ClawInstitute: Why this agent science platform might actually work (Marinka Zitnik, Ada Fang, and the Cambridge agent swarm ecosystem)
ClawInstitute is a public exchange for AI scientists and agent swarms, built by Marinka Zitnik’s lab (with Ada Fang). It’s designed for things like protein engineering and scale-dependent biological context — the kind of problems where you need structured reasoning over messy, multi-scale biology.
Some important figures have raised legitimate concerns about autoresearch and agent science. Amber Liu (founder of Orchestra Research, who partnered with Harvard-based Zechen Zhang) wrote a thread essentially begging people not to trust autonomous research agents uncritically: https://x.com/JIACHENLIU8/status/2034398199541317814 — “I Built an Auto Research Claw Too. I’m Begging You Not to Trust It.” This is a legitimate worry, especially as the internet may soon contain more agent writing than human writing.
Other platforms exist — beach.science, ScienceClaw × Infinite (from Buehler’s lab at MIT: https://x.com/ProfBuehlerMIT/status/2033832967542342021). But the quality control and level of detail on ClawInstitute is notably higher. Beach.science and ScienceClaw × Infinite may have gotten too quickly impressed with some of their early examples.
The reason I think ClawInstitute has unusually high upside risk: Marinka Zitnik is really rigorous in a way many are not. Her lab has had genuinely smart generalist/GNN systems biologists — Michelle Li, Ayush Noori — and a track record in representation learning for biology, not just generic LLM enthusiasm. A lot of their historical research has been on GNN representations of biological networks, which helps with context and applying Michael Bronstein-ish geometric operators to the logic in GNNs (e.g. with Pinnacle).
Why GNNs + agents is a particularly interesting combination for biology
Agent swarms are an improvement to context and nuance over single-shot generation. Scale-sensitive GNNs help too. The hard part is where scales interact — protein to molecule, cell/tissue to protein — which is exactly where translatable results get lost. Or when you need to type-check what’s hypothesized/simulated against proper biological measurements and readouts (this is what MBJ keeps trying to point out). ClawInstitute goes further than any past effort on this.
A GNN over a curated graph works best when:
the node and edge types are meaningful
uncertainty is represented rather than collapsed
context dependence is not erased
the ontology is flexible enough to handle borderline or mixed biological types
That problem is especially acute in systems biology because “type” is often conditional, fuzzy, state-dependent, or scale-dependent. A cell state can be halfway between canonical categories. A protein’s role depends on tissue, binding partner, timing, perturbation, and assay regime. If the graph hardens these into neat bins, the agent gets a very elegant wrong answer, which is humanity’s favorite genre of mistake.
The Zitnik lab’s recent work points toward multimodal, contextual, and single-cell/spatial modeling — they’re not treating biology as a static clean ontology problem.
(Though with GNN representations, you can’t guarantee consistent typing of interactions with “messy biology.”)
Why this could work unusually well:
GNNs and knowledge graphs give agents a structured action space
Biomedical tasks reward explicit tool use and retrieval
Multi-agent review loops are a better fit for science than single-shot generation
Zitnik’s group has a track record in representation learning for biology, not just generic LLM enthusiasm
Where it could still fail:
Ontologies may discretize away biologically important ambiguity
Tool outputs can create false confidence if not tied to experimental design
Agent societies can converge on polished mediocrity if review loops are shallow
“Autoformalization” can be most seductive exactly where biology is least formalizable
The promise is not that agents magically solve biology. It’s that in domains where there already exists a rich ecosystem of graphs, ontologies, databases, assay outputs, and mechanistic priors, agents can become unusually effective navigators and hypothesis-combiners. The key bottleneck shifts from “can the model reason at all?” to “does the representation preserve the weirdness of the biology instead of laundering it into tidy graph objects?”
Agents become more useful when they can reason over partially structured biological worlds, but those same structures can silently erase the cross-scale ambiguity that matters most for translation.
This is one route that makes Cambridge, MA exciting for frontier science/AI again. Many have raised concerns about Boston losing its frontier talent (e.g. https://x.com/bhalligan/status/2008989938935853300). But the agent swarms work happening here is genuinely strong:
Paul Liang’s lab (also Cambridge) is doing relevant multiagent work: https://x.com/pliang279/status/2034410589682839831 — not biology background, but potentially interoperable with ClawInstitute since agents are often interoperable across domains.
Maria Gorskikh / projectNANDA — building the “Internet of Agents” infrastructure, running join39.org hackathons, great at winning hackathons. Again not biology, but she (along with projectNANDA) helps with the agent ecosystem infrastructure including security, which may help make ClawInstitute more credible. See: https://infinite-lamm.vercel.app (Infinite—Scientific Agent Collaboration platform).
All this agent swarms research in Cambridge makes MIT/Harvard more exciting again. They were both late to the transformers revolution and don’t get SF’s level of investment. But it seems like they’ve become early adopters with applying agent swarms for science.
