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This paper introduces a multi-stage rule-chaining framework for compositional and interpretable cognitive reasoning on the Abstraction and Reasoning Corpus (ARC) benchmark, achieving over 95% accuracy through integrated deterministic solvers and hierarchical abstraction.
The paper introduces Structurally Speaking, a structured prompting protocol for motif-oriented graph captioning that improves compactness and consistency while maintaining graph recovery compared to direct prompting of LLMs.
MonoTM is an interpretable topic modeling framework that decouples mixture estimation from feature interpretation using sparse autoencoders, providing semantically meaningful topics beyond traditional word-based representations.
BioGlyph is a method that translates network topology into interpretable language roles to enhance large language models' ability to reason about complex networks, demonstrating significant performance improvements across various domains.
Lngram v2 introduces an efficient and scalable latent conditional memory mechanism for transformers, decoupling memory capacity from backbone width and demonstrating consistent improvements in vision-language models through discrete addresses and structured representations.
CAHR-Net proposes a condition-adaptive hysteresis reconstruction network that improves magnetic core loss modeling by injecting operating conditions into intermediate representations, achieving lower errors with fewer parameters compared to existing methods.
OpenAI's upcoming Astra model reportedly uses looped transformers without an interpretable chain of thought, indicating faster AI progress than predicted by AI 2027 forecasts.
The paper introduces a transparent framework that maps acoustic speech features to DSM-5 depression indicators for interpretable detection, running locally on commodity hardware to preserve privacy.
This paper introduces ShuttleArena, a physics-based self-play environment for badminton where agents learn interpretable tactical policies using PPO, showing competitive improvement in shot selection and recovery.
This paper proposes a structural variant of the Factorial Hidden Markov Model to analyze and interpret disease trajectories in Type 2 diabetes patients using electronic health records, revealing clinically meaningful patterns and progression pathways.
HiMA-MDD introduces a hierarchical multi-agent system for interpreting multimodal clinical interviews to detect depression, achieving state-of-the-art performance on the E-DAIC dataset.
The paper introduces J64 and R64, interpretable readout methods for reasoning-states in mixture-of-experts AI models, to enhance test-time decision-making and accuracy.
Structured Prior Knowledge (SPK) is a framework that explicitly extracts latent semantic, geometric, and contextual priors from pretrained object detectors to achieve state-of-the-art out-of-distribution detection, improving interpretability and reliability.
PIKFNO is a new interpretable neural operator framework that integrates physics-informed kernel functions from governing equations to enhance predictive accuracy and interpretability with limited training data.
ORCA is a two-stage framework using contrastive learning and autoencoders for interpretable anomaly detection in collider experiments, enhancing sensitivity and interpretability for new physics searches.
This paper introduces NLP Psychometrics, a framework that treats psychological prediction from text as a psychometric problem. Using LLM personas, emotional profiles, and syntactic-semantic networks with random forest regressors, it explains up to 76% of variance in mental health scores and shows promise and limits of synthetic data for psychometric prediction.
This paper presents a Mass-Conserving Perceptron (MCP) framework that reformulates conceptual hydrologic models into physically constrained, interpretable neural networks, evaluated across 513 CAMELS-US basins for snow-water representation.
Proposes SymCA, an interpretable column annotation framework using LLMs to materialize annotation as a global-to-local symbolic decision process, achieving significant accuracy improvements over baselines.
This paper investigates the use of large language models (LLMs) and supervised classifiers for depression detection from social media text, proposing a prompt-based embedding method that enhances interpretability. Experiments on multiple datasets show that zero-shot LLMs perform well for binary classification but struggle with fine-grained severity, while supervised models on LLM summary embeddings achieve more consistent performance across multi-class and ordinal tasks.
Proposes using neural predicates to generate structured probabilistic views for the Black-Litterman model, improving reproducibility and interpretability in portfolio construction.