5 papers
Packing Linear Programs and Fractional Knapsack using Comparison Oracles
Ritabrata Barat, Siddharth Barman, Nirjhar Das +1
We study the problem of recovering the objective of a packing linear program when the algorithm accesses only comparison information about optimal solutions under varying constrain…
Contextual Slate GLM Bandits with Limited Adaptivity
Tanmay Goyal, Sukruta Prakash Midigeshi, Gaurav Sinha
We investigate the contextual slate bandit problem with generalized linear rewards under limited adaptivity. At each round, the learner is presented with sets of items, where e…
Characterizing Deep Research: A Benchmark and Formal Definition
Abhinav Java, Ashmit Khandelwal, Sukruta Midigeshi +6
Information tasks such as writing surveys or analytical reports require complex search and reasoning, and have recently been grouped under the umbrella of \textit{deep research} --…
Achieving Limited Adaptivity for Multinomial Logistic Bandits
Sukruta Prakash Midigeshi, Tanmay Goyal, Gaurav Sinha
Multinomial Logistic Bandits have recently attracted much attention due to their ability to model problems with multiple outcomes. In this setting, each decision is associated with…
Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation
Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha +3
We introduce Plan*RAG, a novel framework that enables structured multi-hop reasoning in retrieval-augmented generation (RAG) through test-time reasoning plan generation. While exis…