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20242026
most citedEvaluating Language Models as Synthetic Data Generators

3 citations · 13 across the 29 of their papers we have counts for

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8 papers · 1 filter

cs.CL2026

Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models

Jean de Dieu Nyandwi, Leena Mathur, Yonatan Bisk +2

Which reasoning behaviors are associated with correct answers in reasoning models, and does reasoning-oriented training amplify those behaviors? This distinction is important becau…

cs.CL2026

Cracks in the Foundation: Seemingly Minor Architectural Choices Impact Long Context Extension

Amanda Bertsch, Luca Soldaini, Matthew R. Gormley +4

One might imagine that architectural variations within the dense transformer paradigm have a limited effect on accuracy. However, we demonstrate that this is not the case in the lo…

cs.LG2026

Decomposer: Learning to Decompile Symbolic Music to Programs

Yewon Kim, Apurva Gandhi, David Chung +2

Musical performance involves executing a set of high-level musical instructions, yet recovering those instructions from the performance is a challenging inverse problem. We present…

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…

cs.CL2026

Soohak: A Mathematician-Curated Benchmark for Evaluating Research-level Math Capabilities of LLMs

Guijin Son, Seungone Kim, Catherine Arnett +73

Following the recent achievement of gold-medal performance on the IMO by frontier LLMs, the community is searching for the next meaningful and challenging target for measuring LLM…

cs.AI2026

Reasoning over mathematical objects: on-policy reward modeling and test time aggregation

Pranjal Aggarwal, Marjan Ghazvininejad, Seungone Kim +18

The ability to precisely derive mathematical objects is a core requirement for downstream STEM applications, including mathematics, physics, and chemistry, where reasoning must cul…

cs.CL2026

Gained in Translation: Privileged Pairwise Judges Enhance Multilingual Reasoning

Lintang Sutawika, Gokul Swamy, Zhiwei Steven Wu +1

When asked a question in a language less seen in its training data, current reasoning large language models (RLMs) often exhibit dramatically lower performance than when asked the…