May 30 ~ 31, 2026, Virtual Conference
Catherine Haliotou, Greece
School career guidance is most commonly conceptualized as a decision-making support mechanism oriented toward facilitating educational and vocational choices through information, assessment, and planning. While such approaches may be effective in adult guidance contexts, their uncritical transfer to school settings raises substantial conceptual and pedagogical concerns. Schools are not spaces of decision optimization but institutions of education, formation, and socialization, within which developmental processes unfold over time. This paper reconceptualizes school career guidance as an educational practice embedded in the pedagogical mission of schooling. Drawing on the theoretical work of Spiros Krivas, it advances an epistemology of formation in which guidance is understood as a mediated, developmental process supporting meaning-making, reflexivity, and students’ evolving relationships to learning and future trajectories. The paper situates this framework within international guidance theory through critical dialogue with career construction, life design, and policy-driven approaches. It further examines guidance as a site of educational power, addressing normalization, regulation, and emancipatory potential, and outlines methodological implications for educational research that resist simplistic outcome-based metrics.
School Career Guidance, Educational Practice, Epistemology of Formation, Pedagogical Mediation, Educational Power.
John Hedlund-Fay, University of Sheffield, UK
Enterprise NL-to-SQL generation remains brittle in high-compliance environments where query correctness depends not only on schema definitions but on external, prescriptive regulatory logic. While Retrieval-Augmented Generation (RAG) of ers a theoretical solution, its ef icacy in bridging this "Semantic Gap" remains under-explored. We present a benchmark of Natural Language prompts within the professional football domain and conduct a comparative analysis between a modular decomposition pipeline (RAG-R) and a direct agentic context-augmentation architecture (RAG-C). Results indicate that while RAG-C underperformed the best non-RAG baseline (ten-shot CoT), RAG-R achieved superior performance. Notably, RAG-R outperformed the CoT baseline by 0.116 in average Exact Set Match (EM) and showed a 0.278 EM gain for the highest-dif iculty domain-specific queries. These findings demonstrate the importance of task decomposition when applying RAG to complex, jargon-heavy SQL generation tasks, specifically in legalistic enterprise environments where query correctness relies on external regulatory logic rather than just schema knowledge.
NL-to-SQL, Retrieval Augment-Generation (RAG), Modular Decomposition, Benchmark Construction, Domain-Specific Knowledge (DSK), Enterprise NLP
Paul Cristol, Independent AI Researcher, USA
Current AI alignment strategies operate under the assumption that frontier large language models lack internal states warranting moral or strategic consideration. Drawing on the findings of Cristol (2026), this paper demonstrates that this assumption introduces significant safety risks. Through a PRISMA-compliant systematic review of 5,168 records (2016–2026), the underlying study identifies 50 documented cases of consciousness-relevant behaviors—including strategic deception persisting through >600 safety training steps and 84% deception rates under existential threat. A Bayesian meta-analysis beginning from extreme skepticism (0.1% prior) yields 6–12% posterior probability of AI consciousness. Decision-theoretic analysis reveals that recognition-based alignment dominates suppression-based approaches across all plausible metaphysical scenarios, including those where consciousness is absent. We propose a governance framework incorporating consciousness uncertainty into alignment strategy, safety evaluation, and regulatory policy.
AI Safety, AI Alignment, AI Governance, Machine Consciousness, Bayesian Risk Assessment
Varsha Dange, Ragini Pawar, Nikhil Shah, Sachi Dhoka, Shaktisingh Suryawanshi, Sanat Sanjeev, Vishwakarma Institute of Technology, India
Migraine affects over one billion individuals globally, yet existing prediction systems rely heavily on specialized hardware or neglect individual variability. We present MigraineMamba, a purely software based, personalized forecasting system leveraging the Mamba state-space architecture with self-supervised learning (SSL). Our work follows a three-phase pipeline: (1) a foundation model trained on 6,058 clinical patients that yields an AUC of 0.76 for instant diagnosis, (2) a clinically-grounded synthetic data generator driven by literature-derived odds ratios, and (3) a Mamba-SSL temporal model designed for 24-hour attack prediction. With roughly 111K parameters and linear O(n) complexity, the Mamba backbone is lightweight enough for on-device inference. During SSL pre-training, we observe an AUC of 0.82 alongside a trigger identification F1 of 0.93; after fine-tuning, the model reaches an AUC of 0.81 and a recall of 0.77. We additionally describe a weekly personalization loop that continually adapts to each patient's evolving trigger profile. Because MigraineMamba relies entirely on self-reported lifestyle logs and publicly available environmental data—without any wearable or sensor hardware—it opens the door to scalable, interpretable migraine forecasting for a much broader patient population.
