## Line Charts: Model Performance Metrics vs. Number of Actions ### Overview The image contains two line charts that depict the performance of a language model ("Llama-4-Maverick-17B-128E-Instruct-FP8") as a function of the number of actions taken. The top chart shows the success rate, while the bottom chart shows precision, recall, and progress ratio, each plotted against the number of actions. Error bars are included in the bottom chart to indicate variability. ### Components/Axes **Top Chart:** * **Y-axis:** "Success rate", ranging from 0.0 to 0.6. * **X-axis:** "Number of actions", ranging from 0 to 300. * **Legend (top-right):** * Blue line with circles: "Llama-4-Maverick-17B-128E-Instruct-FP8" * Orange dashed line: "∝ exp(-L/L₀), L₀ = 16.7" **Bottom Chart:** * **Y-axis:** Implicitly ranging from 0.0 to 1.0. * **X-axis:** "Number of actions", ranging from 0 to 400. * **Legend (top-right):** * Blue line with circles and error bars: "Precision" * Orange line with circles and error bars: "Recall" * Green line with circles and error bars: "Progress ratio" ### Detailed Analysis **Top Chart: Success Rate** * **Llama-4-Maverick-17B-128E-Instruct-FP8 (Blue):** The success rate starts at approximately 0.62 for a small number of actions and rapidly decreases as the number of actions increases. It approaches 0 as the number of actions reaches 100. * Approximate data points: (10, 0.62), (20, 0.27), (30, 0.14), (50, 0.05), (100, 0.01), (150, 0.005), (200, 0.003), (250, 0.002), (300, 0.001) * **∝ exp(-L/L₀), L₀ = 16.7 (Orange Dashed):** This exponential decay curve closely matches the trend of the "Llama-4-Maverick-17B-128E-Instruct-FP8" line. It starts at approximately 0.65 and decreases rapidly, approaching 0 as the number of actions increases. **Bottom Chart: Precision, Recall, and Progress Ratio** * **Precision (Blue):** The precision starts high, around 0.95, and remains relatively stable with some fluctuations as the number of actions increases. The error bars indicate some variability. * Approximate data points: (0, 0.95), (50, 0.96), (100, 0.88), (150, 0.89), (200, 0.88), (250, 0.89), (300, 0.85) * **Recall (Orange):** The recall starts high, around 0.8, and decreases as the number of actions increases. The error bars become larger as the number of actions increases, indicating greater variability. * Approximate data points: (0, 0.8), (50, 0.7), (100, 0.6), (150, 0.45), (200, 0.4), (250, 0.3), (300, 0.3) * **Progress Ratio (Green):** The progress ratio starts at approximately 0.45 and decreases rapidly as the number of actions increases, approaching a value close to 0.1. The error bars are relatively large, especially for smaller numbers of actions. * Approximate data points: (0, 0.45), (50, 0.25), (100, 0.15), (150, 0.1), (200, 0.12), (250, 0.1), (300, 0.1) ### Key Observations * The success rate of the model decreases exponentially with the number of actions. * Precision remains relatively stable, while recall decreases as the number of actions increases. * The progress ratio decreases significantly with the number of actions. * The error bars in the bottom chart suggest that the variability in recall and progress ratio increases with the number of actions. ### Interpretation The data suggests that while the model maintains a relatively consistent level of precision as the number of actions increases, its ability to recall relevant information and make progress towards a goal diminishes. The exponential decay of the success rate indicates that the model's performance degrades rapidly with increasing task complexity (as represented by the number of actions required). The exponential decay is well modeled by the equation provided. The increasing variability in recall and progress ratio suggests that the model becomes less reliable in its performance as the number of actions increases.

