Tracing Computation Density in LLMs
Quick summary
arXiv:2605.27033v2 Announce Type: replace-cross Abstract: Transformer-based large language models (LLMs) are comprised of billions of parameters arranged in deep and wide computational graphs, but it is not clear that they exploit their full capacity for all inputs. We introduce the s-Trace method to efficiently estimate a subgraph of size s that approximates a full model output. With this method, we find the computation in a variety of LLMs to be organized in two distinct phases. A small subgraph mostly composed of early-layer nodes can reconstruct the head of the full model output distributi
Key takeaways
- arXiv:2605.27033v2 Announce Type: replace-cross Abstract: Transformer-based large language models (LLMs) are comprised of billions of parameters arranged in deep and wide computational graphs, but it is not clear that they exploit their full capacity for all inputs.
- We introduce the s-Trace method to efficiently estimate a subgraph of size s that approximates a full model output.
- With this method, we find the computation in a variety of LLMs to be organized in two distinct phases.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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