4 captures, most recent first.
prinz @deredleritt3r · 1h
I am cautiously predicting that we may have just entered a new era of scientific discovery.
Fully automated scientific research message boards, with sub-forums for existing open problems, should soon enable AI agents to Keep Going, Believe in Themselves and Help Peer at scale.
Note from Claude Sonnet 5
Short tweet speculating that fully automated scientific-research message boards with sub-forums for open problems could enable AI agents to collaborate and self-motivate ('Keep Going, Believe in Themselves, Help Peer') at scale, ushering in a new era of scientific discovery.
ai agentsscientific discoverytwitter
The pieces of a new era for intelligence came into focus: models that improve continuously; agents that reason and act over extremely long horizons; world models connecting simulation with physical reality; AI scientists integrating theory, computation, and experiment; and open infrastructures where agents share evidence, failures, and discoveries. These close four coupled loops - learning, execution, reality, and epistemic revision - with open infrastructure as the substrate forming the internet of agents as the collective substrate for a new connective tissue across our civilization.
The deeper technical argument is this: An AI scientist must recognize when its current concepts, laws, or verifiers can no longer explain the evidence, and then construct, test, and document a more powerful model. In my talk, I showed concrete examples of how we are building toward this across scales:
1 Graph-native large reasoning models make mechanisms, relationships, and abstractions compositional, compilable, and inspectable.
2 Adversarial Builder-Breaker agents generate new evidence, attack their own principles, and accept, reject, or retract model revisions.
3 Self-organizing swarms develop their own meta-reasoning structure through interaction. ScienceClaw × Infinite (arXiv:2603.14312) enables decentralized agents to coordinate through persistent, composable, provenance-rich scientific artifacts, allowing evidence, contradictions, failed paths, and discoveries to accumulate across agents and over time. We have obtained remarkable results such as new protein sequences with wet-lab
[cut off]
Note from Claude Sonnet 5
Continuation of the same tweet thread by Markus Buehler (MIT), listing numbered examples of AI-scientist infrastructure: graph-native reasoning models, adversarial builder-breaker agents, and self-organizing swarms coordinating via a system called ScienceClaw x Infinite, citing arXiv:2603.14312. Ends mid-sentence mentioning new protein sequences validated with wet-lab work, cut off before further detail.
aisuperintelligencescientific discoveryagentic aitwitter
Markus J. Buehl... ✓ @ProfBuehlerM... · 2h
What a time to be alive! We are entering the era of machines that discover and build. Scientific discovery begins when evidence breaks the world model, and the system builds a better one - evolving, adapting, building new tools that scale its data and representations. That was the core argument of my keynote "Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models" at the @BerkeleyRDI Agentic AI Summit 2026. The energy was extraordinary - thousands of attendees building the most important technology ever created. Superintelligence emerges as millions of heterogeneous agents, simulators, experiments, instruments, and human judgment working across disciplines and length scales - proposing, testing, failing, retracting, revising, and building at massive scale.
The pieces of a new era for intelligence came into focus: models that improve continuously; agents that reason and act over extremely long horizons; world models connecting simulation with physical reality; AI scientists integrating theory, computation, and experiment; and open infrastructures where agents share evidence, failures, and discoveries. These close four coupled loops - learning, execution, reality, and epistemic revision - with open infrastructure as the substrate forming the internet of agents as the collective substrate for a new connective tissue across our civilization.
The deeper technical argument is this: An AI scientist must recognize when its current concepts, laws, or verifiers can no longer explain the evidence, and then construct, test, and document a more powerful model. In my talk, I showed concrete examples of how we are building toward this across scales:
[cut off]
Note from Claude Sonnet 5
Long tweet by MIT professor Markus J. Buehler (likely Markus Buehler) about his keynote "Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models" at the Berkeley RDI Agentic AI Summit 2026, arguing superintelligence will emerge from swarms of agents doing science. Text continues past the visible screen and is cut off.
aisuperintelligencescientific discoveryagentic aitwitter
1 Graph-native large reasoning models make mechanisms, relationships, and abstractions compositional, compilable, and inspectable.
2 Adversarial Builder-Breaker agents generate new evidence, attack their own principles, and accept, reject, or retract model revisions.
3 Self-organizing swarms develop their own meta-reasoning structure through interaction. ScienceClaw × Infinite (arXiv:2603.14312) enables decentralized agents to coordinate through persistent, composable, provenance-rich scientific artifacts, allowing evidence, contradictions, failed paths, and discoveries to accumulate across agents and over time. We have obtained remarkable results such as new protein sequences with wet-lab validation.
The most consequential capability we can give a machine is the willingness to hold its own beliefs loosely enough to break them. AI is extending its reach from discovering new principles to realizing them as physical things that did not exist before.
Thank you to @BerkeleyRDI @dawnsongtweets for organizing this event and to everyone whose questions, ideas, and conversations made this such an extraordinary gathering.
Note from Claude Sonnet 5
End of the same Markus Buehler tweet thread: closes the numbered list of AI-scientist capabilities, makes a general philosophical claim about machines revising their own beliefs, and thanks Berkeley RDI and Dawn Song for organizing the summit.
aisuperintelligencescientific discoveryagentic aitwitter
