RULER shows most long-context LMs drop sharply in performance on complex tasks as length and difficulty increase, with only half maintaining results at 32K tokens.
Can long-context lan- guage models subsume retrieval, rag, sql, and more?
10 Pith papers cite this work. Polarity classification is still indexing.
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A 0.6B LM with length-aware attention adjustments performs competitive in-context retrieval at million-token scale on MS MARCO, NQ, and LIMIT benchmarks.
Proposes the Intelligent Computing Architecture (ICA) as a six-layer framework with dual probabilistic-deterministic planes and three Amdahl-style heuristics to unify design of LLM-based systems.
A memory-efficient SMC clustering method decomposes problems into approximately independent subproblems to handle large-scale online clustering with complex distributions.
RAH improves coding performance on Oolong-Synthetic from 71.75% to 81.36% with fixed GPT-5 backbone by spawning subagent harnesses via executable scripts.
ATLAS is a length-dependent benchmarking framework that evaluates 26 models on 8 capability dimensions and shows substantial rank changes when moving from 128K to 1M token ranges.
Current LLMs remain robust to high levels of inference-time context sparsity across diverse tasks, enabling up to 10x acceleration via sparse kernels.
LLMs recover interpretable topic structures via attention and achieve competitive topic modeling performance as long-context generators.
Presents open-source 7B models for million-token video and language understanding via Blockwise RingAttention, setting new benchmarks in retrieval and long video tasks.
Gemini 2.5 Pro and Flash models are presented as achieving frontier performance in reasoning, coding, and long-context multimodal tasks while spanning a cost-capability Pareto curve.
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