Pith Number

pith:S6PFNZRG

pith:2026:S6PFNZRGQNE7OMGM3K5QGYDO23

not attested not anchored not stored refs pending

SceneSelect: Selective Learning for Trajectory Scene Classification and Expert Scheduling

Deshun Xia, Weijie Zhu, Xinrun Wang, Yuxi Sun

SceneSelect classifies input scenes from geometry and motion features then routes each trajectory to the best expert predictor.

arxiv:2604.24514 v2 · 2026-04-27 · cs.LG

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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

Extensive experiments on three public benchmarks (ETH-UCY, SDD, and NBA) demonstrate that our method consistently outperforms strong single-model and ensemble baselines, achieving an average improvement of 10.5%, showcasing the effectiveness of scene-aware selective learning.

C2weakest assumption

That unsupervised clustering on geometric and kinematic features produces a stable, generalizable latent scene taxonomy, and that the decoupled classification and scheduling modules enable seamless integration with off-the-shelf models and new datasets without joint retraining or performance loss.

C3one line summary

SceneSelect discovers a latent scene taxonomy through clustering, trains a decoupled classifier to assign inputs, and uses a scheduling policy to dispatch to optimal expert trajectory predictors, reporting 10.5% average gains over baselines on ETH-UCY, SDD, and NBA.

Receipt and verification

First computed	2026-05-22T01:04:03.217630Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

979e56e6268349f730ccdabb03606ed6f7afb3e79d89deced6a54fe35186235a

Aliases

arxiv: 2604.24514 · arxiv_version: 2604.24514v2 · doi: 10.48550/arxiv.2604.24514 · pith_short_12: S6PFNZRGQNE7 · pith_short_16: S6PFNZRGQNE7OMGM · pith_short_8: S6PFNZRG

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/S6PFNZRGQNE7OMGM3K5QGYDO23 \
  | 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: 979e56e6268349f730ccdabb03606ed6f7afb3e79d89deced6a54fe35186235a

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "154947f023016fd3db6cf8b544a13f43c89b6794c5c2cdb3ae01606df09473f1",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-04-27T14:15:44Z",
    "title_canon_sha256": "25996b26245029b74ea111330df3f29a6652e4e8abb35bd856a38ea5949466ae"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.24514",
    "kind": "arxiv",
    "version": 2
  }
}