Tootfinder

CDU/CSU führen in den Untergang. Bald rennt FFF dann wieder einmal um die Binnenalster, alle fühlen sich gut und gehen heim. Ende.
https://mastodon.social/@peterjelinek/115201735502740476

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Erika de Casier:
🎵 Miss
#ErikadeCasier
https://jalenjones.bandcamp.com/track/erika-de-casier-miss-jalen-jones-ukg-bootleg
https://open.spotify.com/track/3iewIkYTvvaicW8iysqxuU

🇺🇦 #NowPlaying on #BBC6Music's #6MusicsRaveForever
JIALING:
🎵 Percolate on Acid
#JIALING
https://jialing.bandcamp.com/track/percolate-on-acid
https://open.spotify.com/track/6L0G4sAWQMlGt7XjV5SQDO

🇺🇦 Auf radioeins läuft...
Jalen Ngonda:
🎵 All About Me
#NowPlaying #JalenNgonda
#newRelease 🆕 single
https://jalenngonda.bandcamp.com/album/all-about-me
https://open.spotify.com/track/60mXzhxdo3dtKYz1MNBLFD

Julmasti kettuja kohdelleet turkistarhurit saivat pysyvän eläintenpitokiellon - #eläinoikeudet

Julmasti kettuja kohdelleet turkistarhurit saivat pysyvän eläintenpitokiellon
Ketut olivat sairaita ja laihoja, eikä niistä pidetty huolta. Myöhemmässä tarkastuksessa tarhalta löydettiin kymmeniä muumioituneita eläimiä.

Learning to Detect Unknown Jailbreak Attacks in Large Vision-Language Models: A Unified and Accurate Approach
Shuang Liang, Zhihao Xu, Jialing Tao, Hui Xue, Xiting Wang
https://arxiv.org/abs/2508.09201

Learning to Detect Unknown Jailbreak Attacks in Large Vision-Language Models: A Unified and Accurate Approach
Despite extensive alignment efforts, Large Vision-Language Models (LVLMs) remain vulnerable to jailbreak attacks, posing serious safety risks. Although recent detection works have shifted to internal representations due to their rich cross-modal information, most methods rely on heuristic rules rather than principled objectives, resulting in suboptimal performance. To address these limitations, we propose Learning to Detect (LoD), a novel unsupervised framework that formulates jailbreak detecti…

SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system
Jialiang Shi, Yaguang Dou, Tian Qi
https://arxiv.org/abs/2508.09090 https:…

SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system
Modeling multi-interests has arisen as a core problem in real-world RS. Current multi-interest retrieval methods pose three major challenges: 1) Interests, typically extracted from predefined external knowledge, are invariant. Failed to dynamically evolve with users' real-time consumption preferences. 2) Online inference typically employs an over-exploited strategy, mainly matching users' existing interests, lacking proactive exploration and discovery of novel and long-tail interests. To addres…

🇺🇦 #NowPlaying on KEXP's #MorningShow
Julian Cope:
🎵 World Shut Your Mouth
#JulianCope
https://open.spotify.com/track/2GF0D3d6LKIsDnk8ufpBQa

🇺🇦 #NowPlaying on #BBC6Music's #DonLettsCultureClashRadio
Jalen Ngonda:
🎵 All About Me
#JalenNgonda
#newRelease 🆕 single
https://jalenngonda.bandcamp.com/album/all-about-me
https://open.spotify.com/track/60mXzhxdo3dtKYz1MNBLFD

Towards Self-cognitive Exploration: Metacognitive Knowledge Graph Retrieval Augmented Generation
Xujie Yuan, Shimin Di, Jielong Tang, Libin Zheng, Jian Yin
https://arxiv.org/abs/2508.09460

Towards Self-cognitive Exploration: Metacognitive Knowledge Graph Retrieval Augmented Generation
Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) significantly enhances the reasoning capabilities of LargeLanguage Models by leveraging structured knowledge. However, existing KG-RAG frameworks typically operate as open-loop systems, suffering from cognitive blindness, an inability to recognize their exploration deficiencies. This leads to relevance drift and incomplete evidence, which existing self-refinement methods, designed for unstructured text-based RAG, cannot effectively r…