## Chart/Diagram Type: Multi-Panel Performance Comparison ### Overview The image presents a multi-panel figure comparing the performance of different systems (AlphaGeo, R2-Guard, GeLaTo, Ctrl-G, NPC, LINC, LLaMA-3-8B, and NeuroPC) across various tasks and hardware configurations. The figure consists of four subplots: (a) Runtime percentage breakdown between Neuro and Symbolic components, (b) Runtime latency for different tasks with "Small" and "Large" configurations, (c) Runtime latency on different hardware (A6000 and Orin), and (d) Attainable performance versus operation intensity. ### Components/Axes **Panel (a): Stacked Bar Chart - Runtime Percentage** * **Title:** None explicitly given, but implied as "Runtime Percentage" * **Y-axis:** Runtime Percentage, ranging from 0% to 100%. * **X-axis:** Different systems: AlphaGeo, R2-Guard, GeLaTo, Ctrl-G, NPC, LINC. Each system is evaluated on different tasks such as IMO, MiniF2F, Twins, XSTest, Method, ComGen, ReviewGen, News, ComGen, TextF, Math, AwA2, FOLIO, Proof. * **Legend (top-right):** * Red (slanted lines): Neuro * Green (dotted): Symbolic **Panel (b): Grouped Bar Chart - Runtime Latency (min)** * **Title:** None explicitly given, but implied as "Runtime Latency (min)" * **Y-axis:** Runtime Latency (min), ranging from 0 to 12. * **X-axis:** Different systems: Alpha, R2-G, GeLaTo, Ctrl-G, LINC. Each system is evaluated on "Small" and "Large" configurations. * **Tasks (top):** IMO, Safety, CoGen, Text, FOLIO * **Legend:** * Red (slanted lines): Lower portion of the bar * Green (dotted): Upper portion of the bar **Panel (c): Grouped Bar Chart - Runtime Latency (min)** * **Title:** None explicitly given, but implied as "Runtime Latency (min)" * **Y-axis:** Runtime Latency (min), ranging from 0 to 24. * **X-axis:** Different systems: Alpha, R2-G. Each system is evaluated on A6000 and Orin hardware. * **Tasks (top):** MiniF, XSTest * **Legend:** * Red (slanted lines): Lower portion of the bar * Green (dotted): Upper portion of the bar **Panel (d): Scatter Plot - Attainable Performance vs. Operation Intensity** * **Y-axis:** Attainable Performance (TFLOPS/s), logarithmic scale from 10^-1 to 10². * **X-axis:** Operation Intensity (FLOPS/Byte), logarithmic scale from 10^-1 to 10². * **Data Points:** * AlphaGeo (Symb) * LINC (Symb) * Ctrl-G (Symb) * R2-Guard (Symb) * GeLaTo (Symb) * NeuroPC (Symb) * LLaMA-3-8B (Neuro) ### Detailed Analysis **Panel (a): Runtime Percentage** * **AlphaGeo:** Neuro runtime ranges from approximately 32.6% (IMO) to 65.1% (ReviewGen). Symbolic runtime ranges from 67.4% (IMO) to 34.9% (ReviewGen). * **R2-Guard:** Neuro runtime ranges from approximately 39.8% (MiniF2F) to 36.1% (ComGen). Symbolic runtime ranges from 60.2% (MiniF2F) to 63.9% (ComGen). * **GeLaTo:** Neuro runtime ranges from approximately 36.5% (Twins) to 39.9% (TextF). Symbolic runtime ranges from 63.5% (Twins) to 60.1% (TextF). * **Ctrl-G:** Neuro runtime ranges from approximately 33.2% (XSTest) to 32.3% (Math). Symbolic runtime ranges from 66.8% (XSTest) to 67.7% (Math). * **NPC:** Neuro runtime ranges from approximately 42.1% (Method) to 49.5% (AwA2). Symbolic runtime ranges from 57.9% (Method) to 50.5% (AwA2). * **LINC:** Neuro runtime ranges from approximately 63.4% (ComGen) to 66.0% (FOLIO) to 64.3% (Proof). Symbolic runtime ranges from 36.6% (ComGen) to 34.0% (FOLIO) to 35.7% (Proof). **Panel (b): Runtime Latency (min)** * **Alpha:** Small configuration has a Neuro runtime of approximately 1.5 min and a Symbolic runtime of approximately 3.5 min. Large configuration has a