ColdSplash: A Borrowed Thought for Agent Self-Reflection

As language agents perform increasingly long and complex multi-turn tasks, ro- bust performance requires not only correct initial actions but also the ability to re- cover from errors through re-planning. Self-reflection is a common mechanism for recovery, but agents can remain anchored to the interpretation that caused the fail- ure, leading them to reproduce the same erroneous reasoning despite having access to relevant evidence in the trajectory. We introduce COLDSPLASH, a lightweight cross-model intervention that mitigates this anchoring effect by transferring only the opening of a reflection from a heterogeneous model. The original agent then completes the reflection and resumes the task using the same trajectory and ev- idence. Thus, COLDSPLASH changes the initialization of the reflection process without delegating the task or modifying the agent’s subsequent reasoning pro- cess. We evaluate COLDSPLASH on the self-reflection failure sets of τ 2-bench across Airline, Retail, and Telecom. On the pooled Airline/Retail cohort, a 16- token opening improves pass@any(4), the fraction of tasks solved in at least one of four attempts, from 5.2% to 9.4%, an 80% relative improvement, with gains observed across actors and domains. Diagnostic audits, trajectory-level analyses, and reflection-channel analyses further show that the intervention can change sub- sequent behavior rather than merely the linguistic form of reflection. These results demonstrate that a small, well-placed external nudge can unlock recovery capacity that agents already latently possess, pointing to lightweight cross-model reflection as a practical complement to scaling a single model’s introspection. Code and data is https://anonymous.4open.science/r/coldflash-1D84