AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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9,568 papers
The ARIA framework for MAS-agnostic runtime inspection and amendment is developed, designed to integrate with any MAS that exposes execution trajectories and supports continuation replacement, and a compact model is taught to recognize when intervention is needed and how to correct an ongoing run.
This work studies the benign case: agents follow one protocol, none intends to deceive, but some read a misleading passage, testing three open-weight models on commonsense reasoning, knowledge QA, and moral judgment, and concludes that robustness should be judged against exposure-matched baselines.
Existing methods induce various forms of knowledge from agent traces, from sample workflows to programmatic skills. We propose ARISE (Agent Routine Induction from Successful Experience), which identifies recurring procedures in agent traces and synthesizes them into a reusable library of agent subroutines. Across five...
T2J-BENCH is introduced, a benchmark of 63 whole-repository PyTorch-to-JAX migration tasks spanning 18 model families across both training and serving, and TWINHARNESS, a reference-grounded agent harness that turns the agent's executable PyTorch source into legitimate self-verification feedback.
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Many recent document embedding models are trained on document-as-image representations, embedding rendered pages as images rather than the underlying source. Meanwhile, existing benchmarks for scientific document retrieval, such as ArXivQA and ViDoRe, treat documents as images of pages, implicitly favoring such represe...
Audio-text retrieval systems based on Contrastive Language-Audio Pretraining (CLAP) achieve strong performance on traditional benchmarks; however, these benchmarks rely on caption-style queries that differ substantially from real-world search behavior, limiting their assessment of practical retrieval robustness. We pre...
It is found that external rewards drive most of the accuracy gains, while preserving more coherent reasoning when rewarded for reasoning tokens, while preserving more coherent reasoning when rewarded for reasoning tokens through internal attention rewards.
There has been growing interest in building agents that can interact with digital platforms to execute meaningful enterprise tasks autonomously. Among the approaches explored are tool-augmented agents built on abstractions such as Model Context Protocol (MCP) and web agents that operate through graphical interfaces. Ye...
Large language models (LLMs) increasingly store user preferences in persistent memory to support personalization across interactions. However, in third-party communication settings governed by social and institutional norms, some user preferences may be inappropriate to apply. We introduce BenchPreS, which evaluates wh...
Draft-Thinking is proposed, a lightweight adaptation framework that learns a compact, self-contained working derivation that directly supports the final answer in a long chain-of-thought reasoning model.
We examine how lyric representation shapes computational similarity using four 384-dimensional multilingual MiniLM representations of 7,891 K-pop songs by 1,456 artists from charts spanning 2000 to October 2023. Whole-song truncated embeddings provide the strongest standardized separation between neighbouring and rando...
Crafting effective reward signals remains a central challenge in reinforcement learning (RL) for complex reasoning. Existing methods for automated reward optimization search with a fixed proposer which never learns from its costly trials. We present Dual-Evolving Reinforcement Learning (DERL), a dual-training framework...
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Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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