AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes ECHO, a black-box forecast correction framework that uses AnalogE to assess retrieved historical analogues together with the current context and base forecast, learning transferable corrections through cross-domain pretraining and dynamically combines the two corrections according to their observed f...
Time-series analysis is increasingly studied in multimodal settings, yet most benchmarks pair temporal signals with text or with visualizations derived from the same signal. This leaves open whether multimodal large language models (MLLMs) can integrate time series with real-world visual evidence. We introduce MMTA (mu...
LLM-based agents are increasingly used for time-series forecasting because they can organise contextual information, perform multi-step analysis, and guide the sequence of actions required to complete forecasting tasks. Most existing agents focus only on the current forecasting instance. However, in real-world deployme...
This work introduces the Multi-control Mixed Audio Generation benchmark, a systematic evaluation protocol that measures acoustic fidelity, speech quality, semantic alignment, and temporal accuracy, and establishes MMAG as a comprehensive benchmark for future research.
Evaluations across scientific, multimodal, agentic, and general-purpose benchmarks show that Intern-S2-Preview-397B achieves competitive or leading results in multiple settings.
A multi-layer feature fusion (MLF) adapter that aggregates information from all encoder layers before projecting them into the language model is proposed and shows that MICL does not emerge naturally in ALLMs, but can be effectively acquired through targeted contextual biasing training.
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