Reasoning models

Reasoning models is connected to the Pagish AI graph through source-backed clusters and field-level provenance.

ResearchOct 23, 2026important

SceneBloom builds more realistic practice rooms for embodied agents

SceneBloom addresses an awkward robotics bottleneck: a plausible-looking room is not necessarily a valid training environment. The new preprint combines visual layout generation with geometric checks to produce indoor scenes that embodied agents can actually use.

Why it matters: This is research evidence within a defined evaluation, rather than a guarantee of real-world navigation reliability. Watch whether other teams reproduce the transfer gains and whether the method remains useful in cluttered spaces with unusual objects and layouts.

ResearchAug 26, 2026watch

Trace integrity gives data agents a better reliability target than answer accuracy

Data agents can produce the right answer for the wrong reason, and that is a serious problem in business systems. If the reasoning trace is invalid, a benchmark score may hide a tool that cannot be trusted on unfamiliar data.

Why it matters: This matters for any company putting agents near dashboards, finance workflows, or compliance reports. Pagish will watch whether trace-based evaluation becomes part of production agent monitoring rather than staying in papers.