Tootfinder

Automating Modelica Module Generation Using Large Language Models: A Case Study on Building Control Description Language
Hanlong Wan, Xing Lu, Yan Chen, Karthik Devaprasad, Laura Hinkle
https://arxiv.org/abs/2509.14623

Automating Modelica Module Generation Using Large Language Models: A Case Study on Building Control Description Language
Dynamic energy systems and controls require advanced modeling frameworks to design and test supervisory and fault tolerant strategies. Modelica is a widely used equation based language, but developing control modules is labor intensive and requires specialized expertise. This paper examines the use of large language models (LLMs) to automate the generation of Control Description Language modules in the Building Modelica Library as a case study. We developed a structured workflow that combines s…

On small, local language models: 'In a world increasingly dominated by massive models and opaque APIs, we believe there’s still room for small, transparent, controllable systems. Models you can fine-tune, understand and run on your own terms' https://www.turing.ac.uk…

TDRM: Smooth Reward Models with Temporal Difference for LLM RL and Inference
Dan Zhang, Min Cai, Jonathan Li, Ziniu Hu, Yisong Yue, Yuxiao Dong, Jie Tang
https://arxiv.org/abs/2509.15110

TDRM: Smooth Reward Models with Temporal Difference for LLM RL and Inference
Reward models are central to both reinforcement learning (RL) with language models and inference-time verification. However, existing reward models often lack temporal consistency, leading to ineffective policy updates and unstable RL training. We introduce TDRM, a method for learning smoother and more reliable reward models by minimizing temporal differences during training. This temporal-difference (TD) regularization produces smooth rewards and improves alignment with long-term objectives. I…

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction
Xinhe Li, Jiajun Liu, Peng Wang
https://arxiv.org/abs/2508.13037

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction
Recent studies have demonstrated that Large Language Models (LLMs) have strong mathematical reasoning abilities but rely on hundreds of billions of parameters. To tackle the challenge of poor reasoning in Small Language Models (SLMs), existing methods typically leverage LLMs to generate massive amounts of data for cramming training. In psychology, they are akin to System 1 thinking, which resolves reasoning problems rapidly based on experience and intuition. However, human learning also require…

🇺🇦 Auf radioeins läuft...
Midlake:
🎵 Gone
#NowPlaying #Midlake
https://midlakeband.bandcamp.com/track/aurora-gone
https://open.spotify.com/track/7myBFDQIel9VMa4kGFaYg8

The Commission has issued guidelines to clarify the scope of the obligations for providers of general-purpose AI models under the AI Act. These obligations enter into application on 2 August 2025.
#eu #ai #aiact

Stable Diffusion: Neue Erweiterung bringt KI-Bilder in LibreOffice
Eine neue Erweiterung bringt Bilderzeugung mittels Stable Diffusion in LibreOffice. Dank Freiwilliger ist das gebührenfrei.
https://www.…

Self-Improving Embodied Foundation Models
Seyed Kamyar Seyed Ghasemipour, Ayzaan Wahid, Jonathan Tompson, Pannag Sanketi, Igor Mordatch
https://arxiv.org/abs/2509.15155 https://…

Self-Improving Embodied Foundation Models
Foundation models trained on web-scale data have revolutionized robotics, but their application to low-level control remains largely limited to behavioral cloning. Drawing inspiration from the success of the reinforcement learning stage in fine-tuning large language models, we propose a two-stage post-training approach for robotics. The first stage, Supervised Fine-Tuning (SFT), fine-tunes pretrained foundation models using both: a) behavioral cloning, and b) steps-to-go prediction objectives. …

GLAMs and humanities folk deserve better AI models, number 5654 in a series: 'the subjectivity of a task or instance negatively affects the performance of a model – an observation that is particularly worrying for humanities research'.
From Kaspar Beelen's report, Small Language Models for libraries and computational humanities

HARNESS: Lightweight Distilled Arabic Speech Foundation Models
Vrunda N. sukhadia, Shammur Absar Chowdhury
https://arxiv.org/abs/2509.14689 https://arxiv.o…

HARNESS: Lightweight Distilled Arabic Speech Foundation Models
Large pre-trained speech models excel in downstream tasks but their deployment is impractical for resource-limited environments. In this paper, we introduce HArnESS, the first Arabic-centric self-supervised speech model family, designed to capture Arabic speech nuances. Using iterative self-distillation, we train large bilingual HArnESS (HL) SSL models and then distill knowledge into compressed student models (HS, HST), preserving Arabic-specific representations. We use low-rank approximation t…