How good are LLMs at fixing their mistakes? A chatbot arena experiment with Keras and TPUs

TextQuests: How Good are LLMs at Text-Based Video Games?

Structured Feedback Improves Repair in an LLM Agent Loop

arXiv:2607. 14167v1 Announce Type: cross Abstract: LLM agents often retry after external validation rejects a candidate, but the interface between validation and the next model call remains underspecified.

LLMs help robots understand vague instructions and focus on key details

To help robots do chores in places like homes and factories, a new approach from MIT uses one language model to clarify users’ instructions, then another to ignore irrelevant info.

Judge Arena: Benchmarking LLMs as Evaluators

TriEval: A Resource-Efficient Pipeline for LLM Bias, Toxicity, and Truthfulness Assessment

LLMs have evolved from basic chatbots to the backbone of the AI ecosystem, now widely used in healthcare, schools, and government services. The domain-wide adoption of LLMs necessitates continuous evaluation to ensure their safety and fairness.

When Upstream Messages Override Correct Answers: A Controlled Study of Multi-Agent LLM Collaboration

arXiv:2609.36855v1 Announce Type: new Abstract: Multi-agent LLM systems rely on message passing among specialized agents to accomplish complex tasks. However, an upstream agent may provide useful inf...

If These Walls Could Talk: Critical Play with Large Language Models in Museums

arXiv:2606. 15565v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly being used in museums to as role playing chatbots which let visitors talk to simulated versions of people and artefacts from the past.

Non-engineers guide: Train a LLaMA 2 chatbot

Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits

arXiv:2608. 02955v1 Announce Type: cross Abstract: This research paper describes an exploratory study on the effectiveness of Chat Debugging: troubleshooting malfunctioning analog circuits on breadboards and printed circuit boards (PCB) by undergraduates through conversations with public-domain large language models (LLMs).

Consilium: When Multiple LLMs Collaborate

Components of A Coding Agent

How coding agents use tools, memory, and repo context to make LLMs work better in practice

TriEval: A Resource-Efficient Pipeline for LLM Bias, Toxicity, and Truthfulness Assessment

arXiv:2606. 03036v1 Announce Type: new Abstract: LLMs have evolved from basic chatbots to the backbone of the AI ecosystem, now widely used in healthcare, schools, and government services.