This document provides a detailed technical extraction and analysis of the provided image, which contains four distinct sub-charts labeled (a) through (d). ## [Chart/Diagram Type]: Comparative Analysis of Neuro-Symbolic Runtime and Performance ### Overview The image displays four sub-figures analyzing the runtime distribution and performance characteristics of various "Neuro-Symbolic" AI systems. * **(a)** A stacked bar chart showing the percentage of runtime spent on "Neuro" vs. "Symbolic" components across various tasks and models. * **(b)** A stacked bar chart comparing "Runtime Latency (min)" for different tasks across model sizes (Small vs. Large). * **(c)** A stacked bar chart comparing "Runtime Latency (min)" for specific models on two different hardware platforms (A6000 vs. Orin). * **(d)** A scatter plot (log-log scale) showing "Attainable Performance (TFLOPS/s)" versus "Operation Intensity (FLOPs/Byte)" for various models. **Legend (Applies to a, b, c):** * **Neuro (Red/Pink, hatched):** Bottom segment of bars. * **Symbolic (Green, cross-hatched):** Top segment of bars. --- ### Components/Axes #### Sub-figure (a): Runtime Percentage * **Y-Axis:** Runtime Percentage (0% to 100%). * **X-Axis:** Categorized by model groups: AlphaGeo, R²-Guard, GeLaTo, Ctrl-G, NPC, LINC. * **Tasks:** IMO, MiniF2F, TwinS, XSTest, Mod, ComGen, Review, News, ComGen, TextF1, Math, AwA2, FOLIO, Proof. #### Sub-figure (b): Runtime Latency (min) * **Y-Axis:** Runtime Latency (min) (0 to 12). * **X-Axis:** Models (Alpha, R²-G, GeLaTo, Ctrl-G, LINC) split into "Small" and "Large" variants. * **Tasks:** IMO, Safety, CoGen, Text, FOLIO. #### Sub-figure (c): Hardware Latency * **Y-Axis:** Runtime Latency (min) (0 to 24). * **X-Axis:** Models (Alpha, R²-G) on hardware (A6000, Orin). #### Sub-figure (d): Performance Scatter Plot * **Y-Axis:** Attainable Performance (TFLOPS/s) (Log scale: 10⁻¹ to 10²). * **X-Axis:** Operation Intensity (FLOPs/Byte) (Log scale: 10⁻¹ to 10²). * **Data Points:** LLaMA-3-8B (Neuro), AlphaGeo (Symb), LINC (Symb), GeLaTo (Symb), Ctrl-G (Symb), R²-Guard (Symb), NeuroPC (Symb). --- ### Detailed Analysis #### Sub-figure (a) Data Points (Neuro% / Symbolic%) * **AlphaGeo:** * IMO: 32.6% / 67.4% * MiniF2F: 39.8% / 60.2% * **R²-Guard:** * TwinS: 36.5% / 63.5% * XSTest: 33.2% / 66.8% * **GeLaTo:** * Mod: 42.1% / 57.9% * ComGen: 63.4% / 36.6% * Review: 65.1% / 34.9% * **Ctrl-G:** * News: 61.6% / 38.4% * ComGen: 36.1% / 63.9% * TextF1: 39.9% / 60.1% * **NPC:** * Math: 32.3% / 67.7% * **LINC:** * AwA2: 49.5% / 50.5% * FOLIO: 66.0% / 34.0% * Proof: 64.3% / 35.7% #### Sub-figure (b) Trends * **Trend:** "Large" variants consistently exhibit higher total latency than "Small" variants across all models. * **LINC:** Shows the highest latency overall, with the "Large" variant reaching ~11 minutes. * **R²-G:** Shows the lowest latency overall, with the "Small" variant at ~2.8 minutes. #### Sub-figure (c) Trends * **Trend:** The "Orin" hardware consistently results in significantly higher latency (approx. 4x to 6x higher) compared to the "A6000" hardware for both Alpha and R²-G models. * **Alpha (Orin):** ~21 min total latency. * **Alpha (A6000):** ~4 min total latency. #### Sub-figure (d) Trends * **Trend:** A positive correlation exists between Operation Intensity and Attainable Performance. * **Outlier:** LLaMA-3-8B (Neuro) is positioned at the top-right, significantly outperforming the other (Symbolic) models in both intensity and TFLOPS/s. * **Cluster:** The Symbolic models (AlphaGeo, LINC, GeLaTo, Ctrl-G, R²-Guard, NeuroPC) are clustered in the lower-left quadrant, indicating lower performance and lower operation intensity compared to the Neuro model. --- ### Key Observations 1. **Runtime Dominance:** In sub-figure (a), there is no uniform dominance; some tasks are heavily Symbolic-bound (e.g., NPC/Math at 67.7% Symbolic), while others are Neuro-bound (e.g., LINC/FOLIO at 66.0% Neuro). 2. **Hardware Bottleneck:** Sub-figure (c) demonstrates that the Orin hardware is a significant bottleneck for these specific Neuro-Symbolic workloads compared to the A6000. 3. **Performance Gap:** Sub-figure (d) illustrates a massive performance gap between pure Neuro models (LLaMA-3-8B) and the Neuro-Symbolic systems, which operate at much lower TFLOPS/s. --- ### Interpretation The data suggests that Neuro-Symbolic systems are heterogeneous in their computational requirements. The runtime distribution in (a) indicates that the "Symbolic" component is not a negligible overhead; it often consumes the majority of the runtime. The scatter plot in (d) is a classic "Roofline Model" visualization. The Symbolic models are clustered in the memory-bound region (low intensity, low performance), whereas the Neuro model (LLaMA-3-8B) is pushing toward the compute-bound region. This implies that while Neuro-Symbolic systems aim to combine the reasoning capabilities of symbolic logic with the performance of neural networks, the symbolic components currently act as a significant performance anchor, limiting the overall attainable TFLOPS/s compared to pure neural models. The hardware comparison in (c) confirms that these systems are sensitive to the underlying compute platform, with edge devices (Orin) struggling significantly more than server-grade GPUs (A6000).