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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="description" content="Mi-Memory: A Lifecycle Memory Framework for Personal AI." />
<meta property="og:title" content="Mi-Memory" />
<meta property="og:description" content="A lifecycle memory framework for personal AI across structure, expansion, evolution, and deployment." />
<meta property="og:image" content="figure/banner.png" />
<title>Mi-Memory</title>
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</style>
</head>
<body>
<main class="page">
<nav class="navbar" aria-label="Primary navigation">
<div class="navbar-inner">
<a class="brand" href="#top" aria-label="Mi-Memory home"><span>Mi</span><span>Memory</span></a>
<div class="nav-links">
<a href="#overview">Overview</a>
<a href="#architecture">Architecture</a>
<a href="#results">Results</a>
<a href="#citation">Citation</a>
</div>
</div>
</nav>
<aside class="side-nav" aria-label="Section navigation">
<a href="#overview">Overview</a>
<a href="#architecture">Architecture</a>
<a href="#lifecycle">Lifecycle</a>
<a href="#results">Results</a>
<a href="#citation">Citation</a>
</aside>
<header id="top" class="hero">
<div class="eyebrow">Technical Report · July 2026</div>
<h1><span class="title-accent">Mi</span>-Memory</h1>
<p class="subtitle">A Lifecycle Memory Framework for Personal AI that turns memory from a conversation cache into auditable, multi-source, governable user-state infrastructure.</p>
<div class="meta">
<span>Darwin Agent Team</span>
<span>•</span>
<span>Xiaomi</span>
<span>•</span>
<span>53-page technical report</span>
</div>
<div class="actions">
<a class="button primary" href="https://arxiv.org/abs/2607.18975" target="_blank" rel="noopener noreferrer">Read Report</a>
<a class="button" href="https://github.com/Darwin-Agent/Mi-Memory" target="_blank" rel="noopener noreferrer">GitHub</a>
</div>
</header>
<div class="banner" aria-label="Mi-Memory banner">
<img src="figure/banner.png" alt="Mi-Memory banner visual" />
</div>
<section id="overview">
<h2 class="section-title">Memory for always-on personal AI.</h2>
<p class="lead">Personal AI is moving beyond chat-only interaction toward continuous services across phones, cars, homes, wearables, cameras, and tools. In this setting, memory must preserve durable user state, connect answers to multimodal evidence, support correction and forgetting, bound policy evolution, and remain deployable across edge/cloud constraints.</p>
<div class="grid">
<article class="card">
<h3>Structure</h3>
<p>Memory runtime, storage hierarchy, retrieval, filtering, and context assembly for multi-granularity state.</p>
</article>
<article class="card">
<h3>Expansion</h3>
<p>Multimodal and cross-device evidence acquisition with source identity, provenance, and fusion contracts.</p>
</article>
<article class="card">
<h3>Evolution</h3>
<p>Diagnostic iteration, governed strategy updates, feature gates, rollback records, and auditable improvement.</p>
</article>
<article class="card">
<h3>Deployment</h3>
<p>Substrate-independent memory variants for cloud, edge, and lightweight local environments.</p>
</article>
</div>
<div class="evidence-ribbon">
<div class="evidence-pill"><strong>Controlled reference</strong><span>Public benchmark with a reproduced baseline under a shared harness.</span></div>
<div class="evidence-pill"><strong>Module-level</strong><span>Standalone component on a public benchmark with its own harness.</span></div>
<div class="evidence-pill"><strong>Preliminary/internal</strong><span>Internal benchmark evidence with external validity still pending.</span></div>
<div class="evidence-pill"><strong>Transfer feasibility</strong><span>Capability mapped to a lighter substrate and task style.</span></div>
</div>
</section>
<section id="architecture">
<h2 class="section-title">A shared audit contract across the lifecycle.</h2>
<p class="lead">Mi-Memory links structure, expansion, evolution, and deployment through typed evidence payloads, diagnostic traces, strategy artifacts, and gate/rollback records. The same artifact families support research diagnostics and product-facing memory governance.</p>
<div class="figure-card">
<img src="figure/mimemory.png" alt="Mi-Memory lifecycle overview" />
<p class="caption">Lifecycle overview: Structure, Expansion, Evolution, and Deployment are connected by auditable memory artifacts.</p>
</div>
</section>
<section id="lifecycle">
<h2 class="section-title">Designed for correction, provenance, and transfer.</h2>
<div class="quote">The framework decomposes assistant memory into testable roles: typed state makes failures localizable, source-aware expansion preserves evidence boundaries, governed evolution turns failures into rollback-capable hypotheses, and deployment tests whether audit obligations survive substrate transfer.</div>
<div class="grid two-column" style="margin-top: 18px;">
<article class="card">
<h3>Human-editable memory</h3>
<p>Correction, forgetting, and policy updates are treated as first-class lifecycle events rather than ad hoc prompt patches.</p>
</article>
<article class="card">
<h3>Evidence-aware answers</h3>
