HiLS learns chunk selection for sparse attention
Published on July 7, 2026, the paper presents HiLS, an approach that learns chunk selection for sparse attention using the language-modeling loss.
Source: HiLS aprende a seleção de chunks para sparse attention (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on July 7, 2026, the paper proposes Hierarchical Landmark Sparse (HiLS) Attention, a chunk-based sparse attention method that learns selection end to end using the language-modeling loss. Chunks are scored using aggregated landmark-token scores. The approach is intended for long-context models.
Companies developing or evaluating long-context models can test this strategy on representative tasks and compare quality, memory use, and latency with their current approach. Practical work includes preparing evaluation data, instrumenting tests, and tracking results in a dashboard, without assuming the technique is suitable before validation.
How the consultancy can help
Wendelmaques can diagnose your architecture and long-context requirements, define a HiLS evaluation scope, and implement the instrumentation, tests, and monitoring needed.
Next step
Send a short description of your model, tasks, and performance constraints to receive a scoped proposal.
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