Tootfinder

The ancient Celts upheld values such as truthfulness, loyalty, faithfulness, generosity, hospitality, honesty, righteousness, justice, bravery and courage.
These are part of an honourable life.
We want to try something partly new, using ancient values that meet today's highest moral standards as a compass.
Why not adopt the superior habits of thought and the ancestral knowledge of the druids and go the Nua-Celtic way?
A tree without healthy roots dies and fall…

I had a lunch in the oldest restaurant in the world according to the Guiness. Truthfully, not worth a hassle. @…
#madrid #botin

"Acting is behaving truthfully under imaginary circumstances."
-Sanford Meisner
#acting #coaching #inspiration

In this post "Consent-Based Computing", I think Heidi Waterhouse is articulating some my own feelings, reflections and choices about the tools around us today and the tradeoffs I'm prepared to make. #linkTuesday

@… https://zikzak.substack.com/

Zik Zak | Tarotplane | Substack
Lunch for your ears. Click to read Zik Zak, by Tarotplane, a Substack publication with thousands of subscribers.

“The String… will be much faster”
my brain is breaking https://mastodon.social/@Catfish_Man/114655168110161214

Understanding and Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
Youze Wang, Zijun Chen, Ruoyu Chen, Shishen Gu, Yinpeng Dong, Hang Su, Jun Zhu, Meng Wang, Richang Hong, Wenbo Hu
https://arxiv.org/abs/2506.12336

Understanding and Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
Recent advancements in multimodal large language models for video understanding (videoLLMs) have improved their ability to process dynamic multimodal data. However, trustworthiness challenges factual inaccuracies, harmful content, biases, hallucinations, and privacy risks, undermine reliability due to video data's spatiotemporal complexities. This study introduces Trust-videoLLMs, a comprehensive benchmark evaluating videoLLMs across five dimensions: truthfulness, safety, robustness, fairness, …

Recette maison : 🥚✨ "Oeufs du Matin Tardif": https://blog.rmendes.net/2025/06/02/recette-maison-oeufs-du-matin.html

UCD: Unlearning in LLMs via Contrastive Decoding
Vinith M. Suriyakumar, Ayush Sekhari, Ashia Wilson
https://arxiv.org/abs/2506.12097 https://

UCD: Unlearning in LLMs via Contrastive Decoding
Machine unlearning aims to remove specific information, e.g. sensitive or undesirable content, from large language models (LLMs) while preserving overall performance. We propose an inference-time unlearning algorithm that uses contrastive decoding, leveraging two auxiliary smaller models, one trained without the forget set and one trained with it, to guide the outputs of the original model using their difference during inference. Our strategy substantially improves the tradeoff between unlearni…

Design Tradeoffs in Photonically Linked Qubit Networks
Ely Novakoski, Jungsang Kim
https://arxiv.org/abs/2506.06268 https://arxiv.org…

Design Tradeoffs in Photonically Linked Qubit Networks
Quantum networking can be realized by distributing pairs of entangled qubits between remote quantum processing nodes. Devoted communication qubits within each node can naturally interface with photons which bus quantum information between nodes. With the introduction of CQED to enhance interactions between communication qubits and photons, advanced protocols capable of achieving high entanglement distribution rates with high fidelity become feasible. In this paper, we consider two such protocol…