Agent memory

Evidence in the last complete seven days

Source mentions

5

Previous seven days 4

Distinct sources

4

Previous 3

Comparable growth

Not comparable

Ranking appearances

0

Collection observations and undated records are separate from dated publication counts. Observed in 7d：3 · Undated records：0

Daily history · up to 90 days

Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.

Dotted markers indicate days with no record. They are not counted as zero mentions.

Source evidence

Sources in the evidence shown：arXiv 4 · 量子位 1 · Hacker News 8

1. arXiv · Published Nov 4, 2026

   What Is This Memory Useful For? Measuring Task-Conditioned Utility in Long-Term Agent Memory

   arXiv:2611.03930v1 Announce Type: new Abstract: A stored experience may answer one future question and offer little help with another. We measure this task-conditioned utility rather than assigning each memory a single importance score. A task-blind writer stores a memory without seeing the later task; a frozen reader then answers that task with and without

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2. 量子位 · Published Nov 4, 2026

   让Agent首次拥有“视听双流记忆”：openJiuwen Agent-Memory看懂长视频，关键事件一个不落

   长视频记忆精度飙升，问答时延最高降97%

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3. Hacker News · Published Nov 3, 2026

   fine tuned decision model for agent memory

   future of ai products imho

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4. Hacker News · Published Nov 2, 2026

   The AI Agent Memory Stack: Building Blocks, Patterns, and Frameworks

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5. arXiv · Published Oct 29, 2026

   When Retrieval Is Not Enough: Obsolete Facts in Agent Memory

   arXiv:2610.36308v1 Announce Type: new Abstract: Memory-agent benchmarks often collapse retrieval and answer generation into one score, making it hard to tell whether a failure comes from missing an update or from not using an update that was already retrieved. We examine this distinction on the single-hop FactConsolidation task of MemoryAgentBench. With Qwen

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6. Hacker News · Published Oct 27, 2026

   How to Give Your AI Agent Memory You Can Audit

   View source record →

7. arXiv · Published Oct 27, 2026

   SLICE-Mem: Salience-Gated Lightweight Information Curation for Efficient LLM Agent Memory

   arXiv:2610.33015v1 Announce Type: new Abstract: Long-term memory enables large language model (LLM) agents to maintain personalized and temporally coherent interactions, but repeated LLM inference for memory admission, extraction, and organization introduces substantial cost and latency. We present SLICE-Mem, a lightweight memory framework that combines sali

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8. arXiv · Published Oct 23, 2026

   Why Bother Me Again? Benchmarking Personal Agent Memory for Interaction Preferences

   arXiv:2610.30485v1 Announce Type: new Abstract: Personal intelligence systems are taking on increasingly complex everyday tasks, yet repeated questions, unnecessary confirmations, and unwanted outreach can turn capable assistance into interaction burden. Reducing this burden requires agents to remember how users want to be assisted. However, existing memory

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9. Hacker News · Published Oct 22, 2026

   Show HN: CATT 0.2.1: Test agents/services against a Muse/Instinct-alike, in CI

   Hey HN, we recently open sourced our Consumer Agent Testing Tool to help teams iterate against proprietary assistants like Muse and Instinct. We built this to reduce our own frustrations with building an MCP/API for these agents to consume. The problem: testing against these tools is hard! Agent memory + account statefulness make reproducibility, in the ways

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10. Hacker News · Published Oct 21, 2026

    Wiki-driven development: coding agents update a wiki for you, you set direction

    My team has been working in a way we are calling calling wiki-driven development, where the wiki is written for the human, not the agent and the human uses primarily the wiki to set direction for the agent. This is different from Karparthy's LLm I'm curious whether others do something like it. Once I run several coding agents at a time, I get overwhelmed and

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11. Hacker News · Published Oct 20, 2026

    Should agent-memory benchmarks test stale information?

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12. Hacker News · Published Oct 16, 2026

    Give Your Python Agent Memory in Ten Lines

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13. Hacker News · Published Oct 7, 2026

    We Built an Alternative to Vector RAG for AI Agent Memory

    View source record →

Review priority 70 / 100

This score orders research candidates. It is not search volume, a forecast or verified commercial demand.

• Evidence

  20.7 / 30

  Distinct dated articles or first observations; repeat crawling adds nothing.

• Publisher diversity

  25 / 25

  Conservative owner grouping; syndicated copies count once.

• Comparable change

  0 / 20

  Comparable complete weeks only; no percentage from a zero baseline.

• Recent evidence

  14.7 / 15

  Latest publication or first observation, not repeated fetch time.

• Name specificity

  9.7 / 10

  Explicit entities and technical concepts outrank ambiguous acronyms.

Limitations and interpretation

• First observation is a collection milestone, not proof that a word or product is new to the world.
• Counts describe monitored public sources, not the whole web or search demand.
• First observed here is not the invention date of a name or idea.
• Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
• Owner grouping is conservative; undisclosed syndication may remain.
• An established concept or common product name retained as a baseline, not a newly invented term.
• Both weeks need adequate, comparable collection coverage.
• Sources behind this term changed or lack a comparable baseline.
• Recent evidence includes collection over three days after publication.