M-ORE decouples text and visual update statistics in MLLMs and applies recursive low-rank edits in an orthogonal subspace to reduce cross-modal conflict and long-horizon interference.
MMErroR: A Benchmark for Erroneous Reasoning in Vision-Language Models
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
abstract
Recent advances in Vision-Language Models (VLMs) have improved performance in multi-modal learning, raising the question of whether these models truly understand the content they process. Crucially, can VLMs detect when a reasoning process is wrong and identify its error type? To answer this, we present MMErroR, a multi-modal benchmark of 1997 samples, each embedding a single coherent reasoning error. These samples span 24 subdomains across six top-level domains, ensuring broad coverage and taxonomic richness. Unlike existing benchmarks that focus on answer correctness, MMErroR targets a process-level, error-centric evaluation that requires models to detect incorrect reasoning and classify the error type within both visual and linguistic contexts. We evaluate 12 representative VLMs, and even the best model, Gemini-3-Pro-Preview, classifies the error correctly in only 66.65\% of cases, underscoring the challenge of identifying erroneous reasoning. Furthermore, the ability to accurately identify errors offers valuable insights into the capabilities of multi-modal models. Project Page: https://mmerror-benchmark.github.io
years
2026 10representative citing papers
NeRP corrects asymmetric class confusion in VLMs for unseen classes by combining neutral-prompt priors with sample likelihood to flip predictions on confusable pairs, improving new-class accuracy while preserving base-class performance.
TransSplat uses unbalanced semantic transport to match edited 2D evidence with 3D Gaussians and recover a shared 3D edit field, yielding better local accuracy and structural consistency than prior view-consistency methods.
TransSplat formulates language-driven 3D Gaussian Splatting editing as a multi-view unbalanced semantic transport problem, achieving better cross-view consistency and local editing precision than prior fusion-based methods on 8 benchmark scenes.
COMBINER proposes a new architecture for composed image retrieval using adaptive semantic disentanglement, unified prototype-based composition, and dual attribute-based relation modeling to address visually similar but attribute-unrelated samples.
VEDAL prunes 3D Gaussian Splatting models via variational free energy minimization and prediction-error gating, reporting 5.2x compression with 0.31 dB PSNR drop on standard benchmarks.
HiP-LoRA decomposes LoRA updates into principal and residual spectral channels with a singular-value-weighted stability budget to reduce forgetting and interference during foundation model adaptation.
RankVR introduces GSCP and ASVC modules to improve CIR robustness by decoupling clean samples via low-rank structure and dynamically scoring triplet value in noisy datasets.
IMAGINE uses adaptive schema-imagery via dynamic multimodal prototypes to incorporate implicit semantics into composed video retrieval, claiming SOTA results on CVR and CIR benchmarks.
CoCo-SAM3 improves SAM3 by aligning evidence from synonymous prompts for concept consistency and then running inter-class competition on a unified scale to reduce mask overlaps.
citing papers explorer
-
RF-HiT: Rectified Flow Hierarchical Transformer for General Medical Image Segmentation
TransSplat formulates language-driven 3D Gaussian Splatting editing as a multi-view unbalanced semantic transport problem, achieving better cross-view consistency and local editing precision than prior fusion-based methods on 8 benchmark scenes.