Tootfinder

Formal Algorithms for Model Efficiency
Naman Tyagi, Srishti Das, Kunal, Vatsal Gupta
https://arxiv.org/abs/2508.14000 https://arxiv.org/pdf/2508.14000

Formal Algorithms for Model Efficiency
We introduce the Knob-Meter-Rule (KMR) framework, a unified formalism for representing and reasoning about model efficiency techniques in deep learning. By abstracting diverse methods, including pruning, quantization, knowledge distillation, and parameter-efficient architectures, into a consistent set of controllable knobs, deterministic rules, and measurable meters, KMR provides a mathematically precise and modular perspective on efficiency optimization. The framework enables systematic compos…

The (Short-Term) Effects of Large Language Models on Unemployment and Earnings
Danqing Chen, Carina Kane, Austin Kozlowski, Nadav Kunievsky, James A. Evans
https://arxiv.org/abs/2509.15510

The (Short-Term) Effects of Large Language Models on Unemployment and Earnings
Large Language Models have spread rapidly since the release of ChatGPT in late 2022, accompanied by claims of major productivity gains but also concerns about job displacement. This paper examines the short-run labor market effects of LLM adoption by comparing earnings and unemployment across occupations with differing levels of exposure to these technologies. Using a Synthetic Difference in Differences approach, we estimate the impact of LLM exposure on earnings and unemployment. Our findings …

Quantum Power Iteration Unified Using Generalized Quantum Signal Processing
Viktor Khinevich, Yasunori Lee, Nobuyuki Yoshioka, Wataru Mizukami
https://arxiv.org/abs/2507.11142

Quantum Power Iteration Unified Using Generalized Quantum Signal Processing
We present a unifying framework for quantum power-method-based algorithms through the lens of generalized quantum signal processing (GQSP): we apply GQSP to realize quantum analogues of classical power iteration, power Lanczos, inverse iteration, and folded spectrum methods, all within a single coherent framework. Our approach is efficient in terms of the number of queries to the block encoding of a Hamiltonian. Also, our approach can avoid Suzuki-Trotter decomposition. We constructed quantum c…

Open, Reproducible and Trustworthy Robot-Based Experiments with Virtual Labs and Digital-Twin-Based Execution Tracing
Benjamin Alt, Mareike Picklum, Sorin Arion, Franklin Kenghagho Kenfack, Michael Beetz
https://arxiv.org/abs/2508.11406

Open, Reproducible and Trustworthy Robot-Based Experiments with Virtual Labs and Digital-Twin-Based Execution Tracing
We envision a future in which autonomous robots conduct scientific experiments in ways that are not only precise and repeatable, but also open, trustworthy, and transparent. To realize this vision, we present two key contributions: a semantic execution tracing framework that logs sensor data together with semantically annotated robot belief states, ensuring that automated experimentation is transparent and replicable; and the AICOR Virtual Research Building (VRB), a cloud-based platform for sha…

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