AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,652 papers
Through PlayTrain, an RL framework that combines the abilities of large language models to robustly generate JavaScript games from a minimal human prompt, and an efficient pipeline that can run any JS game in a standard'gym'environment, this work reimagine RL VGE development.
Large Language Models (LLMs) are increasingly deployed in settings where rare but severe harmful generations can have significant consequences. Existing Risk-Averse RLHF addresses this issue by optimizing Conditional Value-at-Risk (CVaR), but it trains policies for fixed risk levels and therefore cannot adjust the desi...
Adapting large language models (LLMs) to specialized financial reasoning typically requires expensive fine-tuning that produces model-locked expertise. Training-free alternatives have emerged, yet our experiments show that leading methods (GEPA and ACE) achieve only marginal gains on the FAMMA financial reasoning bench...
In the era of generative AI, recommender systems are moving from precise prediction to trustworthy generation. Large language models (LLMs) support this shift by inferring user interests and producing natural-language explanations. However, LLM-based recommendation suffers from a fundamental obstacle: popularity bias....
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This work establishes a feedback design strategy that reformulates environments by transitioning from action guidance to observation enrichment during the later stages of both intra-episode exploration and inter-episode evolution, and reveals that FEEs consistently yield performance improvements over standard settings.
This study studies how large language model (LLM)-powered chatbots respond to series of questions about legal reasonableness, and suggests that chatbot responses generally track those of human participants.
A World Values Survey-grounded simulation framework where culturally diverse agents with different communication styles engage in longitudinal, value-laden discussions suggests that current LLM agents can generate plausible conversations, but remain limited proxies for representing and preserving diverse human value pr...
Poster presented at the 10th Research Software Engineering Conference (RSECon26) in Sheffield, UK, 9-11 September, 2026. Abstract The use of generative AI tools to generate source code or to augment a programmer's own code is ever more widespread in research software development. While evidence of associated gains in p...
Poster presented at the 10th Research Software Engineering Conference (RSECon26) in Sheffield, UK, 9-11 September, 2026. Abstract The use of generative AI tools to generate source code or to augment a programmer's own code is ever more widespread in research software development. While evidence of associated gains in p...
Poster presented at the 10th Research Software Engineering Conference (RSECon26) in Sheffield, UK, 9-11 September, 2026. Abstract The use of generative AI tools to generate source code or to augment a programmer's own code is ever more widespread in research software development. While evidence of associated gains in p...
Large Language Models (LLMs) have transformed natural language processing (NLP), but their billions of parameters make them opaque. This lack of transparency is especially problematic in high-risk areas such as healthcare, finance, and content moderation, where understanding model decisions is essential for responsible...
In complex industrial scenarios, traditional Industrial Anomaly Detection methods often perform poorly under extreme conditions such as low light, high-speed motion, and strong light interference. To address this, we propose RGB-Event Industrial Anomaly Detection via Vision-Language Model (ReIAD-VL), integrating RGB im...
A Blog post by Ai2 on Hugging Face
A Blog post by IBM Research on Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A Blog post by IBM Granite on Hugging Face
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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