AI Efficiency

Opening — Why this matters now The AI hardware race is entering a biological phase. As GPUs hit their thermal limits, a quiet counterrevolution is forming around spikes, not tensors. Spiking Neural Networks (SNNs) — the so-called “third generation” of neural models — mimic the brain’s sparse, asynchronous behavior. But until recently, their energy advantage came at a heavy cost: poor accuracy and complicated decoding. The paper Hyperdimensional Decoding of Spiking Neural Networks by Kinavuidi, Peres, and Rhodes offers a way out — by merging SNNs with Hyperdimensional Computing (HDC) to rethink how neural signals are represented, decoded, and ultimately understood. ...

Opening — Why this matters now Large reasoning models like GPT-5 and s1.1-32B can solve Olympiad-level problems — but they’re computationally gluttons. Running them for every query, from basic arithmetic to abstract algebra, is like sending a rocket to fetch groceries. As reasoning models become mainstream in enterprise automation, the question is no longer “Can it reason?” but “Should it reason this hard?” ...

If federated fine-tuning feels like trying to teach calculus to a toddler on a flip phone, you’re not alone. While the privacy-preserving benefits of federated learning are clear, its Achilles’ heel has always been the immense cost of training large models like LLaMA2-13B across resource-starved edge devices. Now, a new method—DEVFT (Developmental Federated Tuning)—offers a compelling paradigm shift, not by upgrading the devices, but by downgrading the expectations. At least, at first. ...

Retrieval-Augmented Generation (RAG) has become the go-to technique for grounding large language models (LLMs) in external data. But as anyone building real-world RAG pipelines knows, there’s a growing tension between accuracy and cost. Existing graph-based RAG solutions promise richer semantics than vanilla vector stores, but suffer from two persistent issues: incomplete graphs and retrieval misalignment. The paper “CUE-RAG: Towards Accurate and Cost-Efficient Graph-Based RAG” proposes a structural rethinking. By integrating a multi-partite graph, hybrid extraction, and a query-driven iterative retriever, CUE-RAG achieves state-of-the-art accuracy while cutting indexing costs by up to 72.58% and even outperforming other methods without using any LLM tokens at all. ...

Cut the Fluff: Leaner AI Thinking When it comes to large language models (LLMs), brains aren’t the only thing growing—so are their waistlines. As AI systems become increasingly powerful in their ability to reason, a hidden cost emerges: token bloat, high latency, and ballooning energy consumption. One of the most well-known methods for boosting LLM intelligence is Chain-of-Thought (CoT) reasoning. CoT enables models to break down complex problems into a step-by-step sequence—much like how humans tackle math problems by writing out intermediate steps. This structured thinking approach, famously adopted by models like OpenAI’s o1 and DeepSeek-R1 (source), has proven to dramatically increase both performance and transparency. ...