Nathan Hu

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@NathanHu12

PhD @stanfordnlp | aspiring LLM biologist | nathanhu0.github.io

Joined September 2021

398 Following 490 Followers 72 Posts

Latest posts 5

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Nathan Hu @NathanHu12 Sep 17

New paper! In subliminal learning, LLMs transmit traits (e.g. loving cats) though seemingly unrelated data (e.g. numbers). We proactively detect these effects as readable prompts. To do so, we use the surprising ability of models to verbalize learned soft prompts.🧵

Nathan Hu @NathanHu12 Oct 19

Cool work applying gradient routing to post-training! I like viewing GRAFT’s forget adapter as an online cousin of things like inoculation prompts or preventative steering vectors (objects that elicit some undesirable behavior during training and are then removed).

Jake Ward @_jake_ward Oct 19

At the start of training, we initialize two new adapters: a "retain" adapter and a sacrificial "forget" adapter. When we detect reward hacking, we only update the "forget" adapter, otherwise we update both. When we deploy the model, we discard the "forget" adapter.

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Nathan Hu @NathanHu12 Sep 23

My biggest takeaway from watching @aryaman2020 work his magic: it’s super natural, but also remarkably effective, to turn an interp task like attribution into something we can directly optimize for.

Aryaman Arora Verified account @aryaman2020 Sep 23

New paper! 🫡

We introduce Matryoshka Attribution, a new attribution method which uses gradient descent to find which parts of a neural network are responsible for a behaviour.

MAttr is #1 on the Mechanistic Interpretability Benchmark by a wide margin (2.9× the runner up).

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Nathan Hu @NathanHu12 Aug 31

Circuit tracing still feels quite magical to me, but manually annotating attribution graphs can get quite tedious. @AmeenPatel0 and @MaxZhang119 show simply asking an LLM to group feature descriptions already yields surprisingly useful annotations.

Max Zhang @MaxZhang119 Aug 29

Circuit tracing is an exciting technique for revealing a LM's internal computation, but interpreting its attribution graphs takes an expert 1–2 hours. Our new #ICML2026 Mech Interp paper presents a simple LLM pipeline for automating this step; $0.05-0.60 and 3 minutes per graph.

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Nathan Hu @NathanHu12 Feb 26

What does reasoning fine-tuning actually change inside a model?
In our new paper, we introduce transcoder adapters to learn sparse, interpretable approximations of how reasoning fine-tuning changes MLP computation. 🧵