The Logic of Efficient Search
**Algorithm-of-Thoughts** (AoT) aims to achieve the power of ToT/GoT but with significantly lower latency and cost. It does this by "Internalizing" the search process, teaching the model to follow specific algorithmic patterns (like DFS) in a single reasoning chain.
The AoT Advantage
We use AoT to build "Fast and Smart" autonomous systems:
- DFS Simulation: Prompting the model to explore a path, evaluate it, and "Simulate Backtracking" in its own text output.
- Efficiency Gains: Achieving "Search-Level" reasoning quality in 1-2 model calls instead of 20-50.
- Algorithmic Pruning: Teaching the model to "Discard" bad paths early in the reasoning chain to save tokens.
- Structured Output: Using specialized markers (e.g.,