Papers with sequential reasoning
Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs (2021.findings-acl)
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| Challenge: | Knowledge Graphs (KGs) store structured human knowledge with nodes and edges being entities and relations between them. |
| Approach: | They propose a deep cognitive reasoning network that uses two phases to find answers in large candidate entity sets. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on benchmark datasets. |
IPS: In-Prompt Process Supervision for Short Video Content Moderation (2026.acl-industry)
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| Challenge: | Multimodal large language models (MLLMs) capture semantics of short video content but fail to account for policy-specific details. |
| Approach: | They propose a framework that integrates In-prompt Process Supervision into MLLMs . they propose sequential reasoning over ancillary questions during fine-tuning . |
| Outcome: | IPS outperforms baseline MLLMs on public and proprietary benchmarks . replacing human-annotated ancillary labels with MLML-generated ones results in performance degradation. |
Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision (N19-1)
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| Challenge: | Distant supervision is an important paradigm for automatically extracting relations . but the examples collected can be noisy and pose significant challenge for labeling . |
| Approach: | They propose a method to predict whether two entities participate in a relation at a given time spot. |
| Outcome: | The proposed model performs better in WIKI-TIME and NYT-10 datasets compared with the best existing models . the proposed model is based on a dataset with a valid period of a certain relation of two entities in the knowledge base . |
Live API-Bench: 2500+ Live APIs for Testing Multi-Step Tool Calling (2026.eacl-long)
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Benjamin Elder, Anupama Murthi, Jungkoo Kang, Ankita Rajaram Naik, Kiran Kate, Kinjal Basu, Danish Contractor
| Challenge: | Large language models rely on external tools and APIs to perform tasks specified in natural language. |
| Approach: | They propose a benchmark that transforms SQL queries from BIRD-SQL into executable API sequences. |
| Outcome: | The proposed benchmark evaluates 10 LLMs and 4 ReACT agents with low task completion rates and 50% task completion rate. |
MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems (2025.naacl-long)
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| Challenge: | Existing chart understanding benchmarks focus on single-chart tasks, neglecting multi-hop reasoning required to extract and integrate information from multiple charts. |
| Approach: | They propose a benchmark that evaluates MLLMs’ capabilities in four key areas: direct question answering, parallel question answering and comparative reasoning. |
| Outcome: | The proposed benchmark evaluates MLLMs’ capabilities in four key areas: direct question answering, parallel question answering and comparative reasoning. |
PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (2026.acl-long)
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Jingcheng Hu, Yinmin Zhang, Shijie Shang, Xiaobo Yang, Yue Peng, Zhewei Huang, Hebin Zhou, Xin Wu, Jie Cheng, Fanqi Wan, Xiangwen Kong, Chengyuan Yao, Kaiwen Yan, Ailin Huang, Hongyu Zhou, Qi Han, Zheng Ge, Xiangyu Zhang, Heung-Yeung Shum
| Challenge: | Parallel Coordinated Reasoning (PaCoRe) overcomes a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window. |
| Approach: | They propose a training-and-inference framework to overcome a central limitation of language models: their inability to scale test-time compute (TTC) under a fixed context window. |
| Outcome: | The proposed model scales to multi-million-token effective TTC without exceeding context limits. |