A Suite of LMs Comprehend Puzzle Statements as Well or Better Than Humans.

Supantho Rakshit, Jennifer Hu, Kyle Mahowald, Adele E Goldberg

Abstract read

In one paragraph

Article in Open mind : discoveries in cognitive science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Unifying the structures of language in a neural population code.Neuron · 2026
Review
Language in vivo vs. in silico: Size matters but Larger Language Models still do not comprehend language on a par with humans due to impenetrable semantic reference.PloS one · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Supantho RakshitPrinceton University, Princeton, NJ, USA.

Jennifer HuJohns Hopkins University, Baltimore, MD, USA.

Kyle MahowaldUniversity of Texas, Austin, Austin, TX, USA.

Adele E GoldbergPrinceton University, Princeton, NJ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper reexamines a recent claim that Large Language Models lag behind humans in language comprehension on what were described as minimally complex statements. We argue that human performance was overestimated and LM performance, underestimated. Moreover, both people and lower-performing LMs are disproportionately challenged by queries involving potentially appropriate inferences, suggesting shared pragmatic sensitivity rather than model-specific deficits. Analysis of more sensitive log probabilities of Llama-2-70B demonstrate ceiling-level accuracy

Indexed as

LLM and human alignmentLLM evaluation

Identifiers

PMID41953133

DOI10.1162/OPMI.a.344

PMCPMC13056333

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.