Effect-driven interpretation: Functors for natural language composition

🖥 Github: https://github.com/UCSC-VLAA/MedReason

📕 Paper: https://arxiv.org/abs/2504.00993v1

🔗 Tasks: https://paperswithcode.com/task/knowledge-graphs

Large Language Model Agent: A Survey on Methodology, Applications and Challenges

27 Mar 2025 · Junyu Luo, Weizhi Zhang, Ye Yuan, Yusheng Zhao, Junwei Yang, Yiyang Gu, Bohan Wu, Binqi Chen, Ziyue Qiao, Qingqing Long, RongCheng Tu, Xiao Luo, Wei Ju, Zhiping Xiao, Yifan Wang, Meng Xiao, Chenwu Liu, Jingyang Yuan, Shichang Zhang, Yiqiao Jin, Fan Zhang, Xian Wu, Hanqing Zhao, DaCheng Tao, Philip S. Yu, Ming Zhang

The era of intelligent agents is upon us, driven by revolutionary advancements in large language models. Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence. This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy, linking architectural foundations, collaboration mechanisms, and evolutionary pathways. We unify fragmented research threads by revealing fundamental connections between agent design principles and their emergent behaviors in complex environments. Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time, while also addressing evaluation methodologies, tool applications, practical challenges, and diverse application domains. By surveying the latest developments in this rapidly evolving field, we offer researchers a structured taxonomy for understanding LLM agents and identify promising directions for future research. The collection is available at https://github.com/luo-junyu/Awesome-Agent-Papers.

Paper: https://arxiv.org/pdf/2503.21460v1.pdf

Code: https://github.com/luo-junyu/awesome-agent-papers

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Crystal Generation with Space Group Informed Transformer

🖥 Github: https://github.com/deepmodeling/crystalformer

📕 Paper: https://arxiv.org/abs/2504.02367v1

🔗 Dataset: https://paperswithcode.com/dataset/alex-20

CPPO: Accelerating the Training of Group Relative Policy Optimization-Based Reasoning Models

28 Mar 2025 · Zhihang Lin, Mingbao Lin, Yuan Xie, Rongrong Ji

This paper introduces Completion Pruning Policy Optimization (CPPO) to accelerate the training of reasoning models based on Group Relative Policy Optimization (GRPO). GRPO, while effective, incurs high training costs due to the need for sampling multiple completions for each question. Our experiment and theoretical analysis reveals that the number of completions impacts model accuracy yet increases training time multiplicatively, and not all completions contribute equally to policy training -- their contribution depends on their relative advantage. To address these issues, we propose CPPO, which prunes completions with low absolute advantages, significantly reducing the number needed for gradient calculation and updates. Additionally, we introduce a dynamic completion allocation strategy to maximize GPU utilization by incorporating additional questions, further enhancing training efficiency. Experimental results demonstrate that CPPO achieves up to
speedup on GSM8K and on Math while preserving or even enhancing the accuracy compared to the original GRPO. We release our code at https://github.com/lzhxmu/CPPO.

Paper: https://arxiv.org/pdf/2503.22342v1.pdf

Code: https://github.com/lzhxmu/cppo

Datasets: GSM8K - MATH

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🌳 Compose Anything is Out! 🌳
#SkyworkAI unveils #SkyReelsA2 — a controllable video generation framework that can assemble arbitrary visual elements (e.g., characters, objects, backgrounds) into fully synthesized videos from text prompts.
Code, models, and evaluation benchmark are all released!
🔗 Resources:
Review: https://t.ly/MEjzL
Paper: https://arxiv.org/pdf/2504.02436
Project: https://skyworkai.github.io/skyreels-a2.github.io/
Repo: https://github.com/SkyworkAI/SkyReels-A2
🤗 Models: https://huggingface.co/Skywork/SkyReels-A2

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This channels is for Programmers, Coders, Software Engineers.

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🐈 TTT Long Video Generation 🐈

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Adding TTT layers into a pre-trained Transformer enables generating a one-minute clip from text storyboards.
Videos, code & annotations released 💙

🔗 Review: https://t.ly/mhlTN
📄 Paper: arxiv.org/pdf/2504.05298
🌐 Project: test-time-training.github.io/video-dit
💻 Repo: github.com/test-time-training/ttt-video-dit

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🔥 #YOLOv12 is out – new SOTA! ⚡️

👉 YOLOv12 is a novel attention-centric YOLO framework that matches the speed of previous CNN-based versions while harnessing the performance benefits of attention mechanisms.

💙 Source Code & Demo released:
▶️ Review: https://t.ly/jj1oR
▶️ Paper: arXiv
👉 Repo: GitHub
🤗 Demo: https://t.ly/w5rno

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Zep: A Temporal Knowledge Graph Architecture for Agent Memory

20 Jan 2025 · Preston Rasmussen, Pavlo Paliychuk, Travis Beauvais, Jack Ryan, Daniel Chalef ·

We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark. Additionally, Zep excels in more comprehensive and challenging evaluations than DMR that better reflect real-world enterprise use cases. While existing retrieval-augmented generation (RAG) frameworks for large language model (LLM)-based agents are limited to static document retrieval, enterprise applications demand dynamic knowledge integration from diverse sources including ongoing conversations and business data. Zep addresses this fundamental limitation through its core component Graphiti -- a temporally-aware knowledge graph engine that dynamically synthesizes both unstructured conversational data and structured business data while maintaining historical relationships. In the DMR benchmark, which the MemGPT team established as their primary evaluation metric, Zep demonstrates superior performance (94.8% vs 93.4%). Beyond DMR, Zep's capabilities are further validated through the more challenging LongMemEval benchmark, which better reflects enterprise use cases through complex temporal reasoning tasks. In this evaluation, Zep achieves substantial results with accuracy improvements of up to 18.5% while simultaneously reducing response latency by 90% compared to baseline implementations. These results are particularly pronounced in enterprise-critical tasks such as cross-session information synthesis and long-term context maintenance, demonstrating Zep's effectiveness for deployment in real-world applications.

Paper: https://arxiv.org/pdf/2501.13956v1.pdf

Code: https://github.com/getzep/graphiti

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