Task Chaining in Modular Systems

• Task chaining is a paradigm that decomposes complex tasks into sequential or conditional subtasks with intermediate outputs guiding each step.
• It is applied in diverse domains such as vision-language systems, dialogue agents, robot skill composition, workflow automation, and test-case synthesis.
• Its formal basis uses compositional chaining where each subtask’s output transforms into the next subtask’s input, enhancing modularity and transparency.

Task chaining is a general paradigm in which a complex task is decomposed into a sequence of interrelated subtasks, each of which is executed in series (or, in some settings, in parallel or with conditional structure). Each subtask’s output provides intermediate representations, context, or execution artifacts that are then consumed as input by the subsequent subtask. Task chaining is a unifying abstraction across multiple domains—including vision-language systems, dialogue agents, robot skill composition, workflow automation, and test-case synthesis—enabling increased modularity, transparency, and compositional generalization over “monolithic” end-to-end approaches.

1. Formal Definitions and Design Space

At its core, task chaining operates by explicitly specifying a chain (sequence) of subtasks $S_1, S_2, \dots, S_n$, each mapping an input $I_i$ to an output $O_i$, with $I_{i+1} = \varphi_i(O_i)$ for some transformation $\varphi_i$. The overall computation is

$$T \equiv (S_1 \circ S_2 \circ \cdots \circ S_n)(I_1),$$

where $\circ$ denotes compositional chaining. Chaining architectures may be strictly sequential or include branching (conditional execution), parallel executions with later