As a relatively new forum, ACM FAccT has become a key space for activists and scholars to critically examine emerging AI and ML technologies. It brings...
Conversational agents are increasingly deployed in knowledge-intensive settings, where correct behavior depends on retrieving and applying domain-specif...
Tropical cyclone (TC) intensity forecasting remains challenging as current numerical and AI-based weather models fail to satisfactorily represent extrem...
Kurtosis-based Independent Component Analysis (ICA) weakens in wide, balanced mixtures. We prove a sharp redundancy law: for a standardized projection w...
Next-generation networks aim to provide performance guarantees to real-time interactive services that require timely and cost-efficient packet delivery....
A dataset server must often distribute the same large payload to many clients, incurring massive communication costs. Since clients frequently operate o...
Large language models are beginning to show steganographic capabilities. Such capabilities could allow misaligned models to evade oversight mechanisms....
Dirac-Frenkel instantaneous residual minimization evolves nonlinear parametrizations of PDE solutions in time, but ill-conditioning can render the param...
This paper proposes a novel distributed semismooth Newton based augmented Lagrangian method for solving a class of optimization problems over networks,...
WebGIS development requires rigor, yet agentic AI frequently fails due to five large language model (LLM) limitations: context constraints, cross-sessio...
We propose a minimal agentic baseline that enables systematic comparison across different AI-based theorem prover architectures. This design implements...
Pre-trained vision encoders like DINOv2 have demonstrated exceptional performance on unimodal tasks. However, we observe that their feature representati...
Despite advances in machine learning-based medical image classifiers, the safety and reliability of these systems remain major concerns in practical set...
In Orabona and Pál [2016], we introduced the shifted KT potentials, to remove the $\ln \ln T$ factor in the parameter-free learning with expert bound. I...
Causal inference is essential for data-driven decision-making, as it aims to uncover causal relationships from observational data. However, identifying...
Causal discovery from observational data remains a fundamental challenge in machine learning and statistics, particularly when variables represent inher...
We develop a predictive-first optimisation framework for streaming hidden Markov models. Unlike classical approaches that prioritise full posterior reco...
Generative virtual staining (VS) models for high-throughput screening (HTS) can provide an estimated posterior distribution of possible biological featu...
Continual post-training of generative models is widely used, yet a principled understanding of when and why forgetting occurs remains limited. We develo...
Machine-learning interatomic potentials (MLIPs) have advanced rapidly, with many top models relying on strong physics-based inductive biases. However, a...
The surge in reinforcement learning (RL) applications gave rise to diverse supporting technology, such as RL frameworks. However, the architectural patt...
Stein's identity is a fundamental tool in machine learning with applications in generative models, stochastic optimization, and other problems involving...
Tool-augmented AI agents substantially extend the practical capabilities of large language models, but they also introduce security risks that cannot be...
While diffusion models have emerged as a powerful class of generative models, their learning dynamics remain poorly understood. We address this issue fi...
The rapid adoption of the Internet of Medical Things (IoMT) is transforming healthcare by enabling seamless connectivity among medical devices, systems,...
Academic integrity continues to face the persistent challenge of examination cheating. Traditional invigilation relies on human observation, which is in...
Finite-context models (FCMs) are widely used for compressing symbolic sequences such as DNA, where predictive performance depends critically on the cont...
We study the problem of training diffusion and flow generative models to sample from target distributions defined by an exponential tilting of a base de...
Calibration - the problem of ensuring that predicted probabilities align with observed class frequencies - is a basic desideratum for reliable ML prediction.
Research in AI using Large-Language Models (LLMs) is rapidly evolving, and the comparison of their performance with human reasoning has become a key con...
Data assimilation (DA) combines model forecasts and observations to estimate the optimal state of the atmosphere with its uncertainty, providing initial...
Computational models support high-stakes decisions across engineering and science, and practitioners increasingly seek probabilistic predictions to quan...
In this article, bipartite ranking, a statistical learning problem involved in many applications and widely studied in the passive context, is approache...
A novel regularization technique, AdaCubic, is proposed that adapts the weight of the cubic term. The heart of AdaCubic is an auxiliary optimization pro...
In this paper, we provide the first investigation into adaptive combinatorial experimental design, focusing on the trade-off between regret minimization...
Minimising a spectral risk objective, defined as a convex combination of expected cost and Conditional Value-at-Risk (CVaR), is challenging when the unc...
Large vision--language models (VLMs) are increasingly applied to long-video question answering, yet inference is often bottlenecked by the number of inp...
In multi-fidelity optimization, biased approximations of varying costs of the target function are available. This paper studies the problem of optimizin...
We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within t...
Transformer attention is typically implemented using softmax normalization, which enforces attention weights with unit sum normalization. While effectiv...
While Multi-Agent Systems (MAS) excel in complex reasoning, they suffer from the cascading impact of erroneous information generated by individual parti...
The complexity of learning a concept class under Gaussian marginals in the difficult agnostic model is closely related to its $L_1$-approximability by l...
Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipeline with min...
Many software systems originate as prototypes or minimum viable products (MVPs), developed with an emphasis on delivery speed and responsiveness to chan...
As forgery types continue to emerge consistently, Incremental Face Forgery Detection (IFFD) has become a crucial paradigm. However, existing methods typ...
We propose a novel amortized optimization method for predicting optimal transport (OT) plans across multiple pairs of measures by leveraging Kantorovich...
In recent years, physics-informed neural networks (PINNs) have gained significant attention for solving differential equations, although they suffer fro...
Getting a real cybersecurity risk assessment for a small organization is expensive -- a NIST CSF-aligned engagement runs $15,000 on the low end, takes w...
Federated learning (FL) is an effective paradigm for distributed environments such as the Internet of Things (IoT), where data from diverse devices with...
Direct Preference Optimization (DPO) is widely used after supervised fine-tuning (SFT) to align language models, yet empirical behavior under small back...
Kimi K2.5 is an open-weight LLM that rivals closed models across coding, multimodal, and agentic benchmarks, but was released without an accompanying sa...
The accelerometer has become an almost ubiquitous device, providing enormous opportunities in healthcare monitoring beyond step counting or other averag...
The persistent storage requirements for high-resolution, spatiotemporally evolving fields governed by large-scale and high-dimensional partial different...
Argumentative LLMs (ArgLLMs) are an existing approach leveraging Large Language Models (LLMs) and computational argumentation for decision-making, with...
Can narratives make arguments more persuasive? And to this end, which narrative features matter most? Although stories are often seen as powerful tools...
Orofacial clefts are among the most common congenital craniofacial abnormalities, yet accurate prenatal detection remains challenging due to the scarcit...
As AI systems are increasingly used to conduct research autonomously, misaligned systems could introduce subtle flaws that produce misleading results wh...
The rapid advancement of large language models (LLMs) has enabled powerful authorship inference capabilities, raising growing concerns about unintended...
The InfoNCE loss in contrastive learning depends critically on a temperature parameter, yet its dynamics under fixed versus annealed schedules remain po...
Large language models (LLMs) increasingly rely on chain-of-thought (CoT) reasoning to solve complex tasks. Yet ensuring that the reasoning trace both co...
Safety is a primary challenge in real-world reinforcement learning (RL). Formulating safety requirements as state-wise constraints has become a prominen...
