5. CoAct-1 Researchers from USC, Salesforce, and UW present CoAct-1, a multi-agent system that combines GUI interaction with direct code execution to improve efficiency and robustness in computer-using agents.
@dair_ai
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Seed Diffusion: Discrete-State LLM for Code Generation
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6. Seed Diffusion A discrete-state diffusion-based LLM optimized for code generation, achieving 2,146 tokens/sec on H20 GPUs while maintaining competitive benchmark performance.
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Apple Launches Multilingual Foundation Models for On-Device AI
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10. Apple Intelligence Foundation Language Models Apple introduces two multilingual, multimodal foundation models: a 3B-parameter on-device model optimized for Apple silicon and a scalable server model using a novel PT-MoE transformer architecture.
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MCPEval: Open-Source LLM Agent Evaluation Framework
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9. MCPEval MCPEval is an open-source framework that automates end-to-end evaluation of LLM agents using a standardized Model Context Protocol, eliminating manual benchmarking.
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Deep Researcher: Test-Time Diffusion for Report Generation
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8. Deep Researcher with Test-Time Diffusion Rather than relying on static inference strategies like CoT or best-of-n sampling, this work frames the report generation process as a diffusion process.
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Inverse Scaling in Large Reasoning Models Test-Time Compute
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6. Inverse Scaling in Test-Time Compute Presents a systematic study of inverse scaling in large reasoning models, where increasing the test-time compute (i.e., reasoning length) harms rather than helps model performance.
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Compute-Optimal Strategies for Many-Shot In-Context Learning
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7. Towards Compute-Optimal Many-Shot In-Context Learning Proposes practical strategies for reducing the cost of many-shot in-context learning while preserving or improving performance. https://
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In-Context Learning Without Weight Updates in LLMs
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5. Learning without Training This paper provides a theoretical and empirical explanation for how LLMs exhibit in-context learning, the ability to learn from examples in a prompt without weight updates.
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Routine: Structured Planning Format for LLM Agent Tool-Calling
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4. Structural Planning for LLM Agents This paper introduces Routine, a structured planning format designed to improve the stability and accuracy of LLM agents executing multi-step tool-calling tasks in enterprise settings.
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LLMs in AIOps: Survey of 183 Papers and Methods
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10. A Survey of AIOps This survey analyzes 183 papers to evaluate how LLMs are being used in AIOps, focusing on data sources, task evolution, applied methods, and evaluation practices.