# Memory & Personalization — Angle B: Industry Blogs

**Project context:** Humanizing AI output and thinking.
**Research value: high** — Industry blog coverage of LLM memory is rich, with convergent architectural patterns (tiered memory, self-editing blocks, graph-based long-term context) and direct implications for making AI feel less stateless and more human.

Date ranges covered: Feb 2024 – Apr 2026. Eighteen primary posts plus several adjacent titles from the same outlets.
**Last updated:** 2026-04-21

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## 1. OpenAI — "Memory and new controls for ChatGPT"

- **URL:** https://openai.com/index/memory-and-new-controls-for-chatgpt/
- **Author / Source:** OpenAI (blog post, product announcement)
- **Date:** Feb 13, 2024 (with updates Sep 2024, Apr 2025, Jun 2025)
- **Summary:** First-party announcement of ChatGPT's cross-chat memory. Introduces two modes: "saved memories" (explicit user instructions to remember) and "chat history reference" (implicit, continuous extraction from past conversations). Rolled out Plus/Pro → Free over 2024–2025 with lightweight short-term continuity for free users.
- **Notable quote:**
  > "Memory now works in two ways: 'saved memories' you've asked it to remember and 'chat history', which are insights ChatGPT gathers from past chats to improve future ones. … The more you use ChatGPT, the more useful it becomes."
- **Relevance to humanization:** Frames memory as the thing that turns a stateless assistant into a continuous relationship — remembering tone, voice, and format preferences so the user doesn't have to repeat. Also names the risk surface (sensitive data, health info) OpenAI is explicitly avoiding remembering by default.

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## 2. Anthropic — "Claude memory and 'Managing context on the Claude Developer Platform'" (context-management announcement)

- **URL:** https://www.anthropic.com/news/context-management (primary); secondary summaries: Kingy AI, TheOutpost, Thomas Wiegold blog
- **Author / Source:** Anthropic
- **Date:** Sep 2025 (developer platform) → Oct 23, 2025 (paid rollout) → Mar 2, 2026 (free-tier + import tool)
- **Summary:** Anthropic ships a project-scoped memory model with three layers: global preferences, per-project conventions, and individual chat memories. Paired with a developer-side **memory tool** on Sonnet 4.5 that lets agents store information in a file-based system *outside* the context window, enabling long-running agents to avoid context exhaustion (reportedly ~84% token reduction in extended workflows).
- **Notable quote (via Kingy AI summary of Anthropic's positioning):**
  > "Unlike competitors, Claude only retrieves past conversations when explicitly asked by users, offering a more privacy-focused approach." Paired with the product claim that each project gets its own "memory space, keeping sensitive conversations contained."
- **Relevance to humanization:** Opt-in, user-legible recall is pitched as a *trust* lever, not just a quality one — Anthropic's stance is that a humanized assistant is one whose memory the user can read, edit, and scope. The developer-side memory tool reframes humanization as something developers must *build for*, not a model feature.

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## 3. Google — "Introducing Gemini with personalization"

- **URL:** https://blog.google/products/gemini/gemini-personalization/
- **Author / Source:** Dave Citron, Senior Director of Product Management, Gemini app (Google blog)
- **Date:** Mar 13, 2025
- **Summary:** Gemini connects to a user's Google account data (starting with Search history, expanding to Photos/YouTube/Gmail) to tailor responses. Powered by Gemini 2.0 Flash Thinking. Personalization is opt-in per source, and the reasoning model decides *when* a user's history would actually help answer the prompt.
- **Notable quote:**
  > "We'll only use your Search history when our advanced reasoning models determine that it's actually helpful. … These updates are all designed to make Gemini feel less like a tool and more like a natural extension of you, anticipating your needs with truly personalized assistance."
- **Relevance to humanization:** Unique angle — Google is the only vendor whose "memory" is grounded in *real-world behavioral data outside the chat window*. The explicit framing ("less like a tool and more like a natural extension of you") is the clearest humanization pitch among frontier vendors.

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## 4. Google — "Use past chats to get more personalized responses" (Personal Context)

- **URL:** https://blog.google/intl/en-ca/feed/use-past-chats-to-get-more-personalized-responses/
- **Author / Source:** Google / Gemini team
- **Date:** 2025 (rollout continued through 2026)
- **Summary:** Introduces the "Personal Context" setting that lets Gemini automatically reference past chats to learn preferences. Setting is on by default but user-controllable.
- **Notable quote (paraphrased from announcement):** Gemini "remembers key details and preferences you've shared, making conversations more natural and relevant."
- **Relevance to humanization:** Confirms the industry has converged on *on-by-default* implicit memory — a major UX bet that natural-feeling conversation trumps opt-in purity.