This paper studies Automated Instruction Revision (AIR), a rule-induction-based method for adapting large language models (LLMs) to downstream tasks usi...
Large language models have shown strong performance on broad-domain knowledge and reasoning benchmarks, but it remains unclear how well language models...
Deep learning in cardiac MRI (CMR) is fundamentally constrained by both data scarcity and privacy regulations. This study systematically benchmarks thre...
Riemannian neural networks have proven effective in solving a variety of machine learning tasks. The key to their success lies in the development of pri...
In this paper, we consider the problem of black-box optimization with noisy feedback revealed in batches, where the unknown function to optimize has a b...
Conditional Average Treatment Effect (CATE) estimation in practice demands three properties simultaneously: heterogeneous effects $τ(x)$, calibrated unc...
Brain-wide recordings of large-scale networks of neurons now provide an unprecedented view into how the brain drives behavior. However, brain activity c...
Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases. In...
We present improved learning-augmented algorithms for finding an approximate minimum spanning tree (MST) for points in an arbitrary metric space. Our wo...
The integration of Large Language Models (LLMs) into autonomous driving has attracted growing interest for their strong reasoning and semantic understan...
Learning-to-Defer routes each input to the expert that minimizes expected cost, but it assumes that the information available to every expert is fixed a...
Frontier models have demonstrated exceptional capabilities following the integration of task-reward-based reinforcement learning (RL) into their trainin...
We introduce **CRYSTAL** (*__C__lear __R__easoning via __Y__ielded __S__teps, __T__raceability and __L__ogic*), a diagnostic benchmark with 6,372 instan...
Modern visual world modeling systems increasingly rely on high-capacity architectures and large-scale data to produce plausible motion, yet they often f...
Density aggregation is a central problem in machine learning, for instance when combining predictions from a Deep Ensemble. The choice of aggregation re...
We study wide Bayesian neural networks focusing on the rare but statistically dominant fluctuations that govern posterior concentration, beyond Gaussian...
It is currently difficult to distill discrete diffusion models. In contrast, continuous diffusion literature has many distillation approaches methods th...
Large language models are increasingly deployed in settings where reliability matters, yet output-level uncertainty signals such as token probabilities,...
Large language models (LLMs) encode vast world knowledge in their parameters, yet they remain fundamentally limited by static knowledge, finite context...
Neural network accelerators have been widely applied to edge devices for complex tasks like object tracking, image recognition, etc. Previous works have...
Reconstructing fluid flows from sparse sensor measurements is a fundamental challenge in science and engineering. Widely separated measurements and comp...
Deep reinforcement learning agents are often misaligned, as they over-exploit early reward signals. Recently, several symbolic approaches have addressed...
Active learning (AL) aims to reduce annotation costs while maximizing model performance by iteratively selecting valuable instances. While foundation mo...
Yield Multi-Corner Analysis validates circuits across 25+ Process-Voltage-Temperature corners, resulting in a combinatorial simulation cost of $O(K im...
Scientific discovery increasingly relies on AI systems to select candidates for expensive experimental validation, yet no principled, budget-aware evalu...
Large language models are increasingly deployed as autonomous coding agents and have achieved remarkably strong performance on software engineering benc...
Firearm violence is a pressing public health issue, yet research into survivors' lived experiences remains underfunded and difficult to scale. Qualitati...
Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the provid...
Predicting the effects of chemical and genetic perturbations on quantitative cell states is a central challenge in computational biology, molecular medi...
Understanding how neural networks transform inputs into outputs is crucial for interpreting and manipulating their behavior. Most existing approaches an...
Large language models (LLMs) are trained on enormous amounts of data and encode knowledge in their parameters. We propose a pipeline to elicit causal re...
Neural network methods for PDEs require reliable error control in function space norms. However, trained neural networks can typically only be probed at...
Conventional fine-tuning on domain-specific datasets can inadvertently alter a model's pretrained multimodal priors, leading to reduced generalization....
Competency Questions (CQs) are a cornerstone of requirement elicitation in ontology engineering. CQs represent requirements as a set of natural language...
We study high-dimensional convex empirical risk minimization (ERM) under general non-Gaussian data designs. By heuristically extending the Convex Gaussi...
The recent advancements in Vision Language Models (VLMs) have demonstrated progress toward true intelligence requiring robust reasoning capabilities. Be...
Computational X-ray absorption near-edge structure (XANES) is widely used to probe local coordination environments, oxidation states, and electronic str...
We propose Chunk-wise Attention Transducer (CHAT), a novel extension to RNN-T models that processes audio in fixed-size chunks while employing cross-att...
LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks f...
Implement Uncle Bob's Clean Architecture in Python with proper layering, the dependency rule, domain models, service layers, repositories, and framework boundaries.
Interactive segmentation enables clinicians to guide annotation, but existing zero-shot models like nnInteractive fail to consistently reach expert-leve...
The dynamics of Saturn's satellite system offer a rich framework for studying orbital stability and resonance interactions. Traditional methods for anal...
Activation steering methods enable inference-time control of large language model (LLM) behavior without retraining, but current approaches face a funda...
In finite-width deep neural networks, the empirical kernel $G$ evolves stochastically across layers. We develop a collective kernel effective field theo...
Mobile Agents can autonomously execute user instructions, which requires hybrid-capabilities reasoning, including screen summary, subtask planning, acti...
Quantum machine learning applies principles such as superposition and entanglement to data processing and optimization. Variational quantum models opera...
Cost-per-click (CPC) in paid search is a volatile auction outcome generated by a competitive landscape that is only partially observable from any single...
Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems. Although modern...
Most familiar equilibrium concepts, such as Nash and correlated equilibrium, guarantee only that no single player can improve their utility by deviating...
Protein sequence generation via stochastic attention produces plausible family members from small alignments without training, but treats all stored seq...
Externalize and validate application configuration with python-dotenv, pydantic-settings, secrets management, multi-environment configs, and the 12-factor config principle.
Federated learning (FL) faces challenges in uncertainty quantification (UQ). Without reliable UQ, FL systems risk deploying overconfident models at unde...
The Tactile Internet demands sub-millisecond latency and ultra-high reliability, as high latency or packet loss could lead to haptic control instability...
AI agents powered by reasoning models require access to sensitive user data. However, their reasoning traces are difficult to control, which can result...
Agentic AI is increasingly judged not by fluent output alone but by whether it can act, remember, and verify under partial observability, delay, and str...
Identifying the full landscape of small and medium-sized enterprises (SMEs) in specialized industry sectors is critical for supply-chain resilience, yet...
Autonomous agents act through sandboxed containers and microVMs whose state spans filesystems, processes, and runtime artifacts. Checkpoint and restore...
GPU kernel optimization is fundamental to modern deep learning but remains a highly specialized task requiring deep hardware expertise. Despite strong p...
Chest X-ray plays a central role in thoracic diagnosis, and its interpretation inherently requires multi-step, evidence-grounded reasoning. However, lar...
The fast-growing demands in using Large Language Models (LLMs) to tackle complex multi-step data science tasks create an emergent need for accurate benc...
Large Language Model (LLM) adapters enable low-cost model specialization, but introduce complex caching and scheduling challenges in distributed serving...
