EvoSpark: Endogenous Interactive Agent Societies for Unified Long-Horizon Narrative Evolution (2026.acl-long)
Copied to clipboard
| Challenge: | Existing systems suffer from social memory stacking and narrative-spatial dissonance . long-horizon narratives suffer from conflicting relational states without resolution . |
| Approach: | They propose a framework to sustain logically coherent long-horizon narratives within endogenous interactive agent societies. |
| Outcome: | Experiments show that the framework outperforms baselines across paradigms. |
Similar Papers
EvoHyper: Evolving Hypergraph Topologies for Unified Collaboration in Multi-Agent Communication (2026.findings-acl)
Copied to clipboard
Heng Zhang, Yihao Zhong, Lubin Gan, Zhihe Chen, Jiajun Wu, Yuling Shi, Xiaodong Gu, Hao Zhang, Haochen You, Jin Huang
| Challenge: | Existing methods for multi-agent collaboration use a fixed communication graph and manage collaboration structure and shared memory in separate modules. |
| Approach: | They propose a framework that uses an evolving hypergraph topology for multi-agent collaboration. |
| Outcome: | The proposed framework achieves 3.2% to 7.8% accuracy gains over state-of-the-art methods and efficient, reducing token consumption by up to 23.5%. |
SPARK: Simulating the Co-evolution of Stance and Topic Dynamics in Online Discourse with LLM-based Agents (2025.emnlp-main)
Copied to clipboard
| Challenge: | a new framework for topic evolution and stance dynamics is needed to understand online discourse . topic evolution is central to understanding fragmentation of debates, spread of misinformation . |
| Approach: | They propose a stance and topic evolution reasoning framework for co-evolution of topics and stances through natural language interactions. |
| Outcome: | The proposed framework captures key empirical patterns across five real-world domains. |
EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for large language models (LLMs) face challenges in the design of research workflows and multi-role collaboration mechanisms. |
| Approach: | They propose a multi-agent scientific collaboration framework which integrates bio-inspired evolution with knowledge graph modeling. |
| Outcome: | EvoSci outperforms baselines in peer-review and ranking evaluations on real-world research topics. |
Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning (2026.eacl-long)
Copied to clipboard
| Challenge: | scalability challenges arise as conversations extend over weeks or months . current approaches fragment conversations into isolated embeddings or graph representations . |
| Approach: | They propose a working memory framework that actively constructs structured memory representations . the framework organizes conversational fragments into episodic narratives based on momentum . |
| Outcome: | Amory improves performance on LOCOMO benchmark while reducing response time by 50%. |
GAM: Hierarchical Graph-based Agentic Memory for LLM Agents (2026.acl-long)
Copied to clipboard
Zhaofen Wu, Hanrong Zhang, Fulin Lin, Wujiang Xu, Xinran Xu, Yankai Chen, Henry Peng Zou, Shaowen Chen, Weizhi Zhang, Xue Liu, Philip S. Yu, Hongwei Wang
| Challenge: | Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise. |
| Approach: | They propose a hierarchical Graph-based Agentic Memory framework that explicitly decouples memory encoding from consolidation to resolve conflict between rapid context perception and stable knowledge retention. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on LoCoMo and LongDialQA. |
Structured Episodic Event Memory (2026.acl-long)
Copied to clipboard
| Challenge: | Current approaches to memory in Large Language Models (LLMs) rely on static Retrieval-Augmented Generation (RAG) this lacks the cognitive organization necessary to model the dynamic and associative nature of long-term interaction. |
| Approach: | They propose a hierarchical framework that transforms interaction streams into structured Episodic Event Frames (EEFs) anchored by precise provenance pointers. |
| Outcome: | The proposed framework outperforms baseline approaches on LoCoMo and LongMemEval benchmarks. |
MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods rely on unstructured retrieval or coarse abstractions, which lead to temporal conflicts, brittle reasoning, and limited traceability. |
| Approach: | They propose a unified memory framework that consolidates long-term agent experiences into three interconnected components that combine structured knowledge and evidence to construct compact yet information-dense contexts for reasoning. |
| Outcome: | The proposed framework significantly improves multi-hop and temporal reasoning accuracy while reducing input context length by over 95% compared to long-context baselines. |
EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning (2026.acl-long)
Copied to clipboard
Chuanrui Hu, Xingze Gao, Zuyi Zhou, Dannong Xu, Yi Bai, Xintong Li, Hui Zhang, Tong Li, Chong Zhang, Lidong Bing, Yafeng Deng
| Challenge: | Existing memory systems for LLMs store isolated records and retrieve fragments . Existing systems store isolated data and fragments, limiting their ability to consolidate evolving experience and resolve conflicts. |
| Approach: | They propose an engram-inspired memory operating system that implements an 'engram'-inspired lifecycle for computational memory. |
| Outcome: | Experiments on LoCoMo, LongMemEval, and PersonaMeM-v2 show that EverMemeOS outperforms state-of-the-art methods on memory-augmented reasoning tasks. |
EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing work on extending specialized agents to multi-agent systems is dependent on human-designed frameworks, limiting the functional scope and scalability of agent systems. |
| Approach: | They propose a generic method to automatically extend specialized agents to multi-agent systems via evolutionary algorithm . they consider existing agent frameworks as the initial individual and apply evolutionary operators to generate multiple agents with diverse settings. |
| Outcome: | The proposed method can extend specialized agents to multi-agent systems . it can generate multiple agents with diverse settings, and improves performance across tasks . |
LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning (2026.findings-acl)
Copied to clipboard
Zhengjun Huang, Zhoujin Tian, Qintian Guo, Fangyuan Zhang, Yingli Zhou, Di Jiang, Zeying Xie, Xiaofang Zhou
| Challenge: | Large Language Models are constrained by limited context windows and lack of persistent memory . recent efforts address these limitations via external memory architectures . |
| Approach: | They propose an end-to-end agentic memory framework for real-time updating and retrieval that integrates hierarchical and temporal indexing layers. |
| Outcome: | The proposed framework outperforms established benchmarks in temporal reasoning, multi-session consistency, and retrieval efficiency. |