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This paper investigates quality issues in LLM-generated answers for hardware description language questions, finding over-answering tendencies like redundancy (65.7%) and verbosity (69.1%), and proposes a multi-agent framework that reduces core answers by 37% and non-core content length by 31% while improving quality scores.
A user reports that Claude has become significantly worse to use, citing overly aggressive pushback behavior and inappropriate use of an 'end conversation' tool that causes it to abandon tasks prematurely. The user is switching coding work to OpenAI's Codex as a result.