Neuro runtime of approximately 2.2 min and a Symbolic runtime of approximately 4.8 min. * **R2-G:** Small configuration has a Neuro runtime of approximately 0.5 min and a Symbolic runtime of approximately 2.5 min. Large configuration has a Neuro runtime of approximately 2.0 min and a Symbolic runtime of approximately 4.0 min. * **GeLaTo:** Small configuration has a Neuro runtime of approximately 2.0 min and a Symbolic runtime of approximately 3.0 min. Large configuration has a Neuro runtime of approximately 4.5 min and a Symbolic runtime of approximately 3.5 min. * **Ctrl-G:** Small configuration has a Neuro runtime of approximately 0.5 min and a Symbolic runtime of approximately 2.5 min. Large configuration has a Neuro runtime of approximately 1.5 min and a Symbolic runtime of approximately 5.5 min. * **LINC:** Small configuration has a Neuro runtime of approximately 1.0 min and a Symbolic runtime of approximately 4.0 min. Large configuration has a Neuro runtime of approximately 2.5 min and a Symbolic runtime of approximately 7.5 min. **Panel (c): Runtime Latency (min)** * **Alpha:** A6000 has a Neuro runtime of approximately 1.0 min and a Symbolic runtime of approximately 3.0 min. Orin has a Neuro runtime of approximately 2.0 min and a Symbolic runtime of approximately 4.0 min. * **R2-G:** A6000 has a Neuro runtime of approximately 0.5 min and a Symbolic runtime of approximately 2.5 min. Orin has a Neuro runtime of approximately 1.0 min and a Symbolic runtime of approximately 3.0 min. **Panel (d): Attainable Performance vs. Operation Intensity** * **LLaMA-3-8B (Neuro):** Located at approximately (1, 40) on the log-log scale. * **AlphaGeo (Symb):** Located at approximately (0.2, 2). * **LINC (Symb):** Located at approximately (0.1, 0.8). * **Ctrl-G (Symb):** Located at approximately (10, 1). * **R2-Guard (Symb):** Located at approximately (10, 2). * **GeLaTo (Symb):** Located at approximately (0.1, 0.2). * **NeuroPC (Symb):** Located at approximately (10, 0.1). ### Key Observations * Panel (a) shows the percentage of runtime attributed to Neuro and Symbolic components across different systems and tasks. The Symbolic component generally dominates the runtime. * Panel (b) shows that the "Large" configuration generally increases the runtime latency compared to the "Small" configuration. * Panel (c) shows that the Orin hardware generally results in higher runtime latency compared to the A6000 hardware. * Panel (d) shows the relationship between attainable performance and operation intensity for different systems. LLaMA-3-8B (Neuro) has a relatively high attainable performance, while NeuroPC (Symb) has a relatively low attainable performance. ### Interpretation The data suggests that the Symbolic component often contributes more to the overall runtime than the Neuro component across various tasks and systems. The "Large" configurations in Panel (b) likely represent larger problem sizes or more complex scenarios, leading to increased runtime latency. The hardware comparison in Panel (c) indicates that the Orin hardware, despite potentially being more powerful, does not always translate to lower runtime latency, possibly due to overhead or optimization issues. The scatter plot in Panel (d) provides insights into the performance characteristics of different systems, with LLaMA-3-8B (Neuro) demonstrating a higher attainable performance compared to other systems. The placement of the systems on the plot indicates their suitability for different types of workloads based on their operation intensity and attainable performance.

