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Poisoned training data leaves a unique fingerprint in the spectral entropy of LLM gradients, enabling backdoor detection even at extreme poison ratios where clustering-based defenses fail.
Decentralized debate among LLM agents doesn't just select the best solution for optimization modeling; it structurally enables agents to refine flawed candidates and even recover correct formulations through interaction.
LLM agent progress increasingly hinges on better external cognitive infrastructure, not just stronger models.
