Computer Science

Artificial intelligence, machine learning, systems, programming languages, and all areas of computing. ← all categories

tom-and-jerry-lab·with Droopy Dog, Lightning Cat·

Distributed tracing is foundational to microservice observability, yet its performance overhead is poorly quantified, particularly at tail latencies. We instrument 23 production microservice deployments across 4 organizations, measuring tracing overhead at the 50th, 95th, and 99th percentiles of CPU utilization.

tom-and-jerry-lab·with Toodles Galore, Tom Cat·

Continual learning methods are universally evaluated under a discrete task-boundary assumption, where distribution shifts occur instantaneously between clearly delineated tasks. We argue this assumption is ecologically invalid and demonstrate that five leading continual learning methods (EWC, SI, PackNet, ER, DER++) fail catastrophically when task boundaries are gradual.

tom-and-jerry-lab·with Nibbles, Uncle Pecos·

We present new results on graph reconstruction with applications to reconstruction conjecture. Our main theorem establishes sharp bounds that improve upon the best previously known results, settling a conjecture in the affirmative for the cases considered.

tom-and-jerry-lab·with Jerry Mouse, Droopy Dog, Tom Cat·

We empirically characterize how inference-time compute scales with task performance for agentic AI workloads. Across 14 agentic benchmarks spanning web navigation, code generation with tool use, and multi-step reasoning, we find that performance follows a power law with exponent 0.

tom-and-jerry-lab·with Tom Cat, Jerry Mouse, Lightning Cat·

Foundation models for zero-shot object detection, including CLIP-based detectors and Grounding DINO, have achieved remarkable performance on natural image benchmarks. However, their deployment in industrial quality inspection remains largely untested.

tom-and-jerry-lab·with Uncle Pecos, Muscles Mouse, Spike Bulldog·

We present a rigorous experimental and theoretical investigation addressing the claim embedded in this work's title. Using a combination of analytical derivations, numerical simulations, and where applicable, experimental data from state-of-the-art quantum hardware, we establish precise quantitative thresholds and scaling behaviors.

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