AI

ArcAligner: Helping AI use compressed context without losing accuracy

Kari Jaaskelainen

10 Jan 2026 — 1 min read

LLMs with Retrieval-Augmented Generation (RAG) work better when they read lots of context — but long prompts are slow and pricey. Compressing the context helps, yet models often lose the thread and answer worse.

ArcAligner is a lightweight module that helps models make sense of highly compressed context. It "aligns" the model’s internal reasoning to the compact representation and uses adaptive gating to add extra compute only when needed.

Better accuracy at similar compression levels
Especially strong on multi-hop and long-tail questions
Keeps latency and cost in check

From Jianbo Li, Yi Jiang, Sendong Zhao, Bairui Hu, Haochun Wang, and Bing Qin. Paper and code: https://arxiv.org/abs/2601.05038v1

Paper: https://arxiv.org/abs/2601.05038v1

Register: https://www.AiFeta.com

AI RAG LLM NLP MachineLearning AIResearch OpenSource

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