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Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Daniel Burkhardt, Xiangwei Cheng
https://arxiv.org/abs/2508.12100

Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Reasoning in interactive problem solving scenarios requires models to construct reasoning threads that reflect user understanding and align with structured domain knowledge. However, current reasoning models often lack explicit semantic hierarchies, user-domain knowledge alignment, and principled mechanisms to prune reasoning threads for effectiveness. These limitations result in lengthy generic output that does not guide users through goal-oriented reasoning steps. To address this, we propose …

Pia Lohikoski: Hallitus näyttää vihreää valoa ympäristön kannalta tuhoisille kaivoshankkeille https://vasemmisto.fi/pia-lohikoski-hallitus-nayttaa-vihreaa-valoa-ympariston-kannalta-tuhoisille-kaivoshankkeille/

Pia Lohikoski: Hallitus näyttää vihreää valoa ympäristön kannalta tuhoisille kaivoshankkeille
Hallitus on näyttänyt vihreää valoa muun muassa Viiankiaavan luonnonsuojelualueelle kaavaillulle kaivoshankkeelle, jonka asiantuntijat ovat arvioineet tekevän peruuttamatonta vahinkoa alueen…

Quantum Relational Knowledge Distillation
Chen-Yu Liu, Kuan-Cheng Chen, Keisuke Murota, Samuel Yen-Chi Chen, Enrico Rinaldi
https://arxiv.org/abs/2508.13054 https://

Quantum Relational Knowledge Distillation
Knowledge distillation (KD) is a widely adopted technique for compressing large models into smaller, more efficient student models that can be deployed on devices with limited computational resources. Among various KD methods, Relational Knowledge Distillation (RKD) improves student performance by aligning relational structures in the feature space, such as pairwise distances and angles. In this work, we propose Quantum Relational Knowledge Distillation (QRKD), which extends RKD by incorporatin…

Trivia Thursday: 20 questions to test your Bears-Cowboys knowledge https://www.dallascowboys.com/news/trivia-thursday-20-questions-to-test-your-bears-cowboys-knowledge

Germany denies knowledge of Patriot missiles en route Ukraine, contradicting Trump's claim: https://benborges.xyz/2025/07/17/germany-denies-knowledge-of-patriot.html

RPKT: Learning What You Don't -- Know Recursive Prerequisite Knowledge Tracing in Conversational AI Tutors for Personalized Learning
Jinwen Tang, Qiming Guo, Zhicheng Tang
https://arxiv.org/abs/2508.11892

RPKT: Learning What You Don't -- Know Recursive Prerequisite Knowledge Tracing in Conversational AI Tutors for Personalized Learning
Educational systems often assume learners can identify their knowledge gaps, yet research consistently shows that students struggle to recognize what they don't know they need to learn-the "unknown unknowns" problem. This paper presents a novel Recursive Prerequisite Knowledge Tracing (RPKT) system that addresses this challenge through dynamic prerequisite discovery using large language models. Unlike existing adaptive learning systems that rely on pre-defined knowledge graphs, our approach rec…

NFLPA dispute emerges over lawsuit awareness https://www.espn.com/nfl/story/_/id/45762449/nflpa-members-disagree-knowledge-lloyd-howell-lawsuit

Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generation
Baolei Zhang, Haoran Xin, Yuxi Chen, Zhuqing Liu, Biao Yi, Tong Li, Lihai Nie, Zheli Liu, Minghong Fang
https://arxiv.org/abs/2509.13772

Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) integrates external knowledge into large language models to improve response quality. However, recent work has shown that RAG systems are highly vulnerable to poisoning attacks, where malicious texts are inserted into the knowledge database to influence model outputs. While several defenses have been proposed, they are often circumvented by more adaptive or sophisticated attacks. This paper presents RAGOrigin, a black-box responsibility attribution framewo…

SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding
Yifei Li, Lingling Zhang, Hang Yan, Tianzhe Zhao, Zihan Ma, Muye Huang, Jun Liu
https://arxiv.org/abs/2508.11347

SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding
Traditional knowledge graph (KG) embedding methods aim to represent entities and relations in a low-dimensional space, primarily focusing on static graphs. However, real-world KGs are dynamically evolving with the constant addition of entities, relations and facts. To address such dynamic nature of KGs, several continual knowledge graph embedding (CKGE) methods have been developed to efficiently update KG embeddings to accommodate new facts while maintaining learned knowledge. As KGs grow at di…

Trivia Thursday: 20 questions to test your Bears-Cowboys knowledge https://www.dallascowboys.com/news/trivia-thursday-20-questions-to-test-your-bears-cowboys-knowledge