## Chart Type: Performance Metrics vs. Number of Actions ### Overview The image displays two separate line charts, stacked vertically, both illustrating performance metrics as a function of the "Number of actions". The top chart shows the "Success rate" of a specific model ("Llama-4-Maverick-17B-128E-Instruct-FP8") and an exponential decay fit. The bottom chart presents "Precision", "Recall", and "Progress ratio" with error bars for an unspecified system, also against the "Number of actions". ### Components/Axes #### Top Chart: Success Rate * **X-axis Label**: "Number of actions" * **Range**: 0 to 300 * **Major Ticks**: 0, 50, 100, 150, 200, 250, 300 * **Y-axis Label**: "Success rate" * **Range**: 0.0 to 0.6 (visually extends slightly above 0.6) * **Major Ticks**: 0.0, 0.2, 0.4, 0.6 * **Legend (Top-right quadrant)**: * **Blue line with circular markers**: "Llama-4-Maverick-17B-128E-Instruct-FP8" * **Orange dashed line**: "∝ exp(−L/L₀), L₀ = 16.7" #### Bottom Chart: Precision, Recall, Progress Ratio * **X-axis Label**: "Number of actions" * **Range**: 0 to 400 * **Major Ticks**: 0, 100, 200, 300, 400 * **Y-axis Label**: (Implicitly a ratio or score, ranging from 0.0 to 1.0) * **Range**: 0.0 to 1.0 * **Major Ticks**: 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 * **Legend (Top-right quadrant)**: * **Blue line with circular markers and error bars**: "Precision" * **Orange line with circular markers and error bars**: "Recall" * **Green line with circular markers and error bars**: "Progress ratio" ### Detailed Analysis #### Top Chart: Success Rate The chart shows a rapid decrease in success rate as the number of actions increases. * **Llama-4-Maverick-17B-128E-Instruct-FP8 (Blue line with circular markers)**: * **Trend**: Starts at a high success rate and rapidly declines, approaching zero. * **Data Points (approximate)**: * At 0 actions: ~0.65 success rate * At ~10 actions: ~0.62 * At ~20 actions: ~0.50 * At ~30 actions: ~0.26 * At ~40 actions: ~0.12 * At ~50 actions: ~0.05 * At ~60 actions: ~0.02 * At ~70 actions: ~0.01 * At ~80 actions: ~0.005 * At 100 actions: ~0.002 * Beyond 100 actions, the success rate remains very close to 0, with minor fluctuations (e.g., ~0.001 at 180, 230, 280 actions). * **∝ exp(−L/L₀), L₀ = 16.7 (Orange dashed line)**: * **Trend**: This exponential decay model closely follows the observed success rate of the Llama-4-Maverick model, indicating a good fit. * **Data Points**: Visually, the orange dashed line is almost indistinguishable from the blue line, especially for the initial rapid decay phase. #### Bottom Chart: Precision, Recall, Progress Ratio This chart displays three metrics with associated error bars, showing their behavior as the number of actions increases. * **Precision (Blue line with circular markers and error bars)**: * **Trend**: Starts high, shows a slight initial dip, then stabilizes at a high level. The error bars are relatively small and consistent. * **Data Points (approximate mean and error range)**: * At 0 actions: ~0.90 (range ~0.85-0.95) * At ~20 actions: ~0.90 (range ~0.85-0.95) * At ~40 actions: ~0.90 (range ~0.85-0.95) * At ~60 actions: ~0.90 (range ~0.85-0.95) * At ~80 actions: ~0.88 (range ~0.80-0.95) * At ~120 actions: ~0.88 (range ~0.80-0.95) * At ~160 actions: ~0.88 (range ~0.80-0.95) * At ~200 actions: ~0.88 (range ~0.80-0.95) * At ~240 actions: ~0.88 (range ~0.80-0.95) * At ~280 actions: ~0.88 (range ~0.80-0.95) * **Recall (Orange line with circular markers and error bars)**: * **Trend**: Starts high, decreases significantly and steadily, with increasing uncertainty (larger error bars) as the number of actions grows. * **Data Points (approximate mean and error range)**: * At 0 actions: ~0.80 (range ~0.70-0.90) * At ~20 actions: ~0.75 (range ~0.60-0.90) * At ~40 actions: ~0.65 (range ~0.50-0.80) * At ~60 actions: ~0.60 (range ~0.40-0.80) * At ~80 actions: ~0.55 (range ~0.30-0.75) * At ~120 actions: ~0.40 (range ~0.20-0.60) * At ~160 actions: ~0.38 (range ~0.15-0.60) * At ~200 actions: ~0.35 (range ~0.10-0.55) * At ~240 actions: ~0.30 (range ~0.05-0.50) * At ~280 actions: ~0.28 (range ~0.05-0.50) * **Progress ratio (Green line with circular markers and error bars)**: * **Trend**: Starts at a moderate level, rapidly decreases, and then flattens out at a very low