Daily edition · AI Research
TILens turns technical updates into a focused daily brief: official releases, trusted reporting, and practitioner analysis, deduplicated and organized by topic.
arXiv:2608.16975v1 Announce Type: cross Abstract: With the rapid advancement of large language models, brain-language decoding has achieved remarkable progress. However, it remains unclear whether decoded content…
arXiv:2608.17293v1 Announce Type: new Abstract: Existing research on irregular time-series forecasting has primarily focused on model design, while evaluation metrics remain insufficiently studied. Existing benchmarks…
arXiv:2608.17556v1 Announce Type: cross Abstract: Large Language Models (LLMs) in real-world applications often face the risks of specially crafted prompts designed to bypass the safety controls. Existing guardrail…
arXiv:2607.19083v3 Announce Type: replace Abstract: Equivariant graph neural networks provide a powerful modeling language for three-dimensional scientific data, but their reuse is often limited by implementations tied…
arXiv:2607.16660v2 Announce Type: replace-cross Abstract: The increasing adoption of Large Language Models (LLMs) as AI components in modern software systems introduces distinct security risks to the software supply…
arXiv:2608.17959v1 Announce Type: cross Abstract: State-of-the-art model-based reinforcement learning methods learn neural world models that allow policy improvement by planning in a latent space, without assumptions on…
arXiv:2608.17366v1 Announce Type: new Abstract: Merging finetuned models combines specialized capabilities without joint training or access to the original data. Most methods operate by linear arithmetic in Euclidean…
arXiv:2607.18046v2 Announce Type: replace Abstract: Graphical User Interface (GUI) agents powered by vision-language models hold promise for automating real-world mobile tasks. However, progress is limited by the lack…
arXiv:2608.17347v1 Announce Type: new Abstract: Repetition is a fundamental mechanism in human learning, where revisiting successful experiences strengthens memory, consolidates skills, and improves future performance.…
arXiv:2605.07304v2 Announce Type: replace Abstract: Bandit algorithms solve diverse sequential decision-making problems, but are often too sample-inefficient for from-scratch personalization. To substantially reduce…
arXiv:2608.17370v1 Announce Type: new Abstract: Generative models promise a route to explainable clinical AI: rather than probe a classifier, model the distributions of healthy and diseased patients and read…
arXiv:2506.14790v3 Announce Type: replace Abstract: Recurring concept drift is pervasive in real-world online time series, where the underlying data-generating process repeatedly alternates between a small set of…
arXiv:2608.17373v1 Announce Type: new Abstract: Sample efficiency is a central challenge in reinforcement learning (RL), particularly in image-based domains where agents must learn from high-dimensional visual inputs.…
arXiv:2511.19513v4 Announce Type: replace Abstract: Decentralized learning often involves a weighted global loss with heterogeneous node weights $\lambda$. We revisit two natural strategies for incorporating these…
arXiv:2606.29519v3 Announce Type: replace Abstract: Long-range learning is hard for recurrent networks trained with stochastic gradient descent, because the influence of a past input fades with the lag $\ell$, and if it…
arXiv:2608.17286v1 Announce Type: new Abstract: Compute-optimal scaling laws guide the training of frontier language models yet remain largely unexplored for visual generation. We present a systematic scaling law study…
arXiv:2608.14635v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents are increasingly trained with reinforcement learning in long-horizon, sandboxed environments. Unlike conventional RL, agentic…
arXiv:2608.16038v2 Announce Type: replace Abstract: Post-hoc Graph Neural Network (GNN) explainers commonly follow a Perturb-Query paradigm, inferring the importance of graph elements based on queried predictions to…
arXiv:2608.17894v1 Announce Type: cross Abstract: This paper investigates the feasibility of using residual learning to improve unsteady aerodynamic load prediction for aeroelastic applications. The machine learning…
arXiv:2608.16932v1 Announce Type: new Abstract: Multi-layer locate-then-edit methods for knowledge editing first optimize target residual-stream activations (anchors) at selected layers, then realize them layer by layer…
arXiv:2504.20131v4 Announce Type: replace Abstract: We introduce the LZ penalty, a penalty specialized for reducing degenerate repetitions in autoregressive language models without loss of capability. The penalty is…
arXiv:2608.17567v1 Announce Type: new Abstract: Pretrained molecular language models are increasingly used as molecular encoders for learning structure-property relationships. However, their practical suitability for…
arXiv:2608.17713v1 Announce Type: new Abstract: Agent evaluations and trace-based learning often compare outputs across transformed views through a post-response correspondence treated as neutral preprocessing. We show…
arXiv:2608.16955v1 Announce Type: cross Abstract: Post-disaster damage to terrestrial infrastructure can disrupt wireless coverage,while Uncrewed Aerial Vehicle (UAV) swarms provide a promising solution for rapid…
arXiv:2608.17971v1 Announce Type: new Abstract: The impact of climate variability on food production has led to the creation of various forecasting models that uses machine learning (ML), numerical weather predictors…
arXiv:2605.09623v2 Announce Type: replace-cross Abstract: In recent years, the use of artificial intelligence on resource-constrained IoT devices has grown significantly. However, existing approaches to AI task…
arXiv:2608.14308v1 Announce Type: cross Abstract: We study score-matching diffusion models with a convolutional architecture. We argue that the inductive bias of locality means that the machinery of effective field…
arXiv:2608.16974v1 Announce Type: new Abstract: Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societal inequalities and harming…
arXiv:2408.13323v3 Announce Type: replace-cross Abstract: Solutions of bilevel optimization problems tend to suffer from instability under changes to problem data. In the optimistic setting, we construct a lifted…
arXiv:2605.09058v2 Announce Type: replace-cross Abstract: We introduce Nonlinear GENERIC-Embedded Neural Networks (N-GENNs), a deep learning framework for discovering evolution equations of systems governed by the…
arXiv:2511.17954v2 Announce Type: replace-cross Abstract: Incorporating spatial information, particularly when related to climate, weather, and demographic factors, is crucial for improving underwriting precision and…
arXiv:2311.09614v5 Announce Type: replace-cross Abstract: This study addresses critical gaps in automated lymphoma segmentation from PET/CT images, focusing on issues often overlooked in existing literature. While deep…
arXiv:2608.16939v1 Announce Type: cross Abstract: Training variational quantum models requires choosing between parameter-shift gradients, which are exact but cost $O(P)$ forward evaluations, and simultaneous…
arXiv:2604.15271v4 Announce Type: replace-cross Abstract: Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decision…
arXiv:2608.16928v1 Announce Type: new Abstract: Automatic sensitivity classification of organizational documents is a critical yet underserved problem, where the consequences of misclassification range from regulatory…
arXiv:2608.17620v1 Announce Type: new Abstract: Machine learning models for electroencephalography (EEG) analysis show great promise across a wide range of applications, but their deployment in high-risk domains is…
arXiv:2505.22533v2 Announce Type: replace Abstract: In this paper, we introduce a novel quantum generative model for synthesizing tabular data. Synthetic data is valuable in scenarios where real-world data is scarce or…
arXiv:2608.17981v1 Announce Type: new Abstract: We describe an inference-time architectural enhancement for off-the-shelf foundation models that markedly reduces perplexity and boosts accuracy across generation and…
arXiv:2607.04728v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) post-training for large language models (LLMs) follows a efficient paradigm of "rollout then update", which inevitably results in…
arXiv:2608.17561v1 Announce Type: cross Abstract: The health of marine ecosystems is a critical indicator of global environmental change, yet the physical constraints of underwater observation and the intrinsic…
arXiv:2608.12704v2 Announce Type: replace-cross Abstract: Multi-objective bilevel optimization has wide applications in the AI area such as automated learning and multi-task meta-learning. Although recently some works…
arXiv:2608.17501v1 Announce Type: cross Abstract: Recent efforts toward fully automated AI scientists have demonstrated that language-model agents can generate hypotheses, execute experiments, and draft scientific…
arXiv:2608.17253v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a powerful approach for improving reasoning in language and vision-language models, yet its strongest successes still depend…
arXiv:2608.16923v1 Announce Type: cross Abstract: Networks provide a fundamental representation of relationships among entities. However, real-world networks are often corrupted by noise caused by measurement errors and…
arXiv:2608.17666v1 Announce Type: new Abstract: Bayesian imaging inverse problems often require sampling from high-dimensional posterior distributions. While recent score-based and diffusion models provide expressive…
arXiv:2608.17849v1 Announce Type: new Abstract: Split federated learning (SFL) has emerged as a powerful paradigm for model training at the edge. However, SFL inherently involves discrete decision variables for model…
arXiv:2608.17471v1 Announce Type: cross Abstract: Recent advances in LLM agents have made them increasingly capable of designing methods for complex AI tasks. This raises two central questions about agent-designed…
arXiv:2608.18004v1 Announce Type: new Abstract: Probabilistic modeling of physical fields benefits from both a data-driven prior and known physical structure such as the governing equations. Energy-based models (EBMs)…
arXiv:2603.26747v3 Announce Type: replace-cross Abstract: Recent text-driven motion generation methods span both discrete token-based approaches and continuous-latent formulations. MotionGPT3 exemplifies the latter…
arXiv:2605.31163v3 Announce Type: replace-cross Abstract: We introduce the \emph{design-model framework}: a way to derive efficient recurrent sequence maps from explicit assumptions about memory. A design model writes…
arXiv:2511.06609v5 Announce Type: replace Abstract: The accurate forecasting of complex, high-dimensional dynamical systems from observational data is a fundamental task across numerous scientific and engineering…
arXiv:2608.17802v1 Announce Type: new Abstract: Let $\varepsilon_1,\ldots,\varepsilon_n$ be independent Rademacher signs and let $a=(a_1,\ldots,a_n)\in\R^n$ satisfy the normalization below. For the normalized Rademacher…
arXiv:2608.17948v1 Announce Type: new Abstract: Recent research has leveraged Large Language Models (LLMs) to enhance Automated Feature Engineering (AutoFE) through semantic descriptions and trajectory-based prompting.…
arXiv:2608.17284v1 Announce Type: new Abstract: Irregular time series forecasting is crucial in many domains, such as healthcare and meteorological observation. However, due to the inherent characteristics of irregular…
arXiv:2608.17450v1 Announce Type: cross Abstract: Learning restricted Boltzmann machines (RBMs) is computationally challenging because it requires expectations whose exact evaluation is generally intractable. The…
arXiv:2506.06584v2 Announce Type: replace Abstract: Learning Gaussian Mixture Models (GMMs) is a fundamental problem in statistics and machine learning, with the Expectation-Maximization (EM) algorithm and its popular…
arXiv:2602.21693v2 Announce Type: replace Abstract: Multimodal time series forecasting has garnered significant attention for its potential to provide more accurate predictions than traditional single-modality models by…
arXiv:2602.14784v1 Announce Type: cross Abstract: Breaking long documents into smaller segments is a fundamental challenge in information retrieval. Whether for search engines, question-answering systems, or…
arXiv:2604.20817v2 Announce Type: replace-cross Abstract: Language models trained on natural text learn to represent numbers using periodic features with dominant periods at $T=2, 5, 10$. In this paper, we identify a…
arXiv:2608.17956v1 Announce Type: new Abstract: In the Code World Model paradigm an LLM synthesizes an executable world model that a classical planner searches, and the model is accepted when it reproduces sampled…
arXiv:2608.17914v1 Announce Type: new Abstract: Static Timing Analysis (STA) is a critical step in the design flow of digital integrated circuits, however, obtaining accurate delay estimations can represent a challenge…
arXiv:2510.02625v5 Announce Type: replace Abstract: Missing data in tabular datasets forces practitioners into a hard choice: deploy a general-purpose imputer that may perform poorly for the problem at hand, or wait for…
arXiv:2403.15594v4 Announce Type: replace-cross Abstract: Domestic violence is commonly viewed as a gendered issue that primarily affects women, which tends to leave male victims largely overlooked. This study presents…
