FailureScope clusters evaluation probes by cross-model failure patterns via LOMO to produce stable taxonomies that generalize across single-turn, multi-turn, and adversarial regimes, with reported metrics of Kendall's tau 0.81 and AUC 0.88.
Failure modes in LLM systems: A system-level taxonomy for reliable AI applications
3 Pith papers cite this work. Polarity classification is still indexing.
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POIROT protocol repurposes agents in LLM multi-agent systems as an internal diagnostic layer for failure detection, outperforming single-LLM evaluators with gains that increase with complexity, agent count, and fault types.
In the Flux environment, RL agents with explicit latent state access achieve ~79% win rate versus ~11% for LLMs on long-horizon tasks, illustrating limitations of sequence prediction for dynamic reasoning.
citing papers explorer
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POIROT: Interrogating Agents for Failure Detection in Multi-Agent Systems
POIROT protocol repurposes agents in LLM multi-agent systems as an internal diagnostic layer for failure detection, outperforming single-LLM evaluators with gains that increase with complexity, agent count, and fault types.
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Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform
In the Flux environment, RL agents with explicit latent state access achieve ~79% win rate versus ~11% for LLMs on long-horizon tasks, illustrating limitations of sequence prediction for dynamic reasoning.