This paper describes TransCore, the NewTranx submission to the WMT26 General Machine Translation Shared Task constrained track. Our system is built upon the Qwen3-8B large language model and adopts a two-stage supervised fine-tuning framework trained exclusively on publicly available data. Our approach focuses on high-quality multilingual data construction, including parallel corpus construction, knowledge-distilled corpus construction, and instruction-following data construction. For the parallel corpus, we construct a high-quality multilingual training corpus through multilingual data collection and automatic quality filtering. For knowledge distillation, we construct a KD corpus from publicly available monolingual resources using a large language model, followed by automatic quality filtering. To support the instruction-following evaluation introduced in WMT26, we further construct instruction data covering immutable entity preservation, document-level translation, and structured translation, which are incorporated into the first-stage supervised fine-tuning. Our system participates in four language directions of the WMT26 General Machine Translation Shared Task: English$\rightarrow$Indonesian, English$\rightarrow$Kazakh, English$\rightarrow$Thai, and Chinese$\rightarrow$Japanese.

LLMs are increasingly used to arbitrate among multiple candidate outputs and compress them into a single final result. The final answer may not be incorrect, yet information compression can still make it suboptimal. We define this problem as source-resolution distortion (SRD), the irreversible loss or incorrect mapping of task-relevant source-side structure in the final output during arbitration.

We model pairwise arbitration as a compression process; we then present a source-bounded SRD measurement framework that aligns the reference structure fixed before arbitration with the final output and uses six metrics to measure its preservation and incorrect mapping. To reduce SRD, we further propose A²R (Auditable Arbitration for Resolution), a reference resolution method based on a decision ledger. Motivated by anti-aliasing in signal processing, A²R extracts query-relevant source units and records the local resolution relations among them in a decision ledger before using it to constrain final generation.

Across 520 pairwise cases, compared with Checklist Prompt, A²R-Decision lowers CER from 0.23 to 0.16 and raises Prov-H from 0.45 to 0.67. Its answer-preference scores against three structured baselines range from 51% to 53%, showing that similarly preferred answers can still differ markedly in source recoverability. A 200-case three-candidate extension preserves the system ordering.