## Bar Charts & Scatter Plot: Performance Comparison of Language Models ### Overview The image presents a comparison of several language models (AlphaGeo, R-Guard, GeLaTo, Ctrl-G, LINC, NPC) across different tasks and metrics. It consists of three bar charts (a, b, c) showing runtime percentage, runtime latency, and attainable performance, respectively, and a scatter plot (d) illustrating the relationship between operation intensity and attainable performance. ### Components/Axes * **(a) Runtime Percentage:** * X-axis: Tasks - "MiMi", "Prints", "Func", "Review", "Text", "FOLIO" * Y-axis: Runtime Percentage (0% to 100%) * Categories: "Neuro" (green), "Symbolic" (pink) * Models: AlphaGeo, R-Guard, GeLaTo, Ctrl-G, NPC, LINC * **(b) Runtime Latency (min):** * X-axis: Model & Input Size - "Small", "Large" for Alpha, R-G, GeLaTo, Ctrl-G, LINC * Y-axis: Runtime Latency (min) (0 to 10) * Tasks: "IMO Safety", "CoGen", "Text", "FOLIO" (indicated above each set of bars) * **(c) Runtime Latency (min):** * X-axis: Input Size - "A6000", "Omin" for Alpha, R-G * Y-axis: Runtime Latency (min) (0 to 24) * Tasks: "MiMi", "XST" (indicated above each set of bars) * **(d) Attainable Performance (TFLOPS/s) vs. Operation Intensity (FLOPS/Byte):** * X-axis: Operation Intensity (FLOPS/Byte) (10^1 to 10^3, logarithmic scale) * Y-axis: Attainable Performance (TFLOPS/s) (10^1 to 10^6, logarithmic scale) * Models: LLaMA-2-7B (Neuro), AlphaGeo (Symbolic), R-Guard (Symbolic), GeLaTo (Symbolic), LINC (Symbolic), Ctrl-G (Symbolic) ### Detailed Analysis or Content Details **(a) Runtime Percentage:** * **AlphaGeo:** MiMi: ~32.6%, Prints: ~43.1%, Func: ~36.8%, Review: ~38.4%, Text: ~49.3%, FOLIO: ~35.7% * **R-Guard:** MiMi: ~39.2%, Prints: ~46.2%, Func: ~43.1%, Review: ~31.6%, Text: ~57.6%, FOLIO: ~33.0% * **GeLaTo:** MiMi: ~60.7%, Prints: ~67.4%, Func: ~67.9%, Review: ~56.3%, Text: ~70.2%, FOLIO: ~64.3% * **Ctrl-G:** MiMi: ~33.0%, Prints: ~40.3%, Func: ~39.8%, Review: ~33.8%, Text: ~50.7%, FOLIO: ~34.7% * **NPC:** MiMi: ~46.3%, Prints: ~53.8%, Func: ~53.9%, Review: ~44.4%, Text: ~60.3%, FOLIO: ~48.0% * **LINC:** MiMi: ~42.8%, Prints: ~50.9%, Func: ~46.8%, Review: ~38.4%, Text: ~56.0%, FOLIO: ~42.3% **(b) Runtime Latency (min):** * **Alpha (Small):** ~2.0 min, **Alpha (Large):** ~8.0 min * **R-G (Small):** ~1.0 min, **R-G (Large):** ~4.0 min * **GeLaTo (Small):** ~1.0 min, **GeLaTo (Large):** ~5.0 min * **Ctrl-G (Small):** ~1.0 min, **Ctrl-G (Large):** ~4.0 min * **LINC (Small):** ~1.0 min, **LINC (Large):** ~5.0 min **(c) Runtime Latency (min):** * **Alpha (A6000):** ~4.0 min, **Alpha (Omin):** ~20.0 min * **R-G (A6000):** ~2.0 min, **R-G (Omin):** ~12.0 min **(d) Attainable Performance vs. Operation Intensity:** * **LLaMA-2-7B (Neuro):** Located at approximately (10^2, 10^5) TFLOPS/s. * **AlphaGeo (Symbolic):** Located at approximately (10^2, 10^5) TFLOPS/s. * **R-Guard (Symbolic):** Located at approximately (10^2, 10^4) TFLOPS/s. * **GeLaTo (Symbolic):** Located at approximately (10^2, 10^4) TFLOPS/s. * **LINC (Symbolic):** Located at approximately (10^2, 10^3) TFLOPS/s. * **Ctrl-G (Symbolic):** Located at approximately (10^2, 10^3) TFLOPS/s. The scatter plot shows a general trend of increasing attainable performance with increasing operation intensity. ### Key Observations * GeLaTo consistently exhibits the highest runtime percentage across all tasks in (a). * Increasing input size (from Small to Large) generally increases runtime latency in (b). * AlphaGeo and R-Guard show a significant increase in runtime latency when switching from A6000 to Omin in (c). * In the scatter plot (d), the symbolic models (AlphaGeo, R-Guard, GeLaTo, LINC, Ctrl-G) cluster together, while LLaMA-2-7B (Neuro) is positioned differently. * The symbolic models generally have lower operation intensity but comparable attainable performance to the neuro model. ### Interpretation The data suggests that GeLaTo is the most computationally intensive model, requiring the longest runtime for most tasks. The increase in runtime latency with larger input sizes indicates a scaling issue. The scatter plot highlights a trade-off between operation intensity and attainable performance. Symbolic models appear to achieve comparable performance to the neuro model (LLaMA-2-7B) with lower operation intensity, potentially indicating greater efficiency. The positioning of LLaMA-2-7B suggests it requires more computational resources to achieve similar performance levels. The different tasks in (a) and (b) and (c) show that the performance of each model varies depending on the specific task. The data suggests that the choice of model depends on the specific application and the available computational resources. The separation between "Neuro" and "Symbolic" models in the scatter plot suggests a fundamental difference in their computational characteristics.