arXiv:2606.12289v2 Announce Type: replace Abstract: As Artificial Intelligence models grow in complexity, interpretability has become an indispensable tool for understanding, debugging, and controlling their…
arXiv:2205.11930v3 Announce Type: replace-cross Abstract: Human ratings are the gold standard in NLG evaluation. The standard protocol is to collect ratings of generated text, average across annotators, and rank NLG…
arXiv:2608.17542v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting future embeddings, but the objective admits a trivial solution of a constant encoder, so…
arXiv:2608.17423v1 Announce Type: cross Abstract: GRPO is increasingly used for reinforcement learning of vision-language-action (VLA) policies because, unlike PPO, it does not require training a critic. This…
arXiv:2608.17836v1 Announce Type: new Abstract: As large language models (LLMs) are granted increasing autonomy, it is essential to investigate methods that can induce unsafe behavior. We propose a novel white-box…
arXiv:2607.26654v3 Announce Type: replace-cross Abstract: Post-training alignment is often shallow, eroding under fine-tuning. It remains untested as to whether constitutional midtraining interventions can produce…
arXiv:2510.14656v3 Announce Type: replace-cross Abstract: This work proposes a two-stage physics-informed deep learning framework that combines neural-network-based sampling with statistical inference and constrained…
arXiv:2608.16926v1 Announce Type: new Abstract: Data selection in supervised fine-tuning aims to select a small set of effective samples from large-scale candidate data, reducing training cost while preserving model…
arXiv:2602.12250v2 Announce Type: replace Abstract: Graph neural networks (GNNs) enable powerful unsupervised learning of communities. However, such inference may inadvertently expose sensitive group structures,…
arXiv:2608.17310v1 Announce Type: new Abstract: Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning. However, long-horizon agentic reasoning introduces increasingly branching interactions and…
arXiv:2605.06866v2 Announce Type: replace Abstract: We study finite-iteration behavior of the exact asynchronous recursions used by categorical distributional temporal-difference methods. The analysis covers scalar…
arXiv:2608.17466v1 Announce Type: cross Abstract: Regularized sparse regression has been extensively studied in the offline setting, but online formulation remains relatively under-explored. This gap stems from four key…
arXiv:2608.17641v1 Announce Type: new Abstract: We present rl-triton, an open-source library of high-performance GPU kernels for reinforcement learning credit assignment, implemented in Triton. The core contribution is…
arXiv:2604.16780v2 Announce Type: replace-cross Abstract: This paper presents FairNVT, a lightweight debiasing framework for pretrained transformer-based encoders that improves prediction fairness while preserving task…
arXiv:2608.16970v1 Announce Type: cross Abstract: LLM-based code generation is now embedded in mission-critical pipelines, but defenses against vulnerable output remain post-hoc -- static analyzers, fine-tuned…
arXiv:2601.22170v2 Announce Type: replace-cross Abstract: Large language models (LLMs) process and predict sequences containing text to answer questions, and address tasks including document summarization, providing…
arXiv:2603.24126v2 Announce Type: replace Abstract: When language models are trained by reinforcement learning (RL) to write probabilistic programs, they can artificially inflate their marginal-likelihood reward by…
arXiv:2606.21828v2 Announce Type: replace-cross Abstract: Neural operators are increasingly used to warm-start Newton solvers for nonlinear PDEs, on the premise that a low test error places the initial guess inside the…
arXiv:2608.17174v1 Announce Type: new Abstract: Chronic kidney disease (CKD) progresses silently and severely undermines quality of life, making early detection critical for improving patient outcomes. We present a…
arXiv:2606.01372v2 Announce Type: replace Abstract: Can neural networks learn algebraic rules from visual inputs, or do they merely fit observed patterns? We study this question using MNIST (or EMNIST letters) as states…
arXiv:2608.16914v1 Announce Type: cross Abstract: Digital learning platforms generate rich behavioural traces (digital markers) that offer the potential to identify struggling students early. This paper investigates…
arXiv:2608.16959v1 Announce Type: cross Abstract: Breast cancer is one of the most common types of cancer among women around the world. Rapid detection and early treatment can hinder its progress to more complex stages…
arXiv:2608.17776v1 Announce Type: new Abstract: We demonstrate that RL finetuning an LLM using debate, a two-player adversarial game between a generator and a critic adjudicated by a weaker LLM judge, reduces reward…
arXiv:2608.17342v1 Announce Type: new Abstract: Forecasting cryptocurrency prices remains a formidable challenge due to inherent non-stationarity, abrupt regime shifts, and multi-scale stochastic dependencies.…
arXiv:2508.05778v2 Announce Type: replace Abstract: Nudging is an empirical data assimilation technique that incorporates an observation-driven control term into the model dynamics. The trajectory of the nudged system…
arXiv:2508.06692v3 Announce Type: replace Abstract: Federated learning systems typically allocate gradient compression by link speed. This is sensible when bandwidth and data informativeness align. However, under…
arXiv:2608.17030v1 Announce Type: cross Abstract: The massive overactuation in the human musculoskeletal system makes it challenging to train musculoskeletal models to generate human-like motion via reinforcement…
arXiv:2608.17646v1 Announce Type: new Abstract: This monograph develops elimination geometry (EG), a typed, native-loss, audit-oriented framework for studying when locally optimal objects can be realized by a shared…
arXiv:2608.12700v2 Announce Type: replace Abstract: Systems that generate GPU kernels with language models report high correctness rates. Those rates come from a single loose test: run the kernel on a few random inputs…
arXiv:2512.22287v4 Announce Type: replace Abstract: Synthetic appliance data are essential for developing non-intrusive load monitoring algorithms and enabling privacy preserving energy research, yet the scarcity of…
arXiv:2608.17093v1 Announce Type: cross Abstract: Existing automotive intrusion detection systems (IDSs) for the Controller Area Network (CAN) largely target discrepancies in message timing, frequency, or sequencing and…
arXiv:2509.06777v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) suffer from oversquashing, where structural bottlenecks limit message propagation between distant nodes, hindering tasks that require…
arXiv:2608.18021v1 Announce Type: new Abstract: WEASEL 2.0 is a dictionary-based time series classifier that combines dilated sliding windows with a randomised hyperparameter ensemble and a fixed-size dense feature…
arXiv:2608.17848v1 Announce Type: new Abstract: Geospatial Foundation Models (GFMs) are emerging as a powerful paradigm for learning semantically rich and geographically consistent visual and physical representations.…
arXiv:2507.03631v5 Announce Type: replace Abstract: Extracting interpretable mathematical models from complex dynamical systems is difficult, especially for chaotic dynamics observed with noisy experimental data. We…
arXiv:2608.17519v1 Announce Type: cross Abstract: Vision-based surgical skill assessment has shown strong in-domain results, yet a fundamental question remains unasked: do these models learn transferable representations…
arXiv:2608.17573v1 Announce Type: cross Abstract: In high-dimensional online prediction, the best predictor may depend on only a few features, so regret should scale with sparsity rather than the ambient dimension.…
arXiv:2605.31156v2 Announce Type: replace Abstract: Causal discovery aims to recover directed causal relations from observational and interventional data, providing a basis for mechanistic understanding and reliable…
arXiv:2604.06416v2 Announce Type: replace-cross Abstract: Although LLM context lengths have grown, there is evidence that their ability to integrate information across long-form texts has not kept pace. We evaluate one…
arXiv:2608.16931v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for EEG decoding by learning reusable representations from large-scale…
arXiv:2606.31856v2 Announce Type: replace Abstract: We study layered models, including feedforward networks, ResNets, and transformers, by limiting each layer to a width of $d = 3$, i.e., $\mathbb{R}^3$ as…
arXiv:2608.12805v2 Announce Type: replace Abstract: Access to clinical data is essential for developing reliable healthcare machine learning systems, but direct use of electronic health records is constrained by privacy…
arXiv:2608.17440v1 Announce Type: new Abstract: Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely solely on sensor…
arXiv:2608.17381v1 Announce Type: cross Abstract: Biomolecular design underpins applications from molecular recognition to therapeutics and synthetic biology, yet de novo interaction design remains…
arXiv:2608.16902v1 Announce Type: cross Abstract: The increasing integration of machine learning in healthcare has highlighted critical challenges related to fairness, transparency, and health equity. Specifically, the…
arXiv:2608.18036v1 Announce Type: cross Abstract: MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements. Parallel developments in sparse phase retrieval have shown that…
arXiv:2608.17678v1 Announce Type: new Abstract: Drug discovery and development underpins healthcare but remains costly and failure-prone. A critical bottleneck lies in predicting molecular properties such as solubility,…
arXiv:2608.16978v1 Announce Type: cross Abstract: Turning a frontier vision-language model into a robot policy usually means fine-tuning it to emit an action representation it never saw in pretraining, which throws away…
arXiv:2608.15940v2 Announce Type: replace-cross Abstract: Modern encoder-decoder systems can produce fluent text even when their input contains no recoverable message. We study this failure in ASR and NMT through the…
arXiv:2608.12447v2 Announce Type: replace Abstract: Trained transformer models develop privileged bases: coordinate axes whose statistics differ from the rest of the residual stream. But what kind of direction does such…
arXiv:2608.17524v1 Announce Type: new Abstract: This preliminary paper outlines a planned evaluation benchmark for Explainable Reinforcement Learning (XRL) methods. Current evaluations rely on functionally-grounded…
arXiv:2608.17180v1 Announce Type: new Abstract: Contextual Reinforcement Learning (CRL) seeks to generalize classical RL by maximizing task coverage across a context space of related tasks. While prior works often train…
arXiv:2608.01658v3 Announce Type: replace-cross Abstract: For mirror descent generated by a Legendre kernel, perhaps one of the most basic question in optimization is this: must every accumulation point of a bounded…
arXiv:2608.16972v1 Announce Type: new Abstract: Machine learning has become an essential component of modern healthcare, where the integration of heterogeneous data sources offers unprecedented opportunities to improve…
arXiv:2401.02020v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs), known for their biologically plausible architecture, face the challenge of limited performance. The self-attention mechanism,…
arXiv:2608.17530v1 Announce Type: cross Abstract: Continual pre-training of large language models must acquire new information without erasing old knowledge. Existing replay methods often choose a global old/new mixture…
arXiv:2608.16930v1 Announce Type: new Abstract: Existing multi-task learning methods rely on hard sharing, multiple paths or experts, adaptive sharing, and dynamic expansion. However, their capacity changes are usually…
arXiv:2608.17753v1 Announce Type: new Abstract: Short-duration heavy-rainfall warning determines whether 1 h rainfall will exceed a threshold within a target-station neighborhood over the next few hours. Multitemporal…
arXiv:2608.17841v1 Announce Type: cross Abstract: Multi-armed bandit algorithms are evaluated by regret, yet comparable regret can coexist with different allocations across independent runs. We study the trade-off…
arXiv:2608.17248v1 Announce Type: cross Abstract: When computational models (either physics-based or data-driven) are used for the sensitivity analysis of engineering systems, the sensitivity estimate is affected by the…
arXiv:2507.15240v2 Announce Type: replace Abstract: For classification with imbalanced class frequencies, i.e., imbalanced classification (IC), standard accuracy is known to be misleading as a performance measure. While…
arXiv:2506.10775v3 Announce Type: replace Abstract: In monotone classification, the input is a multi-set $P$ of points in $\mathbb{R}^d$, each associated with a hidden label from $\{-1, 1\}$. The goal is to identify a…
arXiv:2604.24749v4 Announce Type: replace Abstract: While the optimal sample complexity of binary classification in terms of the VC dimension is well-established, determining the optimal sample complexity of multiclass…
arXiv:2608.17592v1 Announce Type: cross Abstract: The communication demands of distributed model prediction control (DMPC) can overwhelm even advanced wireless communication technologies as agents must exchange a…
