The Signal

Everything that matters in AI, with our take.

Updated through the day. Every headline links straight to the source. The two lines underneath are ours.

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arXiv cs.CLPaper10d ago

Lost in the bf16 Cast: Exporting Ternary Language Models Can Revert Most Low-Learning-Rate Code Changes

This is a hard finding: deployed ternary models (BitNet, Falcon-E, BitCPM) lose most of their ability on standard benchmarks because of an accidental rounding bug in the export step. The labs documented the pipelines correctly, but the actual released weights disagree with the code. If you're using or planning to use ternary models, verify the export step or you're getting crippled performance.

research quantization model-compression bitnet

arXiv cs.LGPaper24d ago

Disaggregated Quantization: Specializing LLM Prefill and Decode

This is a real efficiency win if you're deploying large models in production. The insight is simple: prefill and decode have different bottlenecks, so quantizing them identically is wasteful. The numbers are solid: 32.5 points on MMLU-Pro without increasing inference cost. If you're optimizing inference for latency-sensitive workloads, this becomes part of your baseline. Start testing on your models this week.

research quantization inference llm-optimization