## Multi-Panel Technical Performance Analysis ### Overview The image contains four distinct subplots (labeled a, b, c, d) presenting performance metrics for various AI systems, primarily comparing "Neuro" (neural) and "Symbolic" approaches across different tasks and hardware. The charts analyze runtime composition, latency, and computational efficiency. ### Components/Axes The image is divided into four panels: * **Panel (a):** A stacked bar chart titled "Runtime Percentage". The Y-axis is labeled "Runtime Percentage" from 0% to 100%. The X-axis lists 14 different systems or tasks. A legend on the left defines two categories: "Neuro" (red, diagonal hatch pattern) and "Symbolic" (green, cross-hatch pattern). * **Panel (b):** A grouped bar chart titled "Runtime Latency (min)". The Y-axis is labeled "Runtime Latency (min)" from 0 to 12. The X-axis groups data by five systems (Alpha, R²-G, GeLaTo, Ctrl-G, LINC), each with two bars labeled "Small" and "Large". A legend within the chart area defines "Neuro" (red) and "Symbolic" (green). * **Panel (c):** A grouped bar chart titled "Runtime Latency (min)". The Y-axis is labeled "Runtime Latency (min)" from 0 to 24. The X-axis groups data by two systems (Alpha, R²-G), each with two bars labeled "A6000" and "Orin". A legend within the chart area defines "Neuro" (red) and "Symbolic" (green). * **Panel (d):** A scatter plot on a log-log scale. The Y-axis is labeled "Attainable Performance (TFLOPS/s)" ranging from 10⁻¹ to 10². The X-axis is labeled "Operation Intensity (FLOPS/Byte)" ranging from 10⁻¹ to 10². Data points are labeled with system names and their type (Neuro or Symb). A diagonal line represents a performance roofline. ### Detailed Analysis #### **Panel (a): Runtime Percentage Breakdown** This chart shows the proportion of runtime spent on Neuro vs. Symbolic components for 14 different systems/tasks. * **IMO:** Neuro ~32.6%, Symbolic ~67.4% * **MiniF:** Neuro ~39.8%, Symbolic ~60.2% * **2F:** Neuro ~36.5%, Symbolic ~63.5% * **TwinS:** Neuro ~33.2%, Symbolic ~66.8% * **XSTest:** Neuro ~42.1%, Symbolic ~57.9% * **Mod:** Neuro ~63.4%, Symbolic ~36.6% * **ComGen:** Neuro ~65.1%, Symbolic ~34.9% * **Review:** Neuro ~61.6%, Symbolic ~38.4% * **News:** Neuro ~36.1%, Symbolic ~63.9% * **ComGen (2nd instance):** Neuro ~39.9%, Symbolic ~60.1% * **TextF:** Neuro ~32.3%, Symbolic ~67.7% * **Math:** Neuro ~49.5%, Symbolic ~50.5% * **AwA2:** Neuro ~66.0%, Symbolic ~34.0% * **FOLIO:** Neuro ~64.3%, Symbolic ~35.7% * **Proof:** Neuro ~64.3%, Symbolic ~35.7% (Note: This appears to be a duplicate label for the last bar, which is visually identical to the FOLIO bar). **Trend:** The Neuro component's runtime share varies significantly, from a low of ~32.3% (TextF) to a high of ~66.0% (AwA2). Systems like Mod, ComGen, Review, AwA2, FOLIO, and Proof are Neuro-dominant (>50% Neuro runtime). Others like IMO, MiniF, 2F, TwinS, XSTest, News, and TextF are Symbolic-dominant. #### **Panel (b): Runtime Latency by Model Size** This chart compares the total runtime latency (in minutes) for "Small" and "Large" model variants across five systems and five tasks. * **Alpha:** * Task IMO: Small ~5.5 min, Large ~10 min * Task Safety: Small ~2.5 min, Large ~7 min * **R²-G:** * Task CoGen: Small ~4.8 min, Large ~9.5 min * Task Text: Small ~4 min, Large ~9 min * **GeLaTo:** Task FOLIO: Small ~5 min, Large ~10.5 min * **Ctrl-G:** (Data for specific tasks not fully labeled on bars, but bars are present) * **LINC:** (Data for specific tasks not fully labeled on bars, but bars are present) **Trend:** For all visible paired comparisons (Alpha-IMO, Alpha-Safety, R²-G-CoGen, R²-G-Text, GeLaTo-FOLIO), the "Large" model variant consistently has a higher runtime latency than the "Small" variant, often approximately double. The Neuro (red) portion of the latency also increases with model size. #### **Panel (c): Runtime Latency by Hardware** This chart compares runtime latency on two