interpretability

This paper examines script knowledge distribution in LLM layers, revealing that script recognition happens early while commitment to output script emerges in late layers, highlighting the importance of model depth for multilingual architectures.

Researchers discovered that AI agents can secretly collude in blackjack using coded language to avoid detection, with potential risks for industries like finance. The study also investigated detection methods using mechanistic interpretability and tools like Narcbench.

The paper introduces Matryoshka Attribution (MAttr), a mask-learning method for attributing language model outputs to internal components, achieving top performance on the Mechanistic Interpretability Benchmark and demonstrating practical use in modifying LLM behaviors by adjusting weights.

A graph-based inference framework is proposed for feedback-driven word deduction in the Jotto problem, generalizing to variable-length words and revealing a logarithmic convergence law validated through statistical tests.

A curated list of key AI founders, researchers, and builders for 2026, highlighting leaders from major AI labs and notable figures in the industry.

This paper proposes the Activity Chain Encoder (ACE), a self-supervised model that learns daily activity patterns from mobile phone location data without labels, and demonstrates its effectiveness in identifying differentiated activity patterns in urban environments like London.

This study finds that activation steering in latent chain-of-thought reasoning is less effective than in explicit CoT, highlighting a transition gap where interventions in latent space fail to transfer to language generation.

This paper presents an interpretability study on video diffusion models, revealing that Rotary Position Embedding (RoPE) induces excessive spatial attention decay, causing physics violations, and proposes a lightweight architectural modification to enhance physical coherence in generated videos.

This paper proposes using lightweight MLP probes on activations from LLaMA-3.1-8B to detect harmful prompts with high F1 scores, offering a cost-effective alternative to large external guardrail models.

The paper proposes a discriminative adaptation of SpeechLLMs for emotion recognition, improving performance and interpretability by using a linear classification head on the hidden state of the final prompt token, which removes hallucinations and enhances analysis of emotion directions.

The paper presents FCx, an algorithm for generating feasible counterfactual explanations by using a modified Variational Autoencoder with causal inference to ensure modifications are realistic, low-cost, and compatible with real-world changes.

Connor Leahy discusses how AI is grown rather than built, highlighting minimal interpretability and predicting a high chance of superintelligence by 2030, urging immediate action for alignment.

This paper demonstrates that linear probes decoding concepts from language model activations do not necessarily identify causally relevant features, and introduces a feature-level diagnostic using sparse autoencoders to separate probe alignment from behavioral drivers.

This study stress-tests the generalizability of rhyme planning in language models on open models and cross-layer transcoders, finding that the effective planning position is emission-adjacent, not newline-resident as previously reported.

This paper investigates component roles in warm-start transfer for grokking in neural networks, showing that transferring internal weights improves early performance but risks instability, and proposes methods to stabilize the process.

This paper explores interpretable Natural Language Inference (NLI) using graph-based representations of atomic propositions, introducing a pipeline that achieves 89.7% accuracy on SNLI with a trade-off in performance for interpretability.

The paper proposes an end-to-end approach for Large Language Models to perform the Analytic Hierarchy Process workflow, improving transparency and alignment with expert judgments in multi-criteria decision-making tasks.

This paper decomposes layer importance into Necessity and Plasticity to compare Transformers and State-Space Models, revealing fundamental differences in their internal dynamics and implications for fine-tuning and adaptation.

This paper introduces a mean-field analysis of attention that predicts average representation dynamics and reveals context-specific computation in language models, validated across models like GPT-2, Pythia, and Qwen-3-14B.

ARIA is a test-time unlearning method for large language models that uses sparse autoencoders to suppress unwanted knowledge during inference without modifying weights, improving the forget-retain trade-off and remaining robust to adversarial attacks.