The SAIRC Journal

Background: Migraine is a highly prevalent neurological disorder characterized by an unpredictable prodromal phase, during which subtle physiological and behavioral changes precede headache onset. Advances in digital phenotyping and artificial intelligence (AI) offer the potential to detect these changes through passive data collected from smartphones and wearable devices. Methods: This study investigated the feasibility of predicting migraine onset using machine learning models trained on digital behavioral biomarkers. A publicly available dataset comprising 11,879 daily observations from 100 users (January–August 2024) was analyzed. Features included sleep duration, screen time, stress, mood, hydration, and engineered temporal variables such as lag features, rolling averages, and composite risk scores. Logistic Regression, Random Forest, and Gradient Boosting models were developed and evaluated using ROC-AUC, average precision, precision, recall, and five-fold cross-validation. Additionally, a primary survey explored self-reported changes in typing speed and typing errors during the migraine prodromal phase. Results: All three models demonstrated predictive performance above chance, with Random Forest achieving the strongest overall performance (ROC-AUC = 0.674; accuracy = 61.8%). Temporal behavioral patterns were more informative than same-day measurements, with previous-day stress, sleep duration, and composite risk scores emerging as the most influential predictors. Survey findings indicated that approximately 29% of participants perceived slower typing before migraine onset, supporting typing dynamics as a potential digital biomarker, although most participants were unable to reliably recognize prodromal changes without objective monitoring. Conclusion: AI-based analysis of digital biomarkers can identify meaningful behavioral patterns preceding migraine attacks and may enable passive early-warning systems. Although current predictive accuracy is insufficient for standalone clinical use, integrating longitudinal digital phenotyping with explainable, personalized machine learning models could substantially improve proactive migraine management and reduce disease burden.

This research article presents a novel theoretical framework designed to model and experimentally evaluate synthetic consciousness via a dual-layer cognitive architecture. The proposed paradigm bifurcates foundational computational logic from subjective interpretation, establishing an empirical environment capable of simulating an internal observer within artificial systems. Drawing from contemporary philosophy of mind regarding the "Hard Problem of Consciousness," we argue that current artificial intelligence infrastructures remain fundamentally restricted to symbolic computation, lacking qualitative experiential awareness (qualia). The framework introduces a functional separation: an Outer Layer executing high-throughput computational logic and environmental interactions, and an Inner Layer serving as an isolated experiential observer that interprets internal system states and operational anomalies. By decoupling these modalities, the architecture constructs a digital analog to subjective oversight. Furthermore, this model is contextualized alongside the Vedic metaphysical framework of the Pancha Koshas (Five Sheaths of Existence). Finally, we propose concrete empirical metrics—Unpredictable Choice Variance, Evolutionary Self-Preservation Response, and Cognitive Dissonance Latency—to differentiate genuine internal awareness from sophisticated behavioral simulation. This interdisciplinary framework aims to bridge the historical divides separating neuroscience, artificial intelligence engineering, philosophy of mind, and ancient metaphysical traditions.

Semiconductor wafer manufacturing is highly susceptible to defects that can reduce production yield and increase manufacturing costs. This study investigates the use of machine learning classification models to predict defective semiconductor wafers at an early stage of production using the SECOM dataset from the University of California Irvine. Several classification approaches, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Gradient Boosting, and XGBoost, were evaluated alongside imbalance-handling techniques such as SMOTE oversampling and cost-sensitive learning. Model performance was assessed using precision, recall, F1-score, accuracy, and AUC metrics. Results showed that baseline models achieved high overall accuracy due to the dominance of non-defective samples but performed poorly in detecting defective wafers. Hybrid approaches using SMOTE and class balancing slightly improved minority-class recall, although defect detection performance remained limited because of the severe class imbalance within the dataset. The findings highlight both the challenges and potential of AI-based approaches for semiconductor defect prediction and suggest that future improvements should focus on advanced resampling methods, imbalance-specific algorithms, and ensemble learning techniques to enhance predictive reliability in manufacturing environments.

