Research·arXiv cs.LG·11h ago

LLM agents fail to execute their own plans consistently

Researchers identify a critical gap between how LLM agents declare their planning strategy and how they actually execute tasks. The work introduces Planning-as-Routing, a framework that routes tasks to specialized executors based on the agent's declared mode (Predefined, Sequential, Hierarchical, or Search) rather than relying on generic planning patterns. Testing across multiple benchmarks and LLM variants reveals consistent execution failures even when planning selection succeeds, suggesting that agent reliability depends less on unified reasoning and more on pattern-specific execution fidelity. This finding reshapes how practitioners should architect multi-step reasoning systems, moving away from one-size-fits-all approaches toward mode-aware routing.

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Explainer

The paper's core finding is not just that agents fail at execution, but that failure is predictable and mode-specific. This means the problem is not a general reasoning deficit but a routing-to-executor mismatch that practitioners can actually fix by design.

This connects directly to the Meta-Skill framework (from late September) and AdviSD work, which both treat agent improvement as an environment or guidance problem rather than a weights problem. Planning-as-Routing extends that logic: instead of trying to make a single agent reason better, route its declared intent to a specialized executor. The LongHarness Bench paper from the same period also surfaces a related tension: systems declare one strategy but the actual bottleneck is in execution harness design, not reasoning quality. Both findings push practitioners away from unified architectures toward mode-aware decomposition.

If Planning-as-Routing shows consistent gains when tested on the same benchmarks used to evaluate AdviSD's advisor model (within the next two months), that confirms the routing hypothesis is orthogonal to steering. If instead performance plateaus when combined with external guidance, the bottleneck is deeper than routing and suggests agents need retraining, not better dispatch.

Coverage we drew on

AdviSD: Learning to Advise Frontier LLMs via Targeted Multi-Turn Self-Distillation · arXiv cs.CL

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MentionsLLM agents · Planning-as-Routing · ReAct

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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Do LLM Agents Execute the Plans They Declare? From Planning-Mode Declaration to Pattern-Specific Execution”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.