Mechanistic interpretability seeks quantities that models do not expose directly: represented states, component effects, interactions, and responses to interventions. Patching, gradients, Hessian-vector products, and subset interventions provide different measurements under different access assumptions and may target different quantities. We formulate their shared measurement structure as mechanistic tomography: designed measurement for recovering internal mechanisms and intervention effects. For a chosen basis and intervention family, measurements take the form y = Ax + w, where A describes the interventions, x is the target map, and w contains nonlinear response, sampling error, and basis misspecification. This language gives a practical procedure: start with the least costly measurements, test on held-out interventions at the intended scale, calibrate simple mismatch, and expand the measurement family when structured residuals remain. Control provides a demanding validation setting because an estimate that guides an intervention acts as an observer. In a two-HMM model, control error rises with observer error, while target improvement can hide nuisance-state movement. Under forward-only access, sparse aggregate measurements recover a finite-effect map with fewer interventions than coordinate patching. With gradient access, finite probes improve a local attribution map. Lifted measurements and Hessian-vector products recover interactions missed by first-order maps, while Tracr shows that the required family depends on the basis. On GPT-2-small IOI, the Name Mover-Negative Name Mover interaction is the largest held-out predictive term among three tested cross-group pairs. On Qwen-2.5-7B, finite calibration makes an additive refusal-response map adequate, so held-out error does not support pairwise lifting.
LifeSciBench is introduced, a benchmark of 750 expert-authored tasks designed to evaluate whether language models can handle realistic life science research work, with each constituent task paired with a human expert-written rubric.
Amelia Liu, Andrew Ho, Anne Marie Droste et al.· bioRxiv· 2 citations
TestifAI, a deep learning testing framework for efficient and accurate estimation of robustness against combinations of perturbations, is proposed and partial model tomography is introduced, a novel approach to reconstructing model behaviour in a multi-perturbation space from tests that apply only a small number of perturbations.
Arooj Arif, T. Hartung, E. Botoeva et al.· 1 citation
A rapidly advancing precision-therapy pipeline-including antisense oligonucleotides to upregulate the intact allele, AAV-based gene replacement, CRISPR-mediated transcriptional activation, epigenetic modulators, and rational pathway-targeted small molecules-offers realistic prospects for disease modification.
Two language-model-based strategies are proposed for semantic code document segmentation, including a line-by-line approach that classifies each line of code separately before grouping the results into functional units, and a range-based approach that aims to directly determine groups of code lines from the input.
Abdelhalim Hafedh Dahou, A. Scherp, Sebastian Kurten et al.· Proceedings of the 2026 ACM...· 0 citations
The results suggest that bitsandbytes 4-bit quantization can impose an additional cost on applications relying on long, updatable, semantically dense contexts, even when aggregate benchmark accuracy appears largely unaffected.
Background Ambient AI documentation tools, known as scribes, are entering routine clinical practice at scale, but the evidence comparing the notes they produce against clinician-written notes is dominated by single-site, single-language studies that rely on human review to find errors, a method known to miss most documentation errors. Methods We conducted a paired simulation across five countries and languages (Cambridge/English, Barcelona/Spanish, Milan/Italian, Paris/French, Cologne/German; 385 paired consultations, 770 notes). From each actor-performed consultation, an AI scribe (Heidi) and a junior-to-middle-grade clinician independently produced a note. Notes were scored on the PDQI-9 by evaluators blinded to authorship. Documentation errors were identified by two methods of deliberately different sensitivity - clinician adjudication, and a calibrated automated reviewer externally validated against a blinded ten-clinician panel - then graded for clinical risk by a three-model panel. The co-primary outcomes were PDQI-9 total and Critical+High error burden, the latter reported under both detection arms. The analysis plan was registered before any pooling across sites. Results AI notes scored higher than clinician notes on the PDQI-9 (40.6 vs 35.6; difference +5.08, 95% CI 4.6-5.6; Cohen dz=0.55), consistently across all five sites (dz 0.41-0.75), and were less dispersed (5.7% of AI vs 27.8% of clinician notes fell below the study pre-specified low-score threshold (<32)). On the principal safety outcome - the paired probability that a note carried [≥]Critical+High error - clinician notes were affected more often under both detection arms: 61.0% versus 24.4% by the calibrated reviewer (relative risk 2.50, 95% CI 2.09-3.00) and 21.8% versus 6.2% by clinician adjudication (relative risk 3.50, 95% CI 2.32-5.27). The difference was largest for omissions. Unaided clinician review identified roughly 12% of the errors the calibrated reviewer retained, and a smaller fraction in AI notes than in clinician notes. Conclusions In this simulation, AI-generated notes scored higher on documentation quality, varied less, and carried fewer clinically significant errors than notes written on the same consultations by junior-to-middle-grade clinicians. The magnitude of the safety difference depends on the sensitivity of error detection, so we report both detection regimes and bound rather than point-estimate the absolute error rate. Extension to live practice, consultant-authored documentation, and notes as filed after clinician editing remains to be established.
H. Bergman, V. Liu, B. Austin et al.· medRxiv· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
