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ARXIV:2605.05103 · LLM EVALUATION · SUBMITTED 07 MAY · 20:31 UTC · FRESHNESS STALE
ARXIV:2605.05103LLM EVALUATIONSUBMITTED 07 MAY · 20:31 UTCFRESHNESS STALENicholas S. Kersting · Vittorio Castelli · Chieh Ting Yeh · Xinzhu Wang · Saad Taame · arXiv
A novel 'Concept Field' approach to measure hallucination and novelty in text corpora using sentence embeddings and a Vector Sequence Database.
Opportunity summary
Pain A novel 'Concept Field' approach to measure hallucination and novelty in text corpora using sentence embeddings and a Vector Sequence Database.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A novel 'Concept Field' approach to measure hallucination and novelty in text corpora using sentence embeddings and a Vector Sequence Database. Given a candidate sentence transition, we score its agreement with the field by…
We introduce the **Concept Field** of a text corpus: a local drift field with pointwise uncertainty, estimated in sentence-embedding space from the deltas between consecutive sentences. Given a candidate sentence transition, we score its…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We support the computation with the introduction of a **Vector Sequence Database (VSDB)** that stores embeddings together with sequence-position and next-delta metadata.
LLM Evaluation moved forward this cycle; last verified May 2026. Public score 4.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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A novel 'Concept Field' approach to measure hallucination and novelty in text corpora using sentence embeddings and a Vector Sequence Database.
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10.48550/arXiv.2605.05103A novel 'Concept Field' approach to measure hallucination and novelty in text corpora using sentence embeddings and a Vector Sequence Database.
Abstract
We introduce the **Concept Field** of a text corpus: a local drift field with pointwise uncertainty, estimated in sentence-embedding space from the deltas between consecutive sentences. Given a candidate sentence transition, we score its agreement with the field by $ζ$, the mean absolute z-distance between the observed delta and the field's local Gaussian estimate. The score is black-box (no model internals), corpus-attributable (every score traces to nearby corpus sentences), and admits a direct probabilistic reading. We support the computation with the introduction of a **Vector Sequence Database (VSDB)** that stores embeddings together with sequence-position and next-delta metadata. We evaluate this approach on two large-scale settings: hallucination-style groundedness detection over the U.S. Code of Federal Regulations, and novelty detection over Project Gutenberg. Using controlled LLM-generated rewrites, Concept Fields achieve strong selective classification performance under a grounded / ungrounded / unsure triage policy, which unlike retrieval-centric baselines have similar coverage-risk behavior across both domains, supporting a probability-based interpretation that transfers across domains. We also sketch how divergence and curl of the Concept Field, computed on dense clusters, surface qualitatively meaningful semantic patterns (logic sources, sinks, and implicit topics), which we offer as hypothesis-generating rather than as a quantitative result. Concept Fields provide a fast, lightweight, and interpretable signal for groundedness and novelty, complementary to LLM-as-judge and white-box detectors.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Commercial
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Dimensions overall score 4.0
PROBLEM
A novel 'Concept Field' approach to measure hallucination and novelty in text corpora using sentence embeddings and a Vector Sequence Database. Given a candidate sentence transition, we score its agreement with the field by $ζ$, the mean absolute z-distance between the observed...
METHOD
We introduce the **Concept Field** of a text corpus: a local drift field with pointwise uncertainty, estimated in sentence-embedding space from the deltas between consecutive sentences. Given a candidate sentence transition, we score its agreement with the field by $ζ$, the mean...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We support the computation with the introduction of a **Vector Sequence Database (VSDB)** that stores embeddings together with sequence-position and next-delta metadata.
WHY NOW
LLM Evaluation moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A novel 'Concept Field' approach to measure hallucination and novelty in text corpora using sentence embeddings and a Vector Sequence Database. Given a candidate sentence transition, we score its agreement with the field by $ζ$, the mean absolute z-distance between the observed delta and the field's local Gaussian estimate.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We introduce the **Concept Field** of a text corpus: a local drift field with pointwise uncertainty, estimated in sentence-embedding space from the deltas between consecutive sentences. Given a candidate sentence transition, we score its agreement with the field by $ζ$, the mean absolute z-distance between the observed delta and the field's local Gaussian estimate.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We support the computation with the introduction of a **Vector Sequence Database (VSDB)** that stores embeddings together with sequence-position and next-delta metadata.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Evaluation moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel 'Concept Field' approach to measure hallucination and novelty in text corpora using sentence embeddings and a Vector Sequence Database.
Segment
LLM Evaluation
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Commercial read
4.0/10 public viability
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status
missing
reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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0 references, 3 sources, 50% evidence coverage.
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Buyer clarity
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
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Write integration checklist from prototype path and target workflow.
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Evidence
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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