An 800K-parameter Lattice Deduction Transformer reaches 100% accuracy on Sudoku-Extreme and Snowflake Sudoku and 99.9% on Maze-Hard by using lattice projections and abstract-interpretation supervision, while frontier LLMs score 0%.
Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou
2 Pith papers cite this work. Polarity classification is still indexing.
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BitCal-TTS raises exact-match accuracy by 3.7 points (7B) and 2.8 points (14B) on small GSM8K shards for 4-bit Qwen2.5 models while cutting premature-stop rates and retaining token savings versus fixed-budget decoding.
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Lattice Deduction Transformers
An 800K-parameter Lattice Deduction Transformer reaches 100% accuracy on Sudoku-Extreme and Snowflake Sudoku and 99.9% on Maze-Hard by using lattice projections and abstract-interpretation supervision, while frontier LLMs score 0%.
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BitCal-TTS: Bit-Calibrated Test-Time Scaling for Quantized Reasoning Models
BitCal-TTS raises exact-match accuracy by 3.7 points (7B) and 2.8 points (14B) on small GSM8K shards for 4-bit Qwen2.5 models while cutting premature-stop rates and retaining token savings versus fixed-budget decoding.