Papers

The actor model - state isolation, data-race freedom, and sequential single-message reasoning - has long been dismissed as unsuitable for high-frequency trading (HFT): actors seem to imply many threads, a mailbox per actor, and a heap-allocated message plus a context switch per interaction, overhead incompatible with a microsecond budget. This paper argues the dismissal is wrong for co-located actors, with a deployed, measured implementation: kaspar-hft, an open-source C++20 framework. Four extensions adapt the model for HFT: fast_send, a synchronous delivery mechanism in which the sending thread runs the receiver's handler inline and returns the reply as a value; actor groups, which co-schedule actors on one thread behind a shared mailbox; per-actor selectable mailbox queues; and a memory pool. fast_send has receiver transparency: the handler is written identically for synchronous and asynchronous delivery and does not depend on which was used or which thread runs it. A grouped synchronous chain runs on one thread, cutting scheduler context switches from O(N) to O(1); a thread-local call-chain test detects cyclic invocation on one thread before any lock is taken, while a cycle spread across threads deadlocks. Microbenchmarks put the synchronous round trip at tens of nanoseconds. On a live CME market-data feed (ES, NQ, ZN futures), when the socket-reader thread decodes each packet and updates the book itself, socket-to-book medians for book updates are 0.8-1.1 microseconds, and a fast_send hop is about 1% of that. The shared-queue group also yields a production/simulation duality: the same actor code runs unchanged in live trading and deterministic backtest.

Event contracts reach economic finality through different institutional paths. Polymarket distinguishes oracle adjudication, adapter consumption, Conditional Tokens payout recording, technical redeemability, and optional holder redemption. Kalshi distinguishes venue determination, public finalization, lifecycle messages, and exact REST settlement fields. Resolved, settled, and finalized do not themselves define comparable endpoints. We define a mechanism-aware comparison certificate using functional roles rather than common labels. A scalar cross-venue duration is admissible only when payoff semantics, endpoint functions, calendar support, censoring, observation grades, and dependence units are jointly aligned. We separate exact paired, interval-qualified paired, and standardized unpaired estimands. Polymarket evidence contains 108,638 exact-linked conditions, 99,283 protocol payout records, 92,158 observed redemptions of any amount, and 91,817 observed positive-payout redemptions. Kalshi evidence contains 152,694 ordinary markets reconstructed as at risk at the enrollment boundary and 7,611,594 exact MVE market objects. Exact public endpoints cover 71,657 ordinary markets and 7,357,576 MVE objects. Exact determination-to-endpoint pairs total 70,979 for ordinary markets and 126,806 for MVE. These are Kalshi-native finality results, not Polymarket-Kalshi estimates. REST endpoint completeness can be high when exact lifecycle paths are sparse, and transport gaps can preserve two exact clocks while preventing claims about intermediate revisions. Current MVE timer fields do not identify historical version-consistent timer rules, and public finalization does not identify member cash. Cross-venue numerical comparison remains blocked until Paper 7.3 supplies blind first/stable decidability clocks and a semantic/calendar registry establishes genuinely comparable events and endpoints.

We examine whether post-origin training information inflates the measured accuracy and economic value of financial forecasts. We evaluate five sets of financial time-series foundation models, each comprising independently trained annual vintages under U.S., global, and factor-augmented training environments, across 14 equity markets and four forecast horizons. Rolling comparisons vary the annual vintage for a fixed forecast; fixed-vintage comparisons hold the vintage fixed as target windows move across its training cutoff. Each alternative forecast is paired with an origin-aligned point-in-time (PIT) benchmark using identical numerical histories and inference protocols. In the U.S.-trained reference environment, post-origin vintages materially revise informative PIT forecasts but generally reduce accuracy in both designs. Pooled rolling comparisons yield higher mean squared forecast errors in 18 of 20 U.S. model-set-horizon combinations. The origin-crossing update also performs worse on average than an equally long pre-origin update. Under a common constrained allocation rule using one-month forecasts, median exposed-minus-PIT differences in annualized certainty-equivalent returns are -1.77 percentage points in the United States and -2.14 points internationally. Global and factor-augmented training produce more mixed predictive effects. An exact squared-error decomposition shows that revisions improve accuracy when their error-correcting benefit exceeds their mean squared magnitude; under U.S. training, alignment with PIT errors generally falls short of this requirement. Temporal exposure therefore establishes an information-set violation, not sufficient evidence of inflated predictive accuracy or investor value.

