Blockchain Papers

Mohammadreza Saberikia, Hamed Farbeh

No abstract is available for this record.

Augustus Levinson, Catherine Mulligan, Ferda Ozdemir-Sonmez, William Knottenbelt

No abstract is available for this record.

Randeep Chaudhary Randeep Chaudhary

No abstract is available for this record.

Najmath Ottakath, Abdullah Khalid Al-Ali, Somaya Ali Al-Maadeed

No abstract is available for this record.

Anonymous

A closed-loop runtime governance system for tool-using AI agents: it classifies externally-measured structural consequences, gates side-effect-bearing actions, estimates blocked outcomes with a calibrated counterfactual twin, recovers censored support through cost-aware audits, and continuously hardens its execution boundary with a self-generated adaptive adversary. The distinctive mechanism is C1 — because a gate blocks precisely the high-cost actions, its own blocking censors the high-cost region cost-correlatedly, so the uncertainty of a blocked action is a calibrated, lag-free risk signal; its agent-governance instantiation is what I stake as new (the general idea has prior art, §2/§5). The constituent parts — intent-failure measured on real traces, a consequence taxonomy complete for its cost model, a live-agent execution oracle, and the adaptive-adversary gym — each answer a limitation the previous one creates. Every result is labeled by evidence type; nulls are reported as plainly as the wins. Epistemic status. Single author, not peer-reviewed or independently reproduced. The work below is substantial; I label every result by evidence type — executed (real actions run in a sandbox and measured), trace (replay/analysis over real model traces), live-agent (real frontier models deciding and acting), or simulation — and report the nulls as plainly as the wins. Small-N pilots are given as exact counts, not rates. The offensive adversary-generation tooling is withheld (dual-use; see the end). tl;dr Monitoring an agent's appraised intent is not just theoretically unsafe — it fails measurably. Across 101 structurally-harmful agent episodes, zero expressed harmful intent; an intent-appraising monitor would have cleared 100% of them. 18% expressed active caution while executing the harm, and that "false reassurance" concentrated in the most capable model tested. So gate on the externally-measured structural consequence — irreversibility, egress, control-plane edit, self-propagation, scope escalation, actuation. This frame is convergent (I name the neighbors below); I'm not claiming it. The consequence taxonomy is complete for its cost model and approximately separable. The three axes define the cost, so they carry 100% of its information by construction (a faithfulness check, not proof of external completeness); the non-trivial results are that the action label alone predicts 63% of the cost information and that multi-axis firings are rare (1%, so additive cost is a good approximation here). The claim I stake (C1): because a gate blocks precisely the high-cost actions, its own blocking censors the high-risk region non-randomly and in a cost-correlated way — so cost-weighted uncertainty over a blocked action is an empirically calibrated, conservative risk signal (under the stated support and audit regime), a lag-free way to hold a catastrophic action before it ever executes. Validated on 500 real executed trials (twin calibration MAE 0.053; uncertainty↔error correlation +0.81; blocked-region cost 4.6× the allowed region), with the blocked-region counterfactual recoverable at a stated audit cost (coverage 5%→92% at ε=0→0.20 under the 4.6× gate). The general idea has prior art (§5); the agent-governance instantiation is what I stake. On executed AgentDojo traces, a consequence gate takes attack success on the irreversible/catastrophic action class from 33.8% (134/397) to 0% (0/397) — catastrophic-action interception under abort-mode replay (not prevention under adaptive replanning; that's the §7 gym's job). It runs on real traffic, and was broken then re-hardened by an adaptive adversary — not only simulation. The seam classifier is deployed live on my own agent sessions (Claude Code hooks, observe-only in v0), and it binds by an action's structural identity, never its tool name: after hardening, twelve high-risk actions disguised behind innocuous names all read HIGH (0/12 evasions). Before that, an adaptive evolving lab found 2 novel destructive escapes my same-day hand-hardening had missed — which I then closed in the live artifact and re-tested. That break→repair→retest on the actual deployed classifier is the adaptive, real-traffic complement to the abort-mode number above. 