Public articles linked to the same research event.
arXiv The authors propose MemCo, a memory-centric collaboration framework in which each agent keeps an environment-specific local memory graph while evidence-aggregated, Wilson-lower-bound-filtered transferable workflows are promoted to a shared global memory, and at decision time local and global memories are retrieved and grounded by current state, task phase, and goal; across ALFWorld, PDDL, FEVER, and ScienceWorld with multiple LLM backbones, MemCo attains the highest accuracy in 10 of 12 evaluation settings and the fewest interaction steps in 9, e.g., on Qwen3-4B it improves ALFWorld held-out layout accuracy by 43.47% and reduces steps by 32.10% relative to the runner-up baseline.
The authors propose MemCo, a memory-centric collaboration framework in which each agent keeps an environment-specific local memory graph while evidence-aggregated, Wilson-lower-bound-filtered transferable workflows are promoted to a shared global memory, and at decision time local and global memories are retrieved and grounded by current state, task phase, and goal; across ALFWorld, PDDL, FEVER, and ScienceWorld with multiple LLM backbones, MemCo attains the highest accuracy in 10 of 12 evaluation settings and the fewest interaction steps in 9, e.g., on Qwen3-4B it improves ALFWorld held-out layout accuracy by 43.47% and reduces steps by 32.10% relative to the runner-up baseline.
The authors propose MemCo, a memory-centric collaboration framework in which each agent keeps an environment-specific local memory graph while evidence-aggregated, Wilson-lower-bound-filtered transferable workflows are promoted to a shared global memory, and at decision time local and global memories are retrieved and grounded by current state, task phase, and goal; across ALFWorld, PDDL, FEVER, and ScienceWorld with multiple LLM backbones, MemCo attains the highest accuracy in 10 of 12 evaluation settings and the fewest interaction steps in 9, e.g., on Qwen3-4B it improves ALFWorld held-out layout accuracy by 43.47% and reduces steps by 32.10% relative to the runner-up baseline.
The authors propose MemCo, a memory-centric collaboration framework in which each agent keeps an environment-specific local memory graph while evidence-aggregated, Wilson-lower-bound-filtered transferable workflows are promoted to a shared global memory, and at decision time local and global memories are retrieved and grounded by current state, task phase, and goal; across ALFWorld, PDDL, FEVER, and ScienceWorld with multiple LLM backbones, MemCo attains the highest accuracy in 10 of 12 evaluation settings and the fewest interaction steps in 9, e.g., on Qwen3-4B it improves ALFWorld held-out layout accuracy by 43.47% and reduces steps by 32.10% relative to the runner-up baseline.