TL;DR
CausalPlan introduces a causal reasoning framework for LLM multi-agent collaboration, significantly reducing invalid actions and enhancing coordination without fine-tuning, across diverse models and tasks.
Contribution
This work presents CausalPlan, a novel causal reasoning framework that improves LLM multi-agent planning by integrating explicit causal structure learning and intervention-based action selection.
Findings
Reduces invalid actions in multi-agent tasks
Improves collaboration performance over baselines
Works across various LLM sizes and environments
Abstract
Large language model (LLM) agents-especially smaller, open-source models-often produce causally invalid or incoherent actions in collaborative tasks due to their reliance on surface-level correlations rather than grounded causal reasoning. This limitation undermines their performance in terms of coordination and planning in dynamic environments. We address this challenge with CausalPlan, a two-phase framework that integrates explicit structural causal reasoning into the LLM planning process. At the core of CausalPlan is the Structural Causal Action (SCA) model, which learns a causal graph from agent trajectories to capture how prior actions and current environment states influence future decisions. This structure is then used to guide action selection by assigning causal scores to LLM-generated proposals, reweighting them accordingly, or falling back to causally grounded alternatives…
Peer Reviews
Decision·Submitted to ICLR 2026
1. The proposed two-phase framework is intuitive and modular. 2. The paper is easy to follow. 3. The empirical results are extensive and show consistent performance gains across multiple LLM backbones (Gemma, Llama, Qwen) and evaluation settings (AI-AI collaboration and Human-AI collaboration), outperforming baselines on the Overcooked benchmark.
1. The paper's primary claim rests on "causality-driven planning". However, the SCA model learns a supervised mapping from $(s_t, a_{t-1})$ to $a_t$ based on data collected from a single behavior policy (MEP). It is highly questionable whether this process discovers true "causal" structure as defined by Pearl or simply learns the strong correlations and biases within that specific policy's data. The proof of identifiability (Proposition 1) relies on strong, standard assumptions (e.g., causal suf
1. The paper clearly identifies a real pain point in the LLM-planning space LLM agents often rely on correlations and fail under causal inconsistencies and proposes a targeted solution. The motivation aligns well with current challenges in multi-agent LLM systems. 2. The method section is well-structured and easy to follow. The paper clearly explains the proposed causal model and the way it integrates with LLM action sampling. The theoretical argument that, under standard identifiability assum
1. It does not compare against the most recent LLM-agent + causal reasoning methods. For example, CausalMACE[1] and Causal-aware LLMs[2]. 2. All evaluations are done in the Overcooked kitchen environment. While this benchmark is standard, it is still a fairly constrained action/state space in a stylized cooperative setting. It would be helpful to see results in a more diverse or general multi-agent domain (e.g., social games, robotics simulators). Otherwise, it's unclear how easily the method
1. Consistent gains across very different LLM backbones and layouts, not just one setup. 2. Human-partner results are stronger than baselines and include statistical testing; several settings reach p<0.05 and none show degradation when the method is enabled. 3. The causal backup plan is an effective recovery mechanism when the planner proposes no valid actions; ablation shows it adds measurable benefit beyond the two-prompt tweak. 4. The framework exposes a causal action matrix and publishe
1. The framework learns from trajectories generated by a fixed behavior policy in Overcooked-AI, which means each action is conditioned on the policy’s internal decision process. Since actions aren’t randomized or independently manipulated, the data are observational, not interventional. 2. The Structural Causal Action model optimizes a likelihood loss ( -\log P(a_t \mid s_t, a_{t-1}) ), which captures conditional correlations rather than causal effects ( P(a_t \mid s_t, \text{do}(a_{t-1})) ).
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