AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
It is demonstrated that smaller models can efficiently handle complex language assessments, suggesting dynamic routing to balance performance with sustainable deployment in educational technology.
A comparative benchmarking study evaluating the regulatory comprehension of four open-source large language models, TinyLlama-1.1B-Chat, Gemma-2B-Instruct, Llama-3.1-8B-Instruct, and Mistral-7B-Instruct-v0.3, contributes an empirical characterization of small and medium language model regulatory comprehension and a reu...
BabelFlow is introduced, a benchmark-general agentic workflow that adapts existing agent benchmarks to new languages by analyzing runtime dependencies, coordinating structure-preserving translation, and combining multi-layer verification with human review to preserve task and evaluation semantics.
The effect of introducing the manager-worker scaffold over a shared filesystem workspace, with no training and no per-benchmark tuning, measured against the same model answering in a single pass is investigated, finding several mechanisms behind the gains.
The environmental cost of adapting Large Language Models (LLMs) is a growing concern in artificial intelligence research. We present a systematic comparison between gradient-based fine-tuning and a rule-based prompt optimization framework for financial tabular reasoning. Using the TAT-QA dataset, we demonstrate that ou...
MOSCOPT is proposed, a text-native, parameter-free algorithm that jointly optimizes a pool of skills and a gating skill that dynamically selects skills per step and demonstrates that MOSCOPT consistently outperforms all baselines.
WorldBench is presented: a comprehensive, multilingual benchmark of genuine, persona-grounded everyday workflows, where agents can act in a sandbox via structured actions, and Constrained Task Success (CTS), which combines natural language instructions and testbeds to score task completion, minimal modification, and ot...
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