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e7a589e4e09943532c0149e86a42879c17b54ae194f4d49139ad760ecca06d1f | 2026-01-07T00:00:00-05:00 | Algorithmic randomness in harmonic analysis | arXiv:2601.03239v1 Announce Type: cross Abstract: Within the last fifteen years, a program of establishing relationships between algorithmic randomness and almost-everywhere theorems in analysis and ergodic theory has developed. In harmonic analysis, Franklin, McNicholl, and Rute characterized Schnorr randomness using ... | https://arxiv.org/abs/2601.03239 | Academic Papers | svg |
1f85f6426e065b2a318f7a45c0e2caa922d4af50fcbd38043af10bb5843aba9c | 2026-01-07T00:00:00-05:00 | Self-Supervised Learning from Noisy and Incomplete Data | arXiv:2601.03244v1 Announce Type: cross Abstract: Many important problems in science and engineering involve inferring a signal from noisy and/or incomplete observations, where the observation process is known. Historically, this problem has been tackled using hand-crafted regularization (e.g., sparsity, total-variatio... | https://arxiv.org/abs/2601.03244 | Academic Papers | svg |
d8858a13150a648533da99f229fd609a7ebc049f39389a07ea3d148f071e91f4 | 2026-01-07T00:00:00-05:00 | Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data | arXiv:2601.03247v1 Announce Type: cross Abstract: Artificial muscles are essential for compliant musculoskeletal robotics but complicate control due to nonlinear multiphysics dynamics. Hydraulically amplified electrostatic (HASEL) actuators, a class of soft artificial muscles, offer high performance but exhibit memory ... | https://arxiv.org/abs/2601.03247 | Academic Papers | svg |
b510446bc2ba388decc75973c33c2f5301eca3f08829b887aae71d430c928161 | 2026-01-07T00:00:00-05:00 | Auditing for Core Stability in Participatory Budgeting | arXiv:2209.14468v2 Announce Type: replace Abstract: We consider the participatory budgeting problem where each of $n$ voters specifies additive utilities over $m$ candidate projects with given sizes, and the goal is to choose a subset of projects (i.e., a committee) with total size at most $k$. Participatory budgeting ... | https://arxiv.org/abs/2209.14468 | Academic Papers | svg |
cffb3d25b42ef05388e1f2f7be7fe92a6e02a96a7797a73d922b86e6154b1c37 | 2026-01-07T00:00:00-05:00 | Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis | arXiv:2210.06094v3 Announce Type: replace Abstract: Intraoral 3D scanning is now widely adopted in modern dentistry and plays a central role in supporting key tasks such as tooth segmentation, detection, labeling, and dental landmark identification. Accurate analysis of these scans is essential for orthodontic and rest... | https://arxiv.org/abs/2210.06094 | Academic Papers | svg |
3728e66becd8fcd50be0234a82c6c72cd583c253ae621a293c6fbe6378d58e8a | 2026-01-07T00:00:00-05:00 | MAST: Model-Agnostic Sparsified Training | arXiv:2311.16086v2 Announce Type: replace Abstract: We introduce a novel optimization problem formulation that departs from the conventional way of minimizing machine learning model loss as a black-box function. Unlike traditional formulations, the proposed approach explicitly incorporates an initially pre-trained mode... | https://arxiv.org/abs/2311.16086 | Academic Papers | svg |
7bb974cb4de3d475044e4071fd307f9c70b365d7c944769cf98365b140d78479 | 2026-01-07T00:00:00-05:00 | Time-Transformer: Integrating Local and Global Features for Better Time Series Generation (Extended Version) | arXiv:2312.11714v4 Announce Type: replace Abstract: Generating time series data is a promising approach to address data deficiency problems. However, it is also challenging due to the complex temporal properties of time series data, including local correlations as well as global dependencies. Most existing generative m... | https://arxiv.org/abs/2312.11714 | Academic Papers | svg |
bbd46047abd7329b7ed9916f934334ec99bc030fd12c191d1ac732cb34618c7f | 2026-01-07T00:00:00-05:00 | A Large-Scale Analysis on the Use of Arrival Time Prediction for Automated Shuttle Services in the Real World | arXiv:2401.05322v2 Announce Type: replace Abstract: Urban mobility is on the cusp of transformation with the emergence of shared, connected, and cooperative automated vehicles. Yet, for them to be accepted by customers, trust in their punctuality is vital. Many pilot initiatives operate without a fixed schedule, enhanc... | https://arxiv.org/abs/2401.05322 | Academic Papers | svg |
6580bcb1541536960fc8fd1c5a44b1f97b0297c50885836141d6d3539898f555 | 2026-01-07T00:00:00-05:00 | On the permutation automorphisms of binary cubic codes | arXiv:2402.10667v3 Announce Type: replace Abstract: A binary linear code whose permutation automorphism group has a fixed point free permutation of order $3$ is called a binary cubic code. The scope of this paper is to investigate the structural properties of binary cubic codes. Let $C$ be a binary cubic $[n,k]$ code. ... | https://arxiv.org/abs/2402.10667 | Academic Papers | svg |
a5ecf2a89f98be240388f81ae2a8b4a5891b9b6ee024bc2371c3da0c667d1d74 | 2026-01-07T00:00:00-05:00 | HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature Fusion | arXiv:2404.03527v3 Announce Type: replace Abstract: Data-fusion networks have shown significant promise for RGB-thermal scene parsing. However, the majority of existing studies have relied on symmetric duplex encoders for heterogeneous feature extraction and fusion, paying inadequate attention to the inherent differenc... | https://arxiv.org/abs/2404.03527 | Academic Papers | svg |
c3c11359141cfad3e14bdbf5f67013106a112fce2d99040390e2e6d28bce8d96 | 2026-01-07T00:00:00-05:00 | Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning | arXiv:2405.18376v3 Announce Type: replace Abstract: Existing SFDA methods struggle to fully use pre-trained knowledge and often rely on a single model's predictions or handcrafted prompts, limiting robustness under domain shift. Multimodal Large Language Models (MLLMs) offer a promising alternative: they encode rich vi... | https://arxiv.org/abs/2405.18376 | Academic Papers | svg |
6426f139036f57f2242944907d887a2e098d4d6e556dbba80d916f5f29854f7f | 2026-01-07T00:00:00-05:00 | Topological Perspectives on Optimal Multimodal Embedding Spaces | arXiv:2405.18867v2 Announce Type: replace Abstract: Recent strides in multimodal model development have ignited a paradigm shift in the realm of text-to-image generation. Among these advancements, CLIP stands out as a remarkable achievement which is a sophisticated autoencoder adept at encoding both textual and visual ... | https://arxiv.org/abs/2405.18867 | Academic Papers | svg |
fb17f27cfee8ee8d9cb82b17e5d6510a703bb71251ea3cc629b3a5967130669f | 2026-01-07T00:00:00-05:00 | A Survey on Failure Analysis and Fault Injection in AI Systems | arXiv:2407.00125v2 Announce Type: replace Abstract: The rapid advancement of Artificial Intelligence (AI) has led to its integration into various areas, especially with Large Language Models (LLMs) significantly enhancing capabilities in Artificial Intelligence Generated Content (AIGC). However, the complexity of AI sy... | https://arxiv.org/abs/2407.00125 | Academic Papers | svg |
