LDM2: A Large Decision Model Imitating Human Cognition with Dynamic Memory Enhancement (2023.findings-emnlp)
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| Challenge: | Extensive experiments conducted in two interactive environments have shown that our LDM2 outperforms the baselines in terms of both score and success rate. |
| Approach: | They propose a large decision model with memory that leverages a dynamic memory mechanism to construct dynamic prompts, guiding the LLMs in making proper decisions according to the faced state. |
| Outcome: | The proposed model outperforms baseline models in two interactive environments in terms of score and success rate. |
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| Challenge: | Empirical results on mathematical reasoning benchmarks substantiate the efficacy of Large language models (LLMs). |
| Approach: | They propose a framework that combines an LLM with a process-based verifier to generate plausible candidates and provide timely process-driven feedback to distinguish desirable and undesirable outputs. |
| Outcome: | Empirical results show that LLM2 improves accuracy on GSM8K and self-consistency increases major@20 accuracy. |
Working Memory Identifies Reasoning Limits in Language Models (2024.emnlp-main)
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| Challenge: | Using large language models, we examine the limitations of their cognitive capabilities and their working memory. |
| Approach: | They examine the limitations of large language models from a scaling perspective . they also assess various prompting strategies, revealing their diverse impacts on LLM performance. |
| Outcome: | The proposed models perform poorly on n-back tasks and on prompting strategies. |
FAC2E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are evaluated by overall performance on various text understanding and generation tasks. |
| Approach: | They propose a framework for Fine-grAined and Cognition-grounded LLMs’ Capability Evaluation that dissociates the language-related capabilities from cognition-related ones. |
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GraphMind: LLMs as Dynamic Knowledge Builders for Sequential Decision-Making (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable performance in natural language understanding and generation, establishing themselves as foundational tools across a wide range of domains. |
| Approach: | They propose an LLM agent architecture that integrates a knowledge graph as a graph-based memory module and integrates it into the agent to generate efficient plans. |
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Towards Reasoning in Large Language Models: A Survey (2023.findings-acl)
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| Challenge: | Reasoning is a fundamental aspect of human intelligence that plays a crucial role in many intellectual activities. |
| Approach: | They propose to improve LLMs' ability to elicit reasoning by providing exemplars or prompts to model reasoning. |
| Outcome: | This paper provides a comprehensive overview of the state of knowledge on reasoning in large language models. |
LLM in a flash: Efficient Large Language Model Inference with Limited Memory (2024.acl-long)
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Keivan Alizadeh, Seyed Iman Mirzadeh, Dmitry Belenko, S. Khatamifard, Minsik Cho, Carlo C Del Mundo, Mohammad Rastegari, Mehrdad Farajtabar
| Challenge: | Large language models (LLMs) have high computational and memory requirements, especially for devices with limited memory. |
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Disentangling Memory and Reasoning Ability in Large Language Models (2025.acl-long)
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Mingyu Jin, Weidi Luo, Sitao Cheng, Xinyi Wang, Wenyue Hua, Ruixiang Tang, William Yang Wang, Yongfeng Zhang
| Challenge: | Existing LLMs operate as an opaque process without explicit separation between knowledge retrieval and reasoning steps, making the decision-making process unclear and disorganized. |
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| Outcome: | The proposed paradigm decomposes the inference process into two distinct and clear actions, memory and reason, guiding the model to distinguish between steps that require knowledge retrieval and those that involve reasoning. |
Mapping the Minds of LLMs: A Graph-Based Analysis of Reasoning LLMs (2025.emnlp-main)
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| Challenge: | Large Reasoning Models (LRMs) often display unstable behaviors, e.g., hallucinating unsupported premises, overthinking simple tasks, and displaying higher sensitivity to prompt variations. |
| Approach: | They propose a graph-based analytical framework that clusters long, verbose CoT outputs into semantically coherent reasoning steps, then constructs directed reasoning graphs to capture contextual and logical dependencies among these steps. |
| Outcome: | The proposed framework enables quantitative evaluation of internal reasoning structure and quality beyond conventional metrics and provides practical insights for prompt engineering and cognitive analysis of LLMs. |
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning (2026.acl-long)
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Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Jinhe Bi, Kristian Kersting, Jeff Z. Pan, Hinrich Schuetze, Volker Tresp, Yunpu Ma
| Challenge: | Large Language Models (LLMs) are stateless and limited by a finite context window, preventing them from maintaining knowledge across long conversations or evolving tasks. |
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| Outcome: | The proposed framework outperforms baselines and benchmarks across diverse question types, three benchmarks, and multiple model scales. |
Unveiling Factual Recall Behaviors of Large Language Models through Knowledge Neurons (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models have underscored their exceptional reasoning prowess with natural language understanding across a broad spectrum of tasks. |
| Approach: | They examine whether Large Language Models actively recall or retrieve their internal repositories of factual knowledge when faced with reasoning tasks. |
| Outcome: | The proposed model improves reasoning performance while suppressing it leads to notable degradation. |