Two Paths to Spot Truth in AI Answers

Kari Jaaskelainen

13 Jan 2026 — 1 min read

Two Paths to Spot Truth in AI Answers

Large language models can sound confident while being wrong. This study shows their internal activity holds two distinct "truth signals."

Question-anchored: The model checks the answer against information it drew from the question.
Answer-anchored: The model looks for self-contained evidence inside the answer it just produced.

By selectively "muting" attention routes and swapping token traces, the authors tease these pathways apart and uncover a few surprises: the two signals line up with the model’s knowledge boundaries, and the network’s hidden states can tell which path they’re using.

Why it matters: understanding these routes enables better detectors for hallucinations, and points toward models that know when to trust themselves - and when to ask for help.

Paper: https://arxiv.org/abs/2601.07422

Paper: https://arxiv.org/abs/2601.07422v1

Register: https://www.AiFeta.com

#AI #LLM #Hallucinations #TrustworthyAI #NLP #ExplainableAI

Read more

Meet GRACE: a moral governor for safer, more transparent AI

Meet GRACE: a moral governor for safer, more transparent AI

AI agents are getting powerful—so how do we make sure they do the right thing, not just the effective thing? Meet GRACE, a reason-based moral governor that keeps AI behavior aligned with human norms by separating moral reasoning from goal-driven decision-making. * Moral Module: uses deontic logic and explicit reasons

Why some fine-tuned LLMs miss phishing—and how to fix it

Why some fine-tuned LLMs miss phishing—and how to fix it

Not all fine-tuned LLMs spot phishing equally. A new study tests Llama 3.1 8B, Gemma 2 9B, and Mistral on high-stakes phishing detection—and uses SHAP and mechanistic interpretability to reveal why models do (or don’t) generalize. * Architecture × data diversity matters: Gemma 2 9B hits state-of-the-art performance (F1

AI that explains itself—by following the science

AI that explains itself—by following the science

AI that explains itself—by following the science Black-box AI can be powerful, but it often can’t tell us why it made a decision. Concept Bottleneck Models (CBMs) try to fix that by predicting human-understandable concepts first, then the final answer. The catch: standard CBMs ignore domain-specific cause-and-effect and

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A

TL;DR: Finding reliable answers in genomics is hard. GenomAgent turns one big AI into a coordinating team of specialists—and beats the current leader by 12% on a key benchmark. Genomic facts live across many databases. Standard chatbots struggle because they can’t flexibly query those sources. GeneGPT added