AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 3 of 6 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Compared with a full-context baseline using the same DeepSeek-V4-Flash model, FlowState improves the average success rate on MemoryArena and the average pass rate on $\tau^3$-Bench by 4.55 and 13.95 percentage points, respectively, while reducing total token consumption.
NeoHorse-1 is presented, a family of agent-native models developed to explore this path toward harness-mediated RSI across successive iterations, and provides an initial prototype of this feedback-driven process and a path toward harness-mediated RSI across successive iterations.
The report studies singleton and multi-model routing on agentic benchmarks including DRACO and PinchBench, and argues that agentic routing is not merely cost control, but a data engine for agent-native training.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.