Semantic Clustering

Latest papers

14 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

CLIVO: Differentiable Cluster-Based Instruction Mixing with Learned Validation Objectives
Oct 12, 2026Yuhan Wang, Rui Hu, Batian Di +5Semantic Clustering Instruction-Tuning Data Selection
Peking University · Renmin University of China · Zhejiang University +2
Vision Transformers Learn Word Meaning from Images Alone
Oct 9, 2026Anders Gjølbye, Sebastian Ray Mason, Stella Frank +1Vision Transformer Semantic Clustering
Department of Applied Mathematics and Computer Science Technical University of Denmark
From High Recall to High Utility: Dataset-Adaptive Post-Processing of LLM-Generated Customer Intents
Oct 6, 2026Mahesh Viswanathan, Joan Rossello, Leticia Fernandes +1Semantic Clustering LLM Information Extraction
Cisco Systems, Inc., San Jose, CA, USA
RAILS: Retrieval-Augmented Incremental LLM Clustering at Scale
Sep 21, 2026Armin Oliya, Aleksandra Sawczuk, Radosław BiałobrzeskiClustering Unsupervised Clustering
Zendesk
Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent
Jun 23, 2026Wenliang Zhong, Rob Barton, Lucas Goncalves +7Clustering Unsupervised Clustering
1The University of Texas at Arlington · 2Amazon
Clusters are All You Need: Pre-Training the Tsetlin Machine with Semantic Clusters from Language Models for Interpretability
Jun 18, 2026Jiechao Gao, Rohan Kumar Yadav, Yuangang Li +4Interpretable ML Text Classification
Stanford University · Independent Researcher · University of California, Irvine +1
Agentic Clustering: Controllable Text Taxonomies via Multi-Agent Refinement
May 31, 2026Simon Löwe, Emily SilcockMulti-Agent LLM Systems Clustering
Burning Glass Institute · Harvard University
Arcane: An Assertion Reduction Framework through Semantic Clustering and MCTS-Guided Rule Exploring
May 11, 2026Hongqin Lyu, Yonghao Wang, Zhiteng Chao +2Formal Hardware Verification Formal Verification
State Key Lab of Processors, Institute of Computing Technology, CAS, Beijing, China · University of Chinese Academy of Sciences, Beijing, China
Mapping the Political Discourse in the Brazilian Chamber of Deputies: A Multi-Faceted Computational Approach
Apr 23, 2026Flávio Soriano, Victoria F. Mello, Pedro B. Rigueira +4Political Discourse Analysis Computational Social Science
Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
AGSC: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation
Apr 8, 2026Guanran Luo, Wentao Qiu, Wanru Zhao +4Semantic Clustering Uncertainty Quantification
School of Informatics, Xiamen University
PRISM: LLM-Guided Semantic Clustering for High-Precision Topics
Apr 3, 2026Connor Douglas, Utkucan Balci, Joseph Aylett-BullockSemantic Clustering Topic Modeling
New York University New York, NY, USA · Binghamton University Binghamton, NY, USA · United Nations New York, NY, USA
Language-Assisted Image Clustering Guided by Discriminative Relational Signals and Adaptive Semantic Centers
Mar 25, 2026Jun Ma, Xu Zhang, Zhengxing Jiao +4Image Clustering Clustering
School of Computer Science and Engineering, Southeast University, Nanjing, China · School of Computing Information Sciences, Saint Francis University, Hong Kong, China · Department of Computer Science, City University of Hong Kong, Hong Kong, China
SHIELD: Semantic Heterogeneity Integrated Embedding for Latent Discovery in Clinical Trial Safety Signals
Feb 23, 2026Francois Vandenhende, Anna Georgiou, Theodoros Psaras +1Semantic Clustering Anomaly Detection
ClinBAY Limited, Limassol, Cyprus
CluCERT: Certifying LLM Robustness via Clustering-Guided Denoising Smoothing
Dec 1, 2025Zixia Wang, Gaojie Jin, Jia Hu +1Randomized Smoothing Semantic Clustering
University of Exeter

Oct 12, 2026J/K move · Enter open · S save

CLIVO: Differentiable Cluster-Based Instruction Mixing with Learned Validation Objectives

Yuhan Wang, Rui Hu, Batian Di, Tan Jiang+4

Peking University · Renmin University of China · Zhejiang University+2

Semantic Clustering Instruction-Tuning Data Selection Data Selection Data Mixture Optimization Multimodal Instruction Tuning

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Target-aware multimodal instruction selection requires useful examples and an appropriate balance of supervision. Sample-level rankings can overrepresent similar content without optimizing this balance; group-level methods face coarse units, predefined set-level validation signals, and costly or inaccurate mixture search. We propose CLIVO, a target-aware framework connecting fine-grained semantic allocation, automatically learned validation objectives, and differentiable mixture search. Specifically, semantic clusters provide independently adjustable shares at the hundred-cluster scale. Correlations between per-example merged-proxy losses and real probe performance identify target-relevant validation examples; learned nonnegative weights combine their losses into an objective. A single backward pass through the merged proxy supplies gradients for all shares, avoiding an additional mixture-response regression model. Selecting 100K instructions from an 8M pool, CLIVO attains 97.2% of the full-data four-target average score with only 1.25% of the data. It improves on the strongest evaluated selection baseline by 1.16 points using random within-cluster sampling; combining learned quotas with sample ranking increases this advantage to 1.55 points. Analyses show that finer units benefit most targets, short-trained experts preserve mixture responses, sample weighting retains useful validation information, and direct search consistently reduces the learned objective. CLIVO also supports interpretable allocation, modular selection, and reuse across targets and granularities.