Models improving task handoffs by remembering prior work better
Benchmarking Task Takeover Across Agent Handoffs
Computation and Language
Summary
When multiple AI models work together on long tasks, the handoff from one model to another can be tricky. The new model must understand and continue what the previous model did, which isn’t just about remembering facts but also about keeping promises and plans. The authors created a benchmark called HSC-Bench with over 1,000 tasks to test how well models take over in these situations. They found that giving models structured information from previous work helps them recover important details and continue the task better, but the best model depends on what kind of handoff they face. This helps people pick the right model for continuing tasks based on what was done before.
What this means in practice
- •For software development teams: Improve AI assistants that share work on coding projects by ensuring smooth transition and continuation of tasks between different models.
- •For customer service teams: Enable chatbots or AI agents to better pick up ongoing conversations and service tasks handed off from other agents to provide consistent support.
Authors
Wenyuan Li, Guang Li, Dongyuan Li, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
Abstract
As agents collaborate on longer tasks, responsibility may pass between models before the work is complete. The receiving model must build on prior decisions, observations, and actions while addressing the remaining requirements. Evaluating this transition requires connecting the information preserved at handoff to how the receiver continues the work, a relationship that task-success and memory-retention scores alone do not capture. We introduce the Handoff State Continuity Benchmark (HSC-Bench), comprising 1,039 tasks that evaluate two complementary aspects of task takeover: recovering relevant repository evidence and continuing from established conversational, tool, and file-work state. Comparisons across six receiving models show that structured handoff state improves repository evidence recovery for every model and continuation performance for most models. The strongest receivers differ between the two tracks, showing that evidence-recovery rankings alone do not characterize performance when continuing from inherited state. By combining these comparisons with checks of subsequent responses and actions, HSC-Bench evaluates both access to prior evidence and adherence to established commitments and completed work. This provides a basis for selecting receiving models and assessing handoff representations according to the work that must be carried forward.