Rethinking Environments for Long Horizon Work
Conference Context
- Date/time: 2026-06-29 · 11:40am-12:00pm
- Track/room: Data Quality · Track 9
- Speaker(s): Rayan Garg
- Session type/status: session · confirmed
- Track: Data Quality
- Room: Track 9
- Session type: session
- Status: confirmed
Session Description
As autonomous agents push towards longer-horizon tasks, a number of challenges emerge in measuring and improving frontier model capabilities. In this talk, we discuss how long-horizon tasks are defined and measured, how RL environments and verifiers have to scale for more complex and open-ended tasks, and how we navigate these problems at Theta.
Media Evidence
No related AI Engineer channel video found yet.
Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.
Media Signals
No linked video, transcript, or slide source has been attached yet.
Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.
Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.
People
Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.
Synthesis
Synthesized Breakdown
Rethinking Environments for Long Horizon Work ## Conference Context - Date/time: 2026-06-29 · 11:40am-12:00pm - Track/room: Data Quality · Track 9 - Speaker(s): Rayan Garg - Session type/status: session · confirmed - Track: Data Quality - Room: Track 9 - Session type: session - Status: confirmed ## Session Description As autonomous agents push towards longer-horizon tasks, a number of challenges emerge in measuring and improving frontier model capabilities. In this talk, we discuss how long-horizon tasks are defined and measured, how RL environments and verifiers have to scale for more complex and open-ended tasks, and how we navigate these problems at Theta. ## Media Evidence No related AI Engineer channel video found yet. ## Evidence Graph This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.
Speaker And Company Context
- Rayan Garg — CEO at Theta Software.
Topics Covered
- Topic links are pending transcript-backed classification.
Derived Links And Source Material
Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.
Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.