Semantic Flashlight: Plan-First Context Construction with Response-Adaptive Evidence Exposure for Deep Research

Deep-research agents repeatedly search, inspect evidence, and refine their reasoning, causing their working context to grow with research depth. Existing context-management methods often decide what to retain only after the interaction history has accumulated, requiring a selector to reconstruct the information need behind earlier search decisions. We introduce Semantic Flashlight, a plan-first and response-adaptive context construction framework built around two complementary signals. First, during query formulation, the model already identifies an unresolved information gap and anticipates what evidence is needed next. Flashlight externalizes this information need as a next-context plan, which guides evidence selection for the following decision. Second, the model's response provides a signal about whether the current evidence is being effectively used. Across real research trajectories, higher-quality outcomes are associated with stronger alignment to the current context than to prior model responses. Flashlight therefore uses response feedback to adapt the breadth of evidence exposure, broadening raw evidence when response echo dominates source alignment. A dependency-aware evidence contract carries these two signals across planning, acquisition, review, and synthesis, while the Context Engine enforces provenance and bounded context construction. Across diverse deep-research benchmarks with two closed-source models, Flashlight improves report quality while reducing LLM token consumption by up to 45.6% relative to Full-History, without requiring an auxiliary LLM context-selection call. Optional role-conditioned supervised fine-tuning and reinforcement learning with our Effective Information Acquisition (EIA) reward extend the workflow to an open-weight model. Together, these results suggest that context construction can be integrated into the research decision itself rather than treated as post-hoc transcript compression.