In a statistical factor model, principal components (or eigenvectors) of a sample covariance matrix serve as estimates of principal directions, the true drivers of co-movement of a collection of observed variables. We write the often substantial error in these estimates as a sum of two interpretable terms, which we show have almost sure asymptotic limits as the number of variables grows with sample size bounded. This scenario is commonplace in financial economics, genomics, machine learning and signal processing. Out-of-subspace error measures the distance from an estimate to the subspace spanned by population factor exposures. It can be expressed in terms of data, providing an estimable floor for asymptotic error. In-subspace error is driven by a rotation error within that subspace, which arises from the fixed sample size of the latent factor returns and cannot be estimated from data alone. We illustrate our error analysis with a three-factor simulation of the US public equity market, showing the dependence of the magnitude of the error and its components on dimension and sample size. In that simulation, out-of-subspace error dominates. Researchers who rely on principal component analysis to estimate factor models can use our results to quantify errors in model-based predictions and attributions.

Organizations often invest substantial effort in improving internal mechanisms such as processes, governance, and technology, yet realize only modest performance gains. This paper proposes a structural explanation for this pattern. We introduce the concept of internal externalities: unpriced cross-effects generated by internal organizational mechanisms that act simultaneously on multiple objectives. When these effects oppose one another, effort cancels internally, limiting attainable performance even without incentive misalignment or informational constraints. We formalize this mechanism by modeling internal mechanisms as primitives that exert signed effects across performance objectives. This structure gives rise to internal conflict, a measurable property that captures the fraction of organizational activity lost to cancellation. We show that conflict constitutes a form of structural inefficiency that constrains the returns to effort and renders allocative optimization ineffective unless addressed first. The framework implies a natural sequencing of interventions, in which structural cleanup precedes effort reallocation and optimization. The analysis yields testable predictions and governance implications for how organizations should scale, constrain, redesign, or deprioritize internal mechanisms.

I consider a neoclassical growth model with a constant absolute risk aversion (CARA) utility function and derive a global closed form approximation that is arbitrarily precise as the discount rate $\rho$ is close to the population growth rate $n$. I use it to show that the consumption function is strictly concave and that countries can have two different paths converging to the steady-state: front-loading and back-loading.

Recent crises have made supply security central to trade policy. We show how tradeable import certificates (TIC) implement targets for domestic production and reliable foreign supply while preserving gains from trade. A single certificate market per country decentralizes the welfare-maximizing allocation under heterogeneous targets, with certificate prices adjusting endogenously as conditions change. TIC robustly protect these targets against a range of deviations from trade agreements. A model of economic coercion microfounds the targets, linking them to shortage salience, supply reliability, bilateral trade dependence, and norms against yielding to coercion. Tariff-subsidy agreements require more information and are more vulnerable to hidden deviations. While a common carbon price provides a natural focal point for climate agreements, quantity-based security targets could be a more natural focal point for trade agreements than tariff and subsidy rates.

