# LifePlanner: Evaluating LLM Agents for Geo-spatial Planning with Social Media Data

arXiv: https://arxiv.org/abs/2608.25039 · HTML: https://mlsanity.com/paper/2608.25039/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 15 of 15)

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [GLM-5: from Vibe Coding to Agentic Engineering](https://mlsanity.com/paper/2602.15763/) (2026)
- [TALM: Tool Augmented Language Models](https://mlsanity.com/paper/2205.12255/) (2022)
- [DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints](https://mlsanity.com/paper/2601.18137/) (2026)
- [ChinaTravel: An Open-Ended Travel Planning Benchmark with Compositional Constraint Validation for Language Agents](https://mlsanity.com/paper/2412.13682/) (2024)
- [Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions](https://mlsanity.com/paper/2502.18470/) (2025)
- [Beyond Itinerary Planning-A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks](https://mlsanity.com/paper/2512.22673/) (2025)
- [COMPASS: Benchmarking Constrained Optimization in LLM Agents](https://mlsanity.com/paper/2510.07043/) (2025)
- [SpatialLLM: From Multi-modality Data to Urban Spatial Intelligence](https://mlsanity.com/paper/2505.12703/) (2025)
- [MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios](https://mlsanity.com/paper/2602.22638/) (2026)
- [USTBench: Benchmarking and Dissecting Spatiotemporal Reasoning of LLMs as Urban Agents](https://mlsanity.com/paper/2505.17572/) (2025)
- [LocalGPT: Benchmarking and Advancing Large Language Models for Local Life Services in Meituan](https://mlsanity.com/paper/2506.02720/) (2025)
- [Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents](https://mlsanity.com/paper/2510.06078/) (2025)
- [Urban-R1: Reinforced MLLMs Mitigate Geospatial Biases for Urban General Intelligence](https://mlsanity.com/paper/2510.16555/) (2025)
- [TripScore: Aligning LLMs for Real-World Travel Planning via Expert-Calibrated Reward](https://mlsanity.com/paper/2510.09011/) (2025)

## Abstract (arXiv)

Geo-spatial planning, like trip design, is a realistic testbed for LLM agents because it requires grounded tool use, noisy evidence retrieval, and multi-constraint reasoning. Most benchmarks, however, only provide clean geospatial data and tools, missing the open-ended social signals that people use in daily planning. We introduce LifePlanner, a benchmark that enriches map data with large-scale local social media posts and provides access through an MCP toolset. LifePlanner provides an evaluation suite spanning four task categories and three difficulty levels. Experiments show frontier LLMs perform well on simple retrieval but degrade sharply on complex planning, with the Pass Rate dropping to 40.2%. Results show that failures mainly stem from incomplete evidence acquisition from such a large multimodal database, imprecise tool use, and weak constraint integration rather than model size or reasoning length, suggesting that future progress requires effective grounded planning instead of scaling alone.