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Suhaas Garre

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#artificial intelligence Preprint Oct 2026

DAYJOB: A Benchmark for Long-Horizon Professional Work

Professional work often starts with a brief request that leaves the professional to work out what is needed, which documents matter, and whether the request's premise holds. We introduce DAYJOB, a benchmark of 130 tasks built by professionals in healthcare (50) and finance (80). The tasks are estimated to take a profes...

Stephanie Finley, Liudas Panavas, T. Mikkelson et al. · 0 citations
Preprint Aug 2026

Chartography: A Benchmark for Professional Chart Understanding

Professionals across medicine, engineering, finance, manufacturing, and the sciences often make consequential decisions from charts. Existing chart benchmarks do not sufficiently measure this ability: they are dominated by bar, line, and pie formats, rely on shorter reasoning chains, and are nearing saturation, with fr...

Suhaas Garre, Chris Mutty, Sushant Mehta et al. · 0 citations
Jul 2026

GDP.pdf: Benchmarking Grounded Multimodal Reasoning over Professional PDF Documents

G GDP_pdf is a benchmark built to measure the capabilities of frontier multimodal models inside PDF files, and results for seventeen frontier models on the 100-item benchmark are reported: the best model passes only 30.7% of the items and the worst passes 2%.

Suhaas Garre, Emily Ritchie, Sushant Mehta et al. · 1 citation
Jul 2026

HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following

HANDBOOK_md is presented, a benchmark of 65 agentic tasks modeled on how employees follow company handbooks, and every task modifies one of 10 base handbooks, altering the specific rules and thresholds on which grading depends, so no two tasks share the same set of policies.

Liudas Panavas, Sebastian Minus, Bradley Monton et al. · 1 citation

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