Unverified60% confidenceFactTime unknown
The randomness of LLM outputs in multi-Agent systems causes errors to amplify layer by layer as multiple Agents execute in series; this phenomenon is known as 'Error Propagation'.
2
Sources
60%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Glutra：多Agent协同桌面应用，打造你的AI一人公司
bilibiliseekskyworld
Related Entities
Related Claims
UnverifiedAs the input graph grows larger (with more triples), contradiction and omission errors in fine-tuned models grow faster, while errors in prompted LLMs grow relatively slowly.74% similarUnverifiedIn multi-agent systems, when multiple agents execute in series, output deviations from upstream agents are amplified by downstream agents, leading to error propagation.74% similarUnverifiedIn probabilistic LLM-driven systems, "errors" are often ambiguous and context-dependent, complicating fault-tolerant design74% similarUnverifiedIn multi-step Agent loops, errors in intermediate steps propagate and amplify in the form of contaminated context, producing an error cascade phenomenon.73% similarVerifiedIn multi-Agent scenarios, hallucination problems are cascadingly amplified: the erroneous output of one Agent may become the input of the next, causing errors to accumulate continuously throughout the chain.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/16265API
curl https://kongchang.com/api/v1/knowledge/claims/16265MCP
get_claim(id=16265)