Author

Jangho Park

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Preprint Oct 2026

Foveated Compression: Selective High-Resolution Preservation for Token-Efficient VLMs

Visual tokens are a major source of inference cost in vision-language models, yet simple image downsampling remains a surprisingly strong compression baseline. This raises a complementary question: under a fixed token budget, where should visual fidelity be preserved? We introduce Foveated Compression, which encodes a...

Donghyun Han, Jangho Park, Yuseok Bae · 0 citations

#artificial intelligence Preprint Sep 2026

DriftOPD: Sequence-Level Reverse-KL Distillation for One-Step VLA Policies

Vision-Language-Action (VLA) models increasingly rely on action experts that generate short action chunks under receding-horizon control. While chunk-level training is convenient across robot embodiments, it optimizes local action likelihood without explicitly accounting for long-horizon task success. Sequence-level re...

Youngjun Jun, Kyumin Choi, Young Min Kim et al. · 0 citations

#machine learning Preprint Sep 2026

Adjoint Guidance Flow: Amortized Critic Guidance for VLA Policies

Adjoint Guidance Flow is proposed, which is a deterministic optimal control problem, whose optimal guidance is a costate that carries the terminal critic gradient back through the remaining flow, and regress the guidance network onto this costate while keeping both the VLA and critic frozen.

Jeongsol Kim, Youngjun Jun, Kyumin Choi et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Don't Throw Away the Tail: Action Upcycling for Policy Acceleration

This work proposes Action Upcycling, a training-free algorithm that reuses actions the policy would otherwise discard, without accessing model internals or drawing extra samples, and finds that discarded actions stay close to their replanned versions as long as the action velocity remains smooth.

Taesung Kwon, Jangho Park, Sunwoo Park et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Dynamic Manipulation with World-Action Models via Counterfactual Planning

DPP enables real-time dynamic manipulation on a single consumer GPU without additional training on dynamic data and constructs a counterfactual observation that places a predicted target position in a familiar robot context, allowing the model to invoke an existing manipulation skill rather than generate a recovery beh...

Sunwoo Park, Won-Sang Lee, Seonghyun Jin et al. · 0 citations

FlowLong: Training-free Long Audio-Video Generation via Manifold-constrained Tweedie Matching

FlowLong is presented, a training-free, architecture-agnostic, inference-time framework that harmonizes overlapping windows sampled in parallel via Tweedie matching, a manifold-constrained blend of predicted clean samples on overlaps, and stochastic early-phase sampling that yields arbitrarily long, phase-locked audio-...

Jangho Park, G. Park, Gihyun Kwon et al. · 0 citations