Tootfinder

Recyclers Switch from Smelting to Solvents, Recovering Precious Metals from E-waste with Fewer Emissions https://www.goodnewsnetwork.org/recyclers-switch-from-smelting-to-solvents-recovering-precious-metals-fr…

Recyclers Switch from Smelting to Solvents, Recovering Precious Metals from E-waste with Fewer Emissions
The UN estimated that some three-fourths of all e-waste didn't enter recycling streams, leaving an estimated $62 billion in metals behind

Locality in Image Diffusion Models Emerges from Data Statistics
Artem Lukoianov, Chenyang Yuan, Justin Solomon, Vincent Sitzmann
https://arxiv.org/abs/2509.09672 https://…

Locality in Image Diffusion Models Emerges from Data Statistics
Among generative models, diffusion models are uniquely intriguing due to the existence of a closed-form optimal minimizer of their training objective, often referred to as the optimal denoiser. However, diffusion using this optimal denoiser merely reproduces images in the training set and hence fails to capture the behavior of deep diffusion models. Recent work has attempted to characterize this gap between the optimal denoiser and deep diffusion models, proposing analytical, training-free mode…

NOWJ@COLIEE 2025: A Multi-stage Framework Integrating Embedding Models and Large Language Models for Legal Retrieval and Entailment
Hoang-Trung Nguyen, Tan-Minh Nguyen, Xuan-Bach Le, Tuan-Kiet Le, Khanh-Huyen Nguyen, Ha-Thanh Nguyen, Thi-Hai-Yen Vuong, Le-Minh Nguyen
https://arxiv.org/abs/2509.08025

NOWJ@COLIEE 2025: A Multi-stage Framework Integrating Embedding Models and Large Language Models for Legal Retrieval and Entailment
This paper presents the methodologies and results of the NOWJ team's participation across all five tasks at the COLIEE 2025 competition, emphasizing advancements in the Legal Case Entailment task (Task 2). Our comprehensive approach systematically integrates pre-ranking models (BM25, BERT, monoT5), embedding-based semantic representations (BGE-m3, LLM2Vec), and advanced Large Language Models (Qwen-2, QwQ-32B, DeepSeek-V3) for summarization, relevance scoring, and contextual re-ranking. Specific…

🇺🇦 Auf radioeins läuft...
Midlake:
🎵 The Ghouls
#NowPlaying #Midlake
https://midlakeband.bandcamp.com/track/the-ghouls
https://open.spotify.com/track/7u716E5N8na1jdhc0i85lj

"A 6 or a 9?": Ensemble Learning Through the Multiplicity of Performant Models and Explanations
Gianlucca Zuin, Adriano Veloso
https://arxiv.org/abs/2509.09073 https:/…

"A 6 or a 9?": Ensemble Learning Through the Multiplicity of Performant Models and Explanations
Creating models from past observations and ensuring their effectiveness on new data is the essence of machine learning. However, selecting models that generalize well remains a challenging task. Related to this topic, the Rashomon Effect refers to cases where multiple models perform similarly well for a given learning problem. This often occurs in real-world scenarios, like the manufacturing process or medical diagnosis, where diverse patterns in data lead to multiple high-performing solutions.…

proud to be a DSA member!
https://www.middleeasteye.net/news/largest-us-socialist-organisation-passes-resolution-supporting-palestinian-resistance

Largest US socialist organisation passes resolution supporting Palestinian resistance
The resolution supports boycott campaigns against Israel and principles outlined by the Palestinian Liberation Organisation

Verräterische Vibrationen: Handy-Gespräche abhören mit Radar und KI
Durch Sprache entstehende Mikrovibrationen an Mobiltelefonen können dazu genutzt werden, um mittels Radar und KI Gespräche abzuhören.

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities
Rajendramayavan Sathyam, Yueqi Li
https://arxiv.org/abs/2509.08302 https://

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities
Foundation models are revolutionizing autonomous driving perception, transitioning the field from narrow, task-specific deep learning models to versatile, general-purpose architectures trained on vast, diverse datasets. This survey examines how these models address critical challenges in autonomous perception, including limitations in generalization, scalability, and robustness to distributional shifts. The survey introduces a novel taxonomy structured around four essential capabilities for rob…

MESH -- Understanding Videos Like Human: Measuring Hallucinations in Large Video Models
Garry Yang, Zizhe Chen, Man Hon Wong, Haoyu Lei, Yongqiang Chen, Zhenguo Li, Kaiwen Zhou, James Cheng
https://arxiv.org/abs/2509.08538

MESH -- Understanding Videos Like Human: Measuring Hallucinations in Large Video Models
Large Video Models (LVMs) build on the semantic capabilities of Large Language Models (LLMs) and vision modules by integrating temporal information to better understand dynamic video content. Despite their progress, LVMs are prone to hallucinations-producing inaccurate or irrelevant descriptions. Current benchmarks for video hallucination depend heavily on manual categorization of video content, neglecting the perception-based processes through which humans naturally interpret videos. We introd…

Bias after Prompting: Persistent Discrimination in Large Language Models
Nivedha Sivakumar, Natalie Mackraz, Samira Khorshidi, Krishna Patel, Barry-John Theobald, Luca Zappella, Nicholas Apostoloff
https://arxiv.org/abs/2509.08146

Bias after Prompting: Persistent Discrimination in Large Language Models
A dangerous assumption that can be made from prior work on the bias transfer hypothesis (BTH) is that biases do not transfer from pre-trained large language models (LLMs) to adapted models. We invalidate this assumption by studying the BTH in causal models under prompt adaptations, as prompting is an extremely popular and accessible adaptation strategy used in real-world applications. In contrast to prior work, we find that biases can transfer through prompting and that popular prompt-based mit…