Unverified50% confidenceFactExact time
LLM agents are prone to goal drift in long-horizon tasks and multi-agent collaboration, that is, gradually and cumulatively deviating from the original goal as execution proceeds.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLarge language models are prone to goal drift during multi-step long-duration tasks, gradually deviating from the original core objective77% similarUnverifiedThe phenomenon where minor deviations at each step of a multi-step task executed by an agent may amplify along the chain and lead to results far from the initial intent is called goal drift.75% similarUnverifiedThe parallel sub-agent structure improves execution speed, but also means the actual actions triggered by a single goal invocation can far exceed what the user expects.75% similarUnverifiedA single execution-type agent exhibits three typical problems during long-running tasks: goal drift, shortcut-taking, and mid-task cessation74% similarUnverifiedBackground long-horizon tasks involve engineering challenges such as task state persistence, multi-agent communication coordination, and exception handling and recovery mechanisms — issues that simple agents do not encounter.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1042054API
curl https://kongchang.com/api/v1/knowledge/claims/1042054MCP
get_claim(id=1042054)