DeepInsight: A Unified Evaluation Infrastructure Across the Physical AI Stack
Quick Answer
DeepInsight introduces a unified evaluation infrastructure for Physical AI stacks, enabling cross-layer diagnostics through shared trace identities.
Quick Take
It preserves heterogeneity across tasks, resources, and results while improving benchmark onboarding and scalability, outperforming existing frameworks in speed and accuracy.
Key Points
- DeepInsight evaluates diverse stack operators in a single runtime environment.
- It maintains heterogeneity with three abstractions: task, resource, and result.
- The system scales near-linearly across nodes, enhancing performance and efficiency.
- Cross-layer regressions are easily localized due to shared tracing.
- It reproduces published benchmarks faster on a single node compared to existing frameworks.
Paper Resources
Source Excerpt
arXiv:2606. 17574v1 Announce Type: new Abstract: Evaluating a stack spans operators that differ by more than three orders of magnitude -- from a single foundation-model decoding step to thousands of physics ticks of whole-body control -- varying orthogonally in modality, reward semantics, and resource profile.
No existing framework spans this range, so the stack is evaluated today by stitching together separate harnesses that share neither runtime nor scoring, preserving each segment's local validity but losing the shared identity needed to diagnose cross-layer regressions. We present DeepInsight, an evaluation infrastructure that serves this full spectrum on a single runtime. …
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.