Tootfinder

Google announces Gemma 3 270M, a compact model designed for task-specific fine-tuning with strong capabilities in instruction following and text structuring (Google Developers Blog)
https://developers.googleblog.com/en/introducing-gemma-3-270m/

Introducing Gemma 3 270M: The compact model for hyper-efficient AI- Google Developers Blog
Explore Gemma 3 270M, a compact, energy-efficient AI model for task-specific fine-tuning, offering strong instruction-following and production-ready quantization.

Live-Webinar: Apple-Gerätemanagement mit Microsoft Intune
Intune für das Mobile Device Management von iPhones, iPads & Macs – unser Experte zeigt Tipps und Tricks, wo Grenzen liegen und wie Sie Stolperfallen meistern.

FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation
Yuxuan Jiang, Zehua Chen, Zeqian Ju, Chang Li, Weibei Dou, Jun Zhu
https://arxiv.org/abs/2507.08557

FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation
Text-to-audio (T2A) generation has achieved promising results with the recent advances in generative models. However, because of the limited quality and quantity of temporally-aligned audio-text pairs, existing T2A methods struggle to handle the complex text prompts that contain precise timing control, e.g., "owl hooted at 2.4s-5.2s". Recent works have explored data augmentation techniques or introduced timing conditions as model inputs to enable timing-conditioned 10-second T2A generation, whi…

SI-FACT: Mitigating Knowledge Conflict via Self-Improving Faithfulness-Aware Contrastive Tuning
Shengqiang Fu
https://arxiv.org/abs/2509.10208 https://arxi…

SI-FACT: Mitigating Knowledge Conflict via Self-Improving Faithfulness-Aware Contrastive Tuning
Large Language Models often generate unfaithful responses in knowledge intensive tasks due to knowledge conflict,that is,a preference for relying on internal parametric knowledge rather than the provided context.To address this issue,we propose a novel self improving framework,Self Improving Faithfulness Aware Contrastive Tuning.The framework uses a self instruct mechanism that allows the base LLM to automatically generate high quality,structured contrastive learning data,including anchor sampl…

Toward Human-Robot Teaming: Learning Handover Behaviors from 3D Scenes
Yuekun Wu, Yik Lung Pang, Andrea Cavallaro, Changjae Oh
https://arxiv.org/abs/2508.09855 https://

Toward Human-Robot Teaming: Learning Handover Behaviors from 3D Scenes
Human-robot teaming (HRT) systems often rely on large-scale datasets of human and robot interactions, especially for close-proximity collaboration tasks such as human-robot handovers. Learning robot manipulation policies from raw, real-world image data requires a large number of robot-action trials in the physical environment. Although simulation training offers a cost-effective alternative, the visual domain gap between simulation and robot workspace remains a major limitation. We introduce a …

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Couple shares unique home offering incredibly high standard of living: 'An urban lifestyle without the urban density'
ReGen Homes is building eco-conscious townhomes in British Columbia that promote sustainable living while meeting future homeowners' needs, reported the Vancouver Sun.

NEFMind: Parameter-Efficient Fine-Tuning of Open-Source LLMs for Telecom APIs Automation
Zainab Khan, Ahmed Hussain, Mukesh Thakur, Arto Hellas, Panos Papadimitratos
https://arxiv.org/abs/2508.09240

NEFMind: Parameter-Efficient Fine-Tuning of Open-Source LLMs for Telecom APIs Automation
The use of Service-Based Architecture in modern telecommunications has exponentially increased Network Functions (NFs) and Application Programming Interfaces (APIs), creating substantial operational complexities in service discovery and management. We introduce \textit{NEFMind}, a framework leveraging parameter-efficient fine-tuning of open-source Large Language Models (LLMs) to address these challenges. It integrates three core components: synthetic dataset generation from Network Exposure Fun…

Live-Webinar: Was macOS 26, iOS 26 & Co. für das Apple Gerätemanagement bedeuten
Apples neue Systeme bringen viele Neuerungen für das Gerätemanagement. Erfahren Sie, wie Sie mit Ihnen die Effizienz und Sicherheit Ihres Unternehmens steigern.

A Comprehensive Evaluation framework of Alignment Techniques for LLMs
Muneeza Azmat, Momin Abbas, Maysa Malfiza Garcia de Macedo, Marcelo Carpinette Grave, Luan Soares de Souza, Tiago Machado, Rogerio A de Paula, Raya Horesh, Yixin Chen, Heloisa Caroline de Souza Pereira Candello, Rebecka Nordenlow, Aminat Adebiyi
https://arxiv.org/abs/250…

A Comprehensive Evaluation framework of Alignment Techniques for LLMs
As Large Language Models (LLMs) become increasingly integrated into real-world applications, ensuring their outputs align with human values and safety standards has become critical. The field has developed diverse alignment approaches including traditional fine-tuning methods (RLHF, instruction tuning), post-hoc correction systems, and inference-time interventions, each with distinct advantages and limitations. However, the lack of unified evaluation frameworks makes it difficult to systematica…

Making Qwen3 Think in Korean with Reinforcement Learning
Jungyup Lee, Jemin Kim, Sang Park, SeungJae Lee
https://arxiv.org/abs/2508.10355 https://arxiv.org…

Making Qwen3 Think in Korean with Reinforcement Learning
We present a two-stage fine-tuning approach to make the large language model Qwen3 14B "think" natively in Korean. In the first stage, supervised fine-tuning (SFT) on a high-quality Korean reasoning dataset establishes a strong foundation in Korean logical reasoning, yielding notable improvements in Korean-language tasks and even some gains in general reasoning ability. In the second stage, we employ reinforcement learning with a customized Group Relative Policy Optimization (GRPO) algorithm to…