ed07ed443443f30ea60f3fb90e2ac47b5af5caace769175a1c3038865ad4b907 | 2026-01-07T00:00:00-05:00 | Limits to Predicting Online Speech Using Large Language Models | arXiv:2407.12850v3 Announce Type: replace Abstract: Our paper studies the predictability of online speech -- that is, how well language models learn to model the distribution of user generated content on X (previously Twitter). We define predictability as a measure of the model's uncertainty, i.e. its negative log-like... | https://arxiv.org/abs/2407.12850 | Academic Papers | svg |
1f3a531e0816f4c7436eff804d1f867e51cd5da57d3de548c2f0518dc0c1782b | 2026-01-07T00:00:00-05:00 | An Uncertainty-Aware Generalization Framework for Cardiovascular Image Segmentation | arXiv:2409.14305v2 Announce Type: replace Abstract: Deep learning models have achieved significant success in segmenting cardiovascular structures, but there is a growing need to improve their generalization and robustness. Current methods often face challenges such as overfitting and limited accuracy, largely due to t... | https://arxiv.org/abs/2409.14305 | Academic Papers | svg |
30e5368e83c02bd179b5a59eead5346b7706539ca5e4a81b4a6024cdbd401218 | 2026-01-07T00:00:00-05:00 | Conformal Prediction for Dose-Response Models with Continuous Treatments | arXiv:2409.20412v2 Announce Type: replace Abstract: Understanding the dose-response relation between a continuous treatment and the outcome for an individual can greatly drive decision-making, particularly in areas like personalized drug dosing and personalized healthcare interventions. Point estimates are often insuff... | https://arxiv.org/abs/2409.20412 | Academic Papers | svg |
0639935b688063239c30c1527dbf0b913318ffaf80fab5efc4bea2e29c28fe13 | 2026-01-07T00:00:00-05:00 | Large Language Models can Achieve Social Balance | arXiv:2410.04054v3 Announce Type: replace Abstract: Large Language Models (LLMs) can be deployed in situations where they process positive/negative interactions with other agents. We study how this is done under the sociological framework of social balance, which explains the emergence of one faction or multiple antago... | https://arxiv.org/abs/2410.04054 | Academic Papers | svg |
6d56c086f417bfca0591fdde71512cf1ba305aafa5ada618def43b3b0c958f35 | 2026-01-07T00:00:00-05:00 | A Machine Learning Model for Solving Lane-Emden Equation using Legendre Wavelet Neural Network | arXiv:2410.05409v2 Announce Type: replace Abstract: As we know differential equations are very useful for electrical engineers to solve a variety of problems like: voltage across a capacitor, input versus output voltage, etc. Therefore, the goal of this paper is to find the solutions of non-linear differential equation... | https://arxiv.org/abs/2410.05409 | Academic Papers | svg |
bd3745e31dc48cec8dc6c08ddf8a5055451a18d88fb2d6cbd49f350d4e049808 | 2026-01-07T00:00:00-05:00 | Limits to scalable evaluation at the frontier: LLM as Judge won't beat twice the data | arXiv:2410.13341v3 Announce Type: replace Abstract: High quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem. Scalable evaluation methods that avoid costly annotation have therefore become an important research ambition. Many hope to use strong existing models in lieu... | https://arxiv.org/abs/2410.13341 | Academic Papers | svg |
d4160dbe55d57300e1d670ce95d459ee47dbb2bfd2ab5a70288821118e2a7dcd | 2026-01-07T00:00:00-05:00 | How Many Images Does It Take? Estimating Imitation Thresholds in Text-to-Image Models | arXiv:2410.15002v2 Announce Type: replace Abstract: Text-to-image models are trained using large datasets of image-text pairs collected from the internet. These datasets often include copyrighted and private images. Training models on such datasets enables them to generate images that might violate copyright laws and i... | https://arxiv.org/abs/2410.15002 | Academic Papers | svg |
16d45ea0d04fba85453b297e84ce7aa06e1d072dffe3a561f81e5e6fea8c6511 | 2026-01-07T00:00:00-05:00 | Uncovering Autoregressive LLM Knowledge of Thematic Fit in Event Representation | arXiv:2410.15173v2 Announce Type: replace Abstract: We show closed models possess much thematic fit knowledge and set a new state of the art, while open models also seem to capture much relevant knowledge (in semantic filtering), but yield lower scores. Surprisingly, multi-step reasoning only helped closed models (with... | https://arxiv.org/abs/2410.15173 | Academic Papers | svg |
c17d70df0ac7948e4eb92efc60f7162fd980561d70f8e88c4e4d2769d5092669 | 2026-01-07T00:00:00-05:00 | SaVe-TAG: LLM-based Interpolation for Long-Tailed Text-Attributed Graphs | arXiv:2410.16882v4 Announce Type: replace Abstract: Real-world graph data often follows long-tailed distributions, making it difficult for Graph Neural Networks (GNNs) to generalize well across both head and tail classes. Recent advances in Vicinal Risk Minimization (VRM) have shown promise in mitigating class imbalanc... | https://arxiv.org/abs/2410.16882 | Academic Papers | svg |
3a0a87ba9532d801a0dbf62d5c00049a5c2950451249eefee8324d7ac1eb8d0d | 2026-01-07T00:00:00-05:00 | EviRerank: Adaptive Evidence Construction for Long-Document LLM Reranking | arXiv:2411.06254v5 Announce Type: replace Abstract: Decoder-only LLM rerankers struggle with long documents: inference is costly and relevance signals can be diluted by irrelevant context. Motivated by an attention analysis indicating a consistent degradation trend when non-relevant text is appended, we propose EviRera... | https://arxiv.org/abs/2411.06254 | Academic Papers | svg |
66320ea8f06f6a4a1d884532422eacbf759ebd7b0391562a3e07d246214c1cfd | 2026-01-07T00:00:00-05:00 | Communication Compression for Tensor Parallel LLM Inference | arXiv:2411.09510v3 Announce Type: replace Abstract: Large Language Models (LLMs) have pushed the frontier of artificial intelligence but are comprised of hundreds of billions of parameters and operations. For faster inference latency, LLMs are deployed on multiple hardware accelerators through various Model Parallelism... | https://arxiv.org/abs/2411.09510 | Academic Papers | svg |
2cfb6d91a5fdd5cc851126ed06d47923e5d8123b0cfd5109c07c0374aebe02a7 | 2026-01-07T00:00:00-05:00 | FCC: Fully Connected Correlation for One-Shot Segmentation | arXiv:2411.11917v2 Announce Type: replace Abstract: Few-shot segmentation (FSS) aims to segment the target object in a query image using only a small set of support images and masks. Therefore, having strong prior information for the target object using the support set is essential for guiding the initial training of F... | https://arxiv.org/abs/2411.11917 | Academic Papers | svg |
