CASPIAN introduces unified cross-channel causal monitoring via late-interaction conditional transfer entropy to detect cascade onset and attribute origin, bridge, and amplifier agents in LLM multi-agent systems.
Graphtracer: Graph-guided failure tracing in llm agents for robust multi-turn deep search
5 Pith papers cite this work. Polarity classification is still indexing.
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Agent systems lose uncertainty at decision handoffs, causing downstream over-trust; the paper proposes latent uncertainty as a carrier to preserve pre-commitment fragility across interfaces.
FALAT improves failure attribution in LLM agent trajectories via dependency-guided search, achieving 46.0% step-level accuracy on algorithm-generated and 29.1% on hand-crafted trajectories in the Who&When benchmark.
VerifyMAS improves failure attribution in LLM multi-agent systems via hypothesis verification on full trajectories, error taxonomy-based data construction, and fine-tuned verifier models, outperforming prior direct-prediction methods on Aegis-Bench and Who&When.
This survey categorizes anomalies in agent systems into intra-agent and inter-agent types and introduces the AgentOps framework with four operational stages.
citing papers explorer
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CASPIAN: Online Detection and Attribution of Cascade Attacks in LLM Multi-Agent Systems via Cross-Channel Causal Monitoring
CASPIAN introduces unified cross-channel causal monitoring via late-interaction conditional transfer entropy to detect cascade onset and attribute origin, bridge, and amplifier agents in LLM multi-agent systems.
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Confidence Laundering in Agent Systems: Why Uncertainty Needs a Latent Carrier
Agent systems lose uncertainty at decision handoffs, causing downstream over-trust; the paper proposes latent uncertainty as a carrier to preserve pre-commitment fragility across interfaces.
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FALAT: Tracing Failures in LLM Agent Trajectories via Dependency-Guided Search
FALAT improves failure attribution in LLM agent trajectories via dependency-guided search, achieving 46.0% step-level accuracy on algorithm-generated and 29.1% on hand-crafted trajectories in the Who&When benchmark.
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VerifyMAS: Hypothesis Verification for Failure Attribution in LLM Multi-Agent Systems
VerifyMAS improves failure attribution in LLM multi-agent systems via hypothesis verification on full trajectories, error taxonomy-based data construction, and fine-tuned verifier models, outperforming prior direct-prediction methods on Aegis-Bench and Who&When.
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Agent System Operations: Categorization, Challenges, and Future Directions
This survey categorizes anomalies in agent systems into intra-agent and inter-agent types and introduces the AgentOps framework with four operational stages.