A CPU-based RL controller optimizes adaptive sampling during test-time scaling, reducing computational overhead and latency compared to heuristic methods.
The US government receives 30 days of advance access to new powerful AI models to benefit from their vulnerability detection, while the tech industry was spared longer exclusivity periods.
The disparity between machine-IDs and human accounts is growing so dramatically in cloud-native environments that traditional IAM processes are failing, creating security gaps.
Microsoft has introduced MAI-Thinking-1, its first reasoning model with fine-tuning capability for enterprise, specifically designed for domain-specific customizations.
Linear probes for deception detection in LLMs function reliably only on training data, not under stylistic variations—but style augmentation can restore robustness.
VaSE achieves higher accuracy than existing sparse-attention methods at 4x KV-cache compression, thereby reducing the memory bottleneck of reasoning models.
NVIDIA’s OmniDreams generates complex vehicle simulations in real time, generalizes better to rare scenarios, and can serve as a foundation for more efficient driving policy models.
A new training paradigm enables LLMs to autonomously integrate in-context knowledge into their parameters and continue developing without human supervision.
