# ScienceWorld

With executable capabilities held fixed, presenting them as MCP tools or as CLI commands still sends language agents down different task trajectories. Built from a shared semantic action contract, the comparison spans six models and three interactive environments, and 94.3% of 2,004 paired episodes show structured divergence. DART, which repairs only the interface expression once a semantic decision is validated, raises benchmark score in all six benchmark–interface settings by up to 9.0 points and lowers MCP token cost in all three environments.

With executable capabilities held fixed, presenting them as MCP tools or as CLI commands still sends language agents down different task trajectories. Built from a shared semantic action contract, the comparison spans six models and three interactive environments, and 94.3% of 2,004 paired episodes show structured divergence. DART, which repairs only the interface expression once a semantic decision is validated, raises benchmark score in all six benchmark–interface settings by up to 9.0 points and lowers MCP token cost in all three environments.

The work introduces Hindsight-Divergence Localization (HDL), which picks branch points in a trajectory by how much token log-likelihoods change once verifier feedback and a reflection are added, then builds each training group from a few complete root trajectories plus continuations that reuse the root prefix; across math, code, and agent tasks with three models it cuts generated tokens by 35–61% and rollout wall-clock time by 18–45% relative to GRPO while improving task performance, with gains up to 12.46 percentage points on agent tasks.

The work introduces Hindsight-Divergence Localization (HDL), which picks branch points in a trajectory by how much token log-likelihoods change once verifier feedback and a reflection are added, then builds each training group from a few complete root trajectories plus continuations that reuse the root prefix; across math, code, and agent tasks with three models it cuts generated tokens by 35–61% and rollout wall-clock time by 18–45% relative to GRPO while improving task performance, with gains up to 12.46 percentage points on agent tasks.