most citedHow new data permeates LLM knowledge and how to dilute it

1 citations · 2 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL2025

Compass-Embedding v4: Robust Contrastive Learning for Multilingual E-commerce Embeddings

Pakorn Ueareeworakul, Shuman Liu, Jinghao Feng +7

As global e-commerce rapidly expands into emerging markets, the lack of high-quality semantic representations for low-resource languages has become a decisive bottleneck for retrie…

cs.CV2025

You May Speak Freely: Improving the Fine-Grained Visual Recognition Capabilities of Multimodal Large Language Models with Answer Extraction

Logan Lawrence, Oindrila Saha, Megan Wei +3

Despite the renewed interest in zero-shot visual classification due to the rise of Multimodal Large Language Models (MLLMs), the problem of evaluating free-form responses of auto-r…

cs.CL20251 cited

LongCat-Flash Technical Report

Meituan LongCat Team, Bayan, Bei Li +179

We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities. Stemming f…

cs.CE2025

An Information Bottleneck Asset Pricing Model

Che Sun

Deep neural networks (DNNs) have garnered significant attention in financial asset pricing, due to their strong capacity for modeling complex nonlinear relationships within financi…

cs.CL2025

What is an "Abstract Reasoner"? Revisiting Experiments and Arguments about Large Language Models

Tian Yun, Chen Sun, Ellie Pavlick

Recent work has argued that large language models (LLMs) are not "abstract reasoners", citing their poor zero-shot performance on a variety of challenging tasks as evidence. We rev…

cs.LG2025

xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations

Kaiyuan Chen, Yixin Ren, Yang Liu +30

We introduce xbench, a dynamic, profession-aligned evaluation suite designed to bridge the gap between AI agent capabilities and real-world productivity. While existing benchmarks…