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News/Explaining Attention with Program Synthesis
arxiv
PublishedJune 18, 2026 at 4:00 AM
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Explaining Attention with Program Synthesis

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arxiv.orgfull article ↗
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Publisher summary· verbatim

arXiv:2606.19317v1 Announce Type: cross Abstract: A longstanding goal of research on interpretable deep learning is to replace opaque neural computations with human-meaningful symbolic descriptions. In this paper, we propose an approach for approximating the behavior of components of deep networks w

Models mentioned
01
  • 01meta-llama logo
    Llama-3B
    meta-llama/Llama-3B
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Discussion
Mentioned models
03
  • 01
    GPT-2
  • 02
    TinyLlama-1.1B
  • 03
    Llama-3B
    meta-llama/Llama-3B
Source
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arxiv
Read original ↗All from arxiv →
Tags
04
#interpretable-ai#transformer-models#reverse-engineering#symbolic-transparency

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Mentioned models
03
  • 01
    GPT-2
  • 02
    TinyLlama-1.1B
  • 03
    Llama-3B
    meta-llama/Llama-3B
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#interpretable-ai#transformer-models#reverse-engineering#symbolic-transparency

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