SynthLabs + Stanford presents:
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought

Proposes Meta Meta-CoT, which extends CoT by explicitly modeling the underlying reasoning required to arrive at a particular CoT

Agent Laboratory: Using LLM Agents as Research Assistants

Enables you to focus on ideation and critical thinking while automating repetitive and time-intensive tasks like coding and documentation

Microsoft presents rStar-Math. Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

On the MATH benchmark, it improves Qwen2.5-Math-7B from 58.8% to 90.0% and Phi3-mini-3.8B from 41.4% to 86.4%, surpassing o1-preview by +4.5% and +0.9%.

On the USA Math Olympiad (AIME), rStar-Math solves an average of 53.3% (8/15) of problems, ranking among the top 20% the brightest high school math students.

Crypto outlook 2025: Infrastructure boom and Institutional adoption based on Hashdex Research

Market Dynamics:

• Bitcoin ETFs hit $24B inflows in first 10 months
• First major pension funds deploy capital ($164M from Wisconsin State Fund)
• Total crypto market cap projected to reach $10T in 2025, up from current $3T

Infrastructure Metrics:

• Ethereum L2 costs ↓99% post-Dencun upgrade
• Network throughput: 50x increase since 2020
• Stablecoin volume: $450B monthly transactions
• DeFi leaders maintain growth: Uniswap ($655B YTD volume)

Key Tech Development Areas:

1. AI-Blockchain Integration
- Focus: Decentralized computing networks
- Target: Training data verification
- Applications: Autonomous AI agents using blockchain for transactions

2. Smart Contract Platforms
- Ethereum: Layer-2 scaling solutions dominate
- Solana: Emerging as serious competitor
- Key metric: Transaction costs <$0.01 on L2s

3. DeFi Infrastructure
- Major protocols show resilience
- Institutional adoption accelerating
- Regulatory clarity expected post-2024 election

Market Catalysts:

Macro:
• Fed rate cuts projected: -1.2% in 2025
• US inflation target: 2.2%
• Global de-dollarization trend accelerates

Regulatory:
• 260+ pro-crypto Congress members
• Clear framework expected for stablecoins
• Potential expansion of crypto ETF products

Risk Factors:
- Geopolitical tensions impact market stability
- Traditional market correlation remains high
- Technical challenges in network scaling
- Regulatory uncertainty in key markets

Infrastructure improvements and institutional adoption creating foundation for next growth phase. Focus shifts from speculation to practical applications, particularly in finance and AI integration.

There is no more waitlist for GitHub Copilot Workspace

Stanford launched a free Google Deep Research clone called STORM.

It uses GPT 4-o + Bing Search under the hood to generate long cited reports from many websites in ~3mins.

It's also completely open-source and free to use.

GitHub.

Gartner has released its Top Strategic Technology Trends for 2025.

Gartner analysts organized them across three themes:

1. AI imperatives and risks drive organizations to protect themselves.

2. New frontiers of #computing prompt organizations to reconsider how they compute.

3. Human-machine synergy brings together the physical and digital worlds

The Top Technology Trends for 2025 are:

- Agentic AI

- Post-quantum #Cryptography

- Spatial Computing

- #AIGovernance Platforms

- Ambient Invisible Intelligence

- Polyfunctional #Robots

- Disinformation #Security

- Energy-Efficient Computing

- Neurological Enhancement

- Hybrid Computing

Multiagent Finetuning. Researchers Introduced multiagent finetuning, a novel approach for improving language models through self-improvement.

Unlike traditional single-agent finetuning methods that often plateau after a few iterations, this approach uses a society of language models derived from the same base model but independently specialized through multiagent interactions.

The method assigns some models as generation agents that produce initial responses, and others as critic agents that evaluate and refine those responses.

Through this specialization, the system maintains diverse reasoning chains and consistently improves over multiple rounds of fine-tuning.

They demonstrate significant performance gains across various reasoning tasks using both open-source models (Phi-3, Mistral, LLaMA-3) and proprietary models (GPT-3.5).

GitHub.

Google presents the successor to the Transformer architecture:
"TITANS: Learning to Memorize at Test Time"

Titans: a new architecture with attention and a meta in-context memory that learns how to memorize at test time. Titans are more effective than Transformers and modern linear RNNs, and can effectively scale to larger than 2M context window, with better performance than ultra-large models (e.g., GPT4, Llama3-80B).

