Tootfinder

Interleaving Reasoning for Better Text-to-Image Generation
Wenxuan Huang, Shuang Chen, Zheyong Xie, Shaosheng Cao, Shixiang Tang, Yufan Shen, Qingyu Yin, Wenbo Hu, Xiaoman Wang, Yuntian Tang, Junbo Qiao, Yue Guo, Yao Hu, Zhenfei Yin, Philip Torr, Yu Cheng, Wanli Ouyang, Shaohui Lin
https://arxiv.org/abs/2509.06945

Interleaving Reasoning for Better Text-to-Image Generation
Unified multimodal understanding and generation models recently have achieve significant improvement in image generation capability, yet a large gap remains in instruction following and detail preservation compared to systems that tightly couple comprehension with generation such as GPT-4o. Motivated by recent advances in interleaving reasoning, we explore whether such reasoning can further improve Text-to-Image (T2I) generation. We introduce Interleaving Reasoning Generation (IRG), a framework…

CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization
Xinge Ye, Rui Wang, Yuchuan Wu, Victor Ma, Feiteng Fang, Fei Huang, Yongbin Li
https://arxiv.org/abs/2508.09074

CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization
Reinforcement Learning Fine-Tuning (RLFT) has achieved notable success in tasks with objectively verifiable answers (e.g., code generation, mathematical reasoning), yet struggles with open-ended subjective tasks like role-playing dialogue. Traditional reward modeling approaches, which rely on independent sample-wise scoring, face dual challenges: subjective evaluation criteria and unstable reward signals.Motivated by the insight that human evaluation inherently combines explicit criteria with i…

M2IO-R1: An Efficient RL-Enhanced Reasoning Framework for Multimodal Retrieval Augmented Multimodal Generation
Zhiyou Xiao, Qinhan Yu, Binghui Li, Geng Chen, Chong Chen, Wentao Zhang
https://arxiv.org/abs/2508.06328

M2IO-R1: An Efficient RL-Enhanced Reasoning Framework for Multimodal Retrieval Augmented Multimodal Generation
Current research on Multimodal Retrieval-Augmented Generation (MRAG) enables diverse multimodal inputs but remains limited to single-modality outputs, restricting expressive capacity and practical utility. In contrast, real-world applications often demand both multimodal inputs and multimodal outputs for effective communication and grounded reasoning. Motivated by the recent success of Reinforcement Learning (RL) in complex reasoning tasks for Large Language Models (LLMs), we adopt RL as a prin…

The knots people twist themselves into to defend Perplexity is super funny.
Motivated reasoning is one hell of a drug.

Effective Equation of State Oscillations at Matter-Radiation Equality and Primordial Gravitational Waves
S. D. Odintsov, V. K. Oikonomou
https://arxiv.org/abs/2507.04571

Effective Equation of State Oscillations at Matter-Radiation Equality and Primordial Gravitational Waves
The theory controlling the Universe's evolution in the classical regime has to be motivated by particle physics reasoning and should also generate inflation and dark energy eras in a unified way. One such framework is $F(R)$ gravity. In this work we examine a class of exponential deformations of $R^2$ gravity motivated by fundamental physics of scalaron evolution in a de Sitter background. As we show this class of models describe both inflation and the dark energy era in a viable way compatible…

LLM-Supported Content Analysis of Motivated Reasoning on Climate Change
Yuheun Kim, Qiaoyi Liu, Jeff Hemsley
https://arxiv.org/abs/2508.21305 https://arxiv…

LLM-Supported Content Analysis of Motivated Reasoning on Climate Change
Public discourse around climate change remains polarized despite scientific consensus on anthropogenic climate change (ACC). This study examines how "believers" and "skeptics" of ACC differ in their YouTube comment discourse. We analyzed 44,989 comments from 30 videos using a large language model (LLM) as a qualitative annotator, identifying ten distinct topics. These annotations were combined with social network analysis to examine engagement patterns. A linear mixed-effects model showed that …

Counting Answer Sets of Disjunctive Answer Set Programs
Mohimenul Kabir, Supratik Chakraborty, Kuldeep S Meel
https://arxiv.org/abs/2507.11655 https://

Counting Answer Sets of Disjunctive Answer Set Programs
Answer Set Programming (ASP) provides a powerful declarative paradigm for knowledge representation and reasoning. Recently, counting answer sets has emerged as an important computational problem with applications in probabilistic reasoning, network reliability analysis, and other domains. This has motivated significant research into designing efficient ASP counters. While substantial progress has been made for normal logic programs, the development of practical counters for disjunctive logic pr…

AI4Research: A Survey of Artificial Intelligence for Scientific Research
Qiguang Chen, Mingda Yang, Libo Qin, Jinhao Liu, Zheng Yan, Jiannan Guan, Dengyun Peng, Yiyan Ji, Hanjing Li, Mengkang Hu, Yimeng Zhang, Yihao Liang, Yuhang Zhou, Jiaqi Wang, Zhi Chen, Wanxiang Che
https://arxiv.org/abs/2507.01903

AI4Research: A Survey of Artificial Intelligence for Scientific Research
Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs) such as OpenAI-o1 and DeepSeek-R1, have demonstrated remarkable capabilities in complex domains such as logical reasoning and experimental coding. Motivated by these advancements, numerous studies have explored the application of AI in the innovation process, particularly in the context of scientific research. These AI technologies primarily aim to develop systems that can autonomously conduct rese…

LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research
Shuo Yan, Ruochen Li, Ziming Luo, Zimu Wang, Daoyang Li, Liqiang Jing, Kaiyu He, Peilin Wu, George Michalopoulos, Yue Zhang, Ziyang Zhang, Mian Zhang, Zhiyu Chen, Xinya Du
https://arxiv.org/abs/2506.17335…

LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research
Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery. However, their capability in the fundamental yet crucial task of reproducing code from research papers, especially in the NLP domain, remains underexplored. This task includes unique complex reasoning challenges in the intellectual synthesis of abstract concepts and the comprehension of code repositories with interdependent files. Motivated by this gap, we present LMR-BENCH, a benchmark d…

Wanting to Be Understood Explains the Meta-Problem of Consciousness
Chrisantha Fernando, Dylan Banarse, Simon Osindero
https://arxiv.org/abs/2506.12086 htt…

Wanting to Be Understood Explains the Meta-Problem of Consciousness
Because we are highly motivated to be understood, we created public external representations -- mime, language, art -- to externalise our inner states. We argue that such external representations are a pre-condition for access consciousness, the global availability of information for reasoning. Yet the bandwidth of access consciousness is tiny compared with the richness of `raw experience', so no external representation can reproduce that richness in full. Ordinarily an explanation of experienc…

Efficient Bayesian Sampling with Langevin Birth-Death Dynamics
Alex Leviyev, Francesco Iacovelli, Aaron Zimmerman
https://arxiv.org/abs/2509.01942 https://…

Efficient Bayesian Sampling with Langevin Birth-Death Dynamics
Bayesian inference plays a central role in scientific and engineering applications by enabling principled reasoning under uncertainty. However, sampling from generic probability distributions remains a computationally demanding task. This difficulty is compounded when the distributions are ill-conditioned, multi-modal, or supported on topologically non-Euclidean spaces. Motivated by challenges in gravitational wave parameter estimation, we propose simulating a Langevin diffusion augmented with …