Tootfinder

Single Answer is Not Enough: On Generating Ranked Lists with Medical Reasoning Models
Pittawat Taveekitworachai, Natpatchara Pongjirapat, Krittaphas Chaisutyakorn, Piyalitt Ittichaiwong, Tossaporn Saengja, Kunat Pipatanakul
https://arxiv.org/abs/2509.20866

Single Answer is Not Enough: On Generating Ranked Lists with Medical Reasoning Models
This paper presents a systematic study on enabling medical reasoning models (MRMs) to generate ranked lists of answers for open-ended questions. Clinical decision-making rarely relies on a single answer but instead considers multiple options, reducing the risks of narrow perspectives. Yet current MRMs are typically trained to produce only one answer, even in open-ended settings. We propose an alternative format: ranked lists and investigate two approaches: prompting and fine-tuning. While promp…

@Chevrolet was built for the fan, no matter how bad their team is 😂#ad grateful to partner with chevy after they saw me buy my truck, match made in heaven 🤝 https://www.tiktok.com/@annieagar5/video/7

Hmmm.... kann man auf dem Foto leider nicht gut erkennen:
Auf manchen #Blättern setzen sich bei hoher #Luftfeuchtigkeit und niedrigen #Temperaturen feine

Russland meldet Abschuss von 82 ukrainischen Drohnen
Die russische Flugabwehr hat nach Angaben des Verteidigungsministeriums in Moskau über Nacht 82 ukrainische Drohnen abgeschossen. Die Angaben lassen sich allerdings derzeit nicht unabhängig überprüfen.
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Ukraine-Liveblog: ++ Erneut Tote nach russischem Angriff auf Kiew ++
Nach ukrainischen Angaben sind bei einem Drohnenangriff auf die Hauptstadt Kiew mindestens drei Menschen getötet und 27 verletzt worden. US-Präsident Trump will Kremlchef Putin erst treffen, wenn er sich einer Einigung sicher sein kann.

MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for Video Temporal Reasoning
Sicheng Tao, Jungang Li, Yibo Yan, Junyan Zhang, Yubo Gao, Hanqian Li, ShuHang Xun, Yuxuan Fan, Hong Chen, Jianxiang He, Xuming Hu
https://arxiv.org/abs/2509.21113

MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for Video Temporal Reasoning
Video reasoning has emerged as a critical capability for multimodal large language models (MLLMs), requiring models to move beyond static perception toward coherent understanding of temporal dynamics in complex scenes. Yet existing MLLMs often exhibit process inconsistency, where intermediate reasoning drifts from video dynamics even when the final answer is correct, undermining interpretability and robustness. To address this issue, we introduce MOSS-ChatV, a reinforcement learning framework w…

#Wordle 1,620 3/6*
⬜⬜🟩⬜⬜ <1% of 230,963 (91)
⬜🟨🟩🟩⬜ 0 of 0 (2)
🟩🟩🟩🟩🟩
WordleBot
Skill 93/99
Luck 72/99
So oddly my fingers typed and submitted a different last letter of my 1st guess that I intended. Oddly it would have been a letter in the correct position. I doubt it would have helped me arrive at the answer any quicker though. Just sort of funny.

A weak Lehmer code for type $F_4$
Paolo Sentinelli, Andrea Zatti
https://arxiv.org/abs/2509.20981 https://arxiv.org/pdf/2509.20981

A weak Lehmer code for type $F_4$
We show that, despite the Poincaré polynomial of $F_4$ is a product of $q$-analogues, the Bruhat order of $F_4$ does not admit a product of chains as subposet. This answers negatively, in this type, a question by Billey, Fan and Losonczy. In other words, we show that Lehmer codes for type $F_4$ do not exist. Nevertheless, by introducing weak Lehmer codes, we construct explicitly multicomplexes and Lehmer complexes for any lower Bruhat interval in type $F_4$.

The other day I had a funny conversation. A Finnish person was making excuses for my laziness at learning Finnish. Then she asked «is Italian hard to learn?». I never know how to answer the question so I said «I don't know: at least pronunciation is not too bad for Finns, they may sound funny but they're understandable; they mostly have trouble because they have no concept of separate p and b, and so on». She said «you mean strong p and soft p»? Not how I would have phrased it, but y…

Instruction-tuned Self-Questioning Framework for Multimodal Reasoning
You-Won Jang, Yu-Jung Heo, Jaeseok Kim, Minsu Lee, Du-Seong Chang, Byoung-Tak Zhang
https://arxiv.org/abs/2509.21251

Instruction-tuned Self-Questioning Framework for Multimodal Reasoning
The field of vision-language understanding has been actively researched in recent years, thanks to the development of Large Language Models~(LLMs). However, it still needs help with problems requiring multi-step reasoning, even for very simple questions. Recent studies adopt LLMs to tackle this problem by iteratively generating sub-questions and answers. However, there are disadvantages such as 1) the fine-grained visual contents of images are not available using LLMs that cannot read visual in…

Analysis of instruction-based LLMs' capabilities to score and judge text-input problems in an academic setting
Valeria Ramirez-Garcia, David de-Fitero-Dominguez, Antonio Garcia-Cabot, Eva Garcia-Lopez
https://arxiv.org/abs/2509.20982

Analysis of instruction-based LLMs' capabilities to score and judge text-input problems in an academic setting
Large language models (LLMs) can act as evaluators, a role studied by methods like LLM-as-a-Judge and fine-tuned judging LLMs. In the field of education, LLMs have been studied as assistant tools for students and teachers. Our research investigates LLM-driven automatic evaluation systems for academic Text-Input Problems using rubrics. We propose five evaluation systems that have been tested on a custom dataset of 110 answers about computer science from higher education students with three model…