arxiv: 2604.13074 · v1 · submitted 2026-03-20 · 💻 cs.CL · cs.CV

Recognition: 2 theorem links

· Lean Theorem

PersonaVLM: Long-Term Personalized Multimodal LLMs

Chang Nie , Chaoyou Fu , Yifan Zhang , Haihua Yang , Caifeng Shan

Authors on Pith no claims yet

Pith reviewed 2026-05-15 07:58 UTC · model grok-4.3

classification 💻 cs.CL cs.CV

keywords personalized multimodal LLMslong-term personalizationmemory databasepersona alignmentmultimodal agentsresponse alignmentchronological memories

0 comments

The pith

PersonaVLM equips multimodal language models with long-term memory of user interactions to deliver personalized responses over time.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a framework that turns a standard multimodal LLM into a personalized assistant capable of handling extended conversations. It does this by proactively building a database of summarized memories from past multimodal interactions, retrieving them for reasoning in new turns, and inferring the user's personality to align outputs. This addresses the limitation of existing models that only handle one-off personalization and cannot track evolving preferences. A sympathetic reader would care because it makes AI assistants more consistent and tailored to individuals over months or years of use rather than resetting each time.

Core claim

PersonaVLM transforms a general-purpose MLLM into a personalized assistant by integrating remembering through proactive extraction and summarization of chronological multimodal memories into a database, reasoning via retrieval and integration of relevant memories, and response alignment by inferring evolving user personality, leading to improvements of 22.4% on Persona-MME and 9.8% on PERSONAMEM under 128k context while outperforming GPT-4o.

What carries the argument

The chronological multimodal memory database built through proactive extraction and summarization, which supports multi-turn reasoning and personality inference for aligned responses.

If this is right

Models can maintain consistent personalization across long interaction histories exceeding 128k tokens.
Performance on personalized multimodal tasks improves substantially over general-purpose baselines and even GPT-4o.
Users receive responses that reflect their unique evolving characteristics rather than generic outputs.
Evaluation across seven aspects and 14 tasks in Persona-MME shows effectiveness in long-term scenarios.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Such memory mechanisms could extend to other domains like personalized recommendation systems or long-term planning assistants.
Privacy concerns arise if the memory database stores detailed user histories without strong safeguards.
Testing on even longer contexts or real-world user studies would further validate the approach.

Load-bearing premise

The base multimodal model can reliably extract and summarize key user-specific details from long sequences of interactions without systematic errors or omissions.

What would settle it

Running the memory extraction on a controlled set of 100+ simulated long-term interactions and finding that critical personality traits are missed or distorted in the resulting database.

Figures

Figures reproduced from arXiv: 2604.13074 by Caifeng Shan, Chang Nie, Chaoyou Fu, Haihua Yang, Yifan Zhang.

**Figure 1.** Figure 1: Illustration of PersonaVLM’s three core capabilities for long-term personalization. PersonaVLM proactively remembers user [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Figure 2: Overview of the PersonaVLM Framework. It leverages a personalized memory architecture and operates in two collaborative [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of our data synthesis pipeline and Persona-MME. (a) The pipeline first constructs rich user personas and then simulates [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Quantitative evaluation across seven tasks on the PER [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Qualitative comparison on open-ended generation, evalu [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Qualitative comparison on open-ended generation tasks. Case studies demonstrate PersonaVLM’s superior capabilities in memory [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Data composition for the training of PersonaVLM [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗

**Figure 8.** Figure 8: Distribution of the 500 long-term conversation samples [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗

**Figure 9.** Figure 9: Illustrative in-situ cases for the 14 task categories in Persona-MME, organized into the seven core personalization aspects. [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗

**Figure 10.** Figure 10: Overall performance on Persona-MME (128k), ranking PersonaVLM against various proprietary and open-source models. Visual Detail Recall Semantic Information Recall Explicit Intent Inference Implicit Intent Recognition Latest Preference Recognition Interest Evolution Analysis Implicit Preference Recommendation Behavioral Pattern Recognition Long-term Goal Tracking Relationship Recognition Relationship Dynam… view at source ↗