What each storage architecture does for AI systems, when ML teams need both raw unstructured data and structured query access on the same platform, and how to choose and implement the right architecture in production AI data pipelines.
Training capable software engineering (SWE) agents demands large-scale, executable, and verifiable environments that provide dynamic feedback loops for...
We propose Decentralized Proximal Stochastic Gradient Langevin Dynamics (DE-PSGLD), a decentralized Markov chain Monte Carlo (MCMC) algorithm for sampli...
The concept of ranking aggregation plays a central role in preference analysis, and numerous algorithms for calculating median rankings, often originati...
In modern parametric model training, full-batch gradient descent (and its variants) suffers due to progressively stronger biasing towards the exact real...
Autocorrelation is a defining characteristic of time-series data, where each observation is statistically dependent on its predecessors. In the context...
Using advanced machine learning techniques, we developed a method for reconstructing precisely the arrival direction and energy of ultra-high-energy cos...
Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal components...
Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as by...
Master dependency injection in Python from manual constructor injection to DI containers and FastAPI Depends, with testing strategies and architectural trade-offs.
Online platforms routinely compare multi-armed bandit algorithms, such as UCB and Thompson Sampling, to select the best-performing policy. Unlike standa...
Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and tracea...
Reinforcement learning with verifiable rewards (RLVR) typically optimizes for outcome rewards without imposing constraints on intermediate reasoning. Th...
The ability to provide trustworthy maternal health information using phone-based chatbots can have a significant impact, particularly in low-resource se...
This study presents the development of the PsyCogMetrics AI Lab (psycogmetrics.ai), an integrated, cloud-based platform that operationalizes psychometri...
Recent advances in machine learning have emphasized the integration of structured optimization components into end-to-end differentiable models, enablin...
The language in online platforms, influence operations, and political rhetoric frequently directs a mix of pro-social sentiment (e.g., advocacy, helpful...
Quantization can drastically increase the efficiency of large language and vision models, but typically incurs an accuracy drop. Recently, function-pres...
Vision-language models encode continuous geometry that their text pathway fails to express: a 6,000-parameter linear probe extracts hand joint angles at...
Multi-turn interactions with large language models typically retain the assistant's own past responses in the conversation history. In this work, we rev...
Sparse autoencoders (SAEs) are widely used to extract interpretable features from neural network representations, often under the implicit assumption th...
Automated annotation of pedagogical dialogue is a high-stakes task where LLMs often fail without sufficient domain grounding. We present a domain-adapte...
Graph domain adaptation (GDA) aims to transfer knowledge from a labeled source graph to an unlabeled target graph under distribution shifts. However, ex...
Multimodal web agents that process both screenshots and accessibility trees are increasingly deployed to interact with web interfaces, yet their dual-st...
While Large Language Models (LLMs) have demonstrated significant potential in Tool-Integrated Reasoning (TIR), existing training paradigms face signific...
Federated Multimodal Learning (FML) trains multimodal models across decentralized clients while keeping their image-text pairs private. However, joint e...
Random sample consensus (RANSAC), which is based on a repetitive sampling from a given dataset, is one of the most popular robust estimation methods. In...
Chest X-ray report generation (CXR-RG) has the potential to substantially alleviate radiologists' workload. However, conventional autoregressive vision-...
Neural networks are hypothesized to implement interpretable causal mechanisms, yet verifying this requires finding a causal abstraction -- a simpler, hi...
Multivector retrieval models achieve state-of-the-art effectiveness through fine-grained token-level representations, but their deployment incurs substa...
Safety-aligned language models refuse harmful requests through learned refusal behaviors encoded in their internal representations. Recent activation-ba...
While Graph Foundation Models (GFMs) have achieved remarkable success in homogeneous graphs, extending them to multi-domain heterogeneous graphs (MDHGs)...
Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such for...
Attributing authorship to paintings is a historically complex task, and one of its main challenges is the limited availability of real artworks for trai...
The Hyperspace Analogue to Language (HAL) model relies on global word co-occurrence matrices to construct distributional semantic representations. While...
This paper explores the use of server learning for enhancing the robustness of federated learning against malicious attacks even when clients' training...
Accurately anticipating how complex, diverse scenes will evolve requires models that represent uncertainty, simulate along extended interaction chains,...
As corporate responsibility increasingly incorporates environmental, social, and governance (ESG) criteria, ESG reporting is becoming a legal requiremen...
Deep Research Agents (DRAs) are promising agentic systems that gather and synthesize information to support research across domains such as financial de...
Large Language Models (LLMs) have the potential to accelerate small molecule drug design due to their ability to reason about information from diverse s...
Leader-follower interaction is an important paradigm in human-robot interaction (HRI). Yet, assigning roles in real time remains challenging for resourc...
The transition of Multi-Agent Reinforcement Learning (MARL) policies from simulated cyber wargames to operational Security Operations Centers (SOCs) is...
Large Language Models (LLMs) have been widely deployed, especially through free Web-based applications that expose them to diverse user-generated inputs...
A major limitation of clustering approaches is their lack of explainability: methods rarely provide insight into which features drive the grouping of si...
Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in en...
Deep Neural Networks are powerful tools for solving machine learning problems, but their training often involves dense and costly parameter updates. In...
Reinforcement learning (RL) has become essential to the post-training of large language models (LLMs) for reasoning, agentic capabilities and alignment....
Algorithmic decisions about individuals require predictions that are not only accurate but also fair with respect to sensitive attributes such as gender...
Accurate fault detection and localization in electrical distribution systems is crucial, especially with the increasing integration of distributed energ...
Transformer-based large language models exhibit in-context learning, enabling adaptation to downstream tasks via few-shot prompting with demonstrations....
Reinforcement learning (RL) has become a standard technique for post-training diffusion-based image synthesis models, as it enables learning from reward...
We study fixed budget constrained best-arm identification in grouped bandits, where each arm consists of multiple independent attributes with stochastic...
Existing multi-hazard susceptibility mapping (MHSM) studies often rely on spatially uniform models, treat hazards independently, and provide limited rep...
Standard mixed-precision training of neural networks requires many bytes of accelerator memory for each model parameter. These bytes reflect not just th...
We present FlexiTac, a low-cost, open-source, and scalable piezoresistive tactile sensing solution designed for robotic end-effectors. FlexiTac is a pra...
Flow matching has emerged as a simulation-free alternative to diffusion-based generative modeling, producing samples by solving an ODE whose time-depend...
Obstacle avoidance in unmanned aerial vehicles (UAVs), as a fundamental capability, has gained increasing attention with the growing focus on spatial in...
What is a diffusion model actually doing when it turns noise into a photograph? We show that the deterministic DDIM reverse chain operates as a Partitio...
The complexity of Vietnam's legal texts presents a significant barrier to public access to justice. While Large Language Models offer a promising soluti...
While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulat...
Existing tampering detection benchmarks largely rely on object masks, which severely misalign with the true edit signal: many pixels inside a mask are u...
In this work, we study scaling limits of shallow Bayesian neural networks (BNNs) via their connection to Gaussian processes (GPs), with an emphasis on s...
This study proposes the General Bayes framework for policy learning. We consider decision problems in which a decision-maker chooses an action from an a...
