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REVIEW 5 major objections 6 minor 3 cited by

Explicit memory control in a hierarchical coordinator improves long-horizon multi-agent performance.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · deepseek-v4-flash

2026-08-03 11:30 UTC pith:V4X67XTQ

load-bearing objection A sensible memory-management framework for centralized multi-agent systems, but the headline SOTA claim rests on a nonstandard evaluation protocol that needs fixing before the numbers can be trusted. the 5 major comments →

arxiv 2601.05890 v2 pith:V4X67XTQ submitted 2026-01-09 cs.AI

StackPlanner: A Centralized Hierarchical Multi-Agent System with Task-Experience Memory Management

Ruizhe Zhang , Xinke Jiang , Zhibang Yang , Zhixin Zhang , Jiaran Gao , Yuzhen Xiao , Tao Feng , Yue Fang
show 7 more authors
Yuxuan Liu Ruiqing Li Hongbin Lai Huheng Huang Xu Chu Junfeng Zhao Yasha Wang
This is my paper
classification cs.AI
keywords multi-agent systemscentralized coordinationtask memoryexperience memoryreinforcement learningGRPOlong-horizon reasoningmemory management
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that central multi-agent coordinators fail on long tasks because memory is treated as a passive append-only log, and that making memory an explicit control target fixes it. StackPlanner lets a coordinator actively condense and prune a task-memory stack, and gives it a structured experience store of user profiles, facts, and reusable procedures retrieved before planning. The coordinator is trained with reinforcement learning to decide when to plan, delegate, or revise memory. The reported results show gains over existing approaches on multi-hop QA and agentic benchmarks, with ablations indicating both memory components matter.

Core claim

On its own terms, the discovery is that a coordinator's stability over long horizons is a memory-management problem, not just a planning problem. The system's central coordinator acts through PLAN, DELEGATE, and REVISE actions; REVISE performs condensation (summarizing a contiguous memory segment) or pruning (dropping unproductive segments while recording failure causes). A separate experience memory—split into user profiles, semantic facts, and procedural SOPs—is retrieved to reduce cold-start. The action sequence is optimized with GRPO. The paper reports state-of-the-art F1 on four benchmarks and shows that removing task memory or experience memory degrades scores, with the largest drop wh

What carries the argument

The key mechanism is the task-memory stack, a sequential store that only the coordinator can modify via REVISE. Condensation pops a contiguous segment and pushes back a summary; pruning removes a selected segment and retains a short failure note. This turns memory bloat and error propagation into controllable choices. The second pillar is experience memory—separate from task memory—organized into user profiles, semantic memory, and procedural SOPs, with an Experience Search agent injecting retrieved guidance. Training uses GRPO at the action level, so the coordinator learns when to plan, delegate, or revise memory.

Load-bearing premise

The headline 'state-of-the-art' claim rests on comparing all methods under the same F1 protocol on a 127-question text-only GAIA subset, even though GAIA's official metric is exact-match accuracy; if that protocol is not comparable, the SOTA claim is unsupported.

What would settle it

Run the same coordinator on the official GAIA validation set using exact-match accuracy and compare against published GAIA leaderboard entries. If it does not at least match the strongest published numbers, the state-of-the-art claim fails under the benchmark's intended metric. Alternatively, re-run ablations with a fixed, non-learned memory policy (e.g., always summarize after n steps) to test whether GRPO-trained REVISE beats the heuristic.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If the claim holds, long-horizon agent pipelines should treat memory as part of the action space: coordinators that can trim their own history should degrade more slowly as steps accumulate.
  • The ablations imply that forgetting (pruning) and compression (condensation) are net positives despite information loss, challenging the default practice of appending everything to context.
  • Storing cross-task experience as structured, retrievable procedures and facts should reduce cold-start failures on new tasks of similar type.
  • Learning memory operations with reinforcement learning rather than fixed rules suggests the optimal memory policy can be shaped by task reward, which may transfer to other agentic settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The GAIA comparison uses F1 on a 127-question text-only subset, while GAIA's official evaluation is exact-match accuracy; the state-of-the-art claim should be read as applying to this nonstandard protocol unless confirmed on the official metric.
  • The same stack-based revision mechanism could be adapted to single-agent long-context reasoning, where pruning and condensation may mitigate lost-in-the-middle effects without any multi-agent scaffolding.
  • A natural stress test is to replace the learned REVISE policy with a simple heuristic—e.g., summarize every n steps or prune after each failed delegation—to see how much of the gain comes from the learned memory policy versus the mere existence of memory controls.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes StackPlanner, a hierarchical centralized multi-agent framework in which a coordinator performs high-level planning and delegation, controls a task-memory stack through REVISE operations (condensation and pruning), and consults a structured experience memory (user profiles, semantic memory, procedural SOPs). The coordinator is trained with GRPO, with the policy conditioned on memory and retrieval tools. Experiments are reported on 2WikiMultiHopQA, MuSiQue, GAIA, and FRAMES using Qwen2.5-3B/7B, claiming state-of-the-art performance in F1 on all four benchmarks, with ablations showing that removing task memory or experience memory degrades performance. The paper also provides two qualitative case studies and a discussion of limitations.

