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From the 1 of 14 linked papers with an AI index.

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14 papers

cs.CV2026

UEmbed: Unified Sparse and Dense Multimodal Embeddings

Tingyu Song, Mingxin Li, Yanzhao Zhang +5

Sparse retrieval underpins modern search systems, from web search to retrieval-augmented generation. Existing work has introduced Learned Sparse Retrieval (LSR) to push beyond exac…

cs.IR2026

CORE-Bench: A Comprehensive Benchmark for Code Retrieval in the Era of Agentic Coding

Fuwei Zhang, Yanzhao Zhang, Mingxin Li +5

The paper presents CORE-Bench, a large benchmark designed to evaluate code retrieval tasks needed by coding agents, covering code understanding, issue-to-edit localization, and bro…

cs.LG2026

Fused Gromov-Wasserstein Distance with Feature Selection

Harlin Lee, Ying Yu, Mingxin Li +1

Fused Gromov-Wasserstein (FGW) distances provide a principled framework for comparing objects by jointly aligning structure and node features. However, existing FGW formulations tr…

cs.CL2026

LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval

Jiajie Jin, Yanzhao Zhang, Mingxin Li +4

LLMs have fundamentally transformed dense retrieval, upgrading backbones from discriminative encoders to generative architectures. However, a critical disconnect remains: while LLM…

cs.IR2026

DiffuRank: Effective Document Reranking with Diffusion Language Models

Qi Liu, Kun Ai, Jiaxin Mao +6

Recent advances in large language models (LLMs) have inspired new paradigms for document reranking. While this paradigm better exploits the reasoning and contextual understanding c…

cs.CV2026

Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image Editing

Tingyu Song, Yanzhao Zhang, Mingxin Li +6

Composed Image Retrieval (CIR) is a pivotal and complex task in multimodal understanding. Current CIR benchmarks typically feature limited query categories and fail to capture the…