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## 5. Character.AI — "Helping Characters Remember What Matters Most"

- **URL:** https://blog.character.ai/helping-characters-remember-what-matters-most/
- **Author / Source:** Character.AI product team
- **Date:** 2024
- **Summary:** Ships **Chat Memories** — a 400-character fixed-text slot per chat that the user writes and the Character is "more likely to incorporate." Sits alongside earlier **pinned memories** and **auto-memories** (paid-tier). Explicitly probabilistic, not deterministic.
- **Notable quote:**
  > "While we can't guarantee the Character will always use or reference the information exactly as written, adding it to your chat memories increases the likelihood that it will be incorporated into your Character interactions, especially over longer conversations."
- **Relevance to humanization:** The honesty about non-determinism is notable — Character.AI tells users memory is a *bias on the likelihood of recall*, not a database lookup. This is closer to how human memory actually works and matters for the project's framing of "humanizing AI thinking."

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## 6. Character.AI — "Optimizing AI Inference at Character.AI"

- **URL:** https://blog.character.ai/optimizing-ai-inference-at-character-ai-2/
- **Author / Source:** Character.AI research blog
- **Date:** 2024
- **Summary:** Engineering post on how Character.AI serves 20k QPS of long roleplay dialogues (avg 180 messages of history per turn). Stateful KV-cache on host memory with rolling-hash prefix matching yields 95% cache hit. Multi-Query Attention + hybrid local/global attention + cross-layer KV-sharing cut cache size >20×. Serving cost reduced 33× since late 2022.
- **Notable quote:**
  > "On Character.AI, the majority of chats are long dialogues; the average message has a dialogue history of 180 messages. As dialogues grow longer, continuously refilling KV caches on each turn would be prohibitively expensive."
- **Relevance to humanization:** Rarely cited outside infra circles, but important: *long, persistent, human-feeling conversations have a specific inference-economics problem*. "Humanizing" AI at scale is partly an infra-cost problem, not just a prompting one.

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## 7. MemGPT / Letta — "MemGPT is now part of Letta"

- **URL:** https://letta.com/blog/memgpt-and-letta
- **Author / Source:** Charles Packer & Sarah Wooders (Letta co-founders, ex-UC Berkeley)
- **Date:** Sep 23, 2024
- **Summary:** Rebrand and clarification post. MemGPT = the academic *design pattern* (LLM-OS with self-editing memory tools). Letta = the company and open-source framework continuing the work, focused on deployability, debugging, and monitoring stateful agents.
- **Notable quote:**
  > "MemGPT should refer to the original agent design pattern described in the research paper (empowering LLMs with self-editing memory tools), and use the name Letta to refer to the agent framework."
- **Relevance to humanization:** Establishes the archetype "LLM with self-editing memory" as the canonical industry pattern for agents that can grow with a user.

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## 8. Letta — "Agent Memory: How to Build Agents that Learn and Remember"

- **URL:** https://letta.com/blog/agent-memory
- **Author / Source:** Letta team
- **Date:** Jul 7, 2025
- **Summary:** Letta's canonical reference piece defining the four-tier memory model: **Message Buffer** (recent turns) → **Core Memory** (in-context editable blocks) → **Recall Memory** (full history, searchable) → **Archival Memory** (external vector/graph DBs). Introduces **sleep-time compute**: a separate agent that refines memory asynchronously during idle time.
- **Notable quote:**
  > "Rather than hard-coding human-like memory structures, we should focus on context engineering — designing systems that effectively manage the information available to the model at inference time. … The goal isn't to replicate human memory mechanics but to create memory systems that enable agents to be genuinely helpful, consistent, and capable of learning within the token-based paradigm of LLMs."
- **Relevance to humanization:** Important counter-position to anthropomorphic framing — Letta explicitly says *don't* try to copy human memory, focus on what gets into the context window. This is a sharper frame for "humanizing AI" that's about *behavior*, not biology.

