Feedback-driven knowledge production in computer science domain

Computer Science (CS) knowledge production increasingly operates as a coupled socio-technical process in which human researchers and large language model LLM-augmented teams interact with evolving topic landscapes, benchmarks, and software ecosystems. CS knowledge production is formulated as a feedback control problem, where the “plant” comprises an evolving topic–artifact network and resource-constrained agents; measurable outputs include novelty rate, topic diversity, code-reuse centrality, and time-to-insight; and the control input reallocates exploration–exploitation effort to track explicit objectives under delay, noise, and exogenous shocks. A reproducible hybrid simulation framework is specified that integrates agent-based generation of research outputs with system-level dynamics over a topic co-occurrence graph seeded from OpenAlex and Crossref-derived CS concept metadata. On this simulator, three classes of feedback policies – proportional–integral–derivative (PID) tracking, linear–quadratic regulator (LQR) control on an identified linear surrogate, and a constrained policy-gradient (CPG) controller – are evaluated against status-quo (no control), impact-only, and random-allocation baselines. Across regimes with reporting delays, measurement noise, and funding-cut shocks, closed-loop policies reduce time-to-insight while maintaining diversity targets and exhibiting interpretable stability characteristics (overshoot and settling behavior). The findings support a control-theoretic perspective on governing human–machine knowledge production, while highlighting risks of metric gaming and bias amplification that motivate transparent signal design and auditable control policies.