Tootfinder

Beyond Revenue and Welfare: Counterfactual Analysis of Spectrum Auctions with Application to Canada's 3800MHz Allocation
Sara Jalili Shani, Kris Joseph, Michael B. McNally, James R. Wright
https://arxiv.org/abs/2512.08106 https://arxiv.org/pdf/2512.08106 https://arxiv.org/html/2512.08106
arXiv:2512.08106v1 Announce Type: new
Abstract: Spectrum auctions are the primary mechanism through which governments allocate scarce radio frequencies, with outcomes that shape competition, coverage, and innovation in telecommunications markets. While traditional models of spectrum auctions often rely on strong equilibrium assumptions, we take a more parsimonious approach by modeling bidders as myopic and straightforward: in each round, firms simply demand the bundle that maximizes their utility given current prices. Despite its simplicity, this model proves effective in predicting the outcomes of Canada's 2023 auction of 3800 MHz spectrum licenses. Using detailed round-by-round bidding data, we estimate bidders' valuations through a linear programming framework and validate that our model reproduces key features of the observed allocation and price evolution. We then use these estimated valuations to simulate a counterfactual auction under an alternative mechanism that incentivizes deployment in rural and remote regions, aligning with one of the key objectives set out in the Canadian Telecommunications Act. The results show that the proposed mechanism substantially improves population coverage in underserved areas. These findings demonstrate that a behavioral model with minimal assumptions is sufficient to generate reliable counterfactual predictions, making it a practical tool for policymakers to evaluate how alternative auction designs may influence future outcomes. In particular, our study demonstrates a method for counterfactual mechanism design, providing a framework to evaluate how alternative auction rules could advance policy goals such as equitable deployment across Canada.
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TIL about the Perverse Incentive (aka Cobra Effect), which describes the effect when "incentives are often designed to achieve short-term goals, but in the long run, they lead to bigger problems or undermine the original objectives":
https://en.wikipedia.org/wiki/Perverse_incentiv…

Perverse incentive - Wikipedia

Robust Exploratory Stopping under Ambiguity in Reinforcement Learning
Junyan Ye, Hoi Ying Wong, Kyunghyun Park
https://arxiv.org/abs/2510.10260 https://arx…

Robust Exploratory Stopping under Ambiguity in Reinforcement Learning
We propose and analyze a continuous-time robust reinforcement learning framework for optimal stopping problems under ambiguity. In this framework, an agent chooses a stopping rule motivated by two objectives: robust decision-making under ambiguity and learning about the unknown environment. Here, ambiguity refers to considering multiple probability measures dominated by a reference measure, reflecting the agent's awareness that the reference measure representing her learned belief about the env…

How does it feel to have given $60 of your money to bail out Argentina?
What, you did not know that $60 was pulled out of your pocket (and the pocket of every man, women, and child in the US) to be gifted unto Argentina?
https://www.pbs.org/newsho…

WATCH LIVE: Trump meets with Argentina's Milei as U.S. prepares $20 billion bailout
Experts see two objectives for Milei at the White House. One is to negotiate U.S. tariff exemptions for Argentine products. The other is determine how the U.S. will implement its currency swap line to replenish Argentina's depleted currency reserves ahead of the country's crucial midterm elections this month.

Robust Closed-Form Control for MIMO Nonlinear Systems under Conflicting Time-Varying Hard and Soft Constraints
Farhad Mehdifar, Charalampos P. Bechlioulis, Dimos V. Dimarogonas
https://arxiv.org/abs/2510.11393

Robust Closed-Form Control for MIMO Nonlinear Systems under Conflicting Time-Varying Hard and Soft Constraints
This paper introduces a novel robust closed-form control law to handle time-varying hard and soft constraints in uncertain high-relative-degree nonlinear MIMO systems. These constraints represent spatiotemporal specifications in mechanical systems' operational space, with hard constraints ensuring safety-critical requirements and soft constraints encoding performance or task objectives. Initially, all constraints are consolidated into two separate scalar time-varying hard and soft constraint fu…