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## 9. Letta — "RAG is not Agent Memory"

- **URL:** https://www.letta.com/blog/rag-vs-agent-memory
- **Author / Source:** Letta team
- **Date:** Feb 13, 2025
- **Summary:** Argues RAG — static document retrieval over a fixed corpus — is insufficient for agent memory. Memory needs to be *written to* by the agent, evolved over time, and able to invalidate old facts. RAG is a retrieval tool; memory is an architectural state.
- **Notable quote (from the post's positioning):**
  > "Although RAG provides a way to connect LLMs and agents to more data than what can fit into context, traditional RAG is insufficient for building agent memory."
- **Relevance to humanization:** Directly names a common industry mistake — pasting a vector DB onto a chatbot and calling it memory. For a humanization project, this matters: humans don't just retrieve, they consolidate and update.

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## 10. Letta — "Sleep-Time Compute"

- **URL:** https://www.letta.com/blog/sleep-time-compute
- **Author / Source:** Letta research
- **Date:** Apr 21, 2025
- **Summary:** Introduces the idea of agents spending "idle" time rewriting their own memory — consolidating, deduplicating, and refining memory blocks when not actively serving a user. Analogous to memory consolidation during sleep in biological systems.
- **Notable quote (from related Letta posts summarizing this line of work):**
  > "Instead of lazy, incremental updates during conversations, memory can be reorganized and improved during idle periods."
- **Relevance to humanization:** A rare industry example of explicit biological analogy that pays off structurally, not just vocabulary — idle-time memory consolidation has a real engineering payoff (lower interaction latency, higher memory quality).

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## 11. Letta — "Continual Learning in Token Space"

- **URL:** https://letta.com (blog post, Dec 11, 2025)
- **Author / Source:** Letta research
- **Date:** Dec 11, 2025
- **Summary:** Argues that "learning in token space" — i.e., writing learnings into a persistent external memory the agent itself manages — is the path to agents that improve over time. Importantly, token-space memory is *portable across model generations*, whereas weight-space learning dies with the model.
- **Notable quote:**
  > "We believe that learning in token space is the key to building AI agents that truly improve over time. … Agents that can carry their memories across model generations will outlast any single foundation model."
- **Relevance to humanization:** A deeply relevant pattern for long-lived humanized assistants: the assistant's "personality" and relationship history can survive model upgrades if it lives in tokens, not weights.

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## 12. Zep — "Zep: A Temporal Knowledge Graph Architecture for Agent Memory"

- **URL:** https://blog.getzep.com/zep-a-temporal-knowledge-graph-architecture-for-agent-memory/
- **Author / Source:** Preston Rasmussen & Daniel Chalef (Zep)
- **Date:** Jan 22, 2025 (arXiv 2501.13956)
- **Summary:** Companion blog for the Zep paper. Introduces **Graphiti**, a temporally-aware knowledge graph engine that unifies unstructured conversation data and structured business data, with edges that track validity over time. Benchmarks: 94.8% on Deep Memory Retrieval (beats MemGPT 93.4%); up to 18.5% accuracy improvement with 90% latency reduction on LongMemEval (long-horizon enterprise memory).
- **Notable quote:**
  > "While existing retrieval-augmented generation (RAG) frameworks for large language model (LLM)-based agents are limited to static document retrieval, enterprise applications demand dynamic knowledge integration from diverse sources including ongoing conversations and business data."
- **Relevance to humanization:** Temporal reasoning is one of the clearest places current AI fails to feel human — humans know "Alice got married *last year*" supersedes "Alice is single." Zep's graph represents this as first-class state.

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## 13. Zep — "Beyond Chat Memory: Making AI Interactions More Personal"

- **URL:** https://blog.getzep.com/ai-knowledge-graph-memory/
- **Author / Source:** Daniel Chalef (Zep)
- **Date:** Oct 22, 2024
- **Summary:** Launches Zep's graph API: agents ingest JSON business data (billing, CRM, transactions) alongside conversation history into a unified knowledge graph. Demonstrates with a fictional "PaintWiz" support bot that correctly diagnoses a login failure as a billing-suspension issue, not a password issue, because it can cross-reference user state.
- **Notable quote:**
  > "Effective AI agents require more than conversation memory alone — they need to understand the full context of who they're helping and why."
- **Relevance to humanization:** Reframes "memory" from *chat recall* to *whole-person context*. For a humanization project this is the most natural definition: a human friend knows your job, your bills, your history, not just what you said yesterday.