Significance. If the empirical claims were fully supported, the paper would make a useful contribution to centralized LLM multi-agent design: explicit, learnable memory control is a plausible mechanism for reducing context bloat and error accumulation in long-horizon coordination, and the component-level ablations are directionally consistent with that view. However, the headline 'state-of-the-art across all benchmarks' is not supported on the evidence as presented, primarily because GAIA is evaluated with a nonstandard F1 metric on a 127-question text-only subset rather than the official exact-match protocol, and because the comparison protocol for baselines is not established. The internal ablations are suggestive but lack statistical grounding. The core idea is defensible and could be validated, but the manuscript needs substantial revision before the central claim can be accepted.

major comments (5)
  1. [§3.2, Table 1, Appendix A.2] The headline claim of state-of-the-art performance across all benchmarks rests on F1 scores for GAIA computed on a 127-question text-only subset of the validation set. GAIA's official evaluation metric is exact-match accuracy on the full 165-question validation set. Reporting F1 on a subset is a different protocol and cannot be compared with published GAIA results. The paper must either report official GAIA exact-match accuracy on the full validation set, or restrict the claim to 'under our F1 protocol' and ensure every baseline row in Table 1 is run under the same protocol. As written, the external comparison is invalid and the central claim is unsupported.
  2. [Eq. (1), §2.3] The RL objective contains a reward function r_phi(q,y) that is introduced but never defined anywhere in the paper. Since the coordinator is trained with GRPO using this reward, the central training mechanism is not reproducible. The paper also omits GRPO hyperparameters (beta, epsilon, group size K, learning rate, epochs), the exact threshold of the training-data difficulty filter described in Appendix A.1, and the experience-memory retrieval configuration. These details must be specified for the method to be reproducible and for the RL contribution to be assessable.
  3. [§2.1 vs Appendix C] The formal action space is defined as A={PLAN, DELEGATE, REVISE}, but the coordinator prompt in Appendix C implements five actions: PLAN, REFLECT, SUMMARIZE, DELEGATE, and FINISH. The mapping from REFLECT/SUMMARIZE to the formal REVISE action is not stated, and FINISH is absent from the formal action space. This inconsistency makes the memory-control mechanism under-specified and complicates any attempt to implement or verify the method from the description.
  4. [§3.1, Table 3] Section 3.1 states that the method is evaluated on 'ten benchmarks,' but Table 1 and Table 3 list only four datasets: 2WikiMultiHopQA, MuSiQue, GAIA, and FRAMES. No explanation is given for the missing six benchmarks. This mismatch makes the 'across all benchmarks' claim ambiguous and must be corrected, either by reporting the remaining benchmarks or by fixing the text.
  5. [Tables 1 and 2] All reported numbers are point estimates with no error bars, confidence intervals, or significance tests. For the GAIA subset of only 127 questions, differences of 2–5 F1 points in the ablation study may be within sampling noise. At least multiple random seeds with standard deviations, or a paired significance test, are needed to support the internal component claims and to validate that the differences in Table 2 are not due to chance.
minor comments (6)
  1. [Table 1, ARPO row] The ARPO row contains a garbled entry: '13.387.7113.49' appears to be a missing-separator typo. Since this table is the primary evidence, every entry should be double-checked for formatting.
  2. [§3.2] Please specify exactly how F1 is computed for each benchmark. Token-level F1, character-level F1, and exact-match-after-normalization give different numbers, and the choice is material given the nonstandard use of F1 for GAIA.
  3. [Appendix B] The baseline implementation descriptions are high-level and do not state whether numbers were newly produced for this paper or taken from prior publications. This needs to be explicit for fairness of comparison, especially for MacNet and AFlow where 'missing scores' are reported.
  4. [Appendix C] The prompt examples refer to a 'researcher agent' and a 'replanner agent,' while Section 2.1 names only Search Agent and Report Agent. The terminology should be aligned to avoid confusion about the actual sub-agent set.
  5. [References] Several references use the placeholder style 'and 1 others' (e.g., Chen et al., 2025a; Dong et al., 2025; Mialon et al., 2023). These should be expanded to full author lists.
  6. [Limitations] The limitations paragraph notes cold-start issues in long-term memory and limited multi-turn support, but these limitations are not tested or quantified. A sentence tying these limitations to the observed benchmark results would improve the discussion.

