Apodex 1.0 launches with open-weight models at 0.8B, 2B, and 4B, trained not for general generation but for specialized sub-agent roles—fact-checking external claims and verifying tool call outputs before passing results to a main controller. The design targets long-horizon agent workflows where routing small tasks to lightweight models avoids wasteful use of 70B+ models at every step. AgentHarness, an open-source evaluation framework for local multi-step agent pipelines, is released alongside the weights.
This paper studies transformer expressivity through succinctness: how compactly a formalism describes a language. It proves fixed-precision transformers can be exponentially more succinct than LTL and RNNs, and doubly exponentially more succinct than finite automata. The same succinctness makes verification hard, with basic problems such as emptiness and equivalence shown to be EXPSPACE-complete.