# LongCat-DeepResearch Technical Report

arXiv: https://arxiv.org/abs/2609.36071 · HTML: https://mlsanity.com/paper/2609.36071/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 41 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 29 of 29)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [WebThinker: Empowering Large Reasoning Models with Deep Research Capability](https://mlsanity.com/paper/2504.21776/) (2025)
- [DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents](https://mlsanity.com/paper/2506.11763/) (2025)
- [Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models](https://mlsanity.com/paper/2402.14207/) (2024)
- [Language agents achieve superhuman synthesis of scientific knowledge](https://mlsanity.com/paper/2409.13740/) (2024)
- [DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research](https://mlsanity.com/paper/2511.19399/) (2025)
- [AgentFold: Long-Horizon Web Agents with Proactive Context Management](https://mlsanity.com/paper/2510.24699/) (2025)
- [ResearchRubrics: A Benchmark of Prompts and Rubrics For Evaluating Deep Research Agents](https://mlsanity.com/paper/2511.07685/) (2025)
- [WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research](https://mlsanity.com/paper/2509.13312/) (2025)
- [DeepResearch Bench II: Diagnosing Deep Research Agents via Rubrics from Expert Reports](https://mlsanity.com/paper/2601.08536/) (2026)
- [OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking](https://mlsanity.com/paper/2501.09751/) (2025)
- [Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design](https://mlsanity.com/paper/2603.28376/) (2026)
- [Learning Agent-Compatible Context Management for Long-Horizon Tasks](https://mlsanity.com/paper/2605.30785/) (2026)
- [DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence](https://mlsanity.com/paper/2509.04499/) (2025)
- [FoldAct: Efficient and Stable Context Folding for Long-Horizon Search Agents](https://mlsanity.com/paper/2512.22733/) (2025)
- [FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents](https://mlsanity.com/paper/2602.01566/) (2026)
- [Argus: Evidence Assembly for Scalable Deep Research Agents](https://mlsanity.com/paper/2605.16217/) (2026)
- [S1-DeepResearch: Beyond Search, Toward Real-World Long-Horizon Research Agents](https://mlsanity.com/paper/2606.15367/) (2026)
- [Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revision](https://mlsanity.com/paper/2601.13217/) (2026)
- [Don't Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination](https://mlsanity.com/paper/2604.24978/) (2026)
- [DEEPRUBRIC: Evidence-Tree Rubric Supervision for Efficient Reinforcement Learning of Deep Research Agents](https://mlsanity.com/paper/2606.17029/) (2026)
- [A Tale of Two Graphs: Separating Knowledge Exploration from Outline Structure for Open-Ended Deep Research](https://mlsanity.com/paper/2602.13830/) (2026)
- [Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation](https://mlsanity.com/paper/2605.29861/) (2026)
- [Planner-Centric Reinforcement Learning for Deep Research with Structure-Aware Reward](https://mlsanity.com/paper/2605.30824/) (2026)
- [ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research](https://mlsanity.com/paper/2606.20122/) (2026)
- [CogGen: A Cognitively Inspired Recursive Framework for Deep Research Report Generation](https://mlsanity.com/paper/2604.17072/) (2026)
- [SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration](https://mlsanity.com/paper/2607.15257/) (2026)
- [Personalized Deep Research Query Refinement with Graph-Scaffolded Evidence Grounding](https://mlsanity.com/paper/2608.05876/) (2026)

## Abstract (arXiv)

We present LongCat-DeepResearch, a deep research system that combines an enhanced LongCat model with a multi-agent workflow for producing comprehensive, evidence-grounded reports. The workflow separates global planning from detailed investigation and coordinates revision at the section level. Multiple planning agents first explore external sources and refine an actionable research plan, termed ResearchSpec. Research agents then investigate and draft their assigned sections in parallel, gathering additional evidence in separate contexts as their analyses develop. Once the sections are assembled, global review guides targeted local revisions, reducing reliance on repeated full-report rewriting. This workflow also supports the construction of research tasks and trajectories for the mid-training and post-training of LongCat's general-purpose models. LongCat-DeepResearch achieves 55.25 on DeepResearchBench, 51.35 on DeepResearchBench II, and 79.83 on ResearchRubrics. On an in-house benchmark, it scores 76.04, ranking second among four compared systems. Development-set analyses show benefits from combining planning perspectives, while further planning refinement has mixed effects. Additional editing improves average automatic readability preference across two benchmarks, with different trends on each.