browsecomp-plus-21d90c48·3 events·first seen Aliases: BrowseComp-Plus, BrowseCompPlus
DeepStress is a new evaluation framework that stress-tests search agents by replacing their retrieval module with a controlled synthetic environment, allowing systematic manipulation of document trustworthiness, relevance, and factuality. The authors test several search agents on HotpotQA and BrowseCompPlus, revealing substantial performance differences in handling unreliable information. The work introduces new metrics to capture system outcomes and conflicts between parametric and retrieved knowledge, addressing a gap in realistic benchmarks that rarely surface low-quality evidence scenarios.
WebSwarm is a new multi-agent orchestration framework for LLM-based web search that uses progressive recursive delegation to handle both depth and breadth simultaneously. Each agent node couples a local objective with a search mode and can either solve its task or delegate to child nodes, passing evidence upward for parent-level expansion and aggregation. The system outperforms single-agent and multi-agent baselines on BrowseComp-Plus, WideSearch, DeepWideSearch, and GISA benchmarks. The work addresses known limitations of ReAct-style single-agent search and flat parallel multi-agent approaches.
BRANE is a system that dynamically selects retrieval agent pipeline configurations (LLM, retriever, number of hops, synthesis strategy) at inference time based on per-query characteristics and a cost-quality target. It uses an LLM to extract workload features from each query, then applies lightweight per-configuration predictors to estimate correctness, selecting the configuration that maximizes predicted accuracy penalized by cost. Evaluated on MuSiQue, BrowseComp-Plus, and FinanceBench, BRANE matches best-fixed-configuration accuracy at up to 89% lower cost and outperforms LLM-routing and fine-tuned Qwen3-4B baselines. The work frames per-query pipeline configuration as a practical alternative to static workload-level tuning.