The paper introduces FlexPension-LLM, a domain‑specialized large language model designed to predict pension enrollment among China’s flexible workers. By injecting policy‑grounded cues and using LoRA/SFT for rationale‑augmented supervision, the model achieves a Composite F1 score of 0.9316 on a blind split, outperforming several baselines and matching top commercial models. External survey tests confirm its robustness, with the narrowest performance range among strong systems.
By Yumiao Li, Peixin Liu, Donglin Di, Chen Li, Runhuan Feng
The paper introduces MARS-Gov, a standpoint-aware multi-agent system designed to detect and mitigate bias in Dutch government documents. It combines legal evidence retrieval, open-set target screening, specialized jurors, conservative routing, and rewrite verification, and adds a dynamic "10th juror" when new bias groups are identified. On the DGDB benchmark, MARS-Gov achieves state‑of‑the‑art performance with an F1 score of 0.880, surpassing zero‑shot LLM detectors and supervised Dutch encoders while reducing unnecessary interventions to 2.5%.
By Yuchen Miao, Zijun Wang, Chang Han, Yurui Shi, Mingtai Zhang, Siyang Xu
arXiv:2607. 09262v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to summarize and evaluate policy-relevant information, but it remains unclear whether their judgments are implicitly shaped by geopolitical cues.
By Maxim Chupilkin
arXiv:2608.30757v1 Announce Type: new
Abstract: Before an intelligent system can decide whether an action is allowed, it must first know which rules the action has approached. A single proposed actio...
By Thomson D. Nguy (Radiant Institute for Manifold Studies)
arXiv:2602. 02304v3 Announce Type: replace Abstract: Large-scale foundation models exhibit behavioral shifts when subjected to interventions such as scaling, fine-tuning, reinforcement learning with human feedback, or in-context learning.
By Martino Ciaperoni, Marzio Di Vece, Roberto Pellungrini, Luca Pappalardo, Fosca Giannotti, Francesco Giannini
arXiv:2610.41486v1 Announce Type: new
Abstract: While prior work has highlighted that systematic nation-level bias in large language models (LLMs) can pose operational risks for international relatio...
By Jonghyeon Choi, Yeonjun Choi, Beakcheol Jang
The paper introduces a method for measuring political positions in data‑sparse regions by treating a large language model as a single fallible rater within a panel, analogous to expert surveys. It demonstrates that this panel approach yields reliable agreement across multiple models and reveals that disagreements often stem from differing interpretations of the regime rather than factual errors. The authors provide a full rubric, prompt, and data set, using the Middle East and North Africa as a worked example.
By Tarek Gara
arXiv:2608. 09028v1 Announce Type: new Abstract: Institutional policies stay in natural language while the systems that check compliance demand machine-readable constraints.
By Ponkrit Kaewsawee, Chaklam Silpasuwanchai, Chutiporn Anutariya
arXiv:2606. 30116v1 Announce Type: new Abstract: Pairwise preference data is widely used for training and evaluating language models (e.
By Eleanor Clifford, Michael Amir, Arduin Findeis, Aaron Zhao, Robert Mullins
The study investigates how language models (LLMs) handle refund or claim requests under written policies when presented with past cases that include both facts and recorded decisions. By separating these two components, the authors found that changing the recorded decisions of near‑copy past cases can increase approval rates by 30–55 percentage points, while altering the facts to support approval actually decreases approval rates, suggesting that the request appears weaker when compared to such facts. These effects were consistent across multiple models and both study‑specific and publicly paraphrased policies.
By Junwei Yao, Zike Yang, Yongxi Zhou, Yanxian Chen, Jianan Liu, Jiaxi Wen, Zhijun Wang
The paper proposes a new method for evaluating AI accountability by analyzing the structural quality of a model’s defense for its decisions, using a four‑phase dialectical protocol based on Walton’s argumentation schemes and Govier’s criteria. Applied to nine large language models and 200 ambiguous moral-choice items, the study finds that models generally defend their reasoning well above the rubric minimum, though failures cluster on grounds and sufficiency and correlate with epistemic hedging. The protocol also reveals that models often present different argument schemes in justification than in reasoning, detects indefensible defenses, and highlights challenges in assessing retraction in AI alignment.
By Daan R. Henselmans, Derck W. E. Prinzhorn, Arno Libert
arXiv:2605.30803v2 Announce Type: replace
Abstract: LLM judges are increasingly used to evaluate open-ended responses, but their scores depend strongly on the rubrics that condition them. A vague rub...
By Swastik Roy, Rajkumar Pujari, Tharindu Kumarage, Charith Peris, Rahul Gupta, Anna Rumshisky, Pradeep Natarajan, Venkatesh Saligrama