most citedGemma 2: Improving Open Language Models at a Practical Size

145 citations · 145 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees

S M Rafiuddin, Atriya Sen

Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…

cs.AI2026

Reward-Free Evolving Agents via Pairwise Validator

Minghao Liu, Yu Wang, Jiayun Wang +1

A self-evolving agentic loop repeatedly proposes a tweaked version of an agent (its prompt template or program) and accepts or rejects the change based on a per-iteration quality s…

cs.CL2026

Inference Time Optimization with Confidence Dynamics

Yu Wang, Minghao Liu, Jiayun Wang +3

Inference time optimization techniques, such as repeated sampling, have significantly advanced the reasoning capabilities of Large Language Models (LLMs). However, the critical rol…

cs.CV2026

UniVL: Unified Vision-Language Embedding for Spatially Grounded Contextual Image Generation

Jiayun Wang, Yu Wang, Weijie Gan +2

We introduce spatially grounded contextual image generation, a controllable image generation task that reframes the conditioning paradigm. Instead of supplying a reference image an…

cs.CV2026

Dual-Prompt CLIP with Hybrid Visual Encoders for Occluded Person Re-Identification

Zhangjian Ji, Shaotong Qiao, Kai Feng +1

Occluded person re-identification focuses on matching partially visible pedestrians across multiple camera views. However, occlusions disrupt body-region cues, thereby complicating…

cs.CL2026

HumanLM: Simulating Users with State Alignment Beats Response Imitation

Shirley Wu, Evelyn Choi, Arpandeep Khatua +7

Large Language Models (LLMs) are increasingly used to simulate how specific users respond to a given context, enabling more user-centric applications that rely on user feedback. Ho…

cs.CL2024★ 145 cited

Gemma 2: Improving Open Language Models at a Practical Size

Gemma Team, Morgane Riviere, Shreya Pathak +195

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In th…