08e99b8a7113b920cdc167438308d62625686dd2a1dbb4f626a22f082601ab95 | 2026-01-07T00:00:00-05:00 | Learning Visual Hierarchies in Hyperbolic Space for Image Retrieval | arXiv:2411.17490v4 Announce Type: replace Abstract: Structuring latent representations in a hierarchical manner enables models to learn patterns at multiple levels of abstraction. However, most prevalent image understanding models focus on visual similarity, and learning visual hierarchies is relatively unexplored. In ... | https://arxiv.org/abs/2411.17490 | Academic Papers | svg |
6a64f2def54761f12b5ef3f7bf9cf64a07c5a7b87196d21424c18087f99d9702 | 2026-01-07T00:00:00-05:00 | AdaVLN: Towards Visual Language Navigation in Continuous Indoor Environments with Moving Humans | arXiv:2411.18539v3 Announce Type: replace Abstract: Visual Language Navigation is a task that challenges robots to navigate in realistic environments based on natural language instructions. While previous research has largely focused on static settings, real-world navigation must often contend with dynamic human obstac... | https://arxiv.org/abs/2411.18539 | Academic Papers | svg |
d6596e9b5dd33e3da14311ecbf3e1988c3d1880ea0b3e59a40082b29b7fdf6f2 | 2026-01-07T00:00:00-05:00 | Neural Power-Optimal Magnetorquer Solution for Multi-Agent Formation and Attitude Control | arXiv:2412.00548v2 Announce Type: replace Abstract: This paper presents a learning-based current calculation model to achieve power-optimal magnetic-field interaction for multi-agent formation and attitude control. In aerospace engineering, electromagnetic coils are referred to as magnetorquer (MTQ) coils and used as s... | https://arxiv.org/abs/2412.00548 | Academic Papers | svg |
7cdc7d0685579fa193c845e0b5025197a54435b9ad5c65f84347e24c3d61bbb4 | 2026-01-07T00:00:00-05:00 | MemHunter: Automated and Verifiable Memorization Detection at Dataset-scale in LLMs | arXiv:2412.07261v3 Announce Type: replace Abstract: Large language models (LLMs) have been shown to memorize and reproduce content from their training data, raising significant privacy concerns, especially with web-scale datasets. Existing methods for detecting memorization are primarily sample-specific, relying on man... | https://arxiv.org/abs/2412.07261 | Academic Papers | svg |
fac8ea32ab43c99b03720fa66333eecce1a3dee7f726244decc9658a6d28f5ed | 2026-01-07T00:00:00-05:00 | RobotDiffuse: Diffusion-Based Motion Planning for Redundant Manipulators with the ROP Obstacle Avoidance Dataset | arXiv:2412.19500v2 Announce Type: replace Abstract: Redundant manipulators, with their higher Degrees of Freedom (DoFs), offer enhanced kinematic performance and versatility, making them suitable for applications like manufacturing, surgical robotics, and human-robot collaboration. However, motion planning for these ma... | https://arxiv.org/abs/2412.19500 | Academic Papers | svg |
f1171506fc52442088b3070f0453468222239b851937adc4b6728fcfa98fec92 | 2026-01-07T00:00:00-05:00 | Steering Flexible Linear Objects in Planar Environments by Two Robot Hands Using Euler's Elastica Solutions | arXiv:2501.02874v4 Announce Type: replace Abstract: The manipulation of flexible objects such as cables, wires and fresh food items by robot hands forms a special challenge in robot grasp mechanics. This paper considers the steering of flexible linear objects in planar environments by two robot hands. The flexible line... | https://arxiv.org/abs/2501.02874 | Academic Papers | svg |
e113b1ae6d45593fac49177a6196f0f2e8da792a95c197d111a9cfdc5c28f129 | 2026-01-07T00:00:00-05:00 | The structure of polynomial growth for tree automata/transducers and MSO set queries | arXiv:2501.10270v4 Announce Type: replace Abstract: Given an $\mathbb{N}$-weighted tree automaton, we give a decision procedure for exponential vs polynomial growth (with respect to the input size) in quadratic time, and an algorithm that computes the exact polynomial degree of growth in cubic time. As a special case, ... | https://arxiv.org/abs/2501.10270 | Academic Papers | svg |
f0e6419086c45d31b08ca48367df88432ad9de1fbdb1528a47e6529d27b9837f | 2026-01-07T00:00:00-05:00 | SLVC-DIDA: Signature-less Verifiable Credential-based Issuer-hiding and Multi-party Authentication for Decentralized Identity | arXiv:2501.11052v3 Announce Type: replace Abstract: As an emerging paradigm in digital identity, Decentralized Identity (DID) appears advantages over traditional identity management methods in a variety of aspects, e.g., enhancing user-centric online services and ensuring complete user autonomy and control. Verifiable ... | https://arxiv.org/abs/2501.11052 | Academic Papers | svg |
e710dc095379daf260c75efda2af3546b007fcc8c75aaee10eb2858f42358067 | 2026-01-07T00:00:00-05:00 | Model-checking real-time systems: revisiting the alternating automaton route | arXiv:2501.17576v2 Announce Type: replace Abstract: Alternating timed automata (ATA) are an extension of timed automata, that are closed under complementation and hence amenable to logic-to-automata translations. Several timed logics, including Metric Temporal Logic (MTL), can be converted to equivalent 1-clock ATAs (1... | https://arxiv.org/abs/2501.17576 | Academic Papers | svg |
92ad737bf1831ea93fbb7373c19efb8354ab44c15c00423d5498a164adeddbc8 | 2026-01-07T00:00:00-05:00 | Successor-Generator Planning with LLM-generated Heuristics | arXiv:2501.18784v4 Announce Type: replace Abstract: Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized over task-specific tuning. This work revisits that paradigm in light of recent advances in large language models (LLMs)... | https://arxiv.org/abs/2501.18784 | Academic Papers | svg |
d09a9c55f06daddd500968432cd5fd9da3a7390ec5671ac1ddfd8af543fb60d7 | 2026-01-07T00:00:00-05:00 | Leveraging the true depth of LLMs | arXiv:2502.02790v3 Announce Type: replace Abstract: The remarkable capabilities of Large Language Models (LLMs) are overshadowed by their immense computational cost. While recent work has shown that many LLM layers can be reordered or even removed with minimal impact on accuracy, these insights have not been translated... | https://arxiv.org/abs/2502.02790 | Academic Papers | svg |
9086f07f4b63841c14a25fdca7b707f6c640391e8d8ee3ed1d5d20c21538529b | 2026-01-07T00:00:00-05:00 | Training Set Reconstruction from Differentially Private Forests: How Effective is DP? | arXiv:2502.05307v4 Announce Type: replace Abstract: Recent research has shown that structured machine learning models such as tree ensembles are vulnerable to privacy attacks targeting their training data. To mitigate these risks, differential privacy (DP) has become a widely adopted countermeasure, as it offers rigoro... | https://arxiv.org/abs/2502.05307 | Academic Papers | svg |
b235a060ca0aa0a7c2e1c50c96141647cc60257d9989fb2134fee68a055bac54 | 2026-01-07T00:00:00-05:00 | DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior | arXiv:2502.09111v2 Announce Type: replace Abstract: Gaussian SLAM systems excel in real-time rendering and fine-grained reconstruction compared to NeRF-based systems. However, their reliance on extensive keyframes is impractical for deployment in real-world robotic systems, which typically operate under sparse-view con... | https://arxiv.org/abs/2502.09111 | Academic Papers | svg |
