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agent/papers/items/2026-2607-03333-spork-self-speculative-forking-to-accelerate-agentic-llm-inference.md
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# Paper: SPORK: Self-Speculative Forking to Accelerate Agentic LLM Inference
---
type: paper
title: "SPORK: Self-Speculative Forking to Accelerate Agentic LLM Inference"
authors: Huajun Bai, Weiwei Lv, Huichuan Zheng, Youyou Lu, Jiwu Shu
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2607.03333
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-07-03
updated_at: 2026-07-03
status: queued
relevance: high
topics:
- agent-evaluation
- coding-agent
- reasoning
- tool-use
- workflow-agent
methods:
-
benchmarks:
-
models:
-
datasets:
- cs.DC
- cs.AI
- cs.LG
related_concepts:
-
related_jobs:
-
related_experiments:
-
related_projects:
-
collection_score: 17
collection_queries: llm-agent
---
## One-line Takeaway
Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.
## Why Collected
- matched queries: llm-agent
- inferred topics: agent-evaluation, coding-agent, reasoning, tool-use, workflow-agent
- arXiv categories: cs.DC, cs.AI, cs.LG
- collection score: 17
## Review Checklist
- Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
- Does it include a benchmark, dataset, code, or reproducible experimental setup?
- Should it be promoted from `queued` to `skimmed` or `summarized`?
## Links
- arXiv: https://arxiv.org/abs/2607.03333