We develop a theory of generalization and scaling for Mixture-of-Experts (MoE) Transformers that cleanly separates \emph{active} per-input capacity from...
Despite the remarkable empirical success of score-based diffusion models, their statistical guarantees remain underdeveloped. Existing analyses often pr...
Generalized Rapid Action Value Estimation (GRAVE) has been shown to be a strong variant within the Monte-Carlo Tree Search (MCTS) family of algorithms f...
Given $n$ independent samples from a $d$-dimensional probability distribution, our aim is to generate diffusion-based samples from a distribution obtain...
Generating diverse, readable statistical charts from tabular data remains challenging for LLMs, as many failures become apparent after rendering and are...
Geochemical anomaly detection plays a critical role in mineral exploration as deviations from regional geochemical baselines may indicate mineralization...
Reliable spatial analysis in GIScience requires preserving coordinate semantics, topology, units, and geographic plausibility. Current LLM-based GIS sys...
Stochastic dynamical systems with slow or metastable behavior evolve, on long time scales, on an unknown low-dimensional manifold in high-dimensional am...
Camera motion is a fundamental geometric signal that shapes visual perception and cinematic style, yet current video-capable vision-language models (Vid...
Psychiatric questionnaires are highly context sensitive and often only weakly predict subsequent symptom severity, which makes the prognostic relationsh...
Efficient exploration is a central problem in reinforcement learning and is often formalized as maximizing the entropy of the state-action occupancy mea...
Current network data telemetry pipelines consist of massive streams of fine-grained Key Performance Indicators (KPIs) from multiple distributed sources...
Transformer attention computes a single softmax-weighted average over values -- a one-pass estimate that cannot correct its own errors. We introduce \em...
Understanding the intricate non-convex training dynamics of softmax-based models is crucial for explaining the empirical success of transformers. In thi...
Gradient Boosting Decision Trees (GBDTs) dominate tabular machine learning, with modern implementations like XGBoost, LightGBM, and CatBoost being based...
We study adversarial learning when the target distribution factorizes according to a known Bayesian network. For interpolative divergences, including $(...
Stochastic approximation (SA) is a fundamental iterative framework with broad applications in reinforcement learning and optimization. Classical analyse...
Implement Hexagonal Architecture in Python using Protocol-based ports, swappable adapters, and clear boundaries between application logic and external systems.
Hierarchical time-series forecasting is essential for demand prediction across various industries. While machine learning models have obtained significa...
Inverse problems are the task of calibrating models to match data. They play a pivotal role in diverse engineering applications by allowing practitioner...
The Hierarchical Kernel Transformer (HKT) is a multi-scale attention mechanism that processes sequences at L resolution levels via trainable causal down...
Model predictive control (MPC) with learned world models has emerged as a promising paradigm for embodied control, particularly for its ability to gener...
Batch effects arising from technical variations in histopathology staining protocols, scanners, and acquisition pipelines pose a persistent challenge fo...
We introduce HyCOP, a modular framework that learns parametric PDE solution operators by composing simple modules (advection, diffusion, learned closure...
We introduce Hyper Input Convex Neural Networks (HyCNNs), a novel neural network architecture designed for learning convex functions. HyCNNs combine the...
Purpose: Proton magnetic resonance spectroscopic imaging ($^1$H MRSI) enables the mapping of whole-brain metabolites concentrations in-vivo. However, a...
Unobserved confounding is a key challenge when estimating causal effects from a treatment on an outcome in scientific applications. In this work, we ass...
It is increasingly common in machine learning to use learned models to label data and then employ such data to train more capable models. The phenomenon...
The use of ML in cybersecurity has long been impaired by generalization issues: Models that work well in controlled scenarios fail to maintain performan...
Industrial software development across chip design, GPU optimization, and embedded systems lacks expert reasoning traces showing how engineers reason ab...
Machine learning techniques are now routinely encountered in research laboratories across the globe. Impressive progress has been made through ML and AI...
Understanding the theoretical foundations of attention mechanisms remains challenging due to their complex, non-linear dynamics. This work reveals a fun...
Vision Language models (VLMs) have demonstrated strong performance across a wide range of benchmarks, yet they often suffer from modality dominance, whe...
Standard diffusion models for graph generation typically rely on uniform time-stepping, an approach that overlooks the non-homogeneous dynamics of distr...
Reducing the hardware footprint of large language models (LLMs) during decoding is critical for efficient long-sequence generation. A key bottleneck is...
We present a training-free approach for controllable 3D inpainting based on initial noise optimization. In the structured 3D latent diffusion framework,...
Transformers have emerged as the dominant neural-network architecture, achieving state-of-the-art performance in language processing and computer vision...
Existing research infrastructure is fundamentally document-centric, providing citation links between papers but lacking explicit representations of meth...
We consider stochastic non-stationary linear bandits where the linear parameter connecting contexts to the reward changes over time. Existing algorithms...
An artificial intelligence (AI) model can be viewed as a function that maps inputs to outputs in high-dimensional spaces. Once designed and well trained...
We study the Inverse Contextual Bandit (ICB) problem, in which a learner seeks to optimize a policy while an observer, who cannot access the learner's r...
The natural gradient method is widely used in statistical optimization, but its standard formulation assumes a Euclidean parameter space. This paper pro...
Error Span Detection (ESD) is a crucial subtask in Machine Translation (MT) evaluation, aiming to identify the location and severity of translation erro...
Training Transformer language models is expensive, as performance typically improves with increasing dataset size and computational budget. Although sca...
The Half-Trek Criterion (HTC) is the primary graphical tool for determining generic identifiability of causal effect coefficients in linear structural e...
We propose HILBERT (HIerarchical Long-sequence Balanced Embedding with Reciprocal contrastive Training), a cross-attentive multimodal framework for lear...
Adapter-based methods have become a cost-effective approach to continual learning (CL) for Large Language Models (LLMs), by sequentially learning a low-...
We introduce kernel integrated $R^2$, a new measure of statistical dependence that combines the local normalization principle of the recently introduced...
Recent advances in large language models (LLMs) have increasingly relied on reinforcement learning (RL) to improve their reasoning capabilities. Three a...
Causal generative models provide a principled framework for answering observational, interventional, and counterfactual queries from observational data....
Deep learning models achieve high accuracy in time series classification, yet understanding their class-level decision behaviour remains challenging. Ex...
Large language models (LLMs) undergo alignment training to avoid harmful behaviors, yet the resulting safeguards remain brittle: jailbreaks routinely by...
Multi-turn prompt injection follows a known attack path -- trust-building, pivoting, escalation but text-level defenses miss covert attacks where indivi...
Latent reasoning offers a more efficient alternative to explicit reasoning by compressing intermediate reasoning into continuous representations and sub...
Prior approaches for membership privacy preservation usually update or retrain all weights in neural networks, which is costly and can lead to unnecessa...
Theory of Mind (ToM) reasoning with Large Language Models (LLMs) requires inferring how people's implicit, evolving beliefs shape what they seek and how...
The Flexible Job Shop Scheduling Problem (FJSP) originates from real production lines, while some practical constraints are often ignored or idealized i...
Research on developmentally plausible language models has largely focused on English, leaving open questions about multilingual settings. We present a s...
