Pre-Act Framework: Predictive Planning

• Pre-Act Framework is a predictive agent architecture that interleaves forecast-based reasoning with proactive action selection to optimize performance in dynamic settings.
• It employs a structured sequence of prediction, reasoning, and iterative refinement, allowing correction of deviations between predicted and observed outcomes.
• Empirical evaluations show significant gains in metrics such as Action Recall and Goal Completion, demonstrating its effectiveness in both LLM planning and multi-robot scheduling.

The Pre-Act Framework is a class of agent architectures that leverage explicit prediction as an integral step interleaved with reasoning and action selection. Variants have been developed in both LLM agent planning and multi-robot coordination, with the unifying principle that agents proactively generate plans or demand forecasts and use these predictions—along with observed outcomes—to strategically guide subsequent reasoning and decision-making. This paradigm demonstrably improves complex task performance, action efficiency, and adaptability across diverse domains (Fu et al., 2024, Rawat et al., 15 May 2025, Cao et al., 2024).

1. Core Principles and Conceptual Architecture

At its foundation, the Pre-Act Framework is organized around an explicit pre-action prediction phase. In LLM agent settings, this consists of agents generating forecasts of future environment feedback or multi-step plans before each action. In multi-robot warehousing, Pre-Act performs spatio-temporal forecasting of task demand before scheduling allocation decisions. The architecture can be summarized by the sequence: Prediction → Reasoning → Action (abbreviated as PreAct).

In agentic LLMs, each plan step involves:

• A Thought (reasoning trace on current information and prediction),
• An Action (tool call or answer proposal),
• Observation of environment feedback,
• Iterative refinement based on accumulated (action, observation) pairs until task completion (Rawat et al., 15 May 2025).

In warehousing, the system alternates rounds of:

• Task Flow Prediction (TFP) using recent history,
• Construction of a hybrid pool of real and predicted (virtual) tasks,
• Hybrid Task Allocation (MR-HTA) that schedules robots via joint optimization over real and predicted demand (Cao et al., 2024).

2. Pre-Act in LLM Agent Planning

The Pre-Act algorithm for LLM agents augments the standard Reasoning + Action (ReAct) protocol with a dedicated, multi-step plan generation and iterative refinement loop. Given an instruction $I$ (with optional tool definitions), Pre-Act produces a structured plan $S = \{s_1, s_2, \dots, s_n, s_{fa}\}$, where each $s_i$ is composed of:

• $\text{Thought}_i$: detailed reasoning, potentially referencing prior predictions and feedback,
• $a_i$: the selected action (tool invocation or answer).

At each step, the context $C_{i-1}$ is updated with $(a_{i-1}, o_{i-1})$ pairs, enabling subsequent steps to incorporate observed outcomes. When a predicted outcome diverges from reality, the agent explicitly notes discrepancies to drive further adaptation, thereby enhancing strategic diversity and planning accuracy (Fu et al., 2024, Rawat et al., 15 May 2025).

Empirical evaluation on the Almita dataset demonstrates that Pre-Act yields significant improvements:

• On five models, turn-level Action Recall (AR) increases by ~70% relative to ReAct,
• End-to-end Goal Completion (GC) and Progress Rate (PR) double or more (e.g., on GPT-4, GC: 0.32 → 0.64; PR: 0.44 → 0.69),
• Fine-tuned Llama 3.1 70B Pre-Act achieves AR = 0.9238, GC = 0.82, surpassing both GPT-4 Pre-Act and all ReAct baselines (Rawat et al., 15 May 2025).

The iterative Pre-Act refinement proceeds until the agent produces a final answer, allowing for structured error correction and chaining of complex, multi-step reasoning.

3. Pre-Act for Predictive Multi-Robot Scheduling

In the context of robotic warehousing, Pre-Act formalizes a two-stage pipeline: Task Flow Prediction and Hybrid Task Allocation (Cao et al., 2024). In each scheduling round:

• Task Flow Prediction employs a periodicity-decoupled temporal encoder, extracting frequency components (FFT, DWT) and learning a low-dimensional latent space via Gaussian-RBF embedding for sparse, multi-sector demand representation.
• A 3D spatio-temporal GCN stacks Diffusion GCN, Hypergraph GCN, and Heterogeneous GCN blocks, each preceded by G-TCN modules, to model directed flows, high-order dependencies, and heterogeneous relations, respectively.

