Pith Number

pith:X7LGMLX7

pith:2025:X7LGMLX7PDZADBAF6SOWJYQ4JY

not attested not anchored not stored refs resolved

ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving

Bing Wang, Fang Li, Gangwei Xu, Guang Chen, Haiyang Sun, Hangjun Ye, Kaixin Xiong, Kun Ma, Lijun Zhou, Long Chen, Sixu Yan, Wenyu Liu, Xiangyu Guo, Xinggang Wang, Yongkang Li

ReCogDrive combines a vision-language model for cognition with a reinforced diffusion planner to generate feasible, safe driving trajectories.

arxiv:2506.08052 v2 · 2025-06-09 · cs.CV · cs.RO

Open paper page JSON Open Graph Bundle Merged state What is a Pith Number?

Record completeness

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

ReCogDrive achieves state-of-the-art performance on the NAVSIM and Bench2Drive benchmarks while demonstrating strong scene comprehension across diverse driving scenarios.

C2weakest assumption

The hierarchical data pipeline (generation, refinement, quality control) successfully instills transferable human driving cognition into the VLM without introducing dataset-specific biases that limit generalization to real-world conditions.

C3one line summary

ReCogDrive unifies VLM scene understanding with a diffusion planner reinforced by DiffGRPO to reach state-of-the-art results on NAVSIM and Bench2Drive benchmarks.

References

45 extracted · 45 resolved · 22 Pith anchors

[1] Phi-4 Technical Report · arXiv:2412.08905

[2] Qwen2.5-VL Technical Report · arXiv:2502.13923

[3] Is a 3d-tokenized LLM the key to reliable autonomous driving? CoRR, abs/2405.18361, 2024

[4] GR00T N1: An Open Foundation Model for Generalist Humanoid Robots · arXiv:2503.14734

[5] $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control · arXiv:2410.24164

Formal links

3 machine-checked theorem links

Cited by

31 papers in Pith

Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail

MiMo-Embodied: X-Embodied Foundation Model Technical Report

SimScale: Learning to Drive via Real-World Simulation at Scale

Pseudo-Expert Regularized Offline RL for End-to-End Autonomous Driving in Photorealistic Closed-Loop Environments

DriveLaW:Unifying Planning and Video Generation in a Latent Driving World

Receipt and verification

First computed	2026-05-17T23:38:53.119256Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

bfd6662eff78f2018405f49d64e21c4e19e4814f77ee640dee879085f68a7a56

Aliases

arxiv: 2506.08052 · arxiv_version: 2506.08052v2 · doi: 10.48550/arxiv.2506.08052 · pith_short_12: X7LGMLX7PDZA · pith_short_16: X7LGMLX7PDZADBAF · pith_short_8: X7LGMLX7

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/X7LGMLX7PDZADBAF6SOWJYQ4JY \
  | 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: bfd6662eff78f2018405f49d64e21c4e19e4814f77ee640dee879085f68a7a56

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "0ec7a83949788c53e06ecd2051407aff2c52be5c6823003262d1418c6c3b8e6e",
    "cross_cats_sorted": [
      "cs.RO"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2025-06-09T03:14:04Z",
    "title_canon_sha256": "ba562e3aaab8eef881a9434048224f584cf92f512da83f7c25cb6d1d2022c1d3"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2506.08052",
    "kind": "arxiv",
    "version": 2
  }
}