5. DeepRare Introduces DeepRare, a modular agentic system powered by LLMs to aid rare disease diagnosis from multimodal clinical inputs (text, HPO terms, VCFs).
AGENTS
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Multi-Agent System for Advanced AI Search Tasks
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3. Towards AI Search Paradigm Proposes a modular multi-agent system that reimagines how AI handles complex search tasks, aiming to emulate human-like reasoning and information synthesis.
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MEM1: RL Framework for Efficient Long-Horizon Language Agents
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2. MEM1 This work introduces MEM1, an RL framework for training language agents that operate efficiently over long-horizon, multi-turn tasks by learning to consolidate memory and reasoning into a compact internal state.
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Debunking Five Common AI Agent Myths Today
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5 AI Agent Myths You Need To Stop Believing Now Misconceptions about AI agents can hold back innovation. Let’s debunk five common myths and explore what these tools can really do. Read more https://
bernardmarr.com/5-ai-agent-myt
hs-you-need-to-stop-believing-now/
… #AIAgents #AITruths #DigitalTransformation #BernardMarr -
AGI Alpha Agent: Decentralized AI Agent Platform Launch
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[ AGI Alpha: The Greatest Opportunity of Our Time ] GitHub Repository: https://
github.com/MontrealAI/AGI
-Alpha-Agent-v0
… Website: https://
agialphaagent.com Official contract information: ON-CHAIN Records of the AGI ALPHA AGENT: https://
app.ens.domains/alpha.agent.ag
i.eth
… #AGI #AGIALPHA $AGIALPHA -
Collaborative Note Taking as Collaborative AI Systems
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collaborative note taking ~= collaborative AI
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Building Effective AI Systems with Reasoning and Tools
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a little loop with reasoning
some tools and auth as seasoning
memory and self reflection
objectives give it good direction -
Collaboration Towards Superintelligence with Team Members
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very excited to be working with @shengjia_zhao
, @jhyuxm
, @ren_hongyu
, and @shuchaobi towards superintelligence -
Making LLMs Work: Beyond Out-of-the-Box Limitations
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Studies of LLMs keep looking at the (very real) failures of LLMs working out-of-the-box in complex use cases. I am always surprised that naked LLMs can get so far as generalist systems But if you want to really make an agent handle a complex workflow, you can often figure it out
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Practical Solutions for AI Agent Reliability and Error Reduction
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In practice, for many useful applications, many of the various obvious problems with AI agents (drift, hallucination, compounding errors) are more solvable than they are in theory Clever prompting, tool use, constrained topics,
LLM judges & organizational process close some gaps