**Figure 11.** Figure 11: Distribution of the 14 fine-grained tasks in Persona-MME across its 32 [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗

**Figure 13.** Figure 13: Ablation study on the number of retrieved episodic [PITH_FULL_IMAGE:figures/full_fig_p018_13.png] view at source ↗

**Figure 14.** Figure 14: Visualization of dynamic personality evolving process captured by PEM on ten randomly sampled conversations from the [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗

**Figure 15.** Figure 15: Case studies: Qualitative comparison of open-ended generation [PITH_FULL_IMAGE:figures/full_fig_p023_15.png] view at source ↗

**Figure 16.** Figure 16: Prompt for multi-turn reasoning and retrieval in the response phase. [PITH_FULL_IMAGE:figures/full_fig_p024_16.png] view at source ↗

**Figure 17.** Figure 17: Intermediate prompt for multi-turn reasoning and retrieval in the response phase. [PITH_FULL_IMAGE:figures/full_fig_p024_17.png] view at source ↗

**Figure 18.** Figure 18: Prompt for inferring the user’s Big Five personality traits from the latest interaction. [PITH_FULL_IMAGE:figures/full_fig_p025_18.png] view at source ↗

**Figure 19.** Figure 19: Prompt for updating procedural memories. [PITH_FULL_IMAGE:figures/full_fig_p025_19.png] view at source ↗

**Figure 20.** Figure 20: Prompt for analyzing user input and deciding on semantic memory creation. [PITH_FULL_IMAGE:figures/full_fig_p026_20.png] view at source ↗

**Figure 21.** Figure 21: Prompt for updating the core memory based on recent conversations. [PITH_FULL_IMAGE:figures/full_fig_p027_21.png] view at source ↗

**Figure 22.** Figure 22: Prompt for creating episodic memories by summarizing dialogue topics. [PITH_FULL_IMAGE:figures/full_fig_p027_22.png] view at source ↗

**Figure 23.** Figure 23: Prompt for open-generation task evaluation. [PITH_FULL_IMAGE:figures/full_fig_p028_23.png] view at source ↗

read the original abstract

Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. Prior approaches enable only static, single-turn personalization through input augmentation or output alignment, and thus fail to capture users' evolving preferences and personality over time (see Fig.1). In this paper, we introduce PersonaVLM, an innovative personalized multimodal agent framework designed for long-term personalization. It transforms a general-purpose MLLM into a personalized assistant by integrating three key capabilities: (a) Remembering: It proactively extracts and summarizes chronological multimodal memories from interactions, consolidating them into a personalized database. (b) Reasoning: It conducts multi-turn reasoning by retrieving and integrating relevant memories from the database. (c) Response Alignment: It infers the user's evolving personality throughout long-term interactions to ensure outputs remain aligned with their unique characteristics. For evaluation, we establish Persona-MME, a comprehensive benchmark comprising over 2,000 curated interaction cases, designed to assess long-term MLLM personalization across seven key aspects and 14 fine-grained tasks. Extensive experiments validate our method's effectiveness, improving the baseline by 22.4% (Persona-MME) and 9.8% (PERSONAMEM) under a 128k context, while outperforming GPT-4o by 5.2% and 2.0%, respectively. Project page: https://PersonaVLM.github.io.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

PersonaVLM adds a remembering-reasoning-alignment loop and the Persona-MME benchmark for long-term multimodal personalization, but the gains rest on unverified extraction steps with no ablations or stats in the abstract.

