AI coding agents make empirical specification search fast and cheap, but they also widen hidden researcher degrees of freedom. Building on an open-source agent-loop architecture, this paper adapts that framework to an empirical economics workflow and adds a post-search holdout evaluation. In a forecast-combination illustration, multiple independent agent runs outperform standard benchmarks in the original rolling evaluation, but not all continue to do so on a post-search holdout. Logged search and holdout evaluation together make adaptive specification search more transparent and help distinguish robust improvements from sample-specific discoveries.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on millions of public job advertisements collected from online recruitment platforms, this dataset encompasses monthly recruitment demand.Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research.

To protect deep neural networks (DNNs) from adversarial attacks, adversarial training (AT) is developed by incorporating adversarial examples (AEs) into model training. Recent studies show that adversarial attacks disproportionately impact the patterns within the phase of the sample's frequency spectrum---typically containing crucial semantic information---more than those in the amplitude, resulting in the model's erroneous categorization of AEs. We find that, by mixing the amplitude of training samples' frequency spectrum with those of distractor images for AT, the model can be guided to focus on phase patterns unaffected by adversarial perturbations. As a result, the model's robustness can be improved. Unfortunately, it is still challenging to select appropriate distractor images, which should mix the amplitude without affecting the phase patterns.

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In this paper, we introduce IMPACT (Integrated Multimodal Patent Analysis and Creation Dataset for Design Patents), a large-scale multimodal patent dataset with detailed captions for design patent figures. Our dataset includes half a million design patents comprising 3.61 million figures along with captions from patents granted by the United States Patent and Trademark Office (USPTO) over a 16-year period from 2007 to 2022. We incorporate the metadata of each patent application with elaborate captions that are coherent with multiple viewpoints of designs. Even though patents themselves contain a variety of design figures, titles, and descriptions of viewpoints, we find that they lack detailed descriptions that are necessary to perform multimodal tasks such as classification and retrieval. IMPACT closes this gap thereby providing researchers with necessary ingredients to instantiate a variety of multimodal tasks. Our dataset has a huge potential for novel design inspiration and can be used with advanced computer vision models in tandem. We perform preliminary evaluations on the dataset on the popular patent analysis tasks such as classification and retrieval. Our results indicate that integrating images with generated captions significantly improves the performance of different models on the corresponding tasks. Given that design patents offer various benefits for modeling novel tasks, we propose two standard computer vision tasks that have not been investigated in analyzing patents as future directions using IMPACT as a benchmark viz., 3D Image Construction and Visual Question Answering (VQA).

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The deal will give Palantir sight of a trove of data about how the City of London operates. The deal will give Palantir sight of a trove of data about how the City of London operates. Sun 22 Mar 2026 12.00 EDTLast modified on Sun 22 Mar 2026 12.42 EDT Palantirâ s latest UK contract takes the AI and data analytics company into the heart of one of Britainâ s biggest industries: financial services, which accounts for 9% of the economy. The Miami-based company embedded its technology in the NHS in 2023, the police in 2024 and the military in 2025. Land and expand, they say in the tech industry. Palantir has followed the script building contracts worth more than £500m.

Palantir, co-founded by the billionaire Donald Trump donor Peter Thiel (pictured), has been appointed for a three-month trial period. Palantir, co-founded by the billionaire Donald Trump donor Peter Thiel (pictured), has been appointed for a three-month trial period. Sun 22 Mar 2026 12.00 EDTLast modified on Sun 22 Mar 2026 22.30 EDT Palantir is to be granted access to a trove of highly sensitive UK financial regulation data, in a deal that has prompted fresh concerns about the US AI companyâ s deepening reach into the British state, the Guardian can reveal. The Financial Conduct Authority (FCA) has awarded Palantir a contract to investigate the watchdogâ s internal intelligence data in an effort to help it tackle financial crime, which includes investigating fraud, money laundering and insider trading. The Miami-based company, co-founded by the billionaire Donald Trump donor Peter Thiel, has been appointed for a three-month trial, paying more than £30,000 a week to analyse the FCAâ s vast â data lakeâ, which could lead to a full procurement of an AI system.