arXiv (All)
open access
Spatial Strategies, Not Actions: Vector-Quantized Geodesics as Tools for LLM-Driven Agents
Large language model (LLM) based agents are often criticized for lacking spatial understanding and mainly exploiting statistical text patterns. We investigate their spatial comprehension through an architecture combining geometrical tools with a LLM serving as a high-level orchestrator in grid-world
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
- A map of words: Retrieving the spatial layout of medium-scale geographical maps through distributional semantics#155939
- How LLMs Follow Instructions: Skillful Coordination, Not a Universal Mechanism#998179
- GRASP: Generating, Revising, and Assessing for Strategic Planning with Agentic AI#1078218
- Do LLMs have core beliefs?#119168
- Pass or Fail? Evaluating LLMs on Two Greek Examination Benchmarks#1129417
Record · ID 1155548
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