Modern e-commerce ecosystems generate massive volumes of high-velocity behavioral data, yet extracting actionable purchasing intent remains a significant challenge due to extreme class imbalance and the non-linear nature of human decision-making. In a typical retail environment, over 96% of digital footprints represent non-conversion events, creating approximately a 24:1 ratio that frequently leads to majority-class bias in traditional Machine Learning models. This investigation presents a mathematically grounded, Context-Aware Three-Tower Hybrid Deep Learning Architecture designed to unify disparate behavioral modalities into a single predictive framework. Tower 1 (Collaborative Domain) utilizes high-dimensional Embedding Layers to map visitor and item identifiers into a shared latent space. Tower 2 (Sequential Domain) processes the user's recent browsing trajectory using Global Average Pooling to synthesize short-term micro-intent. Tower 3 (Contextual Domain) integrates cyclical Sine and Cosine temporal embeddings to capture the periodic nature of shopping behavior. Standard Binary Cross-Entropy loss is replaced with Focal Loss (γ = 2.0, α = 0.25), which down-weights easily classified negative examples and focuses optimization on genuine purchasing signals. Experimental results on the 2.7 million-row Retailrocket dataset demonstrate a Validation AUC-ROC of 88.7%. An Ablation Study confirms synergistic uplift: the fully integrated Hybrid Engine achieved 36.83% prediction confidence, representing a substantial absolute gain over isolated Collaborative (29.31%) and Content-based (31.58%) baselines.

Full credit goes to the original author, linked below. All research papers were reposted either with permission of the author, or by anonymous submission by SAIRC members like yourself. We introduce an autonomous multiagent framework for mechanistic interpretability that automates both explaining and finding internal features in large language models. The system runs two coupled loops: (1) explanation refinement, where an agent proposes competing hypotheses and iteratively tests them with targeted prompt controls and a multi-metric evaluation; and (2) feature discovery, where an agent generates prompt sets, constructs a k-nearest-neighbor graph in activation space, and retrieves candidate features using statistical separability and semantic coherence criteria. On Gemma-2 family models and MLP neurons in weight-sparse transformers, our agent improves over one-shot auto-interpretations, discovers language-specific and safety-relevant features, and produces auditable explanation traces, showing that agent-driven empirical loops yield sharper and more falsifiable explanations than one-shot labels.

A central question in computational neuroscience is whether the learning rule used to train a neural network determines how well its internal representations align with those of the human visual cortex. We present a systematic comparison of four learning rules - backpropagation (BP), feedback alignment (FA), predictive coding (PC), and spike-timing-dependent plasticity (STDP) applied to identical convolutional architectures and evaluated against human fMRI data from the THINGS-fMRI dataset (720 stimuli, 3 subjects) using Representational Similarity Analysis (RSA). Crucially, we include an untrained random-weights baseline that reveals the dominant role of architecture. We find that early visual alignment (V1/V2) is primarily architecture-driven: an untrained CNN achieves ρ = 0.071, statistically indistinguishable from BP (ρ = 0.072, p = 0.43). Learning rules only differentiate at higher visual areas: BP dominates at LOC/IT (ρ = 0.018–0.020, d > 2.3 vs. random), and PC with local Hebbian updates achieves IT alignment statistically indistinguishable from BP (p = 0.18). FA consistently impairs representations below the random baseline at V1 (d = 1.1). Partial RSA confirms all effects survive pixel-similarity control. These results demonstrate that the relationship between learning rules and cortical alignment is region-specific: architecture determines early alignment, while supervised objectives drive late alignment.

Reproduced with permission from Chris Zhu et al. We present an in-depth evaluation of LLMs' ability to negotiate, a central business task that requires strategic reasoning, theory of mind, and economic value creation. To do so, we introduce PieArena, a large-scale negotiation benchmark grounded in multi-agent interactions over realistic scenarios drawn from an MBA negotiation course at an elite business school. We develop a statistically grounded ranking model for continuous negotiation payoffs that produces leaderboards with principled confidence intervals and corrects for experimental asymmetries. We find systematic evidence of human-expert-level performance in which a representative frontier language agent (GPT-5) matches or outperforms trained business-school students, despite a semester of general negotiation instruction and targeted coaching immediately prior to the task. We further study the effects of joint-intentionality agentic scaffolding and observe asymmetric gains, with large improvements for mid- and lower-tier LMs and diminishing returns for frontier LMs. Beyond deal outcomes, PieArena provides a multi-dimensional negotiation behavioral profile, revealing novel cross-model heterogeneity, masked by deal-outcome-only benchmarks, in deception, computation accuracy, instruction compliance, and perceived reputation. Overall, our results suggest that frontier language agents are already intellectually and psychologically capable of deployment in high-stakes economic settings, but deficiencies in robustness and trustworthiness remain open challenges.

