Tootfinder

[OT] The force is with us, always? Tuning quantum vacuum forces from attractive to repulsive https://news.asu.edu/20190304-force-us-always-tuning-quantum-vacuum-forces-attractive-repulsive Now can we tune The vOICe soundscapes f…

Fine-Tuning LLMs For ‘Good’ Behavior Makes Them More Likely To Say No https://www.404media.co/fine-tuning-llms-cognitive-bias-decision-making-study/

Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)
Subba Reddy Oota, Akshett Jindal, Ishani Mondal, Khushbu Pahwa, Satya Sai Srinath Namburi, Manish Shrivastava, Maneesh Singh, Bapi S. Raju, Manish Gupta
https://arxiv.org/abs/2505.20029

Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)
Transformer-based language models, though not explicitly trained to mimic brain recordings, have demonstrated surprising alignment with brain activity. Progress in these models-through increased size, instruction-tuning, and multimodality-has led to better representational alignment with neural data. Recently, a new class of instruction-tuned multimodal LLMs (MLLMs) have emerged, showing remarkable zero-shot capabilities in open-ended multimodal vision tasks. However, it is unknown whether MLLM…

Exoplanet Ephemerides Change Observations (ExoEcho). II. Transit timing variation analysis of Brown Dwarfs around Solar-type Stars
Wenqin Wang, Xinyue Ma, Zhangliang Chen, Cong Yu, Shangfei Liu, Bo Ma
https://arxiv.org/abs/2505.21270

Exoplanet Ephemerides Change Observations (ExoEcho). II. Transit timing variation analysis of Brown Dwarfs around Solar-type Stars
Transit timing variation (TTV) is a useful tool for studying the orbital properties of transiting objects. However, few TTV studies have been done on transiting brown dwarfs (BDs) around solar-type stars. Here we study the long-term TTV of a population of close BD companions around solar-type stars using TESS data. We use the measured orbital period change rate to constrain the tidal interaction strength between the host star and the BD companion and put limits on the destruction timescale of t…

Amiguitos, hoy tenemos disco nuevo de la joven y talentosa neozelandesa Ella Marija Lani Yelich-O'Connor, también conocida como Lorde.
«Virgin»
https://open.spotify.com/album/28bHj2enHkHVFLwuWmkwlQ

Aus #Löwenzahnblüten kann man übrigens Sirup kochen, auch als Löwenzahnblütenhonig bezeichnet.
Mein Rezept siehe:https://www.oe…

Unlocking my own understanding of and ability to build #Swift macros feels like a superpower.
…something something great responsibility, though.
Synthesizing boilerplate and statically-verifiable elements like custom function calls based on macro input… is magic—the good kind.
`@GET("/logs/{userId}/{timing}")`
↘️

Exploring Adapter Design Tradeoffs for Low Resource Music Generation
Atharva Mehta, Shivam Chauhan, Monojit Choudhury
https://arxiv.org/abs/2506.21298 http…

Exploring Adapter Design Tradeoffs for Low Resource Music Generation
Fine-tuning large-scale music generation models, such as MusicGen and Mustango, is a computationally expensive process, often requiring updates to billions of parameters and, therefore, significant hardware resources. Parameter-Efficient Fine-Tuning (PEFT) techniques, particularly adapter-based methods, have emerged as a promising alternative, enabling adaptation with minimal trainable parameters while preserving model performance. However, the design choices for adapters, including their archi…

Double-Checker: Enhancing Reasoning of Slow-Thinking LLMs via Self-Critical Fine-Tuning
Xin Xu, Tianhao Chen, Fan Zhang, Wanlong Liu, Pengxiang Li, Ajay Kumar Jaiswal, Yuchen Yan, Jishan Hu, Yang Wang, Hao Chen, Shiwei Liu, Shizhe Diao, Can Yang, Lu Yin
https://arxiv.org/abs/2506.21285 https://arxiv.org/pdf/2506.21285 https://arxiv.org/html/2506.21285
arXiv:2506.21285v1 Announce Type: new
Abstract: While slow-thinking large language models (LLMs) exhibit reflection-like reasoning, commonly referred to as the "aha moment:, their ability to generate informative critiques and refine prior solutions remains limited. In this paper, we introduce Double-Checker, a principled framework designed to enhance the reasoning capabilities of slow-thinking LLMs by fostering explicit self-critique and iterative refinement of their previous solutions. By fine-tuning on our curated 1,730 self-critical instances, Double-Checker empowers long-CoT LLMs to iteratively critique and refine their outputs during inference until they evaluate their solutions as correct under self-generated critiques. We validate the efficacy of Double-Checker across a comprehensive suite of reasoning benchmarks, demonstrating that iterative self-critique significantly enhances the reasoning capabilities of long-CoT LLMs. Notably, our Double-Checker increases the pass@1 performance on challenging AIME benchmarks from 4.4% to 18.2% compared to the original long-CoT LLMs. These results highlight a promising direction for developing more trustworthy and effective LLMs capable of structured self-critique.
toXiv_bot_toot

Learnable Adaptive Time-Frequency Representation via Differentiable Short-Time Fourier Transform
Maxime Leiber, Yosra Marnissi, Axel Barrau, Sylvain Meignen, Laurent Massouli\'e
https://arxiv.org/abs/2506.21440

Learnable Adaptive Time-Frequency Representation via Differentiable Short-Time Fourier Transform
The short-time Fourier transform (STFT) is widely used for analyzing non-stationary signals. However, its performance is highly sensitive to its parameters, and manual or heuristic tuning often yields suboptimal results. To overcome this limitation, we propose a unified differentiable formulation of the STFT that enables gradient-based optimization of its parameters. This approach addresses the limitations of traditional STFT parameter tuning methods, which often rely on computationally intensi…