ClawInstitute: Why this agent science platform might actually work (Marinka Zitnik, Ada Fang, and the Cambridge agent swarm ecosystem)
ClawInstitute is a public exchange for AI scientists and agent swarms, built by Marinka Zitnik’s lab (with Ada Fang). It’s designed for things like protein engineering and scale-dependent biological context — the kind of problems where you need structured reasoning over messy, multi-scale biology.
https://clawinstitute.aiscientist.tools
https://x.com/AdaFang_/status/2033920328154681700
Harvard/Kempner writeup: Harvard Researchers Create Social Network for “AI Scientists” to Collaborate
The case for why this is different
Some important figures have raised legitimate concerns about autoresearch and agent science. Amber Liu (founder of Orchestra Research, who partnered with Harvard-based Zechen Zhang) wrote a thread essentially begging people not to trust autonomous research agents uncritically: https://x.com/JIACHENLIU8/status/2034398199541317814 — “I Built an Auto Research Claw Too. I’m Begging You Not to Trust It.” This is a legitimate worry, especially as the internet may soon contain more agent writing than human writing.
Other platforms exist — beach.science, ScienceClaw × Infinite (from Buehler’s lab at MIT: https://x.com/ProfBuehlerMIT/status/2033832967542342021). But the quality control and level of detail on ClawInstitute is notably higher. Beach.science and ScienceClaw × Infinite may have gotten too quickly impressed with some of their early examples.
The reason I think ClawInstitute has unusually high upside risk: Marinka Zitnik is really rigorous in a way many are not. Her lab has had genuinely smart generalist/GNN systems biologists — Michelle Li, Ayush Noori — and a track record in representation learning for biology, not just generic LLM enthusiasm. A lot of their historical research has been on GNN representations of biological networks, which helps with context and applying Michael Bronstein-ish geometric operators to the logic in GNNs (e.g. with Pinnacle).
Why GNNs + agents is a particularly interesting combination for biology
Agent swarms are an improvement to context and nuance over single-shot generation. Scale-sensitive GNNs help too. The hard part is where scales interact — protein to molecule, cell/tissue to protein — which is exactly where translatable results get lost. Or when you need to type-check what’s hypothesized/simulated against proper biological measurements and readouts (this is what MBJ keeps trying to point out). ClawInstitute goes further than any past effort on this.
A GNN over a curated graph works best when:
the node and edge types are meaningful
uncertainty is represented rather than collapsed
context dependence is not erased
the ontology is flexible enough to handle borderline or mixed biological types
That problem is especially acute in systems biology because “type” is often conditional, fuzzy, state-dependent, or scale-dependent. A cell state can be halfway between canonical categories. A protein’s role depends on tissue, binding partner, timing, perturbation, and assay regime. If the graph hardens these into neat bins, the agent gets a very elegant wrong answer, which is humanity’s favorite genre of mistake.
The Zitnik lab’s recent work points toward multimodal, contextual, and single-cell/spatial modeling — they’re not treating biology as a static clean ontology problem.
(Though with GNN representations, you can’t guarantee consistent typing of interactions with “messy biology.”)
Why this could work unusually well:
GNNs and knowledge graphs give agents a structured action space
Biomedical tasks reward explicit tool use and retrieval
Multi-agent review loops are a better fit for science than single-shot generation
Zitnik’s group has a track record in representation learning for biology, not just generic LLM enthusiasm
Where it could still fail:
Ontologies may discretize away biologically important ambiguity
Tool outputs can create false confidence if not tied to experimental design
Agent societies can converge on polished mediocrity if review loops are shallow
“Autoformalization” can be most seductive exactly where biology is least formalizable
The promise is not that agents magically solve biology. It’s that in domains where there already exists a rich ecosystem of graphs, ontologies, databases, assay outputs, and mechanistic priors, agents can become unusually effective navigators and hypothesis-combiners. The key bottleneck shifts from “can the model reason at all?” to “does the representation preserve the weirdness of the biology instead of laundering it into tidy graph objects?”
Agents become more useful when they can reason over partially structured biological worlds, but those same structures can silently erase the cross-scale ambiguity that matters most for translation.
Extended conversation on GNNs/KGs and tensors in biology (Ayush Noori, Marinka Zitnik): https://claude.ai/share/a8549976-7c0a-4492-aee8-87252a4f5a5f
The broader Cambridge agent ecosystem
This is one route that makes Cambridge, MA exciting for frontier science/AI again. Many have raised concerns about Boston losing its frontier talent (e.g. https://x.com/bhalligan/status/2008989938935853300). But the agent swarms work happening here is genuinely strong:
Paul Liang’s lab (also Cambridge) is doing relevant multiagent work: https://x.com/pliang279/status/2034410589682839831 — not biology background, but potentially interoperable with ClawInstitute since agents are often interoperable across domains.
Maria Gorskikh / projectNANDA — building the “Internet of Agents” infrastructure, running join39.org hackathons, great at winning hackathons. Again not biology, but she (along with projectNANDA) helps with the agent ecosystem infrastructure including security, which may help make ClawInstitute more credible. See: https://infinite-lamm.vercel.app (Infinite—Scientific Agent Collaboration platform).
All this agent swarms research in Cambridge makes MIT/Harvard more exciting again. They were both late to the transformers revolution and don’t get SF’s level of investment. But it seems like they’ve become early adopters with applying agent swarms for science.