By learning the idiosyncrasies of accumulated layers of legacy systems, AI agents can preserve institutional knowledge and provide a unified interface to a range of services.
As AI agents become more autonomous, the key challenge isn't what they can do; it's how to design the human side of the equation.
What machine learning theorists learned using AI agents to generate proofs — and what comes next.
As AI models grow larger, they become less insightful, not more. To ensure that they continue to learn, we need to reduce their inference time.
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March 20, 202615 min readSimplifying and clarifying the assembly code for core operations enabled automated optimization and verification. -
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Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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Transactions of Machine Learning Research2025Despite fast progress, efficiently training large language models (LLMs) in extremely long contexts remains challenging. Existing methods fall back to training LLMs with short contexts (up to a few thousand tokens) and use inference time techniques when evaluating on very long contexts (above 1M tokens). Training on very long contexts is limited by GPU memory availability and the prohibitively long training
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2025Large Language Model (LLM)-powered agents have emerged as a new paradigm for complex, multi-turn human-AI interactions, yet most existing systems adopt a one-size-fits-all approach, neglecting the evolving preferences and goals of individual users. This limitation hinders their ability to maintain alignment and coherence over extended multi-turn interactions and dynamic tasks. To address this gap, we propose
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NeurIPS 2025 Workshop on Aligning Reinforcement Learning Experimentalists and Theorists2025In recommendation systems, diversity and novelty are essential for capturing varied user preferences and encouraging exploration, yet many systems prioritize click relevance. While reinforcement learning (RL) has been explored to improve diversity, it often depends on random exploration that may not align with user interests. We propose LAAC (LLM-guided Adversarial Actor Critic), a novel method that leverages
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NeurIPS 2025 Workshop on Bridging Language, Agent, and World Models (LAW)2025We observe that current state-of-the-art web-agents are unable to effectively adapt to new environments without neural network fine-tuning, without which they produce inefficient execution plans due to a lack of awareness of the structure and dynamics of the new environment. To address this limitation, we introduce ATLAS (Actor-Critic Task-completion with Look-ahead Action Simulation), a memory-augmented
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NeurIPS 2025 Workshop on Efficient Reasoning2025As Large Language Models (LLMs) continue to evolve, practitioners face increasing options for enhancing inference-time performance without model retraining, including budget tuning and multi-step techniques like self-reflection. While these methods improve output quality, they create complex trade-offs among accuracy, cost, and latency that remain poorly understood across different domains. This paper systematically
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