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Get Started Free →Surface the one AI / ML paper to read today from Hugging Face Papers, with the central claim, why it's worth an hour, where it might be wrong, and a time-budgeted read order. Filters out pure benchmark-chasing and incremental scaling reports. Use as a daily morning brief input for AI-savvy operators. Triggers: "one paper to read today", "best AI paper today", "what's the must-read paper", "HF Papers top pick".
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | -45% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -40% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 54% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -25% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 232% | 0% |
One paper per day. Not a digest — one pick, with a short brief that lets the reader decide whether to invest the next hour.
https://huggingface.co/papers).[Paper title]
Authors: A, B, C (Lab)
arXiv: 2505.xxxxx
Central claim: [one sentence, plain English]
Why it's worth an hour:
- [method shift, not benchmark bump — they replace X with Y]
- [claim is falsifiable: ablation in §4 isolates X vs not-X cleanly]
- [code + weights released, reproducible at home]
Where it might be wrong:
- [training distribution is narrow — open question whether the effect transfers]
- [comparison baseline is older than expected]
Read order:
1. §3 (method) — 8 min
2. §4 (ablations) — 12 min
3. §6 (limitations) — 5 min
Optional: §5 (extended experiments)If 0 papers survive the filter:
No surviving picks today. 14 papers filtered out: 6 benchmark-chasing, 5 incremental scaling, 3 position papers.
Worth scrolling yourself: huggingface.co/papersHonesty over manufactured picks.
Other measured skills in the registry, with their headline benchmark lift.