Interpretability & Mechanistic Interp - Weekly Roundup

LLM decoder states exhibit distinct informational and geometric compartments, enabling principled layer selection for speculative decoding that surpasses heuristic approaches.

MLLMs possess an intrinsic "S-Space" that acts as a spatial workspace, encoding object coordinates and enabling viewpoint transformations, even under non-spatial queries.

Source-side training dynamics, not just current values or training time, prospectively predict OOD degradation, even across model architectures.

Standard Shapley values are brittle; interval-valued Shapley values reveal their true robustness landscape in tree models.

A score-based method accelerates cluster DAG learning from interventions by two orders of magnitude, matching state-of-the-art edge recovery even on dense graphs.

LLM decoder states exhibit distinct informational and geometric compartments, enabling principled layer selection for speculative decoding that surpasses heuristic approaches.

MLLMs possess an intrinsic "S-Space" that acts as a spatial workspace, encoding object coordinates and enabling viewpoint transformations, even under non-spatial queries.

Source-side training dynamics, not just current values or training time, prospectively predict OOD degradation, even across model architectures.

Standard Shapley values are brittle; interval-valued Shapley values reveal their true robustness landscape in tree models.

A score-based method accelerates cluster DAG learning from interventions by two orders of magnitude, matching state-of-the-art edge recovery even on dense graphs.

Unifying factual support, counterfactual sensitivity, and exemplar tolerance reveals how GNNs truly reason about molecular properties, not just predict them.

Interpretable archetypes emerge from discrete data using a principled likelihood framework, unlocking insights in genomics and microbiome analysis previously inaccessible to standard AA.

Dynamic critic pessimism, guided by geometric disagreement, unlocks superior locomotion learning and efficiency in off-policy actor-critic methods.

Spectral analysis reveals that increasing latent dimensions in Koopman autoencoders improves dynamics learning by concentrating learned eigenvalues in low-residual regions, not just by reducing one-step prediction error.

Probes can now detect subtle, unverbalized LLM deception with near-perfect accuracy, even when the model's true intentions are hidden from external observation.

LLMs can be steered to reliably interpret complex genomic variants by grounding their reasoning in deterministic biological computations and evidence verification, avoiding hallucinated TF binding changes and fabricated experimental support.

Neural networks can learn interpretable, polytope-based latent representations that preserve performance and enable direct VQ training.

LLMs can attribute responsibility and blame, but their reasoning is incomplete and varies with model scale, with specific attribution dimensions demonstrably encoded in their latent spaces.

A 10x increase in AI time horizon is significantly easier for AI development in the 2-30 minute task range than for tasks outside this sweet spot.

Action chunking unlocks temporally interpretable decision trees that match neural network performance with up to 80% fewer parameters.

Most recalibration methods fail to guarantee calibration for critical outcomes, but this new approach ensures predictions are trustworthy even when focusing on rare or extreme events.

Most gradient-based methods fail to reliably attribute learned behaviors in online RL, with simple fluency metrics often proving more predictive than complex attribution techniques.

Input-blind controls can account for substantial oracle headroom in adaptive computation, challenging the assumption that observed gains directly reflect the efficacy of selected layer programs.

Later Transformer layers in tabular foundation models aren't always the smartest; revisiting earlier thoughts dramatically boosts performance.

Intrusion detectors confidently misclassify unseen attacks, but PairAudit's relational graph tokens uncover these overlooked errors, boosting human review accuracy by 30% on novel threats.

Gaussian regularizers like SIGReg can certify InfoNCE's optimal solutions but diverge significantly in their behavior away from the optimum, especially regarding nuisance channels and style retention.

AI malfunctions, like Clever Hans effects, can be directly traced to specific artifact patterns in the input data using a model-agnostic dictionary of interpretable concepts.

LLM-generated rules, not full trees, unlock efficient and interpretable few-shot tabular classification.

Most alignment model organisms degrade core capabilities and naturalness during training, undermining interpretability findings; a new multi-objective approach preserves these crucial aspects.

A GP's ability to fit data is predictable from its input design alone, revealing potential contradictions before any responses are observed.