Unverified50% confidenceFactExact time
LLM agents may, due to language priors, persistently output actions that violate preconditions with high confidence, thereby falling into ineffective loops.
1
Sources
50%
Confidence
Long-term
Relevance
10/20/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLLM call chains are long and highly non-deterministic, and a single failure may originate from any one of the following: prompt, tool call, context truncation, or model hallucination.79% similarUnverifiedLLM outputs are inherently stochastic, and when multiple agent interaction paths are layered on top of each other, the state space expands exponentially, making exhaustive testing almost impossible to cover all potential failure scenarios.74% similarUnverified研究表明LLM在处理过长、过复杂的指令集时整体遵循率会系统性下降,即指令遗忘现象74% similarUnverifiedThe reliability of LLM applications is moving from the model level down to the engineering level, where hallucination outputs are guarded against through detection, interception, and retry mechanisms within the call chain.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1073879API
curl https://kongchang.com/api/v1/knowledge/claims/1073879MCP
get_claim(id=1073879)