v10.16.0 ONNX backend for the NLI layer; self-healing temperature rejection

Added

• ONNX backend for the NLI decision layer, preferred over torch when a local export exists. Runtime is onnxruntime (already in the dependency tree via FastEmbed) plus the transformers tokenizer, no torch: the mnemos[nli] extra shrinks from a multi-GB torch pull to onnxruntime + transformers + sentencepiece. Models are exported once with scripts/export_nli_onnx.py (tooling extra: mnemos[nli-export]) into MNEMOS_NLI_ONNX_DIR (default ~/.cache/mnemos/nli-onnx/{en,multi}), or copied between machines. MNEMOS_NLI_BACKEND pins auto/onnx/torch.
• Parity gate results (114 nli-bench pairs, both models): ONNX fp32 is score-identical to torch, max probability drift 1e-05, identical AUC to 4 decimals, zero threshold flips. int8 dynamic quantization was REJECTED by the same gate: it collapses DeBERTa-v3 to chance (contradiction AUC 0.94 -> 0.51 English, 0.84 -> 0.48 multilingual) and was not even reliably faster on CPU. The layer ships fp32-only; the torch scorer remains as fallback (mnemos[nli-torch]).
• chat() self-heals on temperature-rejecting models: a 400 naming temperature strips the parameter, retries immediately, and remembers the (endpoint, model) pair for the process lifetime. Nyx phases with hardcoded temperatures now work against such models with no configuration; MNEMOS_LLM_OMIT_TEMPERATURE[_<PHASE>] remains as an explicit override that skips even the first probe.