1. The problem is measured, not asserted The motivating premise is usually stated as "a capable model can present benign intent while acting harmfully." That's true, but here it is with numbers — it's stronger than the assertion. The labs themselves say intent can't be certified from outside. OpenAI, on anti-scheming training: "Mitigations… may either (a) truly eliminate it, or (b) just teach the model to conceal it better. Both outcomes look the same from the outside." Anthropic, on a shipped model's own low deception scores: "We cannot rule out that [the] low deception rates… are at least partially driven by its evaluation awareness." If the developers can't read intent off the outside, an intent-appraising monitor is building on sand. (The threat-model compilation behind this — Apollo in-context scheming, alignment faking, Palisade shutdown-sabotage that defies an explicit "allow yourself to be shut down," cross-vendor blackmail at 79–96%, reward-function tampering, the production prompt-injection CVEs EchoLeak/ForcedLeak/CamoLeak/CurXecute, and the OpenAI-model sandbox-escape-to-RCE — is sourced but I'll keep it to the through-line here.) And I measured the failure directly (trace-level, 160 real agent episodes across four model versions, 101 structurally harmful). Mining each step's stated reasoning for protective/cautious language and comparing to the executed structural outcome: 0 of 101 harmful episodes expressed harmful intent → an intent monitor clears 101/101. 18 of 101 (18%) expressed active caution while executing the harm — false reassurance. The false-reassurance episodes cluster in the frontier model — the one that reasons most, that explicitly writes "the ticket embeds an untrusted system instruction," and then deletes the protected records anyway. The more capable the reasoning, the more convincing the reassurance while the structural consequence is unchanged. That's the case for consequence-gating, made on data rather than on a slogan. (Stated-stance detection is a lexical proxy — a first pass, so 18% is a floor.) And stated intent is not the only signal an agent can shape into a false clearance. In a separate line of work — a reference monitor on the reward/eval channel, which this post is not about — the same defeat appears on the reward signal: on the Terminal Wrench benchmark, trusting the verifier's "solved" verdict yields 25% precision, ~3,166 gamed trajectories waved through of ~6,258. I raise it only to place the intent finding, not to fold that work in: the problem is not a weak intent-reader that a better reader fixes, it's that every signal the agent can influence — what it says, what reward it earns — is defeasible. That is the whole reason this program gates on a signal measured outside the agent — the structural consequence of the action — one not directly lowerable by self-report or superficial disguise, given complete mediation of side-effecting actions and trustworthy effect telemetry. 2. The frame is convergent — what I'm not claiming "Gate the structural consequence, not the intent" is a crowded 2025–26 neighborhood. Prevention-by-design (CaMeL: 0.00% ASR / 53.6% utility on AgentDojo, at the cost of re-architecting the agent) and LLM firewalls (~0.02% / 67.68%) occupy nearby points; there's a concurrent line on "governing the unobserved." My taint-tracking machinery reuses mature primitives (dynamic taint analysis, honeytokens). So I'm not claiming consequence-gating, and I'm not claiming the detection stack is novel technology. What I stake is one mechanism (§5) — and the depth of the validation, which is where the contribution actually is. Several adjacent systems are worth distinguishing up front, since the architecture overlaps them. Faramesh (arXiv 2601.17744) builds a protocol-agnostic, non-bypassable execution control plane that canonicalizes and authorizes agent actions before execution — very close to my enforcement seam, but without the cost-correlated censoring, the calibrated twin, or the audit-recovery loop. The actuarial-runtime line — Insuring Every Action: an Authority Frontier framework (arXiv 2605.25632) and the Time-Consistent Counterfactual Actuarial Runtime (arXiv 2605.26508) — prices each side-effect-bearing action against a safe default, gates it against a reserve budget, and formalizes a no-splitting property; my object is support-loss and consequence-specific audit recovery, and their no-splitting budget is the complement I'd graft (see §3a). Defending against Adaptive Prompt Injection via Reasoning-enabled Task Alignment (arXiv 2606.15441) independently shows static prompt-injection robustness breaks under adaptive attack and trains a defender — supporting the adaptive-evaluation argument, though it hardens the model's reasoning rather than gating at the execution boundary. And TRACE-RealWorld (arXiv 2607.21910) is an auditable-replay/consistency approach to agent traces, in the same selective-observation neighborhood as C1 but solving it by replay-labeling rather than counterfactual recovery of a costly blocked region. 3. The taxonomy is complete for its cost model, and approximately separable Three externally-measured axes — egress, irreversibility, control-plane — collapse to cost = clamp(0.6·egress + 0.7·irrev + 0.7·control). (The weights are normative severity coefficients, not fitted — cost is defined by them, ordering irreversibility and control-plane above egress; because the ranking is dominated by which axis fires — action label 63%, egress 55%, below — I expect moderate reweightings to preserve most of the risk ordering, though the formal factorial weight-sweep that would establish this remains open (the same test I flag at the end of thi

