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新论文 · v1arXiv:2611.06841

ServerBench：你的编码智能体如何设计专用 LLM 服务系统？

ServerBench: How can your coding agents design specialized LLM serving systems?

Xinrui Zhong, Zhiming Wang, Yongqi Liu, Zhaohui Wang, Shanglin Yang, Zikun Li, Zhuoming Chen, Beidi Chen

AI 速读 基于摘要

提出 ServerBench 基准，用于评估编码智能体能否端到端设计并交付经过验证的专用 LLM 服务系统，并以“每美元延迟合格 token 价值”作为统一目标。

cs.AI

arXiv ↗PDF ↗
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Large language model (LLM) coding agents develop software through experimentation, offering an opportunity to design the serving systems supporting their own operation. Effective serving requires coordinating implementation and operating decisions under session dependencies, latency constraints, and correctness requirements. We introduce ServerBench, a benchmark for agentic design of complete, specialized LLM serving systems. Agents experiment against a common objective, dollars per dollar: latency-qualified token value per dollar of GPU cost. Stateful replay preserves session dependencies, and independent verification checks the submitted system. Across 11 coding-agent models and three deployments, Claude Sonnet 5.5 achieves 2.97 dollars per dollar on Gemma E4B, $4.87\times$ the vLLM reference; 22 of 33 runs yield verified systems, and 13 exceed the strongest baseline. Our analysis identifies three findings: high-value designs coordinate capacity improvements with load selection; agents interpret performance feedback through workload-relevant experiments; and reliable delivery requires verification of the execution paths introduced by optimization. Overall, ServerBench supports systematic study of how agents translate service objectives and feedback into effective, verified serving systems for given task specifications.

版本更新 · v5arXiv:2603.28458

HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention

Yufei Xu, Fanxu Meng, Fan Jiang, Yuxuan Wang, Ruijie Zhou, Zhaohui Wang, Jiexi Wu, Zhixin Pan 等 14 位作者

原文摘要

Token-level sparse attention mechanisms, exemplified by DeepSeek Sparse Attention (DSA), achieve fine-grained key selection by scoring every historical key for each query through a lightweight indexer, then computing attention only on the selected subset. While the downstream sparse attention itself scales favorably, the indexer must still scan the entire prefix for every query, introducing an per-layer bottleneck that grows prohibitively with context length. We propose HISA (Hierarchical Indexed Sparse Attention), a plug-and-play replacement for the indexer that rewrites the search path from a flat token scan into a two-stage hierarchical procedure: (1) a block-level coarse filtering stage that scores pooled block representations to discard irrelevant regions, followed by (2) a token-level refinement stage that applies the original indexer exclusively within the retained candidate blocks. HISA preserves the identical token-level top-sparse pattern consumed by the downstream Sparse MLA operator and requires no additional training. On kernel-level benchmarks, HISA achieves up to speedup at 64K context. On Needle-in-a-Haystack and LongBench, we directly replace the indexer in DeepSeek-V3.2 and GLM-5 with our HISA indexer, without any finetuning. HISA closely matches the original DSA in quality, while substantially outperforming block-sparse baselines.

cs.LG · cs.AI

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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1317ACL Anthology · ACL 2026

Fair-CCD: Mitigating Bias in Large Language Models for Tabular Classification Through Context-Contrastive Decoding

Donghan Liu, Han Sun, Zhaohui Wang, Qin Li East China Normal University, Min Zhang

原文摘要

While recent studies show the effectiveness of in-context learning (ICL) for tabular data prediction, they also reveal significant fairness issues in large language models (LLMs). Prior work to mitigate fairness issues often employs interventions relying on subjective demonstration selection. Its effectiveness varies significantly with the specific demonstration content, leading to low controllability. Moreover, the improvement of fairness is highly unstable across different models and tasks. To address the challenges of low controllability and limited stability in fairness interventions, we propose Fairness-Aware Context-Contrastive Decoding (Fair-CCD). Fair-CCD first constructs Structural Bias Templates (SBTs), motivated by behavioral patterns observed in demonstrations, to encode the relationship between sensitive attributes and predicted labels in a structured and controllable form. During inference, Fair-CCD injects multiple SBTs and contrasts the model’s responses, generating two differential signals that guide fairness adjustment and preserve task performance. By leveraging attention signals to scale decoding adjustments guided by the difference signals, Fair-CCD achieves stable and adaptive bias mitigation across models and tasks. Extensive experimental results demonstrate that Fair-CCD consistently improves fairness metrics without degrading task accuracy.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表