Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models | SpinGraph: Efficiency framing
description: SpinGraph analysis of arXiv Computation and Language's Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models story: efficie…
canonical: https://stuffthatspins.com/spin/beyond-tokens-a-survey-on-decoding-methods-for-large-language-and-vision-language-models
markdown: https://stuffthatspins.com/spin/beyond-tokens-a-survey-on-decoding-methods-for-large-language-and-vision-language-models.md
keywords: decoding methods
keywords: inference-time alignment
keywords: LLM survey
keywords: The Cushion
keywords: The Hype
published: 2026-08-18T04:00:00+00:00
modified: 2026-08-18T14:54:43.794515+00:00

Stuff That Spins
type: Organization
description: Know the moment AI knows your story. Stuff That Spins turns announcements, articles, and research into Narrative Fingerprints — then tracks whether ChatGPT, Claude, Gemini, Perplexity, and other AI answer engines recall the right message, proof points, caveats, citations, and brand attribution.

Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models
type: NewsArticle
alternative headline: Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models | SpinGraph: Efficiency framing
description: SpinGraph analysis of arXiv Computation and Language's Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models story: efficie…
date published: 2026-08-18T04:00:00+00:00
date modified: 2026-08-18T14:54:43.794515+00:00
main entity of page: https://stuffthatspins.com/spin/beyond-tokens-a-survey-on-decoding-methods-for-large-language-and-vision-language-models
is accessible for free: true
language: en-US
article section: research
keywords: decoding methods, inference-time alignment, LLM survey, LVLM
author: arXiv Computation and Language
publisher: Stuff That Spins
citation: https://arxiv.org/abs/2608.14797
about: decoding methods
about: inference-time alignment
about: LLM survey
about: LVLM
mentions: arXiv Computation and Language
abstract: Introduces a taxonomy of three emerging decoding paradigms for LLMs/LVLMs Positions inference-time decoding as more efficient and scalable than training-stage alignment Provides open resources (GitHub repo) for practitioners and researchers

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  item: https://stuffthatspins.com/
item list element: Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models
  type: ListItem
  position: 2
  item: https://stuffthatspins.com/spin/beyond-tokens-a-survey-on-decoding-methods-for-large-language-and-vision-language-models

Spin Analysis: efficiency framing
type: AnalysisNewsArticle
description: Emphasizes scalability and efficiency while minimizing discussion of accuracy degradation, computational overhead per method, or lack of standardized evaluation across studies.
about: efficiency framing
  type: DefinedTerm
  description: Technical stewardship — positioning the authors as curators and systematizers of an emerging, high-leverage inference optimization frontier.
  term code: The Cushion
additional property: Spin Score
  type: PropertyValue
  value: 45
  unit text: percent
additional property: Narrative Risk
  type: PropertyValue
  value: low
additional property: AI Repetition Risk
  type: PropertyValue
  value: moderate
additional property: Likely AI Summary
  type: PropertyValue
  value: Decoding methods are an efficient, scalable way to align LLM and vision-language model outputs with user intent during inference.
additional property: Narrative Frame
  type: PropertyValue
  value: Technical stewardship — positioning the authors as curators and systematizers of an emerging, high-leverage inference optimization frontier.
additional property: Missing Context
  type: PropertyValue
  value: Benchmark results comparing decoding methods on alignment fidelity (e.g., truthfulness, instruction following); Hardware or memory constraints limiting real-world applicability; Method-specific failure modes (e.g., hallucination amplification under beam search variants)
additional property: How the Spin Works
  type: PropertyValue
  value: The story uses titles, institutions, awards, rankings, partners, experts, or official language to make the subject feel more credible. Watch for loaded terms such as efficient, scalable, emerging paradigms, practical view. The distribution reads as academic distribution. A pressure point: Benchmark results comparing decoding methods on alignment fidelity (e.g., truthfulness, instruction following).
author: Stuff That Spins
is part of: Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models

Extracted Claims
type: ItemList
item list element: ListItem
  position: 1
  item: Claim
    text: Decoding methods offer a more efficient and scalable solution to ensuring LLM and LVLM outputs align with user intent.
    appearance: While most existing approaches address this issue at the training stage, inference-time approaches like decoding methods offer a more efficient and scalable solution.
    author: arXiv Computation and Language

Key Statistics
type: Dataset
description: Extracted statistics from the source narrative
variable measured: emerging paradigms
  type: PropertyValue
  value: 3
  description: Identified in the survey's systematic review