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2,283 papers
This conceptual research note proposes an invariant-first, falsification-first research program for the P versus NP problem. It does not claim a proof of P=NP or P≠NP. Instead, it asks whether progress can be organized more effectively by shifting attention away from individual candidate algorithms and toward structura...
This conceptual research note proposes an invariant-first, falsification-first research program for the P versus NP problem. It does not claim a proof of P=NP or P≠NP. Instead, it asks whether progress can be organized more effectively by shifting attention away from individual candidate algorithms and toward structura...
An argument that understanding is not a matter of holding the right information, but of building structure that costs something to be wrong about.A student can recite a definition perfectly, reproduce its derivations, and answer routine questions about it — and still be caught flat by a question that requires seeing wh...
Large Language Models have become powerful tools for programming. However, they often overlook essential security practices, producing code with vulnerabilities. Despite this risk, existing work offers limited guidance on steering LLMs toward secure code generation and lacks systematic analysis of how effectively LLMs...
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Text-based person retrieval under a sim-to-real gap (synthetic training data, a real-image gallery) is usually tackled with costly fine-tuned cross-encoders. We ask whether a frozen-encoder system can compete. We present SCOUT, which casts cross-modal retrieval as prediction in embedding space. A trainable predictor ma...
Technology ecosystems often reward whichever firm first controls a tightly held physical or infrastructural asset -the "anchor" -because that control reduces uncertainty, sets de facto standards, and buys market access for a higher-margin software, service, or workflow layer -the "expansion."Existing strategy research...
Reproducibility artifacts for the meta-research study "Available but not runnable out of the box: an audit of execution prerequisites and label reporting in shared code for dysphagia and swallowing artificial intelligence." The protocol keeps the working title it was written under. We audited whether published swallowi...
Enter Banking With Billy: Equation‑Native Intelligence and Autonomous Cognitive Evolution Part One — Mathematical Foundations and System InstantiationBilly Odell Tucker‑Robinson Banking With Billy Qauntum Ai Research and Development ( Independent Researcher ) Abstract Modern artificial intelligence systems are hardware...
Enter Banking With Billy: Equation‑Native Intelligence and Autonomous Cognitive Evolution Part One — Mathematical Foundations and System InstantiationBilly Odell Tucker‑Robinson Banking With Billy Qauntum Ai Research and Development ( Independent Researcher ) Abstract Modern artificial intelligence systems are hardware...
Privacy remains a significant barrier to unlocking the full potential of AI in sensitive domains such as healthcare. In this chapter, we survey methods for preserving privacy while enabling useful data analysis. We review anonymization approaches, including k-anonymity, differential privacy, and federated learning, and...
Reproducibility artifacts for the meta-research study "Available but not runnable out of the box: an audit of execution prerequisites and label reporting in shared code for dysphagia and swallowing artificial intelligence." The protocol keeps the working title it was written under. We audited whether published swallowi...
This work presents PAPC, a platform-mediated mechanism that intercepts information-moving events before they update shared state or external channels and positions event-level mediation as a platform-governance primitive for agent-mediated online work.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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