arXiv:2605.22556v2 Announce Type: replace Abstract: Digital elevation models (DEMs) underpin terrain analysis in Geographic Information Systems (GIS), but commonly as raster representation, they rely on interpolation…
arXiv:2608.17250v1 Announce Type: cross Abstract: This paper develops an adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs. The analysis of spatio-temporal multi-physics…
arXiv:2608.11444v2 Announce Type: replace-cross Abstract: Drug response prediction (DRP) models are an active area of research in pharmacogenomics, with growing potential to accelerate the identification of effective…
arXiv:2603.10302v3 Announce Type: replace Abstract: A plethora of protein language models have been released in recent years. Yet comparatively little work has addressed how to best sample from them to optimize desired…
arXiv:2605.17231v2 Announce Type: replace Abstract: Activation steering has emerged as a lightweight approach for modifying language model behavior without parameter updates, yet existing methods remain brittle:…
arXiv:2608.16971v1 Announce Type: cross Abstract: Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema. Learning this extraction…
arXiv:2608.02965v3 Announce Type: replace Abstract: Magnetic components in high-frequency, high-power-density converters are increasingly driven by non-sinusoidal flux-density waveforms with fast transitions, minor-loop…
arXiv:2608.17744v1 Announce Type: cross Abstract: Take three frontier mixture-of-experts models (Alibaba, OpenAI, NVIDIA; 3.6-4.0B active parameters each) and fine-tune them to reason in a low-resource language. On…
arXiv:2608.18066v1 Announce Type: cross Abstract: Memory-based self-improving agents--those that learn from an online stream of tasks and improve over time by maintaining a textual memory bank--have shown great promise…
arXiv:2608.16873v2 Announce Type: replace Abstract: High-fidelity finite-element simulations can provide accurate numerical predictions for side-branch resonators, but large simulation datasets are expensive to generate…
arXiv:2608.17947v1 Announce Type: cross Abstract: Large language models can generate executable programs, which makes it possible to search directly over procedural content generators rather than individual levels. We…
arXiv:2605.27219v2 Announce Type: replace Abstract: Collaborative analysis of decentralized confidential datasets is important, but direct sharing of original datasets is often restricted by privacy and institutional…
arXiv:2606.06837v2 Announce Type: replace-cross Abstract: Scripted vs spontaneous speech detection is appealing for interview guardrails, but benchmark performance can be inflated by shortcuts tied to corpus identity,…
arXiv:2608.18026v1 Announce Type: new Abstract: Large-scale, high-dimensional tabular regression remains challenging: tree-based models are robust but lack end-to-end representation learning, while deep models enable…
arXiv:2608.17987v1 Announce Type: cross Abstract: The rapid growth of social media has greatly influenced political discourse, highlighting the need to understand individual political ideologies and their temporal…
arXiv:2608.17687v1 Announce Type: cross Abstract: Despite their widespread use, Large Language Models (LLMs) remain limited by a fundamental problem: the generation of plausible but false content, known as…
arXiv:2608.16373v2 Announce Type: replace Abstract: Despite comprising over 70% of its surface, the world's oceans are critically underobserved compared to the land surface or the atmosphere. Understanding the global…
arXiv:2608.16913v1 Announce Type: new Abstract: Road safety monitoring has historically been reactive, relying on crash-record analysis after fatalities and injuries have already occurred. Proactive identification of…
arXiv:2608.17034v1 Announce Type: new Abstract: Post-training can unlock new capabilities and improve performance on specialized tasks, but sometimes at the cost of catastrophic forgetting in other domains. This poses a…
arXiv:2608.17796v1 Announce Type: cross Abstract: Deep learning-based DDoS detectors for 5G-enabled cyber-physical systems face scarce labeled attack data and unrealistic synthetic substitutes, which limit robustness…
arXiv:2608.18040v1 Announce Type: new Abstract: Sampling from a diffusion model typically requires many forward passes through a large neural network, making generation computationally expensive. While much work has…
arXiv:2605.23037v2 Announce Type: replace Abstract: Two-phase heat transfer underpins boiling, condensation, immersion cooling, flow boiling, energy conversion, and electronics thermal management, but its coupled…
arXiv:2211.14297v5 Announce Type: replace-cross Abstract: We introduce and analyze an improved variant of nearest neighbors (NN) for estimation with missing data in latent factor models. We consider a matrix completion…
arXiv:2608.18008v1 Announce Type: new Abstract: Combining large language models with reinforcement learning is increasingly explored, yet the theoretical status of LLM-derived reward signals is often left implicit. We…
arXiv:2608.16925v1 Announce Type: new Abstract: We build an instrument that reads, from a single fit and with no oracle, whether the operator a hybrid PDE-parameter estimator postulates is wrong-and separates that from…
arXiv:2608.17822v1 Announce Type: cross Abstract: Accurate building height information at the individual footprint scale is essential for material stock accounting and post-disaster damage assessments yet remains…
arXiv:2509.25459v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) show promise in generating long-form scientific explanations that synthesize evidence and connect multiple factors. However, in…
arXiv:2608.17091v1 Announce Type: new Abstract: While publicly available electricity market data presents a valuable resource for forecasting research, the field lacks established benchmark datasets for standardized…
arXiv:2608.17203v1 Announce Type: cross Abstract: Contrastive learning has become a cornerstone of modern representation learning, powering CLIP-style models that underpin text-to-image generation, vision-language…
arXiv:2608.16921v1 Announce Type: cross Abstract: Effective cybersecurity operations require timely and accurate analysis of large-scale heterogeneous security information; however, analysts increasingly struggle with…
arXiv:2608.17163v1 Announce Type: new Abstract: Off-policy reinforcement learning (RL) has become increasingly sample-efficient, enabling applications such as RL fine-tuning of Vision-Language-Action models into…
arXiv:2608.17775v1 Announce Type: new Abstract: Anomaly detectors are hardest to deploy exactly where training data is scarcest: a newly commissioned production line has a handful of verified "golden" samples and no…
arXiv:2603.04061v2 Announce Type: replace-cross Abstract: Quantum measurements are the means by which we recover messages encoded into quantum states. They are at the forefront of quantum hypothesis testing, wherein the…
arXiv:2607.26369v2 Announce Type: replace Abstract: Rotary Position Embedding (RoPE) has been widely adopted in transformer-based large language models. However, its log-linear frequency schedule, originally designed to…
arXiv:2608.18025v1 Announce Type: new Abstract: GPT-style models achieve strong performance by representing language with finite vocabularies of reusable discrete tokens. This success has motivated symbolic music…
arXiv:2607.09812v2 Announce Type: replace-cross Abstract: Microbial density is clinically important for tumor assessment and treatment decision-making, and recent advances in deep learning suggest that it can be…
arXiv:2509.05574v3 Announce Type: replace-cross Abstract: We prove that, for many standard link invariants, both the proportion of distinct invariant values and the detection probability among prime alternating links…
arXiv:2608.17941v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models but relies on costly rollout exploration. Assigning the…
arXiv:2501.05819v2 Announce Type: replace Abstract: Uncrewed Aerial Vehicles (UAVs) are increasingly used in modern communication networks. However, challenges in decision-making and digital modeling continue to hinder…
arXiv:2608.17896v1 Announce Type: new Abstract: Recurrent models process long contexts efficiently by compressing their history into a fixed-size state, but modern architectures typically do so in a single causal pass…
arXiv:2608.17173v1 Announce Type: cross Abstract: Online decision making often requires navigating a landscape shaped by both dynamic contexts and strategic interactions. In competitive pricing, for example, hotels must…
arXiv:2608.17923v1 Announce Type: cross Abstract: Appendicitis is one of the most common abdominal emergencies worldwide and requires prompt diagnosis and treatment to prevent life-threatening conditions. However,…
arXiv:2601.20226v3 Announce Type: replace Abstract: We propose two methodologies for modelling aggregated supply and demand curves in the EPEX SPOT Day-Ahead market, emphasizing generative models as a way to recover…
arXiv:2608.16963v1 Announce Type: new Abstract: Learning analytics often treats unsupervised clusters of intelligent tutoring system (ITS) logs as learner types that should predict learning. We test that assumption on…
arXiv:2608.17411v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has become a widely used approach for post-training Large Language Models (LLMs) for reasoning. In GRPO, the group gradients…
arXiv:2608.17055v1 Announce Type: cross Abstract: Large Language Models (LLMs) are machine learning (ML) models that have an increasingly large carbon footprint through their development and use. Efforts to increase the…
arXiv:2608.17069v1 Announce Type: cross Abstract: Federated deployments of variational quantum classifiers are attractive for cross-organisation risk prediction in supply chains, because raw data never leaves the…
arXiv:2312.06576v2 Announce Type: replace Abstract: Graph Transformers (GTs) facilitate the comprehension of complex relationships on graph-structured data by leveraging self-attention of the possible pairs of nodes.…
arXiv:2608.16929v1 Announce Type: new Abstract: Predicting 30-day hospital readmission is essential for assessing patient stability and optimizing healthcare resources. As clinical risk evolves with the accumulation of…
arXiv:2505.22104v2 Announce Type: replace-cross Abstract: Shielding has emerged as a promising approach for ensuring safety of AI-controlled autonomous systems. The algorithmic goal is to compute a shield, which is a…
arXiv:2608.17957v1 Announce Type: new Abstract: Tabular Foundation Models (TFMs) increasingly rely on in-context learning, where a model receives labelled examples at inference time and predicts labels for new inputs…
arXiv:2608.16927v1 Announce Type: new Abstract: As supervised fine-tuning data continues to scale, selecting high-value subsets from large candidate pools is crucial for reducing training cost and improving model…
arXiv:2608.17333v1 Announce Type: cross Abstract: Modern probabilistic time-series forecasters often express uncertainty through forecast samples. While typically converted into nominal prediction regions using…
arXiv:2603.24472v4 Announce Type: replace-cross Abstract: Self-distillation has emerged as an effective post-training paradigm for LLMs, often improving performance while shortening reasoning traces. However, in…
arXiv:2608.17343v1 Announce Type: new Abstract: Data-driven algorithm design frames hyperparameter tuning as a statistical learning problem, but establishing generalization guarantees remains challenging due to the…
arXiv:2608.17616v1 Announce Type: cross Abstract: We present MoNe, a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference without retraining. MoNe reads…
arXiv:2608.17823v1 Announce Type: new Abstract: Powered two-wheeler riders face critical safety challenges in low- and middle-income countries, yet limited studies exist on how cognitive stressors such as Time Pressure…
arXiv:2608.16947v1 Announce Type: cross Abstract: Huang, Lou, and Xiao introduced Dynamic Mixture-of-Experts Serving and gave an O(sqrt(log k))-competitive randomized algorithm for its integral primal problem, where k…
arXiv:2608.17164v1 Announce Type: new Abstract: Textual context such as news, reports, and logs can provide valuable signals for time series forecasting, especially when future dynamics are driven by external events…
arXiv:2608.17147v1 Announce Type: cross Abstract: Image-to-image face generators are widely used, and visual dissimilarity between their outputs and source images is sometimes treated as evidence of privacy. Auditing…
arXiv:2604.22258v2 Announce Type: replace Abstract: Gaseous microemboli (GME) represent a common complication of cardiac structural interventions across both surgical and transcatheter approaches. Intraoperative…
arXiv:2608.18062v1 Announce Type: cross Abstract: Language model tokenizers are typically selected with minimal evaluation, despite the fact that their design choices directly impact model capabilities. This can be…
arXiv:2608.17132v1 Announce Type: new Abstract: Recovering the directed acyclic graph (DAG) of a structural equation model (SEM) from observational data is a central problem in causal discovery. The iterative gradient…
arXiv:2608.09218v2 Announce Type: replace Abstract: Score-driven filters update a time-varying parameter by multiplying a scaled log-likelihood score by a scale parameter that controls the magnitude of the update. We…
