Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive. Naively persisting entire conversation histories is token-inefficient and counterproductive: irrelevant context degrades generation quality. We introduce shared selective persistent memory, an architecture that identifies and retains four categories of reusable context (task specifications, data schemas, tool configurations, and output constraints) while discarding session-specific reasoning traces. Crucially, this memory is shared: workspaces encapsulating selective memory can be transferred across users with role-based access control, enabling collaborative reuse without redundant specification. We implement it in a deployed collaborative workspace platform where LLM agents produce, edit, and maintain git-versioned artifacts (dashboards, reports, and data-driven documents) from heterogeneous sources (CSV, SQL, REST APIs, and MCP servers). A complementary zero-token data refresh mechanism decouples generated programs from runtime data, enabling artifact reuse without re-invocation. Across three enterprise scenarios, shared selective persistent memory achieves 96% task completion (vs. 79% without memory and 71% with full history). Zero-token refresh eliminates LLM re-invocation for recurring updates (14x task-time reduction), while summary-driven generation cuts per-invocation token cost by 97x versus raw data injection. A replication on four public datasets confirms generalizability, with zero-token refresh succeeding in 12/12 trials. Notably, naive full-history persistence actively degrades completion by biasing the agent with stale traces, while selective memory outperforms both extremes.
The study turns the filesystem default from an assumption into a design space for agent memory, and turns the model is not the only lever over a store's shape: changing the tool set alone reshapes the store as strongly as swapping the model.
This work presents PLACEMEM as a systems position on lifelong-agent memory, instantiated by an executable control-plane prototype that demonstrates correction-aware control-plane behavior today and a concrete roadmap for replay-aware serving integration in future lifelong-agent systems.
This work introduces the Super Library Agent problem, where an agent sequentially generates a portfolio of N related applications while maintaining a shared Super Library of reusable cross-application components, and addresses candidate-guided extraction over code chunk summaries, pre-extraction codebase consolidation, and context-aware migration using extraction traces and call-graph information.
Daegyu Sung, Yukyeong Lee, Geon Park et al.· 0 citations
Maintaining open-source repositories demands continuous attention to issue triage, code review, build monitoring, and community support—tasks that overwhelm individual maintainers when existing automation tools operate in isolation without shared context. Static analyzers check code quality, CI/CD systems run automated tests, and simple bots handle basic labeling, yet none of these tools share information with each other, leaving maintainers as the sole integration point between disconnected systems. We present Repogent, a multi-agent system where four specialized AI agents—Issue Manager, Pull Request Reviewer, CI/CD Maintainer, and Community Assistant—collaborate through event-driven coordination and persistent cross-task memory. Our GitHub webhook-driven architecture processes events through a priority queue, routes them to specialized agents via intelligent orchestration, and stores decisions in a persistent context layer that enables cross-task reasoning. Events are assigned CRITICAL, HIGH, or NORMAL priority, while a semantic memory module based on sentence-transformer embeddings supports code-level search, and all agents share a common LLM service using Qwen 3 32B accessed through the Groq API. Evaluation on a dataset of 150 issues, 100 pull requests, 80 CI/CD workflow executions, and 120 community queries collected from five active open-source Python repositories demonstrates that Repogent achieves 93.3% accuracy in issue classification, 69.0% combined review coverage for pull requests, 86.7% accuracy in CI/CD failure categorization with a macro F1-score of 0.884, and an average quality score of 3.96 out of 5 for community responses. These results establish a foundation for sustainable open-source maintenance through context-aware, multi-agent workflow automation. Furthermore, the proposed framework demonstrates that multi-agent orchestration with persistent shared memory can serve as an effective and scalable solution for automating repository maintenance activities, improving coordination across heterogeneous development workflows, reducing maintainer overhead, and enabling more consistent decision-making across repository events.
Venkata Satya Santhi Somisetti, Vijaya Bhaskar Santhuluri, Sai Teja Pathivada et al.· International Journal of Lat...· 0 citations
LLM agents increasingly take on long-running tasks whose history grows far beyond a single model context window. Existing approaches compress earlier interactions or extract selected information into fixed memory representations, committing to what to preserve before future needs are known. We present Scroll, a context manager that treats each agent session as an executable Session Environment. The environment is backed by an append-only Event Log and a sandboxed, persistent Python kernel. The kernel maintains a typed namespace across model calls, allowing tool outputs, retrieved history, and derived state to be bound to variables rather than serialized into the prompt at each call. Model-written code searches, materializes, and transforms session state through exec; only explicitly printed projections enter the model's working view for the next call. Context management thus becomes a programming task that inherits the improving coding abilities of LLMs, while the Event Log preserves lossless historical ground truth. As the working view approaches its budget, stale spans are evicted but remain recoverable: an eviction index keeps compact landmarks tied to exact Event Log addresses, so that the agent navigates directly to evicted regions instead of searching the full log. With Qwen3.8-Max as the backbone, Scroll achieves 94.8% on LongMemEval_S; 73.1% on BEAM_10M, surpassing the best published memory system by 5.1 points; and 86.7% on LOCA_256K, exceeding the best published long-horizon agent by 37.4 points.
This work proposes Graph-Regularized Agentic Context Evolution (GRACE), which maintains the persistent instruction component as a typed semantic graph and validates proposed updates within the local typed neighborhoods of modified nodes.
Daniel Hsu, Lu-Ke Lu· 1 citation
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