Inference-Time Scaling

Opening — Why this matters now Inference-time scaling has quietly replaced parameter scaling as the most interesting battleground in large language models. With trillion-parameter training runs yielding diminishing marginal returns, the industry has pivoted toward how models think together, not just how big they are. Mixture-of-Agents (MoA) frameworks emerged as a pragmatic answer: run multiple models, stack their outputs, and hope collective intelligence beats individual brilliance. It worked—up to a point. But most MoA systems still behave like badly moderated panel discussions: everyone speaks, nobody listens. ...

Most tool-augmented LLMs approach math reasoning like they’re wielding a hammer—good for hitting one nail at a time, but ill-equipped when the problem requires a wrench, a compass, and a soldering iron all at once. Enter Multi-TAG, a clever, finetuning-free framework that aggregates the strengths of multiple tools per reasoning step. Think of it as an LLM with a toolbox, not just a single tool. And it doesn’t just work—it wins, posting 6.0% to 7.5% accuracy gains across MATH500, AIME, AMC, and OlympiadBench against top baselines, using both open and closed LLMs. ...