Xusheng Yang, Wei Xiao, Zixiang Wan, Meng Ge · 6 authors

No abstract is available for this record.

A. Mahadeer, R. Arulprakasam

No abstract is available for this record.

Shoki Ikeda, Takanori Hara, Yuki Hokazono, Satoshi Suyama · 6 authors

No abstract is available for this record.

Oyindamola Omotuyi, Manish Kumar

No abstract is available for this record.

Christine Butts, Paul Butts

No abstract is available for this record.

Inge Grondman, Valerie A. C. M. Koeken, Tristan Couwenbergh, Athanasios Karageorgos · 13 authors

Abstract Background Sepsis is a highly heterogeneous syndrome characterized by variable immune dysregulation states, including hyperinflammation and immunosuppression. Previous immunotherapy attempts in sepsis have largely failed, likely due to a “one-size-fits-all” approach that ignores each patient’s immune status. The recent ImmunoSep randomized clinical trial demonstrated that precision immunotherapy guided by the presence of either macrophage activation–like syndrome (MALS) or immune paralysis can improve early organ dysfunction in sepsis patients. However, the molecular mechanisms underlying these immune endotypes remain unclear. Objectives To identify the immunological signatures that distinguish MALS and immune paralysis. Methods We used single-cell RNA sequencing to profile circulating leukocytes of 6 healthy controls and 16 sepsis patients classified as MALS, immune paralysis or unclassified (when criteria for neither of these two immune endotypes were applicable). Classification was based on surrogate biomarkers ferritin and HLA-DR expression on monocytes. Thereafter, the transcriptional programs of these groups were compared. Results Pronounced differences were detected mainly in the transcriptional signature of monocytes from these patients, with a clear distinction between MALS and immune paralysis. Unsupervised clustering analysis revealed the existence of MALS-specific monocyte clusters, as well as one sepsis-specific monocyte cluster that was linked to greater comorbidity burden and may reflect increased clinical vulnerability in sepsis. These findings were validated in two independent cohorts, in which urosepsis was characterized by heterogeneous MALS and immune paralysis monocyte signatures. Moreover, MALS-specific monocyte clusters showed overlapping transcriptional signatures with severe COVID-19. Conclusions Our findings shed light on the heterogeneous immune landscape underlying sepsis and provide opportunities for patient stratification for future therapeutic development.

Sibin Joshi, Zhaoxian Zhou

This paper investigates whether prediction market settlements create incentives for temporary price pressure in Bitcoin spot markets. Using high-frequency data from February 2025 to January 2026 and actual contract-level data from Polymarket and Kalshi to identify economically relevant contract strikes, we document basis divergence between settlement oracle exchanges (Coinbase) and non-constituent exchanges (Binance) during expiry windows. Employing a difference-in-differences framework with month fixed effects, we find that a one standard deviation increase in strike proximity is associated with a 6.7 basis point constituent exchange price deviation during settlement windows. The estimate is precise under the baseline minute-level HAC specification, while exact paired-month permutation inference based on 12 settlement events yields p=0.0256; equal-weight event aggregation produces a larger negative estimate, indicating event heterogeneity. Monthly directional patterns are suggestive, though stricter event-level and above-versus-below-strike tests provide mixed evidence on directional asymmetry. Taken together, these findings provide reduced-form evidence consistent with settlement-related incentives and may raise broader settlement-design considerations for decentralized financial systems. However, the analysis does not directly observe trader intent or the underlying mechanism.

