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China launches AI framework to improve ‘black box’ transparency and raise standards
China launches AI framework to improve ‘black box’ transparency and raise standards The initiative underscores Beijing’s growing focus on AI governance, as concerns grow over algorithm bias and data security China has pledged to improve the accuracy, reliability and transparency of AI through a new national evaluation framework, as policymakers move to establish common standards for assessing the fast-evolving technology. New guidelines released by the central government said Beijing would...
STABLEVAL: Disagreement-Aware and Stable Evaluation of AI Systems
arXiv:2605.02122v2 Announce Type: replace Abstract: Human evaluation remains the primary standard for assessing modern AI systems, yet annotator disagreement, bias, and variability make system rankings fragile under standard majority vote aggregation. Majority vote discards annotator reliability and item-level ambiguity, often yielding unstable comparisons across annotator subsets. We introduce STABLEVAL, a disagreement-aware evaluation framework that models latent item correctness and...
Broadcom tumbles as revenue miss clouds AI boom bets
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Evaluating Factual Density in Multi-Source RAG: A Study in Medical AI Accuracy
arXiv:2605.31506v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is the current industry standard for grounding AI in real-world facts. Traditional retrieval methods rely on keyword matching and topic proximity, ranking content based on how closely it sounds like the user's query. What they do not measure is how many verified facts the content actually contains.
Supracompetitive Pricing Under AI Monoculture
Announce Type: replace-cross Abstract: When competing sellers delegate pricing to a shared AI model, such as a large language model, correlated recommendations combined with performance-driven updates aggregating seller feedback raise a key question: can standard AI deployment practices inadvertently produce supracompetitive pricing? We develop a stylized duopoly model in which two sellers receive pricing recommendations from a shared AI characterized by two parameters: a propensity...
Beyond Pass/Fail: Using Process Mining to Understand How LLMs Resist (and Fail) Red Team Attacks
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Everywhere Learning: Artificial Intelligence with Pointwise Constraints
Announce Type: new Abstract: Everywhere learning is a new paradigm whereby Artificial Intelligence (AI) systems are trained to satisfy loss constraints with probability one over the data distribution. This is in contrast to the standard paradigm of training AI systems to minimize average losses. We develop an approximate duality theory to substantiate a generalization analysis that establishes the proximity between solutions of empirical and statistical everywhere learning problems.
Do Transformers Need Three Projections? Systematic Study of QKV Variants
Computer Science > Machine Learning [Submitted on 1 Jun 2026] Title:Do Transformers Need Three Projections? Systematic Study of QKV Variants View PDF HTML (experimental)Abstract:Transformers have become the standard solution for various AI tasks, with the query, key, and value (QKV) attention formulation playing a central role.
Do Transformers Need Three Projections? Systematic Study of QKV Variants
Announce Type: new Abstract: Transformers have become the standard solution for various AI tasks, with the query, key, and value (QKV) attention formulation playing a central role. However, the individual contribution of these three projections and the impact of omitting some remain poorly understood. We systematically evaluate three projection sharing constraints: a) Q-K=V (shared key-value), b) Q=K-V (shared query-key), and c) Q=K=V (single projection).