A single GitHub repo summarizing every major AI conference of 2024-2026 — in 5 minutes per paper.
Why This Hits Different Right Now
We are drowning. NeurIPS 2025 alone accepted 2,301 papers. ICLR 2026 dropped another 1,567. CVPR 2026 added 1,330 more. If you read one paper per hour, eight hours a day, you would need over four months just to skim the abstracts of what one researcher — zhaoyang97 — has already summarized in a single GitHub repo. That's the context in which Paper-Notes deserves serious attention.
This isn't a curated "top 10 papers of the year" listicle. This is a systematic, structured attempt to compress the entire frontier of AI research into digestible 5-minute notes, organized by conference and by research domain. With 32 stars at time of writing, almost nobody has found it yet. That's the gap.
What It Actually Does
The repo lives at zhaoyang97/Paper-Notes and publishes as a GitHub Pages site at zhaoyang97.github.io/Paper-Notes/. The structure is deceptively simple but operationally impressive.
The docs/ directory is organized along two axes simultaneously: by conference (ICLR2026/, CVPR2026/, ACL2025/, NeurIPS2025/, etc.) and by research domain within each conference. So if you want to find everything about LLM reasoning from NeurIPS 2025, you'd navigate to docs/NeurIPS2025/llm_reasoning/. That's a sane information architecture — most similar projects force you to pick one or the other.
The 44 research folders cover the full spectrum: from llm_reasoning/ (240 notes) and multimodal_vlm/ (825 notes) to niche domains like earth_science/ (7 notes) and signal_comm/ (37 notes). The breadth is genuinely unusual. Most curated reading lists collapse everything into five or six buckets. This one has aigc_detection/, causal_inference/, knowledge_editing/, and self_supervised/ as distinct categories.
Each note follows a consistent format: title, conference/arXiv link, domain tags, a one-sentence summary, background and motivation, core method breakdown, experimental results, and limitations analysis. That last item — explicit limitations analysis on every paper — is where this distinguishes itself from lazy summarization.
The Technical Architecture Worth Examining
The index.md files at each conference level serve as navigable indices — the docs/CVPR2026/index.md presumably aggregates links across all 1,330 CVPR 2026 notes organized by subdomain. The Python codebase (the repo's listed language) likely handles generation or templating of these notes at scale — though the actual generator scripts aren't exposed in the README, which is a notable omission we'll get to.
The publication layer is MkDocs or a similar static site generator pointed at the docs/ tree, with docs/index.md serving as the root landing page that aggregates across all conferences. This is the right call — GitHub Pages rendering means zero infrastructure cost and instant global CDN.
Coverage numbers that stand out: image_generation/ at 1,018 notes, medical_imaging/ at 597, model_compression/ at 503, and reinforcement_learning/ at 454. These are the domains with the most published research right now, and the note counts roughly track publication volume — which suggests the curation isn't arbitrarily cherry-picked.
The others/ category at 717 notes is the honest admission that taxonomy is hard. I'd rather see 717 papers in an overflow bucket than have them shoehorned into ill-fitting categories.
The Honest Critical Take
Let me be direct about where this breaks down, because credibility matters more than cheerleading.
First: the generation question is unresolved. Ten thousand structured paper notes with consistent formatting, covering conferences that concluded months ago, is a suspicious volume for manual work. The README doesn't address methodology — are these human-written summaries, LLM-generated from abstracts, or some hybrid? For a resource you'd use to make research decisions, that provenance matters enormously. A note that says "core method: we propose a novel