Predicted demand is combined with actual observations to form a hybrid task pool. Hybrid Task Allocation is then formulated as a weighted bipartite matching, factoring in spatial costs, prediction uncertainty (via confidence/entropy decomposition), and sector-level load feedback:

• Cost for matching robot $r_j$ to task $T_i$: $$C_{i,j} = \alpha u(T_i) + \beta d_h(r_j, T_i) + \sigma \eta_{S_i}$$,
• Real tasks set $\alpha=0$; $\eta_{S_i}$ (completion rate) drives sector load balancing,
• The assignment problem is solved via a heuristic-enhanced Hungarian algorithm.

This framework admits continuous reallocation, integrates risk-aware handling of prediction uncertainty, and prioritizes real over predicted tasks when conflicts arise.

4. Comparative Analysis and Evaluation

Agentic LLMs

Model	ReAct AR	Pre-Act AR	Relative Gain	GPT-4 GC	Pre-Act GC
Llama 3.1 8B	0.1537	0.2779	+81%	—	—
Llama 3.1 70B	0.3268	0.4045	+24%	—	—
NeMo 70B	0.1460	0.3908	+167%	—	—
Distilled Llama 3.1	0.3232	0.5036	+56%	—	—
GPT-4-turbo	0.4430	0.5449	+23%	0.32	0.64

Action Recall and Goal Completion show substantial improvements for Pre-Act over ReAct, verified using two-level evaluation: turn-level action matching and end-to-end milestone achievement (Rawat et al., 15 May 2025).

Robotic Warehousing

Baseline	ERR (%)	MPT (min)	MTR (%)	Pre-Act ERR	Pre-Act MPT	Pre-Act MTR
Greedy	57.4	5.96	—	31.9	2.71	0.98
Classic Hungarian	46.1	3.98	—	31.9	2.71	0.98

Pre-Act reduces empty running rates by over 50%, slashes mean pickup time by nearly one-third, and controls mis-assignments (MTR <1%) over months of deployment with hundreds of robots (Cao et al., 2024).

5. Theoretical Considerations and Extensions

Limitations

• Error Accumulation: Predictive accuracy degrades for long forecast horizons (>15 rounds in warehousing); best performance is for short-to-mid-term planning.
• Hyperparameter Sensitivity: Tuning of spatio-temporal encoding parameters, cost weights, and uncertainty metrics is required for site-specific optimization.
• GCN Scalability: Very large or highly granular sector graphs can induce high memory costs in spatio-temporal GCN models (Cao et al., 2024).

Extensions

• Application of periodicity-decoupled (FFT, DWT) modules to non-warehousing time series (e.g., ride-hailing demand, traffic forecasting).
• Adaptive sector partitioning informed by online hot-spot detection.
• Replacement of heuristic allocations with reinforcement learning-based assigners.
• Integration of multi-modal and supply chain data for richer predictive priors.

This suggests cross-domain generality for the Pre-Act paradigm, contingent on demand forecasting tractability and action flexibility.

6. Broader Significance and Related Paradigms

The Pre-Act paradigm advances beyond reactive or single-step agentic reasoning by tightly coupling anticipation with execution. In LLM contexts, this bridges the gap between complex chain-of-thought reasoning and robust task completion, especially under partial observability or when tool use must adapt to dynamic feedback (Fu et al., 2024, Rawat et al., 15 May 2025). In multi-robot settings, it realizes significant efficiency gains by preemptively matching supply to demand.

Empirical and deployment-based results confirm that explicit prediction facilitates diversity, broader strategic coverage in planning, sector-level load balancing, and reduced idle resource waste. The design principle—explicitly forecasting and planning ahead of action—differentiates Pre-Act from techniques that rely solely on immediate reasoning/action alternations, and shows transferability across both language and robotic domains.