Researchers: many big-lab model evals only prove a data mixture gap, not capability limits
josephdviviano · x · 2026-11-03
Researcher tallinzen argues that many comparison experiments against big-lab models are particularly pointless when the conclusion amounts to "this big company hasn't yet included this particular kind of data in their post-training mixture." Joseph Viviano agrees: a wave of "model X fails at task Y" evals mostly measure post-training data mix differences rather than real capability boundaries, offering little scientific insight or actionable guidance.
More from Research
- Stanford preprint: CD19-CAR T cells clear EBV+ autoreactive B cells in autoimmune disease — ahandvanish · 2026-11-04
- Paperclip Datasets: build curated datasets from millions of papers in minutes — james_y_zou · 2026-11-04
- Multi-agent messaging barely helps: 61% of agreed runs still hit merge conflicts — EliasEskin · 2026-11-04
- "Deep lifts" paper: ReLU networks adaptively generalize even with the dumbest random optimizer — yuxiangw_cs · 2026-11-04
- DR Pixel: open-source page-image deep research agent, 27B model matches GPT-5.4 — sewon__min · 2026-11-04
- FlexGlue: open-source pixel-perfect image matching that rejects doppelganger pairs — ducha_aiki · 2026-11-04