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Sample Transform Cost-Based Training-Free Hallucination Detector for Large Langu

BACKGROUND
3 SIGNALSFIRST DETECTED 26 March 2026
Narrative synthesis pending — 3 signals collected.
How the story developed
2 Sept
27 May
26 Mar
6.9
Other feeds and industry voices
21 Apr
Hallucination as Trajectory Commitment: Causal Evidence for Asymmetric Attractor Dynamics in Transformer Generation

arXiv:2604.15400v1 Announce Type: cross Abstract: We present causal evidence that hallucination in autoregressive language models is an early trajectory commitment governed by

REPORT · HOW · ArXiv CS.AIOPEN STORY
27 Apr
PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training

arXiv:2604.22117v1 Announce Type: cross Abstract: Aligned large language models(LLMs) remain vulnerable to adversarial manipulation, and their dependence on web-scale pretraining

REPORT · WHAT · ArXiv CS.AIOPEN STORY
27 Apr
Estimating Tail Risks in Language Model Output Distributions

arXiv:2604.22167v1 Announce Type: cross Abstract: Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of

REPORT · WHEN · ArXiv CS.AIOPEN STORY
7 May
Deployment-Relevant Alignment Cannot Be Inferred from Model-Level Evaluation Alone

arXiv:2605.04454v1 Announce Type: new Abstract: Alignment evaluation in machine learning has largely become evaluation of models. Influential benchmarks score model outputs under

REPORT · WHAT · ArXiv CS.AIOPEN STORY
7 Sept
When Financial Fine-tuning Fails: A Three-Level Detectability Analysis of Numerical Hallucination in Domain-Adapted Language Models

arXiv:2609.04806v1 Announce Type: new Abstract: Financial large language models are increasingly deployed for summarization of reports and disclosures, where numerical

REPORT · WHERE · ArXiv CS.AIOPEN STORY
8 Apr
From Retinal Evidence to Safe Decisions: RETINA-SAFE and ECRT for Hallucination Risk Triage in Medical LLMs

arXiv:2604.05348v1 Announce Type: new Abstract: Hallucinations in medical large language models (LLMs) remain a safety-critical issue, particularly when available evidence is

REPORT · WHEN · ArXiv CS.AIOPEN STORY
13 Apr
From Dispersion to Attraction: Spectral Dynamics of Hallucination Across Whisper Model Scales

arXiv:2604.08591v1 Announce Type: cross Abstract: Hallucinations in large ASR models present a critical safety risk. In this work, we propose the \textit{Spectral Sensitivity

REPORT · WHAT · ArXiv CS.AIOPEN STORY
5 May
BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs

arXiv:2605.00422v1 Announce Type: cross Abstract: Large language models (LLMs) have driven major progress in NLP, yet their substantial memory and compute demands still hinder

REPORT · WHAT · ArXiv CS.AIOPEN STORY
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