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arXiv Machine Learning By Anubha Gupta, Eduardo Pignatelli

LS-AR: Future-Predictive Latent Steering in Autoregressive LLMs

Read the original on arXiv Machine Learning →

The paper introduces LS-AR, a dual‑channel architecture that separates continuous goal steering from discrete token decoding using FiLM conditioning. It employs a Static Goal Encoder for long‑term macro‑objective retention and a Dynamic State Tracker for recurrent latent updates. Experiments show LS‑AR achieves perfect target recall on long‑horizon retrieval tasks, improves throughput by ~35%, reduces VRAM usage by 52.8%, and maintains higher completion rates in Blocksworld planning under perturbations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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