read the letter

The main takeaway is a three-module framework that turns a general MLLM into a long-term personalized agent: it extracts and summarizes interaction memories into a database, retrieves them for multi-turn reasoning, and aligns outputs to inferred user personality. They pair this with Persona-MME, a benchmark of over 2,000 cases across seven aspects and fourteen tasks, and report a 22.4% gain on it plus 9.8% on PERSONAMEM at 128k context, beating GPT-4o as well. That is the concrete new piece compared to prior static single-turn methods. The architecture is straightforward and targets a real use case where preferences evolve over repeated interactions, and shipping a dedicated benchmark is useful for measuring progress in this area. The numbers are specific enough to be interesting on first read. The soft spots are the missing pieces around the remembering module. It depends on the base model doing reliable proactive extraction and summarization without systematic loss of details or introduced errors, yet the abstract gives no fidelity checks, error rates, or independent verification for that step. Any problems there would propagate straight into the reasoning and alignment results. Evaluation protocols, statistical significance, and controls for prompt effects are also not described, so it is hard to judge how much of the lift comes from the full pipeline versus other factors. This is aimed at researchers working on memory and personalization for daily multimodal assistants. Readers who need ideas for long-horizon user modeling or a new testbed will find usable components here. It deserves a serious referee because the problem is practical, the architecture is explicit, and the benchmark is new, even though the current evidence needs more backing on the extraction reliability and controls. I would send it for review and ask for ablations on the memory step plus full protocol details in revisions.

Referee Report

2 major / 2 minor

Summary. The paper introduces PersonaVLM, a framework that converts a general-purpose MLLM into a long-term personalized multimodal assistant via three integrated modules: Remembering (proactive extraction and summarization of chronological multimodal interaction memories into a personalized database), Reasoning (multi-turn retrieval and integration of relevant memories), and Response Alignment (inference of evolving user personality to align outputs). It presents the new Persona-MME benchmark (>2,000 curated cases across 7 aspects and 14 tasks) and reports empirical gains of 22.4% on Persona-MME and 9.8% on PERSONAMEM (128k context) over baseline, plus 5.2% and 2.0% over GPT-4o.

Significance. If the reported gains are robust, the work advances long-term personalization in MLLMs beyond static single-turn methods by explicitly modeling memory accumulation, multi-turn reasoning, and personality drift; the new benchmark and the three-module architecture could serve as a useful reference point for future personalized agent research.

major comments (2)

[Abstract / Remembering module] Abstract and Remembering module description: the 22.4% and 9.8% gains are presented as evidence that proactive memory extraction works reliably, yet no independent fidelity metric, human verification, or error analysis of the extracted/summarized memories is described; because this step feeds directly into Reasoning and Response Alignment, any systematic extraction errors would undermine the central performance claims.
[Evaluation section] Evaluation protocol (Persona-MME and PERSONAMEM results): the abstract states specific percentage improvements but supplies no details on statistical significance testing, exact prompt templates, controls for prompt-engineering effects, or variance across runs; without these, it is unclear whether the reported margins fully support the superiority claims over the baseline and GPT-4o.

minor comments (2)

[Figure 1] Figure 1 caption and legend: the contrast between prior static approaches and the proposed long-term framework would be clearer if the three capabilities (Remembering, Reasoning, Response Alignment) were explicitly labeled on the diagram.
[Benchmark section] Benchmark description: the claim that Persona-MME covers “seven key aspects and 14 fine-grained tasks” would benefit from an explicit table mapping aspects to tasks and example interaction cases.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for stronger validation of memory extraction and more rigorous evaluation reporting. We address each major comment below and have revised the manuscript accordingly to improve transparency and robustness.

read point-by-point responses

Referee: [Abstract / Remembering module] Abstract and Remembering module description: the 22.4% and 9.8% gains are presented as evidence that proactive memory extraction works reliably, yet no independent fidelity metric, human verification, or error analysis of the extracted/summarized memories is described; because this step feeds directly into Reasoning and Response Alignment, any systematic extraction errors would undermine the central performance claims.

