most citedGemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

49 citations · 49 across the 4 of their papers we have counts for

collaborators

6 papers

cs.AI2026

DeLoop: Repeated Actions as Self-Supervision for Long-Horizon Agents

Tianle Chen, Pushkar Singh, Ketan Todi +8

Language-model agents that run long tasks often redo work they have already done: they re-issue a tool call they made earlier and get back exactly the same observation, a step that…

cs.CV2026

Aligning Thoughts with Answers: Probability Rewards to Tame Thinking Drift

Pengzhan Sun, Shiu-hong Kao, Shijie Li +4

This paper studies \textbf{thinking--answer consistency} in vision-language models. We focus on Visual Intention Grounding, where a model infers a target object based on a human in…

cs.CV2025

Some Modalities are More Equal Than Others: Decoding and Architecting Multimodal Integration in MLLMs

Tianle Chen, Chaitanya Chakka, Arjun Reddy Akula +2

Despite remarkable advancements in Multimodal Large Language Models (MLLMs), a fundamental question remains: are MLLMs robust to contradicting modalities? To rigorously study this,…

cs.CL2025★ 49 cited

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…

cs.CL2025

Refract ICL: Rethinking Example Selection in the Era of Million-Token Models

Arjun R. Akula, Kazuma Hashimoto, Krishna Srinivasan +3

The emergence of long-context large language models (LLMs) has enabled the use of hundreds, or even thousands, of demonstrations for in-context learning (ICL) - a previously imprac…

cs.CV2025

Visual Intention Grounding for Egocentric Assistants

Pengzhan Sun, Junbin Xiao, Tze Ho Elden Tse +3

Visual grounding associates textual descriptions with objects in an image. Conventional methods target third-person image inputs and named object queries. In applications such as A…