Auto43%Harbor: pendingMultimodalLong ContextAgentic / Tool Use

ProPerMem

ProPerMem benchmark for memory-driven proactive assistance

Evaluates memory-driven proactive assistance using egocentric video and ~618K token year-long memories, scoring content and timing of interventions.

How to run

ltb · propermem
$ npm i -g lettokenburn-cli
$ ltb bench info propermem

External harness. No Harbor adapter has been generated for this benchmark yet. `ltb bench info` prints the known links.

Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs

Scores

6 sourced entries
HarnessSourceConfidenceLink
Claude Sonnet 515.0ragIndependentmediumOct 15, 2026arxiv.org
GPT-5.310.0ragIndependentmediumOct 15, 2026arxiv.org
Gemini 3.1 Flash Lite8.5ragIndependentmediumOct 15, 2026arxiv.org
Qwen3.8 27B7.0ragIndependentmediumOct 15, 2026arxiv.org
VideoLLM-online1.6ragIndependentmediumOct 15, 2026arxiv.org
MMDuet20.0ragIndependentmediumOct 15, 2026arxiv.org

Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.

Reproduced by the community

All runs
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About

Pairs egocentric video with distractor-rich, year-long user memories (~618K tokens) and human-verified trigger windows. Includes clips requiring silence.

Scores strict accuracy: one grounded response inside the window. Baselines perform poorly (<15%), while oracle evidence yields 96%, highlighting timing and retrieval challenges.