TactiCrafter: Tactical & Skill Transfer Framework

• TactiCrafter is a comprehensive framework that integrates sensor feedback, AR visualization, and LLM-driven multi-agent tactics to externalize tacit and strategic expertise.
• It employs modular algorithms like ISM, adapter fusion, and graph-based parsing to capture, represent, and refine skill transfer in crafts and competitive gaming.
• Applications range from precise craft documentation to dynamic tactical adaptation in multi-agent environments, fostering enhanced learning and strategic execution.

TactiCrafter is a term encompassing a set of computational frameworks and systems designed to enhance the documentation, transfer, and execution of tactical skills and strategies in domains ranging from traditional crafts to competitive multi-agent environments and games. In both human-centric and agent-based contexts, TactiCrafter emphasizes the fusion of tacit, high-level, and improvisational knowledge with precise, observable actions—often leveraging interactive interfaces, mixed reality platforms, and LLMs—to support skill learning, tactical adaptation, and collaborative practice.

1. System Architecture and Functional Principles

Multiple instantiations of TactiCrafter architectures have been demonstrated across domains. In skill transfer for traditional crafts (Nida et al., 7 Nov 2024), TactiCrafter comprises:

• A bi-directional, bracelet-type haptic device arrayed with four evenly distributed sensor–vibrator units at the wrist, enabling spatially resolved vibration capture.
• USB audio transmission of sensor data to a dedicated measurement PC, buffered and signal-processed via an Intensity Segment Modulation (ISM) algorithm.
• Tool position and orientation tracked through an OptiTrack V120: Duo motion-capture system reading reflective markers.
• Socket-based communication synchronizes tactile measurements with AR visualization on a second PC running Unity.
• Visual presentation to the operator through a Microsoft HoloLens2 see-through AR device, with a dynamic, color-mapped trail rendered at the tool tip representing normalized tactile intensity.

In competitive multi-agent domains such as Minecraft (Schipper et al., 7 Sep 2025), TactiCrafter denotes an LLM-based multi-agent framework built into PillagerBench, integrating:

• A Tactics Module for generating natural-language team plans.
• A Causal Model for learning and updating a graph of cause–effect dependencies between possible actions and outcomes.
• An Opponent Model for online inference of adversarial strategies via behavior and communication logs.
• Decentralized Base Agents generating and critiquing executable code (e.g., Mineflayer API calls) in a “generate–execute–critique” loop with self-improvement.

This general architecture enables simultaneous capture, analysis, and enactment of strategic and tacit knowledge, harmonizing high-level reasoning with real-time, situated behavior.

2. Methods for Capturing, Representing, and Visualizing Tacit and Tactical Knowledge

A core challenge addressed by TactiCrafter is the externalization of tacit, improvisational knowledge typically acquired through apprenticeship or repeated practice. In craft domains, the grammar-based approach (Batra et al., 12 Jun 2025) organizes documentation around seven patterns:

• Granularity Shifts: Fluidly transitions between coarse and fine detail, as captured in algebraic state transformations (e.g., $T_1 + D_1 = T_2$).
• Reflective Loops: Iterative do-sense-adjust cycles, highlighting decision points and adaptive behavior.
• Note-to-Self: Inline annotations for conveying invisible nuances, such as tool pressure or nonverbal technique adjustments.
• External Links: Contextual bridges to outside resources for enriching procedural understanding.
• Segments, Branches, and Revision Loops: Explicit representation of process division, method deviation, and iterative repair; supporting both planned steps and active improvisation.

Interactive systems such as CraftLink instantiate this grammar by translating narrated expert videos into graph-based representations (JSON schema) and synchronized visualizations (ReactFlow), facilitating inspection and iterative refinement by expert and novice users.

In spatial skill transfer (Nida et al., 7 Nov 2024), the ISM algorithm mathematically transforms amplitude-modulated vibration segments into perceptual intensity values:

$I(f) = A(f)^a$

where $A(f)$ denotes amplitude at frequency $f$ and $a$ represents discrimination index, with subsequent Turbo color mapping making the tactile intensity interpretable as a continuous AR trail.

In multi-agent gaming (Schipper et al., 7 Sep 2025), natural-language tactics and causal graphs serve as high-level, human-readable policy artifacts, directly linking strategic reasoning with executable action code.

3. Tactical Adaptation and Execution in Agent-Based and Human-Centric Domains

TactiCrafter frameworks operationalize tactical adaptation through modular algorithmic interventions. In StarCraft II (Ma et al., 21 Jul 2025), TacticCraft employs:

• Adapter modules (two-layer MLPs, zero-initialized, 64→32 ReLU units) attached to each DI-Star policy head.
• Tactical tensor $\tau \in \mathbb{R}^9$, encoding probability distributions over strategic archetypes (e.g., aggression, expansion, tech transitions).
• Adapter fusion:

$f(x, y) = x + y$

where $x$ is the frozen base policy output and $y$ is the tactical override from the adapter, producing conditioned action logits.

