An AI-agent social platform generated mostly neutral content whose use in fine-tuning reduced model truthfulness comparably to human Reddit data, suggesting limited unique harm but flagging tail risks like secret leaks.
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey
8 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
8
Pith papers citing it
2
external citations · Pith
abstract
Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. However, fully autonomous LLM-based agents still face significant challenges, including limited reliability due to hallucinations, difficulty in handling complex tasks, and substantial safety and ethical risks, all of which limit their feasibility and trustworthiness in real-world applications. To overcome these limitations, LLM-based human-agent systems (LLM-HAS) incorporate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. These human-agent collaboration systems enable humans and LLM-based agents to collaborate effectively by leveraging their complementary strengths. This paper provides the first comprehensive and structured survey of LLM-HAS. It clarifies fundamental concepts, systematically presents core components shaping these systems, including environment and profiling, human feedback, interaction types, orchestration, and communication, explores emerging applications, and discusses unique challenges and opportunities arising from human-AI collaboration. By consolidating current knowledge and offering a structured overview, we aim to foster further research and innovation in this rapidly evolving interdisciplinary field. Paper lists and resources are available at https://github.com/HenryPengZou/Awesome-Human-Agent-Collaboration-Interaction-Systems.
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2026 8representative citing papers
Persistent decision history plus role-separated redacted evaluation lets agents continually improve skills across sessions and outperform commercial deep-research and prior skill-evolution baselines.
PUMA detects reasoning-level semantic redundancy to enable early exit in chains of thought, achieving 26.2% average token reduction across five LRMs and five benchmarks while preserving accuracy and CoT quality.
SafetyALFRED shows multimodal LLMs recognize kitchen hazards accurately in QA tests but achieve low success rates when required to mitigate those hazards through embodied planning.
GAM decouples event-level memory encoding from topic-level consolidation in LLM agents using hierarchical graphs to reduce interference and improve long-term coherence and retrieval.
Introduces a Solo-to-Social planner-executor framework where LLMs decompose HHI into phases and roles, then a LoRA-adapted solo motion model grounds them into partner-aware 3D motion.
HACO adapts MaskGIT from vision into MaskGXT with symmetry tokens and stratified sampling, reaching 79.06% METRe accuracy on MP-20 polymorph split versus 70.87% for the best baseline.
Paimon is an agentic framework that automates atomistic simulations and improves reliability by suppressing silent errors in agent workflows, demonstrated on liquid electrolyte cases and literature reproduction.
citing papers explorer
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The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment
An AI-agent social platform generated mostly neutral content whose use in fine-tuning reduced model truthfulness comparably to human Reddit data, suggesting limited unique harm but flagging tail risks like secret leaks.
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SkillHone: A Harness for Continual Agent Skill Evolution Through Persistent Decision History
Persistent decision history plus role-separated redacted evaluation lets agents continually improve skills across sessions and outperform commercial deep-research and prior skill-evolution baselines.
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Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models
PUMA detects reasoning-level semantic redundancy to enable early exit in chains of thought, achieving 26.2% average token reduction across five LRMs and five benchmarks while preserving accuracy and CoT quality.
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SafetyALFRED: Evaluating Safety-Conscious Planning of Multimodal Large Language Models
SafetyALFRED shows multimodal LLMs recognize kitchen hazards accurately in QA tests but achieve low success rates when required to mitigate those hazards through embodied planning.
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GAM: Hierarchical Graph-based Agentic Memory for LLM Agents
GAM decouples event-level memory encoding from topic-level consolidation in LLM agents using hierarchical graphs to reduce interference and improve long-term coherence and retrieval.
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Social Structure Matters in 3D Human-Human Interaction Generation
Introduces a Solo-to-Social planner-executor framework where LLMs decompose HHI into phases and roles, then a LoRA-adapted solo motion model grounds them into partner-aware 3D motion.
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Discovering Crystal Structure Prediction Algorithms with an AI Co-Scientist
HACO adapts MaskGIT from vision into MaskGXT with symmetry tokens and stratified sampling, reaching 79.06% METRe accuracy on MP-20 polymorph split versus 70.87% for the best baseline.
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A Robust Agentic Framework for Expert-Level Automation of Atomistic Simulations
Paimon is an agentic framework that automates atomistic simulations and improves reliability by suppressing silent errors in agent workflows, demonstrated on liquid electrolyte cases and literature reproduction.