arXiv:2608.07926v2 Announce Type: replace Abstract: Despite their remarkable capabilities, LLM-based directed input generation produces off-target test inputs when their underlying hypotheses on the target program's behaviors are incorrect. To effectively ground LLM-based directed input generation in actual program behaviors, we propose ReDig, a runtime feedback-guided framework for iterative refinement of LLM-based directed input generation. When a generated input fails to reach a given target code location, ReDig conducts a postmodern analysis to identify incorrect hypotheses by comparing expected runtime values and their actual runtime values observed in the off-target test execution. ReDig utilizes the postmortem analysis result as feedback to refine the inference context, guiding the subsequent test generations toward the target location. In this paper, we present our work-in-progress on ReDig. We illustrate how runtime feedback-guided refinement operates through four case studies with two real-world C/C++ programs (Poppler and Libsndfile). Furthermore, we report preliminary experiment results that show ReDig successfully generates crash-reproducing inputs for 24 out of the 59 CVEs collected from Sec-Bench.