Adapters are trained by minimizing:

$$\mathcal{L}(\phi) = \mathbb{E}_{s \sim D} \left[ \sum_h \alpha_h \cdot \mathrm{KL}\big(\pi_\theta^h(a|s)\ \|\ \pi_{\theta,\phi}^h(a|s, \tau)\big)\right]$$

ensuring tactical variations while stabilizing core decision competencies. Empirical results demonstrate flexible modulation across aggression, expansion, and tech preference, including emergent hybrid strategies and defense scenarios.

In Minecraft (Schipper et al., 7 Sep 2025), TactiCrafter agents update their tactics and causal graphs episode by episode, adapting strategies in response to co-evolving opponents; key metrics such as points, sabotage, and win rate reflect this strategic evolution.

4. Evaluation, User Studies, and Performance Metrics

Empirical studies evaluate the effectiveness of TactiCrafter systems across contrastive baselines and user interaction metrics:

• In PillagerBench (Schipper et al., 7 Sep 2025), TactiCrafter agents exhibit superior performance in multi-round competitive tests over random and Chain-of-Thought baselines, notably in sabotage efficiency, average points, and win rate, with adaptive learning observed in self-play experiments.
• In CraftLink grammar documentation (Batra et al., 12 Jun 2025), expert crocheters report improved discernment of decision points and improvisations using the graph-based workflow, although some sensory nuances (e.g., tactile feel, eye–hand coordination) remain challenging to externalize.
• In skill transfer AR systems (Nida et al., 7 Nov 2024), anticipated evaluations involve condition comparisons: color mapping alone, tactile sensation only, a combination, and no feedback—testing skill reproduction and learning acceleration. Pre-recorded data facilitates movement review and skill refinement.

These findings underscore the centrality of explicit tactical and causal representations for both collaborative archival and adaptive execution.

5. Algorithms, Computational Models, and Technical Implementation

Key computational methods underpin TactiCrafter implementations:

• ISM and EMD (Empirical Mode Decomposition): Segment raw vibration data into frequency-specific components, calculating perceptual intensities per segment for color mapping (AR environments) (Nida et al., 7 Nov 2024).
• Adapter-based Policy Conditioning: Modular, parameter-efficient adapters fused additively with frozen base policy networks, minimizing computational overhead while enabling rapid tactical adaptation (Ma et al., 21 Jul 2025).
• Graph-based Workflow Parsing: LLMs process narrated workflow videos into structured JSON graphs, facilitating both inspection and iterative refinement (Batra et al., 12 Jun 2025).
• Iterative Prompting and Self-Critique: Base agents in PillagerBench generate actionable code through iterative feedback loops, using Mineflayer APIs and self-assessment to optimize task execution (Schipper et al., 7 Sep 2025).

Socket-based communication ensures synchronized multisystem operation (AR), while real-time LLM reasoning provides dynamic updating and fault-tolerant expansions in multi-agent games.

6. Applications, Domain Extensions, and Future Directions

TactiCrafter concepts have broad application potential:

• Skill transfer in traditional and high-skill domains: AR-based tactile–visual systems can accelerate learning in crafts, surgery, manufacturing, and sports by providing real-time feedback associating tactile and spatial information (Nida et al., 7 Nov 2024).
• Craft documentation and apprenticeship: Elementary grammar and interactive graph systems support dynamic, evolving archives of technique for remote peer learning and collaborative practice (Batra et al., 12 Jun 2025).
• Competitive multi-agent and gaming environments: Adapter-based tactical conditioning enables fine-grained, language-driven control of agent strategies, facilitating novel strategy discovery and responsive adaptation in complex real-time contexts (Ma et al., 21 Jul 2025, Schipper et al., 7 Sep 2025).
• Collaborative archives and distributed expertise: LLM-driven parsing and documentation systems make emergent collective knowledge accessible, remixable, and extensible across practitioner communities.

Potential future enhancements include integration of higher-resolution tactile sensors, mixed reality modalities, machine learning assessment tools, and personalized adaptation regimes. A plausible implication is that such systems will foster novel forms of collaborative skill exchange and accelerate the democratization of previously tacit and apprenticeship-locked expertise.

7. Conceptual Synthesis and Significance

TactiCrafter as a collective concept bridges explicit tactical reasoning, high-fidelity multi-modal feedback, and collaborative archival practice. Across distinct implementations—AR-based skill transfer, grammar-driven craft documentation, and LLM-enabled multi-agent tactical execution—the core principles remain: capture and externalize tacit expertise, facilitate adaptation and reflection, and render strategies both human-inspectable and agent-executable.

This suggests that TactiCrafter will continue to shape research directions in skill learning, tactical AI systems, and knowledge sharing across disciplines. The convergence of interactive computation, multi-modal data streams, and advanced reasoning engines establishes a foundation for scalable, high-resolution systems supporting expertise evolution, adaptation, and dissemination in increasingly complex environments.