arXiv subjects

Xinlei Zheng

Publications and source records attributed to Xinlei Zheng.

3 recordsLinked to original sources

arXiv2026

DeFA: Dependency-Guided Failure Attribution for LLM Agents

Errors in LLM agent executions and their visible consequences can be separated by many steps, making decisive-error localization a matter of understanding both step content and step dependencies. We introduce DeFA, a dependency-guided framework for agent failure attribution. DeFA first combines protocol relations and semantic dependencies into an event dependency graph spanning the trajectory. It then identifies events that may violate task requirements and traces their sources and subsequent effects to construct a failure propagation graph. Finally, DeFA uses step evidence and the steps' roles in failure propagation to identify the decisive error, responsible agent, and error category. To support long trajectories, DeFA partitions executions into segments and combines the current segment's detailed content with summaries of the other segments, giving local diagnosis access to global execution context. Across Who and When and the Who and When Pro text subset, DeFA achieves the highest responsible-agent and exact step accuracy with all evaluated backbones, and the highest failure-mode accuracy among taxonomy-aligned methods on Pro. Further experiments on image and video trajectories demonstrate its applicability to multimodal failure attribution. Ablations support the contributions of segmentation, the event dependency graph, and the failure propagation graph. Using DeFA's diagnostic feedback for skill evolution in Trace2Skill improves downstream task accuracy by 6-15 percentage points over the native pipeline, showing that the diagnoses can also support agent improvement on subsequent tasks.

cs.AI↗

arXiv2026

DeepShare: Assurance-Driven Deep Learning Job Scheduling for Multi-Tenant Clusters

Multi-tenant GPU clusters frequently remain underutilized even when tenants experience long queueing delays, because quota control, queue ordering, preemption, and GPU sharing are driven by different local signals. We present DeepShare, a scheduler that uses a continuous tenant-assurance signal to coordinate these decisions at runtime. DeepShare combines elastic quota borrowing, tenant-specific runtime prediction, cost-aware best-effort preemption, and interference-aware MPS colocation, while using the same assurance signal to decide when borrowed capacity should be reclaimed and when sharing should become more conservative. In trace-driven experiments on 23,859 Venus jobs and 3,200 internal jobs, DeepShare achieves an average GPU utilization of 70.58%, a 29.5% improvement over the strongest non-intrusive sharing baseline, while reducing average queueing delay by 46%. On a 16-GPU Kubernetes testbed, it reduces the average job completion time by 34% and maintains 93% QoS compliance for guaranteed tenants. These results show that treating tenant assurance as a runtime control loop achieves a more advantageous utilization-QoS trade-off than optimizing quotas, scheduling, and resource sharing independently.

cs.DC↗

arXiv2021

A hybrid controller for safe and efficient collision avoidance control

We design and experimentally evaluate a hybrid safe-by-construction collision avoidance controller for autonomous vehicles. The controller combines into a single architecture the respective advantages of an adaptive controller and a discrete safe controller. The adaptive controller relies on model predictive control to achieve optimal efficiency in nominal conditions. The safe controller avoids collision by applying two different policies, for nominal and out-of-nominal conditions, respectively. We present design principles for both the adaptive and the safe controller and show how each one can contribute in the hybrid architecture to improve performance, road occupancy and passenger comfort while preserving safety. The experimental results confirm the feasibility of the approach and the practical relevance of hybrid controllers for safe and efficient driving.

cs.RO↗