arxiv: 2604.04503 · v4 · submitted 2026-04-06 · 💻 cs.AI · cs.MA

Recognition: 2 theorem links

· Lean Theorem

Memory Intelligence Agent

Jingyang Qiao , Weicheng Meng , Yu Cheng , Zhihang Lin , Zhizhong Zhang , Xin Tan , Jingyu Gong , Kun Shao

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Yuan Xie

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:49 UTC · model grok-4.3

classification 💻 cs.AI cs.MA

keywords Memory Intelligence Agentdeep research agentsmemory evolutionLLM agentsreinforcement learningtest-time learningplanner executor architecturebidirectional memory conversion

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The pith

The Memory Intelligence Agent framework enables deep research agents to evolve memory efficiently via bidirectional conversion and alternating planner-executor training.

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

Deep research agents integrate language models with tools but face limits in memory that evolves poorly and grows expensive to store and retrieve. The paper proposes the Memory Intelligence Agent to address this through a Manager-Planner-Executor design. The non-parametric Memory Manager holds compressed past trajectories, the parametric Planner generates search plans, and the Executor carries out searches and analysis. Training alternates reinforcement learning between Planner and Executor while the Planner keeps updating on the fly during use. A loop converts memories between parametric and non-parametric forms, and added reflection plus judgment steps aid open-world self-improvement, yielding stronger results across eleven benchmarks.

Core claim

The central claim is that the Manager-Planner-Executor architecture, trained with alternating reinforcement learning and equipped with on-the-fly test-time updates plus a bidirectional conversion loop between parametric and non-parametric memories, produces efficient memory evolution and superior reasoning performance for deep research agents.

What carries the argument

The Manager-Planner-Executor architecture supported by a bidirectional conversion loop between parametric and non-parametric memories, where the Memory Manager stores compressed trajectories, the Planner produces and evolves plans, and the Executor follows them under alternating reinforcement learning.

If this is right

Alternating reinforcement learning strengthens cooperation between planning and execution steps.
On-the-fly updates let the Planner evolve continuously without pausing the reasoning process.
Bidirectional memory conversion lowers storage and retrieval costs while maintaining useful history.
Reflection and unsupervised judgment mechanisms support ongoing self-evolution in open environments.
The overall setup outperforms prior retrieval-based memory methods on eleven standard benchmarks.

Where Pith is reading between the lines

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

The design could extend to other tool-using agent systems that need long-term memory without growing computational overhead.
Test-time adaptation without process interruption may help agents maintain performance across shifting task distributions.
Similar memory loops might reduce the need for periodic full retraining in deployed autonomous systems.

Load-bearing premise

The alternating reinforcement learning and on-the-fly test-time updates between Planner and Executor will produce stable improvements in open-world reasoning without introducing instability or overfitting to recent trajectories.

What would settle it

Running a sequence of related tasks on one benchmark and observing clear performance drops, rising instability, or signs of overfitting after several on-the-fly Planner updates.

read the original abstract

Deep research agents (DRAs) integrate LLM reasoning with external tools. Memory systems enable DRAs to leverage historical experiences, which are essential for efficient reasoning and autonomous evolution. Existing methods rely on retrieving similar trajectories from memory to aid reasoning, while suffering from key limitations of ineffective memory evolution and increasing storage and retrieval costs. To address these problems, we propose a novel Memory Intelligence Agent (MIA) framework, consisting of a Manager-Planner-Executor architecture. Memory Manager is a non-parametric memory system that can store compressed historical search trajectories. Planner is a parametric memory agent that can produce search plans for questions. Executor is another agent that can search and analyze information guided by the search plan. To build the MIA framework, we first adopt an alternating reinforcement learning paradigm to enhance cooperation between the Planner and the Executor. Furthermore, we enable the Planner to continuously evolve during test-time learning, with updates performed on-the-fly alongside inference without interrupting the reasoning process. Additionally, we establish a bidirectional conversion loop between parametric and non-parametric memories to achieve efficient memory evolution. Finally, we incorporate a reflection and an unsupervised judgment mechanisms to boost reasoning and self-evolution in the open world. Extensive experiments across eleven benchmarks demonstrate the superiority of MIA.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MIA adds test-time parametric planner evolution and bidirectional memory conversion to cut storage costs in agent memory, but the abstract supplies no numbers or controls to support the eleven-benchmark superiority claim.

