Please generate a new 16:9 academic presentation based on the provided AIMS5790 individual project report and experiment-summary materials.

Presentation topic:
LLMs for Storage Failure Prediction: A StreamDFP-LLM Extension for Semantic Disk Failure Prediction

General requirements:
1. Use English for all slide text and speaker notes.
2. Use an academic presentation tone, close to a research report or thesis presentation. Avoid casual wording and marketing language.
3. Produce around 19 slides. A range of 18-20 slides is acceptable, but the deck must include a title page and a presentation outline page.
4. Follow the report structure rather than freely reorganizing the content:
   - Title Page
   - Presentation Outline
   - Abstract
   - Related Work
   - Methodology
   - Experiments and Results
   - Future Work
   - Conclusion
5. The outline page must use hierarchical numbering such as 1, 1.1, 2, 2.1, 4, 4.1.
6. Every slide must have one clear central message. Do not only list keywords.
7. Provide detailed speaker notes for every slide. The notes should support a formal 15-20 minute presentation.
8. Use icons to decorate and structure the slides, but do not let icons dominate the content.
9. Use varied layouts: title page, outline page, three-column cards, process flow, architecture figure with text, KPI cards, data tables, comparison tables, roadmap, and conclusion page.
10. Do not use AI image generation. Use the provided figures directly. Flowcharts, tables, and diagrams can be drawn with SVG, HTML/CSS, JavaScript, or native PPT shapes.
11. Keep typography, colors, spacing, and table alignment consistent. Table text must stay inside cells. Leave adequate space between slide titles and explanatory text.
12. Output a PPTX file. Prefer editable shapes where possible. If a compatibility version is available, output that as well.

Recommended slide plan:

1. Title Page
Title: LLMs for Storage Failure Prediction
Subtitle: A StreamDFP-LLM Extension for Semantic Disk Failure Prediction
Author: Daoyang LIU, 1155244610
Course: AIMS5790 Artificial Intelligence Project I
Include the repository link if space allows.

2. Presentation Outline
Use hierarchical numbering:
1. Abstract
   1.1 Motivation and task
   1.2 Core claim
   1.3 Contributions
2. Related Work
   2.1 SMART prediction baselines
   2.2 Streaming evaluation and LLM semantics
   2.3 Research gap
3. Methodology
   3.1 Baseline and architecture
   3.2 Phase 1 summarization
   3.3 Phase 2/3 cache and gates
   3.4 Policy and reproducibility
4. Experiments and Results
   4.1 Overall PASS/FALLBACK snapshot
   4.2 Disk-level result table
   4.3 mc1 stratified_v2 repair
5. Future Work and Conclusion
   5.1 Second-term plan
   5.2 Final conclusion

3. Abstract
Emphasize that the project does not replace StreamDFP with an LLM. It preserves the StreamDFP Python-Java prediction backbone and adds a structured LLM semantic enhancement layer.

4. Core Claim
Use four principles: structured, cached, gated, baseline-matched.
Explain that LLM outputs must be structured, reusable as cache features, selectively injected, and validated under the same no-LLM baseline protocol.

5. Main Contributions and Results
Cover:
- Hybrid StreamDFP-LLM architecture.
- Three-phase semantic enhancement pipeline.
- Disk-model-level policy mechanism.
- mc1 failure diagnosis and repair.
- Reproducible wrappers and local workbench.
Possible KPI values: 8 HDD enabled, 4 fallback, repaired mc1 Recall = 100.0000.

6. Related Work Overview
Position the project through three directions:
- StreamDFP: closest disk-failure prediction baseline.
- MOA: stream-learning evaluation background.
- Time-LLM / S2IP-LLM: LLM-time-series and semantic-transfer inspiration.
Clarify that this project uses the LLM as a semantic extractor, not a direct forecaster.

7. 2.1 SMART Prediction Baselines
This slide must have a clear theme: SMART prediction baseline line.
Use three cards:
- Data foundation: Backblaze Drive Stats provides daily HDD SMART telemetry.
- Baseline foundation: StreamDFP supplies the adaptive Python-Java prediction chain.
- Medium boundary: Xu et al. SSD work supports treating mc1 as a separate SSD pilot.
Conclusion: these works mainly treat SMART records as numerical signals and lack structured causal semantics.

