🍯How Butter Bear Works

Core Capabilities

1. Retrieval-Augmented Generation (RAG) for Deep Knowledge Access

Butter Bear integrates a retrieval-augmented generation framework, allowing it to pull relevant and up-to-date knowledge from:

· Market reports

· Technical whitepapers

· On-chain analytics

· Social media discussions (e.g., Reddit, X/Twitter, Telegram)

This allows users to ask complex questions like:

“What are the risks with staking ETH this week?”, “Which DeFi tokens are showing strong momentum based on TVL growth?”, “Should I claim the ZK airdrop now or wait based on price prediction?”, “Which Layer 2 chains are trending among active developers?”, etc,

and receive grounded, source-backed responses.

RLHF to Reduce Hallucinations and Improve Financial Reasoning

Butter Bear employs reinforcement learning from human feedback (RLHF) and reinforcement learning with external financial signals (RLEFS) to correct LLM hallucinations and ensure predictions are:

· Factual

· Aligned with real market behavior

· Useful for high-stakes decisions

For instance, it penalizes confidently wrong predictions or overly vague strategies, learning instead to output precision-driven, backtest-supported reasoning.

Time-Sensitive Forecasting: Social-Aware Market Response

Crypto moves fast. Unlike traditional finance, price swings in crypto are often triggered by:

· Tweets from key influencers

· Breaking news

· Reddit sentiment Butter Bear is trained on 10 years of influencer posts and corresponding price data. Using a causal transformer-based forecasting model and temporal contrastive learning, it:

· Detects subtle sentiment shifts

· Predicts market movement windows

· Alerts users before price action follows

Every day, Butter Bear is fine-tuned on fresh influencer data and crypto price reaction, keeping it aligned with the latest narratives.