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Qiu

Topic trends: 32,543 papers · similarity ≥ 0.4 · year ≥ 2024 · Data sourced from Semantic Scholar

34,598 papers | Abstracts: 31,650 (91.5%) | Citations: 34,598 (100.0%) | arXiv: 26,074 (75.4%)

Built: Feb 15, 2026, 3:00 AM AMS

22

papers

685

total citations

papers (22)

MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?

Spurious Forgetting in Continual Learning of Language Models

DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images

Benchmarking Agentic Workflow Generation

Implicit Reward as the Bridge: A Unified View of SFT and DPO Connections

NEURIPS 2025arXiv

Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning

NEURIPS 2025arXiv

EgoBlind: Towards Egocentric Visual Assistance for the Blind

NEURIPS 2025arXiv

MMSearch: Unveiling the Potential of Large Models as Multi-modal Search Engines

The Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentation

AlignDiff: Aligning Diffusion Models for General Few-Shot Segmentation

Contextualizing biological perturbation experiments through language

Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective

NEURIPS 2025arXiv

Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives

Hypo3D: Exploring Hypothetical Reasoning in 3D

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training

Modeling the Economic Impacts of AI Openness Regulation

NEURIPS 2025arXiv

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation: Application in De Novo Peptide Sequencing

Curse of High Dimensionality Issue in Transformer for Long Context Modeling

Generalization Performance of Ensemble Clustering: From Theory to Algorithm

Pathformer3D: A 3D Scanpath Transformer for 360° Images

Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning

NEURIPS 2025arXiv