As test-time scaling in large language model(LLM) reasoning has been proven effective in enhancing the model performance through step-by-step generation, this long-context generation incurs substantial Key-Value(KV) cache, posing a critical bottleneck for practical applications deployment(e.g., Agents). While recent KV cache optimizations perform well in the long-context input scenario, the following problems remain unsolved if directly applied to long-context reasoning. (1) Time-consuming layer-wise retrieval operation. The retrieval operation, which selects the important KV pairs in each layer, brings the synchronization overhead that scales with model depth due to the data dependency, resulting in up to 60% latency overhead. (2) Complete retention of the newly generated KV cache. Existing works designed for long-context input choose to retain the KV pair of newly generated tokens to avoid repeated, time-consuming processing on the KV cache, rendering them ineffective in long-context reasoning. (3) Performance degradation with a tiny increase in sequence length. Existing offloading strategies determined before inference cannot adapt to the increasing sequence length, resulting in >80% performance degradation with a tiny increase in sequence length. In this paper, we point out that the objective of the retrieval algorithms is to align with the LLM, which is similar to the objective of knowledge distillation in LLMs. We analyze the similarity in information focus between the distilled language model(DLM) and the original LLM from the perspective of information theory, and thus propose a novel paradigm that leverages a DLM as the retrieval algorithm. Based on the insight, we present SpeContext, an algorithm and system co-design for long-context reasoning. (1) At the algorithm level, SpeContext proposes lightweight retrieval head based on the head-level attention weights of DLM, achieving >90% parameters reduction by pruning the redundancy. (2) At the system level, SpeContext designs an asynchronous prefetch dataflow via the elastic loading strategy, effectively overlapping KV cache retrieval with the LLM computation. (3) At the compilation level, SpeContext constructs the theoretical memory model and implements an adaptive memory management system to achieve acceleration by maximizing GPU memory utilization. We deploy and evaluate SpeContext in two resource-constrained environments, cloud and edge. Extensive experiments show that, compared with the Huggingface and FlashInfer framework, SpeContext achieves up to 24.89× and 2.19× throughput improvement in cloud and 10.06× and 8.02× speedup in edge with negligible accuracy loss, pushing the Pareto frontier of accuracy and throughput.

@inproceedings{abs-2512-00722,
  title = {<i>SpeContext:</i>
                    Enabling Efficient Long-context Reasoning with Speculative Context Sparsity in LLMs},
  author = {Jiaming Xu and Jiayi Pan and Hanzhen Wang and Yongkang Zhou and Jiancai Ye and Yu Wang and Guohao Dai},
  booktitle = {ASPLOS},
  year = {2026},
  doi = {10.1145/3779212.3790224}
}