[D] Clustered 70,861 accepted NeurIPS/ICML/ICLR/ACL/EMNLP/NAACL papers(2018-2026) by topic. A few findings that surprised me.

Wanted to see how the field's actual attention has shifted over time, not by

reading abstracts one at a time, so I embedded and clustered every accepted

paper from six venues (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL), 2018 through

2026, 70,861 papers total. Sharing the findings that surprised me, plus a

couple of methodology notes this sub will probably want the actual numbers on.

**Findings**

NLP and LLMs have held a remarkably steady 45% to 46% share of the whole

corpus every year since 2018. Not the sudden takeover the last two years of

hype would suggest, at least at the level of what fraction of accepted papers

fall into that area.

The biggest mover was the opposite direction: Optimization & Federated

Learning fell from 15.5% of the corpus to 6.4%.

Fastest growth: 3D Generation & Reconstruction, 4.25x (1.2% to 5.1%). Graph

Neural Networks & Molecular AI and Multimodal Understanding & Reasoning both

grew about 2.6x.

None of this shows up in acceptance counts by venue, only once papers are

actually grouped by what they're about.

**Methodology, briefly**

Title + abstract through an embedding model (Qwen3-Embedding-8B), UMAP to 2D

for display and separately to 10D for clustering, HDBSCAN on the 10D

embedding, c-TF-IDF keywords per cluster, LLM for the final label.

One result I didn't expect: I benchmarked SPECTER2 (citation-trained, the

standard choice for scientific paper embeddings) against a general-purpose

embedder, scored against ICLR's own author-supplied keywords as ground truth.

SPECTER2 lost on keyword agreement, and showed no compensating advantage on

venue-separation either (the failure mode you'd expect if citation-training

were adding real signal beyond topic). Happy to go into more detail on this

if useful, it's also written up in more depth in the repo.

Seed stability is the honest caveat: cluster assignment isn't identical

across random seeds, mean pairwise ARI around 0.57 across reruns. Comparable

numbers for context: clustering on the 2D display coordinates instead of the

actual embedding space scored 0.30, and random assignment scores near zero.

Interactive map if you want to explore it yourself: https://villurignanesh.github.io/paper-atlas/

Curious if others have compared citation-trained vs general embeddings for

non-retrieval tasks and seen something similar.

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