redundancy

GraphEcho is a benchmark that evaluates LLM graph agents on distinguishing structural redundancy from distinct evidence provenance, revealing gaps between efficient exploration and effective evidence use.

This paper proposes a long-run persistence framework for AI systems using a redundancy-adjusted Artificial Age Score (AAS), showing that indefinite cyclic operation need not lead to unbounded structural aging.

This article draws parallels between reliability in manufacturing and modern software engineering, highlighting principles like redundancy, root cause analysis, and observability to build resilient systems.

This paper investigates quality issues in LLM-generated answers for hardware description language questions, finding over-answering tendencies like redundancy (65.7%) and verbosity (69.1%), and proposes a multi-agent framework that reduces core answers by 37% and non-core content length by 31% while improving quality scores.

This paper investigates redundancy in Vision-Language-Action (VLA) models and finds that language backbones are highly redundant for robotic manipulation tasks, while vision and action pathways are more critical. The authors propose Drop-Then-Recovery (DTR) and GateProbe to quantify and prune unnecessary blocks, showing that removing half of LLM blocks can even improve performance.

This paper proposes SCBoost, a boosting framework that reduces learner redundancy by projecting residuals onto the orthogonal complement of previous predictions and using covariance-regularized weighting, with theoretical guarantees and strong empirical performance.

This paper systematically investigates when and which parts of the prompt KV cache become dispensable during LLM decoding, showing that redundancy primarily involves chat template scaffolding rather than task content, and replacement with neutral filler preserves accuracy.