arXiv:2510.25890v3 Announce Type: replace-cross Abstract: ATLAS is a constraint-guided generation framework for structured engineering artifacts whose outputs must satisfy explicit schemas, domain rules, and audit requirements. Rather than treating a large language model as a standalone generator, ATLAS places generation inside a model-driven workflow that separates domain representation, constraint compilation, and post-generation validation. ATLAS combines three components. A metamodel-integration stage builds a typed representation of domain entities and relations; in this study, it operates over authoritative AUTOSAR meta-model assets. An Integrated Constraint Model (ICM) compiles heterogeneous requirements into two operational layers: generation-time structural constraints and post-generation semantic/logical obligations. Constraint-Guided, Validation-Backed Generation (CVG) then combines Layer~1 constrained decoding, Layer~2 backend validation, and audit-guided repair. In the AUTOSAR instantiation, these Layer~2 obligations are realized through SHACL/SMT-style checks, illustrating how the same ICM can be connected to domain-specific validation backends. We evaluate ATLAS on AUTOSAR artifact generation at both single-file and multi-file scales. In the evaluated AUTOSAR setting, ATLAS consistently produces schema-valid single-file outputs and preserves perfect file completeness and XSD validity at multi-file scale, while SHACL/SMT checks and result analysis continue to expose residual system-level defects. The empirical picture is therefore one of bounded automation: ATLAS secures structural validity and turns higher-level failures into explicit, diagnosable objects within the generation workflow.

arXiv:2603.03677v1 Announce Type: cross Abstract: Large language models (LLMs) have advanced medical dialogue systems, yet psychiatric consultation poses substantially higher demands due to subjective ambiguity and comorbidity complexity: an agent must continuously extract psychopathological cues from incomplete and inconsistent patient reports in multi-turn interactions and perform rigorous differential diagnostic reasoning. However, existing methods face two fundamental challenges. First, without criteria-grounded clinical supports, they are prone to unsupported clinical assertions when symptoms are atypical or underspecified. Second, in multi-turn interactions, they struggle to mitigate inquiry drift (off-topic or low-yield questioning) and optimize questioning strategies. To address these challenges, we propose MIND, a unified inquiry--diagnosis reinforcement learning framework for psychiatric consultation. Specifically, we build a Criteria-Grounded Psychiatric Reasoning Bank (PRB) that summarizes dialogue context into clinical retrieval states, retrieves semantically similar reference consultations, and distills reusable criteria-grounded clinical supports to guide criteria-aligned inquiry and reasoning. Building on this foundation, MIND enforces explicit clinical reasoning with rubric-based process rewards to provide fine-grained supervision over intermediate decision steps, and incorporates a value-aware trajectory rectification mechanism to jointly improve information acquisition and diagnostic decision-making across turns. Extensive experiments demonstrate that MIND consistently outperforms strong baselines in diagnostic accuracy, empathetic interaction quality, interpretability, and generalization.