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arxiv: 2604.07269 · v1 · submitted 2026-04-08 · 💻 cs.CL

Joint Optimization of Reasoning and Dual-Memory for Self-Learning Diagnostic Agent

Bingxuan Li , Simo Du , Yue Guo This is my paper

Pith reviewed 2026-05-10 17:56 UTC · model grok-4.3

classification 💻 cs.CL
keywords diagnostic agentdual memoryself-learningclinical reasoningreinforcement learningexperience reusecontinual adaptationmedical diagnosis
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The pith

A diagnostic agent with dual memory jointly optimizes reasoning and memory management to convert accumulated experience into reusable clinical rules.

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

The paper argues that current diagnostic agents based on large language models handle each case in isolation and therefore cannot build on prior experience the way human clinicians do. SEA addresses this by adding a cognitively inspired dual-memory module and training the whole system with a reinforcement framework that simultaneously improves reasoning steps and decisions about what to store or consolidate in memory. If the approach works, the agent produces higher accuracy on medical reasoning benchmarks and shows steady gains across sequences of cases rather than unstable or flat performance. Expert review of the rules that emerge from the memory module supports that they capture clinically correct and useful patterns. Readers should care because this points toward agents that can accumulate genuine expertise over time instead of resetting with every new patient.

Core claim

SEA equips a diagnostic agent with a dual-memory module and trains it via a reinforcement framework that jointly optimizes reasoning actions and memory operations so that experience is transformed into consolidated, reusable diagnostic rules; the resulting system records higher accuracy on the MedCaseReasoning dataset and larger, more stable gains on the long-horizon ER-Reason dataset while the induced rules receive positive expert ratings for clinical correctness and usefulness.

What carries the argument

The dual-memory module, which stores recent cases and consolidates them into reusable rules, paired with a reinforcement training framework that jointly optimizes the reasoning policy and memory management decisions.

If this is right

  • Diagnostic agents can maintain and improve performance across long sequences of cases instead of resetting after each one.
  • Experience is turned into explicit, inspectable rules that experts can rate for correctness and usefulness.
  • Joint optimization of reasoning and memory produces larger and more stable accuracy gains than methods that optimize only reasoning.
  • The approach supports continual learning without requiring full retraining when new cases arrive.

Where Pith is reading between the lines

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

  • The same dual-memory pattern could be tested in other sequential reasoning domains such as legal analysis or engineering fault diagnosis.
  • Consolidated rules might function as an interpretable knowledge layer that clinicians can review and edit directly.
  • Future experiments could measure whether the agent requires fewer new examples to reach target accuracy once it has built an initial rule set from prior cases.

Load-bearing premise

Performance gains and rule quality arise because the dual-memory structure and joint optimization genuinely enable experience reuse and continual adaptation rather than from dataset-specific fitting.

What would settle it

If the agent shows no accuracy advantage or unstable gains when evaluated on a fresh collection of medical cases never used in training, or if blind expert review finds the consolidated rules no more clinically correct than those from baselines, the central claim would be falsified.

Figures

Figures reproduced from arXiv: 2604.07269 by Bingxuan Li, Simo Du, Yue Guo.

Figure 1
Figure 1. Figure 1: Overview of SEA: At each round t, the policy model observes a patient case xt and may invoke memory operations before emitting the final output ot (diagnosis and reasoning). The agent controls a short-term memory cluster that stores recent patient cases with a bounded capacity K (list/append/pop) and a long-term memory cluster that consolidates experience into abstracted diagnosis rules (list/consolidate).… view at source ↗
Figure 2
Figure 2. Figure 2: Accuracy trajectories from 10 to 100 rounds for representative methods. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Task setup: At each round t, the environment provides a patient case xt together with a candidate diagnosis set Yt . The agent outputs an action at consisting of a structured reasoning trace and a final prediction yˆt ∈ Yt . The environment then returns feedback ft (e.g., correct/incorrect or graded), which the agent can leverage as experience for subsequent rounds. This interaction repeats for T rounds, m… view at source ↗
read the original abstract

Clinical expertise improves not only by acquiring medical knowledge, but by accumulating experience that yields reusable diagnostic patterns. Recent LLMs-based diagnostic agents have shown promising progress in clinical reasoning for decision support. However, most approaches treat cases independently, limiting experience reuse and continual adaptation. We propose SEA, a self-learning diagnostic agent with cognitively inspired dual-memory module. We design a reinforcement training framework tailored to our designed agent for joint optimization of reasoning and memory management. We evaluate SEA in two complementary settings. On standard evaluation with MedCaseReasoning dataset, SEA achieves 92.46% accuracy, outperforming the strongest baseline by +19.6%, demonstrating the benefit of jointly optimizing reasoning and memory. On the long-horizon with ER-Reason dataset, SEA attains the best final accuracy (0.7214) and the largest improvement (+0.35 Acc@100), while baseline methods show limited or unstable gains. Expert evaluation further indicates that rules consolidated from SEA show strong clinical correctness, usefulness and trust, suggesting that the induced rules in dual-memory module are reliable and practically meaningful. Overall, SEA improves both diagnostic reasoning ability and continual learning by effectively transforming experience into reusable knowledge.

