297 citations · 710 across the 17 of their papers we have counts for
18 papers
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
Continual Harness: Online Adaptation for Self-Improving Foundation Agents
Seth Karten, Joel Zhang, Tersoo Upaa +5
Coding harnesses such as Claude Code and OpenHands wrap foundation models with tools, memory, and planning, but no equivalent exists for embodied agents' long-horizon partial-obser…
FoodSense: A Multisensory Food Dataset and Benchmark for Predicting Taste, Smell, Texture, and Sound from Images
Sabab Ishraq, Aarushi Aarushi, Juncai Jiang +1
Humans routinely infer taste, smell, texture, and even sound from food images a phenomenon well studied in cognitive science. However, prior vision language research on food has fo…
Next-Token Prediction and Regret Minimization
Mehryar Mohri, Clayton Sanford, Jon Schneider +2
We consider the question of how to employ next-token prediction algorithms in adversarial online decision-making environments. Specifically, if we train a next-token prediction mod…
The PokeAgent Challenge: Competitive and Long-Context Learning at Scale
Seth Karten, Jake Grigsby, Tersoo Upaa +28
We present the PokeAgent Challenge, a large-scale benchmark for decision-making research built on Pokemon's multi-agent battle system and expansive role-playing game (RPG) environm…
Gemma 3 Technical Report
Gemma Team, Aishwarya Kamath, Johan Ferret +209
We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision underst…
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1
We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…
Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries
Kiran Vodrahalli, Santiago Ontanon, Nilesh Tripuraneni +21
We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models which is also easy to automatically score. This evaluation…
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei +1132
In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…
Is Learning in Games Good for the Learners?
William Brown, Jon Schneider, Kiran Vodrahalli
We consider a number of questions related to tradeoffs between reward and regret in repeated gameplay between two agents. To facilitate this, we introduce a notion of $\textit{gene…
PaLM 2 Technical Report
Rohan Anil, Andrew M. Dai, Orhan Firat +125
We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 i…
Online Learning with Bounded Recall
Jon Schneider, Kiran Vodrahalli
We study the problem of full-information online learning in the "bounded recall" setting popular in the study of repeated games. An online learning algorithm is -$…
Nonlinear Initialization Methods for Low-Rank Neural Networks
Kiran Vodrahalli, Rakesh Shivanna, Maheswaran Sathiamoorthy +2
We propose a novel low-rank initialization framework for training low-rank deep neural networks -- networks where the weight parameters are re-parameterized by products of two low-…
The Logical Options Framework
Brandon Araki, Xiao Li, Kiran Vodrahalli +3
Learning composable policies for environments with complex rules and tasks is a challenging problem. We introduce a hierarchical reinforcement learning framework called the Logical…
The Platform Design Problem
Christos Papadimitriou, Kiran Vodrahalli, Mihalis Yannakakis
On-line firms deploy suites of software platforms, where each platform is designed to interact with users during a certain activity, such as browsing, chatting, socializing, emaili…
Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform
Mathias Lecuyer, Riley Spahn, Kiran Vodrahalli +2
Companies increasingly expose machine learning (ML) models trained over sensitive user data to untrusted domains, such as end-user devices and wide-access model stores. We present…
A Large Self-Annotated Corpus for Sarcasm
Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli
We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for sarcasm research and for training and evaluating systems for sarcasm detection. The corpus has 1.3 million…
Mapping Between fMRI Responses to Movies and their Natural Language Annotations
Kiran Vodrahalli, Po-Hsuan Chen, Yingyu Liang +8
Several research groups have shown how to correlate fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language a…