2 citations · 2 across the 11 of their papers we have counts for
4 papers · 1 filter
RubricCraft: Where Solutions Diverge, Criteria Emerge
Hosein Hasanbeig, Priyanshu Gupta, Ashish Tiwari +2
Evaluating language agents on open-ended, artifact-producing tasks is difficult: model-based graders can be inconsistent, while exhaustive programmatic checks are often impractical…
Whose turn is it? Context-guided model routing for stateful agents
Anikesh Parashar, Avik Dutta, Vashu Chauhan +6
Model routing can reduce the cost of LLM applications by assigning simpler requests to cheaper models without sacrificing the quality of the response. However, most routers treat r…
Dude, Where's My State? Execution Information Requirements for Stateful Agents
Nikita Mehrotra, Ashish Tiwari, Priyanshu Gupta +1
Long-running agents must preserve information that later steps depend on. We introduce the Execution Information Requirement (EIR), a lower bound on the information that must remai…
LIFT: Last-Mile Fine-Tuning for Table Explicitation
Divij Khaitan, Ashish Tiwari
We propose last-mile fine-tuning, or Lift, a pipeline in which a pre-trained large language model extracts an initial table from unstructured clipboard text, and a fine-tuned small…