Learning the potentials of interacting particle systems is a fundamental task across various scientific disciplines. A major challenge is that unlabeled...
The Normalized Transformer, or nGPT (arXiv:2410.01131) achieves impressive training speedups and does not require weight decay or learning rate warmup....
This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network based on the De...
All prior membership inference attacks for fine-tuned language models use hand-crafted heuristics (e.g., loss thresholding, Min-K\%, reference calibrati...
Although most of the automated theorem-proving approaches depend on formal proof systems, informal theorem proving can align better with large language...
We present a new approach for benchmarking Large Language Model (LLM) capabilities on research-level mathematics. Existing benchmarks largely rely on st...
Variable selection in linear regression models has been a problem since hypothesis testing began. Which variables to include or exclude from a model is...
The choice of loss function in classification involves a fundamental trade-off: smooth losses (like Cross-Entropy) enable fast optimization rates but yi...
Feature maps associated with positive definite kernels play a central role in kernel methods and learning theory, where regularity properties such as Li...
We present LiveSense - a cross-platform that transforms a commercial off-the-shelf (COTS) Wi-Fi Network Interface Card (NIC) on a laptop into a centimet...
Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging....
Large Language Models (LLMs) can generate persuasive influence strategies that shift cooperative behavior in multi-agent populations, but a critical que...
Large language models (LLMs) perform increasingly well on biology benchmarks, but it remains unclear whether they uplift novice users -- i.e., enable hu...
Large language models (LLMs) have driven substantial advances in speech language models (SpeechLMs), yielding strong performance in automatic speech rec...
Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify unc...
We study the fundamental problem of clustering $n$ points into $K$ groups drawn from a mixture of isotropic Gaussians in $\mathbb{R}^d$. Specifically, w...
The massive scale of pretrained models has made efficient compression essential for practical deployment. Low-rank decomposition based on the singular v...
Generative audio requires fine-grained controllable outputs, yet most existing methods require model retraining on specific controls or inference-time c...
Recent advances in diffusion models have significantly improved text-to-video generation, enabling personalized content creation with fine-grained contr...
Causal graph discovery for space-time systems is challenging in high-dimensional gridded data, which often has many more grid cells than temporal observ...
Although Automatic Speech Recognition (ASR) in Bengali has seen significant progress, processing long-duration audio and performing robust speaker diari...
Key-Value (KV) cache has become a de facto component of modern Large Vision-Language Models (LVLMs) for inference. While it enhances decoding efficiency...
In recent times, large datasets hinder efficient model training while also containing redundant concepts. Dataset distillation aims to synthesize compac...
Large language model agents receive instructions from many sources-system messages, user prompts, tool outputs, and more-each carrying different levels...
Research on classroom interaction has long been divided between large-scale observation and in-depth ethnographic work. We propose a framework mapping t...
In this study, we use machine learning to classify and interpolate the phase structure of the Vicsek flocking model across the three-dimensional paramet...
Coarse data arise when learners observe only partial information about samples; namely, a set containing the sample rather than its exact value. This oc...
Steering large-scale swarms in only a few control updates is challenging because real systems operate in sampled-data form: control inputs are updated i...
Recent work on chain-of-thought (CoT) faithfulness reports single aggregate numbers (e.g., DeepSeek-R1 acknowledges hints 39% of the time), implying tha...
Large language model (LLM) agents are fundamentally bottlenecked by finite context windows on long-horizon tasks. As trajectories grow, retaining tool o...
Transformers have been established as the de-facto backbones for most recent advances in sequence modeling, mainly due to their growing memory capacity...
We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-ba...
Navigate the monolith-to-microservices spectrum with Python - bounded contexts, communication patterns, the modular monolith, and practical decision frameworks.
Preference learning has become the foundation of aligning Large Language Models (LLMs) with human intent. Popular methods, such as Direct Preference Opt...
Loss functions play a central role in supervised classification. Cross-entropy (CE) is widely used, whereas the mean absolute error (MAE) loss can offer...
Feature selection is a classical problem in statistics and machine learning, and it continues to remain an extremely challenging problem especially in t...
Multimodal Stance Detection (MSD) is crucial for understanding public discourse, yet effectively fusing text and image, especially with conflicting sign...
Multimodal LLMs can process speech and images, but they cannot hear a speaker's voice or see an object's texture. We show this is not a failure of encod...
Scaling video generation from seconds to minutes faces a critical bottleneck: while short-video data is abundant and high-fidelity, coherent long-form d...
Numerous lines of aim to control $ extit{model disagreement}$ -- the extent to which two machine learning models disagree in their predictions. We adop...
In this paper, we study the problem of learning multi-dimensional Gaussian Mixture Models (GMMs), with a specific focus on model order selection and eff...
Semi-structured documents integrate diverse interleaved data elements (e.g., tables, charts, hierarchical paragraphs) arranged in various and often irre...
Build an extensible CLI tool with plugin discovery, loading, and lifecycle management.
GoF patterns, SOLID principles, DDD, and Hexagonal Architecture - enterprise design patterns implemented idiomatically in Python.
Master Python's object model at engineering depth - classes, instances, dunder methods, encapsulation, inheritance, MRO, composition, abstract base classes, dataclasses, SOLID principles, and production design patterns.
FastAPI in depth, gRPC, event-driven architecture, service mesh patterns, and API contracts - building production Python microservices.
Design production Python systems with clean architecture, hexagonal architecture, dependency injection, plugin systems, 12-factor methodology, and configuration management. The engineering patterns that separate scripts from systems.
Heteroscedasticity -- where the variance of a variable changes with other variables -- is pervasive in real data, and elucidating why it arises from the...
Animal re-identification (ReID) faces critical challenges due to viewpoint variations, particularly in Aerial-Ground (AG-ReID) settings where models mus...
With the explosive growth of digital entertainment, automated video summarization has become indispensable for applications such as content indexing, pe...
We present a scalable methodology for evaluating language models in multi-turn interactions, using a suite of collaborative games that require effective...
Recognizing surgical phases and steps from video is a fundamental problem in computer-assisted interventions. Recent approaches increasingly rely on lar...
We propose a Multivariate Spatio-Temporal Neural Hawkes Process for modeling complex multivariate event data with spatio-temporal dynamics. The proposed...
Modern microscopy routinely produces gigapixel images that contain structures across multiple spatial scales, from fine cellular morphology to broader t...
Matrix multiplication performance has long been the major bottleneck to scaling deep learning workloads, which has stimulated the design of new accelera...
Cox processes model overdispersed point process data via a latent stochastic intensity, but both nonparametric estimation of the intensity model and pos...
Operator learning is reshaping scientific computing by amortizing inference across infinite families of problems. While neural operators (NOs) are incre...
Understanding natural and engineered systems often relies on symbolic formulations, such as differential equations, which provide interpretability and t...
We introduce NOBLE (Nonlinear lOw-rank Branch for Linear Enhancement), an architectural augmentation that adds nonlinear low-rank branches to transforme...
Monte Carlo Tree Search (MCTS) scales poorly in cooperative multi-agent domains because expansion must consider an exponentially large set of joint acti...
Sign languages, of any geographical or accentual variation, understandably face continuous scrutiny under the ever present popularity of verbal dictatio...
