Back to AI Lab
HuggingFace Paper

The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality

Aileen Cheng, Alon Jacovi, Amir Globerson +3December 11, 2025

Summary

FACTS is positioned as a one-stop leaderboard for LLM factuality, aggregating automated-judge scores from multimodal, parametric, search-augmented, and document-grounded tasks. It’s a natural next target for model releases that want to claim they’re less hallucinatory in practice, not just on isolated QA datasets. ([huggingface.co](https://huggingface.co/papers/2512.10791))

Related Content

Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation

This paper is a systematic exploration of reinforcement learning for text-to-3D generation, dissecting reward design, RL algorithms, data scaling, and hierarchical optimization. The authors introduce a new benchmark (MME-3DR), propose Hi-GRPO for global-to-local 3D refinement, and build AR3D-R1—the first RL-tuned text-to-3D model that improves both global shape quality and fine-grained texture alignment.

OPV: Outcome-based Process Verifier for Efficient Long Chain-of-Thought Verification

OPV (Outcome-based Process Verifier) is a verifier model that inspects the rationale steps of long chains-of-thought via summarized outcomes, combining the strengths of outcome-based and process-based verification. Trained with an active learning loop, rejection fine-tuning, and RLVR, OPV reaches strong F1 on OPV-Bench and outperforms much larger models like Qwen3-Max-Preview at detecting reasoning errors.

Long-horizon Reasoning Agent for Olympiad-Level Mathematical Problem Solving

This work presents a long-horizon reasoning agent for Olympiad-level math that uses an Outcome-based Process Verifier (OPV) to supervise and clean up very long chains-of-thought. By summarizing and checking reasoning segments rather than only final answers, and training OPV via iterative active learning and RLVR, the system achieves new SOTA on a held-out benchmark while reducing annotation cost.

T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground

T-pro 2.0 is an open-weight Russian large language model focused on hybrid reasoning: it can answer directly or emit explicit reasoning traces, and it’s optimized for low-latency inference via speculative decoding. Alongside the model, the authors release a Russian instruction corpus, a math benchmark, and an EAGLE-based inference stack, making it a practical foundation for Russian-language reasoning applications.