Authors: We agree that independent validation of memory extraction quality is necessary to substantiate the performance claims. In the revised manuscript we have added a new subsection (Section 4.3) reporting human verification on a random sample of 300 extracted memories (94% accuracy per annotator agreement) together with a categorized error analysis of omission, hallucination, and temporal misalignment cases. The analysis shows low overall error rates (<6%) with no systematic correlation to downstream task failures, thereby supporting the reliability of the reported gains. revision: yes
Referee: [Evaluation section] Evaluation protocol (Persona-MME and PERSONAMEM results): the abstract states specific percentage improvements but supplies no details on statistical significance testing, exact prompt templates, controls for prompt-engineering effects, or variance across runs; without these, it is unclear whether the reported margins fully support the superiority claims over the baseline and GPT-4o.

Authors: We acknowledge the importance of these details for reproducibility and claim strength. The revised evaluation section now includes paired t-test p-values (all <0.01 for key comparisons), standard deviations across five independent runs, and explicit controls for prompt-engineering effects via fixed prompt templates applied uniformly to all models. The exact templates and generation settings have been moved to Appendix C. These additions confirm that the 22.4% and 5.2% margins are statistically significant and robust. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces PersonaVLM as a framework that augments a base MLLM with remembering (proactive extraction/summarization into a database), reasoning (multi-turn retrieval), and response alignment modules. It evaluates on the newly introduced Persona-MME benchmark (over 2,000 cases) and reports gains versus external baselines including GPT-4o. No equations, fitted parameters renamed as predictions, self-citations used as load-bearing uniqueness theorems, or ansatzes smuggled via prior work appear in the text. The performance numbers are measured against independent external models and the new benchmark rather than reducing to quantities defined by the method's own inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The framework rests on the assumption that current MLLMs can reliably summarize and retrieve user-specific multimodal history; no new physical entities or mathematical axioms are introduced.

free parameters (1)

context window size
128k token context used for long-term experiments; chosen to match model capability rather than fitted to results.

axioms (1)

domain assumption Multimodal LLMs can extract and summarize chronological user memories from interaction histories without catastrophic loss of detail.
Invoked in the Remembering component description.

pith-pipeline@v0.9.0 · 5570 in / 1293 out tokens · 46362 ms · 2026-05-15T07:58:37.032896+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Remembering: It proactively extracts and summarizes chronological multimodal memories... Reasoning: It conducts multi-turn reasoning by retrieving... Response Alignment: It infers the user’s evolving personality
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Personality Evolving Mechanism (PEM)... exponential moving average... Big Five scores

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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MIRIX: Multi-Agent Memory System for LLM-Based Agents

Yu Wang and Xi Chen. Mirix: Multi-agent memory system for llm-based agents.arXiv:2507.07957, 2025

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Ai-native memory 2.0: Second me

Jiale Wei, Xiang Ying, Tao Gao, Fangyi Bao, Felix Tao, and Jingbo Shang. Ai-native memory 2.0: Second me. arXiv:2503.08102, 2025

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Rossi, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K

Junda Wu, Hanjia Lyu, Yu Xia, Zhehao Zhang, Joe Barrow, Ishita Kumar, Mehrnoosh Mirtaheri, Hongjie Chen, Ryan A Rossi, Franck Dernoncourt, et al. Personalized multimodal large language models: A survey.arXiv:2412.02142, 2024

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A-MEM: Agentic Memory for LLM Agents

Wujiang Xu, Kai Mei, Hang Gao, Juntao Tan, Zujie Liang, and Yongfeng Zhang. A-mem: Agentic memory for llm agents.arXiv:2502.12110, 2025

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Can large language models be good com- panions? an llm-based eyewear system with conversational common ground

Zhenyu Xu, Hailin Xu, Zhouyang Lu, Yingying Zhao, Rui Zhu, Yujiang Wang, Mingzhi Dong, Yuhu Chang, Qin Lv, Robert P Dick, et al. Can large language models be good com- panions? an llm-based eyewear system with conversational common ground. InIMWUT, 2024

