Unverified50% confidenceFactExact time
TEFM reduces inference costs by one to two orders of magnitude through extreme token compression.
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50%
Confidence
Long-term
Relevance
9/11/2026
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UnverifiedLow-rank decomposition and quantization compression are orthogonally complementary dimensions; quantizing the smaller matrices A and B separately results in smaller errors, and the combined compression ratio can reach several times that of either method alone70% similarUnverifiedIn the trade-off where pursuing interpretability often means introducing greater computational overhead and pursuing efficiency tends to sacrifice reliability, TEFM attempts to break this dilemma through the co-design of behavioral coding and the dual-fidelity objective.70% similarUnverified相比全量走大模型的方案，三层架构可将整体推理成本降低80%-95%，P99延迟从秒级压缩至百毫秒级70% similarUnverifiedllama.cpp的K-quants系列采用超块分组量化策略，相比早期均匀量化在同等压缩率下可降低约20-30%的困惑度损失68% similarUnverifiedModel compression techniques (distillation, pruning, quantization) incur minimal loss on high-frequency, regular tasks, but significantly degrade multi-step reasoning that requires long-range dependencies—a capability that cross-component state management in front-end interaction code heavily relies on.67% similar
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