ISSN 3043-1921
Artificial intelligence research from method to deployed system. Editors assess the method, evaluation and evidence, then publish accepted work continuously with open access from its publication date.
Open access
Mandate Representation and Evidence Control in Switzerland, Germany and Austria
arXiv 2610.02863
A nested client–manager–AI formulation specifies separate client and manager outcomes and a constrained joint objective. Analytical constructions and simulations examine how mandate representation, clarification and evidence-bound approval affect recommendations.
A Design-Science Proposal for Tiered, Reusable Identity Assurance of Natural, Juridical, and Machine Entities
arXiv 2610.00287
Flat maximum verification charges every participant for the rarest high-risk case, and excludes those who cannot clear a bar they never needed to. This model holds the assurance state apart from the capability gate that consumes it, so identity demand follows the act and the weight of its consequences rather than mere presence.
A Hash-Anchored Distributed-Ledger Framework for Portable Identity across Jurisdictions
Digital identity stays rigid while it is bound to provider accounts, mutable handles and local wallet schemes. An accountable hash-anchor tier sits above them: an inert root anchor, unlinkable profile anchors for distinct contexts, and gate-specific assurance evaluated at a point in time.
A Trust Model for Decentralised and Compliant Distributed Settlement Infrastructure
Permissioned ledgers are commonly dismissed as centralised because admission is restricted. Separating permissioning from control distribution makes validator identity externally costly collateral: public legal identity, charter state, liability and audit exposure, with affiliation-aware voting caps and per-member collusion margins.
A Milestone-Based Real-Options Framework for the AI Valuation Uncertainty Problem
arXiv 2609.24181
Discounted cash flow, the IDW S 1 income approach and market multiples compress milestone probabilities, continuation options and risk shifts into opaque aggregate parameters. A milestone-gated real-options overlay decomposes that value into auditable components, with a Success Readiness Index deriving per-option probabilities from structured pairwise comparisons.
Requirements for AI Supported Trading Systems under Regulatory Constraints
arXiv 2609.29108
European electricity trading runs as a constrained multi-layer system in which legal design, exchange microstructure and network physics execute jointly across forward, day-ahead, intraday and balancing horizons. The paper specifies an AI-supported trading architecture with a permission gate on executable actions and fail-closed control logic under REMIT, MiFID II, MiFIR and EMIR.
A tiered multi-agent framework with DLT audit trails for financial operations in DACH
arXiv 2609.27632
Regulation is treated as an orientation layer rather than a deterministic ruleset: a matrix of regulatory intent and exposure is compiled into concrete prohibitions, obligations and runtime budgets. Evidence, decisions and reason codes bind to a permissioned DAG, so a supervisor can replay how an outcome was reached and attribute failure.
Two linked components for higher education: a role-specific multi-agent framework for institutional operations, and a decentralised verification layer for audit, credential authentication and tamper-evident records. GDPR, the EU AI Act, EQF, ECTS and ESG directives are encoded as structural constraints rather than checked after the fact.
Swiss compliant federated AI DLT network using Nash equilibrium and ESG metrics
Centralised AI infrastructure scales, and collides with latency, auditability and energy constraints. The design separates centralised training from decentralised inference and storage across five node classes, tying a size-neutral availability floor to tiered rewards for service level, ESG performance and anti-concentration.
Architecture and methodology for scalable domain adaptation from a unified core framework
Compliance in AI deployments is usually applied afterwards, through prompt engineering, rather than built into the foundation. This architecture encodes regulatory, governance and ESG requirements as an objective-under-constraints problem, so every specialised agent operates within legally admissible and auditable bounds before any domain work begins.
Standing calls
Standing calls identify current editorial priorities within the journal's full scope.
From arXiv
arXiv cs.AI
LLM-guided evolutionary search uses AI agents to improve programs for mathematical and computational problems by iteratively refining solutions and combining high-scoring methods. However, the process is often slow because the LLM generates a long textual chain of thought for each round and must repeat this...
arXiv cs.LG
User embeddings are central components of modern recommendation, personalization, and behavioral modeling systems. Though these embeddings can compactly encode rich behavioral information, they are inherently dense and opaque, limiting interpretability. To address this gap, we argue that user embeddings can be...
arXiv cs.AI
AI-Scientists arrived, but a world for them to grow in did not. Human scientists take theirs for granted - centuries of open problems and shared ideas, waiting in the scientific literature. To AI-Scientists, that world is readable but not runnable, so we build Scientist World for them: every research paper becomes...
arXiv cs.LG
ML code should run where researchers choose, not where it was first written. Today, machine learning code mainly lives in two ecosystems, PyTorch on NVIDIA GPUs and JAX on Google TPUs. Migrating repositories between them means building and testing across frameworks and hardware. This migration is (1) a practical...
arXiv cs.AI
Interpretability, AI auditing and assessments of machine consciousness increasingly rest on mechanism-level quantities: functionals of a system's causal transition law that describe how it computes, not what it outputs. Experiments see that law only through finite, partial and noisy interventions. We ask when such...