arXiv:2608.16906v1 Announce Type: cross Abstract: Dynamic community detection is commonly addressed either by full-snapshot recomputation or by solver-specific dynamic procedures. Full recomputation preserves the…
arXiv:2608.17804v1 Announce Type: new Abstract: Practical LLM unlearning is usually evaluated through two objectives: suppress target-specific knowledge and preserve non-target utility. In generative QA, this leaves a…
arXiv:2608.17223v1 Announce Type: cross Abstract: Financial-news direction prediction has become a popular NLP benchmark, yet reported gains depend critically on whether the train-test split is chronological or random,…
arXiv:2608.17246v1 Announce Type: new Abstract: This article investigates several physics-informed and hybrid machine learning strategies that incorporate physics knowledge in experimental data-driven deep-learning…
arXiv:2605.12763v2 Announce Type: replace Abstract: Rich learning in recurrent neural networks often proceeds through sudden transitions in latent dynamics, but there is little theory predicting how gradient descent…
arXiv:2608.17162v1 Announce Type: new Abstract: What a language model internalizes from fine-tuning is usually diagnosed after the fact. We make it an experimental variable. OraclePhys is a systematic fine-tuning…
arXiv:2512.06208v4 Announce Type: replace-cross Abstract: Inference of standard convolutional neural networks (CNNs) on FPGAs often incurs high latency and a long initiation interval due to the deep nested loops…
arXiv:2608.17079v1 Announce Type: new Abstract: Conformal prediction provides distribution-free prediction intervals but relies on exchangeability, an assumption often violated in economic forecasting because of…
arXiv:2608.12710v2 Announce Type: replace Abstract: Muon, a more recently developed optimizer, is useful for matrix-wise models in AI areas. Although many works have studied Muon and its variants, these methods are…
arXiv:2302.12177v5 Announce Type: replace-cross Abstract: Predicting the binding sites of target proteins plays a fundamental role in drug discovery. Most existing deep-learning methods consider a protein as a 3D image…
arXiv:2608.17070v1 Announce Type: new Abstract: With the growing deployment of machine learning models, formal guarantees of the robustness and fairness of these models have become increasingly important in…
arXiv:2608.16968v1 Announce Type: cross Abstract: In this paper, we consider methods for the diagonal multi-omics integration of heterogeneous datasets. Several approaches to the nature of biological heterogeneity are…
arXiv:2603.08913v2 Announce Type: replace Abstract: Genomic language models (GLMs) have emerged as powerful tools for learning representations of DNA sequences, enabling advances in variant prediction, regulatory…
arXiv:2608.13756v2 Announce Type: replace Abstract: Two GPU kernels implementing the same scaled INT8 GEMM interface are usually treated as interchangeable. We test that assumption: holding the checkpoint, prompts,…
arXiv:2608.17181v1 Announce Type: new Abstract: Reinforcement learning (RL) theory fundamentally depends on probability theory through the Markov chain. There is a deep connection between probability theory and…
arXiv:2608.18033v1 Announce Type: cross Abstract: Categorising invoices into the correct General Ledger (GL) code underpins financial reporting and tax compliance. This is a skilled accounting judgement rather than a…
arXiv:2608.17231v1 Announce Type: new Abstract: Low-cost, scalable screening for dementia remains an open problem. Imaging-based diagnosis is costly and hard to deploy widely. Electroencephalography (EEG) is portable…
arXiv:2608.17087v1 Announce Type: new Abstract: Linear temporal logic is a modal extension of propositional logic that allows one to state how a system should behave over time. Its canonical domain is the booleans, but…
arXiv:2608.15062v2 Announce Type: replace-cross Abstract: Scaling transformer language models creates an inherent tension between expressivity and memory efficiency. While unique weights across layers preserve…
arXiv:2509.00203v3 Announce Type: replace Abstract: Parameterized partial differential equations (PDEs) underpin the mathematical modeling of complex systems in diverse domains, including engineering, healthcare, and…
arXiv:2008.08041v3 Announce Type: replace-cross Abstract: In the big data era, deep learning and intelligent data mining technique solutions have been applied by researchers in various areas. Forecast and analysis of…
arXiv:2608.17063v1 Announce Type: new Abstract: Large language models can be fine-tuned into specialized classifiers that perform well across diverse text tasks and make complex judgments, but they typically expose only…
arXiv:2608.17209v1 Announce Type: cross Abstract: End-to-end vision-language-action (VLA) and world-action models offer an elegant route to general-purpose robotics, but their reliability is bounded by validated…
arXiv:2608.18055v1 Announce Type: cross Abstract: Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan-specific…
arXiv:2608.17628v1 Announce Type: cross Abstract: Developing robots capable of understanding and manipulating objects requires compact, interpretable, and generalizable representations. This work proposes a…
arXiv:2606.20560v2 Announce Type: replace Abstract: LLM reasoning transparency is a critical affordance for understanding model decisions, mitigating misuse and misalignment, and debugging surprising model behaviors.…
arXiv:2608.17103v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have achieved remarkable performance in node classification tasks, motivating growing interest in methods capable of explaining their…
arXiv:2606.01846v2 Announce Type: replace Abstract: Mosquito-borne infectious diseases cause more than 700000 deaths worldwide each year. The long-term use of conventional chemical insecticides has induced serious…
arXiv:2608.04180v2 Announce Type: replace Abstract: Feature selection is a critical step in electronic health record (EHR)-based predictive modeling, where input variables are often high-dimensional, sparse, noisy, and…
arXiv:2604.15794v2 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved remarkable success, underpinning diverse AI applications. However, they often suffer from performance degradation due to…
arXiv:2608.17151v1 Announce Type: cross Abstract: Cell mimicry arises when different cell types appear morphologically similar. Human pathologists resolve this ambiguity using surrounding tissue context, whereas current…
arXiv:2608.17722v1 Announce Type: cross Abstract: Vision-Language models (VLMs) achieve outstanding performance largely due to the amount of training data available on the internet. At the same time, data holders (e.g.,…
arXiv:2608.17213v1 Announce Type: new Abstract: This paper challenges the pessimistic meta-inductive argument against scientific realism by undermining its inductive step rather than its historical premise. Although…
arXiv:2608.16956v1 Announce Type: cross Abstract: API buyers purchase a dated contract, not a model name alone: the contract includes the requested and served model, reasoning-effort term or its omission, output rail,…
arXiv:2502.00213v5 Announce Type: replace Abstract: Transformers are difficult to optimize with stochastic gradient descent (SGD) and largely rely on adaptive optimizers such as Adam. Despite extensive efforts, the…
arXiv:2608.17135v1 Announce Type: new Abstract: Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of…
arXiv:2604.00547v2 Announce Type: replace-cross Abstract: Unified Multimodal Large Models (UMLMs) integrate understanding and generation capabilities within a single architecture. While unified architectures expand…
arXiv:2608.15790v2 Announce Type: replace Abstract: Crevasse mapping from uncrewed aerial vehicle (UAV) imagery matters for glaciological research and for field safety in glaciated terrain. Yet, pixel-level annotation…
arXiv:2608.16965v1 Announce Type: new Abstract: The dominance of majority classes in real-world datasets poses a fundamental challenge to randomized neural networks, often biasing decision boundaries and overlooking…
arXiv:2608.18061v1 Announce Type: new Abstract: We give a two-player zero-sum repeated game between a learner and nature whose value identity generates Bayesian updating and an exact accounting of exponential-weights…
arXiv:2608.17374v1 Announce Type: cross Abstract: Motivated by a conjecture of Vaikuntanathan and Zamir, we study the pseudo-mixing of Kac's walk on $\mathrm{SO}(n)$: whether short trajectories are indistinguishable…
arXiv:2608.17965v1 Announce Type: new Abstract: Online log anomaly detection is critical for maintaining the reliability of large-scale computing systems. Although recent language model-based log anomaly detectors…
arXiv:2608.17452v1 Announce Type: new Abstract: Machine learning methods predict many real-world systems with remarkable accuracy, but they are typically treated as black boxes that offer no insight into which…
arXiv:2602.06323v3 Announce Type: replace Abstract: Epidemiological forecasting from surveillance data is a hard problem and hybridizing mechanistic compartmental models with neural models is a natural direction. The…
arXiv:2608.17190v1 Announce Type: new Abstract: Dimensionality reduction methods are instrumental to visualize high-dimensional data, and t-SNE stands as one of the most widely used methods due to its emphasis on local…
arXiv:2608.17268v1 Announce Type: new Abstract: Curriculum learning has been widely adopted in the post-training of large language models by organizing training data from easy to hard. However, its effectiveness varies…
arXiv:2608.16905v1 Announce Type: cross Abstract: While the existence of postmortem privacy is increasingly acknowledged (such as the protection of the presence of deceased within digital spaces), far less attention has…
arXiv:2605.31281v2 Announce Type: replace Abstract: As wind turbine fleets age, data-driven reliability engineering and maintenance optimisation are essential to manage lifecycle expenditure and support asset life…
arXiv:2608.18072v1 Announce Type: new Abstract: Purpose: To develop and evaluate a locally deployed multi-agent AI system for radiology report structuring and quality assurance. Materials and Methods: This retrospective…
arXiv:2608.17938v1 Announce Type: new Abstract: Small language models can grade open-ended examination answers as reliably as substantially more expensive models when they grade against an explicit rubric. We test this…
arXiv:2608.17810v1 Announce Type: new Abstract: The evaluation of large language models (LLMs) relies heavily on human-designed assessments, implicitly assuming that AI and humans employ similar underlying cognitive…
arXiv:2510.01832v2 Announce Type: replace Abstract: Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complicates usage, and…
arXiv:2604.03553v3 Announce Type: replace-cross Abstract: AI is increasingly supporting, accelerating, and automating scientific discovery across subjects. Yet, the adoption of AI in historical research remains limited…
arXiv:2604.03237v2 Announce Type: replace-cross Abstract: As AI systems increasingly support human decision making, a central challenge is determining what information helps people recognize when to rely on AI…
arXiv:2608.17330v1 Announce Type: cross Abstract: Large language models for medical consultation are often evaluated after a clinical problem has already been made clear, although real consultations may begin with a…
arXiv:2608.17325v1 Announce Type: new Abstract: Tokenization is a fundamental component of language modeling pipelines. Despite its importance, it is often fixed, even though it significantly impacts model performance…
arXiv:2608.17171v1 Announce Type: new Abstract: Many table-centric NLP tasks such as NL2SQL first retrieve relevant tables from large collections using keyword search. Recent work uses LLMs to generate natural-language…
arXiv:2608.16185v2 Announce Type: replace Abstract: LLM agents increasingly answer questions over dynamic raw-document collections, where files may change before preprocessing, and relevant evidence (spans, sections,…
arXiv:2608.17445v1 Announce Type: cross Abstract: Most large language model services use stateless defenses, which judge only the current request, to refuse harmful tasks. Decomposition attacks exploit this limitation…
arXiv:2608.17950v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable multi-hop reasoning capabilities over long contexts, yet the internal mechanisms enabling these distant cognitive leaps…
arXiv:2605.24930v2 Announce Type: replace Abstract: Transformer-based LLMs achieve strong results on many language tasks; however, long inputs remain challenging because context windows are finite, and prefill latency…
arXiv:2608.18027v1 Announce Type: new Abstract: Humans continuously learn from experience, whereas conventional large language model (LLM) evaluations ignore the models' ability to improve through inference-time…
arXiv:2601.03570v2 Announce Type: replace Abstract: Human beings primarily understand the world through concepts (e.g., dog), abstract mental representations that structure perception, reasoning, and learning. However,…
arXiv:2608.17075v1 Announce Type: new Abstract: Next-encounter ICD forecasting predicts which standardized diagnosis codes will be documented at a future visit from the longitudinal record available beforehand. The task…
