arXiv:2606. 16003v1 Announce Type: new Abstract: This work investigates the ability of large language models (LLMs) to generate mathematical equations from scientific texts.
arXiv:2508.14273v3 Announce Type: replace Abstract: As researchers increasingly adopt LLMs as writing assistants, generating high-quality research paper introductions remains both challenging and ess...
arXiv:2607. 25630v1 Announce Type: cross Abstract: Interdisciplinary research is accelerating, yet scientific papers remain difficult to understand outside their home fields.
arXiv:2605. 10574v3 Announce Type: replace Abstract: As artificial intelligence advances, models are not improving uniformly.
arXiv:2607. 22552v1 Announce Type: cross Abstract: The automatic translation of mathematical expressions in scientific literature into executable symbolic code (a process we refer to as Formula Formalization) is hindered by a severe scarcity of high-quality, ground-truth datasets specialized for technical scientific domains.
arXiv:2509. 25459v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) show promise in generating long-form scientific explanations that synthesize evidence and connect multiple factors.
Rubric-based evaluation is a promising approach for assessing open-ended outputs from LLM-based research agents, particularly in paper reproduction, where direct paper-to-repository comparison is prone to hallucination. However, constructing paper-specific rubrics requires substantial expert effort, limiting the scalability of benchmarks such as PaperBench.
arXiv:2608. 13136v1 Announce Type: cross Abstract: With the rapid advancement of large language models (LLMs), research idea generation has attracted increasing attention.
Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential. Realizing this potential requires systematic and scalable methods for evaluating creativity across diverse tasks.
arXiv:2606. 11762v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential.
arXiv:2604.01452v2 Announce Type: replace Abstract: Scientific discovery is slowed by fragmented literature that requires excessive human effort to gather, analyze, and understand. AI tools, includin...
arXiv:2606. 08532v1 Announce Type: new Abstract: A scientific hypothesis is the first step in research and undergoes experimental validation, yet it also reflects a deep understanding of and reasoning about scientific phenomena.
The paper examines whether existing automatic methods can reliably assess creativity in text produced by large language models (LLMs). By collecting human ratings on 11 creativity dimensions for both human and AI short stories, the authors compare these judgments with automated metrics and LLM-as-a-Judge evaluations. The results show a significant misalignment: automated metrics and LLM judges favor AI-generated stories and show near-zero correlation with human assessments, revealing fundamental limitations in current computational approaches to evaluating creative text.