Tag: faang

When Pythian rolled out Google Cloud’s Gemini Enterprise across our 500-person company in 27 countries, the goal was simple: use our own company as a proving ground to discover how enterprise AI actually delivers ROI. What we found changed our strategy entirely. Since the rollout of Gemini Enterprise and our previous enterprise AI deployments, Pythian …

Read more “Reimagining work: How Pythian’s internal AI playbook delivers customer ROI”

Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Post-training pipelines that drive shipped tool-calling agents re-run this stage on a daily or weekly cadence, paying the frontier-teacher cost each cycle, yet the mechanism is generate-and-filter (keep the teacher’s passing trajectories, discard the rest) and each cycle …

Read more “PROOF-Gen: From Optimized Data to Better Distillation”

Unified multimodal models that understand, reason over, and generate interleaved text–image sequences remain structurally fragmented: existing approaches either sacrifice visual fidelity through discrete tokenization, impose structural asymmetry by combining causal text generation with iterative diffusion-based denoising, or degrade pretrained understanding when adapting vision-language models for generation. We observe that autoregressive normalizing flows are autoregressive Transformers—sharing …

Read more “STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation”

Few professions are as exacting as the practice of law. A team reviewing a contract or building a case works inside strictly privileged information, firm-specific playbooks, and a body of law that changes constantly. The work thrives on nuanced, professional judgment — and the systems supporting it inherit real obligations: ethical walls that cannot be …

Read more “Now introducing Gemini Enterprise for Legal”

Multimodal large language models increasingly use visual chain-of-thought (Visual CoT) to reason about spatial, temporal, and embodied environments. By generating intermediate reasoning images, Visual CoT provides an intuitive mechanism for visual foresight but introduces substantial inference overhead, which is particularly problematic for proactive video reasoning. We ask whether models can learn to think visually during …

Read more “Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning”