Abstract
[abridged] Accurately accounting for the AGN phase in galaxy evolution requires a large, clean AGN sample. This is now possible with SRG/eROSITA. The public Data Release 1 (DR1, Jan 31, 2024) includes 930,203 sources from the Western Galactic Hemisphere. The data enable the selection of a large AGN sample and the discovery of rare sources. However, scientific return depends on accurate characterisation of the X-ray emitters, requiring high-quality multiwavelength data. This paper presents the identification and classification of optical and infrared counterparts to eRASS1 sources using Gaia DR3, CatWISE2020, and Legacy Survey DR10 (LS10) with the Bayesian NWAY algorithm and trained priors. Sources were classified as Galactic or extragalactic via a Machine Learning model combining optical/IR and X-ray properties, trained on a reference sample. For extragalactic LS10 sources, photometric redshifts were computed using Circlez. Within the LS10 footprint, all 656,614 eROSITA/DR1 sources have at least one possible optical counterpart; about 570,000 are extragalactic and likely AGN. Half are new detections compared to AllWISE, Gaia, and Quaia AGN catalogues. Gaia and CatWISE2020 counterparts are less reliable, due to the surveys shallowness and the limited amount of features available to assess the probability of being an X-ray emitter. In the Galactic Plane, where the overdensity of stellar sources also increases the chance of associations, using conservative reliability cuts, we identify approximately 18,000 Gaia and 55,000 CatWISE2020 extragalactic sources. We release three high-quality counterpart catalogues, plus the training and validation sets, as a benchmark for the field. These datasets have many applications, but in particular empower researchers to build AGN samples tailored for completeness and purity, accelerating the hunt for the Universe most energetic engines.

Abstract
Artificial intelligence is often measured by the range of tasks it can perform. Yet wide ability without depth remains only an imitation. This paper proposes a Structural-Generative Ontology of Intelligence: true intelligence exists only when a system can generate new structures, coordinate them into reasons, and sustain its identity over time. These three conditions -- generativity, coordination, and sustaining -- define the depth that underlies real intelligence. Current AI systems, however broad in function, remain surface simulations because they lack this depth. Breadth is not the source of intelligence but the growth that follows from depth. If future systems were to meet these conditions, they would no longer be mere tools, but could be seen as a possible Second Being, standing alongside yet distinct from human existence.

AI Insights

• The paper flags three AI confusions: equating imitation with being, hiding structure origins, and treating intelligence as engineering.
• It urges an ontological shift to a philosophically rigorous yet empirically testable framework.
• Generativity is creating new categories that open a world.
• Coordination integrates those categories into a normative space of reasons.
• Sustaining keeps generativity and coordination alive over time, forming a historical subject.
• Breadth alone gives coverage; without coordination it fragments; without sustaining it is episodic.
• Suggested readings: Bostrom’s *Superintelligence*, Floridi’s *Fourth Revolution*, Russell’s *Human Compatible*.

Abstract
This article introduces a novel method for detecting distinctive structural changes in economic data, particularly within frequency distribution tables. The approach identifies significant shifts in the distribution of a variable over time or across populations, capturing changes in category shares, enabling a deeper understanding of the underlying dynamics and trends. The method is applicable to both categorical and numerical data and is especially useful in fields such as industrial economics, demography, social science and market analysis, where comparative analysis is essential. Selected numerical examples illustrate its effectiveness in tracking market structure evolution, where shifts in firm-level market shares may signal changing competitive dynamics. The results offer interpretable insights into structural transformations in economic systems.

AI Insights

• The structure similarity test uses a similarity index to quantify how alike two distributions are, enabling precise detection of subtle shifts.
• Critical values for the index are listed in Table A1, offering a quick reference for significance across datasets.
• The test’s assumption that a high index guarantees similarity can fail for complex, multimodal structures.
• Beyond economics, the method monitors organizational evolution and market share dynamics with equal rigor.
• Quantum-inspired frameworks, like those in “Evolution And Revolution: A Quantum View Of Structural Change In Organizations,” extend the similarity test’s analytical reach.
• Users should validate Table A1 thresholds against their data, as they may not generalize to all structural types.