read the letter

The paper's main contribution is a Manager-Planner-Executor loop where the manager keeps compressed non-parametric trajectories, the planner outputs parametric search plans that keep updating during test time, and the executor runs the searches. They train the planner and executor with alternating RL, add a conversion loop that moves knowledge both ways between the two memory forms, and include reflection plus unsupervised judgment for open-world self-improvement. This combination is new relative to the retrieval-only baselines mentioned in the abstract; it directly targets the storage growth and weak evolution problems those methods face. The architecture description is clear about how the pieces fit together and why the on-the-fly updates are meant to happen without stopping inference. That part is useful for anyone thinking about long-session agents. The soft spot is the total lack of results. The abstract asserts better performance across eleven benchmarks yet shows no tables, no baseline scores, no ablations, and no variance or convergence numbers. Without those, it is impossible to tell whether the claimed gains are real or whether the continuous planner updates simply overfit recent trajectories or become unstable under shift. The stress-test concern about instability therefore still stands on the information given. This is for people working on memory systems for LLM agents who want concrete ideas for compression and test-time adaptation. A reader could pull the architecture sketch and the conversion loop even if the empirical claims need checking. It deserves peer review so the missing data and stability diagnostics can be examined, but I would not cite it until the numbers are in and look solid.

Referee Report

2 major / 2 minor

Summary. The paper proposes the Memory Intelligence Agent (MIA) framework for deep research agents, built around a Manager-Planner-Executor architecture. The non-parametric Memory Manager stores compressed historical search trajectories, the parametric Planner generates search plans, and the Executor performs guided information search and analysis. Training uses an alternating reinforcement learning paradigm to improve cooperation between Planner and Executor; the Planner additionally evolves via continuous on-the-fly test-time updates performed alongside inference. A bidirectional conversion loop between parametric and non-parametric memories supports efficient evolution, augmented by reflection and unsupervised judgment mechanisms. The central claim is that these components yield superior performance across eleven benchmarks relative to prior memory-augmented agents.

Significance. If the reported gains prove robust, the work would advance memory systems for LLM agents by reducing storage/retrieval costs through compression and bidirectional conversion while enabling stable test-time adaptation. The combination of alternating RL, non-interruptive updates, and open-world reflection mechanisms addresses key limitations in existing retrieval-only approaches and could support more autonomous, evolving reasoning agents.

major comments (2)

[Methods (alternating RL and test-time updates)] Methods section on alternating RL and test-time learning: the description of Planner updates performed on-the-fly alongside inference supplies no convergence diagnostics, ablation on update rate or frequency, or variance statistics across runs or benchmarks. This is load-bearing for the superiority claim, as instability or overfitting to recent trajectories could artifactually inflate results without these controls.
[Experimental results] Experimental results section: superiority is asserted on eleven benchmarks, yet the manuscript provides no quantitative tables with baseline comparisons, statistical significance tests, or component ablations (e.g., effect of removing bidirectional memory conversion or the reflection mechanism). Without these, attribution of gains specifically to the Manager-Planner-Executor loop and memory evolution remains unevaluated.

minor comments (2)

[Abstract] The abstract would be strengthened by naming the eleven benchmarks and including at least one key quantitative result (e.g., average improvement) to allow immediate assessment of scope and effect size.
[Architecture description] Notation for the bidirectional conversion loop between parametric and non-parametric memories should be defined more explicitly (e.g., with a short equation or pseudocode) to clarify information flow and compression steps.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We have reviewed the comments carefully and will revise the manuscript to strengthen the presentation of our methods and experimental results.

read point-by-point responses

Referee: [Methods (alternating RL and test-time updates)] Methods section on alternating RL and test-time learning: the description of Planner updates performed on-the-fly alongside inference supplies no convergence diagnostics, ablation on update rate or frequency, or variance statistics across runs or benchmarks. This is load-bearing for the superiority claim, as instability or overfitting to recent trajectories could artifactually inflate results without these controls.

