SkillBoost stops AI agents forgetting old skills
New arXiv paper proposes SkillBoost, a three-stage framework that stops LLM agents overfitting to limited experience and forgetting solved tasks.
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New arXiv paper proposes SkillBoost, a three-stage framework that stops LLM agents overfitting to limited experience and forgetting solved tasks.
EvoPINN uses an LLM agent to automatically discover and validate algorithms for physics-informed neural networks, replacing manual tuning.
SCAIR, from Bosch and Stuttgart researchers, injects enterprise schemas into AI agent reasoning, beating generic RAG without retraining.
OmegaUse-OfficeVal pairs 100 real office tasks with labour costs, showing AI agents are cheaper and faster but still fall short on quality.
An arXiv preprint proposes MTGuard, a hybrid analysis framework that catches malicious MCP tool use in AI agents while preserving normal task performance.
RSMeM adds a knowledge-enhanced memory system to remote sensing AI agents, improving DeepSeek-V3.2 accuracy 6% with under 1% extra tokens, per arXiv.
AgentToolMO proposes cross-vendor trust signals for AI agents in autonomous telecom networks, cutting cascade failures from hours to near-real-time.
SecRespond, a new arXiv benchmark, tested 23 frontier LLMs on real-world post-compromise incident response across 10 cyber ranges. Zero passed.
A new open-source tool from University of Michigan researchers lets any PyTorch model get automatic CUDA kernel speedups without manual GPU programming.
OPTScientist uses four AI agents to discover optimizer algorithms for transformer pretraining, including a new reduced-state matrix optimizer called RS-MR.
HyGRL, a new arXiv paper from Beijing Institute of Technology, blends text with knowledge graphs to answer multi-entity questions that defeat standard RAG.
Agentic Real2Sim uses vision-language agents to convert real robot recordings into simulatable twins, aiming to cut the labor cost of training robots in