# research AI insights

Rangoon Insights reporting and guides tagged research AI.

Canonical: https://rangoon.ai/insights/tags/research-ai/

## Topics
- [Agent systems](https://rangoon.ai/insights/topics/agents/): Agent runtimes, handoffs, tools, and the boundaries that make them reviewable.
- [Governance](https://rangoon.ai/insights/topics/governance/): Policy, identity, authorization, approvals, and evidence for consequential work.
- [Infrastructure](https://rangoon.ai/insights/topics/infrastructure/): Model serving, sandboxes, tool services, and deployment boundaries.
- [Models](https://rangoon.ai/insights/topics/models/): Inference choices, local serving, compatibility, and operational tradeoffs.

## Stories
- [Aeneas made ancient-text parallels an inspectable AI research aid](https://rangoon.ai/insights/archive-aeneas-ancient-inscriptions-2025/)
  Google DeepMind introduced Aeneas on July 23, 2025, reporting a model for contextualizing ancient Latin inscriptions through textual and historical parallels.
- [Chai-2 paired de novo antibody designs with a bounded wet-lab report](https://rangoon.ai/insights/archive-chai2-zero-shot-antibody-2025/)
  Chai Discovery posted its Chai-2 antibody-design study in July 2025, reporting zero-shot designs and a specified set of wet-lab tests.
- [AlphaGenome mapped regulatory predictions without becoming a clinical result](https://rangoon.ai/insights/archive-alphagenome-regulatory-variant-research/)
  Google DeepMind introduced AlphaGenome in June 2025 for regulatory variant-effect research, combining long DNA input with multi-modal molecular predictions and research limits.
- [Boltz-2 put binding-affinity prediction beside structure prediction](https://rangoon.ai/insights/archive-boltz2-binding-affinity-2025/)
  The Boltz team introduced Boltz-2 on June 6, 2025, reporting an open model for joint biomolecular structure and binding-affinity prediction.
- [RFdiffusion2 made enzyme-backbone generation a testable design hypothesis](https://rangoon.ai/insights/archive-rfdiffusion2-enzyme-design-2025/)
  The Institute for Protein Design introduced RFdiffusion2 on April 10, 2025, reporting a model for generating enzyme backbones from minimally specified reaction chemistry.
- [BioEmu-1 made protein-conformation emulation a sampling question](https://rangoon.ai/insights/archive-bioemu-protein-ensembles-2025/)
  Microsoft Research introduced BioEmu-1 on February 20, 2025, reporting a generative model intended to emulate protein equilibrium ensembles for research.
- [Evo 2 broadened genome modeling while keeping sequence design a research claim](https://rangoon.ai/insights/archive-evo2-genome-modeling-2025/)
  Arc Institute introduced Evo 2 on February 19, 2025, releasing a genome-scale biological model, code, weights and training-data materials for research use.
- [Google’s AI co-scientist made hypothesis ranking an auditable research step](https://rangoon.ai/insights/archive-google-ai-co-scientist-2025/)
  Google Research introduced an AI co-scientist on February 19, 2025, describing a Gemini-based system for generating and ranking research hypotheses and plans.
- [MatterGen separated conditional crystal proposals from material confirmation](https://rangoon.ai/insights/archive-mattergen-inorganic-materials-2025/)
  Microsoft Research introduced MatterGen on January 16, 2025, reporting a generative model for inorganic material structures conditioned on target properties.
- [MatterSim v1 made an atomistic emulator release a reproducibility question](https://rangoon.ai/insights/archive-mattersim-v1-december-2024/)
  Microsoft Research released MatterSim v1.0.0 on December 18, 2024, making trained materials-simulation models available alongside earlier atomistic modeling research.
- [Prithvi-EO-2.0 expanded geospatial representation without becoming ground truth](https://rangoon.ai/insights/archive-prithvi-eo-2-global-geospatial/)
  NASA, IBM and Jülich released Prithvi-EO-2.0 in December 2024, extending an Earth-observation foundation model with global multi-temporal satellite data.
- [AlphaQubit treated quantum error decoding as an inference problem](https://rangoon.ai/insights/archive-alphaqubit-quantum-error-decoder/)
  Google DeepMind introduced AlphaQubit on November 20, 2024, reporting a transformer-based decoder for identifying errors in surface-code quantum experiments.


## More information

- [Documentation index](https://rangoon.ai/llms.txt)
- [AI access and policies](https://rangoon.ai/ai/)
- [Source repository](https://github.com/hypler-dev/rangoon)