Raschka's LLM paper list shows research splitting into production layers

Sebastian Raschka published a curated list of LLM papers from January to May 2026. For Radar readers, the interesting part is not the link dump itself, but the map it gives of where practical LLM research is concentrating in the first half of the year.

The public source reads more like a map than a finished synthesis

The page is marked as paid, but the public introduction and a substantial visible portion of the list make the intent clear. Raschka follows up on his organized paper lists from last year and stresses that this is not a complete overview of everything published in 2026.

The list comes from papers he bookmarked as relevant to his own work. He says he reviewed titles, abstracts and topic framing carefully, but only read a subset of the papers in detail. That matters: the source is valuable as a curated filter, not as a final quality judgment on every paper.

The visible text groups topics including architecture and model design, efficient training and scaling, inference efficiency and KV cache, sparse attention and long context, reasoning and test-time compute, reinforcement learning and RLVR, agent systems and tool use, coding agents, diffusion language models and evals.

The useful signal is what repeats across categories

Raschka says his selection is heavy on reasoning models, reinforcement learning and efficient inference. Compared with his 2025 lists, he also mentions more papers around agent harnesses, tool use, long context, diffusion language models and serving infrastructure.

That is a useful signal for engineering teams. Research is not only moving toward larger models. It is moving into the layers that decide cost, latency, memory, tool orchestration and reliability. Put differently: part of the competitive advantage is moving from model weights into the system around the model.

A curated list is not a benchmark or buying advice

A paper roundup can create false confidence. A topic repeating in a list does not mean a specific method works in production, is reproducible or beats a simpler baseline in your use case.

Raschka's own disclaimer is the right one. He read only a subset in detail and the list reflects what he is currently working on. For a product manager or tech lead, this is a reading prioritization tool, not evidence that long context, RLVR or diffusion language models should immediately change the roadmap.

Read primary papers, not just category headings

The proof of value will be whether teams turn the list into concrete experiments: cheaper inference, a better KV cache strategy, a more usable agent harness or more realistic evals for their own product.

The papers to watch are the ones that ship code, ablations and measurements beyond a single benchmark. Without that, even a very good list remains a neatly organized shelf of literature.

Lilith's verdict

Raschka did not build this for anyone to swallow whole. It is a map on the wall: the pins show directions, but every team still has to get its own shoes dirty on the way to proof.