Copertina

Multi-Agent Constraint Factorization Reveals Latent Invariant Solution Structure

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Scofield, Christopher
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Computation and Language Artificial Intelligence Machine Learning Multiagent Systems
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies
di: Zhou, Han, et al.
Pubblicazione: (2025)

MAC: Multi-Agent Constitution Learning
di: Thareja, Rushil, et al.
Pubblicazione: (2026)

Verification-Aware Planning for Multi-Agent Systems
di: Xu, Tianyang, et al.
Pubblicazione: (2025)

AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
di: Trirat, Patara, et al.
Pubblicazione: (2024)

MedAgentBoard: Benchmarking Multi-Agent Collaboration with Conventional Methods for Diverse Medical Tasks
di: Zhu, Yinghao, et al.
Pubblicazione: (2025)

MARCO: Multi-Agent Real-time Chat Orchestration
di: Shrimal, Anubhav, et al.
Pubblicazione: (2024)

$\textit{Agents Under Siege}$: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks
di: Khan, Rana Muhammad Shahroz, et al.
Pubblicazione: (2025)

Can Agents Judge Systematic Reviews Like Humans? Evaluating SLRs with LLM-based Multi-Agent System
di: Mushtaq, Abdullah, et al.
Pubblicazione: (2025)

MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems
di: Wang, Zhexuan, et al.
Pubblicazione: (2026)

Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning
di: Sarkar, Bidipta, et al.
Pubblicazione: (2025)

An Adversary-Resistant Multi-Agent LLM System via Credibility Scoring
di: Ebrahimi, Sana, et al.
Pubblicazione: (2025)

Unleashing Diverse Thinking Modes in LLMs through Multi-Agent Collaboration
di: He, Zhixuan, et al.
Pubblicazione: (2025)

Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence
di: Wang, Ji, et al.
Pubblicazione: (2025)

ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning
di: Wan, Ziyu, et al.
Pubblicazione: (2025)

MLZero: A Multi-Agent System for End-to-end Machine Learning Automation
di: Fang, Haoyang, et al.
Pubblicazione: (2025)

GAMBIT: A Three-Mode Benchmark for Adversarial Robustness in Multi-Agent LLM Collectives
di: Mercier, Alexandre Le, et al.
Pubblicazione: (2026)

Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control
di: Rezazadeh, Alireza, et al.
Pubblicazione: (2025)

Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings
di: Taylor, Russell, et al.
Pubblicazione: (2025)

Harnessing Multi-Agent LLMs for Complex Engineering Problem-Solving: A Framework for Senior Design Projects
di: Mushtaq, Abdullah, et al.
Pubblicazione: (2025)

SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science
di: Seo, Wonduk, et al.
Pubblicazione: (2025)

LLM Agents Making Agent Tools
di: Wölflein, Georg, et al.
Pubblicazione: (2025)

DR. WELL: Dynamic Reasoning and Learning with Symbolic World Model for Embodied LLM-Based Multi-Agent Collaboration
di: Nourzad, Narjes, et al.
Pubblicazione: (2025)

TrustAgent: Towards Safe and Trustworthy LLM-based Agents
di: Hua, Wenyue, et al.
Pubblicazione: (2024)

Recursive Agent Optimization
di: Gandhi, Apurva, et al.
Pubblicazione: (2026)

AgentRec: Agent Recommendation Using Sentence Embeddings Aligned to Human Feedback
di: Park, Joshua, et al.
Pubblicazione: (2025)

Dive into the Agent Matrix: A Realistic Evaluation of Self-Replication Risk in LLM Agents
di: Zhang, Boxuan, et al.
Pubblicazione: (2025)

Language Agents as Optimizable Graphs
di: Zhuge, Mingchen, et al.
Pubblicazione: (2024)

Opponent Shaping in LLM Agents
di: Segura, Marta Emili Garcia, et al.
Pubblicazione: (2025)

Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration
di: Zhang, Yang, et al.
Pubblicazione: (2024)

Memp: Exploring Agent Procedural Memory
di: Fang, Runnan, et al.
Pubblicazione: (2025)

MAEBE: Multi-Agent Emergent Behavior Framework
di: Erisken, Sinem, et al.
Pubblicazione: (2025)

LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions
di: Sun, Chuanneng, et al.
Pubblicazione: (2024)

Toward Super Agent System with Hybrid AI Routers
di: Yao, Yuhang, et al.
Pubblicazione: (2025)

Can We Predict Before Executing Machine Learning Agents?
di: Zheng, Jingsheng, et al.
Pubblicazione: (2026)

Agent Primitives: Reusable Latent Building Blocks for Multi-Agent Systems
di: Jin, Haibo, et al.
Pubblicazione: (2026)

LatentMem: Customizing Latent Memory for Multi-Agent Systems
di: Fu, Muxin, et al.
Pubblicazione: (2026)

SkillAdaptor: Self-Adapting Skills for LLM Agents from Trajectories
di: Yu, Zhuoyun, et al.
Pubblicazione: (2026)

PAACE: A Plan-Aware Automated Agent Context Engineering Framework
di: Yuksel, Kamer Ali
Pubblicazione: (2025)

Doctorina MedBench: End-to-End Evaluation of Agent-Based Medical AI
di: Kozlova, Anna, et al.
Pubblicazione: (2026)

MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
di: Lin, Huawei, et al.
Pubblicazione: (2026)