As deep-learning frameworks grow more capable, understanding the mechanisms behind their emergent behaviors is critical for safety. Mechanistic interpretability seeks to reverse-engineer the internal representations and algorithms that produce model behaviors. One method for interpretability is 'circuit analysis,' which interprets neural networks through subsets of their nodes and edges that replicate the network's behavior. Recent work has demonstrated that multiple, non-unique circuits can implement the same input–output mappings within a network, making the source of a network's behavior non-identifiable (Méloux et al., 2025). However, the conditions under which functionally equivalent circuits arise remain poorly understood. In this work, we train small multi-layer perceptrons on Boolean tasks, varying architectural and training choices to identify conditions under which multiple, functionally equivalent circuits emerge. We find that methods inducing sparsity, such as L1 regularization and orthogonalization, show a weak correlation with uniqueness, whereas increasing hidden layer size leads to a significant increase in the number of equivalent circuits. Finally, we show that task complexity is a strong predictor of non-uniqueness in networks: reductions in task complexity collapse networks to one causal pathway with increasingly redundant circuits. Our findings offer guidance for researchers in determining when circuit-based explanations can uniquely identify the source of a network's behavior.

Reproduced for educational purposes with permission from Nikhil Anand et. al. Large Language Models (LLMs) encode vast amounts of parametric knowledge during pre-training. As world knowledge evolves, effective deployment increasingly depends on their ability to faithfully follow externally retrieved context. When such evidence conflicts with the model's internal knowledge, LLMs often default to memorized facts, producing unfaithful outputs. In this work, we introduce ContextFocus, a lightweight activation steering approach that improves context faithfulness in such knowledge-conflict settings while preserving fluency and efficiency. Unlike prior approaches, our solution requires no model finetuning and incurs minimal inference-time overhead, making it highly efficient. We evaluate ContextFocus on the ConFiQA benchmark, comparing it against strong baselines including ContextDPO, COIECD, and prompting-based methods. Furthermore, we show that our method is complementary to prompting strategies and remains effective on larger models. Extensive experiments show that ContextFocus significantly improves contextual-faithfulness. Our results highlight the effectiveness, robustness, and efficiency of ContextFocus in improving contextual-faithfulness of LLM outputs.

Full credit goes to the original author, linked below. All research papers were reposted either with permission of the author, or by anonymous submission by SAIRC members like yourself. Neuron identification is a popular tool in mechanistic interpretability, aiming to uncover the human-interpretable concepts represented by individual neurons in deep networks. While algorithms such as Network Dissection and CLIP-Dissect achieve great empirical success, a rigorous theoretical foundation remains absent, which is crucial to enable trustworthy and reliable explanations. In this work, we observe that neuron identification can be viewed as the inverse process of machine learning, which allows us to derive guarantees for neuron explanations. Based on this insight, we present the first theoretical analysis of two fundamental challenges: (1) Faithfulness: whether the identified concept faithfully represents the neuron's underlying function and (2) Stability: whether the identification results are consistent across probing datasets. We derive generalization bounds for widely used similarity metrics (e.g. accuracy, AUROC, IoU) to guarantee faithfulness, and propose a bootstrap ensemble procedure that quantifies stability along with BE (Bootstrap Explanation) method to generate concept prediction sets with guaranteed coverage probability. Experiments on both synthetic and real data validate our theoretical results and demonstrate the practicality of our method, providing an important step toward trustworthy neuron identification.

Full credit goes to the original author, linked below. All research papers were reposted either with permission of the author, or by anonymous submission by SAIRC members like yourself. Large language models (LLMs) require precise behavior control for safe and effective deployment across diverse applications. Activation steering offers a promising approach for LLMs' behavioral control. We focus on the question of how steering effectiveness varies across different behavior types and whether the nature of target behaviors can predict steering success. We address this through empirical analysis of activation steering across 50 behaviors that span persona archetypes, personality traits, misalignment behaviors, style cues, and impersonation of public figures. We present a set of comprehensive experiments on coefficient optimization, vector properties, and data requirements to provide comprehensive guidance for the implementation of activation steering. Our analysis demonstrates that steering effectiveness varies significantly by behavior type, with different behavioral categories exhibiting distinct response patterns to intervention strength. We find that trait expression follows an inverted-U curve with a steering coefficient strength. We also show that vector separation metrics do not predict steering success, but larger training datasets enable more aggressive steering. These findings provide empirically grounded guidance for implementing activation steering and demonstrate that steering effectiveness is heavily influenced by behavior type.

