aiqbench analysis
This research introduces Concept-Targeted Attribution (CTA), a new circuit-tracing method that decouples internal concept representations from generated tokens to better explain how linear probes emerge in large language models. By demonstrating that probe-specific circuits predict accuracy across diverse categories, the study advances mechanistic interpretability beyond traditional logit-based explanations. This work provides a critical framework for auditing internal model computations, particularly for safety-critical applications where understanding latent concepts is essential.
How Do Linear Probes Emerge? A Circuit-Tracing Framework with Concept-Targeted Attribution
arXiv:2608.27510v1 Announce Type: cross Abstract: Transcoder attribution graphs are usually trained to explain why a model assigns high probability to a particular next token. We introduce Concept-Targeted Attribution (CTA), which instead trains attribution graphs with respect to a linear probe direction. CTA therefore yields probe-specific circuits that explain why an internal concept representation arises in a prompt, independently of whether it is expressed in the generated token. Using Cross-Layer Transcoders, we show that these probe-targeted graphs contain predictive structure: graph-level features predict probe accuracy across four widely studied concept categories ($\rho = 0.91$, $R^2 = 0.84$), while local features identify the sparse components driving per-prompt classification. This connects probe performance to interpretable circuit structure, allowing us to ask not only whether a probe works, but which internal computations make it work. Causal ablations further show that probe-targeted and logit-targeted graphs capture functionally distinct mechanisms. Removing probe-relevant features reduces internal concept scores while largely preserving generated tokens, whereas removing logit-relevant features changes the generated token in 92% to 100% of cases with near-zero effect on probe scores. CTA provides a framework for moving from behavioral probe accuracy to mechanistic explanations of probe performance, enabling more detailed audits of internal concept representations, including safety-critical ones. Our code is available at https://github.com/vedantpalit/concept-targeted-attribution
Title:How Do Linear Probes Emerge? A Circuit-Tracing Framework with Concept-Targeted Attribution
Abstract:Transcoder attribution graphs are usually trained to explain why a model assigns high probability to a particular next token. We introduce Concept-Targeted Attribution (CTA), which instead trains attribution graphs with respect to a linear probe direction. CTA therefore yields probe-specific circuits that explain why an internal concept representation arises in a prompt, independently of whether it is expressed in the generated token. Using Cross-Layer Transcoders, we show that these probe-targeted graphs contain predictive structure: graph-level features predict probe accuracy across four widely studied concept categories ($\rho = 0.91$, $R^2 = 0.84$), while local features identify the sparse components driving per-prompt classification. This connects probe performance to interpretable circuit structure, allowing us to ask not only whether a probe works, but which internal computations make it work. Causal ablations further show that probe-targeted and logit-targeted graphs capture functionally distinct mechanisms. Removing probe-relevant features reduces internal concept scores while largely preserving generated tokens, whereas removing logit-relevant features changes the generated token in 92% to 100% of cases with near-zero effect on probe scores. CTA provides a framework for moving from behavioral probe accuracy to mechanistic explanations of probe performance, enabling more detailed audits of internal concept representations, including safety-critical ones. Our code is available at this https URL
Accepted to EMNLP 2026 (Main Conference). 29 pages, 18 figures, 9 tables
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2608.27510 [cs.CL]
(or arXiv:2608.27510v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.27510 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 327 words
