Mechanism for adjusting a learned decision boundary under changing context

Determine the mechanism by which an agent that has learned a decision boundary subsequently adjusts that boundary in response to changes in task context.

Background

Empirical studies show that agents flexibly trade speed and accuracy when payoff structures or task statistics change, implying adjustments to decision strategy after learning. Theoretical accounts based on accumulation-to-bound models posit boundary shifts, but neurophysiological evidence has struggled to identify corresponding changes in putative boundary neurons. The paper highlights that the mechanism for post-learning boundary adjustment is not yet clarified.

References

Theoretically, once the agent has learned a given decision boundary, it is not clear through what mechanism the boundary is subsequently adjusted in response to new contexts.

— Sequential sampling without comparison to boundary through model-free reinforcement learning  (2408.06080 - Esmaily et al., 2024) in Introduction

The substantial change from this single-token swap suggests a learned adjustment of the position write, whose purpose remains unclear.

— World Modeling in Transformers  (2609.21748 - Beckmann et al., 18 Sep 2026) in Appendix B, Section "Write strength and noise" (Appendix section labeled \ref{app:weakwrite})

Acquiring useful rules does not guarantee the ability to correctly apply, retain, or revise them in the face of contradictory evidence. Current skill evolution methods achieve gains under familiar conditions, yet these improvements often coincide with poor transfer and capability erosion, while reliable rule revision remains unestablished.

— Beyond Endpoint Performance: Process-Level Evaluation of Self-Evolving Agents  (2609.24663 - Lin et al., 21 Sep 2026) in Section 4, subsection “Main Results,” Takeaway box; reiterated in Section 5, Conclusion