Tootfinder

Spotify's video podcasts deal with Netflix will bring 16 shows to the platform, including The Bill Simmons Podcast, The Rewatchables, and Serial Killers (Spotify)
https://newsroom.spotify.com/2025-10-14/netflix-partnership-the-ringer…

Spotify Studios and The Ringer Video Podcasts Are Coming to Netflix — Spotify
Early next year, your favorite video podcasts are getting a bigger stage. Spotify and Netflix are teaming up to bring sports, culture, lifestyle, and true crime video podcasts from Spotify Studios and The Ringer to Netflix to complement the streamer’s existing programming and unlock new audiences and wider distribution for the shows.  Audiences will be...

Read the Room or Lead the Room: Understanding Socio-Cognitive Dynamics in Human-AI Teaming
Jaeyoon Choi, Mohammad Amin Samadi, Spencer JaQuay, Seehee Park, Nia Nixon
https://arxiv.org/abs/2510.09944

Read the Room or Lead the Room: Understanding Socio-Cognitive Dynamics in Human-AI Teaming
Research on Collaborative Problem Solving (CPS) has traditionally examined how humans rely on one another cognitively and socially to accomplish tasks together. With the rapid advancement of AI and large language models, however, a new question emerge: what happens to team dynamics when one of the "teammates" is not human? In this study, we investigate how the integration of an AI teammate -- a fully autonomous GPT-4 agent with social, cognitive, and affective capabilities -- shapes the socio-c…

The double neutron star PSR J1946 2052 I. Masses and tests of general relativity
Lingqi Meng, Paulo C. C. Freire, Kevin Stovall, Norbert Wex, Xueli Miao, Weiwei Zhu, Michael Kramer, James M. Cordes, Huanchen Hu, Jinchen Jiang, Emilie Parent, Lijing Shao, Ingrid H. Stairs, Mengyao Xue, Adam Brazier, Fernando Camilo, David J. Champion, Shami Chatterjee, Fronefield Crawford, Ziyao Fang, Qiuyang Fu, Yanjun Guo, Jason W. T. Hessels, Maura MacLaughlin, Chenchen Miao, Jiarui Niu, Ziwei Wu, Ju…

The double neutron star PSR J1946+2052 I. Masses and tests of general relativity
We conducted high-precision timing of PSR J1946+2052 to determine the masses of the two neutron stars in the system, test general relativity (GR) and assessed the system's potential for future measurement of the moment of inertia of the pulsar. We analysed seven years of timing data from the Arecibo 305-m radio telescope, the Green Bank Telescope (GBT), and the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The data processing accounted for dispersion measure variations and relat…

A Solution to the Hierarchy Problem with Non-Linear Quantum Mechanics
David E. Kaplan, Surjeet Rajendran
https://arxiv.org/abs/2510.12030 https://arxiv.org…

A Solution to the Hierarchy Problem with Non-Linear Quantum Mechanics
We argue that the hierarchy problem of the standard model of particle physics can be solved by adding a state-dependent term to the Higgs sector. We present an example of a scalar field with a Higgs-like potential with an additional term proportional to the expectation value of the squared Higgs field operator. We show that the mass can be parametrically lighter than the theory's energy-momentum cutoff without fine tuning. We find the Higgs mass can be technically natural, even with a Planck-sc…

HEFT: A Coarse-to-Fine Hierarchy for Enhancing the Efficiency and Accuracy of Language Model Reasoning
Brennen Hill
https://arxiv.org/abs/2509.09801 https://

HEFT: A Coarse-to-Fine Hierarchy for Enhancing the Efficiency and Accuracy of Language Model Reasoning
The adaptation of large language models (LLMs) to specialized reasoning tasks is fundamentally constrained by computational resources. Parameter-Efficient Fine-Tuning (PEFT) methods have emerged as a powerful solution, yet the landscape of these techniques is diverse, with distinct methods operating in either the model's weight space or its representation space. This paper investigates the hypothesis that a synergistic combination of these paradigms can unlock superior performance and efficienc…