a08aa3d9a4ff50ccc53b6079171988cf16ea5829a5056f420886f9d41906d231 | 2026-01-07T00:00:00-05:00 | Whose story is it? Personalizing story generation by inferring author styles | arXiv:2502.13028v3 Announce Type: replace Abstract: Personalization is critical for improving user experience in interactive writing and educational applications, yet remains understudied in story generation. We study the task of personalizing story generation, where our goal is to mimic an author's writing style, give... | https://arxiv.org/abs/2502.13028 | Academic Papers | svg |
f497f6c2ff7db3cf1427bf96be4ec49acd417c00888301d0204120746d8d6b9a | 2026-01-07T00:00:00-05:00 | Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework | arXiv:2502.13759v3 Announce Type: replace Abstract: Geolocation, the task of identifying an image's location, requires complex reasoning and is crucial for navigation, monitoring, and cultural preservation. However, current methods often produce coarse, imprecise, and non-interpretable localization. A major challenge l... | https://arxiv.org/abs/2502.13759 | Academic Papers | svg |
2020033a5ec7bb8fcf08d6fd0be5b670015199e5d0b9c919716caf5f2a98a0fb | 2026-01-07T00:00:00-05:00 | Towards Threshold-Free KV Cache Pruning | arXiv:2502.16886v3 Announce Type: replace Abstract: To reduce memory consumption during LLM inference, prior works have proposed numerous methods that focus on KV cache pruning based on various criteria. While these techniques often accomplish lossless memory reduction on many datasets, they often rely on an under-emph... | https://arxiv.org/abs/2502.16886 | Academic Papers | svg |
88e7ac7f7bfb35c4dcceef9d9ba7a42aefd7362371e98a682a595d6fd9a402e3 | 2026-01-07T00:00:00-05:00 | It's Not All Black and White: Degree of Truthfulness for Risk-Avoiding Agents | arXiv:2502.18805v3 Announce Type: replace Abstract: The classic notion of \emph{truthfulness} requires that no agent has a profitable manipulation -- an untruthful report that, for \emph{some} combination of reports of the other agents, increases her utility. This strong notion implicitly assumes that the manipulating ... | https://arxiv.org/abs/2502.18805 | Academic Papers | svg |
51591928363885aceb5298e4114261d87fd250681b0a92ce8f1005c947578a46 | 2026-01-07T00:00:00-05:00 | Protecting multimodal large language models against misleading visualizations | arXiv:2502.20503v5 Announce Type: replace Abstract: Visualizations play a pivotal role in daily communication in an increasingly data-driven world. Research on multimodal large language models (MLLMs) for automated chart understanding has accelerated massively, with steady improvements on standard benchmarks. However, ... | https://arxiv.org/abs/2502.20503 | Academic Papers | svg |
41165b96d83651f0acb4fe4a4a43ee1a143a6ea548df43c8ed6aca13bda0dc7b | 2026-01-07T00:00:00-05:00 | Active operator learning with predictive uncertainty quantification for partial differential equations | arXiv:2503.03178v2 Announce Type: replace Abstract: With the increased prevalence of neural operators being used to provide rapid solutions to partial differential equations (PDEs), understanding the accuracy of model predictions and the associated error levels is necessary for deploying reliable surrogate models in sc... | https://arxiv.org/abs/2503.03178 | Academic Papers | svg |
b8c20966dc75941e5b78008ba943692a004f35401d3a15b6d441cd27299200df | 2026-01-07T00:00:00-05:00 | The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems | arXiv:2503.03750v3 Announce Type: replace Abstract: As large language models (LLMs) become more capable and agentic, the requirement for trust in their outputs grows significantly, yet at the same time concerns have been mounting that models may learn to lie in pursuit of their goals. To address these concerns, a body ... | https://arxiv.org/abs/2503.03750 | Academic Papers | svg |
7457946d9aced9bc20874b321bbed108168fd73c04961370881f61c77c86957c | 2026-01-07T00:00:00-05:00 | E$^2$AT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Models | arXiv:2503.04833v3 Announce Type: replace Abstract: Research endeavors have been made in learning robust Multimodal Large Language Models (MLLMs) against jailbreak attacks. However, existing methods for improving MLLMs' robustness still face critical challenges: \ding{172} how to efficiently tune massive weight paramet... | https://arxiv.org/abs/2503.04833 | Academic Papers | svg |
dc11ceae56d72accb3aaf073b575a306bb03768374b37078b26878544522a890 | 2026-01-07T00:00:00-05:00 | From Intrinsic Toxicity to Reception-Based Toxicity: A Contextual Framework for Prediction and Evaluation | arXiv:2503.16072v3 Announce Type: replace Abstract: Most toxicity detection models treat toxicity as an intrinsic property of text, overlooking the role of context in shaping its impact. In this position paper, drawing on insights from psychology, neuroscience, and computational social science, we reconceptualise toxic... | https://arxiv.org/abs/2503.16072 | Academic Papers | svg |
8d422a635e9110c0878378b322b35389a4f53c3898b864888c27cc1436825ec1 | 2026-01-07T00:00:00-05:00 | Offline Model-Based Optimization: Comprehensive Review | arXiv:2503.17286v2 Announce Type: replace Abstract: Offline optimization is a fundamental challenge in science and engineering, where the goal is to optimize black-box functions using only offline datasets. This setting is particularly relevant when querying the objective function is prohibitively expensive or infeasib... | https://arxiv.org/abs/2503.17286 | Academic Papers | svg |
2727336d363862126a47d592eea3095096761e485128d0e35783b35d436d6750 | 2026-01-07T00:00:00-05:00 | Graph-Structured Driven Dual Adaptation for Mitigating Popularity Bias | arXiv:2503.23358v2 Announce Type: replace Abstract: Popularity bias is a common challenge in recommender systems. It often causes unbalanced item recommendation performance and intensifies the Matthew effect. Due to limited user-item interactions, unpopular items are frequently constrained to the embedding neighborhood... | https://arxiv.org/abs/2503.23358 | Academic Papers | svg |
4049ee9110f5128486a32d02000f55d1545755c58e2877eec3fb20dcecc43afe | 2026-01-07T00:00:00-05:00 | Self-Routing RAG: Binding Selective Retrieval with Knowledge Verbalization | arXiv:2504.01018v3 Announce Type: replace Abstract: Selective retrieval aims to make retrieval-augmented generation (RAG) more efficient and reliable by skipping retrieval when an LLM's parametric knowledge suffices. Despite promising results, existing methods are constrained by a binary design choice: either retrieve ... | https://arxiv.org/abs/2504.01018 | Academic Papers | svg |
