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Apr 9, 2026 · Artificial Intelligence

Can Parallel Draft‑Distill‑Refine Beat Long Chain‑of‑Thought? Inside Meta’s Muse Spark

Meta’s newly announced Muse Spark model introduces a closed‑source “contemplating mode” that orchestrates multiple parallel reasoning agents using the PDR (draft‑in‑parallel, distill, refine) framework, which the paper shows can surpass traditional long Chain‑of‑Thought reasoning in accuracy while keeping latency unchanged, as demonstrated on AIME 2024/2025 benchmarks.

Chain-of-ThoughtLLMMeta
0 likes · 8 min read
Can Parallel Draft‑Distill‑Refine Beat Long Chain‑of‑Thought? Inside Meta’s Muse Spark
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Mar 3, 2026 · Artificial Intelligence

Enabling Search Agents to Think While Waiting: Diffusion LLMs Deliver 15% Faster Inference Without Accuracy Loss

The paper introduces DLLM‑Searcher, which equips diffusion large language models with a two‑stage training pipeline and a P‑ReAct inference scheme, allowing the model to issue tool calls while simultaneously reasoning, yielding 14‑22% end‑to‑end speedup and matching or surpassing traditional autoregressive agents on multi‑hop QA benchmarks.

Multi-hop QAP-ReActagentic training
0 likes · 10 min read
Enabling Search Agents to Think While Waiting: Diffusion LLMs Deliver 15% Faster Inference Without Accuracy Loss