Tootfinder

🇺🇦 Auf radioeins läuft...
Jalen Ngonda:
🎵 Just as Long as Were Together
#NowPlaying #JalenNgonda
https://jalenngonda.bandcamp.com/track/just-as-long-as-were-together
https://open.spotify.com/track/6uAt2PE7YB5gHeCAFeD7G6

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.

🇺🇦 #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

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…

🇺🇦 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

🇺🇦 #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

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…

Replaced article(s) found for math.LO. https://arxiv.org/list/math.LO/new
[1/1]:
- The complexity of classifying continuous t-norms up to isomorphism
Jialiang He, Lili Shen, Yi Zhou

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

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…