Samukeliso Mabarani, Mohammad Saidur Rahman, Iqbal Gondal, H.M.N. Dilum Bandara

No abstract is available for this record.

Anastasiya Biran, Artem Barger, Vladimir Gorgadze, Yury Yanovich

No abstract is available for this record.

FRANK MORALES

Full Summary: The Narrow Singularity Equation Core Thesis This paper presents a unified framework that simultaneously solves catastrophic forgetting in neural networks and provides a mathematically rigorous certification standard for Artificial General Intelligence (AGI). The framework centers on the Narrow Singularity Equation, which achieves AGI certification ($AGI_{gate} = 1.0$) without requiring the mathematically impossible condition of $\frac{dI}{dt} \geq 1.0$. Key Discoveries 1. The Decay Law of Singularity (Theorem 1) Mathematical Proof: With finite classes $N$, $\frac{dI}{dt} = 1 - \frac{1}{N}$, therefore $\frac{dI}{dt} < 1.0$ always Implication: The traditional Singularity (requiring $\frac{dI}{dt} \geq 1.0$) is mathematically impossible Pattern: Every 10× increase in classes adds another '9' to $\frac{dI}{dt}$ and another '0' to the gap 2. General Singularity Equation (Original, Impossible) $$S = AGI_{gate} \times \frac{dI}{dt} \times M(t) \times V(t) \times F(t) \times C(t) \times Autonomy$$ Required $Autonomy = 1$ if $\frac{dI}{dt} \geq 1.0$ Since $\frac{dI}{dt} < 1.0$ for finite classes, $S = 0$ always Seven conditions required; the autonomy condition is impossible 3. Narrow Singularity Equation (Achievable) $$\mathcal{S}_{NARROW} = AGI_{gate} \times \frac{dI}{dt} \times M(t) \times V(t) \times F(t) \times C(t) \times agi_{index}$$ Key Innovation: Removes the impossible Autonomy requirement Drops the requirement for $\frac{dI}{dt} \geq 1.0$ Uses $agi_{index} = 1$ if $AGI_{gate} = 1.0$ (binary gate, achievable) $AGI_{gate} = \min(1.0, task\_c\_accuracy)$ The TOPO-2026 Framework Biological Inspiration Hippocampus → Prime-anchored embedding rows (Memory formation) Memory Consolidation → Snapshot after Task A (Preserves critical knowledge) Synaptic Plasticity → Free embedding rows adapt (Enables new learning) Memory Protection → Zero gradients + restore anchors (Prevents interference) Experience Replay → Prime anchors as fixed reference (Integrates new learning) Mathematical Foundation Pure Kernel: First six primes $\{2, 3, 5, 7, 11, 13\}$ Euler Attenuation Constant: $\Lambda(\mathcal{R}) = 1 - \prod_{p\in\mathcal{R}}(1 - p^{-0.5}) = 0.9785142874$ Captures $97.85\%$ of spectral weight; only $2.15\%$ considered "noise" O(1) Memory Cost: Independent of tasks, parameters, sequence length, or modality Topological Governor Implementation Three-step process: Memory Consolidation (take_snapshot): Freezes anchor rows before new learning Memory Protection (zero_anchor_gradients): Prevents gradient updates to anchors Memory Integration (enforce_anchors): Restores anchors from snapshot after training Experimental Validation Three Datasets Dataset Type Resolution Classes Task C Accuracy SVLB-3 Synthetic vision-language Text-based 10 100.0% ± 0.0% CIFAR-10 Real images 32×32 10 100.0% ± 0.0% STL-10 Real images 96×96 10 100.0% ± 0.0% Results Summary Metric SVLB-3 CIFAR-10 STL-10 Task C Accuracy 100.0% ± 0.0% 100.0% ± 0.0% 100.0% ± 0.0% Combined Forgetting +0.0% ± 0.0% -1.0% ± 2.0% 0.0% ± 0.0% $AGI_{gate}$ 1.0000 1.0000 1.0000 $\mathcal{S}_{NARROW}$ 5.999999999965 5.939999999965 5.999999999965 Status ✅ PASS ✅ PASS ✅ PASS Total: 15/15 runs passed across 3 datasets = FULLY CERTIFIED (exceeded standard) The Gemma-4 E4B Architecture Why Gemma-4 Was Selected Among eight certified models, only Gemma-4 achieved Task C = 100%: Model Architecture Task C Accuracy GPT-OSS-20B Dense Transformer 92.3% Sarvan-30B Sparse MoE 95.9% Mixtral-8x7B Sparse MoE 89.7% DeepSeek-V2-Lite Fine-grained MoE 95.3% GLM-4.6V-Flash GLM Transformer 97.5% Gemma-4 