Can routing mask future experts to prevent knowledge leakage?

LLMs trained on a fixed web snapshot go stale and, worse, risk temporal leakage — answering as if they know information that postdates a query. Standard pretraining merges all time periods indiscriminately, so the model has no principled way to respect a query's timestamp. TiMoE makes temporal grounding architectural: pre-train a set of GPT-style experts on disjoint two-year slices of a 2013–2024 corpus, then at inference mask every expert whose training window ends after the query timestamp and merge the remaining experts' log-probabilities in a shared space. This guarantees strict causal validity while retaining multi-period breadth.

The result quantifies the trade: on the new 10k-question TSQA benchmark (alternatives labelled past/future/irrelevant), TiMoE cuts future-knowledge errors by up to ~15% and delivers steadier accuracy across years, at a "manageable cost of time-awareness" — a slight underperformance on eight standard NLP tasks rather than a fundamental barrier. The keeper is the design principle: temporal causality can be enforced by routing over time-partitioned parameters, not only by external retrieval or post-hoc verification.

This sits alongside retrieval-time and prompt-time temporal fixes as the parametric option. It complements Does AI text generation unfold through temporal reflection? (the RAG route to temporal grounding) by pushing the same concern into the model's own expert structure, trading some general accuracy for guaranteed causal validity.