Tootfinder

The MAGA cult REFUSES to acknowledge that their great orange leader is involved in the Epstein fiasco.
So they all point their red-hat fingers at Pam Bondi.
YET! The small amount of files that have been released, Trump is all over them.
Just look at the flight logs.
Trump, Trump, Trump! #denial

Dailycast Newstalk Radio (@dailycast.news)
The MAGA cult REFUSES to acknowledge that their great orange leader is involved in the Epstein fiasco. So they all point their red-hat fingers at Pam Bondi. YET! The small amount of files that have been released, Trump is all over them. Just look at the flight logs. Trump, Trump, Trump! #denial

Dealogic: tech deals accounted for $421B of the $1.67T in global deals announced in the first five months of 2025, with 75% of tech M&A involving AI software (Milana Vinn/Reuters)
https://www.reuters.com/business/unglamoro

The Feasibility of a Spacecraft Flyby with the Third Interstellar Object 3I/ATLAS from Earth or Mars
Atsuhiro Yaginuma, Tessa Frincke, Darryl Z. Seligman, Kathleen Mandt, Daniella N. DellaGiustina, Eloy Pe\~na-Asensio, Aster G. Taylor, Michael C. Nolan
https://arxiv.org/abs/2507.15755

The Feasibility of a Spacecraft Flyby with the Third Interstellar Object 3I/ATLAS from Earth or Mars
We investigate the feasibility of a spacecraft mission to conduct a flyby of 3I/ATLAS, the third macroscopic interstellar object discovered on July 1 2025, as it traverses the Solar System. There are both ready-to-launch spacecraft currently in storage on Earth, such as Janus, and spacecraft nearing the end of their missions at Mars. We calculate minimum $ΔV$ single-impulse direct transfer trajectories to 3I/ATLAS both from Earth and from Mars. We consider launch dates spanning January 2025 th…

Mitigating Trojanized Prompt Chains in Educational LLM Use Cases: Experimental Findings and Detection Tool Design
Richard M. Charles, James H. Curry, Richard B. Charles
https://arxiv.org/abs/2507.14207

Mitigating Trojanized Prompt Chains in Educational LLM Use Cases: Experimental Findings and Detection Tool Design
The integration of Large Language Models (LLMs) in K--12 education offers both transformative opportunities and emerging risks. This study explores how students may Trojanize prompts to elicit unsafe or unintended outputs from LLMs, bypassing established content moderation systems with safety guardrils. Through a systematic experiment involving simulated K--12 queries and multi-turn dialogues, we expose key vulnerabilities in GPT-3.5 and GPT-4. This paper presents our experimental design, detai…

MultiVox: Benchmarking Voice Assistants for Multimodal Interactions
Ramaneswaran Selvakumar, Ashish Seth, Nishit Anand, Utkarsh Tyagi, Sonal Kumar, Sreyan Ghosh, Dinesh Manocha
https://arxiv.org/abs/2507.10859

MultiVox: Benchmarking Voice Assistants for Multimodal Interactions
The rapid progress of Large Language Models (LLMs) has empowered omni models to act as voice assistants capable of understanding spoken dialogues. These models can process multimodal inputs beyond text, such as speech and visual data, enabling more context-aware interactions. However, current benchmarks fall short in comprehensively evaluating how well these models generate context-aware responses, particularly when it comes to implicitly understanding fine-grained speech characteristics, such …

Sometimes, you really do have to hand it to Nietzsche.
Recognizing your own potential as literal prey is a useful early step. https://raggedfeathers.com/@lilithsaintcrow/114921877666921523

BD at BEA 2025 Shared Task: MPNet Ensembles for Pedagogical Mistake Identification and Localization in AI Tutor Responses
Shadman Rohan, Ishita Sur Apan, Muhtasim Ibteda Shochcho, Md Fahim, Mohammad Ashfaq Ur Rahman, AKM Mahbubur Rahman, Amin Ahsan Ali
https://arxiv.org/abs/2506.01817

BD at BEA 2025 Shared Task: MPNet Ensembles for Pedagogical Mistake Identification and Localization in AI Tutor Responses
We present Team BD's submission to the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors, under Track 1 (Mistake Identification) and Track 2 (Mistake Location). Both tracks involve three-class classification of tutor responses in educational dialogues - determining if a tutor correctly recognizes a student's mistake (Track 1) and whether the tutor pinpoints the mistake's location (Track 2). Our system is built on MPNet, a Transformer-based language model that combines …