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Tik Tok is back, but with a twist!
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Use AlphaXiv for Instant Literature Reviews
AI Tool of the day
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🧠 How to Use AlphaXiv for Instant Literature Reviews
Turn hours of research into seconds using AlphaXiv’s AI Assistant. Perfect for students, researchers, and builders who want fast, contextual access to arXiv papers.
🔍 Step-by-Step
1️⃣ Go to the AlphaXiv website and sign up
2️⃣ Click on the ‘Assistant’ tab in the dashboard
3️⃣ Enter your question or research prompt
Sample Prompt:
What are the latest breakthroughs in RL fine-tuning?
4️⃣ Wait a few seconds while AlphaXiv processes
📚 What You’ll Get
A comprehensive literature review
Curated, trending arXiv papers
Summarized insights and contextual highlights
Option to use ‘@’ to reference or add specific papers to your conversation
🎓 Whether you're preparing for a thesis, exploring a new field, or just staying ahead—AlphaXiv makes academic research lightning-fast and deeply contextual.
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🚀 TikTok’s Parent Company Unveils New High-Performance Reasoning AI
ByteDance, the tech giant behind TikTok, has entered the reasoning AI competition with Seed-Thinking-v1.5, a new Mixture-of-Experts (MoE) model designed to outperform rivals like DeepSeek R1, Gemini 2.5 Pro, and OpenAI’s o3-mini.
💡 Why it matters:
Specializes in STEM, coding, and structured reasoning (similar to OpenAI’s "o" models).
MoE architecture (20B active params out of 200B total) for efficiency.
Beats DeepSeek R1 in benchmarks and matches Gemini 2.5 Pro in some tests.
Licensing still unclear—will it be open-source or proprietary?
🏆 Benchmark Breakdown: How Seed-Thinking Stacks Up
Model | AIME 2024 | Codeforces (pass@8) | GPQA (Science) | ARC-AGI |
|---|---|---|---|---|
Seed-Thinking-v1.5 | 86.7% | 55.0% | 77.3% | Top-tier |
DeepSeek R1 | ~80% | ~50% | ~70% | Slightly lower |
Gemini 2.5 Pro | ~85% | ~52% | ~75% | Comparable |
OpenAI o3-mini | ~84% | ~53% | ~76% | Comparable |
🔹 Outperforms humans on economically valuable tasks (OpenAI’s AGI benchmark criteria).
🔹 Introduced BeyondAIME, a harder math benchmark to prevent memorization.
🔧 How Seed-Thinking Was Built
✅ Reinforcement Learning (RL) Innovations:
VAPO & DAPO frameworks reduce instability in long reasoning chains.
Two-tier verification:
Seed-Verifier (checks math equivalence).
Seed-Thinking-Verifier (evaluates step-by-step reasoning).
✅ Efficient Training Infrastructure:
HybridFlow framework + Ray clusters for GPU optimization.
Streaming Rollout System (SRS) → 3x faster RL cycles.
FP8 precision, expert parallelism, auto-tuning for MoE efficiency.
✅ Data Strategy:
400K fine-tuning samples (300K STEM/logic, 100K creative).
Elite competition problems (AIME, Codeforces, GPQA).
🔮 What’s Next?
Will ByteDance open-source Seed-Thinking like Meta’s Llama?
Can it challenge OpenAI & Google in reasoning-focused AI?
Will TikTok integrate this AI for content moderation or recommendations?
💬Your Take?
Is reasoning AI the future, or just hype? Would you trust a ByteDance model?
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