<p>Retrieval and context assembly preserve where memory came from, why it was selected, and how it can be inspected.</p>
</article>
</div>
<div class="module-list">
<div class="module-row"><strong>MemStack</strong><span>Structures durable state with layered L0/L1/L2 memory, hybrid retrieval, bounded context assembly, and adapter contracts.</span></div>
<div class="module-row"><strong>MemSense</strong><span>Turns visual and multimodal observations into source-aware evidence rather than disposable attachments.</span></div>
<div class="module-row"><strong>MemFuse</strong><span>Links related observations across cars, homes, phones, wearables, and tools into coherent episodes.</span></div>
<div class="module-row"><strong>LiteMem</strong><span>Transfers selected contract elements to Markdown/Git artifacts and file-tool retrieval for lightweight settings.</span></div>
</div>
<p class="lead" style="margin-top: 26px;">The report grounds the lifecycle with a human-car-home training handoff: a parent, a car route, a phone calendar, and a home camera jointly determine whether the assistant should remind the family about a basketball bag before entering the expressway.</p>
<div class="trace">
<div class="trace-step"><strong>Observe</strong><span>Calendar, vehicle route state, and home camera evidence arrive with source identity and timestamps.</span></div>
<div class="trace-step"><strong>Fuse</strong><span>The system links training time, travel feasibility, and the observed bag location into one episode.</span></div>
<div class="trace-step"><strong>Answer</strong><span>The assistant retrieves the fused episode and answers with inspectable supporting evidence.</span></div>
<div class="trace-step"><strong>Improve</strong><span>User corrections become gated memory-policy updates rather than hidden prompt or retrieval edits.</span></div>
</div>
</section>
<section id="results">
<h2 class="section-title">Evaluation highlights.</h2>
<p class="lead">The report evaluates Mi-Memory across structure benchmarks, expansion modules, governed offline evolution, and deployment transfer. These numbers are evidence anchors rather than a single leaderboard; the PDF is authoritative for protocols and boundaries.</p>
<div class="chart-card">
<div class="bar-chart">
<div class="bar-row"><span class="bar-label">LoCoMo</span><div class="bar-track"><div class="bar-fill" style="width: 93.59%"></div></div><span class="bar-value">93.59%</span></div>
<div class="bar-row"><span class="bar-label">Mem-Gallery</span><div class="bar-track"><div class="bar-fill" style="width: 89.15%"></div></div><span class="bar-value">89.15%</span></div>
<div class="bar-row"><span class="bar-label">LongMemEval</span><div class="bar-track"><div class="bar-fill" style="width: 87.47%"></div></div><span class="bar-value">87.47%</span></div>
<div class="bar-row"><span class="bar-label">PersonaMem-V2</span><div class="bar-track"><div class="bar-fill" style="width: 57.24%"></div></div><span class="bar-value">57.24%</span></div>
<div class="bar-row"><span class="bar-label">MemFuseBench</span><div class="bar-track"><div class="bar-fill" style="width: 35.2%"></div></div><span class="bar-value">35.2%</span></div>
<div class="bar-row"><span class="bar-label">LoCoMo offline</span><div class="bar-track"><div class="bar-fill" style="width: 94.74%"></div></div><span class="bar-value">94.74%</span></div>
<div class="bar-row"><span class="bar-label">LiteMem <span class="mini-tag">lite</span></span><div class="bar-track"><div class="bar-fill" style="width: 90.81%"></div></div><span class="bar-value">90.81%</span></div>
</div>
</div>
<div class="result-notes">
<div class="result-note">D²ACCI/E²MEND improves a LoCoMo offline run from 75.58% to 94.74%.</div>
<div class="result-note">LiteMem is a deployment path, not a benchmark, and reports 90.81% transfer plus 90.0% retention.</div>
</div>
<p class="lead" style="margin-top: 30px;">Mi-Memory is an initial step toward personal-AI memory that can be measured, governed, and improved across its full lifecycle.</p>
<div class="grid two-column" style="margin-top: 16px;">
<article class="card">
<h3>Causal attribution and forgetting</h3>
<p>Future work targets stronger answer-to-evidence tracing and propagation-complete forgetting across derived summaries, profiles, and caches.</p>
</article>
<article class="card">
<h3>Federated and standardized contracts</h3>
<p>Cross-device memory composition, scalable diagnostic evolution, and shared memory interfaces remain key research directions.</p>
</article>
</div>
</section>
<section id="citation">
<h2 class="section-title">Citation.</h2>
<p class="lead">If this report is useful for your research or product work, please cite Mi-Memory.</p>
<pre><code>@techreport{mimemory2026,
title = {Mi-Memory: A Lifecycle Memory Framework for Personal AI},
author = {Darwin Agent Team},
institution = {Xiaomi},
year = {2026},
url = {https://arxiv.org/abs/2607.18975}
}</code></pre>
</section>
<footer>
<span>© 2026 Darwin Agent Team, Xiaomi.</span>
<span> Released as a project introduction page for the Mi-Memory technical report.</span>
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