author
Research Scientist
,
Snorkel AI
I am an aspiring AI researcher with a diverse range of experience in frontier AI research, large scalable machine learning systems, and applied analytics in social science. I believe in the interactionist approach to intelligence development, through granular feedbacks from grounded, open-ended environments, where robust rewards are essential to forge systems that learn, adapt, and evolve through interactions.
The latest from Jason
Blog
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Milestone-Based Evaluation and Training for Long-Horizon AI Agents
Long-horizon agents operate across many dependent states and transitions, often spanning multiple tools, environments, and periods of external feedback. The difficulty comes from preserving coherent progress as earlier decisions constrain later actions. A single workflow may involve researching evidence, changing files or records, waiting for external responses, revising plans, validating intermediate results, and returning to earlier systems with new information….
OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks
Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limita-tions of frontier agents. We introduce OSWORLD 2.0, a benchmark of 108 long-horizoncomputer-use workflows across everyday and professional tasks, designed to capturecomplex and challenging real-world phenomena. Each task represents a realistic end-to-end workflow that takes human users a median of about 1.6 hours to complete andrequires an average of 318 tool calls with Claude Opus 4.7 using maximum thinking,compared with about 30 in OSWORLD 1.0. OSWORLD 2.0 targets challenge phenomenathat are common in real workflows yet underrepresented in...
Research Paper
OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks
Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limita-tions of frontier agents. We introduce OSWORLD 2.0, a benchmark of 108 long-horizoncomputer-use workflows across everyday and professional tasks, designed to capturecomplex and challenging real-world phenomena. Each task represents a realistic end-to-end workflow that takes human users a…
Blog
Cua-Bench: benchmarking computer-use agents on professional software
TL;DR We built a benchmark of 25 expert-authored KiCad schematic-editing tasks and ran a frontier computer-use agent against them. The headline numbers: 1. Why build a computer-use benchmark for electrical engineering? Most computer-use benchmarks today live in the same handful of apps: web browsers, file managers, generic productivity suites. Those evaluations are useful, but they share a structural weakness —…
RIFT: A Rubric Failure Mode Taxonomy and Automated Diagnostics
Rubric-based evaluation is widely used in LLM benchmarks and training pipelines for open-ended, less verifiable tasks. While prior work has demonstrated the effectiveness of rubrics using downstream signals such as reinforcement learning outcomes, there remains no principled way to diagnose rubric quality issues from such aggregated or downstream signals alone. To address this gap, we introduce RIFT: RubrIc Failure mode Taxonomy, a taxonomy for systematically characterizing failure modes in rubric composition and design. RIFT consists of eight failure modes organized into three high-level categories: Reliability Failures, Content Validity Failures, and Consequential Validity Failures. RIFT is developed using grounded theory by...
Research Paper
Accepted to ICLR Brazil 2026
RIFT: A Rubric Failure Mode Taxonomy and Automated Diagnostics
Rubric-based evaluation is widely used in LLM benchmarks and training pipelines for open-ended, less verifiable tasks. While prior work has demonstrated the effectiveness of rubrics using downstream signals such as reinforcement learning outcomes, there remains no principled way to diagnose rubric quality issues from such aggregated or downstream signals alone. To address this gap, we introduce RIFT: RubrIc Failure mode…
