Generalizing CGR to non-math reasoning domains

Ascertain whether Certainty-Guided Reasoning (CGR) extends effectively to coding, commonsense reasoning, and open-domain question answering, maintaining or improving accuracy while reducing token usage.

Background

The paper evaluates CGR primarily on mathematics benchmarks (AIME2024 and AIME2025), showing token savings and stable performance with certainty-driven early exit.

It remains unknown whether CGR’s benefits translate to other reasoning-intensive tasks such as coding, commonsense reasoning, and open-domain QA, which may have different uncertainty and solution structures.

References

Several promising directions remain open for exploration. Finally, while our experiments focused on math problems, the CGR paradigm could extend to other reasoning-intensive domains such as coding, commonsense reasoning, and open-domain question answering.

— Certainty-Guided Reasoning in Large Language Models: A Dynamic Thinking Budget Approach  (2509.07820 - Nogueira et al., 9 Sep 2025) in Conclusions and Future Work