works on

From the 1 of 8 linked papers with an AI index.

activity
20242026
most citedATLAS: Adaptive Transfer Scaling Laws for Multilingual Pretraining, Finetuning, and Decoding the Curse of Multilinguality

1 citations · 1 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CV2026

SeeSE3: Emergence of 3D Space in Vision Features

Caroline Chen, Sayna Ebrahimi, Fedor Kitashov +4

The paper examines whether vision foundation models implicitly encode the geometry of 3D Euclidean space, introducing probes such as a mutual neighborhood metric and a Poincaré Ada…

cs.CV2026

Unique Lives, Shared World: Learning from Single-Life Videos

Tengda Han, Sayna Ebrahimi, Dilara Gokay +8

We introduce the "single-life" learning paradigm, where we train a distinct vision model exclusively on egocentric videos captured by one individual. We leverage the multiple viewp…

cs.CL20261 cited

ATLAS: Adaptive Transfer Scaling Laws for Multilingual Pretraining, Finetuning, and Decoding the Curse of Multilinguality

Shayne Longpre, Sneha Kudugunta, Niklas Muennighoff +6

Scaling laws research has focused overwhelmingly on English -- yet the most prominent AI models explicitly serve billions of international users. In this work, we undertake the lar…

cs.CL2025

Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence

Shangbin Feng, Zifeng Wang, Yike Wang +9

We propose Model Swarms, a collaborative search algorithm to adapt LLMs via swarm intelligence, the collective behavior guiding individual systems. Specifically, Model Swarms start…

cs.CL2025

Reverse Thinking Makes LLMs Stronger Reasoners

Justin Chih-Yao Chen, Zifeng Wang, Hamid Palangi +8

Reverse thinking plays a crucial role in human reasoning. Humans can reason not only from a problem to a solution but also in reverse, i.e., start from the solution and reason towa…

cs.CV2025

Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment

Pritam Sarkar, Sayna Ebrahimi, Ali Etemad +3

Despite their significant advancements, Multimodal Large Language Models (MLLMs) often generate factually inaccurate information, referred to as hallucination. In this work, we add…