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
Recent advances in robot learning have driven rapid progress in humanoid control, enabling increasingly agile behaviors in the real world. However, humanoid control networks are typically obtained through conventional training procedures, while comparatively little attention has been given to directly modeling the...
arXiv cs.LG
Time-series language models typically align numerical signals with language using labels, captions, or question-answer pairs. Although such supervision encourages linguistic representations, it does not explicitly require them to preserve information about how the underlying time-series signal evolves. We introduce...
arXiv cs.AI
Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and intricate coupling between heterogeneous modules. In this paper, we propose HiRe-MoMa (Hierarchical Residual Mobile Manipulation), a...
arXiv cs.LG
Post-training reasoning LLMs often requires making a tradeoff between costly supervision and compute inefficiency: supervised finetuning typically requires reasoning traces, which is data inefficient since in many applications one only has prompt-answer tuples and lacks the groundtruth chain-of-thought (CoT)...
arXiv cs.AI
Training language agents is bottlenecked by the high cost of long-horizon rollouts, yet standard agentic reinforcement learning (RL) learns only from a terminal scalar reward, leaving the supervision in intermediate steps largely unused. Prior work exploits this supervision through value targets that estimate...
arXiv cs.LG
Protein function annotation operates over an expanding label space, making class-incremental learning (CIL) a natural framework for incorporating newly discovered functions without full retraining. Yet existing CIL methods have been developed largely in vision and have not been systematically evaluated over frozen...
arXiv cs.AI
Scaling up transformers has produced models with broad capabilities, deployed across heterogeneous hardware and fluctuating workloads. Yet, model families offer only a few fixed sizes (e.g., S, B, L), forcing users to commit to a model based on hardware and latency constraints. This rigidity is often suboptimal...
arXiv cs.LG
High-throughput perturbation screens increasingly use machine-learning models to predict the effects of gene combinations that were never measured. A difficult case arises when both genes in a test pair were seen individually during training, but their combination was not. In this setting, standard OOD methods...
arXiv cs.AI
An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt. An agent's bid is based on its expected value for the prompt, which is the expectation of the reward for its response minus its inference costs. First-price auctions (FPAs), in which the highest bidder pays its...
arXiv cs.LG
Token-selection methods for on-policy distillation (OPD) prioritize student-visited positions using diverse signals, including uncertainty, teacher-student disagreement, and mismatch direction. We instead derive token importance from the distillation objective itself: how much can each position's loss decrease...
arXiv cs.AI
We introduce Continuum Attention (CA), a causal Transformer attention operator that replaces discrete key and value lookup with integration over a continuous, piecewise linear memory curve. Each query induces an exponential relevance density along the curve. Integrating the density and interpolated values yields...
arXiv cs.LG
The Practical Molecular Optimization (PMO) benchmark standardized evaluation in molecular optimization, but it is built on simple property-based oracles that do not capture structure-based drug design. As the field has moved to docking-based objectives, evaluation practice has become unstandardized, and different...
arXiv cs.AI
Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their visually rich worlds and diverse mechanics, remain challenging and offer a demanding next step toward general digital control. Current game-agent learning pipelines...
arXiv cs.LG
Current scaling law formulations suggest that increasing the number of unique samples in a dataset improves model performance, while repeated exposure to the same samples provides diminishing value. In this work, we examine this assumption for image self-distillation models. Through systematic experiments across...
arXiv cs.AI
Post-training binds the competence of an LLM agent to the interface it was trained on. Semantics-preserving perturbations of that interface, such as renaming an action or changing the notation, leave the underlying task unchanged yet make the agent worse at it. Scoring each sample independently means interface...
arXiv cs.LG
Real measurements are too limited to train an accurate indoor pathloss predictor that works across a wide range of buildings. Large-scale synthetic pretraining offers a way forward, but real building layouts are also scarce, and accurate ray-tracing simulators are slow, expensive, and often proprietary. We address...
arXiv cs.AI
We introduce a novel method for verifying Lean formalizations that goes beyond LLM-judged faithfulness. Unlike most verification methods, which rely on LLMs or trained models, our method combines LLM-based semantic review with mechanical numerical and vacuity checks, providing checkable feedback and detecting...
