probing

This paper presents a systematic frozen-feature probing study comparing vision-language models (VLMs) and video generation models (VGMs) on spatial intelligence tasks. It finds that VLMs excel at semantic tagging and instance grouping, while VGMs provide better dense geometry and camera motion signals, and a naive fusion of both yields strong performance across all axes.

This paper proposes a Polar Probe that linearly recovers semantic structures from LLM activations by representing entity relations through distance and direction in a learned subspace. Testing across arithmetic, visual scenes, family trees, metro maps, and social interactions shows the code emerges in middle layers, generalizes to new entities, and causally influences model predictions.

This paper introduces When2Tool, a benchmark to study when LLM agents actually need to call tools, and reveals that models already know tool necessity from hidden states but fail to act. The proposed Probe&Prefill method reduces unnecessary tool calls by 48% with minimal accuracy loss.

Researchers probe language model representations to predict human reading times across five languages, finding early layers outperform surprisal for early-pass measures while surprisal remains superior for late-pass measures.