---
title: "Nicholas Lourie"
type: "entity"
entity_kind: "person"
status: "hub"
canonical_name: "Nicholas Lourie"
aliases: []
first_seen: "2026-10-23T00:00:00.000Z"
writer_model: "claude-sonnet-5"
connects_to: ["downstream scaling","task-taxonomy","Kyunghyun Cho","Michael Y. Hu","neural scaling laws"]
seek_code_commit: "d40ee39"
---


NYU researcher, lead author of "Scaling Laws Are Unreliable for Downstream Tasks: A Reality Check" (Findings of EMNLP 2025), which classifies 46 downstream LLM-evaluation tasks into six scaling-behavior categories and finds clean linear scaling in only 39% of them.

Matters to this vault beyond that co-authorship: a year later he is also a co-author (fourth of five) on [[claim-neural-neural-scaling-laws-2026-averaging-obscures-per-task-scaling|Hu, Pan, Jhaveri, Lourie & Cho's "Neural Neural Scaling Laws" (2026)]], which cites his own 2025 paper as settled background — "no single parametric family can capture the full spectrum of scaling behaviors [Lourie et al., 2025]" — without extending its taxonomy. He is the direct continuity point between the vault's two Hu-et-al.-adjacent scaling-law notes, and the clearest case in this cluster of a research group citing its own prior answer rather than building on it.

## References
- [[claim-lourie-2025-six-category-downstream-scaling-taxonomy]]
- [[claim-neural-neural-scaling-laws-2026-averaging-obscures-per-task-scaling]]

## Updates

- 2026-10-24: "Neural Neural Scaling Laws" has been accepted as a NeurIPS
  2026 main-track poster and listed as an ICML 2026 workshop poster, both
  under the identical five-author byline (Hu, Pan, Jhaveri, Lourie, Cho) —
  refereed-venue acceptance, still the same author group, not independent
  outside corroboration of the paper's claim.
  ([[claim-hu-2026-neurips-icml-2026-same-byline-not-independent]])