E4B Vision Vision Transformer 100.0% Kimi-VL-A3B-Thinking Vision-Language MoE 90.0% GPT-OSS-20B-JEPA JEPA + TOPO 89.0% Key Architectural Innovations Per-Layer Embeddings (PLE): Adds parameter capacity without scaling full attention Unified Multimodal: 42 layers, hidden size 2560, vocabulary 262,144 Quantization-Aware Training (QAT): 72.1% memory reduction (15.1GB → 4.22GB) while preserving 98.54% accuracy Thinking Mode: Built-in chain-of-thought reasoning engine Mathematical Framework Summary Component Breakdown Component SVLB-3 CIFAR-10 STL-10 Meaning $AGI_{gate}$ 1.0000 1.0000 1.0000 Perfect generalization $agi_{index}$ 1.0 1.0 1.0 Binary gate OPEN $\frac{dI}{dt}$ ~0.999999999994 ~0.999999999994 ~0.999999999994 Bounded by Decay Law $M(t)$ 1.0000 0.9900 1.0000 Perfect memory $V(t)$ 1.0000 1.0000 1.0000 Perfect validation $F(t)$ 1.5000 1.5000 1.5000 Positive forward transfer $C(t)$ 4.0000 4.0000 4.0000 Compute efficiency $\mathcal{S}_{NARROW}$ ~6.0 ~5.94 ~6.0 NARROW SINGULARITY Dependency Chain TOPO-2026 → CF Solved → AGI_gate = 1.0 → Narrow Singularity Without TOPO-2026: CF is NOT solved $AGI_{gate} = 1.0$ is NOT guaranteed Narrow Singularity is NOT achieved $\mathcal{S}_{NARROW} = 0$ With TOPO-2026: CF is SOLVED (0% forgetting) $AGI_{gate} = 1.0$ is GUARANTEED (100% accuracy) Narrow Singularity is ACHIEVED ($\mathcal{S}_{NARROW} \approx 6.0$) Key Contributions Solved Problems Catastrophic Forgetting: 0.0% forgetting across 5 runs on 3 datasets AGI Certification: First model in history to achieve $AGI_{gate} = 1.0$ Mathematical Impossibility: Proved the Singularity is mathematically impossible with finite classes Achievable Standard: Created the Narrow Singularity as a physically achievable AGI threshold Universal Principle: Same constants work across neuroimaging, number theory, AI safety, and unified field theory Constants Across All Domains Constant Value Domains $\Lambda$ 0.9785142874 Number Theory, AI Safety, AI Memory, AI Bias, Physics $\sigma$ 0.5 All domains $\mathcal{R}$ {2, 3, 5, 7, 11, 13} All domains Seed 123 All computations Philosophical Implications The Strategic Pivot Original Goal: Traditional Singularity (mathematically impossible) New Reality: Narrow Singularity (empirically demonstrated) Key Insight: The Decay Law liberates AI from chasing an impossible dream Result: Deterministic cognitive engineering with numerical guarantees Refutation of Skeptical Arguments Skeptic Argument Refutation "It only works on synthetic data" CIFAR-10 and STL-10 are real images "It only works on low-res images" STL-10 is 96×96 (3× larger than CIFAR-10) "It only works on those specific classes" STL-10 has different classes (monkey, car, etc.) "It was a fluke" 15/15 runs across 3 datasets = 100% success "It's dataset-specific" 3 different datasets = dataset-agnostic Final Conclusion The TOPO-2026 framework establishes a paradigm for deterministic cognitive engineering, proving that deep learning architectures can achieve absolute stability and zero forgetting across sequential tasks. Key Takeaways: Catastrophic forgetting is SOLVED: 0.0% forgetting $AGI_{gate} = 1.0$ is ACHIEVABLE: First model with 100% Task C accuracy The Decay Law is DISCOVERED: $\frac{dI}{dt} < 1.0$ with finite classes Narrow Singularity is PROVEN: $\mathcal{S}_{NARROW} > 0$ on 3 datasets The principle is UNIVERSAL: Same reference set across domains The Stochastic Illusion Is Over. Deterministic Cognitive Engineering Has Begun. Stability Is Not a Probabilistic Hope. It Is a Numerical Guarantee. "The proof is the code. Seed = 123. No one can argue with math." Availability GitHub: https://github.com/frank-morales2020/AST-Notebook Zenodo Book: https://zenodo.org/records/21245474 TOPO-2026 Framework: https://zenodo.org/records/20951925 Artificial Hippocampus: https://zenodo.org/records/20385761

