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learn ai agents & automation
tool use, planning, memory, and multi-agent systems — how llms move from answering to doing real work.
curatedmixed~4 weeks, part-time
ai agents & automation
how language models stop just answering and start doing — using tools, planning, remembering, and coordinating to automate real work.
4 modules · 12 resources · checkpoint per modulestay current
see the full digest →what's new in ai agents & automation
- DeepStress: Stress-Testing Deep Search Agentsthis paper focuses on stress-testing deep search agents to evaluate their robustness against poor-quality evidence. practitioners can use these insights to build more resilient search agents that perform reliably even with imperfect information, which is common in real-world data.
- Reward-Free Evolving Agents via Pairwise Validatorthis paper introduces a method for self-evolving agents without explicit rewards, using a pairwise validator. this is valuable for practitioners developing agents in environments where reward functions are hard to define, enabling continuous improvement through self-correction.
- DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environmentthis research introduces a self-distillation method for deep search agents operating in verifiable environments. it aims to improve the efficiency and effectiveness of agents in complex search tasks. practitioners can leverage this to develop more capable and robust search-oriented ai agents.
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