TurboQuant Quantization Explained
TurboQuant is a vector-embedding compression technique that applies a random orthogonal rotation to embedding coordinates, then maps the rotated values…
TurboQuant is a vector-embedding compression technique that applies a random orthogonal rotation to embedding coordinates, then maps the rotated values…
A Hugging Face contributor describes a data-driven quantization scheme for GGUF models that empirically measures per-tensor sensitivity rather than relying…
The paper studies how large language models retrieve and use internal knowledge by performing layerwise interventions on hidden states across…
The paper proposes a Bayesian backward-reasoning method for aggregating decisions from multiple LLM agents, constructing reverse posteriors via explicit likelihood…
The paper introduces TASCO, a test-time adaptation framework that improves LLM reasoning by optimizing lightweight task-level prefixes while keeping the…
The paper proposes a unified four-coefficient parameterization for on-policy knowledge distillation that generalizes existing per-token gating methods by combining forward…
Researchers introduce AgentZip, a memory compression system for AI-agent sandboxes that exploits redundancy across sandbox instances and between sandboxes and…
A joint FBI, NSA, and CISA advisory published in early September 2026 accuses six Chinese AI companies — DeepSeek, Moonshot…
Salesforce unveiled a suite of seven named AI agents — Casey, Paige, Carter, Hunter, Marshall, Piper, and Fin — each…
A suspected Russian-speaking threat actor deployed hundreds of AI agents built on OpenAI’s Codex and a DeepSeek model to exploit…
A federal judge ruled that the Pentagon acted illegally when it designated Anthropic a ‘supply chain risk’ in February 2026.…
In a roughly 3,800-word essay published September 12, 2026, Anthropic CEO Dario Amodei argues that AI companies should deliberately slow…