TenderIntell at ICSOB 2026: Three Days in Gothenburg
GPT-Lab
· gpt-lab.eu · By abrar morshed · November 13, 2026 At ICSOB 2026 in Gothenburg, we presented TenderIntell, our work on multi-agent support for tendering. This post looks back at the conference, the idea behind a panel of three AI reviewers who score each proposal independently, and why evaluating agents reliably is the question we keep coming back to. The post TenderIntell at ICSOB 2026: Three Days in Gothenburg appeared first on GPT-Lab.
More from the blog
Three Lincoln Laboratory technologies receive 2026 R&D 100 Awards
A secure operating system, a quantum microsystem platform, and a 5G interference-canceling algorithm are named among the year's most significant innovations.
Introducing AlphaProof Code: a breakthrough in verifying complex software at depth
AlphaProof Code agents wrote three million lines of proofs in Lean to verify Signal’s core encryption.
MIT affiliates named 2026 Schmidt Sciences AI2050 Fellows
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
Q&A: Hey AI, who should I vote for this year?
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Related papers
ECCOLA - a Method for Implementing Ethically Aligned AI Systems
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
AI-powered Code Review with LLMs: Early Results
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
LLM-based agents for automating the enhancement of user story quality: An early report
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
System for systematic literature review using multiple AI agents: Concept and an empirical evaluation
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.