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In this work, we present ZGCM-1, a fully open 7B dense foundation model trained from scratch with extreme data, system, and algorithmic efficiency. ZGCM-1 is founded on a core premise: compact models cannot passively memorize the open web, but can overcome parametric capacity limits by coupling deliberate internal thin...
OrchBench is established as an efficient and interpretable benchmark for comparing and diagnosing multi-agent orchestration plans, finding that preserving task-critical information is more important than simply increasing the number of agents, and the benefits of parallelism diminish as coordination failures accumulate...
This work proposes HiGram, an evolving hierarchical graph memory framework with path-level localization and rewriting, which organizes the memory into coarse-to-fine architecture composed of upper-level nodes and MemoryUnits, thereby reducing the amount of irrelevant information during retrieval.
AgentRewind is presented, a runtime recovery framework that records aligned checkpoints of the agent context and controlled environment, allowing agents to return to an earlier state and resume execution with information from previous attempts, improving task success rate and average checklist progress over the compare...
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