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20172026
most citedNeural Text Generation with Unlikelihood Training

241 citations · 983 across the 67 of their papers we have counts for

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13 papers · 1 filter

cs.AI2026

Into the Unknown: Benchmarking AI agents on open scientific research problems

Seungone Kim, Jiayi Geng, Young-Jun Lee +25

Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper along with a research direction…

cs.LG2026

cua-speedrun: Standardized Benchmarking of the Speed of Computer-Use Agents

Pranjal Aggarwal, Lawrence Keunho Jang, Sean Welleck +3

Computer use agents (CUAs), which use graphical user interfaces (GUIs) to complete tasks on a computer, have recently surpassed human performance on many standard benchmarks, inclu…

cs.CV2026

Report of the 8th LSVOS Challenge: Complex and Multimodal Video Object Segmentation

Chang Liu, Henghui Ding, Lingyi Hong +36

This report summarizes the 8th Large-scale Video Object Segmentation (LSVOS) Challenge, held in conjunction with ECCV 2026. The challenge evaluates video segmentation in three comp…

cs.AI2026

ImProver 2: Iteratively Self-Improving LMs for Neurosymbolic Proof Optimization

Riyaz Ahuja, Tate Rowney, Jeremy Avigad +1

Formal mathematics libraries are rapidly expanding, creating a growing need to refactor verified proofs for maintainability and to improve training data quality for neural provers.…

cs.CL2026

On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists

Seungone Kim, Dongkeun Yoon, Kiril Gashteovski +55

With the advancement of AI capabilities, AI reviewers are beginning to be deployed in scientific peer review, yet their capability and credibility remain in question: many scientis…

cs.LG2026

Reinforcing Human Behavior Simulation via Verbal Feedback

Weiwei Sun, Xuhui Zhou, Jiarui Liu +13

Humans learn social norms and behaviors from verbal feedback (e.g., a parent saying "that was rude" or a friend explaining "here's why that hurt"). Yet, learning from feedback for…