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# GAIA2

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arXiv Oct 12, 2026
Sub-agents can now ask to be reconfigured mid-task, lifting long-horizon success by 9.1%
POrchestra lets sub-agents proactively send structured requests to the orchestrator during execution, so the orchestrator can revise their subgoal, context, tool set, or execution budget on the spot instead of waiting for the sub-agent to finish or fail. Earlier re-orchestration methods fixed each sub-agent's configuration until its lifecycle ended, delaying adaptation to dynamic environments. Across GAIA, GAIA2, and SWE-Bench Verified, task success improves by an average of 9.1% over the strongest baseline, and the companion OPOD turns that feedback into process supervision, raising small-orchestrator success by an average of 10.0% over training baselines.
POrchestra lets sub-agents proactively send structured requests to the orchestrator during execution, so the orchestrator can revise their subgoal, context, tool set, or execution budget on the spot instead of waiting for the sub-agent to finish or fail. Earlier re-orchestration methods fixed each sub-agent's configuration until its lifecycle ended, delaying adaptation to dynamic environments. Across GAIA, GAIA2, and SWE-Bench Verified, task success improves by an average of 9.1% over the strongest baseline, and the companion OPOD turns that feedback into process supervision, raising small-orchestrator success by an average of 10.0% over training baselines.
Beijing Academy of Artificial Intelligence Renmin University of China
Xueyang Feng（First author）
POrchestra On-Policy Orchestration Distillation Agentic orchestration GAIA2
arXiv Oct 2, 2026
ActiveSaddler co-evolves training scenarios with the harness, raising Pass@1 by 4.4 and 7.5 points on GAIA2 and Terminal-Bench 2.0
The work formulates training-scenario selection in offline harness optimization as an automated curriculum-learning problem and introduces ActiveSaddler, which models the curriculum as a non-stationary bandit with failure-pattern arms, estimates each arm's potential learning progress, and adaptively balances revisiting known weaknesses against exploring unseen scenarios, improving test Pass@1 by 4.4 and 7.5 percentage points over the same harness optimizer with a scenario order fixed before optimization on GAIA2 and Terminal-Bench 2.0 respectively.
The work formulates training-scenario selection in offline harness optimization as an automated curriculum-learning problem and introduces ActiveSaddler, which models the curriculum as a non-stationary bandit with failure-pattern arms, estimates each arm's potential learning progress, and adaptively balances revisiting known weaknesses against exploring unseen scenarios, improving test Pass@1 by 4.4 and 7.5 percentage points over the same harness optimizer with a scenario order fixed before optimization on GAIA2 and Terminal-Bench 2.0 respectively.
Microsoft
ActiveSaddler Curriculum Learning Agent Harness Non-Stationary Bandits GAIA2

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