deep-research

WebRover is an MIT-licensed open-source AI agent that uses natural language to drive the browser for web automation, cross-site data scraping, and deep research, with support for local deployment.

Introduces Deep Research Pretraining (DRP), an offline framework that generates search-open-write trajectories from citation and hyperlink evidence structures. Qwen3-14B models pretrained on 1B tokens with DRP outperform matched no-DRP baselines on deep research benchmarks, even with less supervised fine-tuning data.

Video-DeepResearch (Video-DR) extends multimodal agents from static images to continuous video streams, introducing a decoupled perception-exploration pipeline and a new benchmark Video-DR-Bench. Their Video-DeepResearch-35B-A3B model achieves 64.0% accuracy, surpassing Claude-4.5-Sonnet, GPT-5, and Gemini 2.5 Pro.

This paper introduces SIEVE, a search-inspect-fetch strategy that uses Boolean Query Language to make deep-research agents retrieve only relevant document sections, achieving higher accuracy with 20.7–50.6% fewer tokens across multiple benchmark datasets and agent backbones.

This paper introduces FinanceHarness, a framework for end-to-end automated financial deep research powered by LLM agents, along with FinanceGym, a verifiable point-in-time benchmark. Expert validation shows an 82% pass rate, while leading models score below 40%, and FinanceHarness improves open-weight backbone performance from 25.3% to 32.4%.

This article explains how Similarweb evaluates long-form agent research reports using LangSmith, combining deterministic checks for tool calls and LLM-as-judge scoring for quality, with a focus on making regressions inspectable and enabling A/B comparisons.

Elon Musk announces a /deep-research command for Grok Build that performs research with bounded parallel agents, cross-checks evidence, and generates cited reports.

This paper introduces MisKnow-Agent, a framework for generating misleading knowledge to test DeepResearch agents, showing that limited exposure to credible-looking false information can lead to false conclusions in final reports.

AREX introduces a family of recursively self-improving agents for deep research, alternating between an inner research loop and an outer self-improvement loop, trained with long-horizon reinforcement learning. It substantially outperforms comparable-scale baselines on benchmarks like BrowseComp and Humanity's Last Exam.

Octen is a web search tool that delivers full source-backed reports in under 3 minutes, outperforming OpenAI, Gemini, Grok, and Perplexity by 10-17 points on the DeepResearch Bench, combining speed and accuracy.

Lattics is a brain-like knowledge base that integrates AI writing and deep research capabilities.

The author describes building a custom AI research pipeline using multiple subscriptions and cheaper models to reduce token costs, learning firsthand how to optimize token usage while researching tokenomics.

OpenResearcher is an open-source project that fully reproduces the Deep Research workflow, providing datasets, models, and demos. It enables researchers and AI agent developers to build, train, and evaluate deep research pipelines in an open manner.

OpenResearcher is an open-source project from TIGER-AI-Lab that provides 96,000 deep research trajectories and a recipe to train agentic language models for long-horizon web research without external APIs. It includes a dataset, model, and demo, and has been adopted by NVIDIA's Nemotron models.

An analysis questioning why progress on Deep Research AI products has stalled since their impressive launch in February 2025, noting that known weaknesses like hallucinations and unreliable source verification persist despite incremental improvements.

This paper benchmarks 8 LLM judges for citation quality in deep-research systems, finding that cheaper models remain competitive with frontier models on source relevance and factual support, but differ in directional bias which matters for RL training.

Apodex has released an open-weight DeepResearch fine-tuned model, Apodex-1.0-mini, based on Qwen3.5-35B-A3B. It scores 71.5 on BrowseComp, approaching flagship model performance, and can run efficiently locally. The author packaged an out-of-the-box skill.

LiteResearcher is a scalable reinforcement learning training framework designed for deep research agents.

The article argues that the next major competitive axis for serious AI systems is reliability and trustworthiness, not just capability or fluency. It highlights emerging verification techniques—such as independent checks and rubric-based grading—that aim to catch confident but false outputs, a failure mode termed 'pseudo-correctness.'

Apodex-1.0-H is a new deep research model that introduces a multi-agent architecture where the model decomposes tasks, spawns specialist sub-agents, and uses self-verification and iterative improvement to produce answers. Open-weight variants are available on HuggingFace.