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Using frontier agent harnesses to tackle mathematical research problems has emerged as an effective means of advancing mathematics. However, solving frontier problems in mathematics may require a massive number of agents working in parallel for extended periods to construct proofs, thereby generating an enormous volume...
Agentic reinforcement learning (RL) has emerged as a powerful approach for training large language model agents on multi-step tasks, yet reliance on terminal outcome rewards creates two credit-assignment problems, particularly in long-horizon tasks. First, same-outcome rollout groups provide no learning signal from ter...
Socratic-Geo is proposed, a fully autonomous framework that dynamically couples data synthesis with model learning through multi-agent interaction, and establishes new state-of-the-art for open-source models, sur-passing strong baselines.
RetroAgent is introduced, an LLM agent that bridges symbolic search and neural reasoning through a harness with structured memory, enabling informed decisions grounded in both global progress and domain knowledge in multi-step retrosynthesis planning.
It is argued that the next leap in AI4Math systems requires a decisive shift from predefined problem-solvers to research agents that can address frontier mathematical challenges with rigorous formal mathematical reasoning, highlighting core limitations of existing systems in serving as mathematical research agents.
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