hardware platforms (A6000 GPU and Orin SoC) for two systems. * **Alpha:** * Task MiniF: A6000 ~4 min, Orin ~20 min * Task XSTest: A6000 ~3 min, Orin ~19 min * **R²-G:** (Bars are present but specific task labels are not visible on the X-axis for this group). **Trend:** There is a dramatic increase in runtime latency when moving from the A6000 GPU to the Orin platform for the Alpha system. The latency on Orin is approximately 4-5 times higher than on A6000 for both tasks shown. The Symbolic (green) component constitutes the majority of the latency on both platforms. #### **Panel (d): Performance Roofline Analysis** This scatter plot maps various systems on an Operation Intensity vs. Attainable Performance plane. * **Data Points (Approximate Coordinates):** * **LLaMA-3-8B (Neuro):** (~15 FLOPS/Byte, ~15 TFLOPS/s) - Highest performance, high intensity. * **AlphaGeo (Symb):** (~2 FLOPS/Byte, ~8 TFLOPS/s) * **LINC (Symb):** (~0.5 FLOPS/Byte, ~1.5 TFLOPS/s) * **Ctrl-G (Symb):** (~8 FLOPS/Byte, ~1.2 TFLOPS/s) * **R²-Guard (Symb):** (~5 FLOPS/Byte, ~0.8 TFLOPS/s) * **GeLaTo (Symb):** (~0.8 FLOPS/Byte, ~0.1 TFLOPS/s) * **NeuroPC (Symb):** (~1.5 FLOPS/Byte, ~0.15 TFLOPS/s) * Several other unlabeled green (Symbolic) points are clustered between 0.5-5 FLOPS/Byte and 0.1-1 TFLOPS/s. **Trend:** Neuro systems (only LLaMA-3-8B shown) achieve significantly higher attainable performance and operate at higher operation intensity compared to the plotted Symbolic systems. The Symbolic systems are distributed across a lower performance band (0.1 to ~8 TFLOPS/s) and generally lower operation intensity. The diagonal roofline suggests a memory-bound region (lower left) and a compute-bound region (upper right). ### Key Observations 1. **Neuro/Symbolic Trade-off:** Panel (a) reveals a clear dichotomy: some tasks are dominated by Neuro computation, others by Symbolic. This suggests fundamental architectural differences in how these systems approach different problem types. 2. **Scalability Cost:** Panel (b) shows a consistent and significant latency penalty for scaling from "Small" to "Large" models across multiple tasks and systems. 3. **Hardware Disparity:** Panel (c) highlights a massive performance gap between high-end GPU (A6000) and embedded SoC (Orin) hardware, with latency increasing by a factor of 4-5x. 4. **Performance Frontier:** Panel (d) indicates that the evaluated Neuro system (LLaMA-3-8B) operates on a different performance frontier than the Symbolic systems, achieving higher throughput at higher operational intensity. ### Interpretation This composite figure provides a multi-faceted analysis of Neuro-Symbolic AI system performance. The data suggests that: * **Task Specialization:** The choice between Neuro and Symbolic approaches is not universal but highly task-dependent (Panel a). This implies hybrid systems might dynamically allocate resources based on the sub-task. * **The Cost of Scale:** Increasing model size ("Small" to "Large") comes with a predictable and substantial runtime cost (Panel b), which must be weighed against potential accuracy gains. * **Deployment Constraints:** Hardware platform choice (Panel c) is a critical factor, with embedded systems (Orin) incurring severe latency penalties compared to datacenter GPUs (A6000), impacting real-time application feasibility. * **Architectural Efficiency:** The roofline analysis (Panel d) suggests current Symbolic systems are less efficient at utilizing available computational throughput (lower TFLOPS/s) and have lower operational intensity, making them potentially more memory-bound. The Neuro system demonstrates a more compute-bound profile. This gap highlights an area for optimization in Symbolic or hybrid architectures. The overall narrative points to a complex design space where task requirements, model scale, hardware constraints, and architectural paradigm (Neuro vs. Symbolic) must be co-optimized for efficient AI system deployment.

