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Learning to tackle long-horizon, goal-conditioned tasks requires an agent to reason over extended timescales and act across a broad range of states. In principle, Hierarchical Reinforcement Learning (HRL) addresses both challenges through the interaction between action (temporal) and state (spatial) abstraction. First,...
HorizonMath, a benchmark of 113 predominantly unsolved problems spanning eight domains in mathematics and the mathematical sciences, paired with an open-source evaluation framework for automated verification, is introduced, identifying six novel solutions to research problems that either resolve previously open questio...
CivBench is presented, an open-source benchmark for evaluating language model agents in long-horizon, tool-mediated environments through the Model Context Protocol (MCP), and two interface-level metrics are introduced that the environment makes measurable: Proactive Monitoring Rate (PMR) and RAG@10, capturing whether c...
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