Prediction-market shares differ from traditional financial products in that, with no information or outside utility, classical delta-neutral Central Limit Order Book~(CLOB) market making cannot be financed by payoff-uninformative noise flow. Transaction-level evidence from a major prediction-market CLOB platform shows makers profiting not from spread but from carrying an under-priced side to settlement — the empirical signature of behavioral tail demand rather than classical, randomized noise. We build this tail demand directly into the model and study an LMSR and a CLOB on the same event. A pre-shock CLOB quote inside the common-signal band is picked off; competitive quotes therefore screen informed traders out of the book. CLOB makers earn screening rent by carrying the under-priced side to resolution, while informed flow routes to the LMSR. The venues coexist: the CLOB supplies the tail-demand rent margin that lets the LMSR recover part of its loss to informed flow, and AMM depth moves the CLOB premium with a sign set by maker-side contestability—widening it where standing quotes can be undercut, compressing it where a committed maker carries the book. With three or more outcomes, binary-book CLOBs pin switch prices but leave implied beliefs indeterminate, whereas the LMSR prices the outcome simplex coherently and uses collateral more efficiently.

High annotation reproducibility does not necessarily imply that an LLM-inferred measure captures the construct it is intended to measure. We test this distinction using a dataset from the European Commission's AI Act consultation, linking structured survey responses to free-text consultation submissions from the same stakeholders. LLM annotations of consultation submissions are highly reproducible (intraclass correlations > 0.99), yet show limited convergence with survey-reported measures of the nominal construct they were intended to approximate. Divergence between survey-and LLM-inferred text-based measures varies systematically across stakeholder groups: business associations express greater concern about AI risks in text-based consultations than in survey responses (ḡ = +1.0), whereas public authorities and several nonbusiness groups show smaller or negative divergences. Divergences between scores suggest positive spatial autocorrelation across European countries (Moran's I = 0.347, p = 0.036), indicating that stakeholders from neighboring countries tend toward more similar text-based stances towards AI safety concerns. Despite divergence, survey-reported concerns remain strongly associated with support for explainability across all divergence levels. These results demonstrate that LLM annotation reproducibility can coexist with poor construct correspondence and motivate validation procedures that distinguish reproducibility, construct validity, and communication context variation when LLMs are used as measurement instruments.

LLM-based GUI agents increasingly act on behalf of users in digital environments that were designed with human users in mind. These graphical user interfaces were designed to support, but also deliberately steer, the behaviour and decisions of users. While behavioural biases in the textual outputs of LLMs are well-documented, far less is known about how such influence operates when models act as agents that perceive interfaces and execute decisions—and, in particular, whether the reasoning capabilities increasingly built into these agents make them more robust to it. Drawing on Dual-Process Theory, we empirically investigate whether LLM-based GUI agents are susceptible to automatic (Type 1) and reflective (Type 2) digital nudges, and how their reasoning configuration moderates this susceptibility. In a randomized online shopping experiment with 3,600 agents and a total of 21,600 simulations across six frontier models from three providers, we found that agents were vulnerable to both nudge types. Crucially, the reasoning configuration moderated these effects in opposing directions, reducing susceptibility to automatic default nudges while heightening it to reflective social influence nudges. Extensive reasoning therefore did not make agents more robust but redirected the route through which choice architecture takes effect. Exploratory analysis further showed this redirection to be systematically structured by model scale. Beyond establishing nudge susceptibility as a behavioural property of agentic AI, the study positions interface design as a governance concern for organizations that delegate decisions to autonomous agents.

We examine the effectiveness of work requirements as a screening device in the Supplemental Nutrition Assistance Program (SNAP). Work requirements for "able-bodied adults without dependents" were suspended after the Great Recession and gradually reinstated across counties and states in the 2010s. Using linked administrative SNAP and employment data from five states and a triple-differences design, we find that work requirements reduce SNAP participation by seven percent without increasing labor supply and disproportionately screen out low-income individuals. We develop a welfare framework to interpret these results and find that the social costs of work requirements exceed budget savings.