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## 14. LangChain — "Memory for agents"

- **URL:** https://blog.langchain.dev/memory-for-agents/
- **Author / Source:** Harrison Chase (co-founder, LangChain)
- **Date:** Oct 19, 2024
- **Summary:** Taxonomy post mapping human memory types (from the CoALA paper, Sumers et al. 2024) onto agent memory:
  - **Procedural** — how to perform tasks (weights + code; practically, self-edited system prompts)
  - **Semantic** — facts about the world, including user facts (most common flavor in production personalization)
  - **Episodic** — sequences of past actions (implemented as dynamic few-shot)
  Also contrasts two update strategies: **"in the hot path"** (ChatGPT's approach; blocks response) vs. **"in the background"** (separate process; no latency cost but delayed).
- **Notable quote:**
  > "People often expect LLM systems to innately have memory, maybe because LLMs feel so human-like already. However, LLMs themselves do NOT inherently remember things — so you need to intentionally add memory in. … Memory is application-specific."
- **Relevance to humanization:** Names the *user illusion* problem directly — users expect memory because the thing feels human. The project's humanization goals will live or die on closing that expectation gap.

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## 15. Eugene Yan — "Patterns for Personalization in Recommendations and Search"

- **URL:** https://eugeneyan.com/writing/patterns-for-personalization/
- **Author / Source:** Eugene Yan (MTS, Anthropic; ex-Amazon)
- **Date:** 2022 (still heavily referenced in 2024–2026 LLM recsys discussions)
- **Summary:** Survey of five personalization pattern families as deployed in industry: **bandits** (Netflix artwork, Doordash cuisine, Spotify recsplanations), **embedding + MLP** (TripAdvisor, YouTube, Alibaba DIN), **sequential / transformers** (Alibaba BST, BERT4Rec), **graph-based**, and **session-based**. Emphasizes cold-start handling and the regret math of continuous learning vs. batch training.
- **Notable quote:**
  > "Bandits have several advantages over batch machine learning approaches. … they can continuously learn about the best recommendation for each customer through exploration. … Batch recommenders tend to perform well when we have high certainty about recommendation relevance … However, when we have little or no data (i.e., long-tail, cold-start), batch recommenders ignore possibly relevant items in favor of popular items."
- **Relevance to humanization:** The most rigorous industry frame for "what does personalization actually learn, and how fast?" Bandits and contextual features are the pre-LLM backbone that modern humanized assistants still need in order to avoid mode-collapse onto popular answers.

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## 16. Eugene Yan — "Improving Recommendation Systems & Search in the Age of LLMs"

- **URL:** https://eugeneyan.com/writing/recsys-llm/
- **Author / Source:** Eugene Yan
- **Date:** 2024 (50k+ reads)
- **Summary:** Surveys how industrial recsys is absorbing LLMs: LLM-augmented architectures, LLM-assisted data generation, scaling laws adapted from language to recommendation, unified search-and-recommendation models. The post is cited as canonical context for the LLM-RecSys convergence.
- **Notable quote (from Yan's framing across the piece):** Industrial systems are moving from "learn preferences from clicks" to "learn preferences from language *plus* clicks," with LLMs acting as a universal feature extractor for otherwise-sparse user state.
- **Relevance to humanization:** Any personalized AI product eventually looks like a recsys under the hood. The convergence Yan describes — language-native personalization — is what makes "humanized thinking" commercially tractable.

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## 17. Eugene Yan — "Training an LLM-RecSys Hybrid for Steerable Recs with Semantic IDs"

- **URL:** https://eugeneyan.com/writing/semantic-ids/
- **Author / Source:** Eugene Yan
- **Date:** 2025
- **Summary:** Demonstrates training a model that understands both English and compact *semantic item IDs*, so users can steer recommendations with natural language while preserving the behavioral signal of traditional recsys.
- **Notable quote (from the post's argument):** The hybrid design lets users say "less thrillers, more slow-burn literary fiction" and have that instruction compose with their click history, rather than overriding it.
- **Relevance to humanization:** This is the cleanest industry pattern for *steerable* personalization — users articulating taste in natural language, rather than waiting for inference from behavior. That explicit dialog is a major lever for making AI feel collaborative rather than presumptive.

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## 18. Microsoft — User-Scoped Persistent Memory in Azure AI Foundry

- **URL:** https://learn.microsoft.com/en-us/agent-framework/get-started/memory
- **Author / Source:** Microsoft Learn / Azure AI Foundry team
- **Date:** Mar 31, 2026
- **Summary:** Published a reference architecture for user-scoped persistent memory in Azure AI Foundry, built on Cosmos DB, with per-user isolation enforced through Entra ID. Positions memory as a standard component of the agent framework, not an add-on — the "Step 4" in their getting-started agent guide.
- **Notable point:** The architecture is explicitly user-scoped, not account-global, aligning with the Anthropic model over the ChatGPT model. Signals that enterprise cloud vendors view project-scoped memory as the safe default.
- **Relevance to humanization:** Microsoft's choice of Cosmos DB + Entra ID for memory isolation is an early signal that enterprise memory will be governed by identity infrastructure, not just prompt routing. Implications for who "owns" the memory and its portability.

