Why Do Multi-Agent LLM Systems Fail Even When Each Component Works Correctly?
Compound AI pipelines fail when data representation changes. RePairTQA (ICLR 2026) shows table format alone shifts LLM accuracy across agent workflows.
The Missing Layer in Agentic AI Planning
RECAP introduces intent rewriting: transform multi-turn conversations into clear, structured goals before planning.
Why Agent Coordination Fails and How VERIMAP Improves Reliability
As large language models evolve into agentic systems capable of tool use, planning, and multi-step reasoning, failures increasingly arise from coordination rather than raw reasoning ability. In many multi-agent LLM workflows, each agent may perform its local task correctly. Yet the system still fails. The root cause is often subtle misalignment in task decomposition, output […]
EMNLP 2025 Highlights and Research Directions
Research directions presented at EMNLP 2025 span agentic systems, retrieval, interpretability, multimodality, training, and human–AI interaction, including work contributed by Megagon Labs.
When Vision-language models get conflicting Information, Which Signal Does It Trust?
“Mixed Signals,” exposes hidden biases in VLMs with major implications for healthcare, RAG systems, and AI safety.
2025 Internship Experience Vol. 2
Hands-on AI research: Stories from Megagon Labs interns.
2025 Internship Experience Vol. 1
Megagon Labs Internships: Hands-on AI research, real projects, real growth.
ACL 2025 Highlights: Direction of NLP & AI
We share Megagon Labs’ key takeaways from ACL 2025 — highlighting the trends, debates, and breakthroughs shaping the future of NLP, agentic AI, and trustworthy evaluation.
Blue Streams: Orchestrating Work
Stream processing is a key ingredient when making “agentic workflows” enterprise-ready. Streams support a wide range of workflows and support complexity while at the same time bringing about the right abstractions and scope for facilitating accuracy, scalability, and ease of use.
𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗴𝗲𝘁 𝗟𝗟𝗠-𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝘀𝗸𝗶𝗹𝗹 𝗺𝗮𝗽𝗽𝗶𝗻𝗴 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗟𝗟𝗠-𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗰𝗼𝘀𝘁𝘀?
𝙠𝙉𝙉𝘽𝙀 is a hybrid model combines the efficiency of bi-encoders with the precision of k-NN lookups for skill mapping. With it, organizations can proficiently associate job descriptions with appropriate skills. By employing labeled synthetic sentences, 𝙠𝙉𝙉𝘽𝙀 enhances accuracy while maintaining speed, ideal for large-scale projects.
