SummaryThe paper proposes a framework that separates changes in the number of tasks an occupation performs from changes in occupational uniqueness, meaning how distinct its task mix is relative to other occupations. Using 196,563 task statements from the Dictionary of Occupational Titles and O*NET covering 1977 to 2018, the authors find that occupations becoming less unique are associated with lower wage growth. The wage associations of task additions and removals depend on how widely those tasks are performed elsewhere.
In briefSpeculative Imbalance Positions by Non-Physical Traders and Their Effect on Great Britain's Electricity Balancing
SummaryThis paper provides the first empirical analysis of speculative imbalance positions taken by non-physical traders in Great Britain's electricity system from 2021 to 2024. Using counterfactual simulations that remove the aggregate non-physical trader imbalance, the authors measure effects on balancing volumes, balancing costs and supplier imbalance costs. They find these positions reduced balancing costs by 5.9 to 6.7 percent and supplier imbalance costs by 65 to 75 percent, suggesting consumer bills would have been higher without them.
In briefArithmetic Models for Pricing Commodity Futures, Vanilla, and Calendar Spread Options
SummaryThe paper proposes an arithmetic multi-factor stochastic volatility model for commodity futures and options, giving a low-dimensional Markovian representation of the futures curve and semi-closed-form prices for vanilla, basket, and calendar spread options. It is estimated with an unscented Kalman filter on WTI crude oil futures, vanilla options, and calendar spread options from 2013 to 2024. The authors find three volatility factors are needed, and that including calendar spread options improves the model's treatment of dependence between adjacent futures contracts. The arithmetic model is also reported to fit better than its geometric counterpart.
In briefStronger AI breakthroughs accelerate the exhaustion of their own ideas
SummaryThe paper analyzes 36 years of computer science, including 7.2 million papers, 73,089 peer reviews and 3.7 million researchers. It finds that attention narrowed sharply after the Transformer, and that each major breakthrough was followed by steeper exhaustion of ideas. The authors call for institutions that protect diverse attention in AI.
WhyAnalyzing millions of papers, reviews and researchers, it shows breakthroughs deplete diverse attention, corroborated by structural biology and simulations.
In briefDeep catastrophic-risk-aware reinforcement learning: training neural policies with extreme value theory to control rare-event losses
SummaryThis paper extends an Extreme Value Theory approach for CVaR optimization to high-dimensional neural-network policies. It uses a peaks-over-threshold representation and method-of-moments estimation to obtain a differentiable tail-risk objective whose finite-sample gradient can be computed via automatic differentiation. In a deep-hedging study with rare extreme market moves, EVT-trained policies behaved similarly to empirical-CVaR-trained ones under normal conditions but had lower hedging losses in extreme-scenario evaluation.
Carlos Octavio P\'erez Mendoza, Fr\'ed\'eric Godin
In briefLOB-Impact Benchmark: Market Impact Emerging in Generative Order Book Models
SummaryThis paper introduces the LOB-Impact benchmark for testing whether generative limit order book models reproduce realistic market impact. The authors inject execution-style metaorders into order flow generated by five autoregressive models across five NASDAQ stocks and measure price responses using empirical impact estimators. Models trained only for next-token prediction show emergent impact, with exponents between 0.63 and 1.21, slightly above the square-root law's 0.5. No impact decay during relaxation was observed, which the authors tentatively attribute to short context windows; the benchmark is released as open source.
George Nigmatulin, Valentin Mohl, Sascha Frey et al.
In briefExact risk analysis of teacher training, fixed features, and calibration for candidate-scoring models
SummaryThe paper studies three training choices that determine what a candidate-scoring decision model's probabilities mean: soft targets from a teacher model, a frozen pretrained backbone, and temperature calibration. It analyzes these in Gaussian surrogate models where finite-sample risk can be computed exactly, and tests them on 231,521 CFPB credit card complaints about whether a complaint ends in monetary relief. The authors find that a student inherits a teacher's bias, and that calibration corrects only the probability scale, so probabilities must be checked against realized outcomes.
In briefMulti-agent reinforcement learning for market making on Polymarket order books
SummaryThis paper studies market makers trained with multi-agent reinforcement learning on Polymarket, a prediction market with thin order books. The authors reconstruct order-level LOBSTER-format data from Polymarket and train agents on 417 real markets, splitting the maker into a quoter and a risk manager with reward shaping. Their redesigned single-agent maker quotes more often and has smaller held-out losses than the retrained baseline. However, none of the configurations achieves positive average portfolio value in periodic held-out evaluation, and each was trained with one seed.
In briefProbability-weighting functions from return distribution changes: three orthogonal channels and information-theoretic indices
SummaryThe paper builds probability-weighting functions from changes between prior and posterior normal tempered-stable return distributions rather than a parametric family. Scale, skewness and tail thickness are isolated one at a time with variance held fixed, each producing a distinct sign pattern in the deviation from identity. Three information-theoretic indices summarize the distortions, and the framework is applied to S&P 500 ETF daily returns from 2015 to 2025.
Akash Deep, Bhathiya Divelgama, A. Alexandre Trindade et al.