Pharmacist: Safety Alignment Data Curation for Large Language Models against Harmful Fine-tuning
Guozhi Liu, Qi Mu, Tiansheng Huang, Xinhua Wang, Li Shen, Weiwei Lin, Zhang Li
https://arxiv.org/abs/2510.10085

Pharmacist: Safety Alignment Data Curation for Large Language Models against Harmful Fine-tuning
Harmful fine-tuning issues present significant safety challenges for fine-tuning-as-a-service in large language models. Existing alignment-stage defenses, e.g., Vaccine, Repnoise, Booster, and T-Vaccine, mitigate harmful fine-tuning issues by enhancing the model's robustness during the alignment phase. While these methods have been proposed to mitigate the issue, they often overlook a critical upstream factor: the role of the original safety-alignment data. We observe that their defense perform…

Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey
Abdulhady Abas Abdullah, Arkaitz Zubiaga, Seyedali Mirjalili, Amir H. Gandomi, Fatemeh Daneshfar, Mohammadsadra Amini, Alan Salam Mohammed, Hadi Veisi
https://arxiv.org/abs/2510.12178

Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey
This review surveys the rapid evolution of Meta AI's LLaMA (Large Language Model Meta AI) series - from LLaMA 1 through LLaMA 4 and the specialized parameter-efficient fine-tuning (PEFT) methods developed for these models. We first describe the LLaMA family of foundation models (7B-65B to 288B parameters), their architectures (including native multimodal and Mixtureof-Experts variants), and key performance characteristics. We then describe and discuss the concept of PEFT, which adapts large pre…

Impact of facility timing and coordination for next-generation gravitational-wave detectors
Ssohrab Borhanian, Arianna Renzini, Philippa S. Cole, Costantino Pacilio, Michele Mancarella, Davide Gerosa
https://arxiv.org/abs/2510.11861

Impact of facility timing and coordination for next-generation gravitational-wave detectors
While the Einstein Telescope and Cosmic Explorer proposals for next-generation, ground-based detectors promise vastly improved sensitivities to gravitational-wave signals, only joint observations are expected to enable the full scientific potential of these facilities, making timing and coordination between the efforts crucial to avoid missed opportunities. This study investigates the impact of long-term delays on the scientific capabilities of next-generation detector networks. We use the Fish…

SI-FACT: Mitigating Knowledge Conflict via Self-Improving Faithfulness-Aware Contrastive Tuning
Shengqiang Fu
https://arxiv.org/abs/2509.10208 https://arxi…

SI-FACT: Mitigating Knowledge Conflict via Self-Improving Faithfulness-Aware Contrastive Tuning
Large Language Models often generate unfaithful responses in knowledge intensive tasks due to knowledge conflict,that is,a preference for relying on internal parametric knowledge rather than the provided context.To address this issue,we propose a novel self improving framework,Self Improving Faithfulness Aware Contrastive Tuning.The framework uses a self instruct mechanism that allows the base LLM to automatically generate high quality,structured contrastive learning data,including anchor sampl…

Probing Latent Knowledge Conflict for Faithful Retrieval-Augmented Generation
Linfeng Gao, Baolong Bi, Zheng Yuan, Le Wang, Zerui Chen, Zhimin Wei, Shenghua Liu, Qinggang Zhang, Jinsong Su
https://arxiv.org/abs/2510.12460

Probing Latent Knowledge Conflict for Faithful Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to enhance the factuality of Large Language Models (LLMs). However, existing RAG systems often suffer from an unfaithfulness issue, where the model's response contradicts evidence from the retrieved context. Existing approaches to improving contextual faithfulness largely rely on external interventions, such as prompt engineering, decoding constraints, or reward-based fine-tuning. These works treat the LLM as a black box an…