Circularity Check

0 steps flagged

No circular derivation found; the central empirical claim rests on held-out benchmark comparisons, not on fitted values or self-citation chains.

full rationale

The paper does not attempt a mathematical derivation whose conclusion is built into its premises. StackPlanner's central mechanism—a hierarchical coordinator with active task-memory operations (UPDATE/CONDENSATION/PRUNING) and a structured experience memory—is evaluated on held-out test sets (MuSiQue, GAIA, FRAMES) after training on a filtered 2WikiMultiHopQA split, so there is no by-construction equivalence between the training objective and the reported predictions. The only possible concern is that the GAIA column uses a 127-question text-only subset with an F1 metric rather than GAIA's official exact-match evaluation; this is an external-validity or protocol-comparability issue, not circularity. Similarly, the reward function r_phi in Eq. (1) is left unspecified, which is a reproducibility gap, but it does not mean the reported improvements reduce to fitted inputs. The one self-citation to the authors' prior work (Jiang et al., 2024, cited for the general claim that long contexts overwhelm central agents) is not load-bearing: the paper's own ablations and held-out comparisons carry the argument. No uniqueness theorem, ansatz, or known result is imported via self-citation to force the conclusion. The honest finding is therefore no significant circularity.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

No new physical or formal entities are introduced. The framework's modules (task-memory stack, experience memory) are system components, not invented entities with independent falsifiable handles. The main unstated cost is in free parameters: the reward, GRPO hyperparameters, data filter, and retrieval configuration are all unspecified hand choices.

free parameters (4)
  • GRPO hyperparameters (β, ε, K, learning rate, epochs) = not reported
    Required to reproduce training and chosen by hand; no sensitivity analysis is provided.
  • Reward function r_phi design = not specified
    Equation (1) uses r_phi but never defines it; presumably based on answer correctness but the exact form is a hand-chosen design choice.
  • Training-data difficulty filter threshold = not quantified
    Appendix A.1 filters out instances requiring no retrieval or only a single trivial retrieval step; the precise criterion is unspecified.
  • Experience memory retrieval configuration = not reported
    No details on top-k, similarity metric, or how many retrieved entries are injected into the task memory.
axioms (4)
  • domain assumption The base LLM (Qwen2.5-3B/7B) can reliably execute the coordinator's PLAN/DELEGATE/REVISE actions from the provided prompts over long horizons.
    The central mechanism depends on the LLM making sound memory-condensation and pruning decisions; no error analysis of REVISE actions is provided (§2.1, §2.2, Appendix C).
  • standard math GRPO with token-level importance ratios is a valid optimizer for this action-level RL problem.
    Standard result borrowed from Shao et al. 2024 and applied in §2.3; no new derivation is attempted.
  • domain assumption F1 is an appropriate and comparable evaluation metric for all four benchmarks, including GAIA and FRAMES.
    The paper reports F1 throughout Table 1 without justifying comparability to official GAIA (exact-match) and FRAMES metrics.
  • domain assumption The curated 2Wiki training split represents a useful distribution for zero-shot transfer to MuSiQue, GAIA, and FRAMES.
    Training data is described in Appendix A.1; the transfer assumption is not validated beyond the reported OOD results.

pith-pipeline@v1.3.0-alltime-deepseek · 15326 in / 12532 out tokens · 129987 ms · 2026-08-03T11:30:15.404429+00:00 · methodology

0 comments
read the original abstract

Multi-agent systems based on large language models, particularly centralized architectures, have recently shown strong potential for complex and knowledge-intensive tasks. However, central agents often suffer from unstable long-horizon collaboration due to the lack of memory management, leading to context bloat, error accumulation, and poor cross-task generalization. To address both task-level memory inefficiency and the inability to reuse coordination experience, we propose StackPlanner, a hierarchical multi-agent framework with explicit memory control. StackPlanner addresses these challenges by decoupling high-level coordination from subtask execution with active task-level memory control, and by learning to retrieve and exploit reusable coordination experience via structured experience memory and reinforcement learning. Experiments on multiple deep-search and agent system benchmarks demonstrate the effectiveness of our approach in enabling reliable long-horizon multi-agent collaboration.

