Pith Number

pith:SDI5XCOC

pith:2025:SDI5XCOC4L5OAFROSGD55SLAAF

not attested not anchored not stored refs resolved

Mercury: Ultra-Fast Language Models Based on Diffusion

Aditya Grover, Akash Palrecha, Eric Wang, Harshit Varma, Inception Labs, Samar Khanna, Sawyer Birnbaum, Shufan Li, Siddhant Kharbanda, Stefano Ermon, Volodymyr Kuleshov, Yanis Miraoui, Ziyang Luo

Diffusion LLMs generate code at over 1100 tokens per second while matching frontier quality.

arxiv:2506.17298 v1 · 2025-06-17 · cs.CL · cs.AI · cs.LG

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1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Mercury Coder Mini and Small achieve state-of-the-art throughputs of 1109 tokens/sec and 737 tokens/sec on NVIDIA H100 GPUs and outperform speed-optimized frontier models by up to 10x on average while maintaining comparable quality.

C2weakest assumption

The assumption that independent evaluations by Artificial Analysis and Copilot Arena rankings accurately measure both speed and quality in a way that generalizes beyond the tested benchmarks and real-world developer use.

C3one line summary

Mercury Coder diffusion LLMs achieve throughputs of 1109 and 737 tokens per second on H100 GPUs, up to 10x faster than frontier models with comparable quality.

References

41 extracted · 41 resolved · 15 Pith anchors

[1] URLhttps://api.semanticscholar

[2] Top latest ai code generator statistics and trends in 2024, 2024 2024

[3] GPT-4 Technical Report 2023 · arXiv:2303.08774

[4] Structured denoising diffusion models in discrete state-spaces.Advances in Neural Infor- mation Processing Systems, 34:17981–17993, 2021 2021

[5] Program Synthesis with Large Language Models 2021 · arXiv:2108.07732

Formal links

3 machine-checked theorem links

Cited by

22 papers in Pith

Diffusion Language Models Know the Answer Before Decoding

FS-DFM: Fast and Accurate Long Text Generation with Few-Step Diffusion Language Models

Saber: An Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model

A Survey of Reinforcement Learning for Large Reasoning Models

Efficient-DLM: From Autoregressive to Diffusion Language Models, and Beyond in Speed

Receipt and verification

First computed	2026-05-17T23:38:15.453003Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

90d1db89c2e2fae0162e9187dec9600147e8b3b44ed334d67ec76c97004d3729

Aliases

arxiv: 2506.17298 · arxiv_version: 2506.17298v1 · doi: 10.48550/arxiv.2506.17298 · pith_short_12: SDI5XCOC4L5O · pith_short_16: SDI5XCOC4L5OAFRO · pith_short_8: SDI5XCOC

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/SDI5XCOC4L5OAFROSGD55SLAAF \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 90d1db89c2e2fae0162e9187dec9600147e8b3b44ed334d67ec76c97004d3729

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "42d05162ad40c8417f9651c838467a46967600527416aa5176c6ec3f44016011",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2025-06-17T17:06:18Z",
    "title_canon_sha256": "0b90f499788fdc9bde41d138574e56eb58d75f2a1b9562e557221998c6185b6f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2506.17298",
    "kind": "arxiv",
    "version": 1
  }
}