Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents
Yaoqi Chen, Haibin Lai, Yuru Feng, Chuyu Han, Qianxi Zhang, Baotong Lu, Menghao Li, Xinjiang Wang
Abstract
LLM-based agents increasingly tackle long-horizon tasks with interdependent decisions, where each action reshapes future constraints and intermediate errors can cascade. Existing RAG and agent memory systems organize histories by semantic similarity, retrieving content-relevant entries at decision time. We argue that this design mismatches execution-state dependencies: it fragments decision trajectories and mixes valid and erroneous traces, hindering coherent state reconstruction and error isolation. We propose MAGE (Memory as Agent-Guided Exploration), an active execution-state manager that stores interactions in a hierarchical state tree. The agent derives its state from the active root-to-current path, combining subgoal summaries, recent traces, and hints from prior branches. Four coupled operations maintain the tree: Grow records new traces, Compress summarizes completed subgoals, Maintain validates summaries, and Revise restores a target boundary and resumes on a new branch. This design bounds context growth while preserving state integrity and isolating flawed segments from the active path. Experiments on MemoryArena show that MAGE improves the average task success rate by 7.8--20.4 pp over baselines, while reducing token consumption by 55.1%.
AI Impact Assessments
(1 models)Scientific Impact Assessment: MAGE — Memory as Execution State Management for Long-Horizon Agents
1. Core Contribution
MAGE reframes agent memory from a similarity-driven retrieval system to an execution-state manager built around a two-layer hierarchical state tree. The bottom layer records raw action-observation pairs; the top layer stores compressed subgoal summaries. The agent's current state is derived from the active root-to-current path rather than assembled from semantically retrieved entries. Four coupled operations—Grow, Compress, Maintain, and Revise—form a closed-loop cycle that extends traces, bounds context, validates summaries, and isolates errors via branching.
The key insight is well-articulated: existing RAG and agent memory systems organize information by semantic similarity, which fragments execution-state dependencies and conflates valid and erroneous traces. MAGE addresses both problems structurally. This is a meaningful conceptual shift—from "what is relevant?" to "what is the current execution state?"—and the paper makes a convincing case that this distinction matters for interdependent long-horizon tasks.
2. Methodological Rigor
Strengths in design: The four operations are cleanly defined with pseudocode (Algorithm 1), and the node structure (Table 2) is precise. The cognitive science motivation (prefrontal cortex for hierarchical chunking, anterior cingulate cortex for error monitoring, executive control for selective correction) is well-mapped to operations in Table 1, though this serves more as inspiration than formal grounding.
Experimental evaluation: The paper evaluates on MemoryArena across four domains (Shopping, Travel Planning, Web Search, Formal Reasoning) against seven baselines spanning long-context, RAG, and memory paradigms. The improvements are substantial: 7.8–20.4 pp in task success rate over baselines with 55.1% token reduction versus long-context. The ablation study (Table 4) validates each component's contribution, showing that removing Compress, Maintain, or Revise each degrades performance, while even ablated variants remain competitive with baselines—supporting the claim that the tree-structured path representation itself provides value independent of the full operation loop.
Concerns about rigor:
3. Potential Impact
Direct applications: The framework is immediately applicable to any LLM agent performing sequential tasks with state dependencies: software engineering agents, autonomous web agents, planning systems, and multi-step scientific workflows. The tree-based state management with branching revision is particularly relevant for agents that need to recover from errors without losing valid progress—a common real-world requirement.
Broader influence: The conceptual reframing from "memory as retrieval" to "memory as execution state" could influence how the community designs agent architectures. The observation that existing memory systems often underperform simple long-context approaches (Figure 1) is a valuable empirical finding that challenges current assumptions. If validated more broadly, this could redirect research effort from improving semantic retrieval toward structural state management.
Token efficiency: The 55.1% token reduction is practically significant given the cost of LLM inference at scale. The bounded context growth (Figure 4) is an important property for deployment.
4. Timeliness & Relevance
The paper addresses a genuine and timely bottleneck. As LLM agents are deployed for increasingly complex tasks (coding, research, planning), the gap between memory system design and execution-state requirements becomes critical. The 2025–2026 explosion of agent memory papers (the related work section cites ~15 concurrent works) signals high community interest. MAGE's contribution is well-differentiated from this concurrent work by its focus on structural state management rather than improved retrieval.
The MemoryArena benchmark itself (He et al., 2026) appears relatively new, and MAGE is among the first to achieve strong results on it, establishing early baselines for execution-state-aware approaches.
5. Strengths & Limitations
Key strengths:
Notable limitations:
Additional Observations
The case studies in Appendix D are particularly effective at illustrating the failure modes of baselines and how MAGE avoids them. The paper is well-written with clear figures. The approach is relatively straightforward to implement, enhancing reproducibility potential. However, the reliance on the agent's own ability to identify subgoals and errors introduces a dependency on the backbone LLM's metacognitive capabilities that may not scale uniformly across models or task types.