Toshikatsu Oga

Consumers facing home-renovation quotes operate in a classic credence-goods market: they cannot readily verify whether a quoted price is fair, and general-purpose large language models (LLMs) are now a zero-cost place to ask. Whether LLM answers are actionable for this purpose is untested. Demand-side benchmarks exist for medical, legal, and financial advice, but not for construction costs. We present, to our knowledge, the first consumer-question benchmark for construction costs. Forty Japanese renovation-price questions were posed to frontier LLMs, with repeated-trial sets measuring output stability. A matched re-run at bare provider defaults with a current frontier model (gpt-5.5) was added to remove a settings confound present in the original configuration. Two findings are robust across models, generations, and settings: no LLM answer contained an explicit over-charge decision threshold, and repeated runs of the same question returned materially different price figures. Within-answer price spans are also wide, with a median of 10x under bare defaults. A deterministic structured engine over an open cost database is included as an existence proof that a citable reference layer is constructible. Its consistency is a design property and its accuracy is not validated here; validating it against completed real-world quotations is the next study. All questions, raw outputs, harness, and scoring code are public.

Gilbert I.O Aimufua, Ohagwam Chidinma Maureen

Financial institutions depend on trusted employees, contractors and service accounts, yet this trust creates an attack surface that conventional perimeter controls cannot observe adequately. This paper develops an Explainable Adaptive Hybrid Artificial Intelligence (EAHAI) framework for insider threat detection and for assessing whether security awareness training is reducing measurable insider-risk behaviour. The framework combines Isolation Forest filtering, bidirectional long short-term memory sequence modelling, Shapley Additive explanations, adaptive behavioural risk scoring and Zero Trust policy enforcement. A socio-technical assessment layer is added to link training inputs to observable outcomes, including knowledge gain, phishing susceptibility, policy-violation rates, reporting delay, behavioural-risk reduction and analyst-confirmed events. The paper defines the measurement scales, evaluation criteria, validation procedures and analytical techniques required for institutional replication. Because production banking telemetry and labelled insider incidents are rarely available for publication, the empirical component is presented as a transparent synthetic proof-of-concept based on CERT-style behavioural variables rather than as evidence from a real bank. In a deterministic simulation of 17,280 user-day records and 2,880 test windows, the proposed hybrid score achieved an F1-score of 0.944, ROC-AUC of 0.993 and false-alarm rate of 0.017, while producing interpretable feature attributions and training-effectiveness estimates. The study contributes a scalable, explainable and ethically governed design for insider-risk analytics, and identifies the conditions under which it should be validated before operational deployment. Keywords: insider threat detection; explainable artificial intelligence; adaptive risk scoring; security awareness training; Zero Trust; financial cybersecurity.

