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LongCat-DeepResearch is presented, a deep research system that combines an enhanced LongCat model with a multi-agent workflow for producing comprehensive, evidence-grounded reports and improves average automatic readability preference across two benchmarks, with different trends on each.
The main findings are that diverse, high-quality SFT establishes a strong capability floor and difficulty filtering keeps RL prompts within a productive learning range, and reward reliability provides a practical principle for ordering stages.
We introduce Forecast-Dojo, a replayable environment for benchmarking and training LLM forecasting agents. It combines resolved prediction-market questions with dated news, allowing agents to research an event and revisit their predictions at successive historical dates. The same tasks and tools support repeated evalua...
QUBRIC, a framework that co-designs queries and rubrics can make rubric-based RL a practical complement to RLVR beyond strictly verifiable tasks, provides evidence that co-designing queries and rubrics can make rubrics a practical complement to RLVR beyond strictly verifiable tasks.
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