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ARXIV:2603.06081 · LLM EVALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.06081LLM EVALUATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Detect LLM hallucinations by probing the stability of knowledge representation using lightweight networks, enabling more reliable and trustworthy AI outputs.
Opportunity summary
Pain Detect LLM hallucinations by probing the stability of knowledge representation using lightweight networks, enabling more reliable and trustworthy AI outputs.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Detect LLM hallucinations by probing the stability of knowledge representation using lightweight networks, enabling more reliable and trustworthy AI outputs. Rather than treating hallucination as a straightforward classification task, we conceptualize (M)LLMs as dynamical…
We address hallucination detection in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) by framing the problem through the lens of dynamical systems stability theory. Rather than treating hallucination as a straightforward…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on diverse datasets and models demonstrate consistent improvements over existing baselines.
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Detect LLM hallucinations by probing the stability of knowledge representation using lightweight networks, enabling more reliable and trustworthy AI outputs.
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10.48550/arXiv.2603.06081Detect LLM hallucinations by probing the stability of knowledge representation using lightweight networks, enabling more reliable and trustworthy AI outputs.
Abstract
We address hallucination detection in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) by framing the problem through the lens of dynamical systems stability theory. Rather than treating hallucination as a straightforward classification task, we conceptualize (M)LLMs as dynamical systems, where factual knowledge is represented by stable equilibrium points within the representation space. Our main insight is that hallucinations tend to arise at the boundaries of knowledge-transition regions separating stable and unstable zones. To capture this phenomenon, we propose Lyapunov Probes: lightweight networks trained with derivative-based stability constraints that enforce a monotonic decay in confidence under input perturbations. By performing systematic perturbation analysis and applying a two-stage training process, these probes reliably distinguish between stable factual regions and unstable, hallucination-prone regions. Experiments on diverse datasets and models demonstrate consistent improvements over existing baselines.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
Detect LLM hallucinations by probing the stability of knowledge representation using lightweight networks, enabling more reliable and trustworthy AI outputs. Rather than treating hallucination as a straightforward classification task, we conceptualize (M)LLMs as dynamical system...
METHOD
We address hallucination detection in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) by framing the problem through the lens of dynamical systems stability theory. Rather than treating hallucination as a straightforward classification task, we conceptu...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on diverse datasets and models demonstrate consistent improvements over existing baselines.
WHY NOW
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Detect LLM hallucinations by probing the stability of knowledge representation using lightweight networks, enabling more reliable and trustworthy AI outputs. Rather than treating hallucination as a straightforward classification task, we conceptualize (M)LLMs as dynamical systems, where factual knowledge is represented by stable equilibrium points within the representation space.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We address hallucination detection in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) by framing the problem through the lens of dynamical systems stability theory. Rather than treating hallucination as a straightforward classification task, we conceptualize (M)LLMs as dynamical systems, where factual knowledge is represented by stable equilibrium points within the representation space.
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. Experiments on diverse datasets and models demonstrate consistent improvements over existing baselines.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Detect LLM hallucinations by probing the stability of knowledge representation using lightweight networks, enabling more reliable and trustworthy AI outputs.
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LLM Evaluation
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unverified
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partial
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