Figures

Figures reproduced from arXiv: 2601.05890 by Hongbin Lai, Huheng Huang, Jiaran Gao, Junfeng Zhao, Ruiqing Li, Ruizhe Zhang, Tao Feng, Xinke Jiang, Xu Chu, Yasha Wang, Yue Fang, Yuxuan Liu, Yuzhen Xiao, Zhibang Yang, Zhixin Zhang.

Figure 1
Figure 1. Figure 1: Overview of STACKPLANNER framework. • Pruning : When the coordinator detects unproductive or erroneous exploration, REVISE performs memory pruning by removing a selected segment of memory entries from the stack. Additionally, a concise record of failure causes is retained to guide subsequent ex￾ploration. By exposing memory as an explicit control target, REVISE enables active memory optimization , effec￾ti… view at source ↗
Figure 2
Figure 2. Figure 2: Case Study of STACKPLANNER on a deepresearch task. Task: “Please summarize the recently popular multi-agent system frameworks that are capable of performing report generation tasks.” [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗

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Forward citations

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Reference graph

Works this paper leans on

29 extracted references · 2 linked inside Pith · cited by 2 Pith papers

  1. [1]

    Analyze the current state and select the most appropriate action from available options

  2. [2]

    Provide a clear reasoning for the decision, justifying why the action is optimal

  3. [3]

    decision

    Agentnet: Decentralized evolutionary coordi- nation for llm-based multi-agent systems.Preprint, arXiv:2504.00587. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of thoughts: Deliberate prob- lem solving with large language models.Preprint, arXiv:2305.10601. Shunyu Yao, Jeffrey Zhao, Dian...

    Pith/arXiv arXiv 2023

  4. [4]

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  5. [5]

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    You must carefully check if the current information is sufficient to support the current decision-making requirements. Regardless of whether the information is sufficient or not, you must provide detailed reasoning. If the information is insufficient, you must take appropriate actions to supplement it (for example, by delegating to a sub-agent capable of ...

  6. [6]

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    If choosing DELEGATE, specify the sub-Agent type and task instructions. • If choosing replanner agent: This agent can only handlesearch steps planningand is limited to decomposing retrieval tasks into actionable steps. Do not include any requirements about report writing in the task description. You MUST and ONLY use it at the beginning of the task

  7. [7]

    en-US","zh-CN

    Return results in JSON format with the following fields: • action: Type of action (required) • reasoning: Justification for the decision (required) • params: Action parameters (e.g., agent_type and task_description for DELEGATE) • instruction: Instruction corresponding to the action • locale: Language of the user query (e.g.,"en-US","zh-CN", etc.) {% endi...

  8. [9]

    After the outline is confirmed, you usually need to delegate aresearcher agentto gather sufficient information to support the task fully

    Typically, after confirming the outline, it does not mean that the current information is sufficient to cover the generation requirements. After the outline is confirmed, you usually need to delegate aresearcher agentto gather sufficient information to support the task fully

  9. [11]

    Extract stable user attributes and preference signals intouser_profiles

  10. [12]

    Record atomic factual statements intosemantic_memory

  11. [13]

    Abstract reusable execution patterns intoprocedural_memory(SOPs)

  12. [14]

    user_profiles

    Merge new information with existing_long_term_memory_json, preserving correctness, recency, and non- redundancy. 5.Return JSON only, strictly matching the required schema. Input Task Memory: {{task_memory_json}} Existing Experience Memory (can be empty): {{existing_long_term_memory_json}} Current Timestamp:now_timestamp Output Schema (strictly required) {...

    2025

  13. [16]

    Overview of Popular Multi-Agent Frameworks Framework Name Characteristics Strengths Weaknesses AutoGen Multi-agent conversations, integration with the Microsoft ecosystem Intuitive multi-agent collaboration, strong performance on coding tasks Steep learning curve CrewAI Developer-friendly, strong orchestration capabilities Rapid construction of production...

  14. [17]

    Tool Interaction: Integrated with Semantic Kernel, providing rich API invocation capabilities

    Evaluation of Report Generation Capabilities AutoGen Task Planning: Supports multi-agent collaboration and decomposition of complex tasks. Tool Interaction: Integrated with Semantic Kernel, providing rich API invocation capabilities. Long-Horizon Tasks: Manages task states through conversational mechanisms, suitable for multi-step report generation. Appli...

