TAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents
Language Model (LM) agents have demonstrated remarkable capabilities in solving tasks that require multiple interactions with the environment. However, they remain vulnerable in environments where a single error often leads to irrecoverable failure, particularly under strict feasibility constraints. We systematically analyze existing agent frameworks, identifying imperfect planning and stochastic execution as the primary causes. To address these challenges, we propose Tool-guided Adaptive Planning with constrained Execution (TAPE). TAPE enhances planning capability by aggregating multiple plans into a graph and employing an external solver to identify a feasible path. During execution, TAPE employs constrained decoding to reduce sampling noise, while adaptively re-planning whenever environmental feedback deviates from the intended state. Experiments across Sokoban, ALFWorld, MuSiQue, and GSM8K-Hard demonstrate that TAPE consistently outperforms existing frameworks, with particularly large gains on hard settings, improving success rates by 21.0 percentage points on hard settings on average, and by 20.0 percentage points for weaker base models on average. Code and data available at here.
699e2e67dfbcf0b800aeca26
Jongwon Jeong
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2026-02-25T17:30:24.033Z
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Jongwon Jeong
jongwon-jeong
user
jongwon-jeong
699e2e67dfbcf0b800aeca27
Jungtaek Kim
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2026-02-26T10:02:41.396Z
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Jungtaek Kim
jungtaekkim
user
jungtaekkim
699e2e67dfbcf0b800aeca28
Kangwook Lee
TAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents
Language Model (LM) agents have demonstrated remarkable capabilities in solving tasks that require multiple interactions with the environment. However, they remain vulnerable in environments where a single error often leads to irrecoverable failure, particularly under strict feasibility constraints. We systematically analyze existing agent frameworks, identifying imperfect planning and stochastic execution as the primary causes. To address these challenges, we propose Tool-guided Adaptive Planning with constrained Execution (TAPE). TAPE enhances planning capability by aggregating multiple plans into a graph and employing an external solver to identify a feasible path. During execution, TAPE employs constrained decoding to reduce sampling noise, while adaptively re-planning whenever environmental feedback deviates from the intended state. Experiments across Sokoban, ALFWorld, MuSiQue, and GSM8K-Hard demonstrate that TAPE consistently outperforms existing frameworks, with particularly large gains on hard settings, improving success rates by 21.0 percentage points on hard settings on average, and by 20.0 percentage points for weaker base models on average. Code and data available at here.
TAPE framework improves language model agent performance in complex environments through enhanced planning and constrained execution strategies.
language model agents planning stochastic execution constrained execution adaptive planning external solver feasible path constrained decoding environmental feedback agent frameworks