Generated Jun 5, 2026
Comparison History (20)
Paper 1 (MAGE) addresses a fundamental and broadly applicable problem in LLM-based agent systems—memory management for long-horizon tasks—with a novel hierarchical state tree approach that shows significant empirical improvements (7.8-20.4pp success rate gains, 55% token reduction). This has wide applicability across all agent-based AI systems. Paper 2 makes a solid theoretical contribution to LLM cascading with regret bounds, but addresses a narrower problem (API selection/routing) with primarily theoretical results. Paper 1's practical impact on the rapidly growing agent ecosystem gives it higher potential influence.
Paper 1 offers a more novel, broadly applicable paradigm: unifying task execution with runtime self-reconfiguration via a standardized tool interface, plus a concrete training recipe (CAT) to internalize adaptation. This targets a core limitation of agentic systems (static configurations) and could influence agent architectures, tooling ecosystems, and RLHF/RL training methods across domains. Reported gains are large and suggest strong real-world utility. Paper 2 is rigorous and practical for long-horizon memory/state management, but is a more scoped systems contribution with narrower conceptual breadth.
Paper 2 (MAGE) introduces a novel architectural paradigm for agent memory management that addresses a fundamental limitation in current LLM-based agents. Its hierarchical state tree approach with concrete operations (Grow, Compress, Maintain, Revise) offers a reusable framework applicable across diverse long-horizon tasks. The strong empirical gains (7.8-20.4 pp improvement with 55% token reduction) demonstrate both effectiveness and efficiency. Paper 1 (ANCHOR) addresses an important but narrower problem of safety in self-evolving systems, with findings (diminishing returns of supervision) that, while useful, are less architecturally innovative and have narrower applicability.
Paper 2 introduces a broadly applicable, conceptually novel reframing of agent memory as execution-state management (state tree with explicit branching, validation, and revision), addressing a central bottleneck for long-horizon agents across domains. Its claimed gains are substantial (7.8–20.4 pp success, 55% token reduction) and directly relevant to current agent research. Paper 1 targets an important but narrower enterprise/regulatory assurance niche and shows mixed statistical robustness after correction. Overall, Paper 2 is more likely to influence core agent architectures and be adopted widely.
Paper 2 (MAGE) has higher likely scientific impact due to broader applicability and timeliness: execution-state management for long-horizon agents is a central bottleneck across many agentic systems. The state-tree framework (grow/compress/maintain/revise) reframes “memory” from semantic retrieval to trajectory-consistent state reconstruction, with clear real-world benefits (higher success rates and large token reductions) and easy integration into diverse agent stacks. Paper 1 is novel and practical for skill authoring/governance, but is more specific (YAML/graph skill spec) and closer to systems/engineering impact than a generalizable agent cognition/memory paradigm.
Paper 2 addresses a more fundamental and broadly applicable challenge in LLM-based agents—memory management for long-horizon tasks—which impacts a wider range of applications beyond a single domain. Its hierarchical state tree approach with principled operations (Grow, Compress, Maintain, Revise) offers a novel architectural contribution applicable across diverse agent settings. It also demonstrates strong improvements in both performance and efficiency (55% token reduction). Paper 1, while technically strong with impressive results in hardware verification, targets a narrower domain (testbench generation) with more limited cross-field impact.
Paper 1 has higher potential impact: it introduces a concrete, novel memory/state-management architecture (hierarchical execution-state tree) addressing a timely bottleneck in long-horizon LLM agents, with clear methodological contributions (defined operations, bounded context growth) and quantitative gains on a benchmark plus token savings—supporting rigor and real-world applicability. Its ideas can generalize across agentic systems, tool use, and planning. Paper 2 is primarily a historical/epistemological synthesis; valuable conceptually but less likely to drive new empirical methods or broad downstream technical adoption.
Paper 2 introduces a novel, implementable memory architecture (MAGE) targeting a key bottleneck for LLM agents: long-horizon execution-state management. It offers clear methodological contributions (hierarchical state tree; defined operations; quantitative evaluation with sizable gains and token savings) and is immediately applicable to agentic systems, potentially influencing tooling, benchmarks, and deployments across robotics, software agents, and automation. Paper 1 is timely and valuable for governance and ethics, but as a scoping review it is less methodologically innovative and its impact is more indirect (synthesizing fragmented literature rather than enabling new capabilities).
Paper 2 introduces a broadly applicable conceptual reframing of agent memory—from semantic retrieval to execution-state management—with a concrete, structured mechanism (hierarchical state tree and explicit operations) that targets long-horizon coherence and error isolation. This is timely for general LLM-agent research and can impact many domains (software agents, robotics, workflows) beyond a single application. Paper 1 is strong and practically valuable for autonomous driving efficiency, but is more domain-specific and centers on incremental advances in distillation mechanics. Overall breadth and cross-field relevance favor Paper 2.
Paper 1 addresses a fundamental limitation in the rapidly advancing field of LLM agents by introducing a novel state-tree memory architecture, fundamentally challenging existing RAG-based paradigms. Its methodological innovation and potential to influence general AI agent design give it broader scientific and technological implications. Paper 2, while practically valuable, primarily applies existing deep reinforcement learning techniques to a specific domain (pharmaceutical supply chains), resulting in a narrower scope of scientific impact and less methodological novelty.
