Tootfinder

Axiomatic Choice and the Decision-Evaluation Paradox
Ben Abramowitz, Nicholas Mattei
https://arxiv.org/abs/2509.21836 https://arxiv.org/pdf/2509.21836

Axiomatic Choice and the Decision-Evaluation Paradox
We introduce a framework for modeling decisions with axioms that are statements about decisions, e.g., ethical constraints. Using our framework we define a taxonomy of decision axioms based on their structural properties and demonstrate a tension between the use of axioms to make decisions and the use of axioms to evaluate decisions which we call the Decision-Evaluation Paradox. We argue that the Decision-Evaluation Paradox arises with realistic axiom structures, and the paradox illuminates why…

LLM Agent Meets Agentic AI: Can LLM Agents Simulate Customers to Evaluate Agentic-AI-based Shopping Assistants?
Lu Sun, Shihan Fu, Bingsheng Yao, Yuxuan Lu, Wenbo Li, Hansu Gu, Jiri Gesi, Jing Huang, Chen Luo, Dakuo Wang
https://arxiv.org/abs/2509.21501

LLM Agent Meets Agentic AI: Can LLM Agents Simulate Customers to Evaluate Agentic-AI-based Shopping Assistants?
Agentic AI is emerging, capable of executing tasks through natural language, such as Copilot for coding or Amazon Rufus for shopping. Evaluating these systems is challenging, as their rapid evolution outpaces traditional human evaluation. Researchers have proposed LLM Agents to simulate participants as digital twins, but it remains unclear to what extent a digital twin can represent a specific customer in multi-turn interaction with an agentic AI system. In this paper, we recruited 40 human par…

Steelers' Calvin Austin being evaluated in Dublin hospital after suffering shoulder injury in win over Vikings

https://www.cbssports.com/nfl/news/s…

GW241011 and GW241110 - Exploring Binary Formation and Fundamental Physics with Asymmetric, High-spin #BlackHole Coalescences: https://iopscience.iop.org/article/10.3847/2041-8213/ae0d54 -> Colliding black holes might have formed from earlier cosmic smashups: https://www.caltech.edu/about/news/colliding-black-holes-might-have-formed-from-earlier-cosmic-smashups and https://www.ligo.caltech.edu/news/ligo20251028 and https://www.ozgrav.org/news/twin-black-hole-mergers-reveal-secrets-of-cosmic-evolution/ - two recent detections have given the international LIGO-Virgo-KAGRA collaboration new insights into black hole formation and evolution -> Gravitational Wave Detectors Spot Merging Black Holes That Have Merged Before: https://aasnova.org/2025/10/28/not-their-first-rodeo-gravitational-wave-detectors-spot-merging-black-holes-that-have-merged-before/

ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents
Tianjian Liu, Fanqi Wan, Jiajian Guo, Xiaojun Quan
https://arxiv.org/abs/2508.20973 https://

ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents
Proactive dialogue has emerged as a critical and challenging research problem in advancing large language models (LLMs). Existing works predominantly focus on domain-specific or task-oriented scenarios, which leads to fragmented evaluations and limits the comprehensive exploration of models' proactive conversation abilities. In this work, we propose ProactiveEval, a unified framework designed for evaluating proactive dialogue capabilities of LLMs. This framework decomposes proactive dialogue in…

I need to read it properly, but this looks 🔥 https://arxiv.org/abs/2511.16652

Evolution Strategies at the Hyperscale
We introduce Evolution Guided General Optimization via Low-rank Learning (EGGROLL), an evolution strategies (ES) algorithm designed to scale backprop-free optimization to large population sizes for modern large neural network architectures with billions of parameters. ES is a set of powerful blackbox optimisation methods that can handle non-differentiable or noisy objectives with excellent scaling potential through parallelisation. Na{ï}ve ES becomes prohibitively expensive at scale due to the…

Evaluating Differentially Private Generation of Domain-Specific Text
Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar, Iqra Zahid, Yuping Wu, Warren Del-Pinto, Goran Nenadic, Siew-Kei Lam, Jie Zhang, Anil A Bharath
https://arxiv.org/abs/2508.20452

Evaluating Differentially Private Generation of Domain-Specific Text
Generative AI offers transformative potential for high-stakes domains such as healthcare and finance, yet privacy and regulatory barriers hinder the use of real-world data. To address this, differentially private synthetic data generation has emerged as a promising alternative. In this work, we introduce a unified benchmark to systematically evaluate the utility and fidelity of text datasets generated under formal Differential Privacy (DP) guarantees. Our benchmark addresses key challenges in d…

Evaluating LLMs for Combinatorial Optimization: One-Phase and Two-Phase Heuristics for 2D Bin-Packing
Syed Mahbubul Huq, Daniel Brito, Daniel Sikar, Rajesh Mojumder
https://arxiv.org/abs/2509.22255

Evaluating LLMs for Combinatorial Optimization: One-Phase and Two-Phase Heuristics for 2D Bin-Packing
This paper presents an evaluation framework for assessing Large Language Models' (LLMs) capabilities in combinatorial optimization, specifically addressing the 2D bin-packing problem. We introduce a systematic methodology that combines LLMs with evolutionary algorithms to generate and refine heuristic solutions iteratively. Through comprehensive experiments comparing LLM generated heuristics against traditional approaches (Finite First-Fit and Hybrid First-Fit), we demonstrate that LLMs can pro…

The Lie of the Average: How Class Incremental Learning Evaluation Deceives You?
Guannan Lai, Da-Wei Zhou, Xin Yang, Han-Jia Ye
https://arxiv.org/abs/2509.22580 https://

The Lie of the Average: How Class Incremental Learning Evaluation Deceives You?
Class Incremental Learning (CIL) requires models to continuously learn new classes without forgetting previously learned ones, while maintaining stable performance across all possible class sequences. In real-world settings, the order in which classes arrive is diverse and unpredictable, and model performance can vary substantially across different sequences. Yet mainstream evaluation protocols calculate mean and variance from only a small set of randomly sampled sequences. Our theoretical anal…

Guiding Evolution of Artificial Life Using Vision-Language Models
Nikhil Baid, Hannah Erlebach, Paul Hellegouarch, Frederico Wieser
https://arxiv.org/abs/2509.22447 https://

Guiding Evolution of Artificial Life Using Vision-Language Models
Foundation models (FMs) have recently opened up new frontiers in the field of artificial life (ALife) by providing powerful tools to automate search through ALife simulations. Previous work aligns ALife simulations with natural language target prompts using vision-language models (VLMs). We build on Automated Search for Artificial Life (ASAL) by introducing ASAL++, a method for open-ended-like search guided by multimodal FMs. We use a second FM to propose new evolutionary targets based on a sim…