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multimodal large language models

topic11 events
papersTODAY 04:00 UTC

arXiv Paper Benchmarks Model-Agnostic Keyframe Selection for Long Video MLLMs

A new arXiv preprint evaluates keyframe selection techniques that can be plugged into existing multimodal large language models without modifying them. The work targets the constraint that MLLMs cannot ingest every frame of a long video due to visual-token and compute limits, and compares the main families of approaches proposed to address this. The study positions keyframe selection as a model-agnostic add-on for improving long-video understanding.

video-understanding/multimodal/benchmark/research
papersTODAY 04:00 UTC

EventVL Uses Multimodal LLMs to Interpret Event Camera Streams

Researchers present EventVL, a multimodal large language model designed to interpret event-based camera data rather than relying on CLIP-style encoders. The work targets explicit understanding of event streams, a sensing modality where most prior vision-language approaches have focused only on conventional perception tasks. The paper is a revised cross-listing on arXiv.

multimodal/event-cameras/vision-language/research
papersTODAY 04:00 UTC

UniCAR-RL targets fine-grained perception failures in multimodal math reasoning

A new arXiv paper introduces UniCAR-RL, a reinforcement learning approach aimed at improving how multimodal large language models handle math problems involving diagrams and figures. The authors argue that weak fine-grained visual perception leads models to hallucinate details early, which then causes errors to compound through the rest of the reasoning chain. The method is framed as improving perception before deeper reasoning steps are attempted.

multimodal/reinforcement-learning/visual-reasoning/mllm
papersTODAY 04:00 UTC

Study probes how multimodal LLMs describe bistable images like the duck-rabbit

A new arXiv paper examines whether multimodal large language models report bistable images, such as the duck-rabbit figure, in ways comparable to humans. Humans typically perceive only one interpretation of such images at a time, and the research tests whether MLLMs show a similar pattern. The work falls in the area of model perception and evaluation research.

multimodal/vision/evaluation/research
papersTODAY 04:00 UTC

Valley3: Omni Multimodal LLM Targets Global E-commerce Tasks

Researchers introduce Valley3, a multimodal large language model designed for e-commerce applications across different markets. The model handles text, images, video, and audio within a single framework, aiming to combine understanding and reasoning abilities across those modalities. The paper is a revised arXiv preprint, with an updated version posted to the cs.AI category.

multimodal/e-commerce/llm/arxiv
papersTODAY 04:00 UTC

Circuit-MLLM applies topological logic guidance to circuit schematic reasoning

A new arXiv preprint argues that multi-modal large language models handle circuit schematics poorly because these diagrams pack dense component layouts and connectivity into a single image. The authors propose Circuit-MLLM, which uses topological logic to steer visual reasoning within the model's latent space rather than relying on surface-level image features. The work is cross-listed under arXiv's cs.AI and cs.LG categories.

mllm/visual-reasoning/circuit-schematics/arxiv
papersTODAY 04:00 UTC

CapGeo-Bench separates visual perception from geometric reasoning in multimodal models

A new arXiv paper introduces CapGeo-Bench, a benchmark designed to evaluate geometric understanding in multimodal large language models while distinguishing failures in visual perception from failures in reasoning. The authors note that even strong closed models such as GPT-o3 continue to lag on geometry problems despite success on purely textual math tasks. The benchmark aims to give a clearer picture of where these systems break down.

benchmarks/multimodal/reasoning/geometry
papersTODAY 04:00 UTC

arXiv Paper Proposes Concept-Grounded Reasoning for Medical Imaging Reports

A new arXiv preprint introduces an approach that combines multimodal large language models with prompt-driven localization to produce interpretable structured reports from medical images such as ultrasound and X-ray. The method grounds reasoning in clinical concepts, aiming to align generated findings with standardized diagnostic criteria. The work appears under the cs.AI and cs.LG categories and has not yet been peer reviewed.

medical-imaging/multimodal/interpretability/arxiv
papersSEP 11 04:00 UTC

arXiv paper examines in-context multimodal jailbreaks in AI models

A new arXiv preprint analyzes how harmful examples placed in a prompt can make multimodal large language models produce unsafe outputs without any change to their weights. The authors frame this in-context jailbreak behavior as a vulnerability and propose a posterior reweighting approach to explain it. The work falls under cross-listed machine learning submissions.

safety/jailbreaks/multimodal/research
papersSEP 10 04:00 UTC

Paper proposes contrastive modeling to align reasoning paths in multimodal in-context learning

A newly released arXiv paper examines a weakness in how multimodal large language models use in-context learning, noting that current methods tend to copy superficial patterns from examples rather than the underlying reasoning process. The authors present a contrastive modeling technique designed to align a model's reasoning path with the logic demonstrated in-context. The approach is intended to improve performance across a range of multimodal tasks by fostering genuine reasoning instead of imitation.

multimodal/in-context-learning/reasoning/llms
papersSEP 10 04:00 UTC

Study Links MLLM Hallucinations to Information Drift in Synergy Heads

A new arXiv paper traces hallucinations in multimodal large language models to shifts in how information is distributed across attention components the authors call synergy heads. The researchers argue that existing mitigation techniques built on attention weights track only indirect cues and therefore miss the underlying mechanism. The findings could support more targeted methods for reducing fabricated outputs in multimodal systems.

hallucination/multimodal/llm/interpretability