Tootfinder

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability
Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
https://arxiv.org/abs/2506.15629

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability
In generative commonsense reasoning tasks such as CommonGen, generative large language models (LLMs) compose sentences that include all given concepts. However, when focusing on instruction-following capabilities, if a prompt specifies a concept order, LLMs must generate sentences that adhere to the specified order. To address this, we propose Ordered CommonGen, a benchmark designed to evaluate the compositional generalization and instruction-following abilities of LLMs. This benchmark measures…

Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps
Kangyu Wang, Hongliang He, Lin Liu, Ruiqi Liang, Zhenzhong Lan, Jianguo Li
https://arxiv.org/abs/2508.11452

Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps
Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have ushered in a new era of AI capabilities, demonstrating near-human-level performance across diverse scenarios. While numerous benchmarks (e.g., MMLU) and leaderboards (e.g., Chatbot Arena) have been proposed to help evolve the development of LLMs and MLLMs, most rely on static datasets or crowdsourced general-domain prompts, often falling short of reflecting performance in real-world applications. To bridge this criti…

MetaLint: Generalizable Idiomatic Code Quality Analysis through Instruction-Following and Easy-to-Hard Generalization
Atharva Naik, Lawanya Baghel, Dhakshin Govindarajan, Darsh Agrawal, Daniel Fried, Carolyn Rose
https://arxiv.org/abs/2507.11687

MetaLint: Generalizable Idiomatic Code Quality Analysis through Instruction-Following and Easy-to-Hard Generalization
Large Language Models, though successful in code generation, struggle with code quality analysis because they are limited by static training data and can't easily adapt to evolving best practices. We introduce MetaLint, a new instruction-following framework that formulates code quality analysis as the task of detecting and fixing problematic semantic code fragments or code idioms based on high-level specifications. Unlike conventional approaches that train models on static, rule-based data, Met…

Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning
Ankan Deria, Adinath Madhavrao Dukre, Feilong Tang, Sara Atito, Sudipta Roy, Muhammad Awais, Muhammad Haris Khan, Imran Razzak
https://arxiv.org/abs/2506.15649

Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning
Despite significant advances in inference-time search for vision-language models (VLMs), existing approaches remain both computationally expensive and prone to unpenalized, low-confidence generations which often lead to persistent hallucinations. We introduce \textbf{Value-guided Inference with Margin-based Reward (ViMaR)}, a two-stage inference framework that improves both efficiency and output fidelity by combining a temporal-difference value model with a margin-aware reward adjustment. In th…

Apparent Resonance Splitting in Self-Coupled Excitonic Systems
Avishek Sarbajna, Qitong Li, Dorte Rub{\ae}k Danielsen, Skyler Peitso Selvin, Duc Hieu Nguyen, Manh-Ha Doan, Peter B{\o}ggild, Mark L. Brongersma, S{\o}ren Raza
https://arxiv.org/abs/2508.11370

Apparent Resonance Splitting in Self-Coupled Excitonic Systems
Thin films of high-refractive-index excitonic materials enable self-coupling by simultaneously supporting intrinsic excitonic transitions and optical resonances. These optical resonances take the form of Fabry-Perot resonances in thick films and absorption resonances in ultrathin films placed on metallic substrates. Here, we investigate whether these optical resonances lead to true exciton-photon hybridization. Using far-field reflectance and spectrally resolved photocurrent measurements, we st…

Fine-structure Line Atlas for Multi-wavelength Extragalactic Study (FLAMES) III: [C II] as Tracer, Crisis of SFR, [O III]/[C II] at High-z, New Answers and New Questions
Bo Peng, Gordon Stacey, Amit Vishwas, Catie Ball, Cody Lamarche, Christopher Rooney, Thomas Nikola, Carl Ferkinhoff
https://arxiv.org/abs/2507.12896

Fine-structure Line Atlas for Multi-wavelength Extragalactic Study (FLAMES) III: [C II] as Tracer, Crisis of SFR, [O III]/[C II] at High-z, New Answers and New Questions
In the final paper of this series, we discuss new perspectives and challenges in the study of interstellar medium (ISM), leveraging comprehensive catalogs and physical insights presented in our previous papers. We focus on key questions of far-infrared (FIR) fine-structure lines (FSLs): their origins, diagnostic value, and implications of correlations. Our analysis reveals a strong dependence on elemental abundance, so that FSL/H$α$ traces metallicity, [N II]/[C II] traces N/O, and $\sim$80% o…

A Comparative Approach to Assessing Linguistic Creativity of Large Language Models and Humans
Anca Dinu, Andra-Maria Florescu, Alina Resceanu
https://arxiv.org/abs/2507.12039

A Comparative Approach to Assessing Linguistic Creativity of Large Language Models and Humans
The following paper introduces a general linguistic creativity test for humans and Large Language Models (LLMs). The test consists of various tasks aimed at assessing their ability to generate new original words and phrases based on word formation processes (derivation and compounding) and on metaphorical language use. We administered the test to 24 humans and to an equal number of LLMs, and we automatically evaluated their answers using OCSAI tool for three criteria: Originality, Elaboration, …

Thyme: Think Beyond Images
Yi-Fan Zhang, Xingyu Lu, Shukang Yin, Chaoyou Fu, Wei Chen, Xiao Hu, Bin Wen, Kaiyu Jiang, Changyi Liu, Tianke Zhang, Haonan Fan, Kaibing Chen, Jiankang Chen, Haojie Ding, Kaiyu Tang, Zhang Zhang, Liang Wang, Fan Yang, Tingting Gao, Guorui Zhou
https://arxiv.org/abs/2508.11630

Thyme: Think Beyond Images
Following OpenAI's introduction of the ``thinking with images'' concept, recent efforts have explored stimulating the use of visual information in the reasoning process to enhance model performance in perception and reasoning tasks. However, to the best of our knowledge, no open-source work currently offers a feature set as rich as proprietary models (O3), which can perform diverse image manipulations and simultaneously enhance logical reasoning capabilities through code. In this paper, we make…

HeroBench: A Benchmark for Long-Horizon Planning and Structured Reasoning in Virtual Worlds
Petr Anokhin, Roman Khalikov, Stefan Rebrikov, Viktor Volkov, Artyom Sorokin, Vincent Bissonnette
https://arxiv.org/abs/2508.12782

HeroBench: A Benchmark for Long-Horizon Planning and Structured Reasoning in Virtual Worlds
Large language models (LLMs) have shown remarkable capabilities in isolated step-by-step reasoning tasks such as mathematics and programming, but their proficiency in long-horizon planning, where solutions require extended, structured sequences of interdependent actions, remains underexplored. Existing benchmarks typically assess LLMs through abstract or low-dimensional algorithmic tasks, failing to capture the complexity of realistic planning environments. We introduce HeroBench, a novel bench…

ImpReSS: Implicit Recommender System for Support Conversations
Omri Haller, Yair Meidan, Dudu Mimran, Yuval Elovici, Asaf Shabtai
https://arxiv.org/abs/2506.14231

ImpReSS: Implicit Recommender System for Support Conversations
Following recent advancements in large language models (LLMs), LLM-based chatbots have transformed customer support by automating interactions and providing consistent, scalable service. While LLM-based conversational recommender systems (CRSs) have attracted attention for their ability to enhance the quality of recommendations, limited research has addressed the implicit integration of recommendations within customer support interactions. In this work, we introduce ImpReSS, an implicit recomme…