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HalBench v2.3 shows that sycophancy resistance is largely decoupled from model size and architecture, with a ~27B model outperforming models up to 402B and several closed frontier models on false-premise pushback.
DiffusionGemma's parallel token-generation architecture produces fluent but factually unreliable text, with error rates that grow as topics become more obscure — a concrete limitation that distinguishes it from its autoregressive counterpart for any fact-sensitive use case.
This benchmark directly addresses a gap the post identifies — the lack of tool-calling quality evaluations for popular local GGUF quants — and provides concrete, reproducible evidence that KV cache quantization level and context length have measurable effects on tool-calling accuracy for Qwen3.6-35B-A3B.