REVIEW 16 cited by
Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects
read the original abstract
Intelligent agents stand out as a potential path toward artificial general intelligence (AGI). Thus, researchers have dedicated significant effort to diverse implementations for them. Benefiting from recent progress in large language models (LLMs), LLM-based agents that use universal natural language as an interface exhibit robust generalization capabilities across various applications -- from serving as autonomous general-purpose task assistants to applications in coding, social, and economic domains, LLM-based agents offer extensive exploration opportunities. This paper surveys current research to provide an in-depth overview of LLM-based intelligent agents within single-agent and multi-agent systems. It covers their definitions, research frameworks, and foundational components such as their composition, cognitive and planning methods, tool utilization, and responses to environmental feedback. We also delve into the mechanisms of deploying LLM-based agents in multi-agent systems, including multi-role collaboration, message passing, and strategies to alleviate communication issues between agents. The discussions also shed light on popular datasets and application scenarios. We conclude by envisioning prospects for LLM-based agents, considering the evolving landscape of AI and natural language processing.
Forward citations
Cited by 16 Pith papers
-
SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses
SUPERGLASSES is the first VQA benchmark built from actual smart glasses data, and SUPERLENS is an agent using automatic object detection, query decoupling, and multimodal search that outperforms GPT-4o by 2.19% on it.
-
An Empirical Study of Testing Practices in Open Source AI Agent Frameworks and Agentic Applications
Empirical study of open-source AI agents shows testing effort concentrates on deterministic tools and workflows (over 70%) while the FM-based plan body gets under 5% and prompts appear in only 1% of tests.
-
Agora: Toward Autonomous Bug Detection in Production-Level Consensus Protocols with LLM Agents
Agora's domain-aware multi-agent LLM framework detects 15 previously unknown safety-violating logic bugs across Raft, EPaxos, HotStuff, and BullShark implementations where prior LLM agents found none.
-
MACReD: A Multi-Agent Collaborative Reasoning Framework for Reaction Diagram Parsing
MACReD is a multi-agent collaborative reasoning framework for reaction diagram parsing that reports state-of-the-art F1 scores of 75.2% and 84.6% on the RxnScribe benchmark.
-
Multi-Agent Coordination Adaptation via Structure-Guided Orchestration
MACA frames multi-agent coordination as posterior inference, learns a structural prior to guide orchestration, and reports 8.42% higher performance with 43.19% fewer tokens than adaptive baselines on benchmarks.
-
Hive: A Multi-Agent Infrastructure for Algorithm- and Task-Level Scaling
Hive is a multi-agent infrastructure with a logits cache for reducing cross-path redundancy in sampling and agent-aware scheduling for better compute and KV-cache allocation, shown to deliver 1.11x-1.76x speedups and ...
-
Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution
ReMe enables LLM agents to evolve via multi-faceted experience distillation, context-adaptive reuse, and utility-based memory refinement, achieving new SOTA results on BFCL-V3 and AppWorld while letting an 8B model be...
-
Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies
PGT optimizes latent goal embeddings for frozen policies via trajectory-level preference objectives, reporting 72-81.6% relative gains on 17 Minecraft tasks and 13.4% better OOD performance than fine-tuning.
-
MemTools: A Unified Research Framework for Interoperable Agent Memory
MemTools decouples agent-memory lifecycle stages via declarative data contracts, separates evaluation protocols from benchmark datasets, and unifies symbolic, neural, and multimodal memory in one runtime, enabling hyb...
-
Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
Magentic-One is a modular multi-agent system that matches state-of-the-art performance on GAIA, AssistantBench, and WebArena using an orchestrator-led team of specialized agents.
-
A Survey of Context Engineering for Large Language Models
The survey organizes Context Engineering into retrieval, processing, management, and integrated systems like RAG and multi-agent setups while identifying an asymmetry where LLMs handle complex inputs well but struggle...
-
Large Language Model-Brained GUI Agents: A Survey
A survey consolidating frameworks, data practices, large action models, benchmarks, applications, and research gaps in LLM-brained GUI agents.
-
Large Language Model-Based Agents for Software Engineering: A Survey
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.
-
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.
-
A Survey on the Memory Mechanism of Large Language Model based Agents
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
-
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
This survey frames foundation agents using brain-inspired modular architectures and reviews challenges in evolution, collaboration, and safety.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.