Aixel: A Unified, Adaptive and Extensible System for AI-powered Data Analysis
Meihui Zhang, Liming Wang, Chi Zhang, Zhaojing Luo
https://arxiv.org/abs/2510.12642 https://…

Aixel: A Unified, Adaptive and Extensible System for AI-powered Data Analysis
A growing trend in modern data analysis is the integration of data management with learning, guided by accuracy, latency, and cost requirements. In practice, applications draw data of different formats from many sources. In the meanwhile, the objectives and budgets change over time. Existing systems handle these applications across databases, analysis libraries, and tuning services. Such fragmentation leads to complex user interaction, limited adaptability, suboptimal performance, and poor exte…

PromptFlow: Training Prompts Like Neural Networks
Jingyi Wang, Hongyuan Zhu, Ye Niu, Yunhui Deng
https://arxiv.org/abs/2510.12246 https://arxiv.org/pdf/251…

PromptFlow: Training Prompts Like Neural Networks
Large Language Models (LLMs) have demonstrated profound impact on Natural Language Processing (NLP) tasks. However, their effective deployment across diverse domains often require domain-specific adaptation strategies, as generic models may underperform when faced with specialized data distributions. Recent advances in prompt engineering (PE) offer a promising alternative to extensive retraining by refining input instructions to align LLM outputs with task objectives. This paradigm has emerged …

Task-Aware Reduction for Scalable LLM-Database Systems
Marcus Emmanuel Barnes, Taher A. Ghaleb, Safwat Hassan
https://arxiv.org/abs/2510.11813 https://arxi…

Task-Aware Reduction for Scalable LLM-Database Systems
Large Language Models (LLMs) are increasingly applied to data-intensive workflows, from database querying to developer observability. Yet the effectiveness of these systems is constrained by the volume, verbosity, and noise of real-world text-rich data such as logs, telemetry, and monitoring streams. Feeding such data directly into LLMs is costly, environmentally unsustainable, and often misaligned with task objectives. Parallel efforts in LLM efficiency have focused on model- or architecture-l…

Distributionally Robust Control with End-to-End Statistically Guaranteed Metric Learning
Jingyi Wu, Chao Ning, Yang Shi
https://arxiv.org/abs/2510.10214 https://

Distributionally Robust Control with End-to-End Statistically Guaranteed Metric Learning
Wasserstein distributionally robust control (DRC) recently emerges as a principled paradigm for handling uncertainty in stochastic dynamical systems. However, it constructs data-driven ambiguity sets via uniform distribution shifts before sequentially incorporating them into downstream control synthesis. This segregation between ambiguity set construction and control objectives inherently introduces a structural misalignment, which undesirably leads to conservative control policies with sub-opt…

You Only Train Once: Differentiable Subset Selection for Omics Data
Daphn\'e Chopard, Jorge da Silva Gon\c{c}alves, Irene Cannistraci, Thomas M. Sutter, Julia E. Vogt
https://arxiv.org/abs/2512.17678 https://arxiv.org/pdf/2512.17678 https://arxiv.org/html/2512.17678
arXiv:2512.17678v1 Announce Type: new
Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling. However, most existing feature selection approaches either operate as multi-stage pipelines or rely on post hoc feature attribution, making selection and prediction weakly coupled. In this work, we present YOTO (you only train once), an end-to-end framework that jointly identifies discrete gene subsets and performs prediction within a single differentiable architecture. In our model, the prediction task directly guides which genes are selected, while the learned subsets, in turn, shape the predictive representation. This closed feedback loop enables the model to iteratively refine both what it selects and how it predicts during training. Unlike existing approaches, YOTO enforces sparsity so that only the selected genes contribute to inference, eliminating the need to train additional downstream classifiers. Through a multi-task learning design, the model learns shared representations across related objectives, allowing partially labeled datasets to inform one another, and discovering gene subsets that generalize across tasks without additional training steps. We evaluate YOTO on two representative single-cell RNA-seq datasets, showing that it consistently outperforms state-of-the-art baselines. These results demonstrate that sparse, end-to-end, multi-task gene subset selection improves predictive performance and yields compact and meaningful gene subsets, advancing biomarker discovery and single-cell analysis.
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