CoMATE: Consolidating Memory Across Trajectories for Long-Horizon Interactive Exploration on ARC-AGI-3

As agents take on increasingly complex and demanding tasks, reliable long-horizon performance is becoming essential but remains difficult to achieve. This difficulty is evident in long-horizon interactive exploration, where ARC-AGI-3 remains very hard for frontier models in official direct-play evaluations. Parallel exploration at test time can improve performance by generating complementary experience, but shared notes alone do not establish which claims are supported, when they apply, or how conflicting accounts should be reconciled. We introduce CoMATE, an evidence-grounded memory consolidation framework. A fresh-context model examines agents' written memories and observer-view trajectories, producing collective entries with supporting evidence, epistemic status, and applicability conditions. Agents query this knowledge during subsequent exploration. Because results on ARC-AGI-3 depend heavily on the agent harness, we compare memory treatments under a shared parallel search protocol. Level-wise branching, checkpoint handoff, and retries are common to all conditions, so the no-consolidation baseline also retains a full parallel group at each new level. Under this protocol, CoMATE raises RHAE from 55.1 to 80.1 with six GPT-5.5 agents on the 25 public games, completing 22 games and 171 of 183 levels; nine agents reach 80.9 RHAE and 173 levels. With GPT-5.6 Sol, it raises RHAE from 87.6 to 97.0 and completes all 25 games and 183 levels. Action- and token-budget curves account for both gameplay and memory-related inference, while automated and human audits separately assess memory correctness and citation support. These results support cross-trajectory consolidation on ARC-AGI-3 and its potential for broader long-horizon interactive exploration.