Who really wrote ICML 2026?

ICML 2026 accepted 6,341 papers. Counting lead authors, China has the most papers and the U.S. the most spotlights; two countries appear on most of the program, with 66 countries represented in all; industry has an author on about a third of papers; and the largest topic area is language & NLP. We resolved every author's affiliation to a canonical institution and classified every paper by topic to show what the metadata contains.
01 · Lead authorship

China leads the most papers. The U.S. leads the most spotlights.

Lead authorship — share of papers vs share of spotlights
Spotlight conversion rate by lead-author country
02 · Geography

A two-superpower conference

Country share of papers over time
Papers per country: any author vs first author (top 15)
Blue: papers with at least one author from the country (counted once per country per paper, so blue bars sum past 100% of papers). Gold: papers whose first author is from the country.
Lead ratio — who leads their own work
“Lead ratio” = share of a country's papers with a first author from that country (countries with ≥30 papers).
Self-sufficiency: share of all-domestic papers
03 · The institutions

A handful of mega-labs, and many more

ICML 2026 institution leaderboard
Score credits each paper to its first/corresponding-author institutions, splitting a weight of 1 across the distinct institutions involved (so a paper never counts more than once). Filter by region, or search for an institution.
04 · Academia & industry

Industry doesn't dominate the count — but it's concentrated in certain areas

Every paper by sector
Average team size by sector
Average number of authors per paper, by sector.
Which topics is industry most involved in?
Share of each area's papers with at least one industry author.
Top industry–academia partnerships
Company–university pairs that co-author most often.
The industry leaderboard (top 20)
05 · What ICML 2026 is about

The year of the agent

The shape of ICML 2026 — 15 areas, 87 subtopics
Each paper assigned an area and a subtopic by an LLM agent reading its title and abstract. The taxonomy itself was derived by auditing every title (see Methodology). Click a wedge to zoom in. Inner ring = area, outer ring = subtopic.
Papers by area
The words of ICML 2026 (title terms)
Most frequent meaningful words in paper titles (stopwords removed).
The vocabulary of ICML, 2016–2026 (share of titles)
Where the U.S. and China place their bets
Area mix as a share of each country's own papers.

Who leads each area?

Top institutions by paper count within each area.

06 · How this was made

Methodology & caveats

1. Author extraction. For all 6,341 accepted papers we read each paper's first page and extracted the author list, affiliations, emails, and the first/corresponding-author flags. Names match OpenReview ~93% of the time.

2. Institution disambiguation. Raw affiliation strings were resolved to canonical institutions — each tagged with a country and an academic/industry label — merging spelling, language, and diacritic variants of the same organization into a single canonical English name. The “country share over time” chart runs the same pipeline on every ICML year back to 2016.

3. Lead authorship & score. “Lead author” = the first author. An institution's score splits a weight of 1 per paper across the distinct first/corresponding-author institutions on it, so no paper is ever counted twice; the leaderboard ranks all institutions with a non-zero score.

4. Topic classification. The taxonomy was not hand-picked. Eight independent LLM agents each read every one of the 6,341 titles and flagged recurring themes with no clean home; consolidating their reports produced a 15-area, 87-subtopic taxonomy — adding areas the field has grown into, such as LLM Agents and Causal Inference. A separate LLM agent then classified every paper from its title and abstract into one area and one subtopic.

5. Two labels per paper. Alongside our label, we keep each paper's author-declared ICML primary area — the area its authors themselves chose at submission — and show both in the explorer. Mapping the two schemes through a fixed crosswalk, our LLM label is compatible with the authors' declared area for ~62% of papers. Agreement is high for well-defined areas (e.g. Causal Inference, Reinforcement Learning) and lower for areas the audit introduced, such as LLM Agents, which the official ICML list doesn't separate out.

07 · Explore

Search every paper

Filter all 6,341 papers by topic, country, sector, or spotlight, and search titles and institutions.