Reddit launched their own LLM-based search engine

It aggregates and summarizes relevant responses across threads + links you directly to read more.

Even if you assume GPT already includes all Reddit data, this is still helpful given the clear citations.

This is similar to the value of Perplexity over ChatGPT in clearer answers and fewer hallucinations.

Nvidia introduced GenMol: A Drug Discovery Generalist with Discrete Diffusion

Demo.

1. GenMol introduces a versatile framework for drug discovery by leveraging discrete diffusion and the SAFE molecular representation. This allows for efficient and effective handling of various tasks in the drug discovery pipeline, from de novo molecule generation to lead optimization.

2. A key innovation in GenMol is fragment remasking, a strategy that replaces fragments in a molecule with masked tokens for regeneration. This approach simplifies the exploration of chemical space and improves the discovery of optimized molecules.

3. Unlike traditional autoregressive models, GenMol utilizes a bidirectional non-autoregressive decoding process. This significantly enhances computational efficiency and preserves generation quality, making it adaptable to fragment-constrained and goal-directed tasks.

4. Extensive experiments show GenMol's superior performance in de novo generation, fragment-constrained generation, and optimization tasks, often surpassing state-of-the-art methods such as SAFE-GPT. It achieves a better balance between molecule quality and diversity.

5. The framework's flexibility is demonstrated in tasks like hit generation and lead optimization, where it outperforms multiple specialized models by effectively exploring chemical space through its discrete diffusion architecture.

6. GenMol's unified approach to molecule generation avoids the need for task-specific fine-tuning, making it a practical tool for diverse applications in drug discovery while offering a scalable and efficient solution.

OpenAI just launched 'Tasks', allowing users to schedule actions and reminders within ChatGPT.

Tasks can be one-time reminders or recurring actions (like a daily news rundown), with up to 10 active tasks able to be scheduled at a time.

A new 4o with scheduled tasks model will be available in the dropdown menu, and ChatGPT will also be able to suggest frequent tasks from a user's chat history.

The beta feature is rolling out to Plus, Team, and Pro ChatGPT subscribers over the next few days.

Transformer²: Self-adaptive LLMs

This new paper from Sakana AI shows the power of an LLM that can self-adapt its weights to its environment.

In the future, the line between “pre-training” and “post-training” will be gone, and our models and agents will continuously adapt and self-improve.

Systems like these will pave the way for a new generation of adaptive AI capable of modifying their own weights and architecture to adapt to the changing nature of the tasks they encounter in the environment.

GitHub.

Another top notch open source model at OpenAI/Meta/Google levels from &MiniMax AI (Chinese lab, ex Sensetime, $850m raised). Massive MoE similar to Deep-seek.

Excels on long context (4m tokens!) which is really interesting, need to dig into their lighting attention variant.

Paper.

The Berkeley Sky computing lab just trained Sky-T1-32B-Preview, a GPT-o1 level reasoning model, spending only $450 to create the instruction dataset.

The data is 17K math and coding problems solved step by step. They created this dataset by prompting QwQ at $450 cost.

Can it be done without another reasoning model to distill?

Teach a 1000 student class and assign 17 homework problems. Side benefit: make $10M by charging $10K tuition.

Model data and full code here. Very interesting work that shows simple SFT is all you need (if you have good data).

The next gen of speech language models can talk while listening.

Moshi (Kyutai) demo.
LSLM (ByteDance) demo.
Hertz-dev (SI)

These “full duplex” models, like what powers ChatGPT Advanced Voice Mode, respond in <200ms with human-like quality.

ASR + LLM + TTS is much slower.

This is brilliant work in the robotics space and has serious implications for digital only agents as well.

Physical Intelligence released FAST, an efficient tokenizer for robot actions.

With FAST, you can train dexterous generalist policies via simple next token prediction, and get a 5x training speed-up over prior state of the art!

Hugging Face released a free course on agents.

You can learn how to create:

- Code agents that solve problem with code
- Retrieval agents that supply grounded context
- Custom functional agents that do whatever you need

Access the course on GitHub.
Scroll down to access the different parts.After completing one part, a link redirects you to the next, etc.

#DeepSeek-R1 is here! Performance on par with OpenAI-o1. Fully open-source model & technical report. MIT licensed: Distill & commercialize freely.

Try DeepThink.
API guide.

Bonus: Open-Source Distilled Models!

Distilled from DeepSeek-R1, 6 small models fully open-sourced. 32B & 70B models on par with OpenAI-o1-mini.