vlms

This paper studies how multimodal claim verification models respond to stylistic text changes induced by LLM rewriting. Evaluating 11 open-weight VLMs (2B–38B), the authors find accuracy is largely robust to natural rewriting and controlled LLM-word injection, though hedging-oriented modifications cause consistent probability shifts across nearly all models.

OSWorld-Science introduces a benchmark and evaluation environment for computer-using VLM agents on scientific software, covering 146 expert-designed tasks across domains like molecular drawing, pathology imaging, statistics, and physical simulation with artifact-based evaluators and a special agent harness.

A system paper on hallucination detection in Vision-Language Models has been accepted at EMNLP 2026 for the SHROOM shared task, hosted at the UncertaiNLP workshop, focusing on marking and labeling hallucinated character spans in VLM outputs.

This paper introduces TRACE, a benchmark for post-fire object understanding, and proposes a Feature Recovery Module (FRM) to restore degraded features and improve detection and vision-language tasks under severe physical damage.

This paper introduces an agentic framework combining LLMs and VLMs for consistent multi-instruction video editing across multiple shots, and proposes the MMLVE task and benchmark to evaluate performance.

This survey paper reviews decoding methods for large language models and vision-language models, focusing on inference-time approaches to enhance efficiency and control generation. It identifies paradigms, highlights challenges, and discusses future research directions.

This paper introduces Spatial Memory Agent (SMA), a runtime framework that improves frozen vision-language models' spatial reasoning through verifier-guided reflection and reusable memory without parameter updates or external tools, achieving strong results across five benchmarks and four base VLMs.

Microsoft Research introduces MindTopo, a benchmark that tests whether multimodal AI models understand topological relationships like connectivity, enclosure, and knots. Current models perform well on static recognition but struggle with interactive planning tasks, revealing a key gap for robotics and interactive environments.

A benchmark comparing 8 PDF parsers across 14 capabilities, finding Chandra the most accurate while noting trade-offs like speed; LightOnOCR-1B impresses for its size but hallucinates on illegible text.

GST-Bench is a new VQA benchmark for evaluating global spatial awareness in video understanding, testing whether VLMs can build coherent global scene representations from long-horizon egocentric video. Evaluation of 22 state-of-the-art VLMs shows a large gap versus humans, with the best model scoring 42.68 vs 79.08.

OSReward introduces a standardized benchmark for evaluating VLM judges on computer-use agent trajectories, revealing that even state-of-the-art models have systematic leniency bias. The authors release OS-Shepherd-9B and 35B reward models that provide reliable, low-cost rewards for CUA tasks.

LlamaIndex announces improved routing in LlamaParse that selects lightweight parsing for simple text-heavy PDFs and heavier VLM-based parsing for complex pages with tables or charts, optimizing cost and accuracy.

Z-Reward is a teacher-student framework that decouples complex reasoning from efficient reward deployment for text-to-image training. It achieves 89.6% human preference accuracy with a 27B teacher and 88.6% with a 9B student, outperforming prior methods.

Jerry Liu's team is presenting ParseBench, a comprehensive document understanding benchmark for VLMs, at CVPR 2026. The benchmark includes 2,000 pages of real-world enterprise documents with evaluation metrics for tables, charts, and visual grounding.

This paper introduces a nested geometric decomposition framework to analyze how prompting reorganizes internal representations in large language and vision-language models. The authors show that affine transformations, particularly cross-dimensional linear mixing, are key to explaining prompt-induced behavioral changes.

Fast-dDrive is a block-diffusion VLA model for end-to-end autonomous driving that achieves state-of-the-art trajectory accuracy while delivering over 12x throughput speedup over autoregressive baselines, addressing the trade-off between high-fidelity planning and efficient inference for edge deployment.

This paper presents SpaceNum, a unified framework to evaluate how vision-language models (VLMs) understand numerical values in spatial contexts, finding that current models largely fail to ground numbers spatially and often perform close to random guessing.

GridProbe is a training-free inference paradigm for Long-Video VLMs that adaptively selects relevant frames using posterior probing, achieving sub-quadratic attention costs with minimal accuracy loss.

GeoStack introduces a geometric framework to compose independently trained domain experts in Vision-Language Models without catastrophic forgetting, achieving constant-time inference and a 10x reduction in geometric error.

Researchers from MIT, WPI, and Google propose WRING, a novel post-processing debiasing method for Vision-Language Models that avoids the 'Whac-a-mole dilemma' of amplifying other biases when removing specific ones.