Pith Number

pith:5ZL5BEBA

pith:2026:5ZL5BEBA2K6GXXRXTOM7BATARO

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Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models

Chirag Shah, Harshita Chopra, Krishna Kant Chintalapudi, Ryen W. White, Suman Nath

Prospection-guided retrieval uses imagined future steps to surface low-similarity memories that standard embedding search misses.

arxiv:2605.14177 v1 · 2026-05-13 · cs.IR · cs.AI · cs.CL

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\pithnumber{5ZL5BEBA2K6GXXRXTOM7BATARO}

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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

PGR-TOT substantially improves retrieval, including nearly 3x recall on MemoryQuest over the strongest baseline. In pairwise LLM-as-judge comparisons against baselines, PGR-generated responses are preferred on 89--98% of queries, with blinded human annotations on held-out subsets showing the same trend.

C2weakest assumption

That LLM-generated prospection steps (ToT or linear chains) will consistently produce retrieval probes that surface genuinely relevant low-similarity memories without excessive noise or hallucinated irrelevancies, and that this holds across diverse user histories beyond the tested datasets.

C3one line summary

PGR expands user queries into plausible future steps via Tree-of-Thought or chains and uses them as retrieval probes, delivering nearly 3x recall gains on the new MemoryQuest benchmark for low-similarity memory retrieval.

References

22 extracted · 22 resolved · 2 Pith anchors

[1] Daniel L. Schacter, Roland G. Benoit, and Karl K. Szpunar. Episodic future thinking: Mechanisms and functions. Current Opinion in Behavioral Sciences, 17:41–50, 2017 2017

[2] Wong, and Daniel L 2007

[3] Retrieval augmented language model pre-training 2020

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

[5] From Local to Global: A Graph RAG Approach to Query-Focused Summarization 2024 · arXiv:2404.16130

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:11.289183Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ee57d09020d2bc6bde379b99f082608baf52e242b4d80d54fd75b122fb2bba16

Aliases

arxiv: 2605.14177 · arxiv_version: 2605.14177v1 · doi: 10.48550/arxiv.2605.14177 · pith_short_12: 5ZL5BEBA2K6G · pith_short_16: 5ZL5BEBA2K6GXXRX · pith_short_8: 5ZL5BEBA

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/5ZL5BEBA2K6GXXRXTOM7BATARO \
  | 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: ee57d09020d2bc6bde379b99f082608baf52e242b4d80d54fd75b122fb2bba16

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "62f61cc8c59a1b07970d8ac30e3c3db89577127e144fca86dc181c9ef7f16787",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.IR",
    "submitted_at": "2026-05-13T22:57:54Z",
    "title_canon_sha256": "98ac8736b1b1eb27395ad6dd8b7c2288dc3ca3f976015619d1bacd17111d6cab"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14177",
    "kind": "arxiv",
    "version": 1
  }
}