Apple Machine Learning Research at NeurIPS 2025 - Apple Machine Learning Research
Apple Machine Learning Research at NeurIPS 2025 Apple Machine Learning Research
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[Project] QitOS: A research-first framework for building and evaluating LLM agents
[Project] QitOS: A research-first framework for building and evaluating LLM agents Hey everyone, I wanted to share QitOS, a new framework I’ve been working on that’s built specifically for LLM agent researchers. After working on several agent projects, I found that most existing frameworks didn’t really fit the research workflow: It was too hard to quickly iterate on new agent architectures without rewriting the entire execution stack Strategy (how the agent thinks) and execution (tool calling, tracing, evaluation) were always tangled together Getting set up to evaluate on standard benchmarks took way longer than the actual research Debugging agent trajectories was a mess without proper tooling QitOS was built to solve all these problems: Key Features Clean architecture : Separation betwee
Approximation Algorithms for Capacitated Vehicle Routing Problems: A Comprehensive Survey
arXiv:2306.01826v5 Announce Type: replace Abstract: The Capacitated Vehicle Routing Problem (CVRP) is a core NP-hard problem in the field of combinatorial optimization. It aims to plan optimal routes for a fleet of vehicles with uniform capacity, serving a set of customers with specific demands from a single depot, while minimizing the total travel distance. Due to its extensive applications in logistics, distribution, and supply chain management, CVRP has attracted significant research attention. Theoretically, the problem has been proven to be APX-hard, and in general metric spaces, approximate solutions of arbitrary precision cannot be obtained unless P=NP. These inherent complexities highlight the importance of developing approximation algorithms-finding solutions with provable perform
Bayesian Neural Networks: An Introduction and Survey
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