ValuePilot: A Two-Phase Framework for Value-Driven Decision-Making

• URL: http://arxiv.org/abs/2512.13716v1
• Date: Tue, 09 Dec 2025 12:15:46 GMT
• Title: ValuePilot: A Two-Phase Framework for Value-Driven Decision-Making
• Abstract summary: We propose a value-driven approach to personalized decision-making.<n>Human values serve as stable, transferable signals that support consistent and generalizable behavior.<n>We introduce ValuePilot, a two-phase framework consisting of a dataset generation toolkit (DGT) and a decision-making module (DMM)
• Score: 11.176700333939278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalized decision-making is essential for human-AI interaction, enabling AI agents to act in alignment with individual users' value preferences. As AI systems expand into real-world applications, adapting to personalized values beyond task completion or collective alignment has become a critical challenge. We address this by proposing a value-driven approach to personalized decision-making. Human values serve as stable, transferable signals that support consistent and generalizable behavior across contexts. Compared to task-oriented paradigms driven by external rewards and incentives, value-driven decision-making enhances interpretability and enables agents to act appropriately even in novel scenarios. We introduce ValuePilot, a two-phase framework consisting of a dataset generation toolkit (DGT) and a decision-making module (DMM). DGT constructs diverse, value-annotated scenarios from a human-LLM collaborative pipeline. DMM learns to evaluate actions based on personal value preferences, enabling context-sensitive, individualized decisions. When evaluated on previously unseen scenarios, DMM outperforms strong LLM baselines, including GPT-5, Claude-Sonnet-4, Gemini-2-flash, and Llama-3.1-70b, in aligning with human action choices. Our results demonstrate that value-driven decision-making is an effective and extensible engineering pathway toward building interpretable, personalized AI agents.

