🔹 Title: Efficient Agents: Building Effective Agents While Reducing Cost

🔹 Publication Date: Published on Jul 24

🔹 Abstract: A study on the efficiency-effectiveness trade-off in LLM-driven agent systems identifies optimal agent framework design to reduce costs while maintaining performance. AI-generated summary The remarkable capabilities of Large Language Model ( LLM )-driven agents have enabled sophisticated systems to tackle complex, multi-step tasks, but their escalating costs threaten scalability and accessibility. This work presents the first systematic study of the efficiency-effectiveness trade-off in modern agent systems , addressing the critical need for cost-effective designs without sacrificing performance. We investigate three key questions: (1) How much complexity do agentic tasks inherently require? (2) When do additional modules yield diminishing returns? (3) How much efficiency can be gained through the design of efficient agent framework s? Through an empirical analysis on the GAIA benchmark, we evaluate the impact of LLM backbone selection, agent framework designs, and test-time scaling strategies . Using the cost-of-pass metric, we quantify the efficiency-performance trade-off across these dimensions. Our findings inform the development of Efficient Agents , a novel agent framework that has an optimal complexity to task requirements. Efficient Agents retains 96.7% of the performance of OWL , one leading open-source agent framework , while reducing operational costs from 0.398 to 0.228, resulting in a 28.4% improvement in cost-of-pass . Our work provides actionable insights for designing efficient, high-performing agent systems , advancing the accessibility and sustainability of AI-driven solutions.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.02694

• PDF: https://arxiv.org/pdf/2508.02694

• Github: https://github.com/OPPO-PersonalAI/OAgents

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🔹 Title: A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code

🔹 Publication Date: Published on Aug 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18106
• PDF: https://arxiv.org/pdf/2508.18106
• Github: https://github.com/Tencent/AICGSecEval

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🔹 Title: EmbodiedOneVision: Interleaved Vision-Text-Action Pretraining for General Robot Control

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21112
• PDF: https://arxiv.org/pdf/2508.21112
• Project Page: https://eo-robotics.ai/eo-1
• Github: https://github.com/EO-Robotics/EO-1

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🔹 Title: R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2508.21113
• PDF: https://arxiv.org/pdf/2508.21113
• Github: https://github.com/yannqi/R-4B

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🔹 Title: TalkVid: A Large-Scale Diversified Dataset for Audio-Driven Talking Head Synthesis

🔹 Publication Date: Published on Aug 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13618
• PDF: https://arxiv.org/pdf/2508.13618
• Project Page: https://freedomintelligence.github.io/talk-vid/
• Github: https://github.com/FreedomIntelligence/TalkVid

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🔹 Title: Efficient Code Embeddings from Code Generation Models

🔹 Publication Date: Published on Aug 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21290
• PDF: https://arxiv.org/pdf/2508.21290

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🔹 Title: TiKMiX: Take Data Influence into Dynamic Mixture for Language Model Pre-training

🔹 Publication Date: Published on Aug 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17677
• PDF: https://arxiv.org/pdf/2508.17677

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🔹 Title: UItron: Foundational GUI Agent with Advanced Perception and Planning

🔹 Publication Date: Published on Aug 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21767
• PDF: https://arxiv.org/pdf/2508.21767

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🔹 Title: Morae: Proactively Pausing UI Agents for User Choices

🔹 Publication Date: Published on Aug 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21456
• PDF: https://arxiv.org/pdf/2508.21456

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🔹 Title: AHELM: A Holistic Evaluation of Audio-Language Models

🔹 Publication Date: Published on Aug 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21376
• PDF: https://arxiv.org/pdf/2508.21376
• Project Page: https://crfm.stanford.edu/helm/audio/v1.0.0/

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🔹 Title: Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models

🔹 Publication Date: Published on Aug 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21365
• PDF: https://arxiv.org/pdf/2508.21365

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🔹 Title: HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation

🔹 Publication Date: Published on Aug 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.20085
• PDF: https://arxiv.org/pdf/2508.20085
• Project Page: https://gemcollector.github.io/HERMES/
• Github: https://gemcollector.github.io/HERMES/

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🔹 Title: HPSv3: Towards Wide-Spectrum Human Preference Score

🔹 Publication Date: Published on Aug 5

🔹 Abstract: HPSv3, a human preference score using a wide-spectrum dataset and uncertainty-aware ranking loss, enhances text-to-image generation quality through iterative refinement. AI-generated summary Evaluating text-to-image generation models requires alignment with human perception, yet existing human-centric metrics are constrained by limited data coverage, suboptimal feature extraction, and inefficient loss functions. To address these challenges, we introduce Human Preference Score v3 (HPSv3). (1) We release HPDv3 , the first wide-spectrum human preference dataset integrating 1.08M text-image pairs and 1.17M annotated pairwise comparisons from state-of-the-art generative models and low to high-quality real-world images. (2) We introduce a VLM-based preference model trained using an uncertainty-aware ranking loss for fine-grained ranking. Besides, we propose Chain-of-Human-Preference ( CoHP ), an iterative image refinement method that enhances quality without extra data, using HPSv3 to select the best image at each step. Extensive experiments demonstrate that HPSv3 serves as a robust metric for wide-spectrum image evaluation, and CoHP offers an efficient and human-aligned approach to improve image generation quality . The code and dataset are available at the HPSv3 Homepage.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03789

• PDF: https://arxiv.org/pdf/2508.03789

• Project Page: https://mizzenai.github.io/HPSv3.project/

• Github: https://github.com/MizzenAI/HPSv3

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🔹 Title: Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://www.arxiv.org/abs/2508.20470
• PDF: https://arxiv.org/pdf/2508.20470
• Github: https://dropletx.github.io/

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/DropletX/Droplet3D-4M

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🔹 Title: CLIPSym: Delving into Symmetry Detection with CLIP

🔹 Publication Date: Published on Aug 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.14197
• PDF: https://arxiv.org/pdf/2508.14197
• Github: https://github.com/timyoung2333/CLIPSym

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🔹 Title: A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21148
• PDF: https://arxiv.org/pdf/2508.21148
• Github: https://github.com/open-sciencelab/Awesome-Scientific-Datasets-and-LLMs

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🔹 Title: Model-Task Alignment Drives Distinct RL Outcomes

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21188
• PDF: https://arxiv.org/pdf/2508.21188

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🔹 Title: Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula Discovery

🔹 Publication Date: Published on Aug 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17380
• PDF: https://arxiv.org/pdf/2508.17380
• Github: https://jiaaqiliu.github.io/VIPER-R1/

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🔹 Title: Deep Residual Echo State Networks: exploring residual orthogonal connections in untrained Recurrent Neural Networks

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21172
• PDF: https://arxiv.org/pdf/2508.21172
• Github: https://github.com/NennoMP/deepresesn

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🔹 Title: Quantization Robustness to Input Degradations for Object Detection

🔹 Publication Date: Published on Aug 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19600
• PDF: https://arxiv.org/pdf/2508.19600

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