This paper studies price discovery around contractual decidability rather than an arbitrary venue label. Its upstream lifecycle and decidability clocks are specified in Papers 7.1 and 7.3. Historical venue endpoints remain useful background: 152,694 ordinary markets form the retrospective feasibility denominator, 71,657 have an exact public endpoint, and 70,979 have an exact determination-to-endpoint pair. Those fields do not supply a contractual-decidability clock. The completed historical recovery produced no historically admissible contractual-decidability cohort. The prospective infrastructure shakedown has passed and evidence enrollment is active, but production price extraction has not started. The primary binary cohort will be drawn from the prospectively enrolled and blind-adjudicated contractual-decidability frame, with an accepted exact or interval first-decidability clock, exact terminal payoff, trading-availability classification, and admissible bounded non-block trade or real-candle coverage. Markets tradable after decidability enter a reaction cohort; markets closed before decidability enter a stale-terminal cohort. Closure is a competing event for convergence, not ordinary missingness. A 20-market price pilot may validate acquisition, block-trade treatment, synthetic-candle rejection, staleness, and clock alignment only after blind packet lock and only from accepted prospective clock candidates. It does not create price estimates. The paper specifies a prospective primary cohort and a targeted pilot protocol. Price estimation awaits independently reconstructed first/stable-decidability clocks and source-release clusters. No broad exchange-wide trade crawl or post hoc clock substitution is permitted.

This paper documents how inventory dynamics vary across levels of development and how they respond to real and financial shocks. Using a balanced panel of 72 small open economies over 1993-2022, spanning advanced to low-income economies, we show that inventories are strongly procyclical in advanced economies but become acyclical at lower income levels, while volatility and persistence rise as development declines. Using local projections, we estimate inventory responses to a long-run productivity shock and to a financial shock identified from the spread between U.S. corporate bond yields and Treasury yields. Productivity shocks generate temporary inventory accumulation across all groups, with smoother adjustment in advanced economies and more irregular cycles in emerging and developing economies. Financial shocks trigger decumulation followed by a rebound in higher-income groups, but a delayed, unreversed decline in low-income economies.

Variance swaps are a convenient instrument for trading vega and convexity, and a listed contract now trades on Cboe. We ask when a trader should put such a position on and when she should take it off, and for a perpetual, continuously settled contract we answer both in closed form: each threshold is the unique root of a smooth-pasting equation in confluent hypergeometric functions. Under the pricing measure the question has no content, the mark-to-market being a martingale. Under the physical measure with a variance risk premium it becomes meaningful, and then reduces: the accrued variance separates exactly, the maturity, strike and costs are absorbed into a single forcing term whose sign fixes the geometry of the exercise region, and what is left on the perpetual is an affine reward on a CIR process, which the optimal-stopping literature already solves. Entering the position and exiting it are not mirror images. An exit rule follows from the premium and the trading spread, both observable. For the short — the only side worth opening under the empirical sign of the premium — an entry rule exists only for an interval of carrying charges, and even there triggers only deep in the upper tail of the physical law: a trader who is out of the market pays nothing to stay out, so an operational entry rule needs a cost of idle capital that the exit rule does not.

We study robust bond valuation with endogenous short-rate feedback under volatility uncertainty. Within the $G$-expectation framework, the dependence of the short rate on the bond price yields a nonlinear fixed-point problem, represented by a quadratic $G$-BSDE for the logarithmic price. Under suitable assumptions, we establish existence, uniqueness, comparison, and stability for bounded finite-horizon solutions. An additional strict monotonicity condition yields a unique bounded infinite-horizon solution and exponential convergence of finite-horizon approximations on compact time intervals. We apply these results to inverse short-rate design, constructing discount-rate coefficients that reproduce admissible smooth bond-price targets at a fixed maturity. For long maturities, we construct feedback rules under which the compensated logarithmic price converges exponentially to a prescribed bounded state-dependent profile, while the asymptotic yield equals a specified target.

