Unverified50% confidenceTradeoffExact time
Small-parameter models (such as 9B) are more prone to truncation or missing fields in output format, causing tool call failures, so Agent robustness depends on JSON parsing stability.
1
Sources
50%
Confidence
Long-term
Relevance
9/15/2026
First Seen
Sources
Related Entities
Related Claims
Unverified三值化权重信息熵极低，模型在处理需要精确格式约束的任务（如JSON工具调用）时容错空间极小，易出现能力塌陷的非均匀性69% similarUnverifiedFor models that do not support tool calling, it is generally necessary to simulate tool-calling behavior through carefully designed prompts, though stability and accuracy will decline noticeably.68% similarUnverified量化对结构化输出（如 JSON 工具调用）的影响比对通用对话更敏感，因边界 token 概率分布较窄易受权重噪声漂移触发格式错误67% similarUnverified结构化、类型强约束的工具响应可以显著降低模型在解析阶段产生误解的概率66% similarUnverified小参数模型在代码生成场景下的本质短板是精度不足，表现为函数未声明、空函数体、工具调用格式损坏、变量拼写错误等66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/923256API
curl https://kongchang.com/api/v1/knowledge/claims/923256MCP
get_claim(id=923256)