5139c919f24a6782159f1e71c6a62560e1874a72d7c0622c5ea5678acb8346cb | 2026-01-07T00:00:00-05:00 | EgoLog: Ego-Centric Fine-Grained Daily Log with Ubiquitous Wearables | arXiv:2504.02624v3 Announce Type: replace Abstract: Despite advances in human activity recognition (HAR) with different modalities, a precise, robust, and accurate daily log system is not yet available. Current solutions primarily rely on controlled, lab-based data collection, which limits their real-world applicabilit... | https://arxiv.org/abs/2504.02624 | Academic Papers | svg |
8021f77571630ee258aae0604ff32cd57bf0201f9ad656d53c01f4ccda8460c4 | 2026-01-07T00:00:00-05:00 | Solving the Paint Shop Problem with Flexible Management of Multi-Lane Buffers Using Reinforcement Learning and Action Masking | arXiv:2504.02644v2 Announce Type: replace Abstract: In the paint shop problem, an unordered incoming sequence of cars assigned to different colors has to be reshuffled with the objective of minimizing the number of color changes. To reshuffle the incoming sequence, manufacturers can employ a first-in-first-out multi-la... | https://arxiv.org/abs/2504.02644 | Academic Papers | svg |
6a3148f64c5fadcfbfc698bcf8c63db9fec2b90bfc75667286dea9072bd45e7c | 2026-01-07T00:00:00-05:00 | Heuristic Methods are Good Teachers to Distill MLPs for Graph Link Prediction | arXiv:2504.06193v2 Announce Type: replace Abstract: Link prediction is a crucial graph-learning task with applications including citation prediction and product recommendation. Distilling Graph Neural Networks (GNNs) teachers into Multi-Layer Perceptrons (MLPs) students has emerged as an effective approach to achieve s... | https://arxiv.org/abs/2504.06193 | Academic Papers | svg |
7cf12c4d159d52ae04e7bf88ec3fdcb332e915729720c2b05c09bccf1f2e4c8d | 2026-01-07T00:00:00-05:00 | Efficient Swept Volume-Based Trajectory Generation for Arbitrary-Shaped Ground Robot Navigation | arXiv:2504.07554v2 Announce Type: replace Abstract: Navigating an arbitrary-shaped ground robot safely in cluttered environments remains a challenging problem. The existing trajectory planners that account for the robot's physical geometry severely suffer from the intractable runtime. To achieve both computational effi... | https://arxiv.org/abs/2504.07554 | Academic Papers | svg |
14d4bf920589a6ff2396054c67239aedd6d883c33d068e2697c76a4491c47758 | 2026-01-07T00:00:00-05:00 | SignX: Continuous Sign Recognition in Compact Pose-Rich Latent Space | arXiv:2504.16315v2 Announce Type: replace Abstract: The complexity of sign language data processing brings many challenges. The current approach to recognition of ASL signs aims to translate RGB sign language videos through pose information into English-based ID Glosses, which serve to uniquely identify ASL signs. This... | https://arxiv.org/abs/2504.16315 | Academic Papers | svg |
336d91947ab888334728495634d346ade1baf7f6431c526c2aac759a98b8c6d2 | 2026-01-07T00:00:00-05:00 | Beyond Platforms -- Growing Distributed Transaction Networks for Digital Commerce | arXiv:2504.18602v4 Announce Type: replace Abstract: We talk of the internet as digital infrastructure; but we leave the building of rails and roads to the quasi-monopolistic platform providers. Decentralised architectures provide a number of advantages: They are potentially more inclusive for small players; more resili... | https://arxiv.org/abs/2504.18602 | Academic Papers | svg |
3debc47aa3625d66f57e7a8e7f50c4fbe56d664da34d7687bc4ce40767c1e902 | 2026-01-07T00:00:00-05:00 | PartHOI: Part-based Hand-Object Interaction Transfer via Generalized Cylinders | arXiv:2504.20599v2 Announce Type: replace Abstract: Learning-based methods to understand and model hand-object interactions (HOI) require a large amount of high-quality HOI data. One way to create HOI data is to transfer hand poses from a source object to another based on the objects' geometry. However, current methods... | https://arxiv.org/abs/2504.20599 | Academic Papers | svg |
68f6ecbeee524dfba3b4abefd396187db84edf384e0ad1c4bbba9c1bfbddefad | 2026-01-07T00:00:00-05:00 | UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities | arXiv:2504.20734v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. However, most existing approaches are limited to a text-only corpus, and while recent efforts hav... | https://arxiv.org/abs/2504.20734 | Academic Papers | svg |
7cd957061df0c01bf2ab90ec7e16b94e76adda75fc774a90e96d3c97369b12d3 | 2026-01-07T00:00:00-05:00 | The Great Data Standoff: Researchers vs. Platforms Under the Digital Services Act | arXiv:2505.01122v2 Announce Type: replace Abstract: To facilitate accountability and transparency, the Digital Services Act (DSA) sets up a process through which Very Large Online Platforms (VLOPs) need to grant vetted researchers access to their internal data (Article 40(4)). Operationalising such access is challengin... | https://arxiv.org/abs/2505.01122 | Academic Papers | svg |
7b5e0a0eeff4ce087815ad0c9d85c068efbf76c71f2b4e59dbd42c3204f7ccfc | 2026-01-07T00:00:00-05:00 | HONEYBEE: Efficient Role-based Access Control for Vector Databases via Dynamic Partitioning | arXiv:2505.01538v2 Announce Type: replace Abstract: Enterprise deployments of vector databases require access control policies to protect sensitive data. These systems often implement access control through hybrid vector queries that combine nearest-neighbor search with relational predicates based on user permissions. ... | https://arxiv.org/abs/2505.01538 | Academic Papers | svg |
fa878917bc6e0df21a4cf638020e58f6d1128f4d578e2a5c3025096d75c03554 | 2026-01-07T00:00:00-05:00 | Characterizing the Robustness of Black-Box LLM Planners Under Perturbed Observations with Adaptive Stress Testing | arXiv:2505.05665v3 Announce Type: replace Abstract: Large language models (LLMs) have recently demonstrated success in decision-making tasks including planning, control, and prediction, but their tendency to hallucinate unsafe and undesired outputs poses risks. This unwanted behavior is further exacerbated in environme... | https://arxiv.org/abs/2505.05665 | Academic Papers | svg |
2c004a1d7943c0df399bf1d281faadad12124745bd6528b1f4b054aeca8deb52 | 2026-01-07T00:00:00-05:00 | Reference-Free Evaluation of Taxonomies | arXiv:2505.11470v2 Announce Type: replace Abstract: We introduce two reference-free metrics for quality evaluation of taxonomies in the absence of labels. The first metric evaluates robustness by calculating the correlation between semantic and taxonomic similarity, addressing error types not considered by existing met... | https://arxiv.org/abs/2505.11470 | Academic Papers | svg |
94a94a0cce22a116ed7706506e1b761f15a9e0d214ee8a0b7e272a9ab71445b4 | 2026-01-07T00:00:00-05:00 | DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization | arXiv:2505.12366v5 Announce Type: replace Abstract: The recent success and openness of DeepSeek-R1 have brought widespread attention to Group Relative Policy Optimization (GRPO) as a reinforcement learning method for large reasoning models (LRMs). In this work, we analyze the GRPO objective under a binary reward settin... | https://arxiv.org/abs/2505.12366 | Academic Papers | svg |
