Lancelot: Behavioral AI for Chaotic Worlds (39 chars)

Generated from prompt:

Create a visually cohesive and grant-assessor–ready presentation based on the "Lancelot Grant Pitch Deck v3" using the design principles, brand palette, and typography from the Lancelot brand book. The presentation should include: 1. **Cover Slide** - Title: "Lancelot: A Behavioural World Model for Chaotic Environments" - Subheading: "R&D Grant Proposal" - Brand visuals and logo placement consistent with brand book 2. **Problem Statement** - Highlight failures of current AI in noisy, chaotic environments - Emphasize absence of models for human-triggered behaviour prediction 3. **Why This Matters** - Real-world systems resemble chaotic multi-agent environments - Importance of predicting regime shifts 4. **Use Case: Solana Meme Markets** - Justify as an ideal training environment for the model - Emphasize behavioural data, adversarial actors, measurable outcomes 5. **Project Aim** - Clear, concise statement of goals - Clarify it's not a trading tool 6. **Innovation & Novelty** - Scientific and technological innovation - JEPA + LM reasoning, adversarial modelling 7. **System Architecture** - High-level diagram or bullet structure - Flow from data input to validation 8. **Technological Uncertainties** - Questions that require experimentation - Align with Innovate UK/HMRC standards 9. **Work Packages Overview** - WP1 to WP6 with clear titles 10. **Expected Outcomes** - Scientific, technical, and commercial benefits 11. **Budget** - £200k breakdown visually formatted 12. **Timeline** - Quarter-based roadmap to Q2 2026 13. **Summary for Assessors** - Wrap-up slide emphasizing fit for Innovate UK Use brand colors like Pine Green, Heather Glow, Deep Mud, and neutral tones. Fonts should follow hierarchy: Grapiso for headings and Poppins for body text. Use photography and Celtic knot brand patterns from the brand book to reinforce structure, calm, and discipline. Keep tone professional and structured with minimal distraction. Make sure the visual design follows the aesthetic from the brand book, including layout spacing, alignment, and data visuals.

R&D grant pitch for Lancelot, a JEPA+LM world model predicting human behaviors in chaotic environments like Solana meme markets. Covers AI failures, innovation, architecture, uncertainties, 6 WPs, £20

December 5, 202513 slides

Slide 1 of 13

Slide 1 - Cover Slide

This title slide presents "Lancelot: A Behavioural World Model for Chaotic Environments" as the main project title. The subtitle identifies it as an "R&D Grant Proposal."

Lancelot: A Behavioural World Model for Chaotic Environments

R&D Grant Proposal

Source: Lancelot Grant Pitch Deck v3

Speaker Notes

Title: 'Lancelot: A Behavioural World Model for Chaotic Environments'. Subheading: 'R&D Grant Proposal'. Lancelot logo top-right, Pine Green bg fade, Celtic knot border, Heather Glow accents. Use Grapiso for headings, Poppins body text, brand palette: Pine Green, Heather Glow, Deep Mud, neutrals. Professional layout with Celtic knot patterns for structure.

Slide 2 of 13

Slide 2 - Problem Statement

Current AI fails in noisy, chaotic environments and struggles with unpredictable human behaviors. Existing systems lack models for human-triggered behavior prediction, leaving a gap in multi-agent chaos forecasting.

Problem Statement

Current AI fails in noisy, chaotic environments.
Struggles with unpredictable human behaviors.
No models for human-triggered behavior prediction.
Gap in multi-agent chaos forecasting.

Source: subtle photo bg of turbulent markets

Slide 3 of 13

Slide 3 - Why This Matters

Real-world systems like markets, crowds, and social networks are chaotic multi-agent environments with unpredictable interactions and emergent behaviors that current AI models cannot handle. Accurately predicting regime shifts in these systems enables proactive stability measures to mitigate risks in volatile settings.

Why This Matters

Real-World Systems as Chaotic Multi-Agent Environments	Predicting Regime Shifts for Stability
Markets, crowds, and social systems exhibit chaotic multi-agent dynamics with unpredictable interactions, noise, and emergent behaviours—challenges unmet by current AI models.	Accurately forecasting regime shifts enables proactive stability measures, mitigating risks in financial markets, crowd behaviours, and other volatile environments.

Source: Lancelot Grant Pitch Deck v3

Speaker Notes

Emphasize how real-world chaos mirrors multi-agent systems; stress predictive power for stability in markets and crowds.

Slide 4 of 13

Slide 4 - Use Case: Solana Meme Markets

Solana Meme Markets offer rich behavioral data from thousands of traders and controlled chaos from adversarial actors. They provide measurable price outcomes for model training and serve as an ideal environment for chaotic multi-agent modeling.

Use Case: Solana Meme Markets

Rich behavioral data from thousands of traders
Adversarial actors create controlled chaos
Measurable price outcomes for model training
Ideal environment for chaotic multi-agent modeling

Slide 5 of 13

Slide 5 - Project Aim

The "Project Aim" slide quotes Dr. Eamon Lancelot of Lancelot AI on developing a behavioral world model for chaotic environments using JEPA+LM to predict human-driven regime shifts. It clarifies that this is foundational research, not a trading tool.

Project Aim

> This project aims to develop a behavioral world model for chaotic environments using JEPA+LM, enabling accurate prediction of human-driven regime shifts. Importantly, it is foundational research—not a trading tool.

— Dr. Eamon Lancelot, Founder & Chief Scientist, Lancelot AI

Source: Lancelot R&D Grant Proposal

Speaker Notes

Highlight the research focus: Develop a behavioral world model for chaotic environments using JEPA+LM to predict human-driven regime shifts. Emphasize it's NOT a trading tool. Use Grapiso font for quote, calm photo background.

