26 citations · 56 across the 13 of their papers we have counts for
4 papers · 1 filter
Cost-Aware Best-LLM Identification using Dueling Feedback
Sarvesh Gharat, Nikhil Karamchandani, Jayakrishnan Nair
Inspired by the problem of identifying the best model from a collection of large language models (LLMs) with heterogeneous querying costs, we formulate and analyse a variant of the…
What Makes a Terminal-Bench Task Hard? Separating Genuine Hardness from Fake-Hardness on an Adjudicated Agentic Corpus
Edward Lue Chee Lip, Boden Moraski, Tim Knappe +4
Frontier benchmarks need tasks that current models cannot solve. But a task that no model solves is not automatically a hard task. The same zero pass rate can come from a real capa…
SGHA: Evidence-Grounded Research Problem Discovery with Local Language Models
Sarvesh Gharat, Junpei Komiyama
Recent efforts toward fully automated AI scientists have demonstrated that language-model agents can generate hypotheses, execute experiments, and draft scientific manuscripts. How…
ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling
Vaibhav Singh, Soumya Suvra Ghosal, Sarvesh Gharat +3
Large Reasoning Models (LRMs) improve performance by allocating additional inference-time compute to generate extended chain-of-thought reasoning. However, recent studies reveal th…