TSR Desk · science · 5 November 2026, 01:00 UTC
CogniLife: Continuous Cognitive Distillation for Deployed Multi-Expert Agent Marketplaces
- What
- CogniLife: Continuous Cognitive Distillation for Deployed Multi-Expert Agent Marketplaces
- Who
- arxiv.org
- When
- 4 November 2026, 05:00 UTC
- Category
- Science
- Primary source
- https://arxiv.org/abs/2611.04483
- What is not known
- This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.
On ProfyBench-v1, an 85-task offline benchmark built from replay seeds and controlled internal tests, the Profy Full configuration as a whole achieves a mean overall score of 4.65/5 from the primary LLM judge, compared with 4.01 for Raw LLM and 4.28 for Expert Prompt, with the largest gains in completeness, specificity, and missing-information tasks. It comes from a paper posted to arXiv on 4 November 2026. Multi-expert agent marketplaces -- where creators publish domain-specific AI experts for end users to consult -- are an emerging deployment pattern for language technology. A published expert can receive many requests yet little evidence about its creator, who remains active elsewhere: the cold-expert problem. We frame continuous cognitive distillation as converting attributed creator observations into revisable decision rules, communication patterns, and domain procedures. CogniLife proposes a cross-expert attribution design for this problem, with Creator Profiles for transferable creator patterns and Expert Journals for listing-specific knowledge; this design is not yet implemented or evaluated. A deployed platform partially realizes the surrounding lifecycle through creator-authorized authoring, capability-owned proposal filtering, and reversible consolidation of eligible owner-scoped memory. These configuration-level scores do not test cold-expert mitigation, cross-expert transfer, or longitudinal distillation.
Why it counts
On ProfyBench-v1, an 85-task offline benchmark built from replay seeds and controlled internal tests, the Profy Full configuration as a whole achieves a mean overall score of 4.65/5 from the primary LLM judge, compared with 4.01 for Raw LLM and 4.28 for Expert Prompt, with the largest gains in completeness, specificity, and missing-information tasks.
Sources
Primary source: primary source
What is not known
This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.
No clip. The article still stands.