Recent progress in language models has been defined by scale, yet many practical applications benefit more from robust reasoning than extensive parametric knowledge. We introduce OCC-RAG, a family of models for faithful context-based QA, which prioritizes task-specific reasoning capabilities over knowledge capacity. To train OCC-RAG, we implement a novel pipeline synthesizing over three million multi-context, multi-hop QA examples that target multi-hop reasoning, strict context faithfulness, and calibrated abstention. Mid-trained on this corpus, the models produce structured reasoning traces with source citations grounded in literal quotes from the context. Despite their compact size, OCC-RAG models outperform their same-size general-purpose counterparts on every benchmark and exceed models 2–6× larger on financial reasoning (TAT-QA) and faithfulness (ConFiQA), attaining the best results among all evaluated open-weight models up to 32B parameters. On ConFiQA they rank behind only a frontier proprietary models; on multi-hop reasoning (HotpotQA, MuSiQue) and refusal (MuSiQue-Un) they are on par with 8B-class models and close much of the remaining gap to the largest evaluated systems. We release both our code and family of models.[<https://hf.co/collections/occ-ai/occ-rag>]

Recent work has shown that narrow finetuning can produce broadly misaligned LLMs, a phenomenon termed emergent misalignment (EM). While concerning, these findings were limited to finetuning and activation steering, leaving out in-context learning (ICL). We therefore ask: does EM emerge in ICL? We find that it does: across four model families (Gemini, Kimi-K2, Grok, and Qwen), narrow in-context examples cause models to produce misaligned responses to benign, unrelated queries. With 16 in-context examples, EM rates range from 1% to 24% depending on model and domain, appearing with as few as 2 examples. Neither larger model scale nor explicit reasoning provides reliable protection, and larger models are typically even more susceptible. Next, we formulate and test a hypothesis, which explains in-context EM as conflict between safety objectives and context-following behavior. Consistent with this, instructing models to prioritize safety reduces EM while prioritizing context-following increases it. These findings establish ICL as a previously underappreciated vector for emergent misalignment that resists simple scaling-based solutions.