In biomechanical systems, observable performance is often used as a proxy for underlying system organization. However, this assumption implicitly presum...
Randomized Controlled Trials (RCTs) represent the gold standard for causal inference yet remain a scarce resource. While large-scale observational data...
Modeling neural population dynamics is crucial for foundational neuroscientific research and various clinical applications. Conventional latent variable...
We study dynamic measure transport for generative modelling in the setting of a stochastic process $X_\bullet$ whose marginals interpolate between a sou...
This paper introduces a projected functional gradient descent algorithm (P-FGD) for training nonparametric additive quantile regression models in online...
Online Conformal Prediction (CP) struggles to balance temporal adaptability and structural stability. Feedback-driven methods (e.g., Adaptive Conformal...
Learning algorithms can be significantly improved by routing complex or uncertain inputs to specialized experts, balancing accuracy with computational c...
In computer vision and machine learning for geographic data, out-of-domain generalization is a pervasive challenge, arising from uneven global data cove...
Most PDE foundation models are pretrained and fine-tuned on fluid-centric benchmarks. Their utility under extreme-loading material dynamics remains uncl...
Self-reflection enables language agents to iteratively refine solutions, yet often produces repetitive outputs that limit reasoning performance. Recent...
We study the statistical complexity of estimating partition functions given sample access to a proposal distribution and an unnormalized density ratio f...
While autoregressive Large Vision-Language Models (LVLMs) demonstrate remarkable proficiency in multimodal tasks, they face a 'Visual Signal Dilution' p...
Modern video diffusion models excel at appearance synthesis but still struggle with physical consistency: objects drift, collisions lack realistic rebou...
Offline goal-conditioned reinforcement learning (GCRL) learns goal-conditioned policies from static pre-collected datasets. However, accurate value esti...
Recent progress in text-conditioned human motion generation has been largely driven by diffusion models trained on large-scale human motion data. Buildi...
Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained generat...
Build extensible Python applications with entry_points, importlib.metadata, stevedore, __init_subclass__, and plugin lifecycle management.
Digital firms routinely run many online experiments on shared user populations. When product decisions are compositional, such as combinations of interf...
Explainable Artificial Intelligence (XAI) seeks to enhance the transparency and accountability of machine learning systems, yet most methods follow a on...
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool...
Three-dimensional medical image data and computer-aided decision making, particularly using deep learning, are becoming increasingly important in the me...
The rapidly expanding artificial intelligence (AI) industry has produced diverse yet powerful prediction tools, each with its own network architecture,...
Predictive coding graphs (PCGs) are a recently introduced generalization to predictive coding networks, a neuroscience-inspired probabilistic latent var...
Resource-efficient training optimization techniques are becoming increasingly important as the size of large language models (LLMs) continues to grow. I...
Supervised No Free Lunch Theorems (NFLTs) are well studied, yet unsupervised NFLTs remain underexplored. For elliptical distributions, we prove that the...
In this paper, we propose Precision-Informed Semantic Modeling (PRISM), a structured topic modeling framework combining the benefits of rich representat...
The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforc...
Collecting multiple types of data on the same set of subjects is common in modern scientific applications including, genomics, metabolomics, and neuroim...
Understanding the geometry of learned distributions is fundamental to improving and interpreting diffusion models, yet systematic tools for exploring th...
Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating ste...
Build a FastAPI app with clean architecture, dependency injection, and proper configuration management.
Streaming TTS that receives streaming text is essential for interactive systems, yet this scheme faces two major challenges: unnatural prosody due to mi...
Federated learning (FL) faces two structural tensions: gradient sharing enables data-reconstruction attacks, while non-IID client distributions degrade...
Solve 11 Python clean architecture problems (3 Easy, 4 Medium, 4 Hard). Practice dependency rule, domain model with hints, runnable code, and solutions.
Solve 11 Python configuration management problems. Covers pydantic settings, environment variables. Hints and solutions.
Solve 11 Python dependency injection problems. Covers DI container, constructor injection, FastAPI dependency. Hints and solutions.
Solve 11 Python hexagonal architecture (ports and adapters) problems. Covers hexagonal architecture, ports and, protocol port. Hints and solutions.
Solve 11 Python microservices vs monolith problems. Covers microservices vs, bounded context, modular monolith. Hints and solutions.
Solve 11 Python plugin systems problems (3 Easy, 4 Medium, 4 Hard). Practice plugin system, entry_points exercises with hints, runnable code, and solutions.
Solve 11 Python the 12-factor app problems (3 Easy, 4 Medium, 4 Hard). Practice 12-factor app, 12-factor methodology with hints, runnable code, and solutions.
While second language (L2) learners may acquire target syntactic word order, mapping this syntax onto appropriate prosodic structures remains a persiste...
Diffusion-based generative modeling suggests reversing a noising semigroup by adding a score drift. For continuous-variable Gaussian Markov dynamics, co...
Quantum machine learning is a promising field for efficiently learning features of a dataset to perform a specified task, such as classification. Interv...
Conversational generative AI is rapidly entering healthcare, where general-purpose models must integrate heterogeneous patient signals and support diver...
Randomized-subspace methods reduce the cost of first-order optimization by using only low-dimensional projected-gradient information, a feature that is...
Pathology report generation remains a relatively under-explored downstream task, primarily due to the gigapixel scale and complex morphological heteroge...
Vision transformers have recently made a breakthrough in computer vision showing excellent performance in terms of precision for numerous applications....
Recovering camera parameters from images and rendering scenes from novel viewpoints have long been treated as separate tasks in computer vision and grap...
Engineering breakdown of the ReAct paper (Yao et al., 2022) - the foundation of every AI agent built today. Plain English, production viability rating, implementation notes.
Cardiovascular modeling has rapidly advanced over the past few decades due to the rising needs for health tracking and early detection of cardiovascular...
We propose RecaLLM, a set of reasoning language models post-trained to make effective use of long-context information. In-context retrieval, which ident...
Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing the complex reasoning capabilities of Large Reaso...
Generally capable agents must learn from experience in ways that generalize across tasks and environments. The fundamental problems of learning, includi...
Simulating a Gaussian process requires sampling from a high-dimensional Gaussian distribution, which scales cubically with the number of sample location...
We consider the problem of contextual online RLHF with general preferences, where the goal is to identify the Nash Equilibrium. We adopt the Generalized...
For a risk-averse finite-horizon Markov Decision Problem, we introduce a special class of Markov coherent risk measures, called mini-batch measures. We...
We study multiteacher knowledge distillation for low resource abstractive summarization from a reliability aware perspective. We introduce EWAD (Entropy...
Large language models (LLMs) have revolutionized Text-to-SQL generation, allowing users to query structured data using natural language with growing eas...
Recent research has shown that filtering massive English web corpora into high-quality subsets significantly improves training efficiency. However, for...
Machine learning approaches to spatiotemporal physical systems have primarily focused on next-frame prediction, with the goal of learning an accurate em...
From Chain-of-Thought to production agent architectures. Read the 9 most important agent papers in order — with full engineering context between each one.
From CLIP to GPT-4V to Gemini. Read the 9 most important multimodal AI papers in order — understanding how vision and language were unified.
