deep-search

DeepVoyager-VL proposes a long-horizon multimodal deep-search framework that integrates visual evidence into intermediate reasoning, using a multimodal event graph for data synthesis and fine-tuning without reinforcement learning, achieving strong performance across ten benchmarks.

XYZ AI Lab releases XYZ-Aquila-mini, an open-weight thinking model for agentic deep search, fine-tuned from Qwen3.6-35B-A3B, achieving top benchmark scores among sub-40B open-weight models.

XYZ AI Lab releases XYZ-Aquila-pro, an open-weight thinking model for agentic deep search, post-trained from Qwen3.5-397B-A17B via a bounded-exploration AI4AI pipeline, with strong benchmark results in sub-400B open-weight comparisons.

WebSwarm introduces a recursive multi-agent delegation framework for deep-and-wide web search, dynamically instantiating agentic search nodes that can decompose tasks, expand recursively, and collaborate adaptively. It outperforms baselines on multiple benchmarks.

DeepSearch-Evolve introduces a self-distillation framework for web agents using a verifiable environment (DeepSearch-World) with 420K multi-hop QA tasks, achieving competitive performance without distillation from stronger models.

SearchEyes uses a typed knowledge graph as the backbone of a simulated search world to unify training data, search environment, and reward signals. It proposes Perception-Knowledge Chains (PKC) for multi-hop path sampling and Hop-Anchored Policy Optimization (HaPO) for step-level credit assignment, achieving state-of-the-art performance on multimodal knowledge-intensive benchmarks.

DiscoBench is a new benchmark that evaluates whether LLM-powered search agents can proactively identify ambiguity in user queries, ask clarifying questions, and recover correct reasoning paths through multi-turn interaction.

Visual-Seeker proposes a visual-native multimodal deep search agent that actively reasons over fine-grained visual details and synthesizes multimodal evidence, achieving state-of-the-art performance on five challenging multimodal search benchmarks.

TreeSeeker is an inference-time framework that organizes deep search as branch-and-return over tree-structured states, using textual UCB signals to balance exploitation, exploration, and pruning. It outperforms strong baselines on deep search benchmarks, showing that explicit branch-and-return control improves multi-step web search.

DeepDive is a pattern for building deep search agents that synthesizes QA from knowledge graphs and trains multi-turn browsing with reinforcement learning (GRPO). It includes entity obfuscation and test-time scaling with tool calls.

DeepDive presents an automated approach to training deep search agents using knowledge graphs for data synthesis and multi-turn reinforcement learning, enabling complex multi-step reasoning and web browsing.

Introduces MultiSearch, an RL-based framework that generates multiple queries at each reasoning step and explicitly merges retrieved information to improve signal-to-noise ratio and reasoning accuracy in question-answering tasks.

Google is expanding its new AI-powered Google Finance service to Europe, featuring enhanced AI research, advanced charting visualizations, and live earnings insights with local language support.

This paper introduces On-Policy Data Evolution (ODE) and a visual-native agent harness to improve multimodal deep search agents. By enabling reusable visual evidence and closed-loop data generation, ODE significantly boosts the performance of Qwen3-VL agents across multiple benchmarks, surpassing Gemini 2.5 Pro.

OpenSearch-VL is an open-source framework and paper introducing a recipe for training frontier multimodal search agents using reinforcement learning, featuring specialized data curation and a novel training algorithm.