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## 19. Oracle — Unified Memory Core for Enterprise AI Systems

- **URL:** https://blogs.oracle.com/database/introducing-oracle-ai-agent-memory-a-unified-memory-core-for-enterprise-ai-systems
- **Author / Source:** Oracle Database team
- **Date:** Mar 2026 (expected CY2026 availability)
- **Summary:** Oracle AI Agent Memory extends Oracle Database 26ai into a persistent memory core for agents — stateful, persistent memory stored *inside* the database engine, not as a sidecar. Coupled with an Oracle AI Database Private Agent Factory (no-code agent deployment with portable containers). Oracle's thesis: the database should be the memory primitive, not a vector sidecar.
- **Relevance to humanization:** A counter-bet to the vector-DB-native memory approach of Mem0/Zep/Letta. If enterprise agents store memory in Oracle, every application that can query the same database inherits continuity — a radically different portability story.

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## 20. Mem0 — "State of AI Agent Memory 2026" Report

- **URL:** https://mem0.ai/blog/state-of-ai-agent-memory-2026
- **Author / Source:** Mem0 team
- **Date:** Apr 1, 2026
- **Summary:** Annual report framing memory as a "first-class architectural component" with its own benchmark suite. Key statistics: Mem0 reports 66.9% LoCoMo accuracy vs. OpenAI Memory's 52.9%; the graph-enhanced Mem0g closes the accuracy gap to under 5 points vs. full context while staying at 2.59s p95. Mem0 became the exclusive memory provider for AWS's Agent SDK; PyPI downloads hit ~625k/week. 21 frameworks and platforms integrated.
- **Relevance to humanization:** Mem0 is now infrastructure at AWS scale, not a startup experiment. The "State of" framing signals category maturation — the question is no longer "should agents have memory?" but "which architecture, at what cost?"

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## Cross-Cutting Patterns

1. **Tiered memory is now table stakes.** OpenAI (saved/history), Anthropic (global/project/chat), Letta (message buffer/core/recall/archival), Gemini (personal context/context window), Character.AI (pinned/auto/chat memories), Zep (graph facts/episodes) — every serious player has converged on 3–4 tiers with different recency, cost, and editability properties.
2. **Self-editing memory is the dominant pattern.** MemGPT's "LLM with tools to rewrite its own memory" has been adopted by Anthropic's memory tool, Letta's memory blocks, and (conceptually) by ChatGPT's implicit memory extraction. LangChain explicitly names this as "writing in the hot path."
3. **Asynchronous / sleep-time consolidation is emerging.** Letta formalizes it; LangChain calls it "writing in the background." Both arguments converge: don't block the user on memory ops.
4. **Temporal validity beats naive RAG.** Zep's graph-with-invalidation, Letta's recursive summarization, Anthropic's project-scoped isolation, and Gemini's "only when helpful" gating are all ways of saying *retrieval alone is not memory*.
5. **Privacy/control UX is a differentiation axis.** Anthropic ("only when asked"), OpenAI (temporary chats, manage memories), Google (per-source consent with visible banners) are staking different positions on how legible the memory should be to the user.
6. **Memory as a portability story.** Letta's "continual learning in token space" and Anthropic's import tool (`claude.com/import-memory`) treat the user's accumulated memory as an *asset they can move*, not a platform lock-in. This is new in 2025–2026.
7. **Personalization is both implicit and explicit.** Eugene Yan's bandits/sequential models (implicit, from behavior) are being composed with LLM-steerable natural-language preferences (explicit, from dialog). The semantic-IDs post is the clearest example of deliberate hybridization.
8. **Long-horizon conversation has an infra problem.** Character.AI is the most candid: humanized long dialogue requires KV-cache engineering (95% hit rate on 180-message-average chats), not just a bigger context window.

