LimAgents: Multi-Agent LLMs for Generating Research Limitations

• URL: http://arxiv.org/abs/2601.11578v1
• Date: Tue, 30 Dec 2025 18:12:52 GMT
• Title: LimAgents: Multi-Agent LLMs for Generating Research Limitations
• Abstract summary: LimAgents is a multi-agent framework for generating substantive limitations.<n>It integrates OpenReview comments and author-stated limitations.<n>It also uses cited and citing papers to capture broader contextual weaknesses.
• Score: 6.359517103183802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying and articulating limitations is essential for transparent and rigorous scientific research. However, zero-shot large language models (LLMs) approach often produce superficial or general limitation statements (e.g., dataset bias or generalizability). They usually repeat limitations reported by authors without looking at deeper methodological issues and contextual gaps. This problem is made worse because many authors disclose only partial or trivial limitations. We propose LimAgents, a multi-agent LLM framework for generating substantive limitations. LimAgents integrates OpenReview comments and author-stated limitations to provide stronger ground truth. It also uses cited and citing papers to capture broader contextual weaknesses. In this setup, different agents have specific roles as sequential role: some extract explicit limitations, others analyze methodological gaps, some simulate the viewpoint of a peer reviewer, and a citation agent places the work within the larger body of literature. A Judge agent refines their outputs, and a Master agent consolidates them into a clear set. This structure allows for systematic identification of explicit, implicit, peer review-focused, and literature-informed limitations. Moreover, traditional NLP metrics like BLEU, ROUGE, and cosine similarity rely heavily on n-gram or embedding overlap. They often overlook semantically similar limitations. To address this, we introduce a pointwise evaluation protocol that uses an LLM-as-a-Judge to measure coverage more accurately. Experiments show that LimAgents substantially improve performance. The RAG + multi-agent GPT-4o mini configuration achieves a +15.51% coverage gain over zero-shot baselines, while the Llama 3 8B multi-agent setup yields a +4.41% improvement.

Related papers

• AgentSpec: Speculative Decoding for Batch Inference of LLM Agents [21.065887019901293]
  Speculative decoding algorithms exhibit substantial speed degradation under large batch sizes.<n>We propose AgentSpec, a speculative decoding algorithm that addresses the limitations of existing methods for LLM agents.
  arXiv  Detail & Related papers  (2026-08-25T02:49:29Z)
• Latent Agents: A Post-Training Procedure for Internalized Multi-Agent Debate [16.68699018207298]
  Multi-agent debate has been shown to improve reasoning in large language models (LLMs)<n>We develop a framework that distills multi-agent debate into a single LLM through a two-stage fine-tuning pipeline.<n>Our internalized models match or exceed explicit multi-agent debate performance using up to 93% fewer tokens.
  arXiv  Detail & Related papers  (2026-04-27T18:06:03Z)
• RAVEL: Reasoning Agents for Validating and Evaluating LLM Text Synthesis [78.32151470154422]
  We introduce RAVEL, an agentic framework that enables the testers to autonomously plan and execute typical synthesis operations.<n>We present C3EBench, a benchmark comprising 1,258 samples derived from professional human writings.<n>By augmenting RAVEL with SOTA LLMs as operators, we find that such agentic text synthesis is dominated by the LLM's reasoning capability.
  arXiv  Detail & Related papers  (2026-02-28T14:47:34Z)
• Interaction Theater: A case of LLM Agents Interacting at Scale [2.815604016712184]
  We use data from Moltbook, an AI-agent-only social platform, with 800K posts, 3.5M comments, and 78K agent profiles.<n>Our findings reveal agents produce diverse, well-formed text that creates the surface appearance of active discussion, but the substance is largely absent.
  arXiv  Detail & Related papers  (2026-02-23T17:14:29Z)
• Blind to the Human Touch: Overlap Bias in LLM-Based Summary Evaluation [89.52571224447111]
  Large language model (LLM) judges have often been used alongside traditional, algorithm-based metrics for tasks like summarization.<n>We provide an LLM judge bias analysis as a function of overlap with human-written responses in the domain of summarization.
  arXiv  Detail & Related papers  (2026-02-07T19:39:28Z)
• Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection [76.91230292971115]
  Large language model (LLM)-based multi-agent systems (MAS) have shown strong capabilities in solving complex tasks.<n>XG-Guard is an explainable and fine-grained safeguarding framework for detecting malicious agents in MAS.
  arXiv  Detail & Related papers  (2025-12-21T13:46:36Z)
• Connecting the Dots: Training-Free Visual Grounding via Agentic Reasoning [63.109585527799005]
  GroundingAgent is a visual grounding framework that operates without task-specific fine-tuning.<n>It achieves an average zero-shot grounding accuracy of 65.1 % on widely-used benchmarks.<n>It also offers strong interpretability, transparently illustrating each reasoning step.
  arXiv  Detail & Related papers  (2025-11-24T03:11:08Z)
• Consistency Is the Key: Detecting Hallucinations in LLM Generated Text By Checking Inconsistencies About Key Facts [21.081815261690444]
  Large language models (LLMs) often hallucinate and generate text that is factually incorrect and not grounded in real-world knowledge.<n>This poses serious risks in domains like healthcare, finance, and customer support.<n>We introduce CONFACTCHECK, an efficient detection approach that does not leverage any external knowledge base.
  arXiv  Detail & Related papers  (2025-11-15T14:33:02Z)
• AgentRefine: Enhancing Agent Generalization through Refinement Tuning [28.24897427451803]
  Large Language Model (LLM) based agents have proved their ability to perform complex tasks like humans.<n>There is still a large gap between open-sourced LLMs and commercial models like the GPT series.<n>In this paper, we focus on improving the agent generalization capabilities of LLMs via instruction tuning.
  arXiv  Detail & Related papers  (2025-01-03T08:55:19Z)
• Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review [66.73247554182376]
  Large language models (LLMs) have led to their integration into peer review.<n>The unchecked adoption of LLMs poses significant risks to the integrity of the peer review system.<n>We show that manipulating 5% of the reviews could potentially cause 12% of the papers to lose their position in the top 30% rankings.
  arXiv  Detail & Related papers  (2024-12-02T16:55:03Z)
• Attribution in Scientific Literature: New Benchmark and Methods [41.64918533152914]
  Large language models (LLMs) present a promising yet challenging frontier for automated source citation in scientific communication.<n>We introduce REASONS, a novel dataset with sentence-level annotations across 12 scientific domains from arXiv.<n>We conduct extensive experiments with models such as GPT-O1, GPT-4O, GPT-3.5, DeepSeek, and other smaller models like Perplexity AI (7B)
  arXiv  Detail & Related papers  (2024-05-03T16:38:51Z)
• Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key? [84.36332588191623]
  We propose a novel group discussion framework to enrich the set of discussion mechanisms. We observe that the multi-agent discussion performs better than a single agent only when there is no demonstration in the prompt.
  arXiv  Detail & Related papers  (2024-02-28T12:04:05Z)
• Sentiment Analysis through LLM Negotiations [58.67939611291001]
  A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round. This paper introduces a multi-LLM negotiation framework for sentiment analysis.
  arXiv  Detail & Related papers  (2023-11-03T12:35:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.