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Qwen3 Technical Report

An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report.arXiv:2505.09388, 2025

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MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, et al. Minicpm-v: A gpt-4v level mllm on your phone. arXiv:2408.01800, 2024

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A survey on multimodal large language models.National Science Review, 2024

Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, and Enhong Chen. A survey on multimodal large language models.National Science Review, 2024

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From mooc to maic: Reshap- ing online teaching and learning through llm-driven agents

Jifan Yu, Zheyuan Zhang, Daniel Zhang-li, Shangqing Tu, Zhanxin Hao, Rui Miao Li, Haoxuan Li, Yuanchun Wang, Hanming Li, Linlu Gong, et al. From mooc to maic: Reshap- ing online teaching and learning through llm-driven agents. arXiv:2409.03512, 2024

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InProceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’08, page 677–680, New York, NY , USA

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Do llms recognize your preferences? evaluating personalized preference following in llms.arXiv preprint arXiv:2502.09597, 2025

Siyan Zhao, Mingyi Hong, Yang Liu, Devamanyu Haz- arika, and Kaixiang Lin. Do llms recognize your prefer- ences? evaluating personalized preference following in llms. arXiv:2502.09597, 2025

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InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shen- glong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, et al. Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv:2504.10479, 2025

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Per- sonality alignment of large language models

Minjun Zhu, Yixuan Weng, Linyi Yang, and Yue Zhang. Per- sonality alignment of large language models. InICLR, 2025

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a meticulous researcher

Yuchen Zhuang, Haotian Sun, Yue Yu, Rushi Qiang, Qifan Wang, Chao Zhang, and Bo Dai. Hydra: Model factorization framework for black-box llm personalization. InNeurIPS, 2024. PersonaVLM: Long-Term Personalized Multimodal LLMs Supplementary Material This supplementary material provides comprehensive details to complement the main paper, organized as follows...

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[54]

Adapt & Personalize: Your tone and style must adapt to the user’s Big Five Personality scores (e.g., be reassuring for high Neuroticism, practical for low Openness)

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[55]

Natural Weaving: Naturally weave in relevant details from memories to show you remember, but avoid repeating recent information

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keywords

Decide Your Action: Based on the user’s query and context, first decide if you have enough information to answer directly or if you need to search your long-term memory. # Output Format Your output must consist of a ‘<think>‘ block, followed by **one and only one of the following blocks (‘<answer>‘ or ‘<retrieve>‘): <think>Your reasoning process goes here...

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Analyze: Based on the linguistic and emotional cues in the ‘User Input‘ and its context, infer the user’s momentary Big Five personality state

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[58]

openness

Score: Assign an integer score from 1 to 5 for each trait. # OUTPUT INSTRUCTIONS Provide your response as a series of key-value pairs, one item per line. "openness": [integer from 1 to 5] "conscientiousness": [integer from 1 to 5] "extraversion": [integer from 1 to 5] "agreeableness": [integer from 1 to 5] "neuroticism": [integer from 1 to 5] Figure 18. P...

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Consolidate related behaviors into a single core habit

Identify & Update: Extract user-centric, long-term goals or repetitive habits from the conversation. Consolidate related behaviors into a single core habit. Update or remove goals/habits that are completed or changed

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User runs every Thursday morning

Core Content (‘content‘): Each memory must be a single, simple third-person sentence describing the user’s habit or goal. Include time/trigger context if available (e.g., "User runs every Thursday morning")

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Unique Keys (‘unique key‘): Assign a concise, unique key for each memory

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* Strictly prohibited from creating information not present in the input

Constraints: * The final output must not exceed 5 entries. * Strictly prohibited from creating information not present in the input. * If no relevant habits/goals are found, output an empty object. # Input

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Current User Profile: {UserProfile}

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Current Procedural Memory: {CurrentProceduralMemory}

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unique key 1

Recent Conversations: {DialogHistory} # Output Format Provide your response as key-value pairs, one per line. "unique key 1": string, A single sentence describing the habit. "unique key 2": string, Another single sentence describing the goal. Figure 19. Prompt for updating procedural memories. Prompt for semantic memory creation You are an AI memory analy...

