Three New Research Papers Address AI Agent Frameworks and Citation Verification

New arXiv papers introduce BibAgent for detecting citation errors, a federated learning framework for LLMs, and a declarative layer for agent systems.

Three New Research Papers Address AI Agent Frameworks and Citation Verification

Three research papers published on arXiv address different challenges in AI systems, from citation verification to collaborative learning frameworks.

BibAgent (arXiv:2601.16993v1) introduces an agentic framework designed to detect miscitations in scientific literature. According to the abstract, the paper addresses “widespread miscitations: ranging from nuanced distortions to fabricated references,” noting that “systematic citation verification is currently unfeasible” through manual review alone.

Federated LLM Framework (arXiv:2601.17133v1) presents an “Orchestrated-Decentralized Framework for Peer-to-Peer LLM Federation.” The paper tackles the challenge that “fine-tuning Large Language Models (LLMs) for specialized domains is constrained by a fundamental challenge: the need for diverse, cross-organizational data conflicts with the principles of data privacy and sovereignty,” according to its abstract.

Declarative Agentic Layer (arXiv:2601.17435v1) proposes a declarative layer for intelligent agents in MCP-based server ecosystems. The research notes that while “recent advances in Large Language Models (LLMs) have enabled the development of increasingly complex agentic and multi-agent systems capable of planning, tool use and task decomposition,” empirical evidence indicates many of these systems face unspecified challenges.

All three papers are cross-posted to the AI section of arXiv.

researchAI agentsLLMsfederated learningscientific citationsarXiv