A Tool in the Forward Pass: Frozen Tape Modules as Layers of a Small Language Model

arXiv:2611.00783v1 Announce Type: new Abstract: Calling a tool through generated text adds decoding steps, even when the computation itself is short. We study an alternative: a small neural module that runs inside a language model's forward pass. A learned writer turns hidden states into a symbolic tape, a short sequence of discrete symbols that encodes the computation. The module, trained separately and then frozen, reduces the tape to a result, and a learned reader returns that result to the model. We evaluate arithmetic and object tracking in gemma-3-1b-it. With a writer trained on labelled tapes, the layer answers all 750 items of BIG-Bench Hard's tracking shuffled objects correctly. At seven people it needs about 1/10 of the GPU kernel time of a text-tool baseline that uses the same host model and exact code, with accuracy 1.00 against 0.99. The writer can also be learned from answers alone, guided by a hand-written copying prior, but less reliably: 5 of 6 runs succeed in the 1B host and 2 of 4 in the 4B host. Two independently trained layers keep their task accuracy when attached to one model, and a learned gate keeps them silent on the tested general-text and look-alike prompts. The results support a narrow use case: a bounded computation that returns one symbol can avoid the decoding cost of a textual tool call. Extracting the right input reliably, and deciding when to act, remain the main open problems.

Abstract:Calling a tool through generated text adds decoding steps, even when the computation itself is short. We study an alternative: a small neural module that runs inside a language model's forward pass. A learned writer turns hidden states into a symbolic tape, a short sequence of discrete symbols that encodes the computation. The module, trained separately and then frozen, reduces the tape to a result, and a learned reader returns that result to the model. We evaluate arithmetic and object tracking in gemma-3-1b-it. With a writer trained on labelled tapes, the layer answers all 750 items of BIG-Bench Hard's tracking shuffled objects correctly. At seven people it needs about 1/10 of the GPU kernel time of a text-tool baseline that uses the same host model and exact code, with accuracy 1.00 against 0.99. The writer can also be learned from answers alone, guided by a hand-written copying prior, but less reliably: 5 of 6 runs succeed in the 1B host and 2 of 4 in the 4B host. Two independently trained layers keep their task accuracy when attached to one model, and a learned gate keeps them silent on the tested general-text and look-alike prompts. The results support a narrow use case: a bounded computation that returns one symbol can avoid the decoding cost of a textual tool call. Extracting the right input reliably, and deciding when to act, remain the main open problems.

12 pages, 1 figure, 9 tables. Code, configurations, results and frozen modules: this https URL

Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)

arXiv:2611.00783 [cs.AI]

(or arXiv:2611.00783v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.00783 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

aiqbench analysis

A Tool in the Forward Pass: Frozen Tape Modules as Layers of a Small Language Model

Related coverage