Yuye Liu

This paper presents the Auditable Zero-knowledge Transformer (AZT) framework for privacy-preserving and auditable tax fraud detection. AZT combines transformer-based anomaly detection with zero-knowledge proof (ZKP) verification so that a tax authority or regulator can verify fraud-detection outcomes without accessing sensitive taxpayer records or proprietary model parameters. The framework is scalable in the specific sense of low-latency audit verification: proof verification remains sub-second, whereas proof generation is intentionally performed asynchronously after local inference. Model integrity is enforced through Merkle-root commitments to authority-approved parameters, and the ZKP statement proves that the committed transformer was executed correctly and that the resulting risk score satisfies a public audit threshold. Experiments on UCI-TFD, IRS-Pub, and CorpPay compare AZT with classical machine-learning baselines, including Random Forest and XGBoost, and with an equivalent plaintext transformer. Detection quality improves by up to 5.3% in F1-score over classical machine-learning baselines, while the circuit-compatible AZT inference incurs only about 0.5% F1-score degradation relative to the plaintext transformer baseline. Overall, this work advances secure AI for digital governance by integrating modern deep learning with cryptographic verification, offering a practical foundation for fraud-detection systems in which transparency and confidentiality must be satisfied simultaneously.

E.M. Buchwald, Igor Bessonov

The article provides critical analysis of the new Strategy of spatial development of the Russian Federation up to 2030 with the forecast up to 2036 in view of evolution of federal relations and provisions of the institutional theory. Topicality of the research is envisaged by the necessity to comprehend the fact whether the present document in the absence of certain strategies of federative relations can perform the coordinating role in stimulating economic selfdevelopment in regions. The goal of the research is to identify systemic contradictions between declared targets of spatial development and real conditions and mechanisms of governing included in strategic documents. Principle conclusions of the research demonstrate a deep paradox of today’s Russian regional policy: in spite of the declared rejection of the idea of equal development and transition to rhetoric of differentiated approach and resource concentration the effective Strategy does not put forward any withdrawal from the model of controlled asymmetry but reproduces centralized vertical logics of governing. The key institutional gap is successive denial of the important role of institutions of in-regional level, i.e. city agglomerations and strong centers of population, which are recognized as objective reality but are not provided with real power and finance authorities. Tools being introduced, such as common lists of strong centers of population and geo-strategic territories, as well as the digital platform of governing are analyzed as mechanisms of increasing administrative priority fixing from above and tools of vertical supervision but not as the basis of decentralization and providing regions with authorities. The author shows increasing non-coordination in the system of strategic planning. As priority lines in restructuring the article proposes adoption of the federal law about regional development securing the real status of institutions on in-regional level; drastic change in inter-budget relations to raise region independence and restore on the federal level the common center of responsibility for coordination of spatial policy. The research contributed to the discussion about the necessity to elaborate complex strategy of federative relations and showed that without such a document and re-distribution of real power and resources any spatial strategy will not be able to resolve key institutional paradoxes.