  15. [18]

    Generate a 2025 AI industry trend report

    User Input: The user submits a report request (e.g., “Generate a 2025 AI industry trend report”)

    2025

  16. [19]

    Task Decomposition: A master agent splits the task into subtasks (data collection, analysis, writing, formatting, etc.)

  17. [20]

    Analysis Agent: Processes data and generates charts

    Agent Collaboration: Data Agent: Responsible for web search and data collection. Analysis Agent: Processes data and generates charts. Content Agent: Writes the main body of the report. Formatting Agent: Handles layout and formatting adjustments

  18. [21]

    Technical Challenges and Solutions

    Integrated Output: The master agent aggregates the results of all subtasks and produces the final report. Technical Challenges and Solutions

  19. [22]

    Solutions: Use graph-based structures (e.g., LangGraph) to manage task workflows

    Task Planning and Coordination Challenge: Multi-agent systems must effectively coordinate task assignment and progress synchronization among agents. Solutions: Use graph-based structures (e.g., LangGraph) to manage task workflows. Introduce standardized protocols (e.g., MCP) to ensure consistent communication

  20. [23]

    Solutions: Integrate tool libraries (e.g., CrewAI’s tool system)

    Tool Interaction and API Invocation Challenge: Report generation often requires calls to external APIs (e.g., database queries, web search). Solutions: Integrate tool libraries (e.g., CrewAI’s tool system). Use streaming function calling to improve response latency

  21. [24]

    Solutions: Implement persistent state management (e.g., LangGraph)

    Long-Horizon Task Management Challenge: Report generation may involve multi-step, long-running tasks. Solutions: Implement persistent state management (e.g., LangGraph). Introduce error recovery mechanisms to allow task resumption after interruptions. (b)

  22. [25]

    Solutions: Use version control and data validation mechanisms

    Data Consistency and Accuracy Challenge: Ensuring accuracy and consistency of data transferred across agents. Solutions: Use version control and data validation mechanisms. Introduce a supervisory agent for quality assurance. Industry Trends and Future Outlook

  23. [26]

    Multi-agent systems will become the default form of Agentic AI, evolving from task execution tools into autonomous business process engines

    Multi-Agent Collaboration as the Mainstream Architecture McKinsey predicts widespread adoption of collaborative agent workflows by 2026. Multi-agent systems will become the default form of Agentic AI, evolving from task execution tools into autonomous business process engines

    2026

  24. [27]

    This will foster an open ecosystem for multi-agent systems and enhance cross-platform collaboration

    Standardized Protocols Driving Interoperability The promotion of standardized protocols such as MCP and ACP will enable interoperability across agents from different vendors. This will foster an open ecosystem for multi-agent systems and enhance cross-platform collaboration

  25. [28]

    New roles, such as agent team leaders, will emerge to oversee resource allocation and quality control

    Human–Agent Collaborative Teams By 2026, every employee will become a manager of intelligent agents, shifting focus from execution to strategic supervision. New roles, such as agent team leaders, will emerge to oversee resource allocation and quality control

    2026

  26. [29]

    Super Engine

    China as a “Super Engine” for Multi-Agent Deployment With a complete industrial chain, leading open-source models, and a massive market, China provides an ideal testing ground for multi-agent systems. Government-led digital and intelligent transformation initiatives will drive enterprise upgrades and accelerate multi-agent adoption in sectors such as publ...

  27. [2023]

    InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 180–192, Singapore

    Theory of mind for multi-agent collaboration via large language models. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 180–192, Singapore. As- sociation for Computational Linguistics. Yilong Li, Chen Qian, Yu Xia, Ruijie Shi, Yufan Dang, Zihao Xie, Ziming You, Weize Chen, Cheng Yang, Weichuan Liu, Ye Tian, ...

    Pith/arXiv arXiv 2023

  28. [2025]

    Preprint, arXiv:2508.07976

    Beyond ten turns: Unlocking long-horizon agentic search with large-scale asynchronous rl. Preprint, arXiv:2508.07976. Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V . Chawla, Olaf Wiest, and Xi- angliang Zhang. 2024. Large language model based multi-agents: A survey of progress and challenges. Preprint, arXiv:2402.01680. Xanh Ho...

    arXiv 2024

  29. [2026]

    Report generation typically involves multiple stages, including data collection, analysis, content organization, and formatted output

    By coordinating multiple specialized AI agents to accomplish complex tasks, MAS exhibit strong collaborative capabilities, particularly in scenarios such as report generation that require multi-step reasoning and data integration. Report generation typically involves multiple stages, including data collection, analysis, content organization, and formatted...