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Benjamin Hindrichs, @… für
#Politik und Macht: #Trump scheitert mit seinen Angriffen auf die

Trump scheitert mit seinen Angriffen auf die Demokratie
Seine Politik wirkt autoritär, ist aber ein Zeichen von Schwäche.

This https://arxiv.org/abs/2506.00643 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions
Large language models (LLMs) are increasingly evaluated on single-answer multiple-choice tasks, yet many real-world problems require identifying all correct answers from a set of options. This capability remains underexplored. We introduce SATA-BENCH, the first dedicated benchmark for evaluating LLMs on Select All That Apply (SATA) questions across diverse domains, including reading comprehension, law, and biomedicine. Our evaluation of 27 open-source and proprietary models reveals a significan…

ExAct: A Video-Language Benchmark for Expert Action Analysis
Han Yi, Yulu Pan, Feihong He, Xinyu Liu, Benjamin Zhang, Oluwatumininu Oguntola, Gedas Bertasius
https://arxiv.org/abs/2506.06277

ExAct: A Video-Language Benchmark for Expert Action Analysis
We present ExAct, a new video-language benchmark for expert-level understanding of skilled physical human activities. Our new benchmark contains 3521 expert-curated video question-answer pairs spanning 11 physical activities in 6 domains: Sports, Bike Repair, Cooking, Health, Music, and Dance. ExAct requires the correct answer to be selected from five carefully designed candidate options, thus necessitating a nuanced, fine-grained, expert-level understanding of physical human skills. Evaluating…

A new day a new AI benchmark.
#ai #benchmarks

OpenAI's o3 tops new AI league table for answering scientific questions
SciArena uses votes by researchers to evaluate large language models’ responses on technical topics.

Reasoning as a Resource: Optimizing Fast and Slow Thinking in Code Generation Models
Zongjie Li, Shuai Wang
https://arxiv.org/abs/2506.09396 https://

Reasoning as a Resource: Optimizing Fast and Slow Thinking in Code Generation Models
This position paper proposes a fundamental shift in designing code generation models: treating reasoning depth as a controllable resource. Rather than being an incidental byproduct of prompting, we argue that the trade-off between rapid, direct answers ("fast thinking") and elaborate, chain-of-thought deliberation ("slow thinking") must be explicitly managed. We contend that optimizing reasoning budgets across the entire model lifecycle - from synthetic data creation and benchmarking to real-wo…

Die internationale Bewertungsagentur CRIF hat vermutlich deinen Namen, Anschrift und Geburtsdatum und berechnet damit einen "Score". Banken, Mobilfunker und andere rufen diesen Score ab und entscheiden so, ob du "kreditwürdig" bist.
@noybeu möchte nun Licht ins Dunkel bringen und eine Sammelklage einreichen für jene wo der Score so gar nicht stimmt. Ich habe mitgemacht - hilf auch du auf

CRIF Scoring - Auskunft und Recherche
Die Kreditauskunftei CRIF hat vermutlich deinen Namen, Anschrift und Geburtsdatum und berechnet damit einen "Score". Banken, Mobilfunker und andere rufen diesen Score ab. noyb möchte nun Licht ins Dunkel bringen. Ich habe mitgemacht - hilf auch du:

The embedded deformation problem for monomial ideals
Benjamin Briggs, Elo\'isa Grifo, Josh Pollitz
https://arxiv.org/abs/2506.10827 https://

The embedded deformation problem for monomial ideals
This article is concerned with homological properties of local or graded rings whose defining relations are monomials on some regular sequence. The main result of the article positively answers a question of Avramov for such a ring $R$. More precisely, we establish that an embedded deformation of $R$ corresponds exactly to a degree two central element in the homotopy Lie algebra of $R$, as well as a free summand of the conormal module of $R$. A major input in the proof is an analysis of cohomol…

Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model
Ailin Huang, Bingxin Li, Bruce Wang, Boyong Wu, Chao Yan, Chengli Feng, Heng Wang, Hongyu Zhou, Hongyuan Wang, Jingbei Li, Jianjian Sun, Joanna Wang, Mingrui Chen, Peng Liu, Ruihang Miao, Shilei Jiang, Tian Fei, Wang You, Xi Chen, Xuerui Yang, Yechang Huang, Yuxiang Zhang, Zheng Ge, Zheng Gong, Zhewei Huang, Zixin Zhang, Bin Wang, Bo Li, Buyun Ma, Changxin Miao, Changyi Wan, Chen Xu, Dapeng Shi, Dingyuan Hu, Enle…

Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model
Large Audio-Language Models (LALMs) have significantly advanced intelligent human-computer interaction, yet their reliance on text-based outputs limits their ability to generate natural speech responses directly, hindering seamless audio interactions. To address this, we introduce Step-Audio-AQAA, a fully end-to-end LALM designed for Audio Query-Audio Answer (AQAA) tasks. The model integrates a dual-codebook audio tokenizer for linguistic and semantic feature extraction, a 130-billion-parameter…

This https://arxiv.org/abs/2505.22240 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain
Biomedical reasoning often requires traversing interconnected relationships across entities such as drugs, diseases, and proteins. Despite the increasing prominence of large language models (LLMs), existing benchmarks lack the ability to evaluate multi-hop reasoning in the biomedical domain, particularly for queries involving one-to-many and many-to-many relationships. This gap leaves the critical challenges of biomedical multi-hop reasoning underexplored. To address this, we introduce BioHopR,…

Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding
Emmanouil Zaranis, Ant\'onio Farinhas, Saul Santos, Beatriz Canaverde, Miguel Moura Ramos, Aditya K Surikuchi, Andr\'e Viveiros, Baohao Liao, Elena Bueno-Benito, Nithin Sivakumaran, Pavlo Vasylenko, Shoubin Yu, Sonal Sannigrahi, Wafaa Mohammed, Ben Peters, Danae S\'anchez Villegas, Elias Stengel-Eskin, Giuseppe Attanasio, Jaehong Yoon, Stella Frank, Alessandro Suglia, Chrysoula Zerva, Desmond Elliott, M…

Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding
Despite recent progress in vision-language models (VLMs), holistic understanding of long-form video content remains a significant challenge, partly due to limitations in current benchmarks. Many focus on peripheral, ``needle-in-a-haystack'' details, encouraging context-insensitive retrieval over deep comprehension. Others rely on large-scale, semi-automatically generated questions (often produced by language models themselves) that are easier for models to answer but fail to reflect genuine und…