A paper describes query and item embeddings for efficiently retrieving candidates when relevance computation is expensive. The reported approach uses relevance to selected support items or queries.
Published on July 7, 2026, Lilian Weng’s article examines recursive self-improvement and presents designing and optimizing a model harness as a practical starting point.
Published on July 7, 2026, the material presents Hierarchical Landmark Sparse (HiLS) Attention, a method that learns chunk selection end to end using the language-modeling loss.
A post published on July 7, 2026 describes JetSpec, which uses a single forward pass with causal conditioning to create coherent draft trees. It reports speedups of up to 9.64× on MATH-500 and 4.58× in open-ended conversations.
Published on July 7, 2026, the notice describes a live stream about new chapters of Programming Massively Parallel Processors and an overview of the material.
Published on July 7, 2026, the work presents a method for linking training samples to interpretable attention heads and reports effects of interventions on those circuits.
Published on July 7, 2026, the summary presents a proposal to convert off-policy tokens into tokens treated as on-policy through rejection sampling, addressing variance in LLM alignment.
A study evaluates self-distillation with privileged information in reasoning models and reports degradation in long trajectories across five models tested on AIME.
Published on July 6, 2026, this summary presents Autodata, a method that uses agents to create training and evaluation data by selecting questions that distinguish weak and strong solvers.
A post about a paper describes agents that write, search and organize notes, learning when memory is useful. According to the post, this improved an open 32B model's performance by 2x to 4x in long games.
Published on July 6, 2026, the paper summary describes learned bases and a compact matrix for representing attention, reports linear cost in the number of points, and cites results in three evaluation areas.
A bookmark published on July 6, 2026 reports that NVIDIA's CUDA C++ Programming Guide was updated in June 2026 and describes it as a current reference for engineers working with CUDA C++.
In a post published July 6, 2026, HOLA combines recurrent state with a small KV cache for exact memory. The post reports a 16.1% reduction in perplexity and robust retrieval at 32k tokens.
An article published on July 6, 2026 examines innovations, broad impact, and ongoing work around the LLVM compiler infrastructure, offering context for engineers in the field.
Published on July 6, 2026, the paper presents ReContext, a training-free inference method that resubmits relevant evidence before generation. It reports results on eight 128K datasets and three model backbones.
A study examines language models as in-context retrievers over one-million-token corpora and reports that BlockSearch generalizes up to ten times beyond its training length.