Delphos is a multitask reinforcement learning framework that automates discrete choice model specification by treating it as a sequential decision-making problem. It learns transferable specification strategies across multiple transport choice datasets using a DeepSet‑Q architecture, enabling a shared policy to adapt to varying variable sets. Trained on nine datasets, Delphos outperforms single‑task agents, and when applied to unseen Swissmetro and Decisions datasets, it quickly identifies competitive specifications with higher log‑likelihoods than existing methods.
By Gabriel Nova, Stephane Hess, Sander Van Cranenburgh
arXiv:2609.38334v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-mode...
By Yuhan Guo, Jinming Liu, Liang Xu, Ziqiang Li, Jianguo Huang, Zhicheng Wang, Hu Zhu, Qiuyu Chen, Yuntao Wei, Xin Jin, Wenjun Zeng
arXiv:2607. 11632v1 Announce Type: new Abstract: Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality.
By Jiangtao Han, Shoufeng Ma, Shuxian Xu, Geng Li, Shuai Ling, Ning Jia, Zhengbing He
arXiv:2606. 27483v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capability in sequential decision-making, yet they remains fundamentally reactive in long-horizon tasks.
By Xuan Zhang, Zhijian Zhou, Lingfeng Qiao, Yulei Qin, Ke Li, Xing Sun, Xiaoyu Tan, Chao Qu, Yuan Qi
The paper introduces ProSE, a framework for AI assistants that generate proposals while considering users’ bounded rationality and evaluability constraints. It proposes a KL‑regularised bounded‑rational binary response model and a depth‑2 Bayes‑adaptive planner, “ProSE‑Plan,” which scores proposals by expected responses and resulting belief updates. Experiments on graph simulations show that “ProSE‑Plan” outperforms evaluability‑unaware and myopic baselines, especially when evaluation cost is high, and that informative probes are crucial for effective assistance.
By Yifan Zhu, Sammie Katt, Samuel Kaski
arXiv:2609.15544v1 Announce Type: cross
Abstract: Enabling human stakeholders to specify reward functions that lead to their desired outcomes is a key challenge in deploying reinforcement learning ag...
By Stephane Hatgis-Kessell, W. Bradley Knox, Emma Brunskill
Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines. However, current reward evaluation relies on heterogeneous criteria such as rule-based verifiers, ground-truth references, procedural checklists, and complex rubrics, where a unified mechanism to integrate all types of evidence remains unexplored.
arXiv:2606. 03980v1 Announce Type: new Abstract: Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines.
By Tao Chen, Gangwei Jiang, Pengyu Cheng, Siyuan Huang, Yihao Liu, Jingwei Ni, Jiaqi Guo, Mengyu Zhou, Kai Tang, Junling Liu, Qinliang Su, Xiaoxi Jiang, Guanjun Jiang
arXiv:2610.27280v1 Announce Type: cross
Abstract: Offline goal-conditioned reinforcement learning enables skill acquisition by reaching any goal from any state without dense rewards. Model-based meth...
By Yunzhe Hu, Dong Xu
The paper introduces the Constrained Triple-Head Price Optimization (C3PO) network, a transformer-based model designed for real‑time bi‑level decision making in discrete‑choice settings. C3PO integrates price imitation learning, multi‑task revenue prediction, and in‑context elasticity learning, while a lightweight differentiable constraint layer enforces business pricing rules. Experiments on simulated, synthetic, and real‑world datasets show that C3PO outperforms calibrated MNL baselines by up to 7.9 percentage points in expected revenue and reduces constraint violations by over 50%, with significant improvements in healthcare tender and airline ancillary pricing applications.
By Shivaram Subramanian, Zhengliang Xue, Markus Ettl, Yingdong Lu, Jayant Kalagnanam
arXiv:2507.21790v3 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) are becoming widely used to support various workflows across different disciplines, yet their potential in discr...
By Georges Sfeir, Gabriel Nova, Stephane Hess, Sander van Cranenburgh
arXiv:2607. 06175v1 Announce Type: cross Abstract: Large language models (LLMs) can generate BPMN process models from natural-language descriptions, yet supervised fine-tuning (SFT) limits their output quality to the patterns present in the training data.
By Alexander Rombach, Chantale Lauer, Nijat Mehdiyev