AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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9,568 papers
Court registries publish millions of decisions with structured metadata, which makes them an attractive source of labelled legal benchmarks: the court type, the form of the decision and its subject area come for free. We show that this convenience has a cost that registry-derived benchmarks rarely measure. We build Mul...
This work reframe schema linking as uncertainty-aware schema-need inference over multiple plausible SQL paths, where the system distinguishes required schema items from path-dependent uncertain ones and acquires evidence only where needed.
Real applications provide the training setting closest to phone-agent deployment, but are difficult to reset, scale safely, and verify programmatically. Static screenshots and interaction trajectories preserve realistic evidence but cannot generate new experience. We introduce PhoneWorld, a trace-grounded framework tha...
Large language model (LLM) agents still struggle to effectively use long-term memory, with answer-supporting evidence often scattered across long conversational histories and buried in substantial irrelevant content. Existing memory systems commonly process memory into dedicated units before future queries are known an...
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Results show that decoder-side adaptation alone can improve a meaningful subset of grounding failures under a fixed multimodal representation, while preserving broader multimodal capabilities.
Sequential diagnosis requires ranking diseases from a sparse intake under a public budget of follow-up questions, which is diagnostic triage with missing findings rather than screening of people who have no symptoms. Because laboratory series arrive irregularly and histories remain incomplete, the ranking must be updat...
The results suggest that the representation space in which diffusion operates should itself be treated as a first-class design variable for continuous diffusion in language models.
It is found that merely warning the agents of potential imposter presence is not beneficial: it degrades decisions when no imposter is present, provokes reactions ranging from self-accusation to scapegoating, inflates token use, and reveals to the imposter how it was uncovered.
This work proposes AIPO, an enhanced reinforcement learning framework that improves LLM reasoning through active multi-agent interaction during exploration and consistently improves reasoning performance and generalizes across different policy models, collaborator backbones, and RLVR algorithms.
The model's hidden state provides a way to read, steer, and check tool choice before a call is made, suggesting that the model's hidden state provides a way to read, steer, and check tool choice before a call is made.
Human visual reasoning is governed by active vision, a process where meta-cognitive control drives top-down goal-directed attention, dynamically routing foveal focus toward task-relevant details while maintaining peripheral awareness of the global scene. In contrast, modern Vision-Language Models (VLMs) process visual...
Recurrent LLM architectures have emerged as a promising approach for improving reasoning, as they enable multi-step computation in the embedding space without generating intermediate tokens. Models such as Ouro perform reasoning by iteratively updating internal representations while retaining a standard Key-Value (KV)...
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Our most capable vision-language-action model enhances robot dexterity, reliability, and safety
A Blog post by Ai2 on Hugging Face
A Blog post by IBM Research on Hugging Face
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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