Paper

FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence

Unreadunread

AI summary

arXiv:2605. 16246v1 Announce Type: cross Abstract: This note introduces FRESH (Fusion of Recent Evidence and Subject Histories), a method for incorporating population-level summary results -- published clinical trials, registry summaries, prior natural-history studies, and peer-reviewed indirect comparisons -- into predictive models trained on patient-level data.

Reading status

Priority

About this paper

Authors: Franklin Fuller, Daniele Bertolini, Samantha Liang, Jason Christopher, Aaron M. Smith
Released: 2026-05-18 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, rect, under, source, rate, trained, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.16246

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.