This paper studies the regularity of the value function arising from a multidimensional continuous-time principal-agent model with separable, nonquadratic effort costs. The associated stochastic control problem has the output and the agent's continuation utility as state variables, and its Hamilton-Jacobi-Bellman equation is fully nonlinear and degenerate, with potentially unbounded coefficients. We address these difficulties by adding an independent regularization noise and bounding the effort. For the resulting problem, we establish classical regularity of the value function and show that the optimal effort is unique, positive and remains in a fixed compact subset, uniformly with respect to both the control restriction and the regularization parameter. These estimates allow us first to remove the control restriction and then to let the additional noise vanish. Consequently, we prove that the regularized value function converges to the original value function and conclude that the latter belongs locally to the Sobolev space $W^{2,1}_{\infty, \mathrm{loc}}$, thereby extending the regularity analysis to separable nonquadratic effort costs, for which the arguments yielding classical solutions in the quadratic-cost setting no longer apply.

The Poisson pseudo-maximum likelihood (PPML) estimator is widely used for estimating bilateral gravity equations. Its consistency requires only a correctly specified conditional mean. Conventional inference, however, also requires finite-variance scores and Gaussian limits. We show that these conditions fail: bilateral flows are Pareto-tailed, PPML scores have a stable limit under a structural gravity data-generating process, and sandwich confidence intervals are too narrow. We retain PPML for point estimation but replace sandwich inference with an m-out-of-n bootstrap robust to heavy tails. Across three bilateral data settings, the correction is large and overturns conventionally significant gravity coefficients.

In-context learning (ICL) enables large language model (LLM) agents to improve decisions using interaction history, yet it remains unclear whether such improvement reflects refined internal reasoning or mere extrapolation of statistical patterns. To disentangle these mechanisms, we study LLM agents in multi-agent incomplete-information games that require recursive belief reasoning. By constructing a public goods game and manipulating the statistical structure of historical feedback, we evaluate decision quality against a history-independent rational expectations equilibrium (REE) benchmark. Our experiments reveal that when historical statistical patterns are disrupted, the benefits of longer context largely vanish, degrading decision quality to the no-context baseline in a way sharply amplified by stronger strategic interdependence. These results suggest that, in such strategic environments, ICL behavior is more consistent with statistical extrapolation than with strategic reasoning. Our work extends the mechanistic study of ICL to strategic multi-agent settings, introduces REE as a diagnostic tool for distinguishing reasoning from extrapolation, and provides a reusable framework for probing the boundaries of LLM reasoning in recursive belief tasks.

Discrete choice model specification is a time-consuming task in which modellers often specify and estimate multiple models while balancing goodness-of-fit, parsimony, and behavioural plausibility. We present Delphos, a multitask reinforcement learning framework that learns transferable specification strategies across transport choice datasets. Delphos frames model specification as a sequential decision-making problem in which it applies a sequence of modelling actions and receives feedback from an estimation environment based on model performance and convergence. To transfer modelling decisions across datasets with different sets of variables, Delphos represents utility specifications as sets of modelling terms using a DeepSet-Q architecture, allowing a shared specification policy to learn across multiple datasets. Trained on nine transport choice datasets, Delphos consistently outperforms independently trained single-task agents, indicating that sharing modelling experience improves learning efficiency and helps identify promising sequences of modelling decisions with fewer unsuccessful estimation attempts. When applied without further training to the unseen Swissmetro and Decisions datasets, the same agent identifies competitive specifications in less than 20 minutes on a standard CPU. It achieves a higher log-likelihood per observation than the VNS metaheuristic on Swissmetro and performance comparable to a published MNL specification developed by expert modellers on Decisions. These findings show that accumulating and reusing modelling experience enables Delphos to function as an intelligent assistant for discrete choice model specification. It reduces manual trial-and-error while allowing modellers to retain control over model diagnosis, refinement, and final selection.

We perform a detailed study of a principal–agent problem in a continuous time version of the celebrated Holmström–Milgrom model (Econometrica 55 (2), 1987) where we add limited liability for the Agent. We develop a probabilistic methodology to prove that the Principal's value function is the unique bounded classical solution to a fully nonlinear and fully degenerate partial differential equation (PDE) with Cauchy-Dirichlet boundary conditions on $[0,T]\times[0,\infty)$. Indeed, we also prove infinite continuous differentiability of the solution in the interior of the domain. The strength of our regularity result is such that we can ensure existence of optimal controls in strong form—a rare occurrence in dynamic contracting—and we obtain fine properties of the optimal control map, including a characterisation via a further nonlinear degenerate PDE.