value. The error bars are initially very large, indicating high variability, and then shrink as the ratio approaches zero. * **Data Points (approximate mean and error range)**: * At 0 actions: ~0.45 (range ~0.00-0.80) * At ~20 actions: ~0.30 (range ~0.00-0.60) * At ~40 actions: ~0.20 (range ~0.00-0.40) * At ~60 actions: ~0.15 (range ~0.00-0.30) * At ~80 actions: ~0.12 (range ~0.00-0.25) * At ~120 actions: ~0.10 (range ~0.00-0.20) * At ~160 actions: ~0.09 (range ~0.00-0.18) * At ~200 actions: ~0.08 (range ~0.00-0.15) * At ~240 actions: ~0.07 (range ~0.00-0.15) * At ~280 actions: ~0.07 (range ~0.00-0.15) ### Key Observations * **Top Chart**: The success rate of the Llama-4-Maverick model drops very sharply with an increasing number of actions, indicating that its performance degrades significantly as the task complexity or length (represented by "Number of actions") increases. The exponential decay model provides an excellent fit for this observed behavior. * **Bottom Chart**: * **Precision** remains consistently high (around 0.88-0.90) across the range of actions, suggesting that when the system makes a positive prediction, it is usually correct. The low variability (small error bars) supports this consistency. * **Recall** shows a substantial decline as the number of actions increases, indicating that the system becomes less able to identify all relevant instances. The increasing error bars suggest higher variability in recall at higher action counts. * **Progress ratio** experiences the most dramatic drop, quickly approaching very low values. The large initial error bars highlight significant uncertainty in this metric for fewer actions. ### Interpretation The two charts together likely illustrate the performance characteristics of a language model or an AI agent in tasks requiring a sequence of actions. The **top chart** suggests that the "Llama-4-Maverick" model has a very limited "memory" or "coherence horizon" for tasks involving sequential actions. Its "Success rate" plummets rapidly, implying that beyond a small number of actions (around 50-60), the model is highly unlikely to succeed. The exponential decay fit with L₀ = 16.7 indicates a characteristic length scale for its success, meaning that for every 16.7 actions, the success rate roughly halves. This points to a fundamental limitation in maintaining task coherence or state over extended sequences. The **bottom chart** provides a more nuanced view of performance. * The high and stable **Precision** suggests that when the system *does* attempt an action or make a prediction, it is often correct. This could mean the model is good at local decision-making or generating plausible outputs, even if it misses the overall goal. * The declining **Recall** is a critical indicator. It implies that as the "Number of actions" increases, the system fails to identify or execute a growing proportion of the necessary steps or components to complete a task. This aligns with the "Success rate" drop in the top chart; if the system misses too many required actions, the overall task will fail. The increasing uncertainty in recall further suggests that this failure to recall or execute necessary steps becomes more erratic and unpredictable with longer action sequences. * The rapidly decreasing **Progress ratio** likely measures how much of the task is completed or how much progress is made towards the goal. Its sharp decline and low final values, coupled with high initial variability, reinforce the idea that the system struggles to make substantial progress on tasks requiring many actions. The large error bars at lower action counts might indicate that for simpler tasks, the "progress" can be highly variable, perhaps depending on the specific task instance or initial conditions. In essence, the system (likely the Llama-4-Maverick model or a similar agent) is precise in its individual actions but suffers from a severe recall problem and an inability to sustain progress over longer sequences of actions. This leads to a very low overall success rate for complex, multi-step tasks. The data highlights a common challenge in AI, particularly with large language models, where local coherence can be high (good precision), but global coherence and long-term planning (good recall and progress) remain difficult.

