AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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20,463 papers
It is shown that a single egocentric camera worn on the collector's head replaces the wrist camera, the pose tracker and their separate streams with one, and the full co-trained head-camera policy succeeds in 20/25 against 15/25 for the wrist-camera recipe, and 12/25 for a fixed camera.
The central result is an impossibility: relational purpose violations form a 2-safety hyperproperty with no per-trace locus of blame, so a per-action monitor that never raises a false alarm detects none of them, independent of the underlying model or decision procedure.
By learning heterogeneous inter-graph interactions, the proposed method achieved state-of-the-art accuracy in predicting band gaps of 1,682 hybrid solar cell materials.
ABench is a fine-grained, handcrafted benchmark specifically tailored to taxonomized values in Chinese, grounded in a rigorous hierarchical taxonomy comprising 158 derived value subcategories and utilizing question-specific scoring criteria for nuanced assessment.
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Together, the findings support a claim-indexed reporting standard that connects each claim to the evaluation design needed to test it, and evidence for repeatability and improved procedures remains less developed than product evaluation.
The WORTH Benchmark is proposed: models read original mystery narratives without clue annotations, autonomously select key clue sentences, identify case participants, and explain their reasoning process, and it is suggested that not-fully-cooperative informational relationships may influence evidence selection and judg...
When unique pretraining data is scarce, models see the same tokens many times and are thought to memorize. Random-token replacement, an input-corruption augmentation, is one remedy reported to lower validation loss, but a lower loss alone does not reveal which source of error it reduces. On a controlled source whose en...
Factual recall is a lookup: some representation inside the network serves as the key, and what that key is made of decides which queries can retrieve the fact, what an edit must cover, and what a retriever can read. We call it the factual access state. In autoregressive (AR) models the subject position enriches attribu...
TopologyAware RouteConsistency Modeling (TRCM), which reuses node representations from a standard autoregressive construction pipeline to predict pairwise same-route probabilities and injects them into customer selection as a route-consistency signal, supports explicit route-consistency modeling as an effective structu...
Experiments on DAIC-WOZ demonstrate superior depression assessment performance over specialized depression detection methods and general-purpose speech/omni-LLMs, along with improved reasoning quality, while supplementary evaluation on EATD-Corpus supports AudiPsy's applicability to naturalistic conversations.
Large language models (LLMs) are increasingly used within everyday applications where their responses have significant downstream impacts. For responsible deployment of such systems, we desire insight into how much we can trust their individual outputs. A popular approach for estimating LLM confidence is to train probe...
Recent multimodal LLM (MLLM)-based anomaly detection (AD) methods can generate textual explanations that help interpret their predictions. However, reliably and efficiently evaluating these explanations remains challenging. While LLM judges can assess explanations through structured rubrics, they often require substant...
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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