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.

Referee Report

2 major / 1 minor

Summary. The paper proposes SEA, a self-learning diagnostic agent with a cognitively inspired dual-memory module. It introduces a tailored reinforcement training framework for joint optimization of reasoning and memory management to enable experience reuse and continual adaptation. On the MedCaseReasoning dataset, SEA reports 92.46% accuracy (+19.6% over the strongest baseline). On the long-horizon ER-Reason dataset, it achieves the best final accuracy (0.7214) and largest improvement (+0.35 Acc@100). Expert evaluation indicates that rules consolidated in the dual-memory module exhibit strong clinical correctness, usefulness, and trust.

Significance. If the reported gains can be attributed to the dual-memory design and joint optimization rather than implementation artifacts, the work would meaningfully advance LLM-based diagnostic agents by demonstrating a path to continual learning and reusable clinical patterns. The expert validation of induced rules provides qualitative support for practical relevance beyond raw accuracy metrics.

major comments (2)
  1. [§4 Experiments] §4 Experiments: The manuscript reports numerical improvements (92.46% accuracy, +0.35 Acc@100) as direct evidence for the value of joint optimization, yet provides no ablation studies isolating the dual-memory module, no error bars, no detailed baseline implementations, and no statistical tests. This is load-bearing because the central claim—that the dual-memory plus reinforcement framework enables genuine experience reuse—cannot be confirmed without these controls.
  2. [§3.2 Dual-Memory Module] §3.2 Dual-Memory Module and §3.3 Reinforcement Framework: The description of how memory consolidation interacts with reasoning during joint optimization lacks concrete equations, pseudocode, or reward formulations. Without these, it is impossible to verify that the architecture supports the claimed continual adaptation rather than dataset-specific fitting.
minor comments (1)
  1. [Abstract and §4] The abstract and results sections would benefit from explicit statements of the number of runs, random seeds, and exact baseline configurations to improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will incorporate revisions to improve the clarity and rigor of the manuscript.

read point-by-point responses

  1. Referee: [§4 Experiments] §4 Experiments: The manuscript reports numerical improvements (92.46% accuracy, +0.35 Acc@100) as direct evidence for the value of joint optimization, yet provides no ablation studies isolating the dual-memory module, no error bars, no detailed baseline implementations, and no statistical tests. This is load-bearing because the central claim—that the dual-memory plus reinforcement framework enables genuine experience reuse—cannot be confirmed without these controls.

    Authors: We agree that these controls are necessary to substantiate the central claim regarding experience reuse. The current manuscript does not include ablation studies, error bars, detailed baseline implementations, or statistical tests. In the revised version, we will add ablation experiments that isolate the dual-memory module and the joint optimization objective, report standard deviations across multiple runs with different seeds, provide pseudocode and implementation details for all baselines, and include statistical significance tests (e.g., paired t-tests or Wilcoxon tests) on the reported accuracy gains. These additions will directly address whether the improvements can be attributed to the proposed architecture rather than implementation artifacts. revision: yes

  2. Referee: [§3.2 Dual-Memory Module] §3.2 Dual-Memory Module and §3.3 Reinforcement Framework: The description of how memory consolidation interacts with reasoning during joint optimization lacks concrete equations, pseudocode, or reward formulations. Without these, it is impossible to verify that the architecture supports the claimed continual adaptation rather than dataset-specific fitting.

    Authors: We acknowledge that the current description of the interaction between memory consolidation and reasoning is insufficiently formal. The revised manuscript will include explicit equations defining the memory update rules and their coupling to the reasoning policy, pseudocode for the full joint optimization loop, and the precise reward formulation used during reinforcement training. These additions will clarify the mechanism by which consolidated rules enable continual adaptation across sequential cases, distinguishing it from dataset-specific fitting. revision: yes