\n ## Charts: Performance Metrics vs. Number of Actions ### Overview The image contains two charts displaying performance metrics related to a language model (Llama-4-Maverick-17B-128E-Instruct-FP8) as a function of the number of actions taken. The top chart shows the success rate, while the bottom chart displays precision, recall, and progress ratio. Both charts share the x-axis representing the number of actions. ### Components/Axes **Top Chart:** * **X-axis:** Number of actions (Scale: 0 to 300, increments of 50) * **Y-axis:** Success rate (Scale: 0 to 0.6, increments of 0.1) * **Data Series:** * Llama-4-Maverick-17B-128E-Instruct-FP8 (Blue line with circle markers) * α exp(-L/L₀), L₀ = 16.7 (Orange dashed line) * **Legend:** Located at the top-right corner. **Bottom Chart:** * **X-axis:** Number of actions (Scale: 0 to 400, increments of 100) * **Y-axis:** Metric Value (Scale: 0 to 1.0, increments of 0.2) * **Data Series:** * Precision (Blue line with circle markers) * Recall (Orange line with circle markers) * Progress ratio (Green line with circle markers) * **Legend:** Located at the top-right corner. ### Detailed Analysis or Content Details **Top Chart:** The blue line representing Llama-4-Maverick-17B-128E-Instruct-FP8 starts at approximately 0.65 success rate at 0 actions and rapidly decreases to approximately 0.15 at 50 actions. It continues to decline slowly, reaching approximately 0.08 at 300 actions. The orange dashed line starts at approximately 0.65 at 0 actions and decreases more gradually than the blue line, reaching approximately 0.25 at 300 actions. **Bottom Chart:** * **Precision (Blue):** Starts at approximately 0.9 at 0 actions and remains relatively stable around 0.85-0.95 throughout the range of actions, with some fluctuations. * **Recall (Orange):** Starts at approximately 0.8 at 0 actions and decreases steadily to approximately 0.15 at 100 actions. It continues to decline, reaching approximately 0.08 at 300 actions. * **Progress Ratio (Green):** Starts at approximately 0.2 at 0 actions and decreases rapidly to approximately 0.1 at 50 actions. It continues to decline, reaching approximately 0.05 at 300 actions. Each data point has a significant error bar, indicating high variance. ### Key Observations * The success rate (top chart) decreases significantly with an increasing number of actions. * Precision remains relatively high and stable across all actions. * Recall and progress ratio (bottom chart) both decrease substantially with an increasing number of actions. * The error bars on the recall and progress ratio suggest considerable variability in these metrics. * The orange dashed line in the top chart provides a baseline for the success rate decay. ### Interpretation The data suggests that while the language model starts with a high success rate, its performance deteriorates as the number of actions increases. Precision remains consistently high, indicating that when the model does succeed, it is generally correct. However, the decreasing recall and progress ratio suggest that the model becomes less capable of finding solutions or making progress as more actions are taken. The rapid initial drop in success rate, recall, and progress ratio could indicate an initial period of exploration or learning, followed by diminishing returns. The high variance in recall and progress ratio suggests that the model's performance is sensitive to the specific task or environment. The comparison to the exponential decay function (orange dashed line) in the top chart suggests that the success rate decay follows a similar pattern. This could be used to model or predict the model's performance over time.

