The article describes an experiment where an AI agent was able to identify incorrect model decisions while interacting with a web page, yet failed to correct the errors. By replaying correct decisions through four intentionally flawed browser actions, the author demonstrates that a simple page check missed half of the erroneous clicks. This illustrates that detecting a fault and successfully recovering from it are distinct capabilities.
By Abdullahi Dattijo
The article explores how an AI system can revise its conclusions when presented with new evidence. It examines six different update strategies and introduces a local model to assess their effectiveness. The focus is on whether decisions derived from the AI’s earlier conclusions also need to be updated when those conclusions change.
By Emmimal P Alexander
The article reports on an experiment where an AI agent was allowed to search for evidence whenever it chose. The agent discovered some papers that a standard search missed, but also lost some that the plain search had found, and overall the gains were insufficient to be trusted. The post examines whether agentic Retrieval-Augmented Generation (RAG) truly retrieves better evidence across 298 questions.
By Abdullahi Dattijo
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .
By Sara Nobrega
The article recounts a production incident where a large language model (LLM) was used to evaluate the outputs of another LLM, and the judging model consistently agreed with itself. It explores the implications of relying on one model to assess another’s work, highlighting the potential pitfalls of such an approach. The narrative offers lessons on the limits of trusting automated evaluation systems in real‑world deployments.
By Priyansh Bhardwaj
The article examines how a small reasoning model responds when presented with incorrect answers to 200 exam questions. By inserting wrong answers and observing the model’s reasoning, the author explores whether the model’s justification aligns with the planted hint or diverges. The study highlights the model’s tendency to follow misleading cues and the implications for evaluating its reasoning process.
By Utkarsh Mangal
Because the alternative is much too dangerous The post We Should Train AI to Betray Its Users appeared first on Towards Data Science .
By Nathan Bos
The article titled "Everyone Is Selling AI at You — Here’s How to Keep Your Judgement" discusses three strategies for navigating the current AI hype. It offers practical moves to help readers maintain critical thinking and discern genuine value from marketing claims. The piece was originally published on Towards Data Science.
By Janna Lipenkova
The article discusses how an agent can continue to produce confident answers even when its tools fail, leading to a misleadingly positive dashboard display. It highlights the issue of agents generating responses from nothing after tool breakdowns, which can create a false sense of reliability. The piece serves as a cautionary note for developers deploying agentic AI systems.
By Benjamin Nweke
Mustafa Suleyman argues that artificial models should not be treated as if they possess feelings, preferences, rights, or any entitlement to human welfare. He emphasizes that consciousness underpins our ethical, legal, and political frameworks, and extending such rights to AI would lack evidence and complicate containment and alignment efforts.
The article "Beyond RAGs: Building Actually Truthful AI Harnesses" discusses the limitations of Retrieval-Augmented Generation (RAG) systems, emphasizing that retrieval alone does not guarantee evidence for AI claims. It explores methods for constructing AI systems that can substantiate their statements, moving beyond simple retrieval to more robust proof mechanisms. The piece highlights the importance of developing AI that can verify its own outputs rather than merely retrieve information.
By Ari Joury, PhD
arXiv:2606.16710v2 Announce Type: replace-cross
Abstract: Multi-agent systems (MAS) are credited with solving complex problems, yet their agents routinely act on context that may be false. Prior work...
By Jonas Becker, Jan Philip Wahle, Terry Ruas, Bela Gipp