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20182026
most citedIn-Context Impersonation Reveals Large Language Models' Strengths and Biases

36 citations · 57 across the 22 of their papers we have counts for

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5 papers · 1 filter

cs.LG2026

Learning Functional Subspaces for Neural Network Compression

Massimo Bini, Anders Christensen, Stephan Alaniz +5

Modern transformers pair impressive capabilities with substantial memory and compute demands. Low-rank weight factorization reduces both while keeping the matrices dense, and thus…

cs.CV2026

Look But Don't Touch with Sparse Autoencoders for Unlearning in Diffusion Models

Enrico Cassano, Riccardo Renzulli, Rayyan Ahmed +2

Sparse autoencoders (SAEs) have recently been proposed as interpretable tools for concept-level manipulation, under the assumption that isolated features can serve as controllable…

cs.CV2026

MIRAGE: Benchmarking and Aligning Multi-Instance Image Editing

Ziqian Liu, Stephan Alaniz

Instruction-guided image editing has seen remarkable progress with models like FLUX.2 and Qwen-Image-Edit, yet they still struggle with complex scenarios with multiple similar inst…

cs.CV2026

Explaining CLIP Zero-shot Predictions Through Concepts

Onat Ozdemir, Anders Christensen, Stephan Alaniz +2

Large-scale vision-language models such as CLIP have achieved remarkable success in zero-shot image recognition, yet their predictions remain largely opaque to human understanding.…

cs.CV2026

FINER: MLLMs Hallucinate under Fine-grained Negative Queries

Rui Xiao, Sanghwan Kim, Yongqin Xian +2

Multimodal large language models (MLLMs) struggle with hallucinations, particularly with fine-grained queries, a challenge underrepresented by existing benchmarks that focus on coa…