Pith Number

pith:Y3FVWKAY

pith:2026:Y3FVWKAY3MJARCIYNOTBAXPM2D

not attested not anchored not stored refs resolved

Scaling Retrieval-Augmented Reasoning with Parallel Search and Explicit Merging

Chunxu Shen, Jiabei Liu, Jiancan Wu, Junfei Tan, Lingling Yi, Wenyu Mao, Xiang Wang

MultiSearch retrieves external knowledge from multiple query perspectives in parallel and merges the results to raise signal-to-noise ratio before reasoning.

arxiv:2605.13534 v1 · 2026-05-13 · cs.AI

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\pithnumber{Y3FVWKAY3MJARCIYNOTBAXPM2D}

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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

MultiSearch outperforms baseline methods, enhancing the SNR of retrieval and improving reasoning performance in question-answering tasks.

C2weakest assumption

That generating multiple queries from different perspectives and then performing explicit merging will consistently raise signal-to-noise ratio without introducing new noise sources or prohibitive compute overhead in the merging step.

C3one line summary

MultiSearch uses parallel multi-query retrieval plus explicit merging inside a reinforcement-learning loop to improve retrieval-augmented reasoning, outperforming baselines on seven QA benchmarks.

References

47 extracted · 47 resolved · 13 Pith anchors

[1] A survey on evaluation of large language models.ACM transactions on intelligent systems and technology, 15(3):1–45 2024

[2] Siren’s song in the ai ocean: A survey on hallucination in large language models.Computational Linguistics, 51(4):1373–1418, 2025 2025

[3] A study of generative large language model for medical research and healthcare.NPJ digital medicine, 6(1):210, 2023 2023

[4] Retrieval-Augmented Generation for Large Language Models: A Survey 2023 · arXiv:2312.10997

[5] Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474 2020

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T02:44:24.185809Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c6cb5b2818db120889186ba6105decd0f6dc856362d7ed29b403d7ea21b893b2

Aliases

arxiv: 2605.13534 · arxiv_version: 2605.13534v1 · doi: 10.48550/arxiv.2605.13534 · pith_short_12: Y3FVWKAY3MJA · pith_short_16: Y3FVWKAY3MJARCIY · pith_short_8: Y3FVWKAY

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/Y3FVWKAY3MJARCIYNOTBAXPM2D \
  | 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: c6cb5b2818db120889186ba6105decd0f6dc856362d7ed29b403d7ea21b893b2

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "15ec28207e7fa01c11f4ba4113c5404c7374bdf2c3e3ae988b5c9ae3df33cc15",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-13T13:46:35Z",
    "title_canon_sha256": "5ce489a8eca4b57f6e76fbf9df1ffef77b147666f4ede7b2acb0996faabc7c97"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13534",
    "kind": "arxiv",
    "version": 1
  }
}