🏆 Foundation Models Overview

This document presents cutting-edge AI and LLM projects that push the boundaries in mathematical reasoning, multimodal learning, bias reduction, and agent development.

Projects include DeepSeek-Math-V2, DifficultySampling, Awesome Nano Banana Pro, UDA_Debias, Wave Terminal, and Acontext.

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① DeepSeek-Math-V2 – Self-Verifiable Mathematical Reasoning

DeepSeek-Math-V2 is a large language model focused on achieving self-verifiable mathematical reasoning.

Key Highlights

• Reinforcement learning reward based on correctness of the final answer.
• Recognizes that a correct final answer ≠ correct reasoning, especially in theorem proving.
• Trains an LLM-based verifier to assess reasoning completeness and rigor.
• Uses verifier outputs as the reward signal for proof generation.
• Strong competition results indicate self-verifiable reasoning as a promising research direction.

📎 Quick Access: DeepSeek-Math-V2 Project Link

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🛠 Frameworks, Platforms & Tools

② DifficultySampling – Difficulty-Aware Multimodal Sampling

DifficultySampling explores difficulty-aware data sampling for multimodal post-training without supervised fine-tuning.

Improvements Targeted:

• Mathematical reasoning
• Visual perception
• Chart interpretation
• Other multimodal reasoning tasks

It introduces a difficulty differentiation framework adaptable to various LLM scales and baselines.

📎 Quick Access: DifficultySampling Project Link

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③ Awesome Nano Banana Pro – Prompt Engineering Resources

A curated prompt collection for Nano Banana Pro (Nano Banana 2) AI image models.

Features:

• Covers styles from photorealism to stylized aesthetics.
• Includes complex creative prompt experiments.
• Sources: X (Twitter), WeChat, Replicate, and top prompt engineers.
• Helps maximize model creative potential.

📎 Quick Access: Awesome Nano Banana Pro Project Link

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④ UDA_Debias – Unsupervised Bias Reduction in LLM Evaluation

UDA_Debias combats preference bias in comparative LLM evaluations.

How It Works:

• Adjusts Elo rating system dynamically.
• Uses a compact neural network to set the K factor adaptively.
• Optimizes win probability fully unsupervised.
• Minimizes reviewer score dispersion.

Results:

• Lower score variance between reviewers.
• Better correlation with human judgement.

📎 Quick Access: UDA_Debias Project Link

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⑤ Wave Terminal – Graphical + Command-Line Hybrid

An open-source cross-platform terminal merging traditional CLI power with visual capabilities.

Supported Platforms:

• macOS
• Linux
• Windows

Built-in Tools:

• File preview
• Web browsing
• AI assistance

📎 Quick Access: (link pending)

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📌 Related Resource – AiToEarn

AiToEarn官网 is a global open-source AI content monetization platform.

Core Capabilities:

• Multi-platform publishing to: Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X (Twitter).
• Integrates with analytics and AI model rankings (AI模型排名).
• Streamlines AI content creation → distribution → monetization.

📄 Documentation: AiToEarn文档

💻 Repo: AiToEarn开源地址

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🤖 Agent Development

⑥ Acontext – Context Management for Self-Learning Agents

Acontext is a context data platform improving Agent reliability and success rates.

Core Features:

• Stores contexts and artifacts.
• Observes Agent tasks and user feedback.
• Enables Agent self-improvement via iterative learning.
• Local dashboard for messages, tasks, artifacts, and experiences.
• Works with multi-modal conversational & task-oriented Agents.

📎 Quick Access: Acontext Project Link

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Why It Matters

As Agent products grow complex:

• Context & feedback tracking become critical.
• Tools like Acontext simplify management.
• Platforms like AiToEarn complement development by enabling monetization of AI-driven workflows.

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Would you like me to also provide a summary table of all projects with links, focus areas, and main benefits? That could make this document more scannable.