Full credit goes to the original author, linked below. All research papers were reposted either with permission of the author, or by anonymous submission by SAIRC members like yourself. What do thinking language models learn during training that their base models lack? We first present an unsupervised method that discovers a model's reasoning behaviors by training small Sparse Autoencoders on sentence-level activations of reasoning traces, yielding interpretable reasoning taxonomies. Building on this, we introduce constructive model diffing, which aims to reconstruct the base-to-fine-tuned difference from interpretable components: reasoning mechanisms (category vectors that can induce a reasoning behavior in the base model) and reasoning heuristics (a classifier determining when a mechanism should fire). Across nine base/thinking pairs (four RL-trained, four SFT-distilled, one mixed), two independent findings agree: category vectors in the base model converge to far lower loss for taxonomies derived from purely RL-trained models, and hybrid models recover roughly 76% of the RL base-to-thinking gap but only 11% of the SFT gap. This indicates RL primarily teaches heuristics for orchestrating pre-existing base mechanisms, whereas SFT-distillation installs new ones, offering a new lens on what training paradigms teach, with implications for efficient reasoning-model development.

Full credit goes to the original author, linked below. All research papers were reposted either with permission of the author, or by anonymous submission by SAIRC members like yourself. The Circuit Localization track of the Mechanistic Interpretability Benchmark (MIB) evaluates methods for localizing circuits within large language models (LLMs), i.e., subnetworks responsible for specific task behaviors. In this work, we investigate whether ensembling two or more circuit localization methods can improve performance. We explore two variants: parallel and sequential ensembling. In parallel ensembling, we combine attribution scores assigned to each edge by different methods-e.g., by averaging or taking the minimum or maximum value. In the sequential ensemble, we use edge attribution scores obtained via EAP-IG as a warm start for a more expensive but more precise circuit identification method, namely edge pruning. We observe that both approaches yield notable gains on the benchmark metrics, leading to a more precise circuit identification approach. Finally, we find that taking a parallel ensemble over various methods, including the sequential ensemble, achieves the best results. We evaluate our approach in the BlackboxNLP 2025 MIB Shared Task, comparing ensemble scores to official baselines across multiple model-task combinations.

Full credit goes to the original author, linked below. All research papers were reposted either with permission of the author, or by anonymous submission by SAIRC members like yourself. We study length generalization in sequence models on a composite problem involving both state tracking and associative recall. Prior work finds that recurrent networks handle state tracking well but struggle with recall, whereas Transformers excel at recall yet fail to extend state-tracking capabilities to longer sequences. Motivated by the complementary strengths of these architectures, we construct hybrid models integrating recurrent and attention-based components, and train them on the combined task to evaluate whether both capabilities can be preserved. Our results reveal that, in such hybrids, the Transformer component tends to exploit shortcut solutions, leading to poor length generalization. We identify this shortcut reliance as a key obstacle and propose a simple yet effective training strategy -- delaying the training of the attention layers -- that mitigates this effect and significantly improves length generalization performance. Our experiments show that this approach enables hybrid models to achieve near-perfect accuracy ($>90\%$) on hybrid sequences three times longer than those used during training.

Full credit goes to the original author, linked below. All research papers were reposted either with permission of the author, or by anonymous submission by SAIRC members like yourself. Sparse autoencoders (SAEs) have recently emerged as a powerful tool for language model steering. Prior work has explored top-k SAE latents for steering, but we observe that many dimensions among the top-k latents capture non-semantic features such as punctuation rather than semantic attributes like instructions. To address this, we propose focusing on a single, most relevant SAE latent (top-1), eliminating redundant features. We further identify a limitation in constant SAE steering, which often produces degenerate outputs such as repetitive single words. To mitigate this, we introduce a token-wise decaying steering strategy, enabling more faithful comparisons with mean activation difference baselines. Empirically, we show that steering an SAE latent associated with reasoning reliably elicits step-by-step mathematical reasoning and enhances inference quality, functionally resembling the effect of appending a guiding token. Our results demonstrate that SAEs outperform mean activation difference methods on mathematical reasoning benchmarks and match their performance on IF-Eval.