Read the 8 most important RAG papers in the right order. From the original Lewis et al. through GraphRAG. Full engineering context between each paper.
We study the problem of resilient strategies in the presence of uncertainty. Resilient strategies enable an agent to make decisions that are robust agai...
Many readers today struggle to assess the trustworthiness of online news because reliable reporting coexists with misinformation. The TREC 2025 DRAGUN (...
Data assimilation is the process of estimating the time-evolving state of a dynamical system by integrating model predictions and noisy observations. It...
Large language models have achieved great success in multiple challenging tasks, and their capacity can be further boosted by the emerging agentic AI te...
Reward models are central to aligning large language models (LLMs) with human preferences. Yet most approaches rely on pointwise reward estimates that o...
With advances in imitation learning (IL) and large-scale driving datasets, end-to-end autonomous driving (E2E-AD) has made great progress recently. Curr...
Recent advances in robot learning have accelerated progress toward generalist robots that can perform everyday tasks in human environments. Yet it remai...
In this paper, we propose a novel bounded asymmetric elastic net ($L_{baen}$) loss function and combine it with the support vector machine (SVM), result...
The Unscented Kalman Filter (UKF) is a ubiquitous tool for nonlinear state estimation; however, its performance is limited by the static parameterizatio...
As Large Language Models (LLMs) transition into autonomous multi-agent ecosystems, robust minimax training becomes essential yet remains prone to instab...
Humans solve problems by executing targeted plans, yet large language models (LLMs) remain unreliable for structured workflow execution. We propose RunA...
Safety guarantees are a prerequisite to the deployment of reinforcement learning (RL) agents in safety-critical tasks. Often, deployment environments ex...
Safety-critical task planning in robotic systems remains challenging: classical planners suffer from poor scalability, Reinforcement Learning (RL)-based...
Learning-based quadruped controllers achieve impressive agility but typically lack formal safety guarantees under model uncertainty, perception noise, a...
Recursive self-improvement is moving from theory to practice: modern systems can critique, revise, and evaluate their own outputs, yet iterative self-mo...
We study the sample complexity of learning an $ε$-optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bounds...
While representation and similarity learning have improved the sample efficiency of Reinforcement Learning (RL), they are rarely used to shape policy up...
Evaluation of AI systems often requires synthetic test cases, particularly for rare or safety-critical conditions that are difficult to observe in opera...
Efficient and scalable non-parametric or semi-parametric regression analysis and density estimation are of crucial importance to the fields of statistic...
Large-scale commercial search systems optimize for relevance to drive successful sessions that help users find what they are looking for. To maximize re...
Incremental Few-Shot (IFS) segmentation aims to learn new categories over time from only a few annotations. Although widely studied in 2D, it remains un...
Prompt learning is a parameter-efficient approach for vision-language models, yet its robustness under label noise is less investigated. Visual content...
We identify occlusion reasoning as a fundamental yet overlooked aspect for 3D layout-conditioned generation. It is essential for synthesizing partially...
The discovery rate of optical transients will explode to 10 million public alerts per night once the Vera C. Rubin Observatory's Legacy Survey of Space...
On-policy distillation (OPD) has become a popular training paradigm in the LLM community. This paradigm selects a larger model as the teacher to provide...
Large Language Models (LLMs) increasingly serve as autonomous reasoning agents in decision support, scientific problem-solving, and multi-agent coordina...
When two agents of different computational capacities interact with the same environment, they need not compress a common semantic alphabet differently;...
Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks. However, the truthfulness of their outputs is not guarantee...
Concept learning is a form of supervised machine learning that operates on knowledge bases in description logics. State-of-the-art concept learners ofte...
We introduce Latent Space Distribution Matching (LSDM), a novel framework for semi-supervised generative modeling of conditional distributions. LSDM ope...
Diffusion models achieve state-of-the-art video generation quality, but their inference remains expensive due to the large number of sequential denoisin...
We present a dataset and a model for sentiment analysis of German sign language (DGS) fairy tales. First, we perform sentiment analysis for three levels...
The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in it...
Discrete diffusion models have achieved strong empirical performance in text and other symbolic domains, with masked (absorbing-rate) variants emerging...
We study the population loss landscape of two-layer ReLU networks of the form $\sum_{k=1}^K \mathrm{ReLU}(w_k^\top x)$ in a realisable teacher-student s...
Tendon drives paired with soft muscle actuation enable faster and safer robots while potentially accelerating skill acquisition. Still, these systems ar...
Human motion prediction combines the tasks of trajectory forecasting and human pose prediction. For each of the two tasks, specialized models have been...
Data attribution and valuation are critical for understanding data-model synergy for Large Language Models (LLMs), yet existing gradient-based methods s...
Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion - applied to production Python.
The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model of the wor...
Real-world Table-Text question answering (QA) tasks require models that can reason across long text and source tables, traversing multiple hops and exec...
Pure-vision GUI agents provide universal interaction capabilities but suffer from severe efficiency bottlenecks due to the massive spatiotemporal redund...
Forward-backward (FB) representations provide a powerful framework for learning the successor representation (SR) in continuous spaces by enforcing a lo...
This work proposes novel splitting techniques for argumentation formalisms that incorporate supports between defeasible elements. We base our studies on...
We present SpotIt+, an open-source tool for evaluating Text-to-SQL systems via bounded equivalence verification. Given a generated SQL query and the gro...
With the rise of high-dimensional correlated data, multicollinearity poses a significant challenge to model stability, often leading to unstable estimat...
Real-world systems must continuously adapt to novel concepts from limited data without forgetting previously acquired knowledge. While Few-Shot Class-In...
Sparse autoencoders (SAEs) are widely used to extract human-interpretable features from neural network activations, but their learned features can vary...
This paper focuses on the state estimation problem in distributed sensor networks, where intermittent packet dropouts, corrupted observations, and unkno...
Open-world embodied agents must solve long-horizon tasks where the main bottleneck is not single-step planning quality but how interaction experience is...
Machine learning (ML) inference serving systems host deep neural network (DNN) models and schedule incoming inference requests across deployed GPUs. How...
AI agents increasingly operate in multi-agent environments where outcomes depend on coordination. We distinguish primary algorithmic monoculture -- base...
We study post-training interpretability for Support Vector Machines (SVMs) built from truncated orthogonal polynomial kernels. Since the associated repr...
Multi-view data analysis seeks to integrate multiple representations of the same samples in order to recover a coherent low-dimensional structure. Class...
Long conversations with an AI agent create a simple problem for one user: the history is useful, but carrying it verbatim is expensive. We study persona...
One of the dominant paradigms in self-supervised learning (SSL), illustrated by MoCo or DINO, aims to produce robust representations by capturing featur...
Surgeons don't just see -- they interpret. When an expert observes a surgical scene, they understand not only what instrument is being used, but why it...
Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as precision medicine a...
Realistic long-horizon productivity work is strongly conditioned on user-specific computer environments, where much of the work context is stored and or...
Data plays a fundamental role in consolidating markets, services, and products in the digital financial ecosystem. However, the use of real data, especi...
Reinforcement Learning (RL) lacks benchmarks that enable precise, white-box diagnostics of agent behavior. Current environments often entangle complexit...
Generalization measures have been studied extensively in the machine learning community to better characterize generalization gaps. However, establishin...
