90 citations · 91 across the 8 of their papers we have counts for
7 papers · 1 filter
VisCurate: Evaluating Behavioral Equivalence of Visual Skills
Md Farhan Ishraq, Md Farhan Ishmam, Reaz Hassan Joarder +1
Modern vision-based agents have shifted toward skill-based problem-solving, where a skill is an executable image-to-image operation, e.g., cropping, thresholding, or edge detection…
Don't Retrain, Remix: Adaptation of Web Agents using Mixture Hypernetworks
Md Farhan Ishmam, Minh Pham Dinh, Kenneth Marino
The web is not static or uniform. Websites that serve the same purpose can look and navigate very differently. Even within a single site, successive redesigns can change how famili…
Can LLMs Tell Stories Out of Order? A Corpus and Benchmark for Nonlinear Narrative Generation
Syed Rifat Raiyan, Md. Tariquzzaman, Md Farhan Ishmam +1
Every story has two orders: the order in which its events happen and the order in which they are told. A story is nonlinear when the two differ. We introduce a benchmark of 83 publ…
Teaching Agents in their Own Style: Blackbox Teacher Distillation for Web Agents
Md Farhan Ishmam, Kenneth Marino
Frontier web agents using closed models have high inference and computational costs. Smaller open models can be an alternative, but to be performant, they require training supervis…
The Hard Part Comes After Search: Benchmarking Web Agents on Synthesizing, Organizing, and Displaying Knowledge
Alexander Gill, Md Farhan Ishmam, Xuyen Nguyen +6
Existing computer-use agent benchmarks do not fully evaluate agents acting as assistants. A useful assistant retrieves information across complex, multi-step workflows, synthesizes…
DORA Explorer: Improving the Exploration Ability of LLMs Without Training
Priya Gurjar, Md Farhan Ishmam, Kenneth Marino
Large language model (LLM) agents for sequential decision-making struggle to produce diverse outputs. This leads to insufficient exploration, suboptimal solutions, and repeated act…
TimeWarp: Evaluating Web Agents by Revisiting the Past
Md Farhan Ishmam, Kenneth Marino
As web agents close the gap with humans on benchmarks, one question arises: Do today's agents perform just as well on tomorrow's web? We introduce TimeWarp, a benchmark that emulat…