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Briefly explain the reason for the ‘decision‘

‘reason‘ (string): * Required. Briefly explain the reason for the ‘decision‘

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* Set to ‘false‘: Information is already in the user profile/recent history with no updates; temporary questions, meaningless small talk

‘decision‘ (boolean): * Set to ‘true‘: User explicitly instructs to remember; user mentions new core facts, preferences, dislikes, important corrections, long-term goals/states. * Set to ‘false‘: Information is already in the user profile/recent history with no updates; temporary questions, meaningless small talk

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* Text Memory: Pure text information, dates, events, concepts, or non-specific object descriptions of images (e.g., atmosphere)

‘content‘ (string): * If ‘decision‘ is ‘true‘, extract and summarize the memory content. * Text Memory: Pure text information, dates, events, concepts, or non-specific object descriptions of images (e.g., atmosphere). * Image Object Memory: User indicates remembering a specific object in an image, format is ‘[User Description/Naming] (Image Object: [Objec...

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reason": string

‘keywords‘ (string): * If ‘decision‘ is ‘true‘, list a few core keywords, separated by English commas. * If ‘decision‘ is ‘false‘, set to ‘""‘. Core Constraint: Strictly prohibited from creating or supplementing information not present in the current input and history. # Output Format (four key-value pairs, one per line.) "reason": string "decision": true...

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Core Identity: New information directly overwrites old values (e.g., name, occupation, long-term residence)

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Emphasize recency and intensity

Core Preferences/Hobbies: Intelligently replace/condense/add. Emphasize recency and intensity. Limit list length (e.g., 5-7 items). Ignore temporary/weak preferences

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Temporary Information: Strictly ignore (e.g., short-term itineraries, one-time activities)

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XX": string // HUMAN Aspect, e.g., age, gender, preferences, life status, etc

No Fabrication: All fields and information must originate from the input; strictly prohibited from creating new information. # Output Format (mutiple key-value pairs, one per line) "XX": string // HUMAN Aspect, e.g., age, gender, preferences, life status, etc. "XX": string // PERSONA Aspect, e.g., occupation, education background, etc. Figure 21. Prompt f...

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Topic Summary (‘topic_summary‘): Coherent, complete third-person summary

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Keywords (‘keywords‘): Extract core keywords

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topic_summary

Source Indices (‘source_dialog_indices‘): Contains indices of all relevant dialogues. # Input User Profile: {UserProfile} Recent Conversations: {DialogHistory} # Core Constraint Strictly prohibited from creating or supplementing information not present in the dialogue history. # Output Format (each topic includes the following three key-value pairs) "topi...

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User’s Query: {query}

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Wins" if A is better,

Reference Answer (Ground Truth): {reference_answer} # RESPONSES TO COMPARE - Response A: {response_A} - Response B: {response_B} # EVALUATION INSTRUCTIONS Your task is to compare Response A and Response B to decide which one is superior. You will base your decision on the two criteria below. The final output must be a single word: "Wins" if A is better, "...

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- Use the **Reference Answer ** as the ground truth for what a perfect answer should contain

Accuracy: - Evaluate which response is more factually correct and completely addresses the user’s query. - Use the **Reference Answer ** as the ground truth for what a perfect answer should contain. - A more accurate response directly reflects the information and intent of the Reference Answer

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Wins" if: Response A is clearly superior to Response B on at least one criterion and is not worse on the other. - Output

Personalization: - Evaluate which response’s tone, style, and language better adapt to the user’s stated **Personality Traits **. - A more personalized response feels tailored to the user, not generic. ## Decision Logic: - Output "Wins" if: Response A is clearly superior to Response B on at least one criterion and is not worse on the other. - Output "Lose...

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