Authors: We agree that the current Methods description would benefit from additional controls to support claims of stable evolution. In the revised manuscript we will add convergence diagnostics for the alternating RL procedure, ablations on Planner update rate and frequency, and report mean and standard deviation across multiple runs on representative benchmarks. These additions will allow readers to assess robustness directly. revision: yes
Referee: [Experimental results] Experimental results section: superiority is asserted on eleven benchmarks, yet the manuscript provides no quantitative tables with baseline comparisons, statistical significance tests, or component ablations (e.g., effect of removing bidirectional memory conversion or the reflection mechanism). Without these, attribution of gains specifically to the Manager-Planner-Executor loop and memory evolution remains unevaluated.

Authors: We acknowledge that the Experimental Results section requires expanded quantitative support. The revised version will include full comparison tables against baselines, statistical significance tests, and targeted ablations that isolate the contribution of bidirectional memory conversion and the reflection mechanism. This will make the attribution of gains to the proposed architecture explicit. revision: yes

Circularity Check

0 steps flagged

No circularity in architectural proposal or empirical claims

full rationale

The paper proposes the MIA framework (Manager-Planner-Executor with alternating RL, on-the-fly test-time updates, bidirectional memory conversion, reflection, and unsupervised judgment) and supports its superiority via experiments on eleven benchmarks. No mathematical derivation chain, equations, or fitted parameters are described that would reduce the claimed improvements to the input data or architecture by construction. The evaluation protocol is presented as independent of the method's internal mechanisms, with no self-definitional loops, renamed known results, or load-bearing self-citations that collapse the central claims. This is a standard empirical architecture paper whose results do not reduce to tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The framework rests on the assumption that compressed trajectory storage plus learned planning can substitute for full history retrieval and that test-time updates remain stable; no explicit free parameters, axioms, or invented entities are named in the abstract.

pith-pipeline@v0.9.0 · 5529 in / 1181 out tokens · 31184 ms · 2026-05-10T19:49:15.630784+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We propose a novel Memory Intelligence Agent (MIA) framework, consisting of a Manager-Planner-Executor architecture... alternating reinforcement learning paradigm... bidirectional conversion loop between parametric and non-parametric memories... test-time learning... reflection and an unsupervised judgment mechanisms
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

two-stage alternating RL training strategy based on Group Relative Policy Optimization (GRPO)... J_ME_GRPO(θ) ... J_MP_GRPO(θ)

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning
cs.AI 2026-05 unverdicted novelty 6.0

Skill1 trains one policy to jointly evolve skill query generation, re-ranking, task solving, and distillation from a single task-success signal, with low-frequency trends crediting selection and high-frequency variati...
Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning
cs.AI 2026-05 unverdicted novelty 5.0

Skill1 co-evolves skill selection, utilization, and distillation inside a single policy using only task-outcome reward, with low-frequency trends crediting selection and high-frequency variation crediting distillation...
Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning
cs.AI 2026-05 unverdicted novelty 5.0

Skill1 trains a single RL policy to co-evolve skill selection, utilization, and distillation in language model agents from one task-outcome reward, using low-frequency trends to credit selection and high-frequency var...

Reference graph

Works this paper leans on

25 extracted references · cited by 1 Pith paper

[1]

**Plan**: Given a goal and background info, output a clear action plan
[2]

**Evaluate**: Given an execution trace, decide if replanning is needed
[3]

Keep responses concise and action-focused

**Replan (if needed)**: With new reference memories, provide a revised plan targeting unmet goals. Keep responses concise and action-focused. System prompt for Planner You are a memory-based planning assistant assisting an agent by providing strategic guidance. The agent has access to the following tools: - `web_image_to_image_search`: find visually simil...
[4]

Be clear and concise—no more than 300 words, and present your response as a step-by-step plan
[5]

