Dream2Discern: Self-Generated Hard Examples Predict Multi-Agent Failures With 90%+ Accuracy
RutgersU · hf · 2026-10-19
A Rutgers/UT Austin/Amazon paper introduces Dream2Discern, a runtime auditor for LLM-based multi-agent systems. Instead of costly trajectory execution and verification to expand training data, it implicitly augments data guided by its own prediction loss: perturbations of the auditor's representation operators are selected to raise prediction loss, yielding hard latent variants that serve as a renewable source of hard examples. On AFTRAJ-2K and StepShield it outperforms competitive failure auditors, achieving over 90% trajectory-level accuracy.
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