Bite-sized research notes
Read papers lightly.
Keep the ideas carefully. Focused notes on AI and computer science—built for readers who want the argument, evidence, and limits without the noise.
- Why the paper matters
- The central idea
- How the method works
- What the evidence actually shows
Latest Article Bites
Newest first →Orca: One Task per Worktree
Orca gives each coding-agent task a real Git worktree and a reviewable diff; this beginner walkthrough starts with just one agent.
LightOnOCR-3: OCR + Grounding
LightOnOCR-3 puts transcription, layout boxes, image descriptions, and chart tables into one model, though benchmark formatting and upstream annotation still matter.
Latest Paper Bites
03 selected / updated 2026.10
Beta-KD
Beta-KD learns how strongly a multimodal student should follow its teacher, but its gains depend on the loss and comparison being examined.
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NovaLAD
NovaLAD splits document parsing into parallel semantic and layout detection, then orders text and gates images before optional vision-model enrichment; its DP-Bench lead is author-reported against historical baseline rows.
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TurboQuant
TurboQuant rotates vectors before scalar quantization and adds a residual sketch when unbiased inner products matter; its theory and two application tests need different readings.
Read Paper Bite→From the archive.
View all papers →Less summary.
More signal.
Every note separates the paper’s claim from its evidence—and its evidence from our interpretation.
No invented results. No decorative figures. No certainty where the paper leaves questions open.
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