## Line Graphs: Model Performance Metrics vs. Number of Actions ### Overview The image contains two vertically stacked line charts sharing the same x-axis ("Number of actions"). The top chart plots the "Success rate" of a specific AI model against the number of actions, accompanied by an exponential decay fit. The bottom chart plots three related performance metrics ("Precision", "Recall", "Progress ratio") against the number of actions, with error bars indicating variability. ### Components/Axes **Top Chart:** * **X-axis:** Label: "Number of actions". Scale: Linear, from 0 to 300, with major ticks at 0, 50, 100, 150, 200, 250, 300. * **Y-axis:** Label: "Success rate". Scale: Linear, from 0.0 to 0.6 (approx. 0.7 at top), with major ticks at 0.0, 0.2, 0.4, 0.6. * **Legend (Top-right corner):** * Blue line with circle markers: "Llama-4-Maverick-17B-128E-Instruct-FP8" * Orange dashed line: "∝ exp(−L/L₀), L₀ = 16.7" **Bottom Chart:** * **X-axis:** Label: "Number of actions". Scale: Linear, from 0 to 400, with major ticks at 0, 100, 200, 300, 400. * **Y-axis:** No explicit label, but values range from 0.0 to 1.0, with major ticks at 0.0, 0.2, 0.4, 0.6, 0.8, 1.0. * **Legend (Top-right corner):** * Blue line with circle markers and vertical error bars: "Precision" * Orange line with circle markers and vertical error bars: "Recall" * Green line with circle markers and vertical error bars: "Progress ratio" ### Detailed Analysis **Top Chart - Success Rate:** * **Trend Verification:** The blue data series ("Llama-4-Maverick...") shows a steep, concave-upward decay. It starts high and decreases rapidly, then asymptotically approaches zero. The orange dashed line (exponential fit) follows this trend very closely. * **Data Points (Approximate):** * At ~10 actions: Success rate ≈ 0.63 * At ~20 actions: Success rate ≈ 0.26 * At ~30 actions: Success rate ≈ 0.14 * At ~40 actions: Success rate ≈ 0.09 * At ~50 actions: Success rate ≈ 0.06 * At ~60 actions: Success rate ≈ 0.04 * At ~100 actions: Success rate ≈ 0.02 * From ~150 to 300 actions: Success rate is very close to 0.0, with data points hovering just above the axis. **Bottom Chart - Precision, Recall, Progress Ratio:** * **Trend Verification:** * **Precision (Blue):** Starts high (~0.9) and remains relatively stable, showing a very slight downward trend with large error bars. * **Recall (Orange):** Starts moderately high (~0.8) and shows a clear, steady downward trend. * **Progress Ratio (Green):** Starts moderately high (~0.75) and shows the steepest decline of the three metrics. * **Data Points & Error Bars (Approximate):** * **Precision (Blue):** * ~10 actions: Mean ≈ 0.90, Error bar range ≈ 0.85 to 0.95 * ~50 actions: Mean ≈ 0.92, Error bar range ≈ 0.88 to 0.96 * ~100 actions: Mean ≈ 0.91, Error bar range ≈ 0.84 to 0.98 * ~200 actions: Mean ≈ 0.87, Error bar range ≈ 0.76 to 0.98 * ~300 actions: Mean ≈ 0.87, Error bar range ≈ 0.79 to 0.95 * **Recall (Orange):** * ~10 actions: Mean ≈ 0.79, Error bar range ≈ 0.68 to 0.90 * ~50 actions: Mean ≈ 0.62, Error bar range ≈ 0.40 to 0.84 * ~100 actions: Mean ≈ 0.54, Error bar range ≈ 0.18 to 0.90 * ~200 actions: Mean ≈ 0.38, Error bar range ≈ 0.16 to 0.60 * ~300 actions: Mean ≈ 0.28, Error bar range ≈ 0.10 to 0.46 * **Progress Ratio (Green):** * ~10 actions: Mean ≈ 0.74, Error bar range ≈ 0.22 to 1.00 (very large) * ~50 actions: Mean ≈ 0.26, Error bar range ≈ 0.02 to 0.50 * ~100 actions: Mean ≈ 0.11, Error bar range ≈ 0.02 to 0.20 * ~200 actions: Mean ≈ 0.09, Error bar range ≈ 0.02 to 0.16 * ~300 actions: Mean ≈ 0.04, Error bar range ≈ 0.01 to 0.08 ### Key Observations 1. **Strong Exponential Decay:** The success rate of the "Llama-4-Maverick" model decays exponentially with the number of actions, with a characteristic length scale (L₀) of 16.7 actions. The fit is excellent. 2. **Divergent Metric Trends:** While the model's **Precision** remains high and stable (though with high variance) as actions increase, its **Recall** and **Progress Ratio** degrade significantly. The Progress Ratio degrades the fastest. 3. **Increasing Variability:** The error bars for all three metrics in the bottom chart are substantial, particularly for Recall and Progress Ratio at lower action counts, indicating high variance in model performance across different trials or tasks. 4. **Performance Plateau:** All metrics, especially Success Rate and Progress Ratio, appear to plateau near zero after approximately 150-200 actions, suggesting a functional limit to the model's effective operational range in this context. ### Interpretation This data demonstrates a critical limitation in the evaluated AI model's performance on sequential or multi-step tasks. The exponential decay in success rate indicates that the probability of completing a task successfully diminishes rapidly with each additional action required. The divergence between Precision and Recall is particularly insightful. The model maintains high **Precision** (when it claims to have completed a step or identified something, it is often correct), but its **Recall** plummets (it misses an increasing number of required steps or relevant items as the task length grows). This suggests the model becomes increasingly "conservative" or "forgetful" in longer action sequences—it may avoid making incorrect predictions but at the cost of failing to complete necessary actions. The **Progress Ratio**, which likely measures the proportion of actions that meaningfully advance the task goal, decays fastest. This implies that in longer sequences, a growing fraction of the model's actions are either redundant, corrective, or non-productive. **In summary:** The model is reliable for short action sequences but suffers from a severe "horizon problem." Its ability to maintain goal-directed behavior and recall necessary information degrades exponentially with task length, even while the correctness of its individual, isolated predictions remains relatively stable. This highlights a fundamental challenge in scaling such models to complex, long-horizon problems. The provided exponential fit (L₀=16.7) offers a quantitative benchmark for this limitation.

