arXiv:2604.13706v2 Announce Type: replace Abstract: Professional fact-checkers rely on domain knowledge and deep contextual understanding to verify claims. Large language models (LLMs) and large reas...
arXiv:2606. 28277v1 Announce Type: cross Abstract: Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical theorem proving.
Identifying promising scientific ideas remains an important challenge in research practice. Researchers commonly rely on small-group discussions or one-to-one interactions with a single large language model, yet these approaches often expose them to only a limited range of perspectives and directions.
arXiv:2608. 03283v1 Announce Type: new Abstract: Identifying promising scientific ideas remains an important challenge in research practice.
The paper introduces PERPL, a framework for tailoring fact‑checking explanations to individual readers while preserving factual accuracy. It learns personalized rewriting from relative preferences of simulated LLM readers, using cross‑persona pairing to generate supervision and a progressive learning strategy to refine explanations beyond predefined styles. Experiments show PERPL outperforms larger general‑purpose LLMs in aligning with reader preferences while maintaining high factual fidelity.
arXiv:2605.29522v2 Announce Type: replace Abstract: As scientific literature grows rapidly and research increasingly involves AI agents, automated survey generation has become a key capability for bo...
arXiv:2609.22104v1 Announce Type: new Abstract: As automated scientific discovery advances, Large Language Models (LLMs) can now generate research ideas at an unprecedented scale, shifting the bottle...
arXiv:2611.08196v1 Announce Type: new Abstract: Complex problems with limited context, such as claims, require a means to obtain additional knowledge necessary for solution reasoning. We propose a mu...
The paper evaluates browser-based large language models (LLMs) for extracting detailed, contextualized data from scientific papers. It presents four workflows: (1) expert-curated prompts yield good extraction but struggle with nuance; (2) LLMs can generate effective prompts from simple instructions; (3) autonomous literature discovery is challenging, with missing or hallucinated references; (4) LLMs can build new datasets from guidelines that align closely with human experts, yet still need human oversight. The study outlines a practical, auditable workflow where experts set standards, models cross-check extractions, and researchers resolve disputes, enabling scalable scientific data curation.
arXiv:2606. 11337v1 Announce Type: new Abstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions.
arXiv:2609.13760v1 Announce Type: new Abstract: Publishing a research manuscript is a routine yet demanding part of scientific life: time-consuming, stressful, and often uncertain in outcome. Recent...
The paper introduces Faithful Personalized Fact-Checking Explanation Rewriting, a method that tailors fact‑checking explanations to individual readers while keeping factual accuracy intact. It uses LLM‑simulated readers to generate relative preference data, then applies a two‑stage framework called PERPL: first constructing persona‑specific supervision via cross‑persona pairing, and second refining a lightweight rewriter through exploration‑guided preference optimization. Experiments show PERPL outperforms larger general‑purpose LLMs in aligning with personalized preferences while preserving factual fidelity.