Modern optimizers like Adam and Muon are central to training large language models, but their reliance on first- and second-order momenta introduces sig...
Large language models (LLMs) with reasoning capabilities have fueled a compelling narrative that reasoning universally improves performance across langu...
Small language models (SLMs) have emerged as efficient alternatives to large language models for task-specific applications. However, they are often emp...
Modern machine learning models are deployed in diverse, non-stationary environments where they must continually adapt to new tasks and evolving knowledg...
With the proliferation of Electronic Health Records (EHRs), a critical challenge in building predictive models is determining the optimal historical dat...
This study presents the first systematic, reference-free human evaluation of large language model (LLM) machine translation (MT) for Ancient Greek (AG)...
LLMs are increasingly used as general-purpose reasoners, but long inputs remain bottlenecked by a fixed context window. Recursive Language Models (RLMs)...
Apply the 12-Factor App methodology to Python applications with FastAPI, Docker, and PostgreSQL - covering all 12 factors with production-ready code examples.
Scaling Vision-Language-Action (VLA) models by upgrading the vision encoder is expected to improve downstream manipulation performance--as it does in vi...
Personal Artificial Intelligence is currently hindered by the fragmentation of user data across isolated silos. While Retrieval-Augmented Generation off...
We study the problem of learning in zero-sum matrix games with repeated play and bandit feedback. Specifically, we focus on developing uncoupled algorit...
For each axiom of KM belief update we provide a corresponding axiom in a modal logic containing three modal operators: a unimodal belief operator $B$, a...
Conventional robot social behavior generation has been limited in flexibility and autonomy, relying on predefined motions or human feedback. This study...
The use of algorithmic predictions in decision-making leads to a feedback loop where the models we deploy actively influence the data distributions we s...
Reward models (RMs) have become an indispensable fixture of the language model (LM) post-training playbook, enabling policy alignment and test-time scal...
Singular statistical models-including mixtures, matrix factorization, and neural networks-violate regular asymptotics due to parameter non-identifiabili...
Accurate forecasting of transportation dynamics is essential for urban mobility and infrastructure planning. Although recent work has achieved strong pe...
Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities. However, tool use is not always beneficial; some calls may be...
Large Language Models (LLMs) have advanced Table Question Answering, where most queries can be answered by extracting information or simple aggregation....
The advancement of large language models (LLMs) has accelerated the development of autonomous financial trading systems. While mainstream approaches dep...
We propose a Correlation-Complexity Map as a practical diagnostic tool for determining when real-world data distributions are structurally aligned with...
Vision-language models (VLMs) show promise in drafting radiology reports, yet they frequently suffer from logical inconsistencies, generating diagnostic...
World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting planning under uncertai...
Concept Bottleneck Models (CBMs) are interpretable models that route predictions through a layer of human-interpretable concepts. While widely studied i...
The Room Acoustics and Speaker Distance Estimation (SDE) Challenge at ICASSP 2025 explores the effectiveness of augmented room impulse response (RIR) da...
Randomized controlled trials often do not represent the populations where decisions are made, and covariate shift across studies can invalidate standard...
Forecasting plays a crucial role in modern safety-critical applications, such as space operations. However, the increasing use of deep forecasting model...
YouTube has evolved into a powerful platform that where creators monetize their influence through affiliate marketing, raising concerns about transparen...
Population-based learning paradigms, including evolutionary strategies, Population-Based Training (PBT), and recent model-merging methods, combine fast...
AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-the-art (SOTA) models rely on specialized architectures and ma...
Despite their capabilities, Multimodal Large Language Models (MLLMs) may produce plausible but erroneous outputs, hindering reliable deployment. Accurat...
Deep generative models (DGMs) compress high-dimensional data but often entangle distinct physical factors in their latent spaces. We present an auxiliar...
The recent success of reinforcement learning (RL) in large reasoning models has inspired the growing adoption of RL for post-training Multimodal Large L...
AI-powered scientific research tools are rapidly being integrated into research workflows, yet the field lacks a clear lens into how researchers use the...
Reinforcement Learning with Verifiable Rewards (RLVR) has achieved substantial gains in single-attempt accuracy (Pass@1) on reasoning tasks, yet often s...
Amortized Bayesian Inference (ABI) enables efficient posterior estimation using generative neural networks trained on simulated data, but often suffers...
With the development of deep learning, medical image processing has been widely used to assist clinical research. This paper focuses on the denoising pr...
Explaining Machine Learning (ML) results in a transparent and user-friendly manner remains a challenging task of Explainable Artificial Intelligence (XA...
A growing number of publications address the best practices to use Large Language Models (LLMs) for software engineering in recent years. However, most...
We present a method to identify a valence-arousal (VA) subspace within large language model representations. From 211k emotion-labeled texts, we derive...
The Joint-Embedding Predictive Architecture (JEPA) is often seen as a non-generative alternative to likelihood-based self-supervised learning, emphasizi...
Sparse regularization plays a central role in solving inverse problems arising from incomplete or corrupted measurements. Different regularizers corresp...
Symbolic regression has recently gained traction in AI-driven scientific discovery, aiming to recover explicit closed-form expressions from data that re...
Generative modeling of three-dimensional (3D) molecules is a fundamental yet challenging problem in drug discovery and materials science. Existing appro...
Video agentic models have advanced challenging video-language tasks. However, most agentic approaches still heavily rely on greedy parsing over densely...
Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition. One plausible contrib...
Visual Retrieval-Augmented Generation (VRAG) empowers Vision-Language Models to retrieve and reason over visually rich documents. To tackle complex quer...
Vision-to-code tasks require models to reconstruct structured visual inputs, such as charts, tables, and SVGs, into executable or structured representat...
Large Vision Language Models (LVLMs) achieve strong multimodal reasoning but frequently exhibit hallucinations and incorrect responses with high certain...
Recent work shows that flow matching can be effective for scalar Q-value function estimation in reinforcement learning (RL), but it remains unclear why...
While diffusion models have revolutionized visual content generation, their rapid adoption has underscored the critical need to investigate vulnerabilit...
Background: Patient-facing medical chatbots based on retrieval-augmented generation (RAG) are increasingly promoted to deliver accessible, grounded heal...
Group Relative Policy Optimization (GRPO) has emerged as an effective method for training reasoning models. While it computes advantages based on group...
Identifiability in representation learning is commonly evaluated using standard metrics (e.g., MCC, DCI, R^2) on synthetic benchmarks with known ground-...
Diffusion Language Models (DLMs) are often advertised as enabling parallel token generation, yet practical fast DLMs frequently converge to left-to-righ...
Recent work interprets the linear recoverability of geographic and temporal variables from large language model (LLM) hidden states as evidence for worl...
Model poisoning attacks pose a significant security threat to Federated Learning (FL). Most existing model poisoning attacks rely on collusion, requirin...
We study Stackelberg (leader--follower) tuning of network parameters (tolls, capacities, incentives) in combinatorial congestion games, where selfish us...
Feed-forward transformer models have driven rapid progress in 3D vision, but state-of-the-art methods such as VGGT and $π^3$ have a computational cost t...
Deep learning models, despite their impressive achievements, suffer from high computational costs and memory requirements, limiting their usability in r...
