AI capability and the frontier
What the models can currently do, how fast that is moving, and which milestones are worth taking seriously.
Explainers
- Research Explainer · Litowitz (2026)
The token economy has a finite energy budget, but the real bottleneck is knowing which questions to ask
Litowitz, Polson and Sokolov treat the AI token as a physical quantity with measurable thermodynamic cost, then build a MacKay-style balance sheet showing that projected 2028 US infrastructure could supply 225,000 tokens per person per day, over 1,000× current usage. The binding constraint, they argue, is not compute but the human capacity to formulate questions worth answering.
- Research Explainer · Zandieh (2025)
A random rotation turns vector quantization into a solved problem, within a factor of 2.7 of perfection
TurboQuant achieves near-optimal distortion for both MSE and inner product metrics across all bit-widths, requires zero preprocessing, and matches full-precision LLM quality at 3.5 bits per channel.
- Research Explainer · Wu, Sun, Li, Welleck & Yang (2025)
Smaller models with smarter inference beat bigger models, and it's not even close
A 7-billion-parameter model paired with a novel tree search algorithm consistently outperforms a model five times its size on maths benchmarks, using half the compute. The trick is spending your budget on thinking harder, not on being bigger.
- Research Explainer · Wang et al. (2025)
Letting AI models "think longer" hits a wall, but the math tells you exactly where that wall is
A unified probabilistic model shows that both parallel sampling and sequential rethinking strategies for large reasoning models converge to the same saturation formula, letting you calculate the exact point where more compute stops helping.
- Research Explainer · Song, Han & Goodman (2026)
LLMs ace reasoning benchmarks, but they keep failing in ways that should be embarrassingly easy
The first comprehensive survey of LLM reasoning failures catalogues every known way these models break down, from reversing simple facts to misjudging whether a house fits inside a light bulb, and maps the root causes to a two-axis taxonomy of reasoning type versus failure type.
- Research Explainer · Polo, Somerstep, Choshen, Sun & Yurochkin (2025)
LLM benchmarks are correlated for a reason, and exploiting that lets you predict performance without training the model
Sloth introduces skill-based scaling laws that treat benchmark scores as reflections of three latent abilities, predicting multi-benchmark performance across model families from a single small model per family.
- Research Explainer · Prucs, Csutora, Antal & Marosi (2025)
Reasoning models hit a compute ceiling, but sparse architectures keep climbing
A benchmark study of 19 open-source LLMs finds that Mixture-of-Experts models consistently dominate the accuracy-per-FLOP frontier, while all architectures eventually reach a task-dependent saturation point where more thinking time stops helping.
- Research Explainer · Mohsin et al. (2026)
Scaling LLMs hits five hard ceilings, and more parameters won't break through any of them
A 67-page theoretical synthesis proves that hallucination, context compression, reasoning collapse, retrieval fragility, and multimodal misalignment are mathematical inevitabilities, not engineering problems awaiting bigger budgets.
- Research Explainer · Chi et al. (2024)
LLMs look like causal reasoners, but they're mostly just remembering
When tested on fresh news articles they couldn't have seen during training, four leading language models showed dramatic accuracy drops on cause-and-effect questions, revealing that their apparent causal reasoning is largely a retrieval trick.
- Research Explainer · Zheng et al. (2024)
Treat LLM calls like a program; then the cache starts doing real work
SGLang pairs a small Python DSL with a runtime that understands prompt structure, shared prefixes, and batching. That combination makes agent, reasoning, long-document, and vision workloads measurably faster, while also cutting the amount of glue code needed to build them.
- Research Explainer · Xia, Lu, Zhu et al. (2025)
Most LLM agent evaluation stops at launch; so the same failures keep recurring in production
A multivocal review of 161 sources reveals that academic evaluation overwhelmingly focuses on pre-deployment benchmarks. The authors propose EDDOps, a process model and reference architecture that make evaluation a continuous, governing function across the entire agent lifecycle.
- Research Explainer · Pan, Chodnekar, Roy & Wang (2025)
Running your own LLM can pay for itself in months; but only if you pick the right model size
A cost-benefit analysis of 54 deployment scenarios finds that small open-source models break even against commercial APIs in under three months on a $2,000 GPU, while large models can take years to justify their quarter-million-dollar hardware.
- Research Explainer · Koc, Verre, Blank & Morgan (2025)
Your IDE should watch your AI's metrics; not just your code's syntax
A conceptual framework for wiring real-time LLM telemetry (traces, evaluations, prompt versions) directly into the code editor through the Model Context Protocol, turning prompt engineering from guesswork into a data-driven feedback loop.