New health-time-series benchmark shows LLMs lag behind classic ML baselines

yang_yuzhe · x · 2026-07-22

The post recommends a paper on benchmarking LLM reasoning over health time series, with several non-obvious findings.

• The work argues that health evals have lagged behind model progress.
• It compares LLMs against SOTA machine-learning baselines, which the author calls the real baseline in these settings because they are cheaper and usually more explainable.
• The benchmark is framed as a “living ecosystem” with standard contribution paths.
• One interesting result mentioned is a reverse correlation between signal frequency and LLM performance.
• The paper also surfaces specific health areas where LLMs underperform.

Original post →

More from Research

• Could 10k agents discover learning methods beyond backprop, or just tweak existing ones? — SeunghyunSEO7 · 2026-09-11
• Cognition's SWE-2 uses a KKT duality argument in RL to shift the effort Pareto curve — YouJiacheng · 2026-09-11
• VidMap uses RoMa coarse matching on all frames, fine-scale only for keyframes — ducha_aiki · 2026-09-11
• Bug Hunt Bench author: leaderboard noise is about 2-3 points — PawelHuryn · 2026-09-11
• PNAS paper shows a tiny billiard-ball system is a universal computer — undecidability lives in two dimensions — eigensteve · 2026-09-11
• New paper: Absolute pose estimation from affine cues and gravity direction — ducha_aiki · 2026-09-11

Research channel →