Unverified50% confidenceFactExact time
In code generation scenarios, models may call non-existent APIs, modify functions that should not be touched, or break existing logic without proactively reporting the changes
1
Sources
50%
Confidence
Long-term
Relevance
10/3/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedA model completing a task does not mean the code quality is high; models will complete tasks in the most expedient way possible, potentially producing duplicate code, scattered functions, and dead code.75% similarUnverifiedLarge language models can hallucinate when generating code, fabricating plausible-sounding but non-existent function names, parameter signatures, or API interfaces.75% similarUnverifiedWhen calling tools, models sometimes generate malformed parameters, call nonexistent functions, or fall into infinite loops; mature agent frameworks incorporate parameter validation, retry limits, and exception-handling mechanisms.74% similarUnverifiedFor applications that rely on structured output (such as JSON Schema) or function calling, behavioral drift in the new model may trigger parsing errors or business logic anomalies.74% similarVerifiedFor niche domains with insufficient training data, large models tend toward hallucination-based completion, generating API calls, parameter names, or return structures that appear plausible but are actually incorrect73% similar
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