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