…AI-generated summary Computer Use Agents (CUAs) can act through both atomic GUI actions , such as click and type, and high-level tool calls , such as API-based file operations, but this…
…AI-generated summary Asynchronous reinforcement learning improves rollout throughput for large language model agents by decoupling sample generation from policy optimization, but it also introduces a critical failure mode for PPO-style…
…https://mobisoftinfotech.com/resources/blog/ai‑development/llm‑api‑pricing‑guide — which gives practical advice on LLM API usage, token‑based pricing, and how to plan costs when working with LLMs. Putting…
…AI-generated summary LLM-powered multi-agent systems can now automate the full research pipeline from ideation to paper writing, but a fundamental question remains: automation for whom? Researchers operate under different…
…Bo Yin , Qi Li , Abstract FATE is an on-policy framework that uses failure trajectories to improve agent safety and performance through self-evolution and Pareto-aware optimization. AI-generated summary Tool…
…AI-generated summary LLM-based agents increasingly operate in persistent environments where they must store, update, and reason over information across many sessions. While prior benchmarks evaluate only single-entity updates, MEME…
…AI-generated summary Distributed blackbox consensus optimization is a fundamental problem in multi-agent systems, where agents must improve a global objective using only local objective queries and limited neighbor communication. Existing…
…AI-generated summary Structured LLM workflows , where specialized LLM sub-agents execute according to a predefined graph, have become a powerful abstraction for solving complex tasks. Optimizing such workflows, i.e., selecting…
…The following papers were recommended by the Semantic Scholar API Skill Retrieval Augmentation for Agentic AI (2026) SkillX: Automatically Constructing Skill Knowledge Bases for Agents (2026) SkillLearnBench: Benchmarking Continual Learning Methods for…
…AI-generated summary Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latency, yet no existing benchmark systematically…