Learning to Formalize: Bridging Open-Ended Workloads and Fast Decision Models

Authors: Chengxuan Pei , Ziyao Wang , Teng Mei , Zhe Li

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Abstract: Decision models offer fast choices over sup- plied alternatives, while general-purpose large language models interpret open-ended tasks through flexible generation and reasoning at the cost of sequential decoding. Many work- loads expect a generative backend to determine what can be done and therefore provide no well-defined candidate list. We bridge this in- terface gap with a small candidate formalizer trained on offline teacher-generated lists. The formalizer constructs explicit, validated alter- natives from task context, and a frozen deci- sion model selects an entry. A motivating com- parison with official TypeSafe Jev-1.13.0 on 200 CALVIN-D test episodes reports 11% suc- cess with generic motions versus 46.5% with DeepSeek V4 Flash pose candidates. Sepa- rately, a six-arm privileged-state evaluation on 1,011 CALVIN-D validation annotations finds that a locally deployed decision model can achieve at most 52.6% task success rate with the fine-tuned formalizer, compared with 11.8% without it, and the end-to-end latency is kept to 2.09 s, whereas Qwen3.5-27B is 3.67 s with the exact experiment setting.

Comments:

10 pages, 2 figures, only for concept verification now

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.36595 [cs.AI]

(or

arXiv:2610.36595v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.36595

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Chengxuan Pei [ view email ]

[v1]

Tue, 27 Oct 2026 03:56:32 UTC (131 KB)

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