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LLMs struggle with interactive programming tasks, revealing a significant gap in their algorithmic reasoning abilities under constrained information conditions.
Relying only on affinity can lead to failures in noisy environments, but ANFI's dual approach to neighbor interactions significantly boosts robustness in person re-ID tasks.
Agents can now escape the Self-Confirmation Trap, leading to more reliable experience learning and improved self-evolution.
