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ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

🕒 Published (v1): 2026-03-27 08:52 UTC · Source: Arxiv · Venue: KDD 2026 · link

Why this paper was selected

KDD 2026; ClinicalAgents: multi-agent orchestration with dual-memory for clinical diagnosis

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TL;DR

ClinicalAgents is a multi-agent framework for clinical decision-making that replaces rigid sequential workflows with a Monte Carlo Tree Search (MCTS)-based orchestrator, enabling hypothesis-driven, evidence-seeking reasoning with backtracking. It combines a mutable working memory (patient state) and a static experience memory (guidelines + historical cases) to reduce information fragmentation across multi-agent turns. It achieves state-of-the-art performance on a multi-stage clinical workflow benchmark.

Problem

Existing MAS for clinical decision-making (CDM) suffer from three compounding failures: (1) pre-scripted, linear interaction protocols that cannot replan or backtrack when new evidence emerges; (2) window-bounded dialogue context that fragments critical details across multi-turn agent interactions; (3) static generation framing that misses the iterative hypothetico-deductive nature of real clinical diagnosis, causing premature closure on insufficient evidence.

Method

ClinicalAgents orchestrates a pool of specialized agents (referral, imaging, diagnosis, treatment) through three components:

Clinical Orchestrator (MCTS-based): Formulates orchestration as an MDP \(\langle M^t_\text{work}, \mathcal{A}, R, T, \eta \rangle\). The action space \(\mathcal{A} = \mathcal{A}_\text{agent} \cup \{a_\text{rag}, a_\text{back}, a_\text{term}\}\) includes agent calls, knowledge retrieval, backtracking, and termination. The orchestrator selects top-\(K\) candidate actions, runs \(N\) rollouts per candidate to estimate \(Q(M^t_\text{work}, a)\) as discounted cumulative reward, and uses LLM-prior-guided PUCT for node selection: $\(a^* = \arg\max_{a \in \mathcal{A}_K} \left[ Q(M^t_\text{work}, a) + \lambda \cdot \mathcal{O}(a \mid M^t_\text{work}, I_\text{select}) \right]\)$ The reward function \(R_t = \alpha \cdot \max(0, \Delta E^m_t) + \beta \cdot \max(0, \Delta c_t) - \gamma \cdot \mathbf{1}[\Delta E^m_t \leq 0 \wedge \Delta c_t \leq 0]\) rewards reductions in missing evidence \(\Delta E^m_t\) and gains in top-hypothesis confidence \(\Delta c_t\). Backtracking is triggered when \(|E^m_t| > 0\) and routes to the appropriate prior stage based on the type of missing evidence.

Dual-Memory System: - Working memory \(M^t_\text{work} = \langle E^t, H^t, \tau^t \rangle\): mutable; accumulates evidence, hypotheses, and the action trajectory across all agent interactions. - Experience memory \(M_\text{exp} = \langle D_\text{guide}, D_\text{CDC} \rangle\): static; retrieves guideline chunks via AGRAG (a knowledge-graph-augmented RAG) and identifies potentially missing evidence \(E^p_t\) by scoring candidate evidence items from similar historical cases using cosine-weighted importance: \(\text{Imp}(e \mid E^t, \tilde{D}_\text{cdc}, H^t) = \frac{1}{n_\text{cdc}} \sum_i \text{sim}(E^t \oplus H^t, d_i) \cdot \mathbf{1}[e \in E^*_{d_i}] \cdot \mathbf{1}[e \notin E^t]\).

The reasoning cycle is: Perceive → Hypothesize → Verify → Update, with backtracking re-entering Perceive when evidence is insufficient.

Key Contributions

  • MCTS-based dynamic orchestrator that replaces pre-scripted MAS workflows with a search-theoretic policy over agent actions, enabling evidence-triggered backtracking and replanning.
  • Reward function explicitly encoding the clinical goal of narrowing differential diagnoses via missing-evidence reduction and confidence gain.
  • Dual-memory architecture separating short-term mutable patient state (working memory) from long-term reusable knowledge (experience memory), reducing cross-turn context fragmentation.
  • Hybrid RAG mechanism combining passive guideline augmentation (AGRAG) with active evidence-seeking from historical cases (CDC importance scoring).
  • State-of-the-art results on a multi-stage clinical workflow benchmark over single-agent and multi-agent baselines.

Results

The paper text provided is truncated before the results tables; no specific benchmark scores, dataset names, or numerical comparisons against baselines are available in the supplied excerpt. The abstract and contributions claim "best performance among evaluated baselines" and significant gains in diagnostic accuracy and explainability over strong single-agent and multi-agent baselines, but no concrete numbers can be faithfully reported from the provided text.

Limitations

  • Experimental details and numbers are not available in the provided text excerpt, preventing independent verification of claimed gains.
  • MCTS with \(N\) rollouts per candidate action is computationally expensive; cost relative to sequential MAS baselines is not discussed in the provided text.
  • Experience memory \(M_\text{exp}\) is static — it does not update with new cases encountered at inference time, limiting adaptation to rare or novel diseases.
  • The backtracking mechanism relies on an LLM to detect missing evidence (\(E^m_t\)) and assign importance scores, introducing potential hallucination in the very step designed to reduce diagnostic error.
  • Evaluation is on a single benchmark dataset (details not visible in excerpt); generalizability across clinical specialties or EHR systems is unclear.

Relevance to Agentic AI / LLM Agents

ClinicalAgents is a concrete instantiation of search-augmented agent orchestration: it replaces the common pattern of a static DAG over specialist agents with a learned MDP policy using MCTS, making it directly relevant to research on dynamic agent planning and non-linear workflow execution. The dual-memory design — separating ephemeral working state from persistent external knowledge — is a generalizable pattern for any long-horizon multi-agent task where context fragmentation degrades coordination. The backtracking mechanism operationalizes a key open problem in agentic AI: how agents should detect and recover from information-deficit states rather than committing prematurely. For researchers tracking LLM agents, this paper contributes a formalized reward design for clinical reasoning that could transfer to other evidence-gathering agentic pipelines.