MultiModal Code-Switching: Interleaving Visual Objects into Language for Explicit Object-Level Alignment
Existing Multimodal Large Language Models (MLLMs) predominantly rely on image-text pairs for modality alignment pretraining, mapping global image representations to long textual descriptions. However, this image-level alignment suffers from referential ambiguity: models struggle to infer the correspondences between multiple visual objects and textual entities from the global representation, leading to data inefficiency and suboptimal semantic grounding. To address this, we propose MultiModal Cod
Record details
Published: 11 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment
Retrieved: 12 August 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
From Interpretability to Control: Insights from Six Years of the TrustNLP Workshop
arXiv · 11 August 2026
How to Verify Consistency of Probabilistic Claims
arXiv · 11 August 2026
Dual-Domain Cross-Modal Decoding for Clinical Text-Guided Medical Image Segmentation
arXiv · 11 August 2026
Data Attribution of Emergent Misalignment with Persona Features
arXiv red teaming query · 11 August 2026
AI Guardrail Survival under Single-Cycle Agentic Self-Summarization
arXiv · 11 August 2026
ToolHazard: Scaling Adversarial Environments for Security Evaluation and Alignment of LLM-based Agents
HuggingFace Daily Papers · 11 August 2026
How to cite this record
ethics.ai (11 August 2026), “MultiModal Code-Switching: Interleaving Visual Objects into Language for Explicit Object-Level Alignment,” evidence record 18693, https://ethics.ai/record/18693 (originally published by arXiv cs.LG).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.