LLM-solver loops get a closer look for missing reasoning narration

A new arXiv paper studies the “narration gap” in LLM-solver loops. These systems pair language models with external solvers, but the explanation users see may not fully match the reasoning or constraints handled by the solver.

The issue matters for trust. Solver-backed workflows can improve correctness, but if the model narrates the result poorly, users may misunderstand why an answer is valid or where limitations remain.

As AI systems increasingly combine models with tools, explanation quality becomes part of the system design rather than a cosmetic layer after the fact.