#01Oct 26, 2026
cs.CY
The Watchtower Imperative: Lifespan-Calibrated Evaluation for Frontier AI
Casey M. Williams, Jennifer Victoria Scurrell
The gap between AI capability deployment and safety assurance is structural and cannot be closed through internal evaluations alone. Analyzing documented harms (2017-2026) across the human lifespan reveals that disparate incidents --- from voice assistants endangering children to AI companions implicated in adolescent suicide and automated care denials for the elderly --- reflect a single failure: evaluating systems against an implicit adult user model. We introduce five developmental moderators structured into a Lifespan Vulnerability Matrix across six life stages. The matrix reveals a nonmonotonic risk distribution: vulnerability peaks at both ends of the lifespan, governance coverage is asymmetrical, and authority to convert model errors into real-world impact is scrutinized least. We then propose a set of lifespan design principles generalizing existing child-protective instruments, and an institution-agnostic, three-tier external evaluation framework --- comprising automated simulation, expert red teaming, and post-deployment monitoring --- indexed directly to matrix risk cells.
#02Oct 26, 2026
cs.AI
Beyond Physical Illness: Can Clinical Narratives Reveal Emerging Mental Health Risks in Chronically Ill Patients?
Sudeshna Jana, Siddhartha Laha, Manjira Sinha and 2 more
Mental-health risks such as depression, dementia, and suicidal ideation can emerge during hospitalization among chronically ill patients and may worsen without timely recognition and intervention. However, most existing mental health studies focus on patients with established psychiatric conditions rather than patients hospitalized primarily for physical illness. In this work, we investigate whether latent mental-health risk in chronically ill patients can be inferred from longitudinal clinical narratives. We construct a weakly supervised cohort around 4,000 patients from MIMIC-III using mental-health evidence extracted from discharge summaries. A hierarchical state-space framework derives mental-health representations from day-wise clinical narratives and models their temporal evolution for risk estimation throughout hospitalization. Preliminary results demonstrate improved binary risk prediction using the proposed representations and show distinct model derived risk trajectories between risk and no-risk cohorts, providing a foundation for studying emerging mental-health risks in chronically ill patients.
#03Oct 26, 2026
cs.CR
Atomic Security Concerns: Towards a Foundational Language for Security Knowledge Representation
Hanane Houmani
Cybersecurity governance, risk management, compliance, and security architecture rely on numerous concepts, including security properties, domains, controls, risks, implementation measures, capabilities, and compliance requirements. Although these concepts are widely used, they are often represented through heterogeneous abstractions and vocabularies, making relationships among them difficult to express consistently and leaving much of the associated reasoning dependent on expert knowledge. This paper introduces the concepts of Security Concerns and Atomic Security Concerns (ASCs) and explores the hypothesis that cybersecurity knowledge may be represented through a finite set of foundational concerns. Inspired by the role of atoms in chemistry, the proposed approach views cybersecurity concepts as compositions of more fundamental concerns rather than as independent abstractions. Hence, Atomic Security Concerns could be viewed as a common abstraction layer for the representation of cybersecurity concepts, independently of their domain, nature, or level of granularity. Building upon this perspective, the paper explores how Atomic Security Concerns may provide the vocabulary of a cybersecurity language and how cybersecurity concepts may be represented as concern sets supporting explicit relationships among concepts. The contribution of this work is intentionally conceptual: it introduces a framework and research hypothesis for investigating the foundations of a common language for cybersecurity knowledge representation, reasoning, and future formalization.
#04Oct 26, 2026
cs.LG
Can JEV Support Clinical Trial Criteria Assessment?
Mengxian He, Ziyue Gao, Chun Li and 4 more
Clinical trial prescreening requires distinguishing unmet conditions from insufficient evidence in patient records. Given patient records, criterion questions, and predefined answer options, we retrospectively compared JEV, a non-generative decision model returning native option probabilities, with schema-constrained Qwen Flash across 2,048 test patient-condition pairs from TrialGPT and question-answer datasets derived from MIMIC-III and MIMIC-IV, all evaluated under strict Zero Data Retention (ZDR) policies to ensure clinical data privacy. Both models assessed conditions individually or in batches sharing the same patient context, with performance measured against reference labels. In batch mode, JEV and Qwen achieved similar macro-F1 scores on TrialGPT (0.652 vs. 0.660) and MIMIC-IV (0.807 vs. 0.814), whereas JEV substantially outperformed Qwen on MIMIC-III (0.768 vs. 0.534; $Δ$ = +0.234, 95% CI: 0.187-0.281). This advantage largely reflected higher recall for conditions labeled as having insufficient information (56.3% vs. 9.9%). Across all three datasets, JEV also had lower observed median job latency (1.18-1.40 vs. 2.80-8.90 s) and 53%-74% lower estimated cost per condition. These findings position JEV as an initial assessment component that provides criterion-level judgments and flags potential information gaps for review. JEV's observed errors in interpreting clinical evidence underscore the need to verify its outputs against patient records before they inform eligibility decisions.
#05Oct 26, 2026
cs.CL
Understanding Jev-Style System One Models: Does Structure Reduce Selection Bias?
Mingyang Song, Mao Zheng, Zhiyuan Ma and 1 more
Selection bias in large language models (LLMs) can persist despite instructions to ignore presentation order or prompts that elicit reasoning. Recent efforts to reproduce Jev-style System One behavior have produced a growing family of models that predict probabilities directly over supplied candidates. Yet systematic comparisons of selection bias across Jev versions, Jev reproductions, and generative LLMs remain limited in multi-option tasks. This leaves a central question: does Jev-style decision structure reduce selection bias? We address this question with a simple test that shuffles options while keeping question and candidate content fixed. Across two Jev versions, twelve Jev reproductions, and six generative LLMs, we trace individual answers through eight presentations and compare them with fixed-order repetition. On our primary collection of 1,700 questions from two benchmarks, Jev's accuracy varies by less than two percentage points, yet its cumulative failure set expands by 27--29\% relative to the original order, with first failures continuing into the final presentation. This loss of initial successes is substantially larger than under fixed-order repetition, even for highly repeatable Jev reproductions. Nor does Jev show a general advantage over the LLM baselines in preserving correct choices. Aggregation offers limited protection: voting yields modest gains for Jev, while even unanimous answers are frequently wrong. These results expose a gap between repeatable decisions and decisions that remain correct across equivalent presentations, showing why stable accuracy alone cannot establish resistance to selection bias.