As large language models and multi-agent systems (MAS) grow in complexity and longevity, a critical challenge emerges: the uncontrolled expansion of memory. Over time, agents accumulate vast amounts of interaction history and contextual data. While this information can be valuable, poor management turns it into a noisy, disorganized archive—akin to human "fragmented memory"—leading to reduced reasoning accuracy, increased latency, and soaring computational costs. Traditional approaches, such as vector-based retrieval and hierarchical storage, work well initially but struggle in long-term, multi-task environments. They face three key issues: noise accumulation, where high- and low-value data mix, drowning out useful insights; uncontrolled memory bloat, which inflates context length and degrades performance; and limited cross-domain generalization, where knowledge from one task fails to transfer effectively to another. To address these problems, the Gradient team introduced SEDM (Scalable, Self-Evolving, Distributed Memory), a novel framework that transforms memory from a passive storage system into an active, self-optimizing, and auditable component. SEDM enables memory to evolve over time, adapt to new tasks, and maintain long-term efficiency. The framework is built on three core innovations. First, Verifiable Write Admission ensures that every new memory entry undergoes rigorous validation before being stored. Each candidate memory is wrapped in a Self-Contained Execution Context (SCEC), which allows for environment-agnostic replay and offline verification. The system records cryptographic hashes, versioning, and unique fingerprints, creating a transparent, auditable evidence chain. A/B testing is then conducted: the model’s performance is measured with and without the new memory, evaluating its impact on accuracy, response latency, and token usage. Only entries that yield a net positive score are admitted to the memory store and assigned an initial weight. Second, the Self-Scheduling Controller prevents unbounded memory growth by dynamically managing retention. It uses a combination of utility weight and semantic similarity to determine which memories to prioritize, update, or retire. Memories that repeatedly prove ineffective are automatically downgraded, while high-utility ones are promoted and, over time, abstracted into more general knowledge patterns. Third, Cross-Domain Knowledge Diffusion allows SEDM to transfer insights across different task domains. For example, the team found that knowledge distilled from the FEVER fact-checking benchmark significantly improved performance on HotpotQA, a complex multi-hop reasoning task. This demonstrates SEDM’s ability to extract and generalize useful patterns, enabling transfer learning across diverse applications. Overall, SEDM transforms memory from a static repository into a self-evolving, evidence-based system. It improves reasoning accuracy while effectively curbing token usage and response time, enhancing the long-term sustainability of AI systems. The research has received strong feedback from reviewers. One noted, “This work redefines memory as a verifiable, evolving component—this is a fundamentally new perspective.” Another praised the A/B validation mechanism, calling it “a critical step toward transparent, auditable AI.” A third highlighted the cross-task transfer results as “impressive and promising for real-world deployment.” Looking ahead, the Gradient team envisions SEDM’s application in three key areas over the next 3–5 years. First, personal and enterprise AI assistants can leverage SEDM to maintain long-term, context-aware relationships with users—preserving preferences and key facts without bloating context or increasing latency. Second, in high-stakes, long-context domains such as enterprise knowledge bases, code collaboration tools (Copilot-style systems), and clinical or research decision support, SEDM ensures accurate, efficient, and interpretable reasoning by filtering and scheduling only high-value memories. Third, in scientific research and knowledge management, SEDM can serve as a “research memory” that automatically identifies and retains high-impact findings, prevents redundant work, and enables cross-disciplinary knowledge transfer. The idea originated when the team observed how quickly memory bloat occurred during testing. One member, Haoran, proposed a radical idea: instead of dictating what the system should remember, let it learn to “remember and forget” based on evidence. This shift in philosophy led to the development of verifiable write admission and the self-scheduling controller. The team now acts more as observers—letting the system learn what to retain through objective validation. The team, spread across multiple time zones, often works late into the night, driven by a shared passion. Bill Shi, a key contributor, reflects on the experience as both challenging and deeply rewarding. Future plans include: larger-scale real-world testing in enterprise settings and complex code development systems; integration with planning and reasoning models, so memory actively informs decision-making; and open-sourcing the framework to build a collaborative ecosystem. At its core, SEDM is not just a technical innovation—it represents a shift in philosophy. In an era of ever-larger models and increasing compute demands, sustainability is critical. Without intelligent memory management, even the most powerful systems risk becoming inefficient or unstable. SEDM is a step toward building AI that can work effectively, reliably, and responsibly over time. The research was led by the Gradient team, with core contributions from Haoran, Jiacong, and Zhangke in system design, experimentation, data processing, and toolchain development.