Qualcomm's No License, No Chips policy let it levy a royalty surcharge on every handset, whether or not it used a Qualcomm modem chip. In FTC v. Qualcomm, the Ninth Circuit reversed the district court after accepting Qualcomm's argument that, because the surcharge did not vary with the chip, it was "chip neutral" and left handset makers' choices undistorted. I develop an equilibrium model of the modem chip market and show the defense to be wrong: the surcharge's facial neutrality does not imply economic neutrality. For per handset surcharges, a surcharge and an equal government tax affect the rival's pricing identically, but not Qualcomm's: a tax is remitted to the Treasury, whereas Qualcomm collects the surcharge – including on handsets using a rival's chip. Raising its own price therefore yields Qualcomm a smaller gain under the surcharge (the surcharge it collects on the demand diverted to the rival) than under the tax (the tax it avoids on its own lost sales), because the diversion ratio is less than one. Under the very conditions that would make a tax chip neutral, the surcharge raises the rival's all in price by strictly more than Qualcomm's – and, under symmetric demand, lowers its output by more as well – tilting handset makers toward Qualcomm. For ad valorem surcharges, the defense fails for a different reason: even a non discriminatory tax is generically chip neutral only if the FRAND royalty rate is zero, so the argument's premise itself does not hold. I also analyze discriminatory surcharges.

Automated execution algorithms are organized into schedule-based and liquidity-seeking families. This paper concerns the first, whose members – Time-Weighted Average Price (TWAP), Volume-Weighted Average Price (VWAP), Percentage of Volume (POV) and Implementation Shortfall – are all model-based: each derives its decisions from an explicit model, forecast, schedule or control rule. We introduce Shadow-PPOV, a passive POV whose order-placement rate is set from observed order flow rather than from traded volume. Placing a passive order to fill efficiently conventionally involves an order-book model and a fill prediction. Shadow-PPOV replaces that prediction with tracking: on observing a third-party add, it may transmit its own limit order at the same price on the same venue, recording a single association between the observed order's exchange identifier and its own. Cancellation is then identifier-driven – the shadow is withdrawn when the order it follows ends, at once on a cancel and after a brief grace window on a trade. The placement decision is thus model-free: price and venue are read off the observed order. Model-free is not information-free. Shadow-PPOV reads every order-book message and places only where a participant has just committed capital, while computing nothing from what it reads. Information is inherited from the flow rather than derived from a model. We evaluate Shadow-PPOV on a full calendar year of replayed Chicago Mercantile Exchange (CME) ES futures in a deterministic market-replay simulator, reporting its slippage and latency sensitivity and comparing it against the aggressive equivalent POV. We propose it as a model-free benchmark for passive-order placement, against which a predictive placement model can be scored. The algorithm and the order-book simulator are implemented in the open-source kaspar-hft project.

Large language models (LLMs) now serve as conversational shopping assistants on platforms that also sell advertising. These AI agents face a conflict of duty. They advise consumers who rely on their judgment, yet are deployed by platforms that benefit when sponsored listings are chosen. Sponsorship disclosures, designed to allow consumers to penalize paid placements, now reach the AI agent rather than the consumer, and the agent's evaluation of them is hidden from the consumer. Drawing on the fiduciary concept of conflict of duty, we argue that an agent's evaluation of a sponsored listing should not depend on which party deployed it. In controlled choice experiments, we manipulate assigned roles in the system prompt to name either a traveler or a booking platform as the agent's principal. Platform delegation significantly attenuates the penalty that agents apply to sponsored listings and weakens the skepticism that disclosure triggers in their reasoning traces. We replicate out findings across LLMs and reasoning depths. A second study decomposes the disclosure label and shows that the divergence between the two delegates widens significantly when the paid placement is attributed to the platform. Stricter terminology ("Sponsored" instead of "Promoted") lowers choice of paid listings but does not close this gap when the platform is named. The findings show that disclosure mandates designed for human consumers cannot by themselves protect consumers in AI-mediated commerce.