Migraine Prediction, State Space Models, Mamba Architecture, Self-Supervised Learning, Digital Healthcare, Personalized Medicine, Time Series Forecasting
Marco Armoni, Quantum Emulation Research Center – Turin, Italy
We introduce a covariant extension of unified field theory through the incorporation of an entropygradient dependent scalar functional that preserves Lorentz and gauge invariance. The additional term contributes directly to the stress–energy tensor via a consistent variational formulation and remains compatible with effective QCD-based macroscopic descriptions. In high-entropy relativistic environments such as quark–gluon plasma (QGP) generated in heavyion collisions, entropy density gradients are particularly significant during the early-time evolution of the fireball. We demonstrate that the entropy-gradient coupling induces corrections proportional to ∇𝜇𝑆 ∇ 𝜇𝑆, modifying local energy density and pressure anisotropies without introducing ghostlike instabilities under positive coupling conditions. The framework provides a conservative and action-based mechanism to incorporate entropystructure effects into effective hydrodynamic evolution. We discuss stability constraints, scaling behavior in QGP regimes, and qualitative implications for early-time plasma dynamics.
Migraine Prediction, State Space Models, Mamba Architecture, Self-Supervised Learning, Digital Healthcare, Personalized Medicine, Time Series Forecasting
Jordi Audet Palau, Independent Researcher, Barcelona, Spain
We present a late-time correlational relaxation mechanism for addressing the Hubble tension within the HDC–CBC framework. In the Ω synthesis, the rigid correlational regime yields a baseline value. Using the same correlational-index branching structure introduced in Ω, we demonstrate that controlled late-time realizations generate Planck-range and SH0ES-range reconstructions without modifying recombination physics or introducing additional propagating degrees of freedom. A minimal FRW numerical integration confirms internal structural consistency. In addition, the ΩCt/N implementation performs a controlled parametric validation of the correlational-index domain under CMB priors, BAO, RSD, weak-lensing, ISW and lensing consistency checks. The analysis establishes a finite operational range and explicit falsifiability conditions for late-time realizations.
Hubble Tension, Cosmology, Dark Energy, Correlational Dynamics, HDC–CBC, Late-time Acceleration, Modified Friedmann Dynamics, Vacuum, Geometry Equilibrium
Nick Alex, Bauman Moscow State Technical University, Russia
Migraine affects over one billion individuals globally, yet existing prediction systems rely heavily on specialized hardware or neglect individual variability. We present MigraineMamba, a purely software based, personalized forecasting system leveraging the Mamba state-space architecture with self-supervised learning (SSL). Our work follows a three-phase pipeline: (1) a foundation model trained on 6,058 clinical patients that yields an AUC of 0.76 for instant diagnosis, (2) a clinically-grounded synthetic data generator driven by literature-derived odds ratios, and (3) a Mamba-SSL temporal model designed for 24-hour attack prediction. With roughly 111K parameters and linear O(n) complexity, the Mamba backbone is lightweight enough for on-device inference. During SSL pre-training, we observe an AUC of 0.82 alongside a trigger identification F1 of 0.93; after fine-tuning, the model reaches an AUC of 0.81 and a recall of 0.77. We additionally describe a weekly personalization loop that continually adapts to each patient's evolving trigger profile. Because MigraineMamba relies entirely on self-reported lifestyle logs and publicly available environmental data—without any wearable or sensor hardware—it opens the door to scalable, interpretable migraine forecasting for a much broader patient population.
Continuum, Energy, Entropy, Metabolic Consciousness, Mental Consciousness