Slide 6 of 13

Slide 6 - Innovation & Novelty

This slide on "Innovation & Novelty" showcases JEPA world modeling fused with LM reasoning alongside a pioneering behavioral world model architecture. It also highlights novel adversarial human behavior simulation and a breakthrough in chaotic environment prediction.

Innovation & Novelty

JEPA world modeling fused with LM reasoning.
Novel adversarial human behavior simulation.
Breakthrough in chaotic environment prediction.
Pioneering behavioral world model architecture.

Source: Lancelot Grant Pitch Deck v3

Speaker Notes

Highlight tech diagram snippet; emphasize JEPA-LM synergy, adversarial simulation, and chaotic prediction breakthroughs.

Slide 7 of 13

Slide 7 - System Architecture

The System Architecture slide depicts a pipeline using Solana feeds for real-time chaotic data input. It integrates JEPA modeling for behavioral dynamics, LM reasoning for predictions, adversarial simulations for regime shifts, and validation against market outcomes.

System Architecture

!Image

Solana feeds provide real-time chaotic data input
JEPA modeling captures behavioral world dynamics
LM reasoning enables predictive inference
Adversarial simulations test regime shifts
Validation against measurable market outcomes

Source: Wikipedia - Data Flow Diagram

Speaker Notes

High-level overview of the system's pipeline: from Solana data ingestion through predictive modeling, reasoning, simulation, to validation. Emphasize JEPA innovation and adversarial robustness testing.

Slide 8 of 13

Slide 8 - Technological Uncertainties

The "Technological Uncertainties" slide lists key open questions about optimal JEPA scaling in chaotic environments, LM integration efficacy in multi-agent systems, and adversarial modeling's impact on prediction accuracy. It also notes the need for experiments aligned with Innovate UK standards.

Technological Uncertainties

Optimal JEPA scale for chaotic environments?
Efficacy of LM integration in multi-agent systems?
Impact of adversarial modeling on prediction accuracy?
Experiments required, per Innovate UK standards?

Source: Lancelot Grant Pitch Deck v3

Speaker Notes

Highlight key risks and mitigation via experiments, aligned to Innovate UK standards. Use question icons for visual emphasis.

Slide 9 of 13

Slide 9 - Work Packages Overview

This agenda slide titled "Work Packages Overview" lists the project's six key work packages. They include WP1: Data Pipeline, WP2: JEPA Core, WP3: LM Reasoning, WP4: Adversarial Module, WP5: Validation, and WP6: Reporting.

Work Packages Overview

WP1: Data Pipeline
WP2: JEPA Core
WP3: LM Reasoning
WP4: Adversarial Module
WP5: Validation
WP6: Reporting

Source: Lancelot Grant Pitch Deck v3

Slide 10 of 13

Slide 10 - Expected Outcomes

The "Expected Outcomes" slide highlights 85% regime shift accuracy as a key technical performance benchmark. It also features delivery of 1 new behavioral model for scientific innovation and 3 fintech IP assets to unlock commercialization potential.

Expected Outcomes

85%: Regime Shift Accuracy

Technical performance benchmark

1: New Behavioral Model

Scientific innovation delivered

3: Fintech IP Assets

Commercialisation potential unlocked

Slide 11 of 13

Slide 11 - Budget

The Budget slide outlines a total project funding of £200k. It allocates 40% (£80k) to personnel costs, 25% (£50k) to compute resources, and 20% (£40k) to data acquisition.

Budget

£200k: Total Budget

Full project funding

40%: Personnel Costs

£80k for expert team

25%: Compute Resources

£50k for model training

20%: Data Acquisition

£40k for quality datasets

Slide 12 of 13

Slide 12 - Project Timeline

The project timeline spans Q4 2024 to Q2 2026, with phases including Foundations (WP1-2) for data pipelines and baseline models, Core Development (WP3-4) for JEPA-LM integration, Training & Validation (WP5) on Solana meme data, and final WP6 optimizations with reporting. It focuses on building, training, and validating a behavioral world model for chaotic environments.

Project Timeline

Q4 2024: WP1-2: Foundations Establish data pipelines, baseline models, and initial behavioural analysis for chaotic environments. Q1 2025: WP3-4: Core Development Integrate JEPA architecture with LM reasoning and implement adversarial modelling components. Q2-Q3 2025: WP5: Training & Validation Train behavioural world model on Solana meme market data and validate predictive accuracy. Q4 2025 - Q2 2026: WP6 & Reporting Optimize model, conduct final evaluations, and deliver comprehensive project report.

Slide 13 of 13

Slide 13 - Summary for Assessors

The slide summarizes how Lancelot fits Innovate UK criteria with innovative AI for chaotic environments, high-risk R&D with uncertainties, and strong commercial potential. It closes with a call to "Fund this breakthrough!", contact details, and thanks assessors for their consideration.

Summary for Assessors

Lancelot fits Innovate UK:

Innovative AI for chaotic environments
High-risk R&D with uncertainties
Strong commercial potential

Closing: Fund this breakthrough!

[Logo] | contact@lancelot.ai

Thank you for your consideration

Source: Lancelot Grant Pitch Deck v3

Speaker Notes

Lancelot perfectly aligns with Innovate UK criteria: groundbreaking AI innovation for chaotic environments, high-risk R&D, and clear commercial pathway. End with strong CTA: 'Fund this breakthrough!' Include logo and contact details. Thank assessors.

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