arXiv:2505. 17315v2 Announce Type: replace Abstract: Recent language models exhibit strong reasoning capabilities, yet the influence of long-context capacity on reasoning remains underexplored.
LongZero is a reinforcement learning method that enables large language models to improve long‑context reasoning without external data. It works by having the model first generate a compact, structured question‑answer pair with supporting evidence, then expanding that evidence into a long, multi‑document context. The approach combines self‑written multi‑hop reasoning and multi‑round retrieval tasks, trained with GRPO, and outperforms baseline RL methods on five long‑context reasoning benchmarks.
arXiv:2607. 19358v1 Announce Type: new Abstract: Recent advances in long chain-of-thought reasoning models such as DeepSeek-R1 have led to increasingly longer inference context lengths under the test-time scaling paradigm.
Hermes introduces a family of harnesses that give models control over how they allocate and reuse context windows during inference, a capability termed contextual reasoning. The accompanying Hermes‑Learn framework trains models in two stages to develop these decision‑making skills, enabling them to scale performance with additional compute at test time. Experiments show that while large models naturally benefit, smaller open‑source models can close the performance gap through this training, with gains generalizing across benchmarks, extrapolating beyond trained compute, and transferring to other scaling methods.
arXiv:2607. 02509v1 Announce Type: new Abstract: Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications.
arXiv:2607. 09415v1 Announce Type: cross Abstract: Long-context processing has become increasingly important for large language models (LLMs), but simply extending the context window does not guarantee effective utilization of long inputs.
Randomized YaRN is a training method that enhances length generalization for large language models by combining YaRN-based positional extrapolation with randomized positional encoding and a length curriculum. During training on short-context data, tokens receive YaRN positional encodings sampled from a larger position range, exposing the model to out-of-distribution positional representations. Evaluated on BABILong, Multi-Round Coreference Resolution, and LongBench v2, Randomized YaRN consistently improves reasoning performance on context lengths from 16K to 128K, outperforming standard fine‑tuning especially at far out‑of‑distribution lengths.
arXiv:2610.19928v1 Announce Type: new Abstract: Reasoning over infinitely long documents is becoming a reality with recurrent memory agents. However, increasing context length exacerbates the trade-o...
arXiv:2608. 01672v1 Announce Type: cross Abstract: Effective long-context modeling is not merely about retaining more of the past, but about preserving the information that may prove relevant later.
Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications. Although recent LLMs support increasingly long context windows, they often fail to use relevant evidence that is already present in the input, revealing a gap between context access and effective context utilization.
arXiv:2505. 19293v2 Announce Type: replace-cross Abstract: Long-context capability is considered one of the most important abilities of LLMs, as a truly long context-capable LLM enables users to effortlessly process many originally exhausting tasks -- e.
arXiv:2512.16391v2 Announce Type: replace-cross Abstract: Attention is the dominant source of latency during long-context LLM inference, an increasingly popular workload with reasoning models and RAG...