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Formal Algorithms for Transformers

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arxiv 2207.09238 v1 pith:CFASU4YA submitted 2022-07-19 cs.LG cs.AIcs.CLcs.NE

classification cs.LGcs.AIcs.CLcs.NE
keywords algorithmsarchitecturestheytransformerswhataimsarchitecturalassumed
verification ladder T0 review T1 audit T2 compute T3 formal
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This document aims to be a self-contained, mathematically precise overview of transformer architectures and algorithms (*not* results). It covers what transformers are, how they are trained, what they are used for, their key architectural components, and a preview of the most prominent models. The reader is assumed to be familiar with basic ML terminology and simpler neural network architectures such as MLPs.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 52 citations worldwide. Full citation record

  1. FastTPS: An Optimized Method for LLM Token Phase for AI accelerators

    cs.LG 2026-07 conditional novelty 6.0 of 10

    FastTPS accelerates LLM token-phase inference via reloading-free static KV-cache management, tiled fused RoPE attention, and interlaced-weight MLP fusion, yielding up to 6× speedup at 93% bandwidth on AMD NPUs.

  2. Transformers Learn Faster with Semantic Focus

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Input-dependent top-k sparse attention makes small transformers converge faster and generalize as well as full attention, while input-agnostic sparsity does not, and the effect is tied to reduced dispersion of attenti...

  3. InTraVisTo: Inside Transformer Visualisation Tool

    cs.CL 2025-07 conditional novelty 5.0 of 10

    A GUI tool that decodes hidden states into tokens, visualizes information flow via a Sankey diagram, and supports embedding injection for interactive probing of transformer LLMs.

  4. Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks

    cs.LG 2025-09 conditional novelty 4.0 of 10

    Six input-conditioned DeepONet variants match or approach modified DeepONet accuracy on four PDE benchmarks with roughly half the training time.

  5. MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems

    cs.AI 2025-08 reject novelty 4.0 of 10

    The authors claim their MultiFluxAI orchestration framework achieves 95% accuracy and 0-10 ms responses by combining rule-based routing, caching, and graph knowledge stores for multi-service RAG queries.

  6. What can we learn from signals and systems in a transformer? Insights for probabilistic modeling and inference architecture

    cs.LG 2025-08 conditional novelty 4.0 of 10

    The paper frames transformer layers as fixed-point updates on conditional probability surrogates and gives an explicit such update for hidden Markov models.

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