SCOUT achieves state-of-the-art long-text understanding with up to 8x lower token use by actively foraging for sparse query-relevant information and updating a compact provenance-grounded epistemic state.
arXiv preprint arXiv:2508.13167 , year=
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MAD-OPD recasts on-policy distillation teachers as a debating collective to supply better supervision, lifting agentic and code performance over single-teacher OPD across multiple model sizes.
SOD reweights on-policy distillation strength step-by-step using divergence to stabilize tool use in small language model agents, yielding up to 20.86% gains and 26.13% on AIME 2025 for a 0.6B model.
Agent-World autonomously synthesizes verifiable real-world tasks and uses continuous self-evolution to train 8B and 14B agents that outperform proprietary models on 23 benchmarks.
Paper Circle is an open-source multi-agent system that retrieves papers via offline and online sources, applies multi-criteria scoring and diversity ranking, and converts papers into typed knowledge graphs for structured analysis and question answering.
A vision paper outlining a two-pronged research agenda for scaling mobile agents from isolated devices to distributed intelligent systems.
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SCOUT: Active Information Foraging for Long-Text Understanding with Decoupled Epistemic States
SCOUT achieves state-of-the-art long-text understanding with up to 8x lower token use by actively foraging for sparse query-relevant information and updating a compact provenance-grounded epistemic state.
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MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate
MAD-OPD recasts on-policy distillation teachers as a debating collective to supply better supervision, lifting agentic and code performance over single-teacher OPD across multiple model sizes.
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SOD: Step-wise On-policy Distillation for Small Language Model Agents
SOD reweights on-policy distillation strength step-by-step using divergence to stabilize tool use in small language model agents, yielding up to 20.86% gains and 26.13% on AIME 2025 for a 0.6B model.
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Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Agent-World autonomously synthesizes verifiable real-world tasks and uses continuous self-evolution to train 8B and 14B agents that outperform proprietary models on 23 benchmarks.
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Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework
Paper Circle is an open-source multi-agent system that retrieves papers via offline and online sources, applies multi-criteria scoring and diversity ranking, and converts papers into typed knowledge graphs for structured analysis and question answering.
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Scaling Mobile Agent Systems: From Capability Density to Collective Intelligence
A vision paper outlining a two-pronged research agenda for scaling mobile agents from isolated devices to distributed intelligent systems.