arXiv:2608.17168v1 Announce Type: new Abstract: Reasoning has become a standard technique and feature for contemporary LLMs; however, its application and quality in the context of demanding legal-oriented tasks, such as…
arXiv:2608.17205v1 Announce Type: new Abstract: Vision-language models (VLMs) combine images and text, but when the two conflict and one becomes harder to read, it is unclear how a model shifts its reliance between…
arXiv:2608.17911v1 Announce Type: new Abstract: As LLM agents operate across structured workflows and sessions, preserving long-term history does not ensure that later contexts can recover relevant evidence through a…
arXiv:2608.17827v1 Announce Type: new Abstract: Public institutions face a persistent challenge in selecting LLMs suited to their specific context. Existing benchmarks, however, are of limited use as they primarily…
arXiv:2603.02876v2 Announce Type: replace Abstract: Large Language Model personas, explicit profiles specifying a user's attributes, preferences, and behavioural tendencies, are increasingly used to simulate human…
arXiv:2608.02148v3 Announce Type: replace-cross Abstract: Multimodal representation learning is a cornerstone of modern AI. By encoding multimodal queries and targets into vectors, it powers industrial search and…
arXiv:2604.08525v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are trained to align with user preferences through methods like reinforcement learning. Yet models are beginning to be deployed not…
arXiv:2608.18041v1 Announce Type: new Abstract: Language has two parameters. Count how often words occur together and you estimate amplitude, the strength of association. Word embeddings and attention weights refine…
arXiv:2608.13538v2 Announce Type: replace Abstract: Sparse autoencoders (SAEs) are proposed to extract numerous features from large language model (LLM) representations, yet explaining these features still relies…
arXiv:2602.17127v2 Announce Type: replace Abstract: Large language models increasingly serve as reasoning layers in multi-agent systems, where one provider's models may generate, judge, and summarize within a single…
arXiv:2608.17587v1 Announce Type: new Abstract: Expert-written natural language skills can improve tool-using agents, yet agent-authored skills perform 8-11 points worse than using no skill. This gap suggests that…
arXiv:2607.07436v2 Announce Type: replace-cross Abstract: A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures? Skill retirement is the structural…
arXiv:2608.17379v1 Announce Type: new Abstract: We introduce PTXBench, a benchmark for evaluating and adapting large language models (LLMs) to use architecture-specific PTX for GPU kernel optimization. PTXBench measures…
arXiv:2608.12313v2 Announce Type: replace-cross Abstract: Video creative agents still lack an effective way to learn from high-quality human films, limiting their ability to produce cinematic-grade videos. A key…
arXiv:2507.06185v2 Announce Type: replace-cross Abstract: In July 2025, 18 academic manuscripts on arXiv contained hidden instructions that manipulated AI-assisted peer review (indirect prompt injection). Instructions…
arXiv:2503.20182v2 Announce Type: replace Abstract: As large language models (LLMs) increasingly function as human-like assistants exhibiting human-like personality traits, understanding their behavioral characteristics…
arXiv:2608.17188v1 Announce Type: new Abstract: Multi-agent AI workflows are limited not only by model quality but by token cost, latency, and context-window quality. This paper presents a practitioner framework for…
arXiv:2608.17931v1 Announce Type: new Abstract: Recent advances in AI have revolutionized speech processing, yet effective speech understanding requires discerning not just what is said, but how it is said. Speech…
arXiv:2603.30025v3 Announce Type: replace Abstract: Automated fact-checking pipelines typically begin with a filtering stage that decides which claims are worth verifying, given that the later evidence retrieval and…
arXiv:2606.23671v4 Announce Type: replace Abstract: Prior work shows that large language models (LLMs) exhibit introspective capability on benign tasks. We extend the question to safety contexts and examine how reliably…
arXiv:2608.17288v1 Announce Type: new Abstract: GPT attention measures token compatibility through dot-product similarity. This mechanism is simple, effective, and memory-efficient. But it does not explicitly model…
arXiv:2608.17536v1 Announce Type: new Abstract: Legal consultation questions exhibit multi-level complexity. A single retrieval strategy often leads to over-reasoning for simple questions and poor interpretability for…
arXiv:2608.17809v1 Announce Type: new Abstract: Humans naturally form and express beliefs in daily communication, e.g., "I think the answer is 3" or "I suppose that's right." Such beliefs inevitably intertwine with fact…
arXiv:2608.17644v1 Announce Type: cross Abstract: Agents increasingly interpret a person's natural-language preferences by querying an LLM for numerical preference judgments, e.g., by asking how much the person would be…
arXiv:2603.09872v2 Announce Type: replace Abstract: Recent work has found that contemporary language models such as transformers can become so good at next-word prediction that the probabilities they calculate become…
arXiv:2501.06286v3 Announce Type: replace Abstract: Multi-hop question answering requires a system to identify and integrate evidence distributed across documents, yet large language models remain vulnerable to…
arXiv:2603.06505v2 Announce Type: replace Abstract: Automatic speech recognition (ASR) has benefited from advances in pretrained speech and language models, yet most systems remain constrained to monolingual settings…
arXiv:2608.17150v1 Announce Type: cross Abstract: To effectively collaborate with users on knowledge-intensive tasks, Large Language Models (LLMs) must perform information calibration: matching content to a user's…
arXiv:2608.17053v1 Announce Type: cross Abstract: A bounded agent may obtain information for a decision from its own past, from peers, or from both sources. Retaining task-relevant history can reduce later…
arXiv:2608.12746v2 Announce Type: replace-cross Abstract: Object hallucination in multimodal large language models arises when language priors and corpus co-occurrence bias outweigh the visual evidence, with nothing…
arXiv:2608.17994v1 Announce Type: new Abstract: Using LLMs as judges has become standard practice for evaluating model outputs at scale. This is particularly common for subjective, open-ended tasks such as assessing…
arXiv:2601.07398v2 Announce Type: replace-cross Abstract: Polarization research has demonstrated how people cluster in homogeneous groups with opposing opinions. However, this effect emerges not only through interaction…
arXiv:2608.17102v1 Announce Type: new Abstract: Modern multimodal foundation models (MFMs) have made rapid progress on tasks requiring integrated perception across speech, vision, and language, including emotion…
arXiv:2608.17516v1 Announce Type: new Abstract: Gender bias or other social biases in large language models (LLMs) are frequently evaluated with question answering or survey benchmarks where the LLM needs to give a…
arXiv:2608.16934v1 Announce Type: cross Abstract: RTL code generation is a critical stage in hardware design, and the emergence of agentic systems offers new opportunities to automate this process. To generate correct…
arXiv:2608.17605v1 Announce Type: new Abstract: Conversational AI is moving beyond isolated text prompts toward sustained, multimodal interaction. In real conversations, users clarify goals, revise requests, interrupt…
arXiv:2608.16620v2 Announce Type: replace Abstract: Palmyra x6 is a large language model optimized for use with enterprise-oriented agentic tasks. The model was built by post-training a Mixture-of-Experts base model…
arXiv:2608.07531v2 Announce Type: replace Abstract: Search-augmented language agents should retrieve external information only when necessary and ground their answers in retrieved evidence. Existing external rewards…
arXiv:2608.13706v2 Announce Type: replace Abstract: Existing defenses against hallucination in retrieval-augmented and multi-agent pipelines remain partial: evidence is trusted despite modality disagreement, debate…
arXiv:2608.17096v1 Announce Type: new Abstract: The Voynich manuscript (Beinecke MS 408) is usually analysed on three unstated assumptions: that its glyphs are letters, that the strings between blanks are words, and…
arXiv:2602.13110v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as scalable judges in pairwise evaluation, but they remain prone to miscalibration and biases. We propose…
arXiv:2608.17356v1 Announce Type: new Abstract: Most automated essay scoring (AES) systems output a single holistic score without interpretable evidence and rely on closed APIs that introduce data privacy and cost…
arXiv:2602.13840v2 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly deployed in personalized tasks involving sensitive, context-dependent information, where privacy violations may…
arXiv:2606.21802v2 Announce Type: replace Abstract: Standard tokenwise diffusion LMs keep training corruption and inference commitment at token granularity throughout denoising. At high noise, this leaves scattered…
arXiv:2608.17719v1 Announce Type: cross Abstract: Context: Software systems that depend on commercial large language model APIs must migrate to successor versions when vendors deprecate older models. Migration decisions…
arXiv:2608.15382v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy rather than reasoning…
arXiv:2608.17153v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has significantly enhanced the performance of large language models (LLMs), yet these systems remain vulnerable to knowledge-poisoning…
arXiv:2601.18899v3 Announce Type: replace Abstract: Large Language Model (LLM)-powered Automatic Speech Recognition (ASR) systems achieve strong performance with limited resources by linking a frozen speech encoder to a…
arXiv:2608.17399v1 Announce Type: new Abstract: Text which has been translated from another language tends to carry with it evidence of translation$\unicode{x2014}$hence, it is often referred to as…
arXiv:2608.08032v2 Announce Type: replace-cross Abstract: Safety alignment in multilingual models is uneven: a model that reliably refuses a harmful request in English will often comply with the same request in a…
arXiv:2608.17050v1 Announce Type: new Abstract: Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to external knowledge, but…
arXiv:2607.08642v3 Announce Type: replace Abstract: Speculative decoding accelerates LLM inference by drafting tokens and verifying them in parallel. Block-diffusion drafters such as DFlash model only per-position…
arXiv:2604.02118v2 Announce Type: replace-cross Abstract: Natural language explanations of time series data are increasingly produced by foundation models in high stakes domains, making factual correctness critical.…
arXiv:2608.17866v1 Announce Type: new Abstract: Reconstructing prompts that can elicit a desired answer or behaviour in an LLM is an open and important research topic. Optimisation methods which aim at minimising the…
arXiv:2511.23119v3 Announce Type: replace Abstract: High-quality main content extraction from web pages is a critical prerequisite for constructing large-scale training corpora. While traditional heuristic extractors…
arXiv:2608.17218v1 Announce Type: new Abstract: Works of literature are complicated; they balance plot, suspense, surprise, and artistic expression. Summaries of literature prioritize plot, and therefore may deviate…
arXiv:2608.16894v1 Announce Type: cross Abstract: ML venues shape what kinds of research claims become legible to reviewers and what forms of evidence count as rigorous. The NeurIPS and ICML Position Paper Tracks were…
arXiv:2608.17795v1 Announce Type: new Abstract: Text-to-SQL systems are commonly evaluated using ground-truth SQL queries or reference execution results, but such supervision is unavailable at inference time in…
arXiv:2605.17443v3 Announce Type: replace Abstract: We analyze how automatic speech recognition (ASR) errors propagate through ASR--LLM cascades in Korean spoken question answering (SQA), focusing on downstream semantic…
arXiv:2608.12852v2 Announce Type: replace Abstract: Language can describe states of affairs that are false and states of affairs that could not be the case at all. Whether an AI model internally distinguishes these…
arXiv:2608.16909v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into financial advisory systems, yet their role in reproducing religious bias remains underexamined. This study…
arXiv:2608.12841v2 Announce Type: replace Abstract: We study recursive self-improvement at the level of quantitative-investment research: whether an autonomous system can use evidence from earlier experiments to improve…
arXiv:2608.17583v1 Announce Type: new Abstract: Online video platforms can expose young users to harmful content, but independent audits remain difficult because video annotation is costly and moderation judgments vary…
arXiv:2311.06273v1 Announce Type: cross Abstract: The rise of ChatGPT has brought a notable shift to the AI sector, with its exceptional conversational skills and deep grasp of language. Recognizing its value across…
