Unverified50% confidenceFactExact time
Small models are often unstable in tool-call format, producing missing parameters, wrong field names, or mixed natural-language output, which causes parsing failures and task interruption.
1
Sources
50%
Confidence
Long-term
Relevance
10/20/2026
First Seen
Sources
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Related Claims
UnverifiedTool error messages should be returned to the model as ordinary tool results, containing a plain-language explanation of the problem and what to do, and 'Do not retry' can stop the model from repeatedly retrying hopeless calls.80% similarUnverifiedLow-resource languages have scarce public code with outdated style and a lack of tests, making it hard for models to cover edge cases; at the same time, the absence of evaluation benchmarks creates a vicious cycle: without benchmarks, model defects cannot be quantified, which in turn makes it difficult to collect data and improve models in a targeted way.78% similarUnverifiedThe same type of error may correspond to completely different causes: unclear instructions and retrieval that fails to cover key documents both manifest as "answering the wrong question"; lacking real-time values and lacking private knowledge both manifest as "fabricating content"; insufficient base model capability and noisy training data labels may also produce seemingly similar errors.78% similarUnverifiedTool Use/Function Calling places extremely high demands on the stability of a model's output format; web model output is not optimized for tool calling, and early versions frequently produced format errors.78% similarUnverifiedMisreporting tool execution errors as protocol errors causes the model to miss useful hint information and be unable to self-correct, causing the agent to stop or enter an infinite loop.77% similar
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