[
    {
        "id": "osp-19805",
        "type": "article-journal",
        "title": "Permutation-Robust Decision Modeling with Candidate-Independent Block-Causal Attention",
        "author": [
            {
                "family": "Amit",
                "given": "Guy"
            }
        ],
        "URL": "https://omanscience.com/en/articles/permutation-robust-decision-modeling-with-candidate-independent-block-causal-attention",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Decision models often score a variable-sized set of candidate actions encoded in a single sequence. This setting is increasingly relevant for System 1 components inside generative systems, where candidates may be proposed or ordered differently across runs. Standard causal cross-encoding is expressive, but it can make a candidate's score depend on serialization order rather than on the underlying decision problem. We introduce candidate-independent block-causal attention, which preserves causal computation within the shared context and each candidate while blocking cross-candidate information flow and resetting candidate positions. We compare this architecture with standard causal attention and complementary invariant baselines across Gemma 3 1B, Qwen3 1.7B, and Qwen3 4B backbones. Candidate-independent attention consistently reduces permutation sensitivity while retaining competitive decision quality; ablations indicate that candidate isolation is the primary source of the effect, with position resetting completing the intended symmetry. A larger Qwen3-4B study further examines the behavior of the proposed architecture with substantially more training data. Code is available at the \\href{https://github.com/guyAmit/ci-decision-models}{\\textcolor{blue}{project repository}}, and the \\href{https://huggingface.co/Guy-Amit/qwen3-4b-ci-decision-4096-poc}{\\textcolor{blue}{Qwen3-4B model artifact}} is available on Hugging Face."
    }
]