Papers with WM
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)
Copied to clipboard
Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu
| Challenge: | Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent. |
| Approach: | They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs. |
| Outcome: | The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models. |
Memory efficiency and resource-rational encoding in sentence processing (2026.acl-long)
Copied to clipboard
| Challenge: | Existing studies have shown that language models need to be constrained in their use of working memory for context, the analogue to human working memory (WM). |
| Approach: | They propose to inject noise into hidden representations of Transformer-based LMs to capture constraint on memory encoding. |
| Outcome: | The proposed model improves alignment with human reading times and makes them more compressed and categorical. |
LR-DWM: Efficient Watermarking for Diffusion Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Current methods for large language models rely on tokens being generated sequentially . left-right Diffusion watermarking uses a fixed, deterministic left-to-right order . |
| Approach: | They propose a scheme that biases tokens based on both left and right neighbors . left-Right Diffusion Watermarking is a low-latency alternative to autoregressive models . |
| Outcome: | The proposed method can be watermarked efficiently with minimal runtime and memory overhead. |