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ARXIV:2605.07209 · LLM HALLUCINATION DETECTION · SUBMITTED 11 MAY · 20:36 UTC · FRESHNESS STALE
ARXIV:2605.07209LLM HALLUCINATION DETECTIONSUBMITTED 11 MAY · 20:36 UTCFRESHNESS STALEAkshita Singh · Prabesh Paudel · Siddhartha Roy · arXiv
A lightweight, open-weight model system that detects LLM hallucinations by analyzing generated text activations, outperforming existing methods.
Opportunity summary
Pain A lightweight, open-weight model system that detects LLM hallucinations by analyzing generated text activations, outperforming existing methods.
Evidence 0 refs | 4 sources | 83% coverage
Blocker Evidence partial
A lightweight, open-weight model system that detects LLM hallucinations by analyzing generated text activations, outperforming existing methods. Instead of looking inside the generating model, our system reads already-generated text through a small locally hosted…
We introduce a proxy-analyzer framework for detecting hallucinations in large language models. Instead of looking inside the generating model, our system reads already-generated text through a small locally hosted open-weight model and spots hallucinations…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Both RAGTruth and LLM-AggreFact include outputs from multiple LLM families, so our results are not skewed toward any particular generator. A public repository is…
LLM Hallucination Detection moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A lightweight, open-weight model system that detects LLM hallucinations by analyzing generated text activations, outperforming existing methods.
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Paper Pack
10.48550/arXiv.2605.07209A lightweight, open-weight model system that detects LLM hallucinations by analyzing generated text activations, outperforming existing methods.
Abstract
We introduce a proxy-analyzer framework for detecting hallucinations in large language models. Instead of looking inside the generating model, our system reads already-generated text through a small locally hosted open-weight model and spots hallucinations using the reader's own internal activations. This works just as well when the generator is a closed API like GPT-4 as when it is any open-weight model. We built eighteen features grounded in how transformers process text, covering residual stream norms, per-head source-document attention, entropy, MLP activations, logit-lens trajectories, and three new token-level grounding statistics. We trained a stacking ensemble on 72,135 samples from five hallucination datasets. We tested across seven analyzer architectures from 0.5 billion to 9 billion parameters: Qwen2.5 at 0.5B and 7B, Gemma-2 at 2B and 9B, Pythia at 1.4B, and LLaMA-3 at both 3B and 8B. Across all seven, we consistently beat ReDeEP's token-level AUC of 0.73 on RAGTruth by 7.4 to 10.3 percentage points. Qwen2.5-7B reached an F1 of 0.717, just above ReDeEP's 0.713, while Qwen2.5-0.5B hit 0.706. The most striking finding is how tightly all seven models cluster: AUC spans only 2.3 percentage points across an eighteen-fold difference in model size. Even more surprising, our 3B LLaMA outperforms our 8B LLaMA on RAGTruth, showing that bigger is not always better even within the same model family. Both RAGTruth and LLM-AggreFact include outputs from multiple LLM families, so our results are not skewed toward any particular generator.
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Proof status
partial0 refs; 4 sources; 83% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
A lightweight, open-weight model system that detects LLM hallucinations by analyzing generated text activations, outperforming existing methods. Instead of looking inside the generating model, our system reads already-generated text through a small locally hosted open-weight mod...
METHOD
We introduce a proxy-analyzer framework for detecting hallucinations in large language models. Instead of looking inside the generating model, our system reads already-generated text through a small locally hosted open-weight model and spots hallucinations using the reader's own...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Both RAGTruth and LLM-AggreFact include outputs from multiple LLM families, so our results are not skewed toward any particular generator. A public repository is linked, so build verification can inspect...
WHY NOW
LLM Hallucination Detection moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A lightweight, open-weight model system that detects LLM hallucinations by analyzing generated text activations, outperforming existing methods. Instead of looking inside the generating model, our system reads already-generated text through a small locally hosted open-weight model and spots hallucinations using the reader's own internal activations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We introduce a proxy-analyzer framework for detecting hallucinations in large language models. Instead of looking inside the generating model, our system reads already-generated text through a small locally hosted open-weight model and spots hallucinations using the reader's own internal activations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Both RAGTruth and LLM-AggreFact include outputs from multiple LLM families, so our results are not skewed toward any particular generator. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Hallucination Detection moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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A lightweight, open-weight model system that detects LLM hallucinations by analyzing generated text activations, outperforming existing methods.
Segment
LLM Hallucination Detection
Adoption evidence
Public code linked for build inspection
Commercial read
7.0/10 public viability
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 4 sources / 83% coverage
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.
Market urgency
missing
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Evidence
0 references, 4 sources, 83% evidence coverage.
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Buyer clarity
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Defensibility signals are missing.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
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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
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