CAR: Empowering Agents with Dynamic Tool Synthesis and Global Trajectory Rectification (2026.findings-acl)
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| Challenge: | Existing LLM agents are brittle in open-ended environments due to two limitations: 1) a closed action space; 2) myopic error recovery. |
| Approach: | They propose a novel architecture that augments the action space and revises global strategies by adding a reflective replanning mechanism to the system. |
| Outcome: | Experiments show that CAR outperforms baselines in a diagnostic benchmark with pruned toolsets to simulate tool scarcity. |
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