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Drix10 Blog

AI Research - WMT2026

, 10 items in AI Education, 5 min read

In this digest (10 items)

The MITRA team announced two research papers accepted at WMT2026: Mitrasaṃgraha, a large Sanskrit-English parallel corpus, and MITRA, a large-scale parallel corpus and 9B translation model for Buddhist languages.

Key points

• Mitrasaṃgraha is the largest Sanskrit-English parallel corpus to date with 391k sentence pairs. • MITRA is a large-scale parallel corpus and 9B translation model for Buddhist languages. • The research papers were accepted at WMT2026.

Sources

AI Adoption - Banking

A new survey found that only 15% of proposed AI use cases in banking actually reach production, with 85 out of 100 never getting deployed. The gap is not due to talent or budget, but rather three missing layers: system-of-record integration, infrastructure-level governance, and ownership.

Key points

• Only 15% of proposed AI use cases in banking reach production. • 85 out of 100 AI use cases never get deployed. • The gap is due to three missing layers: system-of-record integration, infrastructure-level governance, and ownership.

Sources

Elicit - Shared Skills

Shared skills, projects, and collaborative editing are now live in Elicit. Skills allow users to define templates or processes once and reuse them again and again.

Key points

• Shared skills, projects, and collaborative editing are now live in Elicit. • Skills allow users to define templates or processes once and reuse them again and again. • Elicit has a feature that finds information about authors in papers.

Sources

Consensus - Collection Sharing

Consensus has introduced Collection Sharing, a feature that allows groups to search, save, and organize papers in one shared Collection.

Key points

• Consensus has introduced Collection Sharing. • Groups can search, save, and organize papers in one shared Collection. • Collection Sharing reduces the time spent on research.

Sources

Higher Education - AI

The OutRival team discussed the future of higher education, AI, and student success with the @umdglobalcampus team. They focused on using technology with purpose, not simply chasing the latest trend.

Key points

• The OutRival team discussed the future of higher education, AI, and student success. • The @umdglobalcampus team focused on using technology with purpose. • The bottleneck was not talent or budget, but rather infrastructure.

Sources

Inference + Graph Queries

Inference + graph queries can be metered, served, and validated in $TRAC between @origin_trail nodes on @Base. This is the latest in a series of prototypes that validated query and inference can be metered.

Key points

• Inference + graph queries can be metered, served, and validated in $TRAC. • This is the latest in a series of prototypes that validated query and inference can be metered. • $TRAC has a protocol level for publishing.

Sources

Neurosymbolic Marketplace

OT-RFC-69 discusses whether query and inference should join the protocol level in $TRAC, forming a neurosymbolic marketplace with consequential economic benefits.

Key points

• OT-RFC-69 discusses whether query and inference should join the protocol level in $TRAC. • This would form a neurosymbolic marketplace with consequential economic benefits. • $TRAC has a protocol level for publishing.

Sources

AI in Healthcare

China's Tsinghua has opened "Agent Hospital" with 42 AI doctors, 21 specialties, and 300+ diseases, integrated with a physical Beijing hospital. Meanwhile, US orgs leading patient-facing AI are not Harvard-affiliated centers, but rather nimble community hospitals.

Key points

• China's Tsinghua has opened "Agent Hospital" with 42 AI doctors and 21 specialties. • The hospital is integrated with a physical Beijing hospital. • US orgs leading patient-facing AI are not Harvard-affiliated centers.

Sources