Swiss-Knife: A Framework for Reconfigurable Externalised Multi-Objective Alignment at Decode Time
Abstract
Decode-time alignment methods steer a frozen language model by scoring candidate continuations with an external reward and selecting the maximiser. We argue that this shared design is a single degenerate point in a much larger space. We introduce Swiss-Knife, a framework for externalised multi-objective alignment in which the alignment specification is a first-class runtime object: hot-swappable scoring blades, a batch normaliser, a pairwise aggregation operator, and a selection rule. Six axioms characterise the admissible aggregation operators, and we prove a representation theorem: every operator satisfying them has the form Ri = ∑j ≠ i g((μi − μj)/s(σi,σj)), a two-parameter family containing probit and logistic comparison rules and pointwise argmax as named coordinates. Within it, pairwise aggregation is Lipschitz-stable under adversarial reward contamination while argmax is not, and Candidate-Batch Normalization (CBN) makes the weight simplex invariant to the rescalings under which reward models are only ever identified. Our reference instantiation pairs DPO-LoRA blades with an uncertainty-aware pairwise tournament. Sweeping the helpfulness/honesty/harmlessness simplex, it attains the best balanced frontier of six decode-time methods (harmonic F1 0.797 vs. 0.750 for the strongest baseline, p < 10−14) with the lowest refusal rate and highest helpfulness of any arm, and reconfigures its objectives in 0.05 ms with no gradient computation. Ablating CBN costs 0.217 F1, and the metadata confirms the predicted mechanism: the lowest-variance blade retains 9% of its nominal 33% influence, and the collapse follows the predicted ordering.
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BibTeX
@misc{karmakar2026swissknife,
title = {{Swiss-Knife: A Framework for Reconfigurable Externalised Multi-Objective Alignment at Decode Time}},
author = {Agnibh Karmakar and Mayur Parvatikar and Shreyash Dhoot and Amit Dhanda and Aman Chadha and Kapil Wanaskar and Vinija Jain and Amitava Das},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.22226},
eprint = {2609.22226},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.22226},
}
RIS
TY - UNPB
TI - Swiss-Knife: A Framework for Reconfigurable Externalised Multi-Objective Alignment at Decode Time
AU - Agnibh Karmakar
AU - Mayur Parvatikar
AU - Shreyash Dhoot
AU - Amit Dhanda
AU - Aman Chadha
AU - Kapil Wanaskar
AU - Vinija Jain
AU - Amitava Das
PY - 2026
DA - 2026/09/03
PB - arXiv
DO - 10.48550/arxiv.2609.22226
UR - https://arxiv.org/abs/2609.22226
AB - Decode-time alignment methods steer a frozen language model by scoring candidate continuations with an external reward and selecting the maximiser. We argue that this shared design is a single degenerate point in a much larger space. We introduce Swiss-Knife, a framework for externalised multi-objective alignment in which the alignment specification is a first-class runtime object: hot-swappable scoring blades, a batch normaliser, a pairwise aggregation operator, and a selection rule. Six axioms characterise the admissible aggregation operators, and we prove a representation theorem: every operator satisfying them has the form R_i = ∑_j ≠ i g((μ_i − μ_j)/s(σ_i,σ_j)), a two-parameter family containing probit and logistic comparison rules and pointwise argmax as named coordinates. Within it, pairwise aggregation is Lipschitz-stable under adversarial reward contamination while argmax is not, and Candidate-Batch Normalization (CBN) makes the weight simplex invariant to the rescalings under which reward models are only ever identified. Our reference instantiation pairs DPO-LoRA blades with an uncertainty-aware pairwise tournament. Sweeping the helpfulness/honesty/harmlessness simplex, it attains the best balanced frontier of six decode-time methods (harmonic F_1 0.797 vs. 0.750 for the strongest baseline, p < 10^−14) with the lowest refusal rate and highest helpfulness of any arm, and reconfigures its objectives in 0.05 ms with no gradient computation. Ablating CBN costs 0.217 F_1, and the metadata confirms the predicted mechanism: the lowest-variance blade retains 9% of its nominal 33% influence, and the collapse follows the predicted ordering.
ER -