Inverting the Shield: Systematically Generating Safety Tests from Policy Specifications

By: Xiaoyue Lu · Xianglin Yang · Haijun Liu · Jiahao Liu · Kuntai Cai · Yan Xiao · Jin Song Dong

26 May 2026 at 12:00

arXiv:2605.24883v1 Announce Type: new Abstract: The widespread integration of Large Language Models (LLMs) necessitates rigorous and systematic safety evaluation. Existing paradigms either rely on constructed benchmarks to assess safety from predefined perspectives, or employ dynamic red-teaming to probe potential vulnerabilities. While effective, these approaches face challenges, as they depend heavily on expert domain knowledge, offer limited systematic guarantees, and are vulnerable to rapid obsolescence. To address these limitations, we introduce a novel framework POLARIS that brings the rigor of specification-based software testing to AI safety. POLARIS first compiles unstructured natural-language policies into First-Order Logic (FOL) representations, establishing a traceable link between high-level rules and concrete test cases. This formalization enables the construction of a Semantic Policy Graph, where complex policy violation scenarios are encoded as traversable paths. By systematically exploring this graph, POLARIS uncovers compositional violation patterns, which are then instantiated into executable natural-language test queries, enabling coverage-driven and reproducible safety testing. Experiments demonstrate that POLARIS achieves higher policy coverage and attack success counts compared to established baselines. Crucially, by bridging formal methods and AI safety, POLARIS provides a principled, automated approach to ensuring LLMs adhere to safety-critical policies with verifiable traceability. We release our code at https://github.com/huac-lxy/POLARIS.

Uncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis

cs.AI, q-bio.NC updates on arXiv.org

By: Xiaoyang Fan · Yufan Cai · Zhe Hou · Jin Song Dong

26 May 2026 at 12:00

arXiv:2605.25566v1 Announce Type: new Abstract: Clinical decision-making requires reasoning over incomplete, imprecise, and linguistically expressed patient narratives. While large language models (LLMs) excel at extracting latent information from natural language, they lack the verifiability and interpretability essential for trustworthy medical AI. We propose a neuro-symbolic reasoning framework that aligns LLMs with formal logic to enable explainable and formally verifiable medical diagnosis. Patient descriptions and clinical guidelines are embedded into a neural knowledge base, where LLMs extract structured medical entities, temporal relations, and fuzzy symptom patterns, which are decoded into a symbolic knowledge base expressed in fuzzy logic and declarative rules. We perform two-stage reasoning: (1) inductive symbolic generalization to capture diagnostic patterns from encoded narratives, and (2) inference verification via a logic programming engine to derive and validate diagnoses consistent with clinical standards. Each symptom is treated as a fuzzy predicate with probabilistic weights, and inference paths are auditable, adjustable, and compatible with physician feedback. Unlike purely statistical methods, our system supports iterative refinement: misalignment between LLM-generated diagnoses and ground truth can be traced, explained, and corrected through formal rules. By combining logic-based transparency, LLM adaptability, and probabilistic robustness, the framework enables human-aligned healthcare inference with strong generalization and verifiable, step-by-step reasoning chains. We validate our framework on public benchmarks, demonstrating effective reconciliation of symbolic reasoning and LLMs with real-world clinical narratives. Results show performance comparable to state-of-the-art LLMs, while additionally providing interpretable reasoning paths and formally verifiable diagnostic conclusions.

Recent advances in biosensing platforms utilizing exosomal biomarker profiling for cancer diagnosis

MRD

By: Sojin Song · Sujin Hyung · Jeeyun Lee · Hong Nam Kim · Nakwon Choi

20 May 2026 at 18:00

Biosens Bioelectron. 2026 May 15;309:118809. doi: 10.1016/j.bios.2026.118809. Online ahead of print.

ABSTRACT

Tumor-derived exosomes carry multidimensional molecular cargo, including surface proteins, microRNAs, and lipids that encode tumor identity and disease dynamics. These features support their application as biomarkers for liquid biopsy-based cancer diagnostics. Circulating tumor DNA undergoes rapid nuclease-mediated degradation, whereas exosomes retain stable molecular information that reflects the proteomic, transcriptomic, and metabolic states of parent tumor cells. However, clinical translation of exosome-based sensing remains limited by variability in isolation, biological heterogeneity, and the analytical difficulty of detecting low-abundance biomarkers in clinical samples. In this review, we examine cancer-specific exosomal signatures across breast, lung, colorectal, and gastric cancers and evaluate biosensing platforms for exosomal biomarker profiling. We integrate engineering principles, clinical performance metrics, and AI-assisted analysis across complementary biosensing modalities to establish a cross-platform analytical framework. We compare optical platforms based on surface plasmon resonance, localized surface plasmon resonance, and surface-enhanced Raman scattering with photoluminescence- and electrochemical-based platforms in terms of sensitivity, clinical compatibility, and translational potential. Furthermore, we examine artificial intelligence (AI)-assisted biosensing frameworks, including classical machine learning classifiers, deep convolutional networks, ensemble models, explainable AI methods, and large language model interfaces. We evaluate how each framework addresses high-dimensional spectral complexity, nonlinear relationships among signals, and inter-patient variability in exosomal data. Finally, we identify remaining challenges, such as the lack of standardized isolation protocols and the absence of large-scale clinical validation. We further highlight minimal residual disease monitoring and early-stage cancer detection as important and underexplored directions for AI-integrated exosomal biosensing in precision oncology.

PMID:42161118 | DOI:10.1016/j.bios.2026.118809

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