# Aligning LLM agents with human learning and adjustment behavior: a dual agent approach arXiv: https://arxiv.org/abs/2511.00993 ยท HTML: https://mlsanity.com/paper/2511.00993/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. - Builds on 27 of its 64 references that are in the map. - First posted on arXiv 2 Nov 2025. ## Placed in - LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/) ## Builds on (most cited first, 27 of 27) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022) - [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021) - [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023) - [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024) - [Where Would I Go Next? Large Language Models as Human Mobility Predictors](https://mlsanity.com/paper/2308.15197/) (2023) - [LLMLight: Large Language Models as Traffic Signal Control Agents](https://mlsanity.com/paper/2312.16044/) (2023) - [Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap](https://mlsanity.com/paper/2503.21411/) (2025) - [Large Language Models are Zero-Shot Next Location Predictors](https://mlsanity.com/paper/2405.20962/) (2024) - [Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Framework](https://mlsanity.com/paper/2412.06681/) (2024) - [Large Language Models for Travel Behavior Prediction](https://mlsanity.com/paper/2312.00819/) (2023) - [AI-Driven Day-to-Day Route Choice](https://mlsanity.com/paper/2412.03338/) (2024) - [From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs](https://mlsanity.com/paper/2502.17701/) (2025) - [A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards](https://mlsanity.com/paper/2206.10598/) (2022) - [GATSim: Urban Mobility Simulation with Generative Agents](https://mlsanity.com/paper/2506.23306/) (2025) - [Automating Traffic Model Enhancement with AI Research Agent](https://mlsanity.com/paper/2409.16876/) (2024) - [LLM-ABM for Transportation: Assessing the Potential of LLM Agents in System Analysis](https://mlsanity.com/paper/2503.22718/) (2025) - [Day-to-Day Dynamic Traffic Assignment with Imperfect Information, Bounded Rationality and Information Sharing](https://mlsanity.com/paper/1811.10729/) (2018) - [Guided Persona-based AI Surveys: Can we replicate personal mobility preferences at scale using LLMs?](https://mlsanity.com/paper/2501.13955/) (2025) - [Synthesizing Attitudes, Predicting Actions (SAPA): Behavioral Theory-Guided LLMs for Ridesourcing Mode Choice Modeling](https://mlsanity.com/paper/2509.18181/) (2025) - [Addressing the alignment problem in transportation policy making: an LLM approach](https://mlsanity.com/paper/2510.13139/) (2025) - [Stable day-to-day dynamics for departure time choice](https://mlsanity.com/paper/1801.09653/) (2018) - [Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation](https://mlsanity.com/paper/2505.21880/) (2025) - [Towards Locally Deployable Fine-Tuned Causal Large Language Models for Mode Choice Behaviour](https://mlsanity.com/paper/2507.21432/) (2025) - [Benchmarking Retrieval-Augmented Generation Strategies for Large Language Model-Based Travel Mode Choice Prediction](https://mlsanity.com/paper/2508.17527/) (2025) ## Built on by (most cited first, 3 of 3) - [PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models](https://mlsanity.com/paper/2605.20873/) (2026) - [An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction](https://mlsanity.com/paper/2608.20320/) (2026) - [Warned alike, AI agents avoid the less-crowded road while people take it](https://mlsanity.com/paper/2609.30883/) (2026) ## Abstract (arXiv) Effective modeling of how human travelers learn and adjust their travel behavior from interacting with transportation systems is critical for system assessment and planning. However, this task is also difficult due to the complex cognition and decision-making involved in such behavior. Recent research has begun to leverage Large Language Model (LLM) agents for this task. Building on this, we introduce a novel dual-agent framework that enables continuous learning and alignment between LLM agents and human travelers on learning and adaptation behavior from online data streams. Our approach involves a set of LLM traveler agents, equipped with a memory system and a learnable persona, which serve as simulators for human travelers. To ensure behavioral alignment, we introduce an LLM calibration agent that leverages the reasoning and analytical capabilities of LLMs to train the personas of these traveler agents. Working together, this dual-agent system is designed to track and align the underlying decision-making mechanisms of travelers and produce realistic, adaptive simulations. Using a real-world dataset from a day-to-day route choice experiment, we show our approach significantly outperforms existing LLM-based methods in both individual behavioral alignment and aggregate simulation accuracy. Furthermore, we demonstrate that our method moves beyond simple behavioral mimicry to capture the evolution of underlying learning processes, a deeper alignment that fosters robust generalization. Overall, our framework provides a new approach for creating adaptive and behaviorally realistic agents to simulate travelers' learning and adaptation that can benefit transportation simulation and policy analysis.