Jie Wang

Jiechao Gao, Rohan Kumar Yadav, Yuangang Li et al.

Pre-trained language models such as BERT achieve strong text classification performance but lack transparency, limiting their use in high-stakes settings. The Tsetlin Machine (TM) offers fully interpretable, clause-based reasoning but captures little semantic information, and prior attempts to bridge the two rely on static word embeddings that miss contextual meaning. We propose a semantic pre-training framework that transfers knowledge from a pre-trained language model into a TM without using embeddings. Text samples are grouped into semantically coherent clusters with K-means or Top2Vec, and the resulting cluster-sample pairs pre-train a non-negated TM with enhanced Type I feedback. The TM thereby learns interpretable semantic keywords that are fine-tuned on downstream tasks. Across five datasets, our method substantially outperforms vanilla and embedding-based TMs and reaches performance competitive with BERT while remaining interpretable.

Jiechao Gao, Yuandong Pan. Yuangang Li, Jie Wang et al.

Agentic systems have advanced quickly on digitally native tasks, yet they have barely touched the industries where coordinated automation could matter most: logistics, healthcare operations, construction, and the many sectors whose work is spread across incompatible tools and many hands. We argue that the reason is a missing abstraction. The value in these settings does not come from a single capable model invocation; it comes from \emph{orchestration}, the runtime that coordinates multi-step workflows, enforces hard domain constraints, manages human approval, and bridges legacy systems. We develop this idea into a usable conceptual frame. We give an operational test for which workflows are orchestration-bound, a decomposition that separates how tangled a workflow is from how much of its effort is coordination and what that coordination is worth, and a feature-level account of why today's multi-agent frameworks leave a specific gap. We then advance our central claim: the right automation path is staged, and which architectural guarantee carries the most weight depends on a sector's dominant source of friction. Constraint enforcement is load-bearing under regulatory friction; explainability is load-bearing under liability friction. We close with the research program this view implies.