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20242026
most citedInternLM2 Technical Report

29 citations · 31 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

Our Own Jev: Typed Decisions in One Forward Pass, from an In-House Judge to Open LoRA Models

Yuchuan Weng, Chi Liu, Xin Chen +1

A typed decision maps evidence, a question and a fixed set of options to a probability for every option, computed in one forward pass, by reading a language model's logits or a sma…

cs.AI2026

Beyond Output Matching: Preserving Internal Geometry in NVFP4 LLM Distillation

Fangbo Tu, Junhua Zhao, Chi Liu +4

Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost constrained production enviro…

cs.CV2026

Q-Mask: Query-driven Causal Masks for Text Anchoring in OCR-Oriented Vision-Language Models

Longwei Xu, Feng Feng, Shaojie Zhang +7

Optical Character Recognition (OCR) is increasingly regarded as a foundational capability for modern vision-language models (VLMs), enabling them not only to read text in images bu…

cs.CV2026

MASQuant: Modality-Aware Smoothing Quantization for Multimodal Large Language Models

Lulu Hu, Wenhu Xiao, Xin Chen +4

Post-training quantization (PTQ) with computational invariance for Large Language Models~(LLMs) have demonstrated remarkable advances, however, their application to Multimodal Larg…

cs.CL2026

Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models

Xin Chen, Saili Uday Gadgil, Jiarong Qiu

Retrieval augmented generation mitigates limitations of large language models in factual consistency and knowledge updating by introducing external knowledge. However, practical ap…

cs.LG2026

Pragma-VL: Towards a Pragmatic Arbitration of Safety and Helpfulness in MLLMs

Ming Wen, Kun Yang, Xin Chen +4

Multimodal Large Language Models (MLLMs) pose critical safety challenges, as they are susceptible not only to adversarial attacks such as jailbreaking but also to inadvertently gen…