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Spatial omics gets an agent

Spatial omics gets an agent
Nº 01 · The Lede bioRxiv Agents · Infrastructure

Memory-augmented agent runs spatial omics

Memory-augmented agent runs spatial omics
Fig. IbioRxiv · Filed 26 May 2026.

SpatialClaw automates spatial-omics analysis end-to-end, pairing a persistent memory store with tool-calling agents to handle the messy chain from raw spatial transcriptomics through cell-type calling and niche analysis. The system remembers prior runs and reuses learned analysis paths, cutting the bespoke-pipeline overhead that has kept spatial omics labor-intensive. It's one of the first agent ecosystems built specifically for a modality where every dataset has historically demanded a custom workflow.

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LLMs infer complex composition
Fig. IIbioRxiv · Filed 26 May 2026.
Nº 02 bioRxiv Field report

LLMs infer complex composition

Model consensus calls macromolecular composition from experimental data, using multiple LLMs voting over candidate stoichiometries and component identities for protein complexes. The approach grafts language-model reasoning onto structural-biology evidence rather than replacing it — narrowing the same gap AlphaFold's pairing work targeted between cryo-EM density maps and confident composition calls, but without a human in the loop.

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Temporal knowledge graph for clinical reasoning
Fig. IIIarXiv · Filed 26 May 2026.
Nº 03 arXiv Clinical AI · Evaluation

Temporal knowledge graph for clinical reasoning

ChronoMedKG times-stamps clinical knowledge and pairs the graph with a reasoning benchmark where guideline changes and drug-label updates have explicit effective dates. Static medical KGs have quietly poisoned clinical-LLM evaluations by treating outdated facts as ground truth; this anchors a counterargument and a reference benchmark for any model claiming clinical reasoning competence.

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Also Filed · One Brief from the queue
Nº 04 arXiv Computational biology

FAIR platform closes materials-to-bio loop

AIMBio-Mat wires materials discovery to biomedical translation in a single FAIR-compliant closed loop, with agents managing synthesis, characterization, and downstream biological assays. Raises the floor for what an AI-native discovery platform should ship when the use case crosses from materials into implants, delivery vehicles, or biosensors.

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Agentic Discovery  ·  Nº 23  ·  26 May 2026

Editor's Note

Quiet day on the platforms, but the preprint stack delivers: memory-augmented agents, complex-composition inference, and a temporal knowledge graph that calls out static benchmarks.

 

Nº 01 · The Lede  —  bioRxiv  —  Agents · Infrastructure

Memory-augmented agent runs spatial omics

Memory-augmented agent runs spatial omics

Fig. I  bioRxiv · Filed 26 May 2026.

SpatialClaw automates spatial-omics analysis end-to-end, pairing a persistent memory store with tool-calling agents to handle the messy chain from raw spatial transcriptomics through cell-type calling and niche analysis. The system remembers prior runs and reuses learned analysis paths, cutting the bespoke-pipeline overhead that has kept spatial omics labor-intensive. It's one of the first agent ecosystems built specifically for a modality where every dataset has historically demanded a custom workflow.

Read the source →

Why it matters

Spatial omics moves from artisanal pipelines toward agent-driven analysis — resets what's reasonable to expect from a spatial-transcriptomics turnaround and pressures every commercial spatial platform to answer whether their software inherits this autonomy.

 

Nº 02  —  bioRxiv  —  Field report

LLMs infer complex composition

Fig. II  bioRxiv · Filed 26 May 2026.

LLMs infer complex composition

Model consensus calls macromolecular composition from experimental data, using multiple LLMs voting over candidate stoichiometries and component identities for protein complexes. The approach grafts language-model reasoning onto structural-biology evidence rather than replacing it — narrowing the same gap AlphaFold's pairing work targeted between cryo-EM density maps and confident composition calls, but without a human in the loop.

Read more →

 

Nº 03  —  arXiv  —  Clinical AI · Evaluation

Temporal knowledge graph for clinical reasoning

Fig. III  arXiv · Filed 26 May 2026.

Temporal knowledge graph for clinical reasoning

ChronoMedKG times-stamps clinical knowledge and pairs the graph with a reasoning benchmark where guideline changes and drug-label updates have explicit effective dates. Static medical KGs have quietly poisoned clinical-LLM evaluations by treating outdated facts as ground truth; this anchors a counterargument and a reference benchmark for any model claiming clinical reasoning competence.

Read more →

 

Also Filed  ·  One Brief from the queue

Nº 04  —  arXiv  —  Computational biology

FAIR platform closes materials-to-bio loop

AIMBio-Mat wires materials discovery to biomedical translation in a single FAIR-compliant closed loop, with agents managing synthesis, characterization, and downstream biological assays. Raises the floor for what an AI-native discovery platform should ship when the use case crosses from materials into implants, delivery vehicles, or biosensors.

Read →

 

· · ·

Reply with your discoveries. A human reads them. Forward freely.