Papers with progressive reasoning
GLGR: Question-aware Global-to-Local Graph Reasoning for Multi-party Dialogue Reading Comprehension (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches for multi-hop reasoning are lacking for local graph reasoning . existing approaches neglect local semantic structures in utterances . |
| Approach: | They propose a question-aware global-to-local graph reasoning approach that expands the canonical Interlocutor-Utterance graph by introducing a query node. |
| Outcome: | The proposed approach outperforms existing methods on Molweni and FriendsQA. |
MuSe: Multi-Stage Graph Reasoning via Vision-Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Graph Neural Networks (GNNs) and graph transformers are inadequate for tasks with limited generalization. |
| Approach: | They propose a multi-stage graph reasoning framework based on vision-language models that incrementally samples and visualizes task-relevant subgraphs. |
| Outcome: | The proposed framework outperforms existing benchmarks in Graph-related tasks. |
HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation Task (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks for code generation tasks are inadequate, but performance declines on self-invoking tasks. |
| Approach: | They propose a general recipe for generating more challenging versions of existing benchmarks . they propose to use instruction-tuned models to evaluate LLMs on self-invoking code generation tasks . |
| Outcome: | The proposed model improves on humanEval and MBPP but on self-invoking code generation tasks. |
Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play (2026.acl-long)
Copied to clipboard
Xiachong Feng, Deyi Yin, Xiaocheng Feng, Yi Jiang, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Qiming Li, Yuxuan Gu, Bing Qin, Lingpeng Kong
| Challenge: | Existing self-play approaches to developing general reasoning in language models rely on terminal game outcomes. |
| Approach: | They propose a game-based reasoning transfer model that addresses two barriers to reasoning transfer. |
| Outcome: | The proposed model improves mathematical reasoning, general reasoning, and code generation benchmarks. |