Fathom-DeepResearch: Unlocking Long Horizon Information Retrieval and Synthesis for SLMs

• URL: http://arxiv.org/abs/2509.24107v1
• Date: Sun, 28 Sep 2025 22:58:11 GMT
• Title: Fathom-DeepResearch: Unlocking Long Horizon Information Retrieval and Synthesis for SLMs
• Authors: Shreyas Singh, Kunal Singh, Pradeep Moturi,
• Abstract summary: We introduce Fathom-DeepResearch, an agentic system composed of two specialized models.<n>The first is Fathom-Search-4B, a DeepSearch model optimized for evidence-based investigation through live web search and targeted webpage querying.<n>The second is Fathom- Synthesizer-4B, trained from Qwen3-4B, which converts multi-turn DeepSearch traces into structured, citation-dense DeepResearch Reports.
• Score: 7.3517692707289415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tool-integrated reasoning has emerged as a key focus for enabling agentic applications. Among these, DeepResearch Agents have gained significant attention for their strong performance on complex, open-ended information-seeking tasks. We introduce Fathom-DeepResearch, an agentic system composed of two specialized models. The first is Fathom-Search-4B, a DeepSearch model trained from Qwen3-4B and optimized for evidence-based investigation through live web search and targeted webpage querying. Its training combines three advances: (i) DUETQA, a 5K-sample dataset generated via multi-agent self-play that enforces strict web-search dependence and heterogeneous source grounding; (ii) RAPO, a zero-overhead extension of GRPO that stabilizes multi-turn Reinforcement Learning with Verifiable Rewards through curriculum pruning, reward-aware advantage scaling, and per-prompt replay buffers; and (iii) a steerable step-level reward that classifies each tool call by cognitive behavior and marginal utility, enabling explicit control over search trajectory breadth, depth, and horizon. These improvements enable reliable extension of tool-calling beyond 20 calls when warranted. The second is Fathom-Synthesizer-4B, trained from Qwen3-4B, which converts multi-turn DeepSearch traces into structured, citation-dense DeepResearch Reports for comprehensive synthesis. Evaluated on DeepSearch benchmarks (SimpleQA, FRAMES, WebWalker, Seal0, MuSiQue) and DeepResearch-Bench, the system achieves state-of-the-art performance in the open-weights category while demonstrating strong generalization to diverse reasoning tasks including HLE, AIME-25, GPQA-Diamond, and MedQA.

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