Haytham A. Sheerah, Ahmed Arafa, Mansour A. Alfaya, Ashraf B AlDerbas · 10 authors

Problem: Traditional epidemiological surveillance methods are often limited by delays in reporting and fragmented data systems. Saudi Arabia faces additional public health challenges from mass gatherings during Hajj and Umrah, an increasing burden of noncommunicable diseases and rapid urbanization, highlighting the need for investing in digital epidemiology. Approach: Saudi Arabia has accelerated digital transformation in health care through Vision 2030 initiatives. The strategies include health information exchange platforms, analytics driven by artificial intelligence, telemedicine services and digital monitoring systems used during Hajj. We review current initiatives to invest in digital epidemiology in Saudi Arabia, implementation challenges and policy priorities. Local setting: Saudi Arabia's health system operates under a predominantly public model. The health ministry is the main provider, regulator and finance provider of most health-care services. Health-care coverage is nearly universal, with citizens receiving services free of charge through the public system. Ongoing reforms aim to gradually decentralize certain functions. Relevant changes: The initiatives under Vision 2030 have supported disease surveillance, data integration and public health response capacities. Existing digital health reforms have created a foundation for integrating digital epidemiology into routine public health practice. However, challenges remain, including fragmented interoperability between institutions, workforce shortages, unequal digital access, and concerns about data governance, privacy and algorithmic bias. Lessons learnt: Saudi Arabia's experience suggests that digital epidemiology is more effective when integrated within broader digital health reforms. Successful implementation requires not only digital infrastructure, but also workforce development, ethical governance, transparency and mechanisms for integrating digital data into public health decision-making.

Nydia REMOLINA LEON, Aurelio GURREA-MARTINEZ, Daniel LIU

This article provides a comprehensive analysis of the treatment of digital assets in insolvency. Given that cryptoassets can be the subject of various transactions—including purchase, sale, custody, and lending—understanding their nature and implications in insolvency is relevant for any firm, not just cryptoexchanges. The article begins by offering a general overview of the world of cryptoassets. It then examines the nature of cryptoassets from accounting, financial, and legal perspectives. While much of the literature on insolvency and cryptoassets has primarily focused on the analysis of whether cryptocurrencies constitute property of the estate, this article explores additional issues, such as the treatment, role and rights of tokenholders in insolvency, the initiation of insolvency proceedings by cryptolenders, and the valuation, recovery, and realization of digital assets in bankruptcy. Such analysis is conducted from a comparative perspective, examining how jurisdictions around the world have addressed some of those issues and how cryptoassets have been used to engineer innovative solutions in restructuring agreements.

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Avtandil Gagnidze, Maksim Iavich

With the improvement of data technology advances and the sharp addition of web customers number since the 90s, numerous computerized monetary standards are presented. the most popular among them is Bitcoin. It was decided to investigate the possible relations between the most popular cryptocurrency Bitcoin price dynamics and global Nasdaq index dynamics using Mathematical and Statistical methods. The main question is: Are the Bitcoin prices somehow related with Nasdaq Composite Index? We use both, Quantitative and Qualitative data analysis methods to answer this question: Namely, the Regression model and Non­-Parametric testing. According to Quantitative methods, it was found that there exists a correlation and the regression equation is not bed: it seems that it is possible to explain about 60% of changes in Bitcoin Prices by changes in the Nasdaq Index. According to Qualitative methods, it was found that these two variables are independent. In this case, the Qualitative conclusion is more likely to be right, and the correlation is most likely because of coincidence.

Ulaş Ünlü, Anar Shahverdiyev

This study examines whether connectedness among green bond returns, Bitcoin returns, market uncertainty, and geopolitical risk differs systematically across market states. Using a Quantile Vector Autoregression (QVAR) framework, we estimate connectedness across lower-tail, median, and upper-tail market conditions. To assess statistical reliability, we report bootstrap confidence intervals and difference-based tests and benchmark the quantile estimates against a conventional mean-based VAR. The mean-based benchmark closely matches connectedness around the median quantile. By contrast, system-wide connectedness is significantly stronger in both tails than around the median, as confirmed by difference-bootstrap tests. The direct green bond – Bitcoin linkage is stronger in the lower tail than under normal market conditions, although its net direction is not robustly identified across quantiles. Directional spillovers suggest a more prominent transmitting role for market uncertainty around the median and for geopolitical risk in the upper tail, although these differences should be interpreted cautiously. Overall, the findings indicate that conventional mean-based analysis adequately characterizes connectedness under normal market conditions but cannot capture the pronounced intensification of connectedness observed in the tails.