Abstract
We analyze a dynamic labor market in which a worker with career concerns chooses each period between (i) self-employment that makes output publicly observable and (ii) employment at a firm that pays a flat wage but keeps individual performance hidden from outside observers. Output is binary and the worker is risk averse. The worker values future opportunities through a reputation for talent; firms may be benchmark (myopic) (ignoring the informational content of an application) or equilibrium (updating beliefs from the very act of applying). Three forces shape equilibrium: an insurance - information trade-off, selection by reputation, and inference from application decisions. We show that (i) an absorbing employment region exists in which low-reputation workers strictly prefer the firm's insurance and optimally cease producing public information; (ii) sufficiently strong reputation triggers self-employment in order to generate public signals and preserve future outside options; and (iii) with equilibrium firms, application choices act as signals that shift hiring thresholds and wages even when in-firm performance remains opaque. Comparative statics deliver sharp, testable predictions for the prevalence of self-employment, the cyclicality of switching, and wage dynamics across markets with different degrees of performance transparency. The framework links classic career-concerns models to contemporary environments in which some tasks generate portable, public histories while firm tasks remain unobserved by the outside market (e.g., open-source contributions, freelancing platforms, or sales roles with standardized public metrics). Our results rationalize recent empirical findings on the value of public performance records and illuminate when opacity inside firms dampens or amplifies reputational incentives.

AI Insights

• Absorbing employment arises when low‑reputation workers trade insurance for opaque firms, stopping public output.
• Strong reputation prompts early‑career self‑employment to broadcast signals and secure future options.
• Application inference shifts hiring thresholds and wage dispersion even when in‑firm performance stays hidden.
• The model predicts a cyclic switch: high‑reputation workers alternate between self‑employment and firms.
• Career Concerns: workers privately know productivity; employers infer via application choices.
• Application‑Based Inference: employers use online signals to update beliefs before hiring.
• See Altonji & Pierret (2000) on employer learning; Bar‑Isaac & Tadelis (2015) on seller reputation.

Abstract
In the LIBOR era, banks routinely tied revolving credit facilities to credit-sensitive benchmarks. This study assesses the Across-the-Curve Credit Spread Index (AXI) -- a transparent, transaction-based measure of wholesale bank funding costs -- as a complement to SOFR, summarizing its behavior, construction, and loan-pricing implications. AXI aggregates observable unsecured funding transactions across short- and long-term maturities to produce a daily credit spread that is IOSCO-aligned and operationally compatible with SOFR-based infrastructure. The Financial Conditions Credit Spread Index (FXI) is a broader market companion to AXI and serves as its fallback. FXI co-moves closely with AXI in normal times; under stress, the correlation of daily changes exceeds 0.9 for economy-wide shocks and remains strong in bank-specific stress, around 0.8 during the Silicon Valley Bank episode. Empirically, AXI is strongly correlated with standard credit-spread measures and market-stress indicators and is inversely related to financial-sector performance. SOFR+AXI exhibits correlations with macroeconomic variables with the signs and magnitudes expected of a credit-sensitive rate. In loan-pricing applications, SOFR+AXI reduces funding risk and can support spread discounts of up to 65 basis points without lowering risk-adjusted returns. In stress scenarios, banks relying on SOFR-only pricing can fail to recover as much as 15 basis points on revolving credit lines over as little as three months. Taken together, AXI restores the credit sensitivity lost in the USD LIBOR transition while avoiding reliance on thin short-term markets, delivering significant economic value.

AI Insights

• AXI’s volatility is σ(Δti)=1/T∑p LTw·STw·|LT spreadt−ST spreadt|, averaging daily Bernoulli spreads.
• Correlations peak at 0.714 with OAS, 0.617 with LIBOR‑OIS, 0.611 with TED, and 0.622 with NFCI.
• A one‑month lag of NFCI still gives a contemporaneous correlation of 0.595, essentially unchanged.
• Using unweighted dollar volumes to estimate probabilities lowers σ(Δti) and unlocks a larger spread reduction.
• The model assumes Δti takes only two values—LT spread minus AXI or ST spread minus AXI—potentially limiting realism.
• For deeper insight, read “Fixed Income Securities: Tools for Today’s Markets” and the paper “Credit spread variability facing individual banks.”
• AXI lets banks price loans with preserved credit sensitivity while avoiding thin short‑term markets.