Agentic is moving fast, second conference had record attendance, 5k in person and over 100k online.
Here is my poster presented at the Agentic AI Summit 2026 (August 1-2 @ UC Berkeley) on
Presentation Abstract
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 large language model serving as a high-level orchestrator in grid-world environments. The agent first collects geodesic trajectories, which are then vector‑quantized to extract a representative subset. Each resulting trajectory defines a reusable skill (tool) that outputs spatial metrics such as the remaining distance to the target and local geometric information, including nearby obstacles. The LLM is used offline to interpret the learned skills by projecting them into natural language descriptions of the underlying behavioral patterns. Online, the LLM chooses the appropriate skill conditioned on the current state and goal. Low-level control is handled by primitive actions that execute the selected skill. From an agentic AI perspective, this approach separates learning into two levels. Skill discovery is handled through unsupervised quantization of trajectories, while reasoning and decision-making are handled by the LLM. New skills emerge when trajectories contrast from the existing ones (implemented technically a signed measure vector-quantization). Further investigations can help assess applicability, robustness and effectiveness for sequential decision-making settings such as robotic manipulation or navigation in partially observable or higher-dimensional environments.
the slides of the presentation.
Youtube video version.