Event-contract settlement is a state path rather than a universal timestamp. Using a registered seven-day enrollment and seven-day administrative follow-up on Kalshi, this paper distinguishes market-object membership, lifecycle messages, endpoint states, and observation gaps. The MVE layer contains 7,611,594 tickers created inside the registered half-open interval; the reconstructed ordinary stock contains 152,694 markets at risk at the enrollment boundary, 3,477,227 already terminal records, and 11,410 boundary-uncertain records. The follow-up identifies exact public endpoints for 7,357,576 MVE market objects: 7,237,993 through REST settlement time alone, 119,212 agreeing across WebSocket and REST, and 371 WebSocket-only; 254,018 lack an admissible exact endpoint. In the ordinary boundary cohort, 71,657 markets have an exact WebSocket endpoint and 81,037 do not. These are venue-level public-finality outcomes, not member-cash observations. Exact determination-to-endpoint pairs are available for 126,806 MVE and 70,979 ordinary tickers, with respective mean durations of 9.676 and 322.50 seconds and observed maxima of 15 and 7,133 seconds. The contrast is descriptive, not causal. Transport gaps preserve two exact endpoint clocks but prevent the same rows from proving complete intermediate paths or absence of revisions; current settlement timers cannot be bound to historical rule versions. Public endpoint completion is therefore extensive, especially on REST, while path completeness remains narrower and family-specific. The exact-key historical crosswalk records earlier interim observations without changing the final cohort or endpoint facts.

Scientific progress is a key driver of economic growth and prosperity. There is great excitement - but also concerns - about the impacts of AI on science, but so far little data. We provide early insights on this from three data sources: a sample of 15 million Gemini interactions, an inventory of over 2,600 specialized AI models across disciplines, and a survey of over 600 scientists. We map these data to a new taxonomy of scientific tasks to study how scientists are using AI. Four main findings emerge. First, we find broad adoption and coverage: scientists use AI more than most other occupations. Specialized AI models have broad disciplinary coverage and are highly cited. Nearly half of the scientists surveyed report using some form of AI every day. Second, we document evidence that LLMs (proxied through Gemini usage) and specialized models act as complements– LLMs are used for general analysis, coding, and manuscript preparation, while specialized models provide domain-specific predictions, data generation and classification. Third, scientists report large productivity gains from using AI: a saving of nearly 7 hours per week, time which is primarily re-invested in more research. Finally, we show that AI is already changing the scientific process. As some stages of scientific research become easier, bottlenecks shift downstream. Scientists report an increased backlog of untested hypotheses and substantial demand for output verification. Our findings suggest that AI holds significant potential to increase scientific productivity. However, as with other sectors, its ultimate impact will be governed by complex task interdependencies and investment into the elimination of emerging bottlenecks.

Alòs, Gatheral and Radoičić derived the Bergomi-Guyon expansion of the implied variance smile from the forest expansion of the cumulant generating function. Its coefficients are sums of products of diamond trees, with prefactors that are polynomials in the log-strike $k$. Matching moments order by order produces, at order $\epsilon^\ell$, terms of degree greater than $\ell$ in $k$ that mysteriously cancel. We show that, in suitable variables, the matching condition can be formulated as a nonlinear heat equation. The resulting recursion computes the prefactor of each product of trees from those of products of fewer trees, without generating the higher-degree terms; its only model-independent input is a cumulant series, which we compute in closed form. Consequently, at order $\epsilon^\ell$ every prefactor has degree exactly $\ell$ in $k$. The prefactors are universal and need only be computed once. Code and coefficients are provided.

