CORRECT: COndensed eRror RECognition via knowledge Transfer in
  multi-agent systems
Multi-agent systems (MAS) are increasingly capable of tackling complex
real-world tasks, yet their reliance on inter-agent coordination, tool use, and
long-horizon reasoning makes error recognition particularly challenging. Minor
errors can propagate across agents, escalating into task failures while
producing long, intertwined execution trajectories that impose significant
costs for both human developers and automated systems to debug and analyze. Our
key insight is that, despite surface differences in failure trajectories (e.g.,
logs), MAS errors often recur with similar structural patterns. This paper
presents CORRECT, the first lightweight, training-free framework that leverages
an online cache of distilled error schemata to recognize and transfer knowledge
of failure structures across new requests. This cache-based reuse allows LLMs
to perform targeted error localization at inference time, avoiding the need for
expensive retraining while adapting to dynamic MAS deployments in subseconds.
To support rigorous study in this domain, we also introduce CORRECT-Error, a
large-scale dataset of over 2,000 annotated trajectories collected through a
novel error-injection pipeline guided by real-world distributions, and further
validated through human evaluation to ensure alignment with natural failure
patterns. Experiments across seven diverse MAS applications show that CORRECT
improves step-level error localization up to 19.8% over existing advances while
at near-zero overhead, substantially narrowing the gap between automated and
human-level error recognition.
68db5416d2bf1f4b15ec74f5
Yifan Yu
68db5416d2bf1f4b15ec74f6
Moyan Li
68db5416d2bf1f4b15ec74f7
Shaoyuan Xu
68db5416d2bf1f4b15ec74f8
Jinmiao Fu
68db5416d2bf1f4b15ec74f9
Xinhai Hou
68db5416d2bf1f4b15ec74fa
Fan Lai
68db5416d2bf1f4b15ec74fb
Bryan Wang
CORRECT: COndensed eRror RECognition via knowledge Transfer in
  multi-agent systems
Multi-agent systems (MAS) are increasingly capable of tackling complex
real-world tasks, yet their reliance on inter-agent coordination, tool use, and
long-horizon reasoning makes error recognition particularly challenging. Minor
errors can propagate across agents, escalating into task failures while
producing long, intertwined execution trajectories that impose significant
costs for both human developers and automated systems to debug and analyze. Our
key insight is that, despite surface differences in failure trajectories (e.g.,
logs), MAS errors often recur with similar structural patterns. This paper
presents CORRECT, the first lightweight, training-free framework that leverages
an online cache of distilled error schemata to recognize and transfer knowledge
of failure structures across new requests. This cache-based reuse allows LLMs
to perform targeted error localization at inference time, avoiding the need for
expensive retraining while adapting to dynamic MAS deployments in subseconds.
To support rigorous study in this domain, we also introduce CORRECT-Error, a
large-scale dataset of over 2,000 annotated trajectories collected through a
novel error-injection pipeline guided by real-world distributions, and further
validated through human evaluation to ensure alignment with natural failure
patterns. Experiments across seven diverse MAS applications show that CORRECT
improves step-level error localization up to 19.8% over existing advances while
at near-zero overhead, substantially narrowing the gap between automated and
human-level error recognition.
CORRECT is a lightweight, training-free framework that uses an online cache of distilled error schemata to improve error localization in multi-agent systems with minimal overhead.
multi-agent systems inter-agent coordination tool use long-horizon reasoning error recognition error schemata CORRECT CORRECT-Error error localization error-injection pipeline