Where to read AI paper explanations in Japanese and English (2026)

Keeping up with AI research is less about access — arXiv is free — and more about digestion. The papers land faster than anyone can read them, and the raw PDF assumes a lot. Here's an honest roundup of where to read plain-language explanations in 2026, in both English and Japanese, and how they differ.

Disclosure: Stack Picks is operated by TechAthletes, which makes one of the sites below (PaperLens). It's marked, and the others are the established places we read first.

What a good explanation gives you

• The claim in one breath — what's new, before the notation.
• Why it matters — where it sits against prior work, not just what it does.
• The catch — limitations and where the result probably won't generalize.
• A path back to the source — a link to the actual paper, because summaries are a starting point, not a citation.

English

• Papers with Code — links papers to implementations and benchmarks. Not "explanation" per se, but the fastest way to see if a result is real and reproducible.
• The Gradient / distill-style writeups — long-form, editorial explanations. Deep and beautifully done, but low-volume and not daily.
• Author threads & lab blogs — often the clearest explanation of a paper is the authors' own thread or blog post. Quality varies; discoverability is the problem.
• Newsletters (Import AI, etc.) — curated weekly digests that explain the few papers that mattered. Great signal, weekly cadence.

Japanese

• Qiita / Zenn — the main home of Japanese-language paper explanations, written by practitioners. Enormous breadth and genuinely good deep-dives, but quality and freshness vary post-to-post and you're searching, not browsing a curated shelf.
• University lab & researcher blogs — high quality, sporadic.
• AI系YouTube / podcast — good for intuition; harder to skim or cite.

The honest gap in Japanese specifically: plenty of individual explainer posts, but few places that present a curated, bilingual, figure-first shelf you can browse by topic.

Where PaperLens fits (ours)

PaperLens aims at that gap: plain-language explanations of AI/ML topics and papers, in both Japanese and English, with diagrams, a "what it says / why it matters / how it works / the catch" structure, and links back to the source. There's a free tier; unlimited reading is a paid subscription (¥490/mo).

Honestly: it's much smaller than the collective output of Qiita/Zenn, and it doesn't try to cover every paper. What it offers is editorial consistency and bilingual, figure-first structure — a curated shelf rather than a search box. If you already have a Qiita/Zenn workflow you love, keep it; PaperLens is for people who want the digested, both-languages version in one place.

How we'd choose

• Is this result real? Papers with Code.
• Deep, occasional, English: The Gradient / lab blogs.
• Broadest Japanese coverage, don't mind searching: Qiita / Zenn.
• Curated, bilingual, figure-first, in one place: PaperLens.

Whatever you read, treat every explanation — ours included — as a map, not the territory: skim to decide if a paper is worth your time, then read the source before you build on it.