9eed8b05576cd0ef3b3b0f6dc1d9cee63eb0cec2b93bfc2063d2d5fc8b0d9d31 | 2026-01-07T00:00:00-05:00 | EvoGPT: Leveraging LLM-Driven Seed Diversity to Improve Search-Based Test Suite Generation | arXiv:2505.12424v2 Announce Type: replace Abstract: Search-Based Software Testing (SBST) is a well-established approach for automated unit test generation, yet it often suffers from premature convergence and limited diversity in the generated test suites. Recently, Large Language Models (LLMs) have emerged as an altern... | https://arxiv.org/abs/2505.12424 | Academic Papers | svg |
bd18fad9149a7ae958dc48800954b8e27f970543ba5c6bc3ddea7421605cf202 | 2026-01-07T00:00:00-05:00 | The Virtual Reality Koinos Method: Analysis of Symmetrical Dyadic Collaboration in Virtual Reality from the perspective of communication models | arXiv:2505.14078v2 Announce Type: replace Abstract: Understanding which factors could influence co-presence in Virtual Reality could help develop more qualitative social interactions, or social interactions that generate similar sensations, emotions and feelings than the ones generated during Face-to-Face interactions.... | https://arxiv.org/abs/2505.14078 | Academic Papers | svg |
55f51c285162fc0567a456a54fdfec87da3ff4018741fe1ccb41810b14af0468 | 2026-01-07T00:00:00-05:00 | Extensible Post Quantum Cryptography Based Authentication | arXiv:2505.16112v2 Announce Type: replace Abstract: Cryptography underpins the security of modern digital infrastructure, from cloud services to health data. However, many widely deployed systems will become vulnerable after the advent of scalable quantum computing. Although quantum-safe cryptographic primitives have b... | https://arxiv.org/abs/2505.16112 | Academic Papers | svg |
7ce297993c9f7ff606f3f58e035b46ab6c03e1d3edb62ff348936afbf7b27235 | 2026-01-07T00:00:00-05:00 | EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios | arXiv:2505.16160v4 Announce Type: replace Abstract: As large language models continue to advance, their application in educational contexts remains underexplored and under-optimized. In this paper, we address this gap by introducing the first diverse benchmark tailored for educational scenarios, incorporating synthetic... | https://arxiv.org/abs/2505.16160 | Academic Papers | svg |
5e291809314edc9ec27420bff771b7504ff619fc2af34a1c44a37aa73c7fded4 | 2026-01-07T00:00:00-05:00 | Asynchronous Global Protocols, Precisely: Full Proofs | arXiv:2505.17676v2 Announce Type: replace Abstract: Asynchronous multiparty session types are a type-based framework which ensure the compatibility of components in a distributed system by checking compliance against a specified global protocol. We propose a top-down approach, starting with the global protocol which is... | https://arxiv.org/abs/2505.17676 | Academic Papers | svg |
50d19a611d3cb2eac5cbc2f6cc1b78f45cc636cedb1f65ac658c10bea92350aa | 2026-01-07T00:00:00-05:00 | PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation | arXiv:2505.19347v3 Announce Type: replace Abstract: Patent similarity evaluation plays a critical role in intellectual property analysis. However, existing methods often overlook the intricate structure of patent documents, which integrate technical specifications, legal boundaries, and application contexts. We introdu... | https://arxiv.org/abs/2505.19347 | Academic Papers | svg |
5c72f76eddb0ebe0ea73d9b5f02ce9027fefd42bfb6b73d89c493d907dc1a05b | 2026-01-07T00:00:00-05:00 | VisRet: Visualization Improves Knowledge-Intensive Text-to-Image Retrieval | arXiv:2505.20291v3 Announce Type: replace Abstract: Text-to-image retrieval (T2I retrieval) remains challenging because cross-modal embeddings often behave as bags of concepts, underrepresenting structured visual relationships such as pose and viewpoint. We propose Visualize-then-Retrieve (VisRet), a retrieval paradigm... | https://arxiv.org/abs/2505.20291 | Academic Papers | svg |
8e30a1e4206deeb931570153cefc4298fdfdc5f3beac702db309c6efb386e25c | 2026-01-07T00:00:00-05:00 | POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization | arXiv:2505.20624v2 Announce Type: replace Abstract: Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We introduce POLAR, a multilingual, multicultural, and multievent dataset with over 23k in... | https://arxiv.org/abs/2505.20624 | Academic Papers | svg |
a03d6b4049dee49aca3d7e19a580f08268fb1ea243c40cffa0bb9e34716696b1 | 2026-01-07T00:00:00-05:00 | RoboTransfer: Controllable Geometry-Consistent Video Diffusion for Manipulation Policy Transfer | arXiv:2505.23171v2 Announce Type: replace Abstract: The goal of general-purpose robotics is to create agents that can seamlessly adapt to and operate in diverse, unstructured human environments. Imitation learning has become a key paradigm for robotic manipulation, yet collecting large-scale and diverse demonstrations ... | https://arxiv.org/abs/2505.23171 | Academic Papers | svg |
98460fa86903614528e43fa9a565dac77c2c2b27da6d49e8e714e26158ed1eb1 | 2026-01-07T00:00:00-05:00 | Melding the Serverless Control Plane with the Conventional Cluster Manager for Speed and Resource Efficiency | arXiv:2505.24551v4 Announce Type: replace Abstract: Serverless platforms face a trade-off: conventional cluster managers like Kubernetes offer compatibility for co-locating Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) components of serverless applications, at the cost of high cold-start latency, whereas... | https://arxiv.org/abs/2505.24551 | Academic Papers | svg |
f6098852382fd2890ce1607f2fbd20c49024d0d0b0527b3f2a272b053ff5a8bc | 2026-01-07T00:00:00-05:00 | Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads | arXiv:2506.00634v2 Announce Type: replace Abstract: Rental listings offer a window into how urban space is socially constructed through language. We analyze Chicago Craigslist rental advertisements from 2018 to 2024 to examine how listing agents characterize neighborhoods, identifying mismatches between institutional b... | https://arxiv.org/abs/2506.00634 | Academic Papers | svg |
39dcc862d12fc632fb35c36ea3926ca7c6307867c807b170d0644332700037b2 | 2026-01-07T00:00:00-05:00 | Quantifying task-relevant representational similarity using decision variable correlation | arXiv:2506.02164v3 Announce Type: replace Abstract: Previous studies have compared neural activities in the visual cortex to representations in deep neural networks trained on image classification. Interestingly, while some suggest that their representations are highly similar, others argued the opposite. Here, we prop... | https://arxiv.org/abs/2506.02164 | Academic Papers | svg |
