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版本更新 · v2arXiv:2611.06079

TauLang: A Language and Compiler for Space-Time Programming on Statically Scheduled Accelerators

Xuxiao Yang, Junchi Wu, Yuqi Peng, Feiyang Wu, Zhuoran Li, Wei Kong, Shuo Tao, Dongyi Fan 等 13 位作者

原文摘要

Statically scheduled accelerators such as Groq's Tensor Streaming Processor (TSP) achieve low-latency large language model inference by stripping dynamic synchronization hardware from the chip. With no caches, arbiters, or hardware schedulers, the compiler must fix the exact slice and issue cycle of every operation, so when an operation runs is a condition for correctness. Existing kernel languages cannot target such machines because they rely on hardware to resolve cycle timing and instead encode schedules implicitly in code structure. We present TauLang, a kernel language and compiler that makes time a first-class citizen of the program. The programmer schedules operations, loops, and composed kernels with parameterized explicit times, so exploring schedules means changing parameter values rather than rewriting code. TauLang validates explicit times algebraically in TauIR, its timed intermediate representation. It then solves tensor placement and cycle scheduling jointly in an integer linear program, using explicit times to group connected operations into timing clusters that prune the search space. On a cycleaccurate model of a TSP-like machine, joint search achieves a $1.40\times$ latency speedup across ten kernels over a space-first flow. A few explicit time pins keep latency within one cycle of the untimed schedules and scale joint search to 1,636 GEMM blocks, whereas untimed search finds no schedule at 32.

cs.PL · cs.AR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.15173

Time-Deterministic and Instruction-Dynamic Execution: A field-programmable gate array quantum-control processor

Xiaoqin Luo, Jiayun Song, Xiaolu Su

原文摘要

Long-running quantum computations, including error-corrected workloads, demand instruction availability, measurement-dependent control, and calibration updates. We present Time-Deterministic and Instruction-Dynamic Execution, an FPGA quantum-control processor supporting runtime revision at both instruction and parameter levels. Dynamic Instruction Parameter Update supplies one-shot, next-match patches before parameter capture, while Dynamic Instruction Stream Overwrite provides guarded replacement, logical deletion, and out-of-line insertion in eligible future regions of a resident program. A moving fetch-visibility guard coordinates instruction updates with execution, and event commitment isolates complete descriptors for independent hardware-timed dispatch. Revision applicability depends on update timing and execution progress; deterministic service applies to committed events under the stated scheduling conditions. The Xilinx ZCU102 implementation uses a 250-MHz control core and a 425-MHz timing/update domain. FPGA-resident measurements at the registered digital interfaces confirmed programmed-cycle dispatch for all tested descriptors committed at least one timing-domain cycle before their timestamps, with downstream ready and the stated FIFO ordering conditions. Separate tests confirmed post-commit timestamp and payload invariance. The minimum all-success mapped DIPU margin was four control-domain cycles, and a replacement of L instruction words completed in L+5 update-domain cycles under continuous payload delivery. Within the tested guard-distance range, rejected DISO requests reported an error and preserved the resident path, whereas all admitted revisions tested here executed complete revised sequences. These results provide a hardware execution basis for incorporating runtime-generated sequences and parameter corrections into precisely timed quantum computations with feedback.

quant-ph · cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04066

Graph-Localized Coverage for Conformal Prediction on Inductive Graphs

Jinwen Xu, Qin Lu, Georgios B. Giannakis

原文摘要

Conformal prediction (CP) provides marginal coverage guarantees, but marginal coverage does not determine where prediction errors occur on a graph. Queries from different graph neighborhoods can exhibit substantially different levels of uncertainty even when aggregate coverage remains near its target. We introduce GLocalCP, an online CP framework for inductive node classification and edge prediction that combines marginal and graph-local calibration with uncertainty-adaptive prediction sets. Specifically, GLocalCP puts forth a novel uncertainty-adaptive nonconformality score that relies on the mutual information obtained from Monte-Carlo (MC) dropout based prediction. Then, it augments a global online threshold with a kernel-based local correction that propagates coverage feedback among similar graph queries. On the theoretical horizon, we establish pathwise $O(N^{-1/2})$ marginal calibration error without exchangeability or stationarity and, for the exact-kernel formulation, an explicit $O(N^{-1/2})$ graph-localized calibration guarantee. Experiments on seven node-classification and seven edge-prediction benchmarks show that similar marginal coverage can conceal substantially different local reliability. Our proposed GLocalCP maintains coverage near the target while consistently reducing graph-local calibration error. Uncertainty scaling in GLocalCP further reduces prediction-set size across all evaluated node and edge datasets while leaving marginal coverage essentially unchanged.

cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04062

StreamLTT: Risk Certification for Policy Selection under Stream Dependence

Jinwen Xu, Qin Lu

原文摘要

Learn-then-Test (LTT) provides a general framework for certifying deployment policies against a prescribed risk target, but its standard formulation targets a fixed population risk estimated from calibration losses. In sequential deployment, losses may instead be dependent and nonstationary, so the risk of a fixed policy can evolve with the observed history. We introduce StreamLTT, which replaces this fixed population risk with average predictable risk, defined by conditioning each decision's loss on the information available before its outcome is revealed. Conditional centering turns empirical deviations from this target into a martingale sum, enabling both a test that adapts to predictable variance and a finite-sample Hoeffding alternative. Combined with family-wise error rate control, StreamLTT yields a simultaneously certified policy region that supports subsequent deployment selection. Across seven forecasting datasets and seven deployment risks, StreamLTT places the certified frontier substantially closer to the prescribed risk target than IID, robust-variance, block-bootstrap, and concentration-based alternatives, while empirical family-wise error closely follows the prescribed level. A finite-sample ablation further shows that the predictable-risk formulation drives the tighter risk frontier, while adaptation to predictable variance makes more effective use of the available family-wise error budget. StreamLTT therefore extends risk-certified policy selection to dependent deployment streams by addressing both what risk should be certified and how it should be tested.

cs.LG · math.ST · stat.ML

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1571ACL Anthology · ACL 2026

ART: Attention Replacement Technique to Improve Factuality in LLMs

Ziqin Luo Baidu Inc., Yihao Quan Rutgers University, Xiaofeng Zhang, Xiaosong Yuan, Chen Shen Alibaba Group

原文摘要

Hallucination in large language models (LLMs) continues to be a significant issue, particularly in tasks like question answering, where models often generate plausible yet incorrect or irrelevant information. Although various methods have been proposed to mitigate hallucinations, the relationship between attention patterns and hallucinations has not been fully explored. In this paper, we analyze the distribution of attention scores across each layer and attention head of LLMs, revealing a common and intriguing phenomenon: Shallow layers of LLMs primarily rely on uniform attention patterns, where the model distributes its attention evenly across the entire sequence. This uniform attention pattern can lead to hallucinations, as the model fails to focus on the most relevant information. To mitigate this issue, we propose a training-free method called Attention Replacement Technique (ART), which replaces these uniform attention patterns in the shallow layers with local attention patterns. This change directs the model to focus more on the relevant contexts, thus reducing hallucinations. Through extensive experiments, ART demonstrates significant reductions in hallucinations across multiple LLM architectures, proving its effectiveness and generalizability without requiring fine-tuning or additional training data.

cs.CL

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