AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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20,463 papers
Verification Learning Theory formalises this possibility for claim-evaluation practice followed by correct, independent, rule-specific feedback and links perceived assistant reliability to evidence checking and links the history of those checks to retained rule use.
The findings show that recognition performance alone cannot characterize relational structure: food similarity depends on representation, modality, and abstraction level rather than being a fixed property of the food collection.
This work introduces Seq@K, a natural evaluation protocol that measures whether a model can solve a problem within K sequential attempts, provided feedback after each previous failure, and establishes how Seq@K offers a more faithful metric for intelligent systems that typically operate in such feedback loop, and the n...
LibStructBench is introduced, a document-grounded benchmark for reconstructing sequencing-library structures and molecular workflows and shows that frontier agents often produce approximately correct reconstructions but remain unreliable at exact reconstruction, particularly in molecular-state representation and workfl...
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Partner--Outsider Positive Listening (PoPLi) is the difference between the two agents in how much their action changes when the message is reworded with its meaning kept, and it is computed after the fact by replaying logged prompts.
This is the first study in smart contract synthesis to gather missing requirements before generation and check the result against an independently written executable model, and proposes an elicitation-first architecture.
This work accounts for what ontology-derived checks catch and what they wrongly flag, and shows how each source trades caught errors against false alarms, how a curated schema used as published can contradict its own data, which errors structural checks miss, and whether combining sources or giving the agent the checke...
This work presents WebAnything, a fully automated framework that reconstructs source-grounded, reusable Web environments from real URLs without per-site manual implementation or repair after initial setup and authorization.
Evaluating seven LLM agents on the HQ-50 subset under a standardized budget, it is found that closed-book rediscovery is rare, at most 7 of 40 resolved problems, whereas live-web success mostly reflects retrieval of published solutions.
ThinkJev builds a fast and a slow system from one open hybrid-thinking model that can answer directly or after a thought, and chooses when to think from System 1's confidence alone saves less, because the decisions System 1 is least sure of are often not the ones thinking fixes.
An application to the k-$\epsilon$ turbulence closure in a single-column ocean model, with atmospheric surface forcing as context, shows that localized posterior calibration reduces temperature and zonal-velocity errors relative to deterministic and global-posterior calibration.
LRMoE is presented, a memory-aware MoE training system that jointly recalibrates backward demand and balances the retained workload, and reduces cumulative MoE-layer training time to fixed-loss targets by 21.2-44.8% relative to SYMI.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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