arXiv:2608.17534v1 Announce Type: new Abstract: Large language models increasingly serve as persistent conversational assistants, requiring memory that preserves relevant experience and maintains continuity across…
arXiv:2608.17979v1 Announce Type: new Abstract: Authorship verification (AV) assumes that an author's writing style remains sufficiently stable to distinguish it from that of other writers. In practice, however, this…
arXiv:2502.13428v2 Announce Type: replace Abstract: This work investigates how to improve large language model (LLM)-based reasoning for knowledge base question answering (KBQA) via Monte Carlo Tree Search (MCTS).…
arXiv:2608.17895v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have made significant strides in visual comprehension, their ability to reason about text-dense, professional documents…
arXiv:2605.07725v3 Announce Type: replace Abstract: Tool-integrated reasoning (TIR) is difficult to scale to small language models due to instability in long-horizon tool interactions and limited model capacity. While…
arXiv:2608.17120v1 Announce Type: new Abstract: Children learn hundreds of words over the first years of their lives, in a process that begins slowly but quickly picks up speed. Prior models describe vocabulary growth…
arXiv:2608.17843v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong performance on structured reasoning tasks, but what they encode and whether it informs model behavior remain unclear.…
arXiv:2603.23047v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) fine-tuning has shown substantial improvements over vanilla RAG, yet most studies target document question answering, leaving open…
arXiv:2608.18011v1 Announce Type: new Abstract: Reasoning in LLMs is overwhelmingly studied in domains that provide a model with rules: mathematics and code. Linguistic puzzles invert this: the solver must first…
arXiv:2607.27155v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents are increasingly expected to assist users in completing tasks. However, existing benchmarks provide limited support for…
arXiv:2608.17051v1 Announce Type: new Abstract: Secondary use of electronic health records requires de-identification, yet existing systems miss \emph{institutionally situated} protected health information (PHI) such as…
arXiv:2608.17454v1 Announce Type: new Abstract: This paper presents a pipeline for analyzing media bias and framing in online news. The pipeline groups articles into topics and events, adds named-entity and sentiment…
arXiv:2402.09696v3 Announce Type: replace Abstract: Current Grammar Error Correction (GEC) initiatives tend to focus on major languages, with less attention given to low-resource languages like Esperanto. In this…
arXiv:2608.04772v2 Announce Type: replace Abstract: Scaling supervision for multi-turn medical agents is difficult because expert dialogue annotation is costly and clinical conversations are privacy-restricted. We…
arXiv:2608.17550v1 Announce Type: cross Abstract: Academic papers are a primary carrier of scientific knowledge, yet most of this knowledge remains locked in PDFs that are optimized for human reading rather than machine…
arXiv:1908.06361v2 Announce Type: replace Abstract: Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. However, methods for measuring and removing such biases…
arXiv:2503.00992v3 Announce Type: replace-cross Abstract: Background. Evidence that large language models (LLMs) reproduce human judgments does not establish conceptual mastery: the correspondence may reflect…
arXiv:2607.15209v2 Announce Type: replace Abstract: Multilingual pre-trained language models such as XLM-R perform well for major languages but struggle with low-resource Ge'ez-script languages, largely due to high…
arXiv:2604.06474v2 Announce Type: replace Abstract: Deep research with Large Language Model (LLM) agents is emerging as a powerful paradigm for multi-step information discovery, synthesis, and analysis. However,…
arXiv:2608.17084v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) increasingly answer questions whose correctness depends on visual, textual, temporal, acoustic, document, chart, or embodied…
arXiv:2608.17184v1 Announce Type: new Abstract: Job Safety Analysis (JSA) and pre-task planning can benefit from prior incident records, yet historical accident data is often stored as unstructured narratives that are…
arXiv:2608.17088v1 Announce Type: new Abstract: A central goal of multilingual NLP is to achieve high monolingual performance per language and cross-lingual alignment for large-scale language coverage with a…
arXiv:2608.17781v1 Announce Type: new Abstract: ML systems increasingly condition decisions on downstream model identity, but this is useful only if model-specific differences form reusable structure rather than…
arXiv:2601.18747v3 Announce Type: replace-cross Abstract: Modern AI agents increasingly rely on search infrastructure to execute complex, neuro-symbolic reasoning workflows. These workflows often compile into deeply…
arXiv:2605.07507v2 Announce Type: replace Abstract: The rapid growth of academic publications has created a need for tools that extract structured knowledge from unstructured scientific texts. Although large language…
arXiv:2608.09106v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong performance across a wide range of legal tasks. In legal practice, time is a critical concept that governs the…
arXiv:2608.17596v1 Announce Type: cross Abstract: In this study, we investigate developmental mechanisms that enable small, resource-constrained systems such as cm-sized millirobots to autonomously explore, learn, and…
arXiv:2503.14229v5 Announce Type: replace Abstract: Vision-and-Language Navigation (VLN) has been studied mainly in either discrete or continuous spaces, with little attention to dynamic, crowded environments. We…
arXiv:2608.13333v3 Announce Type: replace Abstract: Large neighborhood search normally selects a random subset of decision variables for iterative optimization. To efficiently solve various problems, researchers tend to…
arXiv:2608.17638v1 Announce Type: new Abstract: What a reasoning model writes is only a partial record of the process that produces it. We introduce a two-level internal readout for mixture-of-experts reasoning. We…
arXiv:2608.17933v1 Announce Type: new Abstract: Financial time series exhibit non-stationary and heterogeneous statistical properties, making change-point detection challenging because no single unsupervised algorithm…
arXiv:2608.17829v1 Announce Type: cross Abstract: LLMs increasingly rely on external contexts, such as pre-defined system prompts or retrieved documents, to improve generation quality. However, processing these contexts…
arXiv:2608.17597v1 Announce Type: cross Abstract: Large language models are increasingly deployed through agent harnesses that manage tools, extensions, persistent state, permissions, and external actions. Existing…
arXiv:2608.18056v1 Announce Type: new Abstract: Urban traffic congestion reduces productivity and increases travel cost and emissions. Network-wide live travel-time shortest-path rerouting can be highly effective in…
arXiv:2608.17588v1 Announce Type: new Abstract: Agent Skills package reusable natural language procedures with executable resources, enabling software agents to acquire task specific capabilities without model…
arXiv:2608.04457v2 Announce Type: replace-cross Abstract: As "AI Scientists" emerge to drive research via the Model Context Protocol (MCP), systems relying on ephemeral scripts will fail. The sheer scale of stateful,…
arXiv:2608.17167v1 Announce Type: cross Abstract: Active inference selects actions by minimising an expected free energy functional over predicted futures. However, adding an expectation over yet-unobserved outcomes…
arXiv:2608.17007v1 Announce Type: new Abstract: Agent Skills can specify procedural and resource obligations for tool use, and language models instantiate them as concrete programs. However, when models turn this…
arXiv:2608.17659v1 Announce Type: cross Abstract: LLM-powered GUI agents that autonomously operate smartphones are rapidly transitioning from research prototypes to early real-world deployment. However, because these…
arXiv:2406.08534v4 Announce Type: replace-cross Abstract: This paper addresses the NP-hard problem of optimizing container handling at ports by integrating Quay Crane Dual-Cycling (QCDC) and dockyard rehandle…
arXiv:2512.06040v2 Announce Type: replace-cross Abstract: Voice authentication systems deployed at the network edge face dual threats: a) sophisticated deepfake synthesis attacks and b) control-plane poisoning in…
arXiv:2608.17071v1 Announce Type: new Abstract: We present KernelArc, a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads. Strategy-specialized agents run in parallel and…
arXiv:2606.08093v2 Announce Type: replace Abstract: Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices. While artificial intelligence (AI) has the…
arXiv:2608.17499v1 Announce Type: new Abstract: User-facing tool agents must coordinate dialogue and tool use as user goals unfold over multiple turns. Yet interactive reinforcement learning typically reduces each…
arXiv:2608.17301v1 Announce Type: new Abstract: Post-training with supervised chain-of-thought fine-tuning and reinforcement learning from verifiable rewards has substantially improved the mathematical reasoning…
arXiv:2608.16918v1 Announce Type: cross Abstract: Patent prior-art retrieval is a recall-oriented search task over long and highly structured technical documents. Dense retrieval improves semantic matching, but…
arXiv:2608.17684v1 Announce Type: new Abstract: Self-evolving agents turn experience into reusable skills, workflows, or memories, but post-evolution accuracy alone does not show whether learned behavior preserves…
arXiv:2608.16630v1 Announce Type: cross Abstract: Repository-scale coding requires an agent to keep tests, imports, configuration, and migration rules consistent within a bounded context window. We model this as…
arXiv:2608.10442v2 Announce Type: replace-cross Abstract: Automatic stress detection from facial video offers a practical path to non-intrusive affect monitoring, yet existing video-based approaches commonly decompose…
arXiv:2608.17928v1 Announce Type: cross Abstract: In the Lifelong Multi-Agent Path Finding (L-MAPF) problem, agents must repeatedly move from one destination to another while avoiding obstacles and inter-agent…
arXiv:2608.14603v2 Announce Type: replace-cross Abstract: Cooperative perception enables vehicles and infrastructure to exchange sensor data via Vehicle-to-Everything (V2X) communication, extending sensing coverage…
arXiv:2608.17760v1 Announce Type: cross Abstract: Intelligent channel state information (CSI) feedback is essential for realizing the high capacity and spectral efficiency goals of future 6G systems, yet existing deep…
arXiv:2608.17564v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) are motivated by the hope that understanding and generation reinforce each other but controlled ablations repeatedly find that adding a…
arXiv:2605.16024v2 Announce Type: replace Abstract: Desktop GUI agents operate under partial observability: visually similar screens can correspond to different underlying workflow states, so locally plausible actions…
arXiv:2608.17571v1 Announce Type: cross Abstract: Motivation: Low-dimensional embeddings are widely used to explore cell-state heterogeneity in single-cell and other high-dimensional biological data. Although many…
arXiv:2608.03744v2 Announce Type: replace Abstract: Clinical decision support is moving toward committees of language-model agents deliberating on a shared workspace. We ask whether such committees can be gamed by…
arXiv:2608.17906v1 Announce Type: new Abstract: Autonomous research systems are increasingly capable of executing long research workflows, yet automation alone does not ensure that the resulting process remains…
arXiv:2608.17574v1 Announce Type: new Abstract: How cautious should an agent be while it is still learning its environment? We propose RATTL (Risk-Adversarial Total-Reward Learning), which ties caution to epistemic…
arXiv:2608.17671v1 Announce Type: cross Abstract: Automated security patch backporting is critical for mitigating N-day vulnerabilities. Recent tools report success rates above 80% on their respective datasets. However,…
arXiv:2608.17176v1 Announce Type: cross Abstract: An AI audit record is useful only if its durability and trust boundary are explicit. Returning a guarded decision before any durable write minimizes latency, but it…
arXiv:2608.17067v1 Announce Type: new Abstract: As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and…
arXiv:2607.01854v2 Announce Type: replace-cross Abstract: Can a platform tell, before deployment, whether an open-weight checkpoint has had its refusal mechanism stripped? Runtime guards cannot: they score generations,…
arXiv:2409.14644v4 Announce Type: replace-cross Abstract: The advent of large language models (LLMs) has significantly advanced artificial intelligence in software engineering, with source code embeddings playing a…
arXiv:2509.10818v2 Announce Type: replace Abstract: When consequential decisions depend on knowledge that exists nowhere in writing, LLMs hallucinate not from retrieval failure but from model absence. RAG and…
arXiv:2608.17360v1 Announce Type: cross Abstract: Reliable jailbreak evaluation is essential for assessing LLM safety, but most existing studies rely solely on attack success rate (ASR) without accounting for its…
