mlsanityData to 7 Oct 2026

Bridging the Semantic Chasm: Synergistic Conceptual Anchoring for Generalized Few-Shot and Zero-Shot OOD Perception

arXiv 2602.00340 ↗First posted 30 Jan 2026cs.CV cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

This manuscript presents a pioneering Synergistic Neural Agents Network (SynerNet) framework designed to mitigate the phenomenon of cross-modal alignment degeneration in Vision-Language Models (VLMs) when encountering Out-of-Distribution (OOD) concepts. Specifically, four specialized computational units - visual perception, linguistic context, nominal embedding, and global coordination - collaboratively rectify modality disparities via a structured message-propagation protocol. The principal contributions encompass a multi-agent latent space nomenclature acquisition framework, a semantic context-interchange algorithm for enhanced few-shot adaptation, and an adaptive dynamic equilibrium mechanism. Empirical evaluations conducted on the VISTA-Beyond benchmark demonstrate that SynerNet yields substantial performance augmentations in both few-shot and zero-shot scenarios, exhibiting precision improvements ranging from 1.2% to 5.4% across a diverse array of domains.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 40 of its 51 references that are in the map. First posted on arXiv 30 Jan 2026.

Builds on

40 of 51 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024LLaVA-Read: Enhancing Reading Ability of Multimodal Language Models27 Jul
  2. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks21 Dec

Other references · 38

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
  3. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  4. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,848
  5. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  6. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  7. 2021Learning to Prompt for Vision-Language Modelscited by 2,269
  8. 2021Align before Fuse: Vision and Language Representation Learning with Momentum Distillationcited by 1,612
  9. 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,544
  10. 2022Conditional Prompt Learning for Vision-Language Modelscited by 1,427
  11. 2022Reproducible scaling laws for contrastive language-image learningcited by 1,163
  12. 2021CLIP-Adapter: Better Vision-Language Models with Feature Adapterscited by 1,021
26 more
  1. 2021Robust fine-tuning of zero-shot modelscited by 785
  2. 2021DenseCLIP: Language-Guided Dense Prediction with Context-Aware Promptingcited by 530
  3. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  4. 2024LLM2Vec: Large Language Models Are Secretly Powerful Text Encoderscited by 396
  5. 2021How Much Can CLIP Benefit Vision-and-Language Tasks?cited by 321
  6. 2021AdaViT: Adaptive Vision Transformers for Efficient Image Recognitioncited by 228
  7. 2022Task Residual for Tuning Vision-Language Modelscited by 122
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  17. 2025MM-CoT:A Benchmark for Probing Visual Chain-of-Thought Reasoning in Multimodal Modelscited by 33
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