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Forecasting an unresolved event requires finding relevant evidence and deciding how strongly it supports each outcome. Yet outcome-based training of Large Language Model (LLM) forecasters often begins with preassembled evidence at a single prediction date, leaving evidence acquisition outside the learning loop. We intr...
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.
Forecasting requires large language model (LLM) agents to gather evolving evidence, revise their beliefs, and make calibrated predictions over time. Yet evaluating and improving these capabilities is challenging because real-world events unfold continuously and outcomes may take months to resolve. We introduce Forecast...
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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