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