VQV Signal
Lingtai introduces a method to observe large language model computations during autoregressive inference without training probes by projecting residual states onto a domain-specific bank of concept anchors. This approach requires no labeled examples, outcome labels, gradient fitting, or activation-...
Lingtai introduces a method to observe large language model computations during autoregressive inference without training probes by projecting residual states onto a domain-specific bank of concept anchors. This approach requires no labeled examples, outcome labels, gradient fitting, or activation-...
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Observing what a large language model computes during autoregressive inference--online and without training probes--remains difficult. We introduce Lingtai, a training-free concept telemetry layer: at each generation step, residual states are projected onto a domain-specific bank of named concept anchors,...
Understanding LLM inference in real-time without additional training or probes can improve interpretability and transparency of model behavior. Lingtai's training-free concept telemetry offers a new tool for analyzing model computations step-by-step during generation.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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