Paper

Generative Modeling by Value-Driven Transport

Unreadunread

AI summary

arXiv:2605. 22507v1 Announce Type: cross Abstract: We propose a new framework for generative modeling based on a discrete-time stochastic control formulation of measure transport.

Reading status

Priority

About this paper

Authors: Pablo Moreno-Mu\~noz, Adrian M\"uller, Gergely Neu
Released: 2026-05-22 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Generative Modeling by Value-Driven Transport 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: code, strong, class, rect, good, eval, line, rate. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.22507

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Generative Modeling by Value-Driven Transport. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.