pith:RY2TXKHF
LLM-Oriented Information Retrieval: A Denoising-First Perspective
Denoising to maximize evidence density and verifiability becomes the central task in information retrieval for large language models.
arxiv:2605.00505 v2 · 2026-05-01 · cs.IR · cs.AI · cs.CL
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\pithnumber{RY2TXKHF67AJGWLTCK37JRCI5M}
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Record completeness
Claims
denoising—maximizing usable evidence density and verifiability within a context window—is becoming the primary bottleneck across the full information access pipeline
That LLMs' limited attention budgets and unique vulnerability to noise represent a fundamental paradigm shift in IR that requires a new denoising-first framework, rather than incremental extensions of existing relevance and quality techniques.
Denoising to maximize usable evidence density and verifiability is becoming the primary bottleneck in LLM-oriented information retrieval, conceptualized via a four-stage framework and addressed through a pipeline taxonomy of optimization techniques.
Receipt and verification
| First computed | 2026-05-20T00:04:33.298604Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
8e353ba8e5f7c093597312b7f4c448eb0e6f55ca850c9e2852adc558b68adb24
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/RY2TXKHF67AJGWLTCK37JRCI5M \
| 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: 8e353ba8e5f7c093597312b7f4c448eb0e6f55ca850c9e2852adc558b68adb24
Canonical record JSON
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