## Trends

- **2024:** First-party memory ships (OpenAI Feb, Character.AI pinned/auto, LangChain memory types, Zep graph API).
- **2025 H1:** Architectural consensus — everyone publishes their tiered model. Letta formalizes MemGPT→Letta, Zep publishes the temporal-KG paper, LangChain splits memory into LangGraph state.
- **2025 H2:** Async/sleep-time compute and continual learning in token space. Anthropic ships project-scoped memory + developer memory tool. Humanloop sunsets Sep 2025 into Anthropic Console.
- **2026 Q1:** Portability (Claude import, agent file formats, git-backed memory via Letta Code Context Repositories), free-tier rollout (Claude Mar 2026), and context-provenance (Zep's source-traceable retrieval). Microsoft publishes Azure AI Foundry user-scoped memory reference architecture (Mar 31). Oracle announces Unified Memory Core for Database 26ai. GPT-5.4 ships with improved cross-chat memory and Codex agentic coding.
- **2026 Q2:** Memory as infrastructure: Mem0 becomes exclusive memory provider for AWS Agent SDK; "State of AI Agent Memory" annual report signals category maturation. Security governance literature surges (SSGM, InjecMEM, memory control flow attacks). Enterprise incumbents (Microsoft, Oracle) enter the architectural conversation directly.

## Gaps / Under-Covered Areas

- **Voice and persona consistency across long timelines.** Posts talk about remembering facts; few address how to remember *how you talked* (style vector, register, running jokes) without letting it drift or collapse. HorizonBench (Apr 2026) begins to address this on the benchmark side, but no product has shipped a style memory primitive.
- **User-visible memory editing UX.** Every vendor claims the user is "in control," but there's almost no published study of how users actually curate memory, what they delete, or how trust builds.
- **Forgetting as a feature, not a bug.** Human memory forgets deliberately; most industry posts treat forgetting as a failure mode to be engineered away. Little published work on *principled* forgetting (privacy, relevance decay, intentional graceful loss).
- **Multi-user / shared memory.** Letta's Conversations API (Jan 2026) is an early signal, but the patterns for "this agent serves a couple / a team / a family and needs a shared + private memory model" are thin.
- **Evaluation of humanization per se.** Benchmarks (LongMemEval, LoCoMo, DMR, Letta Leaderboard) measure retrieval accuracy, not felt humanness. No industry-standard eval for whether memory makes the assistant *feel more like a person*.
- **Memory and hallucination interaction.** When the agent misremembers, is that worse or better than making it up fresh? Almost no post engages this trade-off head-on.
- **Memory × sycophancy is now confirmed, not conjectured.** MIT / Penn State CHI 2026 demonstrated that condensed user profiles in memory are the largest driver of LLM sycophancy — more so than interaction context. No industry vendor has published a memory architecture that mitigates this. The "relationship-driven" memory use case and sycophancy amplification are directly linked.
- **Memory security governance.** SSGM (arXiv 2603.11768) and the memory security survey (arXiv 2604.16548) have articulated attack classes (poisoning, semantic drift, control flow attacks) that industry posts still largely ignore. OWASP Top 10 for Agentic Applications 2026 includes persistent memory as a named risk category.

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## Sources

- OpenAI — https://openai.com/index/memory-and-new-controls-for-chatgpt/
- Anthropic — https://www.anthropic.com/news/context-management (with secondary coverage: Kingy AI, TheOutpost, Thomas Wiegold blog)
- Google Blog — https://blog.google/products/gemini/gemini-personalization/
- Google Blog — https://blog.google/intl/en-ca/feed/use-past-chats-to-get-more-personalized-responses/
- Character.AI — https://blog.character.ai/helping-characters-remember-what-matters-most/
- Character.AI Research — https://blog.character.ai/optimizing-ai-inference-at-character-ai-2/
- Letta — https://letta.com/blog/memgpt-and-letta
- Letta — https://letta.com/blog/agent-memory
- Letta — https://www.letta.com/blog/rag-vs-agent-memory
- Letta — https://www.letta.com/blog/sleep-time-compute
- Letta — Continual Learning in Token Space (Dec 11, 2025), https://letta.com/blog-categories/research
- Zep — https://blog.getzep.com/zep-a-temporal-knowledge-graph-architecture-for-agent-memory/
- Zep — https://blog.getzep.com/ai-knowledge-graph-memory/
- LangChain — https://blog.langchain.dev/memory-for-agents/
- Eugene Yan — https://eugeneyan.com/writing/patterns-for-personalization/
- Eugene Yan — https://eugeneyan.com/writing/recsys-llm/
- Eugene Yan — https://eugeneyan.com/writing/semantic-ids/
