Hallucination detection remains a significant challenge for large language models. Existing agentic applications rely on LLMs to self-assess the factuality of their outputs using single-step “LLM-as-a-judge” prompts. However, even when equipped with ground truth information, current LLMs still fall short in detecting hallucinations, and this one-shot evaluation offers neither the transparency nor the granularity needed to diagnose where and why the detection fails. To address this gap, we introduce PROBE (Process-based Benchmark for Hallucination Detection), a comprehensive benchmark that breaks down hallucination detection into four critical steps: claim decomposition, evidence finding, evidence evaluation, and hallucination localization, and evaluates each step individually. PROBE consists of 12,000 test cases across three task types—summarization, question answering, and style transfer. Critically, we demonstrate that when hallucination detection is treated as a multi-step process, all models achieve considerably better performance. Through extensive evaluation, we show that current LLMs struggle chiefly with evidence finding, and that finetuning on our released training data substantially improves performance on this step. PROBE represents a significant step toward more transparent, diagnosable, and robust hallucination detection systems.

Diffusion language models (LMs) enable parallel decoding, but still lag behind autoregressive (AR) LMs in accuracy, and behind multi-token prediction (MTP) methods in efficiency. This raises three questions: should diffusion LMs compete with AR LMs or be harmonized with them; can diffusion provide a stronger acceleration mechanism than MTP; and does diffusion decoding have sufficient long-term potential to justify further exploration? We study these questions through Nemotron-Labs-Diffusion, a tri-mode LM that unifies three decoding modes—AR, diffusion, and self-speculation (diffusion drafts, AR verifies)—within a single model to sustain high throughput across all concurrency levels. Our study shows that (1) AR and diffusion objectives are mutually beneficial rather than competing, (2) diffusion-based self-speculation achieves higher acceptance lengths and better system vs. per-user throughput trade-offs than MTP baselines, and (3) diffusion decoding shows strong potential for parallel decoding under an optimal sampler in a speed-of-light analysis. Based on these insights, we develop the Nemotron-Labs-Diffusion family at 3B, 8B, and 14B scales. Our models outperform state-of-the-art open-source AR and diffusion LMs in both accuracy and speed, e.g., achieving 6x tokens per forward over Qwen3-8B with comparable benchmark accuracy, translating to 4x throughput on SPEED-Bench measured with SGLang on an NVIDIA GB200 GPU.