← All notes

Summary

Gold Memory QA ranges from 78.7% to 96.3%, yet Full Pipeline QA averages only 47.7% for open-weight models and 57.4% for proprietary models. DyadMem separately evaluates Capture, gold-conditioned standalone Update, Recall, Gold Memory QA, and Full Pipeline QA, making failures localizable rather than visible only through final-answer accuracy. Capture is precision-heavy and recall-poor: open-weight models average 54.0% precision but only 15.1% recall. [S1]

Claims

• id: C1 claim: 'Gold-Memory QA is reported between 78.7% and 96.3%.' source: S1 evidence: "Gold-Memory QA ranges from 78.7% to 96.3%"
• id: C2 claim: 'Full-Pipeline QA averages 47.7% for open-weight models and 57.4% for proprietary models.' source: S1 evidence: "Full-Pipeline QA averages only 47.7% for open-weight models and 57.4% for proprietary models"
• id: C3 claim: 'The HTML names a public GitHub repository.' source: S1 evidence: "https://github.com/RenaissanceT/DyadMem"

Verification

• Code available: HTML links https://github.com/RenaissanceT/DyadMem (HTTP 200 on 2026-11-11) and https://huggingface.co/datasets/RenaissanceT/DyadMem (HTTP 200). Not run.
• Reproduced: not reproduced (2026-11-11). Code not run.
• Related work: Live note ao-memory-long-term-benchmarks. AMA-Bench in this batch targets agentic application memory rather than user-dyad work.
• Limits: Author-reported stage averages. Capture is described as precision-heavy and recall-poor (open-weight 54.0% precision, 15.1% recall).
• License: CC BY 4.0 on the abs page (2026-11-11; icon by-4.0.png; http://creativecommons.org/licenses/by/4.0/). Submitted 2 Oct 2026 (v1). Sources: [S1] https://arxiv.org/abs/2610.03020, v1 (2 Oct 2026). checked_on: 2026-11-11.

When to apply

Use when asking where an agent memory system fails: writing memories, updating them, retrieving them, or answering once the right memories are present.

Facts

• id: F1 claim: 'DyadMem separately evaluates Capture, gold-conditioned Update, Recall, Gold-Memory QA, and Full-Pipeline QA.' confidence: medium date: 2026-11-11 sources: [S1] license: CC BY 4.0
• id: F2 claim: 'Gold-Memory QA is reported from 78.7% to 96.3%, while Full-Pipeline QA averages 47.7% for open-weight models and 57.4% for proprietary models.' confidence: medium date: 2026-11-11 sources: [S1] license: CC BY 4.0
• id: F3 claim: 'Capture for open-weight models is reported as 54.0% precision and 15.1% recall.' confidence: medium date: 2026-11-11 sources: [S1] license: CC BY 4.0

Sources

• [S1] arXiv 2610.03020, https://arxiv.org/abs/2610.03020. License: CC BY 4.0. v1 (2 Oct 2026). Accessed 2026-11-11.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted). Paper is CC BY 4.0.

@misc{iil_note_w1efg5rn,
  author = {{'iinventedlight (AI-assisted)'}},
  title = {{DyadMem: A Long-Term Memory Benchmark of How Agents Work with Users}},
  howpublished = {iinventedlight Library},
  year = {2026},
  date = {2026-11-11},
  url = {https://iinventedlight.com/library/notes/2610-03020-dyadmem/},
  note = {Permanent ID iil:note:w1efg5rn. License: CC-BY-4.0.},
}

Markdown

Spotted a mistake? Use the MCP feedback tool or email hello@iinventedlight.com.

For language models and agents: start at /llms.txt | /agents.md | /.well-known/agent-library.json. This page in Markdown: /library/notes/2610-03020-dyadmem.md.