Tootfinder

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation
Yashothara Shanmugarasa, Ming Ding, M. A. P Chamikara, Thierry Rakotoarivelo
https://arxiv.org/abs/2506.12699

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation
Large language models (LLMs) are sophisticated artificial intelligence systems that enable machines to generate human-like text with remarkable precision. While LLMs offer significant technological progress, their development using vast amounts of user data scraped from the web and collected from extensive user interactions poses risks of sensitive information leakage. Most existing surveys focus on the privacy implications of the training data but tend to overlook privacy risks from user inter…

ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM
Yujun Wang, Jinhe Bi, Yunpu Ma, Soeren Pirk
https://arxiv.org/abs/2506.14766

ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM
Multimodal Large Language Model (MLLM) often suffer from hallucinations. They over-rely on partial cues and generate incorrect responses. Recently, methods like Visual Contrastive Decoding (VCD) and Instruction Contrastive Decoding (ICD) have been proposed to mitigate hallucinations by contrasting predictions from perturbed or negatively prefixed inputs against original outputs. In this work, we uncover that methods like VCD and ICD fundamentally influence internal attention dynamics of the mod…

I noticed that OpenStreetMaps somehow lost Lake Michigan, so I went investigating. Apparently back in April somebody accidentally changed it from a "lake" to a "school" and it's taking months to be fixed across all regions/renderers.
https:/…

This https://arxiv.org/abs/2504.16633 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nli…

Unveiling Solitonic Collisions in Mechanical Metamaterials
Interactions between solitary waves have been pivotal to understanding nonlinear phenomena across various disciplines. The dynamics of rarefaction solitary waves holds great potential, yet their fundamental characteristics and interactions remain only partially understood through experimental means in mechanical metamaterials. Previous studies highlighted their existence and proposed applications, such as waveguides, impact mitigation, and energy harvesting. Challenges, including energy dissipa…

A federal judge on Monday declared some of the Trump administration’s cuts to National Institutes of Health grants
“void and illegal,”
accusing the government of racial discrimination and prejudice against L.G.B.T.Q. individuals.
Ruling from the bench, Judge William G. Young of the Federal District Court for the District of Massachusetts
delivered a damning assessment of the Trump administrations’ motives in targeting hundreds of grants that focused on the health of Bl…

Trump Live Updates: Senate Republicans Propose Deeper Medicaid Cuts in Major Policy Bill

Self-Interacting Dark Matter with Mass Segregation: A Unified Explanation of Dwarf Cores and Small-Scale Lenses
Daneng Yang, Yi-Zhong Fan, Siyuan Hou, Yue-Lin Sming Tsai
https://arxiv.org/abs/2506.14898

Self-Interacting Dark Matter with Mass Segregation: A Unified Explanation of Dwarf Cores and Small-Scale Lenses
In two-component SIDM models with inter-species interactions, mass segregation arises naturally from collisional relaxation, enhancing central densities and gravothermal evolution without requiring large cross sections. We propose a model with velocity-dependent interactions, both within and between species, that connects observations across several halo mass scales while remaining consistent with cluster-scale constraints. This combination enables modest mass segregation in low-mass and typica…

Density estimation via periodic scaled Korobov kernel method with exponential decay condition
Ziyang Ye, Haoyuan Tan, Xiaoqun Wang, Zhijian He
https://arxiv.org/abs/2506.15419

Density estimation via periodic scaled Korobov kernel method with exponential decay condition
We propose the periodic scaled Korobov kernel (PSKK) method for nonparametric density estimation on $\mathbb{R}^d$. By first wrapping the target density into a periodic version through modulo operation and subsequently applying kernel ridge regression in scaled Korobov spaces, we extend the kernel approach proposed by Kazashi and Nobile (SIAM J. Numer. Anal., 2023) and eliminate its requirement for inherent periodicity of the density function. This key modification enables effective estimation …

Bayesian Knowledge Transfer for a Kalman Fixed-Lag Interval Smoother
Ond\v{r}ej Skalsk\'y, Jakub Dokoupil
https://arxiv.org/abs/2506.14572 https://

Bayesian Knowledge Transfer for a Kalman Fixed-Lag Interval Smoother
A Bayesian knowledge transfer mechanism that leverages external information to improve the performance of the Kalman fixed-lag interval smoother (FLIS) is proposed. Exact knowledge of the external observation model is assumed to be missing, which hinders the direct application of Bayes' rule in traditional transfer learning approaches. This limitation is overcome by the fully probabilistic design, conditioning the targeted task of state estimation on external information. To mitigate the negati…

Hello all!
I wasn't able to process the photos of my recent trips, but I wanted to share this one from last year.
I went there on a hot day to "take a couple of photos". In the end I spent 3h just at this waterfall searching for the right composition & interpretation, the right motive etc.
I love the result, but I'm surprised how much time I spent there to get it.

Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model
Anirud Aggarwal, Abhinav Shrivastava, Matthew Gwilliam
https://arxiv.org/abs/2506.15682

Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model
Diffusion-based image generation models excel at producing high-quality synthetic content, but suffer from slow and computationally expensive inference. Prior work has attempted to mitigate this by caching and reusing features within diffusion transformers across inference steps. These methods, however, often rely on rigid heuristics that result in limited acceleration or poor generalization across architectures. We propose Evolutionary Caching to Accelerate Diffusion models (ECAD), a genetic a…