chromadb/context-1
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Alexander
AlexanderKyng
AI & ML interests
Deeply passionate in all things AI. Junior Data Scientist.
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Happy New Year, Hugging Face!
It's been a crazy year for me! This year I launched VANTA Research as a solo operator and managed to push out 14 original open source finetunes and 5 datasets in the span of about 4 months, completely on my own.
The reception has been much higher than I ever anticipated and sincerely appreciate everyone that's checked out my work thus far.
The good news is, I'm just getting started! In 2026 you can expect even more original models from VANTA Research, more open source datasets, and maybe some other cool things as well? 👀
2026 is gonna be big for AI in general, and I can't wait to experience it with all of you!
It's been a crazy year for me! This year I launched VANTA Research as a solo operator and managed to push out 14 original open source finetunes and 5 datasets in the span of about 4 months, completely on my own.
The reception has been much higher than I ever anticipated and sincerely appreciate everyone that's checked out my work thus far.
The good news is, I'm just getting started! In 2026 you can expect even more original models from VANTA Research, more open source datasets, and maybe some other cool things as well? 👀
2026 is gonna be big for AI in general, and I can't wait to experience it with all of you!
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Missing file preprocessor_config.json?
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9 Recent advances in Multi-Agent Systems (all open-source)
The idea to split tasks across multiple agents instead of relying on one universal agent is now seen as one of the most effective ways to build an AI stack. Concepts like “agent swarms” were highlighted at the AI Engineer Code Summit in NYC (Nov 20–21) as the winning architecture. And this trend is not only about coding and software. It applies across all AI domains.
So here is some recent research that helps keep multi-agent systems (MAS) better and up-to-date:
1. LatentMAS → Latent Collaboration in Multi-Agent Systems (2511.20639)
AI agents share their hidden "thoughts" directly in latent space instead of talking through text. This makes collaboration and reasoning way faster and accurate (no extra training needed)
2. Puppeteer → Multi-Agent Collaboration via Evolving Orchestration (2505.19591)
Uses a “puppeteer” LLM that dynamically decides which agents (“puppets”) to call and in what order. By learning this orchestration with reinforcement learning (RL), the system solves complex tasks more efficiently and with fewer compute costs
3. MADD → MADD: Multi-Agent Drug Discovery Orchestra (2511.08217)
A MAS with 4 agents for drug discovery. It lets researchers describe a drug discovery task in plain language. Then MADD automatically builds and runs the full hit-identification pipeline, making AI-driven drug design a simple end-to-end workflow
4. Multi-Agent Tool-Integrated Policy Optimization (MATPO) → Multi-Agent Tool-Integrated Policy Optimization (2510.04678)
Lets one LLM act as multiple agents (like a planner and a worker) by using different prompts and training them together with RL. So you get the benefits of a multi-agent system without needing multiple models
If you're interested in trends in multi-agent for software development of the future, explore my article with the emergent playbook. This is super interesting → https://www.turingpost.com/p/aisoftwarestack
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
Read further below ⬇️
The idea to split tasks across multiple agents instead of relying on one universal agent is now seen as one of the most effective ways to build an AI stack. Concepts like “agent swarms” were highlighted at the AI Engineer Code Summit in NYC (Nov 20–21) as the winning architecture. And this trend is not only about coding and software. It applies across all AI domains.
So here is some recent research that helps keep multi-agent systems (MAS) better and up-to-date:
1. LatentMAS → Latent Collaboration in Multi-Agent Systems (2511.20639)
AI agents share their hidden "thoughts" directly in latent space instead of talking through text. This makes collaboration and reasoning way faster and accurate (no extra training needed)
2. Puppeteer → Multi-Agent Collaboration via Evolving Orchestration (2505.19591)
Uses a “puppeteer” LLM that dynamically decides which agents (“puppets”) to call and in what order. By learning this orchestration with reinforcement learning (RL), the system solves complex tasks more efficiently and with fewer compute costs
3. MADD → MADD: Multi-Agent Drug Discovery Orchestra (2511.08217)
A MAS with 4 agents for drug discovery. It lets researchers describe a drug discovery task in plain language. Then MADD automatically builds and runs the full hit-identification pipeline, making AI-driven drug design a simple end-to-end workflow
4. Multi-Agent Tool-Integrated Policy Optimization (MATPO) → Multi-Agent Tool-Integrated Policy Optimization (2510.04678)
Lets one LLM act as multiple agents (like a planner and a worker) by using different prompts and training them together with RL. So you get the benefits of a multi-agent system without needing multiple models
If you're interested in trends in multi-agent for software development of the future, explore my article with the emergent playbook. This is super interesting → https://www.turingpost.com/p/aisoftwarestack
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
Read further below ⬇️
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Building Smarter AI Agents: A Tool-Based Architecture for Modularity and Trust
Over the past year, our AI engineering team at GoDaddy has been rethinking how to make agent systems more modular, transparent, and production-ready. Instead of viewing an AI agent as a monolithic process, we’ve decomposed it into four core tools that separate decision-making from execution — a design that’s proving critical for scale and observability:
🧩 MemoryTool – maintains persistent context and user continuity
✅ CompletionTool – determines when a task is truly complete
💬 UserInteractionTool – manages clarifications, approvals, and confirmations
🔁 DelegationTool – enables agents to hand off tasks to other agents or humans
This approach makes every step of an agent’s workflow explicit, testable, and auditable, allowing us to scale AI systems in production with higher confidence. We see this as a step toward a more open, composable agent ecosystem — one where frameworks can interoperate and agents can build trust through transparency and version control.
Read the full write-up here → Building AI Agents at GoDaddy – An Agent’s Toolkit https://www.godaddy.com/resources/news/building-ai-agents-at-godaddy-an-agents-toolkit
We’d love to collaborate and exchange ideas with the community:
- How are you designing modular agent architectures?
- What design patterns or abstractions have helped you manage agent complexity?
Let’s build smarter, safer agents together.
#AI #Agents #Architecture #MachineLearning #OpenSource #AgentFrameworks #TrustInAI
Over the past year, our AI engineering team at GoDaddy has been rethinking how to make agent systems more modular, transparent, and production-ready. Instead of viewing an AI agent as a monolithic process, we’ve decomposed it into four core tools that separate decision-making from execution — a design that’s proving critical for scale and observability:
🧩 MemoryTool – maintains persistent context and user continuity
✅ CompletionTool – determines when a task is truly complete
💬 UserInteractionTool – manages clarifications, approvals, and confirmations
🔁 DelegationTool – enables agents to hand off tasks to other agents or humans
This approach makes every step of an agent’s workflow explicit, testable, and auditable, allowing us to scale AI systems in production with higher confidence. We see this as a step toward a more open, composable agent ecosystem — one where frameworks can interoperate and agents can build trust through transparency and version control.
Read the full write-up here → Building AI Agents at GoDaddy – An Agent’s Toolkit https://www.godaddy.com/resources/news/building-ai-agents-at-godaddy-an-agents-toolkit
We’d love to collaborate and exchange ideas with the community:
- How are you designing modular agent architectures?
- What design patterns or abstractions have helped you manage agent complexity?
Let’s build smarter, safer agents together.
#AI #Agents #Architecture #MachineLearning #OpenSource #AgentFrameworks #TrustInAI