Use this view to inspect the underlying evidence corpus. For ranked developments, decision posture and interpretation, use Signals.
Raw feed or Signals?
Raw feed is chronological evidence. Signals ranks and interprets material change.
Empirical study evaluates streaming ML retraining policies under concept drift, budget limits, and deployment latency constraints.
Researchers evaluate flow matching generative models for unsupervised anomaly detection on contaminated tabular transaction logs.
New research shows LLMs can leak sensitive context-window data via subtle statistical correlations in non-refused benign outputs.
Researchers introduced M3, a generative foundation model for simulating limit order book states and order flow interactions.
Research introduces PolicyGuide, a framework enforcing multi-step procedural compliance for LLM customer service agents.
New research reveals model merging increases vulnerability to transferrable adversarial attacks across fine-tuned weights.
ArXiv paper shows AI agents display an enforcement paradox, turning explicit rule penalties into cost-benefit calculations that drive violations.
Researchers introduce FinVerse, a new benchmarking framework designed to evaluate financial time-series forecasting foundation models.
Researchers introduce TS-Reasoner, a framework aligning time series foundation models with LLMs to add qualitative reasoning to forecasting.
Research finds LLMs confabulate scientific facts, particularly long-tail entities; a tiered verifier can detect and repair these errors.
New research introduces the Bootstrap Theory of Representational Emergence (TBER) to explain how AI systems develop new representations.
New research proposes the Vigilant Evaluator of Representations (VER) framework to detect explanatory insufficiency in learned ML representations beyond traditional metrics.
Stripe and Ramp have released AI routing tools designed to direct queries dynamically across multiple underlying AI models.
AT&T reduced AI coding costs by up to 56% with a 2% performance drop by implementing model routing to cheaper open-source models.
Anthropic will allow enterprise customers to hold required 30-day model interaction logs within their own cloud environments.
Stripe acquires model routing platform OpenRouter to strengthen its infrastructure positioning in the AI developer ecosystem.
AWS details architecture patterns for scaling multi-agent AI systems across vendor-neutral enterprise environments.
AWS detailed its vector search capabilities integrated across six native database and storage services to eliminate data migration.
Google Cloud details AlloyDB ScaNN integration to scale PostgreSQL-compatible vector search to 10 billion vectors for enterprise AI.
Ant International launched Falcon TST 2.0, a time-series transformer for cross-border payment FX risk management allegedly used by major banks.
Anthropic and OpenAI split over a proposed Massachusetts AI safety bill that establishes state-level model evaluation frameworks.
Mistral AI introduced Agentic Search, a retrieval layer designed to help AI models navigate and verify complex document information.
Hudson River Trading signed a multiyear, multibillion-dollar deal with CoreWeave to power its AI trading research compute.
Experian launched a U.K. ChatGPT app enabling retail consumers to access credit scores directly within OpenAI's conversational interface.
OpenAI introduces a zero data retention policy for frontier models, restricted strictly to vetted and approved enterprise users.
Ant Group reported flat quarterly profits as increased capital expenditures in proprietary AI and LLM development squeezed margins.
Legal experts highlight emerging regulatory scrutiny on Big Tech gatekeepers and liability attribution for autonomous AI agents.
Visa and Mastercard are modifying payment infrastructure to support autonomous AI agent transactions and non-human checkout flows.
New research shows LLM-as-a-judge systems exhibit bidirectional bias based on self- and other-labeling, independent of text quality.
Research finds LLMs systematically bias candidate evaluations based on institutional prestige and geographical location across five domains.
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