Continual pre-training (CPT) adapts a pretrained language model to a target domain, but it often degrades general capabilities even when general-data replay is used to mitigate forgetting. We revisit this failure through replay supervision: prior CPT replay mainly tunes data mixtures and token budgets while applying one-hot next-token prediction (NTP) uniformly to domain and replay tokens. This target is natural for learning domain text, but on replay tokens it introduces target variance: it is a noisy, corpus-dependent proxy for preserving the frozen base model’s conditional behavior. We study self-distilled replay: domain tokens keep NTP, while replay tokens are trained to match the frozen base model distribution. Diagnostics show that this target substitution lowers target error, produces more directionally consistent replay gradients, and reduces base-behavior drift. Empirically, self-distilled replay improves the SmolLM3 domain–general trade-off over matched NTP replay across diverse token budgets and replay ratios. It also remains effective for Qwen3 models whose pretraining mixtures are unavailable: under the same mismatched, non-source replay setting, it improves both general score and PubMedQA in all eight matched comparisons. Overall, these results suggest supervising CPT replay to preserve base behavior, rather than fit empirical next tokens, as a way to reduce target variance.

Payment operations are a critical financial infrastructure, but the value of large language models in this domain remains unclear because payment rules change quickly, evidence is fragmented, and decisions depend on transaction state, participant role, region, and payment rail. Existing benchmarks do not isolate whether failures come from missing payment-rule knowledge, poor use of supplied evidence, or brittleness under imperfect harness inputs. We introduce BenchCompass, a payment-domain benchmark whose construction pipeline builds scenario-grounded tasks from typed evidence packs, applies LLM-based quality checks, creates task-input attack variants, and reserves final item admission for domain experts. The release contains an expert-reviewed Pro benchmark covering payment knowledge, context-grounded scenario reasoning, and Attacked Open robustness, plus a lower-assurance Normal pool for inspection and future curation. Across 16 model variants, BenchCompass shows qualitatively different failure modes: missing parametric payment knowledge, incomplete reasoning over supplied rules, and failure to reject plausible but invalid workflows. The benchmark remains unsaturated: the best frontier model reaches 89.6% on Open Context-Grounded Reasoning and 81.7% under attacked inputs, while a representative 32B open-weight model reaches 69.8% and 42.6%. The benchmark, data, and code are publicly available at https://github.com/ant-intl/BenchCompass.

Traditional industrial agents rely on modular pipelines, including Router, Retriever, Planner, Executor, Responder, Reviewer and so on, which inevitably fracture into a labyrinth of ad-hoc patches, leading to cascading errors and high latency. We propose OneModel, an applicable paradigm shift from external workflows to internalized knowledge representation. Unlike modular systems that slice fluid user intents into static steps, OneModel consolidates complex business logic and SOPs directly into the model’s parameters.Through Continual Pre-training (CPT) and logic-compilation SFT, we transform fragmented business rules into the model’s intuitive reasoning within a unified attention space. Deployed in our global financial service system, OneModel effectively breaks the impossible triangle of latency, accuracy, and complexity. Online A/B testing demonstrates end-to-end latency reduction of more than 50% (18.7s → 8s) while the Intelligent Resolution Rate (IRR) jumps from 64.3% to 83.3%. The results demonstrate our paradigm OneModel effectively replaces brittle engineering logic with internalized cognitive intuition, offering a scalable and future-proof blueprint for transitioning industrial agents from complex, error-prone workflow to unified model architectures.