Author

R. Kompella

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Preprint Oct 2026

Test-Time Training as Residual Memory for Robot Policies

Memory is essential for long-horizon robotic manipulation, where successful actions may depend on past events that are no longer recoverable from the current observation. As episodes grow longer, however, retaining the full history becomes increasingly costly, creating a fundamental scalability challenge for memory-aug...

Hao-Xuan Wang, Geng-Yu Zhang, R. Kompella et al. · 0 citations

Preprint Oct 2026

ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies

Robots need to anticipate how their actions will change the world, since manipulation success hinges on the resulting contacts and object motions. However, existing Vision-Language-Action (VLA) policies that predict future observations from shared features leave the forecast decoupled from the actions the policy will a...

Zhe Tao, Fei-Ran Wang, Gao-Wen Liu et al. · 0 citations

Jul 2026

The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model

A principled density model for the CLIP latent space based on Mixtures of von Mises-Fisher distributions defined on the unit hypersphere is proposed, establishing a simple and geometrically consistent probabilistic framework for modeling and understanding multimodal representations.

Zijie Yu, Gaowen Liu, R. Kompella et al. · arXiv.org · 0 citations

Jul 2026

Symbolic Preference Distillation: Advancing Small Language Models for Mental Health Analysis.

This paper proposes symbolic preference distillation (SyPD), a reasoning optimization strategy that leverages specialized domain knowledge to perform SLMs' error analysis and generate symbolic knowledge via a teacher, and proposes a preference distillation method that guides an SLM to align with high-quality and clinic...

Lu Yu, Weikang Xiang, Kang Han et al. · IEEE journal of biomedical a... · 0 citations

Open access 2026

Resource Management for Distributed Quantum Computing in Quantum Data Centers

Simulation results suggest that the lightweight strategy is sufficient for current DQC settings, offering a practical solution for managing remote-gate resource contention in distributed quantum circuits and improving overall system performance.

Zebo Yang, Chenliang Tian, Rajkumar Jain et al. · IEEE Access · 0 citations

Jun 2026

Drop-Then-Recovery: How Redundant Are Vision-Language-Action Models?

The results suggest that current VLA benchmarks may exert limited pressure on deep language grounding and compositional instruction understanding, and that future VLA architectures should allocate capacity more deliberately across language, vision, and action components.

Guoheng Sun, Kai Feng, Shwai He et al. · arXiv.org · 0 citations