Papers with DES
When Does Auxiliary Modality Matter in Solving Geometric Problems? A Comprehensive Study of Textual, Formal, and Visual Modalities (2026.eacl-short)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) face challenges in integrating linguistic and spatial reasoning, which limits their performance on geometry problems. |
| Approach: | They compare four auxiliary modalities on open- and closed-source multimodal LLMs . they show that DES boosts the accuracy of open-source LLM models . |
| Outcome: | The proposed modalities improve performance on open- and closed-source LLMs. |
Bring Linguistics Back to Cryptanalysis: Using Attestation to Break the Advanced Encryption Standard (2026.acl-srw)
Copied to clipboard
| Challenge: | Current DES cryptanalysis methods use mathematical methods to find the most likely approximate key in the entire probability space. |
| Approach: | They propose an algorithm that eliminates AES keys using linguistic attestation . the algorithm proposes trimming the probability space by removing unattested keys . |
| Outcome: | The proposed algorithm eliminates AES keys using linguistic attestation . the proposed algorithm can be analyzed in seconds with current DES algorithms . |
Scale Down to Speed Up: Dynamic Data Selection for Reinforcement Learning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Current approaches to Reinforcement Learning (RL) rely on massive static datasets, leading to computational inefficiency and redundant gradient updates. |
| Approach: | They propose a data-centric RL framework that dynamically selects the most informative training samples to optimize RL for mathematical reasoning. |
| Outcome: | The proposed framework achieves comparable performance to full-data training methods while requiring only 1.5K samples instead of 220K, reducing training time from 13 days to just 4 hours on 8A800 GPUs. |