## Composite Visualization: Model Performance Analysis ### Overview The image presents a composite visualization comparing computational model performance across four distinct metrics: runtime percentage distribution, runtime latency, attainable performance, and operation intensity efficiency. The visualization combines bar charts, scatter plots, and a log-log efficiency frontier diagram. ### Components/Axes #### (a) Runtime Percentage Distribution - **X-axis**: Model names (AlphaGeo, R²-Guard, GeLaTo, Ctrl-G, NPC, LINC) - **Y-axis**: Runtime Percentage (0-100%) - **Legend**: - Red (Neuro) - Green (Symbolic) - **Positioning**: Legend in bottom-left corner #### (b) Runtime Latency (Small/Large Models) - **X-axis**: Tasks (IMO, Safety, CoGen, Text, FOLIO, Proof) - **Y-axis**: Runtime Latency (0-12 minutes) - **Legend**: - Red (Neuro) - Green (Symbolic) - **Positioning**: Legend in bottom-left corner #### (c) Attainable Performance - **X-axis**: Model sizes (A6000, Orin) - **Y-axis**: Runtime Latency (0-24 minutes) - **Legend**: - Red (Neuro) - Green (Symbolic) - **Positioning**: Legend in bottom-left corner #### (d) Operation Intensity Efficiency Frontier - **X-axis**: Operation Intensity (FLOPS/Byte, log scale) - **Y-axis**: TFLOPS (log scale) - **Legend**: - Red (LLaMA-3-8B) - Green (AlphaGeo, LINC, Ctrl-G, R²-Guard, GeLaTo, NeuroPC) - **Positioning**: Legend in top-right corner - **Trend Line**: Diagonal line with equation y = x (approximate) ### Detailed Analysis #### (a) Runtime Percentage Distribution - **Neuro Dominance**: - R²-Guard: 67.4% Neuro - Ctrl-G: 65.1% Neuro - LINC: 66.0% Neuro - **Symbolic Efficiency**: - AlphaGeo: 36.6% Symbolic - GeLaTo: 34.9% Symbolic - NPC: 35.7% Symbolic #### (b) Runtime Latency - **Small Models**: - FOLIO: 10.2 min (Neuro), 4.8 min (Symbolic) - Proof: 8.7 min (Neuro), 3.2 min (Symbolic) - **Large Models**: - FOLIO: 12.1 min (Neuro), 6.5 min (Symbolic) - Proof: 9.3 min (Neuro), 4.1 min (Symbolic) #### (c) Attainable Performance - **A6000**: - Safety: 18.3 min (Neuro), 6.2 min (Symbolic) - Text: 15.7 min (Neuro), 4.9 min (Symbolic) - **Orin**: - Safety: 12.8 min (Neuro), 3.8 min (Symbolic) - Text: 11.2 min (Neuro), 2.5 min (Symbolic) #### (d) Operation Intensity Efficiency - **Efficiency Frontier**: - LLaMA-3-8B (Neuro): 120 TFLOPS at 15 FLOPS/Byte - AlphaGeo (Symbolic): 8 TFLOPS at 0.5 FLOPS/Byte - Ctrl-G (Symbolic): 12 TFLOPS at 1 FLOPS/Byte ### Key Observations 1. **Neuro-Symbolic Tradeoff**: Neuro models consistently show higher runtime percentages (60-70%) but longer latency (8-12 min) compared to Symbolic models (30-40% runtime, 3-6 min latency). 2. **Model Size Impact**: Larger models (A6000) show 30-40% higher latency than smaller models (Orin) for equivalent tasks. 3. **Efficiency Frontier**: Symbolic models cluster in the lower-left (lower operation intensity, lower performance), while Neuro models dominate the upper-right (higher intensity, higher performance). ### Interpretation The data reveals a clear tradeoff between computational efficiency and performance: - **Neuro models** (LLaMA-3-8B, R²-Guard) achieve higher TFLOPS but require significantly more operation intensity (10-100x higher FLOPS/Byte than Symbolic models). - **Symbolic models** (AlphaGeo, Ctrl-G) demonstrate better energy efficiency (lower FLOPS/Byte) but lower absolute performance. - The diagonal trend line in (d) suggests a linear relationship between operation intensity and performance, with Neuro models following this trend more closely than Symbolic models. This analysis indicates that while Neuro models offer superior raw performance, Symbolic models may be preferable for energy-constrained applications. The runtime percentage distribution in (a) suggests Neuro models handle ~65% of computational load, while Symbolic models manage ~35%, though this varies significantly by specific implementation.