arXiv:2602.15200v1 Announce Type: new Abstract: Post-training compression of Transformer models commonly relies on truncated singular value decomposition (SVD). However, enforcing a single shared subspace can degrade accuracy even at moderate compression. Sparse dictionary learning provides a more flexible union-of-subspaces representation, but existing approaches often suffer from iterative dictionary and coefficient updates. We propose COMPOT (Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers), a training-free compression framework that uses a small calibration dataset to estimate a sparse weight factorization. COMPOT employs […]

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Auditing AI Systems: A Practical Guide to Testing Models for Bias, Compliance, Security, and Explainability Photo by Stephen Dawson on Unsplash Why accuracy alone is not enough and how organizations can audit AI systems before regulators, attackers, or users expose the failures. Artificial intelligence systems are now embedded in decisions that affect people’s lives credit approvals, fraud detection, hiring, underwriting, and customer support automation. But while AI adoption has accelerated rapidly, governance frameworks have struggled to keep up. Most […]

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arXiv:2603.05637v1 Announce Type: new Abstract: The rapid adoption of foundation models has significantly expanded the capabilities of software systems, enabling them to perform complex language, reasoning, and interaction tasks that were previously difficult to automate. However, this progress has also introduced novel challenges that were largely absent in previous generations of software. In particular, the increasing integration of foundation models with external tools and resources raises new concerns regarding reliability, security, and robustness. The Model Context Protocol (MCP) […]

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arXiv:2602.04986v1 Announce Type: new Abstract: We endorse and expand upon Susan Schneider’s critique of the linear model of AI progress and introduce two novel concepts: “familiar intelligence” and “strange intelligence”. AI intelligence is likely to be strange intelligence, defying familiar patterns of ability and inability, combining superhuman capacities in some domains with subhuman performance in other domains, and even within domains sometimes combining superhuman insight with surprising errors that few humans would make. We develop and defend a […]

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arXiv:2603.20227v1 Announce Type: new Abstract: Background: Telework has benefits for many people with disabilities. The pandemic may create new employment opportunities for people with disabilities by increasing employer acceptance of telework, but this crucially depends on the occupational structure. Objective: We compare people with and without disabilities in the expansion of telework as the pandemic began, and the evolution of telework during the pandemic. Methods: We use U.S. data from the American Community Survey from 2008 to 2020 […]

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arXiv:2601.05465v1 Announce Type: new Abstract: Answering real-world open-domain multi-hop questions over massive corpora is a critical challenge in Retrieval-Augmented Generation (RAG) systems. Recent research employs reinforcement learning (RL) to end-to-end optimize the retrieval-augmented reasoning process, directly enhancing its capacity to resolve complex queries. However, reliable deployment is hindered by two obstacles. 1) Retrieval Collapse: iterative retrieval over large corpora fails to locate intermediate evidence containing bridge answers without reasoning-guided planning, causing downstream reasoning to collapse. 2) Learning Instability: […]

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Vector search in Postgres usually starts simply. You add an embedding column, run a nearest-neighbor query, and order by distance. SELECT content FROM documents ORDER BY embedding <=> ‘[0.1, 0.2, …]’ LIMIT 10; For a while, that is enough. That simplicity breaks down as the workload becomes real. The table grows, filters become part of the query path, and recall starts affecting user experience. The index still has to stay fast while new data keeps arriving. That is […]

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