Circularity Check

0 steps flagged

No significant circularity in claimed derivation chain

full rationale

The paper advances an empirical proposal for the SEA agent and reports performance gains on two external datasets (MedCaseReasoning and ER-Reason) plus expert rule validation. No mathematical derivation, first-principles result, or prediction is presented that reduces to its own inputs by construction. The central claims rest on comparative accuracy numbers and qualitative expert scores rather than self-definitional loops, fitted parameters renamed as predictions, or load-bearing self-citations. The work is self-contained against the stated benchmarks and does not invoke uniqueness theorems or ansatzes from prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated beyond the high-level description of the dual-memory module as cognitively inspired.

pith-pipeline@v0.9.0 · 5504 in / 1310 out tokens · 41751 ms · 2026-05-10T17:56:03.389494+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

20 extracted references · 20 canonical work pages

  1. [1]

    Mixup for Node and Graph Classification

    Accessed: 2026-01-03. Qi Peng, Jialin Cui, Jiayuan Xie, Yi Cai, and Qing Li. Tree-of-reasoning: Towards complex medical diagnosis via multi-agent reasoning with evidence tree. InProceedings of the 33rd ACM International Conference on Multimedia, pp. 1744–1753, 2025. Pengcheng Qiu, Chaoyi Wu, Junwei Liu, Qiaoyu Zheng, Yusheng Liao, Haowen Wang, Yun Yue, Qi...

    work page doi:10.1145/3442381.3449795 2026

  2. [3]

    DoNOTinvent diagnoses or use synonyms not appearing in the list

    work page

  3. [5]

    • Do not rely on external knowledge beyond the provided descriptions

    UseONLYinformation from the Patient Profile: • Do not assume missing symptoms, labs, or history. • Do not rely on external knowledge beyond the provided descriptions

    work page

  4. [6]

    • Thefewest contradictions

    If multiple candidates are plausible, choose the one with: • Themost specific and comprehensive match. • Thefewest contradictions

    work page

  5. [7]

    Prefer diagnoses that explainkey distinguishing features(e.g., critical symptoms, lab findings, temporal patterns)

    work page

  6. [8]

    reasoning

    DoNOToutput multiple answers, uncertainty, or extra commentary. Reasoning Requirements: • Citekey evidencefrom the Patient Profile (symptoms, history, labs, timeline). • Justify why the selected diagnosisfits best. • Optionally explain why close alternatives are less suitable. • Keep reasoningconcise, evidence-grounded, and non-speculative. Output Format ...

    work page

  7. [12]

    UseONLYinformation from the Patient Profile

    work page

  8. [14]

    reasoning

    Choose the diagnosis with: • strongest evidence match, • highest specificity, • minimal contradictions. Reasoning Requirements: • Ground decisions inexplicit patient evidence. • Prefer concise, structured, and evidence-based reasoning. Output Format (strict): ReturnONLYa valid JSON object (no markdown, no extra text): { "reasoning": "Condensed reasoning s...

    work page

  9. [15]

    SelectEXACTLY ONEdiagnosis from the Candidate Diseases list

    work page

  10. [16]

    DoNOTinvent diagnoses or use synonyms not in the list

    work page

  11. [18]

    UseONLYinformation from the Patient Profile and provided Memory

    work page

  12. [19]

    DoNOTassume missing facts

    work page

  13. [20]

    reasoning

    Choose the diagnosis with: • strongest evidence match, • highest specificity, • minimal contradictions. Reasoning Requirements: • Ground decisions inexplicit patient evidence. • Incorporaterelevant memorywhen helpful. • Prefer concise, structured, and evidence-based reasoning. Output Format (strict): ReturnONLYa valid JSON object (no markdown, no extra te...

    work page

  14. [23]

    Base decisions on Patient Profile, optionally supported by Memory

    work page

  15. [24]

    reasoning

    DoNOTassume missing information. Reasoning Requirements: • Ground reasoning in explicit patient evidence. • Incorporate relevant memory when beneficial. • Clearly connect evidence (and memory, if used) to the diagnosis. Output Format (strict): { "reasoning": "Concise reasoning integrating patient evidence and relevant memory.", "final_diagnosis": "EXACT d...

    work page

  16. [25]

    SelectEXACTLY ONEdiagnosis from the Candidate Diseases

    work page

  17. [26]

    Output the diagnosis nameEXACTLY as written

    work page

  18. [27]

    Base decisions on the Patient Profile, optionally supported by Memory

    work page

  19. [28]

    DoNOTassume missing information

    work page

  20. [29]

    reasoning

    DoNOTinvent diagnoses or use synonyms not appearing in the candidate list. Reasoning Requirements: • Ground reasoning in explicit patient evidence. • Incorporate relevant memory only when it provides useful support. • Clearly connect the selected diagnosis to the strongest supporting evidence. • Prefer concise, structured, and non-speculative reasoning. O...

    work page