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Apodex-1.0-mini

**Apodex-1.0** is a verification-centric model for deep research. The trained model alone, Apodex-1.0, runs as a standard tool-using ReAct agent. Deployed in our **heavy-duty mode** — an asynchronous agent team in which sub-agents specialize in retrieval and verification, route their reports through a shared evidence pool, and feed a global verifier that reasons over the assembled evidence graph to produce the final answer — it becomes **Apodex-1.0-H**. Under the hood, a high-quality data pipeline and a three-stage post-training recipe (SFT, agentic DPO, RL on long agentic rollouts) substantially raise the deep-research capability of the Qwen3.5 base while *preserving* its general knowledge, coding, reasoning, and instruction-following capabilities — the recipe is additive on the deep-research axis rather than a trade across axes. Apodex-1.0-H sets a new state of the art across both open- and closed-source models on deep-research benchmarks. Every claim in the final report it produces is backed by an explicit evidence chain and independently audited by a verification team before delivery.

翻訳準備中のため原文を表示しています

**Apodex-1.0** is a verification-centric model for deep research. The trained model alone, Apodex-1.0, runs as a standard tool-using ReAct agent. Deployed in our **heavy-duty mode** — an asynchronous agent team in which sub-agents specialize in retrieval and verification, route their reports through a shared evidence pool, and feed a global verifier that reasons over the assembled evidence graph to produce the final answer — it becomes **Apodex-1.0-H**. Under the hood, a high-quality data pipeline and a three-stage post-training recipe (SFT, agentic DPO, RL on long agentic rollouts) substantially raise the deep-research capability of the Qwen3.5 base while *preserving* its general knowledge, coding, reasoning, and instruction-following capabilities — the recipe is additive on the deep-research axis rather than a trade across axes. Apodex-1.0-H sets a new state of the art across both open- and closed-source models on deep-research benchmarks. Every claim in the final report it produces is backed by an explicit evidence chain and independently audited by a verification team before delivery.

情報源

huggingface.co ↗