Tootfinder

Is #AI really just dumb statistics? "Olympiad-level physics problem-solving presents a significant challenge for both humans and artificial intelligence (AI), as it requires a sophisticated integration of precise calculation, abstract reasoning, and a fundamental grasp of physical principles," says the (abstract of the) paper https://arxiv.org/abs/2511.10515: "The Chinese Physics Olympiad (CPhO), renowned for its complexity and depth, serves as an ideal and rigorous testbed for these advanced capabilities. In this paper, we introduce LOCA-R (LOgical Chain Augmentation for Reasoning), an improved version of the LOCA framework adapted for complex reasoning, and apply it to the CPhO 2025 theory examination. LOCA-R achieves a near-perfect score of 313 out of 320 points, solidly surpassing the highest-scoring human competitor and significantly outperforming all baseline methods." Oops ...?

This is just such a waste of money.
https://ec.social-network.europa.eu/@EUCommission/115343249383566862

European Commission (@EUCommission@ec.social-network.europa.eu)
Attached: 1 image Europe is upping its game in AI. Researchers, innovators & policymakers now have stronger support to shape the future of science. Today we launched two AI strategies to boost EU leadership in scientific innovation: 🥼 AI in Science 🔹Access to AI Factories & Gigafactories 🔹Stronger EU & global cooperation 🔹 Impact on major challenges such as climate change and health 🔬 Apply AI: 🔹Enhances EU tech sovereignty 🔹Targeted actions to boost AI in key secto…

David Sacks says AI preemption won't apply to state laws on child safety, communities won't be forced to host data centers they don't want, and more (David Sacks/@davidsacks)
https://x.com/davidsacks/status/1998125180753944985

David Sacks (@DavidSacks) on X
ONE RULEBOOK FOR AI I wanted to share a few thoughts on AI preemption and address some of the concerns. First, this is not an “AI amnesty” or “AI moratorium.” It is an attempt to settle a question of jurisdiction. When an AI model is developed in state A, trained in state B,

PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks
Yunuo Liu, Dawei Zhu, Zena Al-Khalili, Dai Cheng, Yanjun Chen, Dietrich Klakow, Wei Zhang, Xiaoyu Shen
https://arxiv.org/abs/2510.12409

PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks
We present PricingLogic, the first benchmark that probes whether Large Language Models(LLMs) can reliably automate tourism-related prices when multiple, overlapping fare rules apply. Travel agencies are eager to offload this error-prone task onto AI systems; however, deploying LLMs without verified reliability could result in significant financial losses and erode customer trust. PricingLogic comprises 300 natural-language questions based on booking requests derived from 42 real-world pricing p…

Found this article quite interesting: #AI #medicine #health

Interesting talk by Elle O’Brien on “the future of scientific code in the era of generative AI.”
#DigitalHumanities

Homophily-induced Emergence of Biased Structures in LLM-based Multi-Agent AI Systems
Aliakbar Mehdizadeh, Martin Hilbert
https://arxiv.org/abs/2510.02637 https://

Homophily-induced Emergence of Biased Structures in LLM-based Multi-Agent AI Systems
This study examines how interactions among artificially intelligent (AI) agents, guided by large language models (LLMs), drive the evolution of collective network structures. We ask LLM-driven agents to grow a network by informing them about current link constellations. Our observations confirm that agents consistently apply a preferential attachment mechanism, favoring connections to nodes with higher degrees. We systematically solicited more than a million decisions from four different LLMs, …

Eliciting Secret Knowledge from Language Models
Bartosz Cywi\'nski, Emil Ryd, Rowan Wang, Senthooran Rajamanoharan, Neel Nanda, Arthur Conmy, Samuel Marks
https://arxiv.org/abs/2510.01070

Eliciting Secret Knowledge from Language Models
We study secret elicitation: discovering knowledge that an AI possesses but does not explicitly verbalize. As a testbed, we train three families of large language models (LLMs) to possess specific knowledge that they apply downstream but deny knowing when asked directly. For example, in one setting, we train an LLM to generate replies that are consistent with knowing the user is female, while denying this knowledge when asked directly. We then design various black-box and white-box secret elici…

InfoAgent: Advancing Autonomous Information-Seeking Agents
Gongrui Zhang, Jialiang Zhu, Ruiqi Yang, Kai Qiu, Miaosen Zhang, Zhirong Wu, Qi Dai, Bei Liu, Chong Luo, Zhengyuan Yang, Linjie Li, Lijuan Wang, Weizhu Chen, Yuan Zhang, Xin Li, Zhaoyi Liu, Xin Geng, Baining Guo
https://arxiv.org/abs/2509.25189…

InfoAgent: Advancing Autonomous Information-Seeking Agents
Building Large Language Model agents that expand their capabilities by interacting with external tools represents a new frontier in AI research and applications. In this paper, we introduce InfoAgent, a deep research agent powered by an innovative data synthesis pipeline and orchestrated web search tools. To construct challenging, hard-to-find queries,we build entity trees and apply sub-tree sampling with entity fuzzification to systematically increase question difficulty. Unlike prior work tha…

A University of Texas Medical Branch Case Study on Aortic Calcification Detection
Eric Walser, Peter McCaffrey, Kal Clark, Nicholas Czarnek
https://arxiv.org/abs/2509.23930 http…

A University of Texas Medical Branch Case Study on Aortic Calcification Detection
This case study details The University of Texas Medical Branch (UTMB)'s partnership with Zauron Labs, Inc. to enhance detection and coding of aortic calcifications (ACs) using chest radiographs. ACs are often underreported despite their significant prognostic value for cardiovascular disease, and UTMB partnered with Zauron to apply its advanced AI tools, including a high-performing image model (AUC = 0.938) and a fine-tuned language model based on Meta's Llama 3.2, to retrospectively analyze im…

GPS Denied IBVS-Based Navigation and Collision Avoidance of UAV Using a Low-Cost RGB Camera
Xiaoyu Wang, Yan Rui Tan, William Leong, Sunan Huang, Rodney Teo, Cheng Xiang
https://arxiv.org/abs/2509.17435

GPS Denied IBVS-Based Navigation and Collision Avoidance of UAV Using a Low-Cost RGB Camera
This paper proposes an image-based visual servoing (IBVS) framework for UAV navigation and collision avoidance using only an RGB camera. While UAV navigation has been extensively studied, it remains challenging to apply IBVS in missions involving multiple visual targets and collision avoidance. The proposed method achieves navigation without explicit path planning, and collision avoidance is realized through AI-based monocular depth estimation from RGB images. Unlike approaches that rely on ste…