arXiv:2608.17220v1 Announce Type: cross Abstract: Autonomous AI agents are emerging as interfaces for decentralized finance (DeFi) actions such as swaps, lending operations, and yield management. Because these agents…
arXiv:2608.01324v2 Announce Type: replace Abstract: Deep search has become a fundamental capability of large language models (LLMs) for solving open-domain complex tasks. However, existing approaches typically rely on…
arXiv:2608.18058v1 Announce Type: new Abstract: Autonomous LLM agents that converse on a user's behalf are an emerging design pattern in matching platforms, yet their viability depends on a condition rarely examined:…
arXiv:2604.09508v3 Announce Type: replace-cross Abstract: Visual Retrieval-Augmented Generation (VRAG) empowers Vision-Language Models to retrieve and reason over visually rich documents. To tackle complex queries…
arXiv:2608.17183v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly deployed in resource-constrained, privacy-sensitive settings, where safety and bias failures can cause security and societal…
arXiv:2608.17917v1 Announce Type: cross Abstract: Automatic Target Detection and Recognition (ATD/R) is critical for military decision support and (semi-)autonomous operations. Recent advances in object detection and…
arXiv:2608.17306v1 Announce Type: cross Abstract: While adversarial prompt tuning can enhance robustness of vision-language models efficiently, we find that existing methods aggravate robust generalization overfitting…
arXiv:2608.15565v2 Announce Type: replace Abstract: Experience-learning agents for optimization modeling improve by storing verified skills, but existing learners admit knowledge by checking against known answers, which…
arXiv:2608.16177v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as agents that operate equipment, execute instructions, and act inside institutional hierarchies, raising…
arXiv:2608.17715v1 Announce Type: cross Abstract: Credit decisioning is a high-stakes task in which model outputs must be accurate and explainable to support compliant decisions. Although modern credit risk models such…
arXiv:2608.07994v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is essential for enterprise knowledge question answering (QA), particularly in domains with complex product documentation like…
arXiv:2607.16207v2 Announce Type: replace Abstract: Public open-weight language models are often fine-tuned on private or domain-specific data before deployment, creating a need to audit whether individual records were…
arXiv:2608.17237v1 Announce Type: cross Abstract: Converting structural framing plans into editable finite-element model drafts remains labor-intensive and prone to transcription error. Existing drawing-understanding…
arXiv:2605.27563v2 Announce Type: replace-cross Abstract: We prove an elementary bounded-differences inequality for functions of non-isotropic Gaussian vectors. Specifically, if $f$ has bounded coordinate differences…
arXiv:2608.18076v1 Announce Type: cross Abstract: Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning, yet conventional pipelines typically optimize…
arXiv:2608.17299v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have recently emerged as a highly promising paradigm for cross-domain zero-shot forecasting. However, existing evaluation protocols…
arXiv:2608.17304v1 Announce Type: cross Abstract: Formal verification is a crucial technique for ensuring the functional correctness of hardware designs. In the context of property checking, a key challenge is how to…
arXiv:2608.18050v1 Announce Type: new Abstract: AI agents increasingly perform knowledge work (i.e., produce and modify persistent digital artifacts such as code repositories, documents, spreadsheets, slides, reports),…
arXiv:2608.17634v1 Announce Type: new Abstract: The $\operatorname{do}$-operator is described graphically by deleting arrows into its targets and functionally by replacing their mechanisms with constants. To call these…
arXiv:2608.16896v1 Announce Type: cross Abstract: This paper presents the design and outcomes of a seven-weekend AI storytelling program developed for Black girls aged 10-12. Grounded in Afrofuturism and Black feminist…
arXiv:2608.17128v1 Announce Type: new Abstract: A personal AI system needs a model of the user's goals, constraints, and ongoing commitments to plan and act on their behalf, and the quality of that model is bounded by…
arXiv:2604.26703v2 Announce Type: replace-cross Abstract: Scientific discovery often begins where observation and prediction disagree. As computation and machine learning survey chemical space, experiment-simulation…
arXiv:2606.14438v4 Announce Type: replace-cross Abstract: End-to-end (E2E) autonomous-driving planners trained by imitation are prone to statistical shortcuts: they associate scene elements that merely co-occur with…
arXiv:2608.17834v1 Announce Type: cross Abstract: Large language models are pushing data science toward increasingly autonomous and agentic workflows, with recent systems already supporting multi-step and long-running…
arXiv:2608.18017v1 Announce Type: new Abstract: Improving flight safety with flight data requires not only accurate detection of risk events, but more importantly, clear interpretation of their underlying causes at the…
arXiv:2608.17341v1 Announce Type: new Abstract: AI planning is concerned with finding a sequence of actions that achieves a specified goal. It relies on explicit models of the world, commonly represented in the Planning…
arXiv:2608.17433v1 Announce Type: new Abstract: LLM agents have been widely adopted to operate mission-critical infrastructure (MCI). These agents normally rely on a harness that determines what information they can…
arXiv:2608.17344v1 Announce Type: cross Abstract: Neural network surrogates are an emerging alternative to traditional electromagnetic wave simulators like finite-difference time-domain (FDTD); their goal is to replace…
arXiv:2507.15455v3 Announce Type: replace-cross Abstract: We propose a mesh-free policy iteration framework that combines classical dynamic programming with physics-informed neural networks (PINNs) to solve…
arXiv:2606.01008v2 Announce Type: replace-cross Abstract: We present a benchmark for evaluating AI models and agents on real-world formal software verification tasks. We first scrape 11,039 property-based tests (PBTs)…
arXiv:2608.07565v2 Announce Type: replace-cross Abstract: Conversational assistants increasingly recommend follow-up edits to help users continue a task. Existing systems primarily target text-only interactions, leaving…
arXiv:2608.16897v1 Announce Type: cross Abstract: Large-scale urban simulation plays a pivotal role in social science, traffic safety, and transportation policy. Recent work has shown that large language models, when…
arXiv:2608.17522v1 Announce Type: cross Abstract: Persistent shortages in the surgical workforce and inherent limitations of traditional training methods highlight the necessity of automated, data-driven approaches in…
arXiv:2608.17255v1 Announce Type: cross Abstract: X-ray imaging can be approximately modeled as the projection of an underlying volumetric attenuation field, with each measurement recording the accumulated attenuation…
arXiv:2608.17244v1 Announce Type: cross Abstract: This paper addresses the limitations of Gaussian distribution assumptions in statistical sparse learning, particularly in modeling correlated and heterogeneous data.…
arXiv:2406.01586v4 Announce Type: replace-cross Abstract: Diffusion models have been verified to be effective in generating complex distributions from natural images to motion trajectories. Recent diffusion-based…
arXiv:2608.17756v1 Announce Type: new Abstract: Memory is a key capability of LLM agents. Persistent memory extends this across sessions---enabling recall, revision, and personalization. Yet its multi-stage pipeline…
arXiv:2608.16900v1 Announce Type: cross Abstract: Language-model agents increasingly produce quantum-science results; we ask whether the same agentic paradigm can also scrutinize them in an auditable, reproducible, and…
arXiv:2506.18682v3 Announce Type: replace-cross Abstract: Recent advances in autonomous driving (AD) have highlighted the potential of hyperspectral imaging (HSI) for enhanced environmental perception, particularly in…
arXiv:2607.12982v3 Announce Type: replace Abstract: Math reasoning has achieved significant progress with the rapid advancement of Multimodal Large Language Models (MLLMs), however analytic geometry remains largely…
arXiv:2608.17731v1 Announce Type: new Abstract: Diversity is a fundamental criterion for evaluating generative artificial intelligence (AI) systems, yet its measurement remains inherently ambiguous. Existing approaches…
arXiv:2606.07464v2 Announce Type: replace-cross Abstract: Monolithic vision-action models represent an emerging paradigm in autonomous driving. However, this architecture produces token sequences that quickly exceed…
arXiv:2608.16907v1 Announce Type: cross Abstract: Generative AI (GenAI) is rapidly reshaping education by unlocking the potential for personalized tutoring. Yet, emerging platforms largely focus on GenAI chatbot tutors…
arXiv:2608.17694v1 Announce Type: cross Abstract: Existing LLM-based approaches to Architecture Decision Record (ADR) generation share a critical and largely unexamined assumption: that input is already reasonably…
arXiv:2608.16910v1 Announce Type: cross Abstract: National AI strategies increasingly guide governance, workforce development, innovation, and competitiveness, but less is known about how they frame education as a…
arXiv:2608.16984v1 Announce Type: cross Abstract: Recent monocular depth estimators achieve strong zero-shot generalization, yet often struggle to preserve fine-grained structures and object boundaries. We attribute…
arXiv:2608.17718v1 Announce Type: new Abstract: Long-horizon agents increasingly operate across many steps, tools, and observa- tions. In this setting, the relevant oversight question is not only whether each action is…
arXiv:2608.17195v1 Announce Type: cross Abstract: We present Graphectory Viewer, a web-based tool for interactive, process-centric analysis of software-agent trajectories. Building on the Graphectory representation…
arXiv:2608.17741v1 Announce Type: new Abstract: OWL 2 DL ontologies, grounded in the description logic $\mathcal{SROIQ}$, express large knowledge bases in biomedicine and the Semantic Web. Neuro-symbolic (NeSy) learners…
arXiv:2506.01297v5 Announce Type: replace Abstract: Representation learning of geospatial locations remains a core challenge in achieving general geospatial intelligence, with increasingly diverging philosophies and…
arXiv:2608.17932v1 Announce Type: cross Abstract: Groups routinely complete projects that no single member can plan, execute, or verify alone. We propose a formal model of this phenomenon, Collective Counterfactual…
arXiv:2608.14825v2 Announce Type: replace-cross Abstract: Frontier LLM agents increasingly transact on behalf of separate principals, often using natural language rather than structured APIs. Much of the safety…
arXiv:2608.17856v1 Announce Type: new Abstract: Tabular prediction is a critical task across numerous applications. The recent success of large language models has sparked various approaches for adapting them to the…
arXiv:2608.17799v1 Announce Type: cross Abstract: Ground-to-Air (G2A) drone detection in medium- and long-wave infrared (MWIR/LWIR) imagery is challenging due to reduced texture information, sensor noise, weak thermal…
arXiv:2608.17092v1 Announce Type: cross Abstract: Autonomous vehicles (AVs) depend on reliable Global Navigation Satellite System (GNSS) positioning. However, spoofed GNSS signals can induce plausible but incorrect…
arXiv:2608.17148v1 Announce Type: cross Abstract: A personal language agent learns a fact from one audience and may later place it in the prompt it assembles for another. This memory-to-context step is an attack…
arXiv:2608.17336v1 Announce Type: new Abstract: Long-context prefill in large language models (LLMs) incurs substantial computation and memory traffic because dense self-attention computes quadratic query-key scores.…
arXiv:2608.17468v1 Announce Type: new Abstract: Storyboards turn screenplays into visual shot plans for automated short drama production. Professional storyboarding relies on tacit directorial expertise and remains an…
arXiv:2608.17234v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly used to interact with screenshots, scanned documents, diagrams, and other visually grounded inputs. This shift…
arXiv:2608.17434v1 Announce Type: new Abstract: We study Gaussian regression over the explicit vector-valued Parhi--Nowak deep-RBV^2 architecture with depth L, width w, layer-sum variation budget A, and output bound B.…
arXiv:2608.17352v1 Announce Type: new Abstract: Distributed Denial-of-Service (DDoS) attacks threaten network availability, requiring a cognitive detection process that senses traffic, infers intent, and supports an…
arXiv:2508.08748v2 Announce Type: replace-cross Abstract: Robotic pick-and-place tasks in convenience stores pose challenges due to dense object arrangements, occlusions, and variations in object properties such as…
arXiv:2608.17983v1 Announce Type: cross Abstract: Ultrasound tongue contour segmentation remains challenging under cross-dataset domain shift, where limited annotations, probe variability, and acquisition noise often…
