IntelligenceCCoommpprreessssiioonn
We build deep research agents as a path to structured world models. Real machine intelligence means solving compression.
Defining the future of World Models
Read our thesis on CompressionIntroducing Intsum
A deep research decision support agent. Intsum ingests thousands of news articles, social posts, and documents daily and provides users with an Intelligence Summary to aid decision-making. It's like your own presidential level daily brief.
Try Intsum (Open Agent-v0)Current Task
Analyzing 152 Papers
Proactive Decision Support
Intsum is a proactive deep research agent designed to maintain critical context across your daily workflows.
Powered by our Autonomous Research Engine, it runs continuously in the background—performing literature search, data analysis, and hypothesis generation.
It doesn't just answer questions; it anticipates decision points, synthesizing fragmented updates into coherent, actionable intelligence.
class Intsum(DecisionAgent):
def __init__(self):
self.memory = PersistentContext()
self.engine = ResearchEngine( mode="autonomous")
async def monitor_workflow(self, user_state):
# Proactively bridge knowledge gaps
current_context = self.memory.retrieve(user_state)
gaps = self.engine.identify_blindspots(current_context)
if gaps.is_critical():
# Launch deep research cycle
insight = await self.engine.deep_dive(gaps)
self.memory.update(insight)
return insight.synthesize()
Active Context Monitor
Scanning Decision Landscape
Research
Read our latest papers on world models, entropy reduction, and decision support systems.
Read the papers (Coming soon)Open Source
We believe in open science. Our core compression algorithms are available for the community to study and improve.
Explore our research on GitHub or contribute to the next generation of world models.
Join us in building something you can never finish.
Get early access updates and express your interest in joining the lab. We review every submission and invite promising builders to an on-site interview.