TIME trains LLMs to trigger compact, context-triggered reasoning via time tags and tick events, improving TIMEBench scores while cutting explicit reasoning tokens by an order of magnitude.
DOTS: Learning to reason dynamically in LLMs via optimal reasoning trajectories search
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End-to-end RL in authentic web environments produces LLM research agents that outperform prompt-engineering and RAG-based baselines by up to 28.9 and 7.2 points respectively while exhibiting emergent planning, cross-validation, and self-reflection.
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TIME: Temporally Intelligent Meta-reasoning Engine for Context-Triggered Explicit Reasoning
TIME trains LLMs to trigger compact, context-triggered reasoning via time tags and tick events, improving TIMEBench scores while cutting explicit reasoning tokens by an order of magnitude.
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DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments
End-to-end RL in authentic web environments produces LLM research agents that outperform prompt-engineering and RAG-based baselines by up to 28.9 and 7.2 points respectively while exhibiting emergent planning, cross-validation, and self-reflection.