Paper 2 introduces a fundamental paradigm shift in federated learning by proposing typed federated artifacts, enabling cross-architecture collaboration and formal privacy guarantees without weight sharing. This solves a major bottleneck in decentralized AI for heterogeneous LLMs. While Paper 1 offers a strong, practical improvement for agent memory management, Paper 2's theoretical innovation and ability to bridge distinct model families offer broader, transformative impacts across the machine learning and privacy communities.
Paper 1 proposes a fundamental architectural innovation for LLM agents by reframing memory as execution-state management rather than semantic retrieval. This approach directly addresses critical bottlenecks in long-horizon tasks, such as error cascades and context bloat, offering broad, practical applications across agentic workflows. While Paper 2 introduces a valuable benchmark for planning, Paper 1 provides a foundational methodological advancement with significant potential to improve the efficiency and reliability of real-world AI systems.
Paper 2 addresses the critical and broadly impactful problem of LLM deployment efficiency through ultra-low-bit quantization. Its dramatic improvements (e.g., 55.8→6.74 perplexity on LLaMA-3-8B at ~1 bit, 50% memory reduction, 1.5x speedup) represent a significant practical advance with immediate real-world applicability to democratizing LLM inference. The graph-guided approach to determining quantization groups is methodologically novel. Paper 1 proposes an interesting memory management framework for agents but is evaluated on a single benchmark (MemoryArena) with more incremental gains, and the agent memory space is less mature with fewer downstream applications currently.
Paper 1 introduces a novel memory/state-management paradigm (execution-state tree with branch isolation, revision, and validated summaries) that directly targets a core bottleneck in long-horizon LLM agents, and reports sizable empirical gains (success rate +7.8–20.4 pp, tokens −55%). Its approach is actionable for building more reliable, scalable agents and could influence agent architecture design broadly. Paper 2’s entropy metrics are useful and timely but are primarily an evaluation add-on, likely incremental and more sensitive to metric validity/adoption. Overall, Paper 1 has higher innovation and systems-level impact potential.
Paper 2 (MAGE) introduces a novel conceptual framework for agent memory management that addresses a fundamental limitation in current LLM-based agents. Its contribution—reframing memory as execution-state management rather than semantic retrieval—is a generalizable architectural insight applicable across many agent systems. It offers clear methodological rigor with well-defined operations and strong empirical gains. Paper 1 (Interfaze) is primarily an engineering/product contribution combining existing techniques (CNNs, transformers, adapters) with benchmark results against commercial models, but offers less conceptual novelty and is harder to reproduce or build upon scientifically.
Paper 1 targets a timely, high-stakes problem—trust and security failures from long-term memory in personal agents—and frames memory retrieval as a control boundary. Its contribution (MemGate) is lightweight, deployable across multiple existing memory frameworks and LLM backbones, and evaluated in a real-world agent environment with tool use, suggesting broad practical adoption and cross-field impact (LLM security, agent alignment, RAG). Paper 2 offers a strong systems idea for long-horizon state management, but is more task/performance oriented and appears evaluated in a narrower benchmark setting.
Paper 2 addresses a more broadly impactful problem—memory management for long-horizon LLM agents—which is relevant across many application domains (robotics, planning, software engineering, etc.). Its hierarchical state tree with principled operations (Grow, Compress, Maintain, Revise) offers a novel architectural contribution applicable beyond any single task. The strong empirical gains (7.8–20.4 pp improvement with 55.1% token reduction) demonstrate both effectiveness and efficiency. Paper 1 solves a narrower problem (SMILES validity recovery) within a specific chemistry subdomain, limiting its breadth of impact despite solid methodology.
Paper 2 likely has higher impact: it tackles a broad, widely-relevant problem (multilingual fine-tuning interference) with a generally applicable optimization framework. The localized gradient conflict resolution is novel, scalable for distributed training, and comes with stronger theoretical guarantees (Refined Pareto Stationarity), increasing methodological rigor and credibility. Its applicability spans many LLMs, languages, and downstream tasks, giving broad cross-field and industry relevance. Paper 1 is innovative for agent memory/state management and shows strong efficiency gains, but its impact is more niche to long-horizon agent architectures and specific benchmarks.
Paper 1 proposes a fundamental architectural shift in how agent memory is managed (execution state trees vs. semantic similarity), directly addressing critical bottlenecks in long-horizon tasks like context limits and error cascading. Its substantial performance gains and token reductions offer broad, practical applicability. Paper 2, while important for safety evaluation, is primarily an empirical measurement study. Paper 1's innovative methodology and broader utility in advancing agent capabilities give it higher potential scientific impact.
Paper 2 has higher likely impact: it proposes a broadly applicable, model-agnostic memory/state management paradigm for long-horizon LLM agents, addressing a timely bottleneck (state coherence, error isolation, and context cost) across many domains (tool use, workflows, coding, planning). The hierarchical state-tree with explicit operations is a clear methodological contribution with strong efficiency gains and sizable success-rate improvements on a relevant benchmark. Paper 1 is novel and valuable for embodied assembly with new benchmarking, but its impact is narrower (brick-like assembly) and current gains still leave low end-to-end success.