Related papers

• Enhancing Decision-Making with Large Language Models through Multi-Agent Fictitious Play [53.56274149236814]
  Multi-Agent Fictitious Play (MAFP) is a novel MAS paradigm that represents stakeholder stances as agents.<n>MAFP iteratively updates each agent's decision by best responding to the empirical mixture of other agents' past decisions.<n>We evaluate MAFP on challenging decision-making tasks that test the capability of deciding strategies for competitive scenarios prior to acting.
  arXiv  Detail & Related papers  (2026-06-17T17:31:06Z)
• Preference-Aware Rubric Learning for Personalized Evaluation [59.539429430690156]
  Existing evaluation methods fail to capture user-specific preferences embedded in long-term interaction histories.<n>We propose Personalized Evaluation as Learning, a paradigm that formulates personalized evaluation as a learning problem rather than a static judgment.<n>Experiments show that PARL consistently induces high-fidelity rubrics that reliably identify user-aligned responses and generalize across users.
  arXiv  Detail & Related papers  (2026-05-29T17:00:55Z)
• Learning the Value Systems of Societies with Preference-based Multi-objective Reinforcement Learning [4.735670734773144]
  Value-aware AI should recognise human values and adapt to the value systems (value-based preferences) of different users.<n>We propose algorithms for learning models of value alignment and value systems for a society of agents.
  arXiv  Detail & Related papers  (2026-02-09T16:06:36Z)
• AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress [71.02263260394261]
  Large language models (LLMs) still encounter challenges in multi-turn decision-making tasks.<n>We build process reward models (PRMs) to evaluate each decision and guide the agent's decision-making process.<n>AgentPRM captures both the interdependence between sequential decisions and their contribution to the final goal.
  arXiv  Detail & Related papers  (2025-11-11T14:57:54Z)
• How can we assess human-agent interactions? Case studies in software agent design [52.953425368394306]
  We make two major steps towards the rigorous assessment of human-agent interactions.<n>We propose PULSE, a framework for more efficient human-centric evaluation of agent designs.<n>We deploy the framework on a large-scale web platform built around the open-source software agent OpenHands.
  arXiv  Detail & Related papers  (2025-10-10T19:04:28Z)
• Boosting Virtual Agent Learning and Reasoning: A Step-Wise, Multi-Dimensional, and Generalist Reward Model with Benchmark [72.46357004059661]
  Generalist Virtual Agents (GVAs) have shown significant promise in autonomous task execution.<n>To address these challenges, we propose Similar, a Step-Wise Multi-Dimensional Generalist Reward Model.<n>Similar offers fine-grained signals for agent training and can choose better action for inference-time scaling.
  arXiv  Detail & Related papers  (2025-03-24T13:30:47Z)
• ValuePilot: A Two-Phase Framework for Value-Driven Decision-Making [4.392948275297139]
  We propose a two-phase value-driven decision-making framework comprising a dataset generation toolkit DGT and a decision-making module DMM trained on the generated data.<n>DGT is capable of generating scenarios based on value dimensions and closely mirroring real-world tasks, with automated filtering techniques and human curation to ensure the validity of the dataset.<n>In the generated dataset, DMM learns to recognize the inherent values of scenarios, computes action feasibility and navigates the trade-offs between multiple value dimensions to make personalized decisions.
  arXiv  Detail & Related papers  (2025-03-06T16:02:53Z)
• Multi-Attribute Steering of Language Models via Targeted Intervention [56.93583799109029]
  Inference-time intervention (ITI) has emerged as a promising method for steering large language model (LLM) behavior in a particular direction.<n>We introduce Multi-Attribute Targeted Steering (MAT-Steer), a novel steering framework designed for selective token-level intervention across multiple attributes.
  arXiv  Detail & Related papers  (2025-02-18T02:27:23Z)
• Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes [50.544186914115045]
  Large language models (LLMs) are increasingly embedded in everyday applications.<n> Ensuring their alignment with the diverse preferences of individual users has become a critical challenge.<n>We present a novel framework for few-shot steerable alignment.
  arXiv  Detail & Related papers  (2024-12-18T16:14:59Z)
• Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks [6.520709313101523]
  This work investigates cognitive bias identification in high-stake decision making process by human experts. We propose bias-aware AI-augmented workflow that surpass human judgment. In our experiments, both the proposed model and the agentic workflow significantly improves on both human judgment and alternative models.
  arXiv  Detail & Related papers  (2024-11-13T10:42:11Z)
• Democratizing Reward Design for Personal and Representative Value-Alignment [10.1630183955549]
  We introduce Interactive-Reflective Dialogue Alignment, a method that iteratively engages users in reflecting on and specifying their subjective value definitions. This system learns individual value definitions through language-model-based preference elicitation and constructs personalized reward models. Our findings demonstrate diverse definitions of value-aligned behaviour and show that our system can accurately capture each person's unique understanding.
  arXiv  Detail & Related papers  (2024-10-29T16:37:01Z)
• Measuring Value Alignment [12.696227679697493]
  This paper introduces a novel formalism to quantify the alignment between AI systems and human values. By utilizing this formalism, AI developers and ethicists can better design and evaluate AI systems to ensure they operate in harmony with human values.
  arXiv  Detail & Related papers  (2023-12-23T12:30:06Z)
• Rational Decision-Making Agent with Internalized Utility Judgment [88.01612847081677]
  Large language models (LLMs) have demonstrated remarkable advancements and have attracted significant efforts to develop LLMs into agents capable of executing intricate multi-step decision-making tasks beyond traditional NLP applications.<n>This paper proposes RadAgent, which fosters the development of its rationality through an iterative framework involving Experience Exploration and Utility Learning.<n> Experimental results on the ToolBench dataset demonstrate RadAgent's superiority over baselines, achieving over 10% improvement in Pass Rate on diverse tasks.
  arXiv  Detail & Related papers  (2023-08-24T03:11:45Z)
• Heterogeneous Value Alignment Evaluation for Large Language Models [91.96728871418]
  Large Language Models (LLMs) have made it crucial to align their values with those of humans. We propose a Heterogeneous Value Alignment Evaluation (HVAE) system to assess the success of aligning LLMs with heterogeneous values.
  arXiv  Detail & Related papers  (2023-05-26T02:34:20Z)
• Learning to Advise Humans in High-Stakes Settings [8.921629768446625]
  Expert decision-makers (DMs) in high-stakes AI-assisted decision-making (AIaDM) settings receive and reconcile recommendations from AI systems before making their final decisions. We identify distinct properties of these settings which are key to developing AIaDM models that effectively benefit team performance.
  arXiv  Detail & Related papers  (2022-10-23T20:46:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.