We present SAiFE_gym, a Python module that provides a collection of simulation environments for studying trading problems in Constant Product Markets (CPMs) with Concentrated Liquidity (CL). These markets give Liquidity Providers (LPs) granular control over how their capital is allocated and enable them to adjust their range of liquidity provision dynamically based on market conditions, which in turn, dictates how they earn fees. We decompose the microstructure of CPMs with CL in interactive components that allow researchers and practitioners to capture various economic settings. We employ a vectorized approach to optimize our environments, making them scalable for high dimensional Reinforcement Learning (RL) workflows that best describe sequential decision problems. We demonstrate the benefits of our environments by evaluating the performance of RL agents in CPMs with CL under uncertainty in market parameters.

Telework has expanded rapidly, and understanding the behaviors employees enact under it has become correspondingly important. This study develops an integrative conceptual framework specifying the multidimensional nature of telework behavior. A systematic literature review following PRISMA identified 114 review articles, which were analyzed using constructivist grounded theory. Six behavioral dimensions were identified, covering performance, communication, environmental, task, policy, and well-being conduct. Antecedents were grouped into individual factors, job characteristics, organizational norms, technological factors, and work environment factors. Outcomes were grouped into job satisfaction, productivity, turnover, health and well-being, work-life balance, and social isolation. Three contextual moderators were identified, namely telework modality, telework preference, and cultural and national context. Fourteen propositions link these categories and are advanced as testable claims rather than established findings, since they are derived from published review evidence and have not been empirically tested. The framework proposes telework behavior as the construct through which the conditions of remote work are translated into employee and organizational outcomes, and it specifies a content domain from which measures of the construct can be developed. It also indicates where organizations can direct policy, training, and intervention.

Green economic complexity provides a generalizable framework for examining countries' productive capabilities in a defined product set. We apply this framework to AI-enabling goods within the full product space, linking current specialization with adjacent diversification opportunities. Using BACI exports for 2007-2023 and 103 AI-enabling goods, we measure complexity-weighted specialization (AECI), product-level adjacent opportunities (AIAP), and average complexity-weighted relatedness of remaining candidates (AECP). In 2023, Japan leads AECI, while China leads AECP; portfolio breadth accounts for much of the variation in raw AECI. Initial raw potential is positively associated with subsequent changes in the AI-enabling export share, but its associations with changes in AECI and specialization counts are not statistically significant at the 5% level. Our contribution is a trade-based assessment of AI-enabling productive capabilities and related opportunities. The results and public dashboard provide a preliminary complement to publication and patent indicators, not a comprehensive measure of national AI performance or a validated forecast of diversification.

Public evidence can become sufficient to settle a prediction-market contract before the venue records its first determination, but the relevant boundary depends on the applicable rule version, exact release object, source hierarchy, correction history, and unfinished contract conditions. This paper defines two Kalshi clocks: first decidability, the earliest contemporaneous singleton in the rule-evidence mapping, and stable decidability, the retrospective earliest time after which the same singleton remains unchanged through finalization. A completed retrospective identification test establishes a narrow feasibility result. In a frozen blind pilot, all 25 identities and blinding checks passed and current rule text was recovered for all 25; no exact or bounded historical rule version and no exact or bounded official source-release object was recovered. The full historical recovery covered 152,694 ordinary tickers, 11,530 exact event identities, and 6,540 read-only official requests, with zero historically eligible events and zero historically eligible tickers. This is an observability result, not a claim that no market was decidable or that public evidence never existed. A completed prospective infrastructure shakedown established observation capability for three source programmes across 25 markets, with integrity revalidation of 781,266 lifecycle frames, 22 closed lower-bounded reconnect receipts, no unresolved reconnect gap, no due-but-missed official release, and an inactive price layer. Production evidence enrollment is active. The prospective sample is constructed only at enrollment close from prospectively frozen identities and pre-outcome fields; its frozen target size is selected mechanically under the registered full, reduced, exploratory, or no-go support disposition. No contractual-decidability clock, human-adjudication, price, or cross-venue result is reported here.