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Memory-augmented reinforcement learning strengthens LLM agents'ability to solve complex long-horizon tasks. Skills are one such form of memory, pairing instructions with an applicability condition over task types. However, retaining every skill indiscriminately as the policy improves lets obsolete or harmful entries ac...
Visual object tracking requires effective temporal integration, yet most Transformer trackers still rely on predominantly feed-forward feature extraction. Existing temporal mechanisms typically update templates, prompts, queries, or prediction states, while intermediate representations are rarely reused to modulate cor...
Overall, RATL shifts the retrieved object from historical target values to base-model-specific historical forecast errors, providing a plug-in, residual-memory-based paradigm for learned feedback correction in continuous-output forecasting.
This work introduces Polarity-Prompt Contrastive Decoding (PopCD), a test-time behavior control method that generalizes contrastive decoding to broader enhancement settings and is applicable to both LLMs and Vision-Language Models without additional training.
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