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Progress Reward Modeling for Robotic Learning: A Comprehensive Survey
arXiv:2607.21655v1 Announce Type: new Abstract: Robotic learning takes place in dynamic environments with large behavior spaces. A terminal success signal only tells the robot whether the task is completed. It does not explain whether the current behavior is making progress, remaining unchanged, or undoing earlier progress. For this reason, recent studies have increasingly explored progress rewards that provide feedback during task execution. However, the current literature lacks a shared framework. Existing methods use different observations, goal specifications, output signals, supervision sources, and evaluation protocols. This makes it difficult to compare them and understand what their results actually validate. In this survey, we provide a unified view of progress reward modeling for robotic learning. We organize the field in three connected steps. We first study the interface of a progress model. This defines the problem from the outside by asking what information the model rece
On the Depth Scalability of Logic Gate Networks
arXiv:2607.21633v1 Announce Type: new Abstract: Logic Gate Networks (LGNs) implement computation through compositions of Boolean operations, yet unlike classical Boolean circuits, existing LGNs do not reliably benefit from increased depth. We identify two distinct causes: optimization collapse in deep relaxed LGNs and a topology-induced limitation that persists even when skip-biased initialization and straight-through estimation stabilize training. Thus, trainability alone is insufficient; deeper layers must also receive information that supports useful computation. We introduce Input-Anchored Logic Gate Networks (IALGNs), in which each gate combines an evolving hidden feature with a direct input anchor. This topology preserves a computational spine while conditioning every layer on the original input. We show that a depth-D path can depend on up to D+1 input bits and establish a strict path-wise depth hierarchy. Random-k anchor relaxation further improves anchor selection without rela
Cloud-Native Evaluation-as-a-Service: A Microservices Architecture for Scalable AI Monitoring with Conformal Guarantees
arXiv:2607.21623v1 Announce Type: new Abstract: We present EaaS, a cloud-native reference architecture that operationalizes AI evaluation methods as six stateless Kubernetes microservices: conformal prediction with finite-sample-corrected Adaptive Prediction Sets, calibration assessment, drift detection via RFF-approximated Maximum Mean Discrepancy, fairness monitoring with bootstrap confidence intervals, a DAG-based pipeline orchestrator, and a result storage API. We validate four key methodological concerns. First, empirical coverage is consistent with the marginal conformal guarantee across K=50 random calibration/test splits, with mean coverage within 1.4 percentage points of the nominal target. Second, all four MMLU answer tokens appear in the top-20 logprobs with 0% imputation needed, and simulated imputation at 10% produces less than 1.5% coverage impact. Third, RFF-MMD achieves 100% detection power for mild and severe drift at the median heuristic bandwidth, with Type I error b
Learning Diverse Humanoid Tasks via Synthetic Video Scenarios without Real World Data
arXiv:2607.21648v1 Announce Type: new Abstract: The human-like morphology of humanoid robots grants them exceptional potential for agile and versatile motor capabilities, but it also introduces significant challenges in acquiring complex skills. Traditional Learning-from-Demonstrations methods are often constrained by the high cost of collecting real-world data, the difficulty of capturing motion-specific behaviors, and the limited diversity of demonstrations across individuals. Moreover, even for the same task, humans may execute the motion in multiple distinct ways. In this paper, we propose a new framework that leverages the power of Generative AI to convert textual prompts into realistic and diverse sequences of human body movements, enabling the robot to observe multiple variations of how a single task can be performed. These synthetic demonstrations are then used as a training resource, allowing the robot to learn a broad range of task-execution styles without requiring direct hu
How to integrate AI into the Business and Systems Analysis Function
I came to this model through numerous experiments with AI in my own work. By observing how AI handles context, evidence, requirements, and Continue reading on Medium »
Every AI Platform Is Actually Two Businesses. Most Are Only Designed For One.
Here s how I d design an ideal AI Platform, if I were building one from scratch: not as one product with one set of users, but as two Continue reading on Medium »
I Didn’t Set Out to Co-Found a Climate Tech Company. I Set Out to Solve Problems.
How curiosity, data, and persistence shaped my journey into AI and climate technology.Continue reading on Medium »
A health AI can know the answer and still miss the patient
High medical benchmark scores and unsafe patient advice are not a contradiction. The gap is context.Continue reading on nimaraa.ai Journal »
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Are brain waves the next unlock for physical AI?
Forget YouTube videos—frontier physical AI models need multiple camera angles, dense annotation, and soon, brain wave readings.
Every Technological Revolution Begins with a Failure of Imagination
Why trust is becoming the next great layer of digital infrastructure.Continue reading on Medium »
Anthropic Is Standing Against History — Can They Make it?
What s happening right now will be remembered. Not as a debate about AI safety, but as the moment one company tried to pull up the ladder Continue reading on Medium »