SAGE: Mitigating Long-Horizon Reasoning Biases via Topological Guidance
- Published
- Source
- arXiv
- Paper number
- 1129
- Field
- Reasoning
- arXiv ID
- 2609.30192
Key points
- It defines long-horizon reasoning failures along two axes, exploration bias and compounding bias, and presents a theoretical analysis (Symbolic Closure Analysis).
- Algebraic sparsification reduces spurious branches, while hyperbolic embedding provides dense depth-wise signals, each addressing one of the two biases.
- It consistently outperformed existing methods across 12 benchmarks and 7 model families.
- It achieved up to an 8-fold improvement on the real-world open Andrews-Curtis problem.
Paper links
External research summaries. These are not HDATF publications or measured product results.