arXiv cs.LG
Regret matching (and its variants) and counterfactual regret minimization are central algorithms in practice for normal-form and extensive-form game solving. Yet these have historically lacked the stronger theoretical convergence guarantees available to optimistic and extra-gradient methods. Recent work introduced...
arXiv cs.AI
On-policy distillation (OPD) trains a student model on its own rollouts with token-level supervision from a teacher, assuming that the teacher can reach a correct answer from any prefix the student writes. We call this property the recoverability of the teacher and show it fails on incorrect rollouts: the teacher...
arXiv cs.LG
Low-rank adaptation (LoRA) applies every rank-one component of its update to every token, and mixture-of-experts variants replace this with a learned router that activates a fixed number of components. We argue that the number of components a token needs is a detection question with a classical answer. A token's...
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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.
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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.
Recent Daily Papers
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Distilling capable language-model agents into smaller models requires aligning the student's behavior with the teacher's. This raises a fundamental question: at which states and at what granularity should this alignment be performed? Offline imitation provides dense but token-specific supervision and can suffer...
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Generated images often have coherent layouts and visual content, yet local errors in small text and complex characters still limit their readability and practical use. Scene-text refinement requires accuracy, preservation, and efficiency. Existing methods based on variational autoencoders (VAEs) can lose fine...
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Effective social interaction requires more than fluent responses: agents must reach plans that fit the participants' needs and circumstances. Existing evaluations of social agents mainly rely on LLM judges that rate the conversation, without checking the resulting plan. We introduce HICO-Bench, a benchmark of 180...
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Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for sequence synthesis rather than edit prediction. This setting is poorly matched to modern AI-assisted IDEs, where edit prediction must infer the next code change from...
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Existing robot policy memories typically evolve with observation sequences, implicitly treating each update as the same amount of physical time. Yet identical observation histories can span different durations and require different actions, exposing physical time as a missing dimension of policy memory. We...
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How can we build an agent that thinks beyond human language, yet still acts through it in the external environment? Modern LLM agents still reason and interact through text, while latent reasoning offers an efficient internal medium but lacks native tool access and reasoning transparency. We bridge these two...
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Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their visually rich worlds and diverse mechanics, remain challenging and offer a demanding next step toward general digital control. Current game-agent learning pipelines...
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Vision-language-action (VLA) models have rapidly advanced generalist robot manipulation by harnessing the semantic knowledge of pretrained vision-language backbones. However, their visual representations are typically grounded in 2D image coordinates rather than calibrated camera geometry. This limitation is...
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Verifiable instruction-following benchmarks emphasize output-constraint satisfaction but can overlook whether responses meaningfully answer the underlying request, creating an evaluation blind spot. We show that frontier models can receive full credit for responses with little substantive answer content. Using the...
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Agents driven by large language models are increasingly deployed on long-horizon tasks, where they use tools and modify real workspaces. How well they perform depends as much on the harness around the model as on the model itself. Existing benchmarks measure downstream task success under a fixed harness, leaving...
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Benchmarks are essential for evaluating and improving large language models (LLMs). Yet many widely used benchmarks are redundant, making evaluation unnecessarily expensive. Benchmark compression lowers this cost by estimating full-benchmark scores and rankings from a partial evaluation. Research in this area has...
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Foundation models are driving a paradigm shift in robotic intelligence, moving beyond low-level action prediction to goal-directed orchestration of data collection, model training, task planning, and real-world execution. We characterize this emerging paradigm as an agentic robots, in which agents reason over human...
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Self-distillation in agentic reinforcement learning has produced a growing number of methods. However, existing evaluations often reuse published baseline scores under differing experimental conditions, provide few direct comparisons among contemporaneous methods, and give limited attention to training stability...
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Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requires weighting trajectories of different lengths and updating the policy while rollouts are still being collected. We examine a single-rollout asynchronous recipe...
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Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities. However, enabling multimodal agents to continually acquire useful information from complex, heterogeneous multimodal data remains a...
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How well can a language model turn a large, incomplete and partly contradictory body of documents into a plan when no single correct answer exists? We introduce Mission Analysis (MA)-Bench, built on mission analysis, the first step of military operational planning, in which a staff reads the full situation and...
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Multi-teacher on-policy distillation (MOPD) extends on-policy distillation (OPD) by routing domain-specific data to specialized teachers, integrating their capabilities through dense token-level supervision on student-generated trajectories. However, domain routing only distinguishes the supervision sources for...
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