✨MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data

📝 Summary:
This study demonstrates efficient MEG-based speech decoding using transfer learning. A Conformer model, pre-trained on listening data, significantly improves accuracy and enables reliable cross-task decoding between speech perception and production with limited fine-tuning. Shared neural processe...

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18253
• PDF: https://arxiv.org/pdf/2602.18253
• Github: https://github.com/hitz-zentroa/meg-phone-decoding

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#MEG #TransferLearning #SpeechProcessing #Neuroscience #DeepLearning

✨Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills

📝 Summary:
Trace2Skill generates transferable LLM agent skills by analyzing diverse execution traces in parallel and consolidating them via inductive reasoning. This framework significantly improves performance, transfers across LLM scales, and generalizes to new settings without model updates.

🔹 Publication Date: Published on Mar 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25158
• PDF: https://arxiv.org/pdf/2603.25158

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#LLM #AgentAI #TransferLearning #MachineLearning #AIResearch

🔥 OpenSkill: Open-World Self-Evolution for LLM Agents

💡 The paper introduces OpenSkill, a framework that enables self-evolving agents to develop skills and verification signals from scratch using open-world resources without target-task supervision. The problem addressed is that existing approaches to self-evolving agents require a usable learning loop, such as curated skills or successful trajectories, which may not be available in real-world deployment scenarios. OpenSkill solves this problem by bootstrapping the learning loop, acquiring grounded knowledge and verification anchors from documentation, repositories, and the web, synthesizing them into transferable skills, and refining those skills against self-built virtual tasks.

The method used in OpenSkill involves three main steps. First, it acquires knowledge and verification anchors from open-world resources. Second, it synthesizes this knowledge into transferable skills. Third, it refines these skills against self-built virtual tasks grounded in the anchors, rather than in target answers. This approach allows the agent to develop skills and verification signals without requiring target-task supervision.

The results of OpenSkill are impressive, with the framework attaining the best automated pass rate across three benchmarks and two target agents, while satisfying the no-supervision constraint. Analysis of the results shows that the skills learned by OpenSkill transfer across models without requiring model-specific adaptation, and the self-built verifier aligns with ground-truth outcomes despite never accessing them. Overall, OpenSkill provides a novel approach to open-world self-evolution, enabling self-evolving agents to develop skills and verification signals from scratch using open-world resources, and achieving high automated performance across benchmarks.

📅 Published on Jun 4

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.06741
• PDF: https://arxiv.org/pdf/2606.06741
• Project Page: https://openlair.github.io/openskill/

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📢 By: https://xn--r1a.website/PaperNexus

#OpenWorldLearning #SelfEvolvingAgents #LLMAgents #TransferLearning #OpenWorldResources