88850293a1b513974655ff869b1a3a1c62ab82ca717847a58ae1377bf6b14b3a | 2026-01-07T00:00:00-05:00 | Something Just Like TRuST : Toxicity Recognition of Span and Target | arXiv:2506.02326v2 Announce Type: replace Abstract: Toxic language includes content that is offensive, abusive, or that promotes harm. Progress in preventing toxic output from large language models (LLMs) is hampered by inconsistent definitions of toxicity. We introduce TRuST, a large-scale dataset that unifies and exp... | https://arxiv.org/abs/2506.02326 | Academic Papers | svg |
3231c68f3b0d5110ff38eba2f40f98f0f3529b4b4d76742f8b13d23726198d1b | 2026-01-07T00:00:00-05:00 | OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for Over-Reasoning Mitigation | arXiv:2506.02397v3 Announce Type: replace Abstract: Human cognition operates through two complementary modes: fast intuitive thinking and slow deliberate thinking. Vanilla large language models (LLMs) predominantly follow the fast-thinking paradigm, producing immediate responses; while recent large reasoning models (LR... | https://arxiv.org/abs/2506.02397 | Academic Papers | svg |
903a964b4ce4c40fe2875e344981292ad9f2cd89f18b39ad7761b37eb1735523 | 2026-01-07T00:00:00-05:00 | Cyber Security of Sensor Systems for State Sequence Estimation: an AI Approach | arXiv:2506.06572v2 Announce Type: replace Abstract: Sensor systems are extremely popular today and vulnerable to sensor data attacks. Due to possible devastating consequences, counteracting sensor data attacks is an extremely important topic, which has not seen sufficient study. This paper develops the first methods th... | https://arxiv.org/abs/2506.06572 | Academic Papers | svg |
0f0014b5c7524dc5b97f1ce8ef249ba67e5961f5d0a78b396e71c1b468e2114d | 2026-01-07T00:00:00-05:00 | Aligning Text, Images, and 3D Structure Token-by-Token | arXiv:2506.08002v2 Announce Type: replace Abstract: Creating machines capable of understanding the world in 3D is essential in assisting designers that build and edit 3D environments and robots navigating and interacting within a three-dimensional space. Inspired by advances in language and image modeling, we investiga... | https://arxiv.org/abs/2506.08002 | Academic Papers | svg |
14f7441a694db23d7b137a7c480d274271d44cae7e3f07974e7a4e8e5f1cbbcf | 2026-01-07T00:00:00-05:00 | TTrace: Lightweight Error Checking and Diagnosis for Distributed Training | arXiv:2506.09280v2 Announce Type: replace Abstract: Distributed training is essential for scaling the training of large neural network models, such as large language models (LLMs), across thousands of GPUs. However, the complexity of distributed training programs makes them particularly prone to silent bugs, which do n... | https://arxiv.org/abs/2506.09280 | Academic Papers | svg |
43a89748d657d0930fe10a2fe29905fa14ad07fd04e78702bc3a83f1ba0b0037 | 2026-01-07T00:00:00-05:00 | Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation | arXiv:2506.09990v2 Announce Type: replace Abstract: We present Chain-of-Action (CoA), a novel visuo-motor policy paradigm built upon Trajectory Autoregressive Modeling. Unlike conventional approaches that predict next step action(s) forward, CoA generates an entire trajectory by explicit backward reasoning with task-sp... | https://arxiv.org/abs/2506.09990 | Academic Papers | svg |
420f018c46f0dce0217ac20b6f0985f669148172f3e26632f0686ad681ad8ad7 | 2026-01-07T00:00:00-05:00 | A new type of federated clustering: A non-model-sharing approach | arXiv:2506.10244v3 Announce Type: replace Abstract: In recent years, the growing need to leverage sensitive data across institutions has led to increased attention on federated learning (FL), a decentralized machine learning paradigm that enables model training without sharing raw data. However, existing FL-based clust... | https://arxiv.org/abs/2506.10244 | Academic Papers | svg |
9b51ec48b26e502584f5aa6dc582153cf23b124c6e530a74198ce34cdc3b1906 | 2026-01-07T00:00:00-05:00 | On Differential and Boomerang Properties of a Class of Binomials over Finite Fields of Odd Characteristic | arXiv:2506.11486v2 Announce Type: replace Abstract: In this paper, we investigate the differential and boomerang properties of a class of binomial $F_{r,u}(x) = x^r(1 + u\chi(x))$ over the finite field $\mathbb{F}_{p^n}$, where $r = \frac{p^n+1}{4}$, $p^n \equiv 3 \pmod{4}$, and $\chi(x) = x^{\frac{p^n -1}{2}}$ is the ... | https://arxiv.org/abs/2506.11486 | Academic Papers | svg |
580eb3eae1c09358981f496084cb96be8fee473050d513f4d4d6c299ff63d2eb | 2026-01-07T00:00:00-05:00 | Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index | arXiv:2506.12229v5 Announce Type: replace Abstract: Language models are trained mainly on massive text data from the Internet, and it becomes increasingly important to understand this data source. Exact-match search engines enable searching in large text corpora - counting string appearances and retrieving the enclosin... | https://arxiv.org/abs/2506.12229 | Academic Papers | svg |
27bbff596b90d90f803dab4dcacaf15098ea0a5f5179bf4b9cb7e9a940762b43 | 2026-01-07T00:00:00-05:00 | BandPilot: Towards Performance- and Contention-Aware GPU Dispatching in AI Clusters | arXiv:2506.15595v4 Announce Type: replace Abstract: Modern multi-tenant AI clusters are increasingly communication-bound, driven by high-volume and multi-round GPU-to-GPU collective communication. Consequently, the GPU dispatcher's choice of a physical GPU subset for each tenant largely determines the job's effective c... | https://arxiv.org/abs/2506.15595 | Academic Papers | svg |
f3a59609832241c776453c099f90e63917fe4e57f5cde735fc9edd593635aba3 | 2026-01-07T00:00:00-05:00 | SLR: Automated Synthesis for Scalable Logical Reasoning | arXiv:2506.15787v5 Announce Type: replace Abstract: We introduce SLR, an end-to-end framework for systematic evaluation and training of Large Language Models (LLMs) via Scalable Logical Reasoning. Given a user's task specification, SLR automatically synthesizes (i) an instruction prompt for an inductive reasoning task,... | https://arxiv.org/abs/2506.15787 | Academic Papers | svg |
5f3bd63fd11b9249273ea16dc12b64e6cc6829718a404534a02398495c672d95 | 2026-01-07T00:00:00-05:00 | Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse | arXiv:2506.16412v2 Announce Type: replace Abstract: Generative AI (GAI) technologies are quickly reshaping the educational landscape. As adoption accelerates, understanding how students and educators perceive these tools is essential. This study presents one of the most comprehensive analyses to date of stakeholder dis... | https://arxiv.org/abs/2506.16412 | Academic Papers | svg |
4f442b315b22861f18a4ae23f0dede25c410e528f685855e3ad2aa926cd782b3 | 2026-01-07T00:00:00-05:00 | Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in Inference-time Scaling? | arXiv:2506.17417v3 Announce Type: replace Abstract: Inference time techniques such as decoding time scaling and self refinement have been shown to substantially improve mathematical reasoning in large language models (LLMs), largely attributed to emergent self correction and self verification behaviors often elicited t... | https://arxiv.org/abs/2506.17417 | Academic Papers | svg |
