We present HADA (Human-AI Agent Decision Alignment), a protocol- and framework agnostic reference architecture that keeps both large language model (LLM) agents and legacy algorithms aligned with organizational targets and values. HADA wraps any algorithm or LLM in role-specific stakeholder agents -- business, data-science, audit, ethics, and customer -- each exposing conversational APIs so that technical and non-technical actors can query, steer, audit, or contest every decision across strategic, tactical, and real-time horizons. Alignment objectives, KPIs, and value constraints are expressed in natural language and are continuously propagated, logged, and versioned while thousands of heterogeneous agents run on different orchestration stacks. A cloud-native proof of concept packages a production credit-scoring model (getLoanDecision) and deploys it on Docker/Kubernetes/Python; five scripted retail-bank scenarios show how target changes, parameter tweaks, explanation requests, and ethics triggers flow end to end through the architecture. Evaluation followed the Design-Science Research Methodology. Walkthrough observation and log inspection demonstrated complete coverage of six predefined objectives: every role could invoke conversational control, trace KPIs and value constraints, detect and mitigate ZIP-code bias, and reproduce full decision lineage, independent of the underlying LLM or agent library. Contributions: (1) an open-source HADA architecture, (2) a mid-range design theory for human-AI alignment in multi-agent systems, and (3) empirical evidence that framework-agnostic, protocol-compliant stakeholder agents improve accuracy, transparency, and ethical compliance in real-world decision pipelines.
Position: Safety and Fairness in Agentic AI Depend on Interaction Topology, Not on Model Scale or Alignment
Department of Computer Science, University of British Columbia, Vancouver, Canada（英属哥伦比亚大学计算机科学系）；Department of Artificial Intelligence, IIT Hyderabad, Hyderabad, India（印度海得拉巴理工学院人工智能系）；Amazon, Delhi, India（印度德里亚马逊公司）
National University of Defense Technology（国防科技大学）；The University of New South Wales（新南威尔士大学）；University of Amsterdam（阿姆斯特丹大学）
Entity alignment (EA) is the task of identifying the entities that refer to the same real-world object but are located in different knowledge graphs (KGs). For entities to be aligned, existing EA solutions treat them separately and generate alignment results as ranked lists of entities on the other side. Nevertheless, this decision-making paradigm fails to take into account the interdependence among entities. Although some recent efforts mitigate this issue by imposing the 1-to-1 constraint on the alignment process, they still cannot adequately model the underlying interdependence and the resu…
Zhejiang University（浙江大学）；Hangzhou Innovation Center, Zhejiang University（浙江大学杭州创新中心）
Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies. Although great promise they can offer, there are still several limitations. The most notable is that they identify the aligned entities based on cosine similarity, ignoring the semantics underlying the embeddings themselves. Furthermore, these methods are shortsighted, heuristically selecting the closest entity as the target and allowing multiple entities to match the same candidate. To address these limitations, we model entity alignment as a sequential decision-making task, in which an agent …
In this work, we propose and explore Deep Graph Value Network (DeepGV) as a promising method to work around sample complexity in deep reinforcement-learning agents using a message-passing mechanism. The main idea is that the agent should be guided by structured non-neural-network algorithms like dynamic programming. According to recent advances in algorithmic alignment, neural networks with structured computation procedures can be trained efficiently. We demonstrate the potential of graph neural network in supporting sample efficient learning by showing that Deep Graph Value Network can outper…
University of Toronto（多伦多大学）；Vector Institute（向量研究所）
Recently, methods such as Decision Transformer that reduce reinforcement learning to a prediction task and solve it via supervised learning (RvS) have become popular due to their simplicity, robustness to hyperparameters, and strong overall performance on offline RL tasks. However, simply conditioning a probabilistic model on a desired return and taking the predicted action can fail dramatically in stochastic environments since trajectories that result in a return may have only achieved that return due to luck. In this work, we describe the limitations of RvS approaches in stochastic environme…
The Alignment Paradox of Medical Large Language Models in Infertility Care: Decoupling Algorithmic Improvement from Clinical Decision-making Quality
Department of Obstetrics and Gynecology, West China Second University Hospital（妇产科部门，西昌第二大学医院）；Reproductive Medical Center, Department of Obstetrics and Gynecology, West China Second University Hospital（生殖医学中心，妇产科部门，西昌第二大学医院）
Nanyang Technological University（南洋理工大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所（IHPC））；Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research（新加坡科技研究局前沿人工智能研究中心（CFAR））；Beihang University（北京航空航天大学）；New Jersey Institute of Technology（新泽西理工学院）；Zhejiang Sci-Tech University（浙江理工大学）
A Decision-Theoretic Approach for Managing Misalignment
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Georgia Institute of Technology（佐治亚理工学院）；University of California, Berkeley（加州大学伯克利分校）；Amrita Vishwa Vidyapeetham（阿米特拉维瓦大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Unifying Perplexing Behaviors in Modified BP Attributions through Alignment Perspective
University of Science and Technology of China（中国科学技术大学）；National Lab of Pattern Recognition, Institute of Automation, CAS（中国科学院自动化研究所模式识别国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
As intelligent systems gain autonomy and capability, it becomes vital to ensure that their objectives match those of their human users; this is known as the value-alignment problem. In robotics, value alignment is key to the design of collaborative robots that can integrate into human workflows, successfully inferring and adapting to their users' objectives as they go. We argue that a meaningful solution to value alignment must combine multi-agent decision theory with rich mathematical models of human cognition, enabling robots to tap into people's natural collaborative capabilities. We presen…
Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment
Department of Electrical and Computer Engineering, Seoul National University（电子与计算机工程系，首尔国立大学）；Samsung SDS, Korea（三星SDS，韩国）；AIIS, ASRI, INMC, ISRC, and IPAI, Seoul National University（人工智能研究所、人工智能研究机构、智能网络中心、智能系统研究室和人工智能政策研究所，首尔国立大学）
