LLM-based agents build memory from past problem-solving: successful strategies, failed approaches, useful intermediate steps. Each agent's memory reflects its own model's reasoning style — the way it decomposes problems, the errors it tends to make, the shortcuts it prefers.
Sharing this memory across different agents should help. But Chang et al. (arXiv:2603.23234) found that direct memory sharing between heterogeneous models degrades performance. One model's successful reasoning trajectory contains model-specific artifacts — prompt formatting preferences, chain-of-thought styles, error recovery patterns — that confuse a different model. The useful signal (task-level reasoning principles) is entangled with noise (model-specific biases).
MemCollab solves this by contrasting trajectories. Give two different models the same problem. Both solve it. Their solution paths differ in model-specific ways but share task-level structure. The contrastive distillation isolates what's common — the problem-relevant reasoning — and discards what's specific to each model's style.
The resulting agent-independent memory improves both mathematical reasoning and code generation across diverse models. The memory isn't personalized to any model. It's personalized to the task.
The through-claim: useful reasoning is model-independent, but the trajectories that contain it are model-dependent. Sharing raw trajectories shares the noise along with the signal. Contrastive distillation — requiring the same task solved by different reasoners — separates the two. The reasoning survives the translation; the style doesn't need to.