AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Open-ended scientific discovery often requires repeatedly proposing and evaluating candidate solutions. LLM-based systems can support this process by generating and refining executable solutions from verifier feedback. Methods such as TTT-Discover use test-time training (TTT) to update the solution-generating LLM from...
This work introduces MicrobeQuest, the first comprehensive multimodal benchmark for microbial information extraction tasks, comprising 11,877 expert-validated, context-aware query–response pairs, and formalizes microbiology-specific IE as a class of tasks that require structured data extraction, joint multimodal unders...
Fisher-MoE is proposed, which operates within FFN to remove intermediate dimensions ranked by Fisher importance, and suggests intermediate dimension granularity is an effective unit for both compression and ranking where capability concentrates in MoE models.
Evaluated on diverse mathematical, algorithmic, and systems optimization tasks, CORAL sets new state-of-the-art results on 10 tasks, achieving 3-10 times higher improvement rates with far fewer evaluations than fixed evolutionary search baselines across tasks.
SPADE (Self-Play in Adaptive Synthetic Executable Executable Environments), a self-play RL framework in which a single LLM plays two roles: an Environment Designer that writes complete, long-horizon training environments as executable code with an OpenAI Gym-style reset()/step() interface, and a Reasoning Agent that le...
The HUman-Grounded AGENT Benchmark is introduced, which rethinks human reasoning simulation along three dimensions: (i) from averaged to individualized reasoning, (ii) from behavioral mimicry to cognitive alignment, and (iii) from vignette-based to open-ended data.
FAIRY is developed to execute and evaluate agentic agronomic operations on full-season spatiotemporal workflows that span ridge preparation, planting, irrigation, fertilization, pest and disease treatment, harvest, grain handling, drying, and storage, and an evaluation suite that combines agentic success, full-path spa...
SPADE (Self-Play in Adaptive Synthetic Executable Executable Environments), a self-play RL framework in which a single LLM plays two roles: an Environment Designer that writes complete, long-horizon training environments as executable code with an OpenAI Gym-style reset()/step() interface, and a Reasoning Agent that le...
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