## Line Graph: Success Rate vs. Number of Actions ### Overview The image contains two charts. The top chart is a line graph comparing a model's success rate to the number of actions, with an exponential decay model overlaid. The bottom chart is a bar graph showing three metrics (Precision, Recall, Progress ratio) across the same action range, with error bars. ### Components/Axes **Top Chart**: - **X-axis**: "Number of actions" (0 to 300, linear scale). - **Y-axis**: "Success rate" (0 to 0.6, linear scale). - **Legend**: - Blue line: "Llama-4-Maverick-17B-128E-Instruct-FP8" (actual data). - Orange dashed line: "∝ exp(−L/L₀), L₀ = 16.7" (exponential decay model). **Bottom Chart**: - **X-axis**: "Number of actions" (0 to 400, linear scale). - **Y-axis**: Metric values (0 to 1.0, linear scale). - **Legend**: - Blue circles: "Precision" (mean ± error bars). - Orange circles: "Recall" (mean ± error bars). - Green circles: "Progress ratio" (mean ± error bars). ### Detailed Analysis **Top Chart**: - The blue line (actual data) starts at ~0.62 success rate at 0 actions and decays exponentially, closely following the orange dashed model line. - Key data points: - At 0 actions: ~0.62 (blue), ~0.62 (orange). - At 50 actions: ~0.25 (blue), ~0.25 (orange). - At 100 actions: ~0.05 (blue), ~0.05 (orange). - At 150+ actions: ~0.01 (blue), ~0.01 (orange). **Bottom Chart**: - **Precision**: - Stable at ~0.9 across all actions, with small error bars (±0.02–0.05). - **Recall**: - Starts at ~0.8 at 0 actions, declines to ~0.3 at 300 actions. - Error bars increase with actions (e.g., ±0.1 at 100 actions, ±0.2 at 300 actions). - **Progress ratio**: - Starts at ~0.75 at 0 actions, declines to ~0.1 at 300 actions. - Error bars are large (e.g., ±0.1 at 100 actions, ±0.2 at 300 actions). ### Key Observations 1. **Exponential decay**: The top chart confirms the model’s success rate follows an exponential decay with a characteristic length scale L₀ = 16.7. 2. **Metric divergence**: Precision remains high, but Recall and Progress ratio degrade significantly over actions. 3. **Error variability**: Recall and Progress ratio exhibit higher uncertainty (larger error bars) compared to Precision. ### Interpretation - The exponential decay in success rate suggests the model’s performance degrades predictably with increased actions, likely due to task complexity or data distribution shifts. - The divergence between Precision (stable) and Recall/Progress ratio (declining) implies the model maintains accuracy in predictions but struggles with completeness (Recall) and incremental improvement (Progress ratio). - Large error bars for Recall and Progress ratio indicate high variability in these metrics, possibly due to sparse data or task-specific challenges. - The model’s L₀ = 16.7 implies a "half-life" of ~16.7 actions, after which success rate halves. This quantifies the rate of performance degradation.