Tootfinder

How Well Can Reasoning Models Identify and Recover from Unhelpful Thoughts?
Sohee Yang, Sang-Woo Lee, Nora Kassner, Daniela Gottesman, Sebastian Riedel, Mor Geva
https://arxiv.org/abs/2506.10979

How Well Can Reasoning Models Identify and Recover from Unhelpful Thoughts?
Recent reasoning models show the ability to reflect, backtrack, and self-validate their reasoning, which is crucial in spotting mistakes and arriving at accurate solutions. A natural question that arises is how effectively models can perform such self-reevaluation. We tackle this question by investigating how well reasoning models identify and recover from four types of unhelpful thoughts: uninformative rambling thoughts, thoughts irrelevant to the question, thoughts misdirecting the question a…

Egyptian police violently obstruct the international Gaza Peace March.
Ocupación israelí | La policía egipcia impide con violencia la marcha internacional a Gaza - El Salto - Edición General
https://www.elsaltodiario.com/ocupacion-is

La policía egipcia impide con violencia la marcha internacional a Gaza
La marcha de 4.000 personas a Gaza es reprimida por la policía del régimen de Al-Sisi. La organización pide a las embajadas que reaccionen y protejan a sus ciudadanos.

23andMe privacy ombudsman recommends company obtains consent for sale of customer data https://therecord.media/23andme-privacy-ombudsman-recommends-consent-sale

#silentSunday #RockyMountainNationalPark
View from Many Parks Curve. October 2016

Apple-Paper: Warum Reasoning-Modelle wohl nicht denken
Sie brauchen viel Leistung, liefern aber nicht immer bessere Ergebnisse: Large Reasoning Models sollen die KI revolutionieren. Eine Apple-Studie kritisiert das.

Context-Adaptive Graph Neural Networks for Next POI Recommendation
Yu Lei, Limin Shen, Zhu Sun, Tiantian He, Yew-Soon Ong
https://arxiv.org/abs/2506.10329 …

Context-Adaptive Graph Neural Networks for Next POI Recommendation
Next Point-of-Interest (POI) recommendation is a critical task in location-based services, aiming to predict users' next visits based on their check-in histories. While many existing methods leverage Graph Neural Networks (GNNs) to incorporate collaborative information and improve recommendation accuracy, most of them model each type of context using separate graphs, treating different factors in isolation. This limits their ability to model the co-influence of multiple contextual factors on us…

Ach ne, das kann doch nicht sein… oder oder… oder doch?! Marketing #Propaganda ist ja nicht neu:
»Ist Reasoning ein #Marketing-Trick? Apple-Forscher zeigen gravierende Probleme von #KI-Modellen:

Ist Reasoning ein Marketing-Trick? Apple-Forscher zeigen gravierende Probleme von KI-Modellen - t3n – digital pioneers
Apple-Forscher:innen haben ein neues Paper veröffentlicht, das die Fertigkeiten von Reasoning-Modellen infrage stellt. Demnach sollen aktuelle Modelle von OpenAI, Google und Anthropic nur in Benchmarks zuverlässig funktionieren, aber an echten Aufgaben grandios scheitern. Immer mehr KI-Unternehmen setzen ihren Fokus auf Reasoning-Modelle, die in der Lage sein sollen, komplexe Gedankengänge zu haben. Doch selbst OpenAI musste […]

Mistral launches its first reasoning models: Magistral Small, on Hugging Face under an Apache 2.0 license, and Magistral Medium, in preview on Mistral's Le Chat (Kyle Wiggers/TechCrunch)
https://techcrunch.com/2025/06/10/mistral-releases-a-pai…

Mistral releases a pair of AI reasoning models | TechCrunch
Like other reasoning models, Magistral works through problems step-by-step for improved consistency and reliability across topics such as math and physics.

Resa: Transparent Reasoning Models via SAEs
Shangshang Wang, Julian Asilis, \"Omer Faruk Akg\"ul, Enes Burak Bilgin, Ollie Liu, Deqing Fu, Willie Neiswanger
https://arxiv.org/abs/2506.09967

Resa: Transparent Reasoning Models via SAEs
How cost-effectively can we elicit strong reasoning in language models by leveraging their underlying representations? We answer this question with Resa, a family of 1.5B reasoning models trained via a novel and efficient sparse autoencoder tuning (SAE-Tuning) procedure. This method first trains an SAE to capture reasoning abilities from a source model, and then uses the trained SAE to guide a standard supervised fine-tuning process to elicit such abilities in a target model, all using verified…

LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
Yanhui Li, Dongxia Wang, Zhu Sun, Haonan Zhang, Huizhong Guo
https://arxiv.org/abs/2506.10347

LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
Recently, Graph Neural Networks (GNNs) have become the dominant approach for Knowledge Graph-aware Recommender Systems (KGRSs) due to their proven effectiveness. Building upon GNN-based KGRSs, Self-Supervised Learning (SSL) has been incorporated to address the sparity issue, leading to longer training time. However, through extensive experiments, we reveal that: (1)compared to other KGRSs, the existing GNN-based KGRSs fail to keep their superior performance under sparse interactions even with S…