Papers by Jianglin Lu
ACBQ: Adaptive Cross-Block Quantization of Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for post-training quantization struggle to support weight–activation joint quantization and extreme low-bit weight quantization. |
| Approach: | They propose a framework that addresses weight–activation joint quantization and extreme weight quantization. |
| Outcome: | The proposed framework achieves superior performance under both W4A4 and highly aggressive W2 settings while incurring negligible additional computational overhead. |
Representation Potentials of Foundation Models for Multimodal Alignment: A Survey (2025.emnlp-main)
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| Challenge: | foundation models learn highly transferable representations through large-scale pretraining on diverse data. |
| Approach: | They examine the representation potentials of foundation models by examining their latent capacity to capture task-specific information within a single modality while providing a transferable basis for alignment and unification across modalities. |
| Outcome: | The foundation models exhibit remarkable similarities across architectures and modalities, the authors show . the models can capture task-specific information within a single modality while providing a transferable basis for alignment and unification across modality. |
Revealing the Seen, Imagining the Beyond: A Survey of Image-Grounded Chain-of-Thought Reasoning in Multimodal LLMs (2026.acl-long)
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| Challenge: | Recent advances in Multimodal Large Language Models (MLLMs) have shifted visual reasoning from tool-calling to end-to-end perceptionreasoning. |
| Approach: | They synthesize the emerging paradigm of Image-Grounded Chain-of-Thought (IG-CoT) they propose a method-centric taxonomy covering prompting, supervised fine-tuning, and reinforcement learning . |
| Outcome: | The proposed model is based on a method-centric taxonomy and benchmarks. |
Unequal Scientific Recognition in the Age of LLMs (2025.findings-emnlp)
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| Challenge: | a new study evaluates the extent to which popular and frontier LLMs recognize scientists . recognition of scientists remains uneven across gender and geography . |
| Approach: | They evaluate the extent to which popular and frontier LLMs recognize scientists . they compare their outputs against OpenAlex and Wikipedia . |
| Outcome: | The proposed models show that they exhibit selective and inconsistent recognition patterns . women researchers and researchers from Africa, Asia, and Latin America are significantly underrecognized . |