Tootfinder

Trump learned about the timing of Musk’s departure in the same manner that the president himself has often used to share significant personnel news:
via a social-media post
"The president knew that Musk would be leaving soon, but not the exact timing until the post was sent"

'I don't like being surprised': Trump reportedly blindsided by Elon Musk comment
Donald Trump and Elon Musk have a "complicated" relationship characterized by at least one recent instance of lack of communication, according to a new report.The conservative Wall Street Journal late Friday evening published a report entitled "Inside Trump and Musk’s Complicated Relationship" in wh...

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
Yifan Hao, Xingyuan Pan, Hanning Zhang, Chenlu Ye, Rui Pan, Tong Zhang
https://arxiv.org/abs/2506.01901

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
Supervised fine-tuning (SFT) on domain-specific data is the dominant approach for adapting foundation models to specialized tasks. However, it has been observed that SFT models tend to forget knowledge acquired during pretraining. In vision models, ensembling a pretrained model with its fine-tuned counterpart has been shown to mitigate this issue. In this work, we demonstrate that the same holds for language models, and, more strikingly, we observe an overadaptation phenomenon: the ensemble mod…

Movable Antenna Enhanced Federated Fine-Tuning of Large Language Models via Hybrid Client Selection Optimization
Yang Zhao, Yue Xiu, Chengxiao Dai, Ning Wei, Dusit Niyato
https://arxiv.org/abs/2506.00011

Movable Antenna Enhanced Federated Fine-Tuning of Large Language Models via Hybrid Client Selection Optimization
Large language model (LLM) training in 6G networks faces stringent latency and energy constraints while operating over bandwidth-limited wireless links. A commonly adopted workflow separates training into a centralized pre-training phase and a federated fine-tuning phase on domain-specific data; however, over-the-air (OTA) gradient aggregation during fine-tuning remains vulnerable to fading and interference. This study explores the integration of movable antennas (MAs), whose element positions …

Thinning algorithms for the Monte Carlo simulation of kinetic Ising models
V. I. Tokar, H. Dreyss\'e
https://arxiv.org/abs/2505.24414 https://

Thinning algorithms for the Monte Carlo simulation of kinetic Ising models
The thinning method for numerical generation of the nonhomogeneous Poisson process (NHPP) arrival times has been adapted to accelerate Monte Carlo simulations of the kinetic Ising models (KIMs) with the Glauber spin-flip dynamics. The performance of the suggested algorithms has been illustrated by simulation of the decay of metastable states in stationary KIMs and of hysteresis in KIMs in a periodic external field. The thinning has been implemented by means of piecewise constant majorizing func…

Fine-tuning for Data-enabled Predictive Control of Noisy Systems by Reinforcement Learning
Jinbao Wang, Shiliang Zhang, Jun Liu, Xuehui Ma, Haolin Liu
https://arxiv.org/abs/2505.24572

Fine-tuning for Data-enabled Predictive Control of Noisy Systems by Reinforcement Learning
Data-enabled predictive control (DeePC) leverages system measurements in characterizing system dynamics for optimal control. The performance of DeePC relies on optimizing its hyperparameters, especially in noisy systems where the optimal hyperparameters adapt over time. Existing hyperparameter tuning approaches for DeePC are more than often computationally inefficient or overly conservative. This paper proposes an adaptive DeePC where we guide its hyperparameters adaption through reinforcement …

Fine-Tuning ASR for Stuttered Speech: Personalized vs. Generalized Approaches
Dena Mujtaba, Nihar Mahapatra
https://arxiv.org/abs/2506.00853 https://

Fine-Tuning ASR for Stuttered Speech: Personalized vs. Generalized Approaches
Stuttering -- characterized by involuntary disfluencies such as blocks, prolongations, and repetitions -- is often misinterpreted by automatic speech recognition (ASR) systems, resulting in elevated word error rates and making voice-driven technologies inaccessible to people who stutter. The variability of disfluencies across speakers and contexts further complicates ASR training, compounded by limited annotated stuttered speech data. In this paper, we investigate fine-tuning ASRs for stuttered…

IAE Optimized PID Tuning with Phase Margin and Crossover Frequency Constraints
Senol Gulgonul
https://arxiv.org/abs/2506.00923 https://

IAE Optimized PID Tuning with Phase Margin and Crossover Frequency Constraints
This paper presents PMwc-Tune, a novel PID tuning method that uniquely combines frequency-domain robustness constraints with time-domain performance optimization through constrained nonlinear programming. The key contribution is a unified formulation that simultaneously enforces phase margin and crossover frequency requirements (via nonlinear equality constraints) while minimizing the Integral Absolute Error (IAE) of the closed-loop response. The algorithm employs Sequential Quadratic Programmi…

MLorc: Momentum Low-rank Compression for Large Language Model Adaptation
Wei Shen, Yaxiang Zhang, Minhui Huang, Mengfan Xu, Jiawei Zhang, Cong Shen
https://arxiv.org/abs/2506.01897

MLorc: Momentum Low-rank Compression for Large Language Model Adaptation
With increasing size of large language models (LLMs), full-parameter fine-tuning imposes substantial memory demands. To alleviate this, we propose a novel memory-efficient training paradigm called Momentum Low-rank compression (MLorc). By directly compressing and reconstructing momentum rather than gradients, MLorc avoids imposing a fixed-rank constraint on weight update matrices and better preserves the training dynamics of full-parameter fine-tuning, in contrast to existing low-rank approache…

CoP: Agentic Red-teaming for Large Language Models using Composition of Principles
Chen Xiong, Pin-Yu Chen, Tsung-Yi Ho
https://arxiv.org/abs/2506.00781 ht…

CoP: Agentic Red-teaming for Large Language Models using Composition of Principles
Recent advances in Large Language Models (LLMs) have spurred transformative applications in various domains, ranging from open-source to proprietary LLMs. However, jailbreak attacks, which aim to break safety alignment and user compliance by tricking the target LLMs into answering harmful and risky responses, are becoming an urgent concern. The practice of red-teaming for LLMs is to proactively explore potential risks and error-prone instances before the release of frontier AI technology. This …

Whispers of Many Shores: Cultural Alignment through Collaborative Cultural Expertise
Shuai Feng, Wei-Chuang Chan, Srishti Chouhan, Junior Francisco Garcia Ayala, Srujananjali Medicherla, Kyle Clark, Mingwei Shi
https://arxiv.org/abs/2506.00242

Whispers of Many Shores: Cultural Alignment through Collaborative Cultural Expertise
The integration of large language models (LLMs) into global applications necessitates effective cultural alignment for meaningful and culturally-sensitive interactions. Current LLMs often lack the nuanced understanding required for diverse cultural contexts, and adapting them typically involves costly full fine-tuning. To address this, we introduce a novel soft prompt fine-tuning framework that enables efficient and modular cultural alignment. Our method utilizes vectorized prompt tuning to dyn…