arXiv (All)
open access
CALM: A Calibrated LLM Choice Network Framework for Activity-Based Traveler Simulation
We present CALM, a reproducible hybrid framework that integrates an optional large language model (LLM) activity planner with calibrated stochastic choice, shared network feedback, memory and habit, typed feasibility checks, and deterministic offline replay. Unlike trip-mode classifiers or diary-onl
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling#363259
- Learning Action Models with Conditional and Quantified Effects via Uncertainty-Guided Exploration#818997
- Learning Action Models with Conditional and Quantified Effects via Uncertainty-Guided Exploration#809005
- Probe with Participation Trophies: Random-Reward RL as a Probe of LLM Capability#1156001
- Environments as Scaffold: Enriching Feedback to Bootstrap Self-Evolving Agents in Long-Horizon Tasks#985760
Record · ID 1036935
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive