Don’t Waste Your Search-and-Visit Signals in Deep Research

Deep research agents repeatedly search and visit the web, yet task-relevant signals exposed or derived during these interactions may not be retained in the updated research state. Representative approaches improve planning, exploration, or evidence organization, while systematic reuse of such unretained signals remains underexplored. We therefore introduce **WasteHub**, a plug-and-play mechanism that stores Unvisited-result, Visit-content, and Source-appraisal waste as typed, provenance-preserving residuals and selectively reuses them through a stage-aware gate without modifying the underlying framework's planner or search/visit interfaces. With ReAct as the underlying framework, WasteHub outperforms the Base setting and two baselines that augment Base with process reward model feedback (PRM-F) and post-answer exploration (PAE), respectively, on the aggregate metrics of DeepResearch Bench and DeepResearch Bench II. WasteHub uses fewer cumulative input tokens and has lower end-to-end latency than both PRM-F and PAE. Ablations show that all three residual types and both reuse phases contribute to the full method. Cross-framework experiments with Self-Manager and WebWeaver show that WasteHub achieves the best aggregate performance on both benchmarks.