arXiv Machine Learning
PERK: Long-Context Reasoning as Test-Time Learning
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arXiv AI
Jun 4
Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoning
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.
arXiv Machine Learning
Oct 22
LongZero: Enhancing Long-Context Reasoning in LLMs from Zero Data
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 AI
Jul 23
LISA: Linear-Indexed Sparse Attention for Efficient Long-Context Reasoning
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.
arXiv AI
Oct 1
Hermes: Learning Contextual Reasoning Unlocks Test-Time Scaling
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 AI
Jul 3
ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning
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 AI
Jul 13
Self-Guided Test-Time Training for Long-Context LLMs
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.