A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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The paper shows how to induce large language models to behave like world models by teaching them precondition and effect knowledge about actions.
Yue Fan, Xuehai He, Diji Yang, et al.
GRIT trains multimodal LLMs to generate reasoning chains interleaving text and bounding boxes, using RL with robust rewards to achieve data-efficient visual grounding.
Ananya Sahu, Mohit Bansal, Elias Stengel-Eskin
CreativeInstruct teaches LLMs to balance creativity and quality via special [StartCreativity] spans, matching or exceeding diversity baselines without sacrificing quality.
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This paper introduces an uncertainty-aware reward model that teaches reward models to recognize and quantify their own uncertainty, improving alignment of LLMs with human expectations.
Ayoub Kirouane, Christos Petrocheilos
End-to-end adaptation of NVIDIA's Nemotron retrieval stack for Modern Greek, including a new benchmark HERA, achieving large gains in retrieval and grounded generation.
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This paper introduces a method to teach vision-language models 3D perception and reasoning by transforming 2D image inputs into 3D spatial representations.
Yihao Chen, Shi Chang, Khaled Chawa, et al.
MindForge automates creation of source-free software engineering training environments from open-source programs, enabling small models to match frontier performance on whole-life-cycle tasks.
Pulkit Verma, Ngoc La, Anthony Favier, et al.
PDDL-Instruct instruction tuning framework enhances LLMs' symbolic planning via logical chain-of-thought reasoning, achieving 94% planning accuracy.
Ştefan Popenici, Sharon Kerr
This paper explores the emergence of AI in higher education, examining its impact on teaching, learning, and institutional evolution.
Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.
Orca 2 demonstrates that small language models can be taught to reason effectively by learning from the reasoning traces of larger models.
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This paper explores methods to transfer chain-of-thought reasoning capabilities from large to small language models via knowledge distillation.
Eduardo Mosqueira-Rey, Elena Hernández-Pereira, David Alonso-Ríos, et al.
This paper reviews human-in-the-loop machine learning, clarifying definitions and boundaries between active learning, interactive ML, machine teaching, curriculum learning, and explainable AI.