The torrent of new machine-learning papers has become impossible to follow alone—arXiv logged over 220 ML submissions just recently and broke its monthly record again last autumn.
ArXivIQ exists to turn that fire-hose into jet-streams of insight: every paper is hand-picked by a human editor, then pushed through a purpose-built multi-agent AI pipeline that dissects methods, experiments, and limitations in minutes instead of hours.
Below you’ll find the what, why, and how of this new publication.
Why ArXivIQ—and why now?
FOMO is real. Researchers openly admit they “have thousands of papers to read—and not much time”. I myself have this problem.
Existing digests are either auto-generated or fully manual (like my own GonzoML). The first group saves time but usually producess way too general descriptions, can hallucinate or miss nuance; the second can’t keep pace with the firehose of daily uploads.
Hybrid wins. Substack’s fastest-growing newsletters already blend human voice with AI production tools, but readers want transparency about where the machine stops and the expert begins (or vice versa).
ArXivIQ plants its flag squarely in that hybrid space.
What makes ArXivIQ different?
1. Manual curation you can trust
Every issue starts with a living shortlist of papers surfaced from conference trackers, RSS feeds, peer recommendations, and good old arXiv trawling. Selection criteria:
Relevance: novel method, good authors, surprising negative result, or milestone benchmark.
Rigor: presence of code, ablation studies, or peer-review acceptance.
Signal-to-noise: only a handful per week make the cut—no “listicles” of 50 titles.
For each post I usually choose one paper (the most interesting one), but it also may be a set of related papers. I’m also open to feedback, suggestions and specific requests.
2. Automatic review powered by a multi-agent stack
Selected papers flow into a custom Multi-Agent Review Engine (MARE):
Analytic agent extracts interesting bits of information.
Skeptic agent hunts for methodological red flags (p-hacking, missing baselines).
Synthesizer agent produces a structured outline—methods, results, limitations.
The are also other agents which may contribute occasionally.
The pipeline is fully logged; I spot-check outputs before hitting Publish because LLM hallucinations are still a thing (even that I mostly solved it with the help of my most critical agents). 🤖
I may also add my own remarks and thoughts, so it might be more human-touched than it is stated. 🙂
Anatomy of a typical post
TL;DR (Free): What was done & why it matters, in ≤ 250 words.
Deep Dive (Members): Architecture details, theory, experiments, and more.
Errata & Discussion: Threaded comments; corrections rolled into a public changelog.
If you’d like a taste, check the pilot review of DeepMind’s AlphaEvolve (and a few more upcoming posts here)—same structure, fewer bells and whistles.
What’s next?
Launch cadence: one curated ML paper per weekday; other special formats (say, “Sunday Synthesis” or something like that) case by case.
Reader polls: vote on which preprint gets dissected next.
Meta-posts: behind-the-scenes of the multi-agent pipeline as it evolves.
Hit Subscribe—free or paid—and let’s turn that arXiv overload into actionable intelligence.
Your attention is scarce; ArXivIQ exists to make sure it’s never wasted.