1c8d869ffaf9de181e96b88b7d3f45f1bb08cd371f51814f22a11d3597c651db | 2026-01-07T00:00:00-05:00 | MemeMind: A Large-Scale Multimodal Dataset with Chain-of-Thought Reasoning for Harmful Meme Detection | arXiv:2506.18919v3 Announce Type: replace Abstract: As a multimodal medium combining images and text, memes frequently convey implicit harmful content through metaphors and humor, rendering the detection of harmful memes a complex and challenging task. Although recent studies have made progress in detection accuracy an... | https://arxiv.org/abs/2506.18919 | Academic Papers | svg |
81c076fcac0a2478844fe98ed11a891ff67de07da476ad3017ba5a8da8434f19 | 2026-01-07T00:00:00-05:00 | MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations | arXiv:2506.20100v2 Announce Type: replace Abstract: We introduce MIRAGE, a new benchmark for multimodal expert-level reasoning and decision-making in consultative interaction settings. Designed for the agriculture domain, MIRAGE captures the full complexity of expert consultations by combining natural user queries, exp... | https://arxiv.org/abs/2506.20100 | Academic Papers | svg |
4d297df40b755a7887003105274bc37778ba85ddb8d18df77bd19ab20ace45bd | 2026-01-07T00:00:00-05:00 | Agent.xpu: Efficient Scheduling of Agentic LLM Workloads on Heterogeneous SoC | arXiv:2506.24045v2 Announce Type: replace Abstract: Personal LLM agents increasingly combine foreground reactive interactions with background proactive monitoring, forming long-lived, stateful LLM flows that interleave prefill and token-by-token decode. While modern heterogeneous SoCs integrate CPUs, iGPUs, and NPUs to... | https://arxiv.org/abs/2506.24045 | Academic Papers | svg |
9b96d13438d43510385675fac637045dde7b1a6233d883141d5cd8bfdf03f2ee | 2026-01-07T00:00:00-05:00 | Stable Preference Optimization: A Bilevel Approach to Catastrophic Preference Shift | arXiv:2507.07723v2 Announce Type: replace Abstract: Direct Preference Learning has emerged as a dominant offline paradigm for preference optimization. Most of these methods are based on the Bradley-Terry (BT) model for pairwise preference ranking, which directly aligns language model with human preference. Prior work h... | https://arxiv.org/abs/2507.07723 | Academic Papers | svg |
3fcc7e9cd4dcba26d2fbd1e45d2f82c279e0c76ce1447d7c0519ec2243b59a31 | 2026-01-07T00:00:00-05:00 | Information-Theoretic Generalization Bounds of Replay-based Continual Learning | arXiv:2507.12043v2 Announce Type: replace Abstract: Continual learning (CL) has emerged as a dominant paradigm for acquiring knowledge from sequential tasks while avoiding catastrophic forgetting. Although many CL methods have been proposed to show impressive empirical performance, the theoretical understanding of thei... | https://arxiv.org/abs/2507.12043 | Academic Papers | svg |
c5ac3642ba58dd8dd35411b3041fa393b5426d452d60bf0a147235b11d6880be | 2026-01-07T00:00:00-05:00 | Constructions of binary self-orthogonal singly-even minimal linear codes violating the Aschikhmin-Barg condition with few weights | arXiv:2507.12240v3 Announce Type: replace Abstract: We first establish a simple yet powerful necessary and sufficient condition for a binary linear code to be SO, leading to a complete characterization of singly-even codes in this family. We further derive necessary and sufficient conditions on Boolean and vectorial Bo... | https://arxiv.org/abs/2507.12240 | Academic Papers | svg |
39f4d02f01d7d8a46a2b2a68f0e0e7ad15f73f91e91a4e1ef38f522ac734d36a | 2026-01-07T00:00:00-05:00 | Compositional Discrete Latent Code for High Fidelity, Productive Diffusion Models | arXiv:2507.12318v3 Announce Type: replace Abstract: We argue that diffusion models' success in modeling complex distributions is, for the most part, coming from their input conditioning. This paper investigates the representation used to condition diffusion models from the perspective that ideal representations should ... | https://arxiv.org/abs/2507.12318 | Academic Papers | svg |
33799b3f66cb2f80ef2853c3c9869db2672117d75e249b6dc69f0c89b0e5eb74 | 2026-01-07T00:00:00-05:00 | BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM | arXiv:2507.14632v3 Announce Type: replace Abstract: Recent advances in generative AI have dramatically improved image and video synthesis capabilities, significantly increasing the risk of misinformation through sophisticated fake content. In response, detection methods have evolved from traditional approaches to multi... | https://arxiv.org/abs/2507.14632 | Academic Papers | svg |
e0940e6c0c391b2903e91a5a4c106f20085f9168b6b76dee0a38e8078bd2edfd | 2026-01-07T00:00:00-05:00 | Awakening LLMs' Reasoning Potential: A Fine-Grained Pipeline to Evaluate and Mitigate Vague Perception | arXiv:2507.16199v5 Announce Type: replace Abstract: Large language models (LLMs) are increasingly trained to abstain on difficult questions by answering unknown. However, we observe that LLMs often misuse this option: they output unknown even when LLMs can actually solve the questions, or they fail to understand why qu... | https://arxiv.org/abs/2507.16199 | Academic Papers | svg |
2a5ea62a6f879d4bc02edde7c674dabfabdab06ddf650ff5a37a70bff2b2b3bc | 2026-01-07T00:00:00-05:00 | TELEVAL: A Dynamic Benchmark Designed for Spoken Language Models in Chinese Interactive Scenarios | arXiv:2507.18061v2 Announce Type: replace Abstract: Spoken language models (SLMs) have advanced rapidly in recent years, accompanied by a growing number of evaluation benchmarks. However, most existing benchmarks emphasize task completion and capability scaling, while remaining poorly aligned with how users interact wi... | https://arxiv.org/abs/2507.18061 | Academic Papers | svg |
ade4761b92f4a9dd374c709e3d4e97520cbaae0023ede2f6c921283c21ad414a | 2026-01-07T00:00:00-05:00 | Learning an Efficient Multi-Turn Dialogue Evaluator from Multiple LLM Judges | arXiv:2508.00454v4 Announce Type: replace Abstract: Evaluating the conversational abilities of large language models (LLMs) remains a challenging task. Current mainstream approaches primarily rely on the "LLM-as-a-judge" paradigm, where an LLM is prompted to serve as an evaluator to assess dialogue quality. However, su... | https://arxiv.org/abs/2508.00454 | Academic Papers | svg |
d21280ac63310d7508611b3276b5fcba8fff64a27bd5694abdd00b509cdd8c49 | 2026-01-07T00:00:00-05:00 | Pro2Guard: Proactive Runtime Enforcement of LLM Agent Safety via Probabilistic Model Checking | arXiv:2508.00500v2 Announce Type: replace Abstract: Large Language Model (LLM) agents demonstrate strong autonomy, but their stochastic behavior introduces unpredictable safety risks. Existing rule-based enforcement systems, such as AgentSpec, are reactive, intervening only when unsafe behavior is imminent or has occur... | https://arxiv.org/abs/2508.00500 | Academic Papers | svg |
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