#agenticsearch

Dive deep into the engineering behind DeepSeek AI’s latest open weight release. Learn how the DeepSeek-V4 MoE language model tackles the toughest logic, mathematics, and programming tasks. Explore its unique hybrid attention architecture—using Compressed Sparse Attention and Heavily Compressed Attention to achieve extreme efficiency in handling long contexts. How does it handle 1-million-token contexts without massive costs? It uses Agentic Search, which enables the model to repeatedly call tools for difficult questions cheaply. Read the full article!

Explore why the Chroma Context-1 agentic search model excels at multi-domain retrieval. How can a compact model beat trillion-parameter giants? Context-1 utilizes Reinforcement Learning from Verifiable Rewards (RLVR), teaching the system to aggressively prune its workspace. By being trained to constantly edit out unnecessary contexts (94.1% accuracy rate), it maintains high fidelity without context rot. Learn about its incredible speed, as it achieves a remarkable 2.56 tool calls on average. With a 0.98 F1 score in out-of-domain email search, Context-1 (4x) completely outperformed GPT-5.4 and Opus 4.6 on the BrowseComp+ benchmark.