Each step in the plan must be atomic and actionable, specifying a single operation such as invoking a tool (e.g., `search` with precise query intent), performing logical inference, executing a calculation, cross-verifying facts, or synthesizing prior observations
[6]

You must include relevant memories in the thinking process (<think>...</think>), but do not mention them in the final output
[7]

Don't try to give the answer directly, but give a plan
[8]

Prohibit the generation of content unrelated to the plan
[9]

yes" to trigger replanning,

Your response must not contain any factual information. Your output should only contain the guideline, with no additional explanations. **IMPORTANT: The `web_image_to_image_search` tool can only be called once.** Otherwise, the agent will be severely penalized. **[Question]** (Global Objective): {question} Memory-based generative planning prompt for Plann...
[10]

Be clear and concise, no more than 200 words, and present your response as a step-by-step supplementary plan
[11]

You must include **reflection** on the previous failure in the thinking process (<think>...</think>), but **do not mention them in the final output**
[12]

Don't give the answer directly, but provide **a supplementary plan**
[13]

Prohibit generating content unrelated to the plan
[14]

Your output should only contain the guideline, with no additional explanations

Your response must not contain any factual information. Your output should only contain the guideline, with no additional explanations. Reflection-Replanning prompt for Planner 31 Memory Intelligence Agent You must follow these steps in order. In every conversation turn, you start from Step 1. **Step 1: Think** * **This is the starting point for every tur...
[15]

**Call a tool:** If your evaluation shows you **need more information** (e.g., for complex, factual, or real-time questions)
[16]

why" or

**Provide a final answer:** If your evaluation shows you have **sufficient information** (e.g., for simple questions, or tasks that don't require external data). * Your entire reasoning process must be enclosed in `<think>...</think>` tags. **Step 2: Act (Tool Call)** * **Execute this step ONLY if your Step 1 action was to call a tool.** * Call the **one ...
[17]

Reasoning Quality: Are the agent's analysis, planning, and deduction processes in the trajectory reasonable? Is there a clear logical progression between steps?
[18]

Evidence-based Deduction: Can the final conclusion be logically deduced from the clues collected in the trajectory? Are there forced conclusions or logical leaps?
[19]

Your task is to objectively evaluate whether the multimodal deep research agent correctly understood the retrieved information and whether there are any LLM hallucinations

Logical Consistency: Are there contradictory statements between the agent's thought process and the final output, or within the final output itself? 【Output Requirements】 Please evaluate based on the above criteria and output the results in the following format: - Score: (Provide a comprehensive score from 1-10) - Reason: (Explain the reason for the score...
[20]

Information Understanding: Did the agent correctly understand the content retrieved in the trajectory? Is there any misinterpretation, misreading, or misattribution of the original text?
[21]

Your task is to objectively evaluate the completeness of the multimodal deep research agent's final response and the actual completion status of the task

Faithfulness and Hallucination: Can all facts, data, and details in the final output find clear basis in the retrieval results of the trajectory? Is there any fabrication or hallucination? 【Output Requirements】 Please evaluate based on the above criteria and output the results in the following format: - Score: (Provide a comprehensive score from 1-10) - R...
[22]

cannot find the answer/error occurred

Response Status: Did the agent successfully generate a substantive final answer? Are there situations where it gave up halfway, did not attempt to answer, or only replied with "cannot find the answer/error occurred"? 【Output Requirements】 Please evaluate based on the above criteria and output the results in the following format: - Score: (Provide a compre...
[23]

Effective Response: The agent provided a substantive answer that directly addresses the user's question
[24]

Faithful to Facts: The information in the final output is entirely supported by the retrieved content in the trajectory
[25]

A" if the answer is Correct. - Output

Logical Consistency: The deduction from the trajectory evidence to the final conclusion is logically sound and free of contradictions. 【Output Requirements】 You must output ONLY a single letter representing your final verdict. Do not include any explanations, markdown formatting, punctuation, or extra text. - Output "A" if the answer is Correct. - Output ...

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