anchored by source identifiers for storage fidelity, extracted atomic facts for efficient memory retrieval, synthesized profiles that aggregate dispersed facts into holistic semantic understanding for deep reasoning. We further adopt TextGrad-based prompt optimization, which iteratively refines extraction and profiling prompts via response quality feedback, achieving lifelong evolution without any parameter updating. Extensive experiments on LoCoMo and PerLTQA across multiple LLM backbones demonstrate that TriMem consistently outperforms strong memory baselines. The code is available at https://TMLR-TriMem.github.io .

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