arXiv:2608.17271v1 Announce Type: new Abstract: Artificial superintelligence (ASI) requires AI to move beyond mastering existing knowledge toward exploring the unknown, creating new knowledge, and turning new ideas into…
arXiv:2608.17289v1 Announce Type: new Abstract: Group-relative policy optimization has emerged as a key paradigm for training agentic large language models (LLMs) on multi-turn interactive tasks. However, most existing…
arXiv:2608.11210v2 Announce Type: replace Abstract: Bayesian calibration of process-based models requires a prior distribution for each model parameter. Despite decades of methodological work, researchers almost always…
arXiv:2411.15455v3 Announce Type: replace-cross Abstract: Accurately predicting the popularity of micro-videos is a critical but challenging task, characterized by volatile, `rollercoaster-like' engagement dynamics.…
arXiv:2605.09948v2 Announce Type: replace Abstract: Current Vision-Language-Action (VLA) models typically treat the deepest representation of a vision-language backbone as universally optimal for action prediction.…
arXiv:2608.17618v1 Announce Type: cross Abstract: Learning analytics models can identify students at risk of poor performance, but they do not directly indicate which interventions are feasible, actionable, and…
arXiv:2608.17749v1 Announce Type: new Abstract: Decentralised partially observable Markov decision processes (DecPOMDPs) provide a general framework for modelling multi-agent decision making under uncertainty. However,…
arXiv:2608.17323v1 Announce Type: cross Abstract: Robotic manipulation policies trained via imitation learning, such as Action Chunking with Transformers (ACT), can achieve strong performance under ideal conditions but…
arXiv:2605.29976v3 Announce Type: replace-cross Abstract: We evaluate the climate simulation capabilities of ArchesWeather and ArchesWeatherGen, two machine learning models originally trained for weather forecasting and…
arXiv:2608.17919v1 Announce Type: cross Abstract: Background and Context: Question and inquiry are integral parts of knowledge seeking and learning. Despite their importance, students tend not to ask enough questions in…
arXiv:2606.31222v2 Announce Type: replace Abstract: Composed image retrieval requires identifying a target image from a gallery by integrating a reference image with a textual modification instruction. In a…
arXiv:2608.17515v1 Announce Type: cross Abstract: Background: Large Language Models (LLMs) are increasingly being applied to Software Engineering (SE) tasks, achieving high accuracy across problems such as clone…
arXiv:2608.17282v1 Announce Type: new Abstract: Existing agentic reasoning systems typically rely on centralized protocols. This design introduces routing bottlenecks and static role allocations that often fail when…
arXiv:2606.03895v3 Announce Type: replace-cross Abstract: Large language model (LLM) agents can persist across tasks, acquire memory, activate Skills, synthesize tools, fork child processes, attach remote resources, and…
arXiv:2607.09520v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are the perceptual backbone of embodied AI, but their energy footprint on edge hardware remains poorly understood. Existing…
arXiv:2608.15546v2 Announce Type: replace Abstract: Most LLM-based automated algorithm design methods optimize a designated component within a human-specified scaffold, fixing overall organization and component…
arXiv:2605.14889v4 Announce Type: replace-cross Abstract: Online surgical phase recognition must commit to a prediction at every frame of a procedure that runs for hours, from past frames alone and at a per-frame cost…
arXiv:2608.14947v2 Announce Type: replace Abstract: Detecting opioid craving from wearable physiological signals is critical yet difficult, with the potential to support proactive interventions for individuals with…
arXiv:2608.17124v1 Announce Type: new Abstract: Combining the answers a large language model (LLM) samples for a question into one decision is a test-time information fusion problem, usually solved by majority voting.…
arXiv:2608.17929v1 Announce Type: new Abstract: Robust Markov decision processes optimize one policy against a set of plausible transition functions. This can be conservative when the unknown dynamics are fixed and…
arXiv:2608.16903v1 Announce Type: cross Abstract: The deployment of machine learning systems capable of detecting covert consciousness in neurologically injured patients creates a profound challenge at the intersection…
arXiv:2608.16916v1 Announce Type: cross Abstract: Calculating average distances in large-scale networks is computationally intensive and constrained by limited main memory, posing a significant challenge in graph…
arXiv:2608.17443v1 Announce Type: new Abstract: Knowledge Graph Reasoning (KGR) aims to discover latent facts by leveraging the structural evidence available in KGs, posing a challenge to the structural semantic…
arXiv:2608.17426v1 Announce Type: cross Abstract: We introduce Semantic Task Completion Video Generation, an outcome-oriented video generation task. Under this formulation, success requires both achievement of the…
arXiv:2608.11733v2 Announce Type: replace-cross Abstract: In this work, we presented, to the best of our knowledge, the first reported 12-CNOT decomposition of the double qubit excitation operator. We compared our new…
arXiv:2608.01035v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a prominent paradigm for end-to-end autonomous driving; however, their efficient deployment is severely…
arXiv:2608.17247v1 Announce Type: new Abstract: Personalized agents must decide whether retrieved user memory should be used, ignored, updated, or queried before it affects a current task. We use this setting to develop…
arXiv:2608.13584v2 Announce Type: replace-cross Abstract: UltraArUco is a lightweight multilingual library and framework for low latency, realtime marker-based tracking in mobile augmented reality. Unlike standard…
arXiv:2608.16924v1 Announce Type: cross Abstract: This work-in-progress (WIP) innovative practice category paper presents LLM Odyssey, an open source, browser-based serious gaming platform comprising 13 interactive…
arXiv:2608.17997v1 Announce Type: cross Abstract: Artificial intelligence (AI) is becoming part of the working infrastructure of the biosciences. AI models can predict biomolecular structures, design proteins, rank…
arXiv:2608.17528v1 Announce Type: new Abstract: Modern agents operate inside agent harnesses that manage tools, context, and control flow, making the harness a critical part of the agent system. Our original Agent…
arXiv:2608.16891v1 Announce Type: new Abstract: Agentic AI systems request tool actions that can modify files, send messages, launch jobs, or change workflow state. This shifts the safety problem from harmful text…
arXiv:2608.16890v1 Announce Type: new Abstract: Clinical trial programming -- transforming study protocols into analysis-ready datasets under CDISC standards -- is a bottleneck in regulatory submissions, yet LLM-based…
arXiv:2608.16919v1 Announce Type: cross Abstract: Recent advances in large language models and agent-based recommendation frameworks have introduced new opportunities for more flexible and context-aware recommendation.…
arXiv:2602.20134v2 Announce Type: replace-cross Abstract: Epidemiological models increasingly rely on crowdsourced, self-reported behavioral data such as vaccination status, mask usage, and social distancing adherence.…
arXiv:2608.16893v1 Announce Type: cross Abstract: Expert surveys are widely used in security research to study practitioner workows and decision-making, yet recruiting domain experts - especially in Security Operations…
arXiv:2608.17632v1 Announce Type: cross Abstract: Large language models (LLMs) can both expand underspecified queries and encode text as dense representations, suggesting a unified model for query expansion and…
arXiv:2608.12898v2 Announce Type: replace-cross Abstract: Document parsing aims to transform unstructured documents into structured and machine-readable representations. Recent advances in Vision-Language Models (VLMs)…
arXiv:2608.17625v1 Announce Type: new Abstract: Saudi Arabia will host the 2034 FIFA World Cup and already operates crowd management at Hajj scale. Drone-based counting must hold accuracy on footage unlike anything in…
arXiv:2608.17044v1 Announce Type: cross Abstract: The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and physical AI. Since its 2017…
arXiv:2606.07897v2 Announce Type: replace Abstract: Current AI models frequently exhibit epistemic sycophancy, endorsing claims to agree with a user. Existing evaluations typically measure this either by assessing what…
arXiv:2608.17800v1 Announce Type: new Abstract: Recent advances in Large Language Models(LLMs) and agents have substantially improved the ability of AI systems to execute complex tasks. Yet existing benchmarks largely…
arXiv:2509.12040v3 Announce Type: replace-cross Abstract: Open-Vocabulary Remote Sensing Image Segmentation (OVRSIS), an emerging task that adapts Open-Vocabulary Segmentation (OVS) to the remote sensing (RS) domain,…
arXiv:2511.16088v3 Announce Type: replace-cross Abstract: Traditional threat modeling remains reactive-focused on known TTPs and past incident data, while threat prediction and forecasting frameworks are often…
arXiv:2608.17262v1 Announce Type: cross Abstract: This paper considers robust nonadaptive regulation for general nonlinear systems in an output-feedback setting with arbitrarily high relative degree. We develop a…
arXiv:2604.12616v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) expand the attack surface of safety-aligned systems by coupling visual perception with text generation. Existing multimodal jailbreak…
arXiv:2510.08713v3 Announce Type: replace Abstract: Enabling embodied agents to imagine future states is essential for robust and generalizable visual navigation. Yet, state-of-the-art systems typically rely on modular…
arXiv:2608.17017v1 Announce Type: cross Abstract: The implementation of artificial intelligence techniques and tools in the media will systematically and continuously alter their work and that of their professionals…
arXiv:2608.17202v1 Announce Type: new Abstract: Safety alignment in open-weight language models is trivially removable: abliteration projects a refusal-mediating direction out of the weights in minutes, and no…
arXiv:2608.17665v1 Announce Type: new Abstract: LLM-driven agents can autonomously exchange opinions on online platforms and form communities. Such agent-operated social platforms raise a new security concern: attackers…
arXiv:2608.17703v1 Announce Type: cross Abstract: Mobile robots that operate in side by side with humans and critical facilities must reach their goals at low cost, despite often unknown true traversal costs of the map…
arXiv:2509.15236v2 Announce Type: replace-cross Abstract: Data-driven surrogate models are increasingly used in computational fluid dynamics, and their reliability depends on the quality of the training data. These…
arXiv:2608.17393v1 Announce Type: new Abstract: Reinforcement learning for coding agents increasingly relies on long-running agent harnesses to manage tool integration, repository contexts, and execution feedback.…
arXiv:2608.17711v1 Announce Type: new Abstract: Prediction cascades significantly reduce energy consumption of Artificial Intelligence (AI) models while maintaining high predictive performance. The idea is that easy…
arXiv:2608.17170v1 Announce Type: new Abstract: Algorithm selection for constraint satisfaction problems requires extracting features that capture problem structure. Manually designing feature extractors demands deep…
arXiv:2608.17033v1 Announce Type: cross Abstract: Visual Place Recognition (VPR) aims to recognize the location of a query image by comparing it with a set of geo-referenced images. Although many datasets have been…
arXiv:2606.15315v2 Announce Type: replace Abstract: Personalized public transit routing in public transit systems remains challenging due to the difficulty of capturing and integrating diverse user preferences into…
arXiv:2602.23545v3 Announce Type: replace Abstract: In the real world, planning is often challenged by distribution shifts. As such, a model of the environment obtained under one set of conditions may no longer remain…
arXiv:2605.25477v2 Announce Type: replace-cross Abstract: The ability to efficiently and reliably learn new tasks has been a foundational challenge in robotics. Vision-Language-Action (VLA) models have demonstrated…
arXiv:2608.17270v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for scientific hypothesis generation. However, evaluating generated hypotheses remains a challenge for trustworthy…
arXiv:2608.17319v1 Announce Type: new Abstract: Browser agents perform well on short, clean demonstrations, but real deployment is fundamentally different: agents must sustain dozens of decisions on live websites while…
arXiv:2608.10448v2 Announce Type: replace Abstract: Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues. However, most existing approaches…
arXiv:2608.17275v1 Announce Type: cross Abstract: AI agents increasingly act rather than merely read: across the Model Context Protocol (MCP) ecosystem, the share of deployed tools that modify external state has risen…
arXiv:2608.16977v1 Announce Type: new Abstract: AI systems are increasingly capable of contributing to mathematical research. In research practice, frontier-model reasoning is a limited resource, and expert mathematical…
