AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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InterTab is a structure-aware framework for CoT reasoning over table images that interleaves chain-of-thought with tool calls that crop structure-aligned table regions, and improves the average accuracy of its backbone from 68.28% to 73.17% and achieves the best average performance among all compared methods.
Reinforcement Fine-Tuning (RFT) has enabled medical Multimodal Large Language Models (MLLMs) to produce Chain-of-Thought (CoT) reasoning for visual question answering, yet these models suffer from $\textit{confidence miscalibration}$---a systematic gap between expressed certainty and actual diagnostic accuracy that und...
MetaRAG is proposed, a belief-action aligned policy optimization framework for agentic RAG that consistently improves the accuracy-efficiency trade-off over strong RL-based agentic RAG baselines, with gains that transfer to deep research settings, different optimizers, and multiple model backbones.
Across counter-commonsense visual benchmarks, TACT substantially improves visual reasoning while preserving general capabilities, demonstrating effective text-to-vision cross-modal transfer.
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