Lin-Feng Zhang

Large Knowledge Model: From Papers to a Scientific Reasoning Landscape

Bohrium + SciMaster: Building the infrastructure and ecosystem for agentic science at scale

AI agents are emerging as a practical way to run multi-step scientific workflows that interleave reasoning, tool use, and verification. Scaling such agentic science remains difficult because workflows are hard to observe and reproduce, many scientific tools and laboratory systems are not agent-ready, and execution trac...

Beyond Token Alignment: Event Completion for Cross-Tokenizer On-Policy Distillation

ESCD aggregates prefix-related teacher events and supervises the total probability of byte-compatible one-step student completions, avoiding tokenizer-dependent probability splits among individual tokens, and support event entry and event completion as complementary supervision targets for cross-tokenizer knowledge tra...

Large Knowledge Model: A Knowledge Foundation for Agentic Science at Scale

Agentic science envisions many autonomous agents investigating concurrently while building on a shared, evolving body of scientific knowledge. This requires a knowledge foundation that supports high-concurrency access, preserves traceable and reusable reasoning, and grows incrementally. We propose the Large Knowledge M...