Datasets:

GXMZU
/

llm-rag-agent-papers

id int64 401 617	file_name stringlengths 10 39	paper_id stringlengths 9 9	title stringlengths 6 175	abstract stringlengths 4 1.92k	link stringlengths 32 155	year int64 2.02k 2.03k	content stringlengths 16k 771k	category stringclasses 1 value
401	2210.03629v3.md	Agent_001	ReAct: Synergizing Reasoning and Acting in Language Models	While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we ...	https://arxiv.org/abs/2210.03629	2,023	## REACT : SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS ## ABSTRACT While large language models (LLMs) have demonstrated impressive performance across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan ge...	Agent
402	2501.04227v1.md	Agent_002	Agent Laboratory: Using LLM Agents as Research Assistants	Historically, scientific discovery has been a lengthy and costly process, demanding substantial time and resources from initial conception to final results. To accelerate scientific discovery, reduce research costs, and improve research quality, we introduce Agent Laboratory, an autonomous LLM-based framework capable o...	https://arxiv.org/abs/2501.04227	2,025	## Agent Laboratory: Using LLM Agents as Research Assistants Historically, scientific discovery has been a lengthy and costly process, demanding substantial time and resources from initial conception to final results. To accelerate scientific discovery, reduce research costs, and improve research quality, we introduce...	Agent
403	osagent.md	Agent_003	OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use	The dream to create AI assistants as capable and versatile as the fictional J.A.R.V.I.S from Iron Man has long captivated imaginations. With the evolution of (multimodal) large language models ((M)LLMs), this dream is closer to reality, as (M)LLM-based Agents using computing devices (e.g., computers and mobile phones) ...	https://os-agent-survey.github.io/paper.pdf	2,024	## OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use https://os-agent-survey.github.io/ ## Abstract The dream to create AI assistants as capable and versatile as the fictional J.A.R.V.I.S from Iron Man has long captivated imaginations. With the evolution of (multimodal) large language models...	Agent
404	2501.03936v1.md	Agent_004	PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides	"Automatically generating presentations from documents is a challenging task that requires balancing(...TRUNCATED)	https://arxiv.org/abs/2501.03936	2,025	"## PPTAgent PPT : Generating and Evaluating Presentations Beyond Text-to-Slides\n\n## Abstract\n\nA(...TRUNCATED)	Agent
405	2410.12361v3.md	Agent_005	Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance	"Agents powered by large language models have shown remarkable abilities in solving complex tasks. H(...TRUNCATED)	https://openreview.net/forum?id=sRIU6k2TcU	2,025	"## PROACTIVE AGENT: SHIFTING LLM AGENTS FROM REACTIVE RESPONSES TO ACTIVE ASSISTANCE\n\n## ABSTRACT(...TRUNCATED)	Agent
406	2409.05556v1.md	Agent_006	SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning	"A key challenge in artificial intelligence is the creation of systems capable of autonomously advan(...TRUNCATED)	https://arxiv.org/abs/2409.05556	2,024	"## SCIAGENTS: AUTOMATING SCIENTIFIC DISCOVERY THROUGH MULTI-AGENT INTELLIGENT GRAPH REASONING ∗\n(...TRUNCATED)	Agent
407	2402.01030v4.md	Agent_007	Executable Code Actions Elicit Better LLM Agents	"Large Language Model (LLM) agents, capable of performing a broad range of actions, such as invoking(...TRUNCATED)	https://dl.acm.org/doi/10.5555/3692070.3694124	2,024	"## Executable Code Actions Elicit Better LLM Agents\n\n## Abstract\n\nLarge Language Model (LLM) ag(...TRUNCATED)	Agent
408	2502.14499v1.md	Agent_008	MLGym: A New Framework and Benchmark for Advancing AI Research Agents	"We introduce Meta MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developin(...TRUNCATED)	https://arxiv.org/abs/2502.14499	2,025	"# MLGyM: A New Framework and Benchmark for Advancing Al Research Agents. \n\nWe introduce Meta MLG(...TRUNCATED)	Agent
409	2303.17760v2.md	Agent_009	CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society	"The rapid advancement of chat-based language models has led to remarkable progress in complex task-(...TRUNCATED)	https://dl.acm.org/doi/10.5555/3666122.3668386	2,023	"# CAMEL: Communicative Agents for \"Mind\" Exploration of Large Language Model Society\n\n## Abstra(...TRUNCATED)	Agent
410	2311.12983v1.md	Agent_010	GAIA: a benchmark for General AI Assistants	"We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milesto(...TRUNCATED)	https://openreview.net/forum?id=fibxvahvs3	2,024	"# GAIA: A Benchmark for General Al Assistants\n\nWe introduce GAIA, a benchmark for General AI Assi(...TRUNCATED)	Agent

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llm-rag-agent-papers

Research papers on LLM, RAG, and AI Agents - Knowledge base for RAG pipeline

Dataset Structure

This dataset contains three subsets:

llm: Large Language Model related content
rag: Retrieval-Augmented Generation related content
agent: AI Agent related content

Usage

from datasets import load_dataset

# Load all subsets
dataset = load_dataset("GXMZU/llm-rag-agent-papers")

# Load specific subset
llm_data = load_dataset("GXMZU/llm-rag-agent-papers", "llm")
rag_data = load_dataset("GXMZU/llm-rag-agent-papers", "rag")
agent_data = load_dataset("GXMZU/llm-rag-agent-papers", "agent")

Use Case

This dataset is designed as a knowledge base for RAG (Retrieval-Augmented Generation) pipelines, providing domain-specific content about LLM, RAG, and AI Agent technologies.

License

This dataset is licensed under MIT.

Citation

If you use this dataset in your research, please cite:

@misc{llm-rag-agent-papers,
  title={LLM RAG Agent papers},
  author={real-jiakai},
  year={2025},
  url={https://huggingface.co/datasets/GXMZU/llm-rag-agent-papers}
}

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