The paper proposes a new method for credit scoring research articles that distinguishes between a paper’s original contribution and the prior work it builds upon. It introduces a hierarchical ‘contribution tree’ framework that conserves importance across a document’s structure and separates original from citation-derived credit. Large language models are employed as noisy comparative estimators to scale the analysis, and the approach is extended to collections of articles via weighted citation graphs to produce corpus-level contributions and normalized influence scores.
By Sana Ebrahimi, Suraj Shetiya, Abolfazl Asudeh
arXiv:2609.14248v1 Announce Type: cross
Abstract: Faithful citation attribution begins with identifying the intended source for a scientific claim. We study this source-identification capability thro...
By Yee Man Choi, Xuehang Guo, Songcheng Cai, Yimu Wang, Yi R. Fung, Qingyun Wang
arXiv:2609.22774v1 Announce Type: new
Abstract: This paper presents our system for Track 2 of the NLPCC 2026 Shared Task 10 on citation-level faithfulness in AI-assisted scientific reporting. Given a...
By Yanling Li, Zirui Li, Mingyu Wan
The paper "Learning to Ideate for Scientific Impact" explores using delayed signals of scientific uptake—specifically citation-normalized impact—as feedback to steer large language models toward generating high‑impact research ideas. The authors build a dataset of over 100,000 computer science papers, train a reward model to predict citation impact from goal‑idea pairs, and align an idea generator via supervised fine‑tuning and reinforcement learning. Evaluation with a reference‑grounded protocol shows that the RL‑tuned model consistently produces ideas with higher estimated impact than baseline models.
By Shubham Kale, Aniketh Garikaparthi, Manasi Patwardhan
Retrieval-augmented generation systems for legal question answering typically retrieve passages based on semantic similarity and provide them to a language model, which then generates cited answers. Prior work assumes that highly ranked passages are most likely to be usefully cited by the model.
arXiv:2608.21376v1 Announce Type: cross
Abstract: Many NLP tasks require systems to provide attribution in their outputs--i.e. citations to grounding sources. Attribution serves as a bulwark against...
By Yu Hou, Hal Daum\'e III, Rachel Rudinger, William Walden
Large language models are increasingly deployed in citation-augmented settings, yet the effect of citation presence on model behavior independent of factual content remains poorly understood. We introduce AuthorityBench, a 220,564-prompt multi-domain benchmark that isolates how citation-based authority signals influence epistemic behavior in LLMs.
arXiv:2606. 24894v2 Announce Type: replace-cross Abstract: Large language models have shown strong fluency in scientific writing, yet the evaluation of related work generation (RWG) remains limited.
By Anzhe Xie, Weihang Su, Jiaxin Mao, Yiqun Liu, Shaoping Ma, Qingyao Ai
arXiv:2608. 19230v1 Announce Type: cross Abstract: As language models move from drafting prose to running literature-search agents with tool calls, fabricated references are becoming easier to catch and constrain.
By Sina Alemohammad, Denghui Zhang, Bolong Tang, Anthony Qin, Gengchen Mai, Ahmed Abbasi, Richard Baraniuk, Zhangyang Wang
arXiv:2609.01432v1 Announce Type: cross
Abstract: Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentionin...
By Yixuan Liu, Lin Chen, Zhuoqi Liu, Jianglin Lu, Dakota Murray
arXiv:2606.22342v2 Announce Type: replace
Abstract: How does research evolve, and can we trace it at the level of individual claims? Scientific progress is not simply a uniform accumulation of facts....
By Abdul Muntakim, Md Abdullah Al Hafiz Khan, Sadid Hasan, Yong Pei
arXiv:2603. 22973v2 Announce Type: replace Abstract: Applying computational methods to law at scale requires separating genuine legal reasoning from surface similarity.
By Avrile Floro (UPHF), Tamara Dhorasoo (UPHF), Soline Pellez (UPHF), Nils Holzenberger