Low-rank adaptation, data augmentation, and chain-of-thought reasoning are among the techniques enabling accent-free polyglot outputs, improved expressiveness, and reliable synthesis.
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.
Customer-obsessed science
Research areas
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March 20, 202615 min readSimplifying and clarifying the assembly code for core operations enabled automated optimization and verification. -
March 19, 202611 min read -
February 25, 202611 min read
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February 17, 20263 min read
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Now open across seven research areas, including Agentic AI and Robotics. Applicants receive unrestricted funds, AWS promotional credits, and training resources. Submission deadline is May 6
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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AES Show 20252025Surround sound systems commonly distribute loudspeakers along standardized layouts for multichannel audio reproduction. However in less controlled environments, practical layouts vary in loudspeaker quantity, placement, and listening locations / areas. Deviations from standard layouts introduce sound-field errors that degrade acoustic timbre, imaging, and clarity of audio content reproduction. This work
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IEEE Big Data 20252025Enterprise relational databases increasingly contain vast amounts of non-semantic data—IP addresses, product identifiers, encoded keys, and timestamps—that challenge traditional semantic analysis. This paper introduces a novel Character-Level Autoencoder (CAE) approach that automatically identifies and groups semantically identical columns in nonsemantic relational datasets by detecting column similarities
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IJCNLP-AACL 20252025In recent years, dense retrieval has been the focus of information retrieval (IR) research. While effective, dense retrieval produces uninterpretable dense vectors, and suffers from the drawback of large index size. Learned sparse retrieval (LSR) has emerged as promising alternative, achieving competitive retrieval performance while also being able to leverage the classical inverted index data structure
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2025Previous AutoML systems have made progress in automating machine learning workflows, but still require significant manual setup and expert knowledge. This paper presents a novel multi-agent system that integrates Large Language Models (LLMs) with external knowledge bases of existing machine learning tools to automate the complete end-to-end solution. To address the limitations of pure LLM solutions, including
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NeurIPS 2025 Workshop on Evaluating the Evolving LLM Lifecycle2025Recent research has demonstrated that debate mechanisms among Large Language Models (LLMs) show remarkable potential for enhancing reasoning capabilities and promoting responsible text generation. However, it remains an open question whether debate strategies can effectively generalize to Multi-Modal Large Language Models (MLLMs). In this paper, we address this challenge by proposing a location-aware debate
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