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Introduces Deco-G, a decoding framework that separates format adherence from problem-solving in LLMs, using a Format Estimation Module to ensure compliance without degrading reasoning. Achieves improved accuracy on mathematical reasoning, event extraction, and LLM-as-a-judge tasks.
The article compares two patterns for deploying AI agents in the cloud: directly in sandboxes vs decoupling components. It explains the limitations of the sandbox approach due to cloud failures, and highlights Anthropic's Claude Managed Agent as a solution that decouples session store, agent runtime, and sandbox for resilience.
This paper identifies and addresses the 'editing decoupling failure' in Multimodal LLMs, where knowledge updates via multimodal inputs fail to generalize to unimodal queries. The authors propose DECODE, a method to disentangle and localize modality-specific neurons for more effective knowledge editing.
Electrobun 2.0 will decouple from Bun due to a Rust rewrite, and will add first-class support for Rust, Zig, Go and more; yt-dlp's support of Bun is deprecated citing issues with vibe coding and supply chain attacks.
This paper introduces a B-spline-based decoupling framework for compressing transformer models, with a robust alternating least-squares algorithm (R-CMTF-BSD) that achieves substantial parameter reduction while maintaining competitive accuracy on Vision and Swin Transformer architectures.
This paper introduces SLIM, a minimal architecture that decouples communication from policy representation in multi-agent reinforcement learning, achieving state-of-the-art performance under bandwidth constraints with minimal degradation.