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The author finds that Qwen3.8-27B has weaker knowledge recall compared to Qwen3.6 based on personal benchmarks and offline tests, suggesting it may not be suitable for airgapped knowledge retrieval.
A case study on how quantization affects factual knowledge in Qwen3.6 27B, showing that knowledge loss scales nonlinearly and obscure facts degrade most at low bit widths, unlike benchmark scores.
This paper introduces a decoupled evaluation paradigm to separate memory recall from actual utilization in LLM personalization, finding that agents often recall user preferences but fail to act on them in behavioral scenarios. Large-scale experiments across 16 systems and five memory architectures reveal a significant Know-Act gap, especially for health-related preferences.
Researchers trace how LLMs recall relational facts by probing per-head attention contributions, showing these are strong linear features whose fidelity correlates with relation specificity and entity connectedness.
This paper challenges the assumption that LLMs can reliably distinguish between hallucinated and factual outputs through internal signals, arguing that internal states primarily reflect knowledge recall rather than truthfulness. The authors propose a taxonomy of hallucinations (associated vs. unassociated) and show that associated hallucinations exhibit hidden-state geometries overlapping with factual outputs, making standard detection methods ineffective.