EvoSpatial: Experience-Driven Skill and Tool Evolution for Spatial Reasoning Agents
Tool-augmented vision-language agents compose perception and measurement to reason about spatial relationships, but effective execution also requires tracking geometric assumptions and checking intermediate evidence.
ProofPaper ↗
Key points
- Preserving this experience across tasks requires retaining procedural knowledge together with the computations it governs.
- We introduce EvoSpatial, an experience-driven framework that evolves a library of linked skills and tools.
- Further experiments show cross-benchmark transfer and improved reasoning through skill-guided distillation without external skills at evaluation.
- These results support accumulating spatial experience for both inference-time reuse and trajectory-based training.
Sources (1)
- [1]EvoSpatial: Experience-Driven Skill and Tool Evolution for Spatial Reasoning AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 12:21 PM
Tool-augmented vision-language agents compose perception and measurement to reason about spatial relationships, but effective execution also requires tracking geometric assumptions and checking intermediate evidence.
Preserving this experience across tasks requires retaining procedural knowledge together with the computations it governs.
Extractive summary: sentences quoted from the sources.
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