When Do Task Vectors Interfere? Mapping the Validity Boundaries of Weight-Space Composition

Title:When Do Task Vectors Interfere? Mapping the Validity Boundaries of Weight-Space Composition

Abstract:Task arithmetic composes skills by adding weight displacements, and merged models are then judged on benchmark suites. We measure when that composition is functionally additive, and find that the answer depends as much on how the model is prompted as on which tasks are merged. Across two-dimensional composition surfaces -- five model settings from 0.5B to 8B, two families, LoRA and full fine-tuning -- pairwise non-additivity is real, seed-stable, and transfers in coarse order to unseen task pairs: all eight preregistered sign predictions held. But it is input-conditioned everywhere we measured: the same merged model that shows a six-point interaction contrast on code prompts shows none on math prompts, and wrapping the identical code prompts in the instruction template the adapters were trained on collapses the contrast twenty-fold, from +6.9 to +0.3 points -- while re-serializing them in an untrained chat template leaves it intact (+12.5), falsifying our own preregistered prediction. Execution benchmarks (pass@1) inherit the training-format wrapper's blindness. Weight-space composition therefore supports coarse, input- and format-conditioned functional statements -- not a universal merging-performance predictor, and not one that training-format evaluations can see.

Machine Learning (cs.LG)

arXiv:2608.09490 [cs.LG]

(or arXiv:2608.09490v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.09490 Focus to learn more arXiv-issued DOI via DataCite

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