Pith Number

pith:C6THR7ZL

pith:2025:C6THR7ZLECF7SQFPQGDUB3JANB

not attested not anchored not stored refs pending

Domain-Aware Machine Learning for Accelerating MILP-Based Motion Planning with Temporal Logic and Chance Constraints

Bistra Dilkina, Brendan Long, Junyang Cai, Jyotirmoy V. Deshmukh, Lars Lindemann, Matthew Cleaveland, Weimin Huang

Neuro-symbolic guidance accelerates MILP motion planning for temporal logic and chance constraints by about 20 percent.

arxiv:2508.07515 v3 · 2025-08-11 · eess.SY · cs.SY

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

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{C6THR7ZLECF7SQFPQGDUB3JANB}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

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

Through extensive experiments, we show that neuro-symbolic search techniques yield scalability gains. Our approach yields substantial improvements across all three classes of planning problems, achieving an average performance gain of about 20% over state-of-the-art solver across key metrics, including runtime and solution quality.

C2weakest assumption

Graph neural network-based learning methods can reliably guide traditional symbolic MILP solvers on branching variable selection and solver parameter configuration for the three classes of planning problems (STL, CPP, CaTL).

C3one line summary

Neuro-symbolic GNN guidance accelerates MILP solvers for STL, CPP chance constraints, and CaTL planning problems with average 20% gains in runtime and solution quality over state-of-the-art solvers.

Cited by

1 paper in Pith

ID-PaS+ : Identity-Aware Predict-and-Search for General Mixed-Integer Linear Programs

Receipt and verification

First computed	2026-07-30T01:23:45.844224Z
Builder	pith-number-builder-2026-05-17-v1
Signature	unsigned_v0
Schema	pith-number/v1.0

Canonical hash

17a678ff2b208bf940af818740ed206850d0468592a1a720f260f5a796a77d43

Aliases

arxiv: 2508.07515 · arxiv_version: 2508.07515v3 · doi: 10.48550/arxiv.2508.07515 · pith_short_12: C6THR7ZLECF7 · pith_short_16: C6THR7ZLECF7SQFP · pith_short_8: C6THR7ZL

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/C6THR7ZLECF7SQFPQGDUB3JANB \
  | 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: 17a678ff2b208bf940af818740ed206850d0468592a1a720f260f5a796a77d43

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "587ad48b5349983db7ad971d0a24573ff5b01244092b51df1570f9800f730365",
    "cross_cats_sorted": [
      "cs.SY"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SY",
    "submitted_at": "2025-08-11T00:13:36Z",
    "title_canon_sha256": "ac71cdb9066e3991a0ab3a255caf55ff19634c46228e0c53123e91f7b7058add"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2508.07515",
    "kind": "arxiv",
    "version": 3
  }
}