8. 2.2 Streaming Evaluation and LLM Semantics
This slide must have a clear theme: evaluation constraint plus semantic enhancement architecture.
Use three cards:
- MOA motivates stream-learning and time-ordered evaluation.
- Time-LLM / LLM4TS show direct LLM time-series forecasting routes, but this is not the chosen architecture.
- S2IP-LLM / ChronosX motivate semantic or exogenous side-information injection, which is closest to this project's cache-based design.

9. Research Gap
Explain that practical SMART predictors and semantic explanation mechanisms are disconnected.
Bridge proposed by this project: window-tied inputs, bounded outputs, and baseline-matched validation.

10. Methodology Overview
Use the overall architecture figure.
Explain three layers:
- StreamDFP baseline layer.
- LLM semantic enhancement layer.
- Orchestration and reproducibility layer.

11. StreamDFP Baseline
Use a process flow:
Daily SMART CSV -> Python preprocessing -> Java/MOA simulation -> metric parsing.
Emphasize that the no-LLM chain defines the fairness baseline.

12. Phase 1: Window Summarization
Show how a SMART window becomes structured summary_text.
Mention typical blocks: WINDOW, DATA_QUALITY, RULE_SCORE, RULE_TOP2, ANOMALY_TABLE, CAUSE_EVIDENCE, RULE_PRED.
Emphasize that Phase 1 is not only formatting, but part of the experimental-validity chain.

13. Phase 2 and Phase 3
Use the Phase 1 -> Phase 2 -> Phase 3 dataflow figure.
Phase 2 extracts root_cause, events, risk_hint, confidence, hardness, and related semantic fields.
Phase 3 evaluates compact9, compact14, and full70 feature profiles with q/severity/rule gates before reinjection.

14. Policy and Reproducibility
Use KPI cards for the acceptance rule:
- Recall(LLM) >= Recall(noLLM)
- ACC(LLM) >= ACC(noLLM) - 1.0pp
- Output = disk-model-level enabled/fallback policy
Use onboarding workflow and workbench UI figures if space allows.

15. Overall Result Snapshot
This slide must use data from `llm_recent_experiments_master_summary_20260305.md`.
Show:
- Tracked entries = 13
- PASS = 9
- FALLBACK = 4
- N/A = 0
Explain the unified acceptance rule: Recall guard plus ACC guard.

16. Disk-Level PASS/FALLBACK Table
Use the complete disk-level result table from `llm_recent_experiments_master_summary_20260305.md`.
Required columns:
model_key, status, LLM Recall, no-LLM Recall, Delta Recall, Delta ACC, best_recall_params.
Must mention representative examples:
- hds723030ala640: Delta Recall +70.0000
- st3000dm001: Delta Recall +28.0157
- wdcwd30efrx: Delta Recall +30.0000
- wdcwd10eads: Delta Recall -90.0000, fallback
Conclusion: the evidence supports selective activation, not universal LLM superiority.

17. mc1 Stratified V2 Result
This slide must use data from `mc1_stratified_v2_final_summary_20260323.md`.
Explain that the old mc1 issue was caused by sequential sampling and was repaired with stratified_day_disk.
Use two tables:
Table 1: Phase 2 extraction quality comparison.
Models: Qwen3.5-4B, Qwen3.5-Plus, Qwen3-4B-Instruct-2507.
Columns: unknown %, mapped_event_ratio, avg confidence, avg risk_hint, avg llm_q_score.
Table 2: Phase 3 final comparison.
All three models reach the same optimum: compact14, q=0.00, sev=0.0, rule=0.
Final result: Recall = 100.0000, ACC = 99.5489, Delta Recall = +2.2296, Delta ACC = +0.0532.
Conclusion: after input repair, mc1 also obtains stable positive gains under the current strict Phase 3 protocol.

18. Future Work
Use a roadmap or timeline:
- Consolidate policy registry.
- Reduce Phase 3 cost.
- Improve Phase 2 robustness.
- Strengthen validation.
- Polish final reporting.

19. Conclusion
Final message:
LLM-derived semantic signals can improve StreamDFP when they are structured, cached, gated, and conservatively evaluated.
The conclusion must be conservative: this is not universal LLM superiority, but selective, policy-based semantic enhancement.