Hugging Face Trending Papers
No More Free Lunch: Corpus Task Complexity Matters as Corpora Grow
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arXiv AI
Sep 25
No More Free Lunch: Corpus Task Complexity Matters as Corpora Grow
The paper introduces Corpus Task Complexity (CTC), a metric that captures how a task’s difficulty scales with corpus size. It distinguishes low‑CTC tasks, whose difficulty grows linearly, from high‑CTC tasks, whose difficulty grows quadratically or more, and presents ten new high‑CTC tasks. Experiments show that models performing well on low‑CTC tasks often fail on high‑CTC tasks, highlighting the need for new approaches to large‑corpus reasoning.
arXiv AI
Jul 10
Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench
arXiv:2607. 08284v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them.
arXiv AI
Jul 21
Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage
arXiv:2601. 03043v4 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing significant demands on inference efficiency.
arXiv Machine Learning
Jun 24
The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs
arXiv:2504. 17768v3 Announce Type: replace-cross Abstract: Sparse attention offers a promising strategy to extend long-context capabilities in Transformer LLMs, yet its efficiency-accuracy trade-offs remain unclear due to the lack of comprehensive evaluation.
arXiv AI
Jun 9
Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps
arXiv:2605. 16928v2 Announce Type: replace-cross Abstract: Long-context inference in large language models is bottlenecked by the quadratic cost of full attention.
Hugging Face Trending Papers
Aug 17
Cost Scales with Change, Not Corpus Size: Incrementally Maintaining an Evolving Semantic Substrate
Retrieval-augmented and agentic question-answering systems increasingly re-derive the meaning of a corpus at query time. Put plainly, instead of re-deriving what a corpus means on every question, the work is done once when a document arrives and is thereafter merely consulted -- a compiler, not an interpreter, of meaning.