The Spectral Geometry of Thought: Phase Transitions, Instruction Reversal, Token-Level Dynamics, and Perfect Correctness Prediction in How Transformers Reason
arXiv 2604.15350•dfb48773bf65fd67b4fd737c00c7034ed60d3640ba81556cff2ee1314fe01665
FP16LoRATurboQuantattention-kernelsattractor-dynamicscompressiondata-leakagefine-tuninghallucinationinference-divergenceintegritykv-cachelarge-language-modelsmemory-footprintmodel-efficiencymodel-predictionnumerical-stabilityperformance-optimizationprobabilistic-language-triesquantizationreasoning-dynamicssequential-compressionside-channelspectral-analysistransformers
Paper metadata
- arXiv ID
- 2604.15350
- Version
- Not specified by this published record
- Category
- Computer Science — Machine Learning (cs.LG)
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Evidence and limitations
- Source ID
- arxiv_cs_lg
- Record identifier
- dfb48773bf65fd67b4fd737c00c7034ed60d3640ba81556cff2ee1314fe01665
- Enrichment time
- 2026-04-20T08:52:11Z
- AI-assisted enrichment
- Yes
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