arXiv cs.LG
To interpret a direction in a language model's residual stream, we can look for text that strongly activates it. But when such text is found by scanning a corpus, it is limited to what that corpus contains. We develop Maximally-Activating Example Metamodels (MAEMs), language models which map residual stream...
arXiv cs.AI
Reproducing machine learning experiments requires reconstructing procedures from information scattered across papers, released code, and supporting artifacts. For coding agents, reproduction success can therefore depend on their ability to fill in missing details, rather than on the released artifacts alone. We ask...
arXiv cs.LG
Adversarial Training (AT) is an important method for improving model robustness. We study the dynamics of deterministic Gradient Descent (GD) in AT. Most existing works in this direction focus on the small or vanishing step sizes regime, where GD resembles a continuous-time flow. However, such step sizes can make...
arXiv cs.AI
Typed-decision models score options that the caller supplies at request time, so their accuracy depends on label wording that the model's developer does not control. We compare two releases of a 307M-parameter multilingual cross-encoder, Ru-Jev, trained on thirteen Russian-language tasks; the later release adds an...
arXiv cs.LG
On-policy distillation (OPD) trains a student on its own rollouts under a frozen teacher's per-token supervision. Does this dense signal reduce the training FLOPs needed to reach a target accuracy compared with reinforcement learning? We systematically compare vanilla reverse-KL OPD with Group Relative Policy...
arXiv cs.AI
Neural networks are likely to be among the most consequential artefacts in human history. As these systems become more capable and autonomous, their potential to cause societal harm also grows, making AI safety one of the defining challenges of our time. Yet we continue to build increasingly powerful systems...
arXiv cs.LG
A defender anticipating an attacker needs a probability distribution over the attacker's next action from information available before that action occurs. Two structures complicate this: the attacker's behavioral regime persists and can switch within an episode, and each action combines an operation with...
arXiv cs.AI
LLM-based medical assistants that follow a patient across visits need a memory that keeps the patient's clinical state correct as it changes. We examine the errors of five memory systems on longitudinal medical dialogue and find three recurring causes: the retrieved evidence gives an outdated state, includes...
arXiv cs.LG
Discovering continuous symmetries directly from differential-equation data can reveal geometric structure for model reduction and scientific machine learning without symbolic determining equations or a closed-form representation of the governing system. We formulate intrinsic Lie point-symmetry discovery as an...
arXiv cs.AI
Chat remains the default surface for many large language model (LLM) tasks, even as agentic tools such as OpenCode excel at coding and automation. Yet the model sees the current dialogue but not the workspace around it. We explore meta access---giving the assistant a structured, permissioned view of the interface...
arXiv cs.LG
Molecular representations such as fingerprints and learned embeddings play a central role in cheminformatics and small-molecule machine learning. They serve as the primary input for a diverse array of tasks, including clustering, dataset splitting, and virtual screening, yet their evaluation is often limited to...
arXiv cs.AI
Vision-Language-Action models (VLAs) enable general-purpose embodied intelligence, but their performance is brittle under distribution shift, where unanticipated failures can damage equipment or endanger people. Deploying VLAs in real-world systems therefore requires a runtime monitor that is timely, calibrated,...
arXiv cs.LG
Low-rank adaptation (LoRA) is commonly used in both supervised fine-tuning (SFT) and reinforcement learning (RL), often under similar optimizer settings. Yet, how the different dynamics of the two stages manifest in the low-rank increment that LoRA optimizes remains unclear. Tracking the singular spectrum and...
arXiv cs.AI
Coding agents are among the most economically consequential deployments of LLMs to date, making reliable evaluation increasingly important. Yet even benchmarks that are carefully validated at release can become stale. As models improve, they can discover valid solutions that existing verifiers reject, while changes...
arXiv cs.LG
Uncertainty quantification is crucial in scientific machine learning, where models inform safety-critical tasks such as flood forecasting, financial risk management, and thermal control in machines. Conformal prediction provides distribution-free coverage guarantees, but in time-dependent settings common to physics...
For authors
Please submit your manuscript, in English or German, as either a LaTeX package or a PDF. Every submission is reviewed by the editors, and every author receives a reasoned response.
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The journal publishes work by researchers, doctoral candidates and practitioners in industry and public institutions. Every manuscript is assessed on its method, evidence and contribution.
Named authors take responsibility for the manuscript and record their individual contributions.
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The journal publishes collaborative research conducted across universities, laboratories, companies and public bodies. Evidence from systems in operation is central to its scope.
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Editorial decisions are assigned by subject competence. Each manuscript selected for peer review is assessed double-blind by independent specialists. Reviewer identities remain confidential, and the responsible editor makes the final publication decision.
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Editorial standard
Every submission is assessed against six criteria, including reproductions and negative results.
Scientific integrity
Kodex Wissenschaftliche Integrität
Swissuniversities, the Swiss National Science Foundation and Innosuisse drew up a code of conduct for scientific integrity together, under the lead of the Swiss Academies of Arts and Sciences.
© 2021 Akademien der Wissenschaften Schweiz. Open-access publication under CC BY 4.0, source doi.org/10.5281/zenodo.4707584.
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