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Matching papers

Spark-to-Paper: End-to-End Research Paper Generation as a Composable Skill

176 upvotes · 12 AUG 2026 · Zhuoyang Qian, Biao Wu, Yiran Wang et al.

This paper presents a system that can generate research papers end-to-end, from retrieving literature to producing publication-ready figures, and includes checks to ensure the accuracy and validity of the research. Practitioners might care about this system because it could streamline the process of writing research papers and reduce errors.

research paper generationnatural language processingcoding assistantsintegrity checksexperiment planningmanuscript writing

AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

156 upvotes · 8 SEP 2026 · Ziyang Ma, Zhikang Niu, Wenming Tu et al.

This paper introduces AuK, an open-source model for speech generation and editing, which can perform a range of tasks with a single interface and achieves state-of-the-art performance on several tasks. Practitioners may care about this model for its potential to improve speech synthesis and editing capabilities in applications such as voice assistants and audio editing software.

speech generationspeech editingnatural language processingdeep learning modelsaudio processing

VibeVideoBench: Evaluating Multimodal Agents on Code-Driven Video Generation

89 upvotes · 27 OCT 2026 · Muxi Diao, Wenbin Xu, Lele Yang et al.

This paper evaluates how well multimodal agents can generate videos that include synchronized code, sounds, and on-screen events, and why a practitioner might care because it could lead to more realistic video generation in applications like educational content, music videos, and video games.

multimodal agentscode-driven video generationvideo synthesisbenchmarkingnatural language processingreinforcement learning

The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement

88 upvotes · 10 SEP 2026 · Yi Duan, Ying Liu, Zirui Tang et al.

This paper explores how AI systems can improve themselves in a self-sustaining way, allowing them to adapt and learn from their experiences without human intervention. Practitioners might care about this research if they're looking for ways to create more autonomous and efficient AI systems.

AI self-improvementrecursive learningautonomous systemsmeta-learningnatural language processing

HABIT-Bench: Evaluating Habit Induction from Longitudinal Weak Evidence in Agent Memory

84 upvotes · 20 OCT 2026 · Junming Zhang, Rubin Wei, Xu Cheng et al.

This paper evaluates how well large language model agents can infer and apply habits from incomplete or weak evidence, and how this relates to their performance in real-world conversations. Practitioners caring about conversational AI might care about this research because it can help improve agents' ability to understand user behavior and adapt to changing habits.

agent memoryhabit inductioncontinual learningnatural language processingdialogue systems