CADSmith: Multi-Agent CAD Generation with Programmatic Geometric Validation

Existing methods for text-to-CAD generation either operate in a single pass with no geometric verification or rely on lossy visual feedback that cannot resolve dimensional errors. We present CADSmith, a multi-agent pipeline that generates CadQuery code from natural language. It then undergoes an iterative refinement process through two nested correction loops: an inner loop that resolves execution errors and an outer loop grounded in programmatic geometric validation. The outer loop combines exact measurements from the OpenCASCADE kernel (bounding box dimensions, volume, solid validity) with holistic visual assessment from an independent vision-language model Judge. This provides both the numerical precision and the high-level shape awareness needed to converge on the correct geometry. The system uses retrieval-augmented generation over API documentation rather than fine-tuning, maintaining a current database as the underlying CAD library evolves. We evaluate on a custom benchmark of 100 prompts in three difficulty tiers (T1 through T3) with three ablation configurations. Against a zero-shot baseline, CADSmith achieves a 100% execution rate (up from 95%), improves the median F1 score from 0.9707 to 0.9846, the median IoU from 0.8085 to 0.9629, and reduces the mean Chamfer Distance from 28.37 to 0.74, demonstrating that closed-loop refinement with programmatic geometric feedback substantially improves the quality and reliability of LLM-generated CAD models.

Synthesizing the Kill Chain: A Zero-Shot Framework for Target Verification and Tactical Reasoning on the Edge

Deploying autonomous edge robotics in dynamic military environments is constrained by both scarce domain-specific training data and the computational limits of edge hardware. This paper introduces a hierarchical, zero-shot framework that cascades lightweight object detection with compact Vision-Language Models (VLMs) from the Qwen and Gemma families (4B-12B parameters). Grounding DINO serves as a high-recall, text-promptable region proposer, and frames with high detection confidence are passed to edge-class VLMs for semantic verification. We evaluate this pipeline on 55 high-fidelity synthetic videos from Battlefield 6 across three tasks: false-positive filtering (up to 100% accuracy), damage assessment (up to 97.5%), and fine-grained vehicle classification (55-90%). We further extend the pipeline into an agentic Scout-Commander workflow, achieving 100% correct asset deployment and a 9.8/10 reasoning score (graded by GPT-4o) with sub-75-second latency. A novel "Controlled Input" methodology decouples perception from reasoning, revealing distinct failure phenotypes: Gemma3-12B excels at tactical logic but fails in visual perception, while Gemma3-4B exhibits reasoning collapse even with accurate inputs. These findings validate hierarchical zero-shot architectures for edge autonomy and provide a diagnostic framework for certifying VLM suitability in safety-critical applications.

Semantic Intelligence: Integrating GPT-4 with A Planning in Low-Cost Robotics

Classical robot navigation often relies on hardcoded state machines and purely geometric path planners, limiting a robot's ability to interpret high-level semantic instructions. In this paper, we first assess GPT-4's ability to act as a path planner compared to the A* algorithm, then present a hybrid planning framework that integrates GPT-4's semantic reasoning with A* on a low-cost robot platform operating on ROS2 Humble. Our approach eliminates explicit finite state machine (FSM) coding by using prompt-based GPT-4 reasoning to handle task logic while maintaining the accurate paths computed by A*. The GPT-4 module provides semantic understanding of instructions and environmental cues (e.g., recognizing toxic obstacles or crowded areas to avoid, or understanding low-battery situations requiring alternate route selection), and dynamically adjusts the robot's occupancy grid via obstacle buffering to enforce semantic constraints. We demonstrate multi-step reasoning for sequential tasks, such as first navigating to a resource goal and then reaching a final destination safely. Experiments on a Petoi Bittle robot with an overhead camera and Raspberry Pi Zero 2W compare classical A* against GPT-4-assisted planning. Results show that while A* is faster and more accurate for basic route generation and obstacle avoidance, the GPT-4-integrated system achieves high success rates (96-100%) on semantic tasks that are infeasible for pure geometric planners. This work highlights how affordable robots can exhibit intelligent, context-aware behaviors by leveraging large language model reasoning with minimal hardware and no fine-tuning.