Don't Solve, Just Compare: Tiny Advisors for Runtime Intervention in LLM Agents
LLM agents are emerging as an important paradigm for real-world tasks that require reasoning, tool use, and sequential decision-making. As these agents operate over longer horizons, runtime intervention offers a way to improve reliability without retraining the underlying actor. Effective intervention must provide a useful direction for recovery besides a warning. Existing approaches often rely on an expert solver or a critic that generates task-specific corrections, incurring either the cost of another capable solver or the capacity demands of a task-capable critic. We introduce Comparison-Only Tiny Advisor (COTA) for constructive runtime intervention, which reduces the learned intervent...
| Authors | Yanze Jiang, Mingxuan Li, Yuhao Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 August 2026 |
| Last updated at source | 28 September 2026 |
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