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070804a71f3ad7b5fba1693e156850933a8f5c97c3392889931819b81e5b45a7 | 2026-01-01T00:00:00-05:00 | Invisible Languages of the LLM Universe | arXiv:2510.11557v2 Announce Type: replace Abstract: Large Language Models are trained on massive multilingual corpora, yet this abundance masks a profound crisis: of the world's 7,613 living languages, approximately 2,000 languages with millions of speakers remain effectively invisible in digital ecosystems. We propose... | https://arxiv.org/abs/2510.11557 | Academic Papers | svg |
d12d972c0fe1fb74aa35f9b1464c64078046806569607a9d0da21565afa69e8d | 2026-01-01T00:00:00-05:00 | Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention | arXiv:2510.13940v2 Announce Type: replace Abstract: Recent progress in large language models (LLMs) has focused on test-time scaling to improve reasoning via increased inference computation, but often at the cost of efficiency. We revisit test-time behavior and uncover a simple yet underexplored phenomenon: reasoning u... | https://arxiv.org/abs/2510.13940 | Academic Papers | svg |
a5e741ffd83819e5050b0f3a5f11006f4674f111e49d0f80db7ec76019f396ab | 2026-01-01T00:00:00-05:00 | CoT-PL: Visual Chain-of-Thought Reasoning Meets Pseudo-Labeling for Open-Vocabulary Object Detection | arXiv:2510.14792v2 Announce Type: replace Abstract: Open-vocabulary object detection (OVD) seeks to recognize and localize object categories beyond those seen during training. Recent approaches typically leverage vision-language models (VLMs) to generate pseudo-labels using image-text alignment, allowing detectors to g... | https://arxiv.org/abs/2510.14792 | Academic Papers | svg |
a2996d446e5b5289d6a67c4f8bfd1a787a3b23c202411525730154fb38fa4240 | 2026-01-01T00:00:00-05:00 | Graph Learning is Suboptimal in Causal Bandits | arXiv:2510.16811v2 Announce Type: replace Abstract: We study regret minimization in causal bandits under causal sufficiency where the underlying causal structure is not known to the agent. Previous work has focused on identifying the reward's parents and then applying classic bandit methods to them, or jointly learning... | https://arxiv.org/abs/2510.16811 | Academic Papers | svg |
3792aeacaea09e8590952a157b990a7722ec155254acda5f11d617c793a3b5ef | 2026-01-01T00:00:00-05:00 | When Intelligence Fails: An Empirical Study on Why LLMs Struggle with Password Cracking | arXiv:2510.17884v3 Announce Type: replace Abstract: The remarkable capabilities of Large Language Models (LLMs) in natural language understanding and generation have sparked interest in their potential for cybersecurity applications, including password guessing. In this study, we conduct an empirical investigation into... | https://arxiv.org/abs/2510.17884 | Academic Papers | svg |
341b18941ebd6c9d36e4742c611059f8c29511328960f57a9ff30da4e6105063 | 2026-01-01T00:00:00-05:00 | Space Object Detection using Multi-frame Temporal Trajectory Completion Method | arXiv:2510.19220v3 Announce Type: replace Abstract: Space objects in Geostationary Earth Orbit (GEO) present significant detection challenges in optical imaging due to weak signals, complex stellar backgrounds, and environmental interference. In this paper, we enhance high-frequency features of GEO targets while suppre... | https://arxiv.org/abs/2510.19220 | Academic Papers | svg |
624ad4711b5fe11edd71d133e869e271939550586d6d1a1751f865377c0b3f7d | 2026-01-01T00:00:00-05:00 | A Unified Approach to Submodular Maximization Under Noise | arXiv:2510.21128v2 Announce Type: replace Abstract: We consider the problem of maximizing a submodular function with access to a noisy value oracle for the function instead of an exact value oracle. Similar to prior work, we assume that the noisy oracle is persistent in that multiple calls to the oracle for a specific ... | https://arxiv.org/abs/2510.21128 | Academic Papers | svg |
9a10beb52433a715bc873109000d043c5fb79b91df38ebf883830cdb32b36b9e | 2026-01-01T00:00:00-05:00 | VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree | arXiv:2510.22693v3 Announce Type: replace Abstract: Video anomaly detection (VAD) focuses on identifying anomalies in videos. Supervised methods demand substantial in-domain training data and fail to deliver clear explanations for anomalies. In contrast, training-free methods leverage the knowledge reserves and languag... | https://arxiv.org/abs/2510.22693 | Academic Papers | svg |
963c299ed6065ab00a1e0bbdb30a5fac4d37797c7ca7252f56b5da1e539edc7a | 2026-01-01T00:00:00-05:00 | Towards Generalisable Foundation Models for Brain MRI | arXiv:2510.23415v3 Announce Type: replace Abstract: Foundation models in artificial intelligence (AI) are transforming medical imaging by enabling general-purpose feature learning from large-scale, unlabeled datasets. In this work, we introduce BrainFound, a self-supervised foundation model for brain MRI, built by exte... | https://arxiv.org/abs/2510.23415 | Academic Papers | svg |
cff7f67de6a0c3cc49bfd4258fe8d22e6e5ad4559c4a0f3639587596be7bf22e | 2026-01-01T00:00:00-05:00 | Human- vs. AI-generated tests: dimensionality and information accuracy in latent trait evaluation | arXiv:2510.24739v2 Announce Type: replace Abstract: Artificial Intelligence (AI) and large language models (LLMs) are increasingly used in social and psychological research. Among potential applications, LLMs can be used to generate, customise, or adapt measurement instruments. This study presents a preliminary investi... | https://arxiv.org/abs/2510.24739 | Academic Papers | svg |
3d809f7e518586d1b60df6b639fe926a2bc25e70c4d7a287d609c51204dc91b0 | 2026-01-01T00:00:00-05:00 | Learning Spatial-Aware Manipulation Ordering | arXiv:2510.25138v2 Announce Type: replace Abstract: Manipulation in cluttered environments is challenging due to spatial dependencies among objects, where an improper manipulation order can cause collisions or blocked access. Existing approaches often overlook these spatial relationships, limiting their flexibility and... | https://arxiv.org/abs/2510.25138 | Academic Papers | svg |
e17f45f0cdca732299f2d9bfc20e63037b075093ef7396fb0a787bd77ad4aaba | 2026-01-01T00:00:00-05:00 | ATLAS: Artifact Generation Through Layered Constraints and LLM x MDE Synergy | arXiv:2510.25890v2 Announce Type: replace Abstract: ATLAS unifies Large Language Models with Model-Driven Engineering to generate regulator-ready artifacts and machine-checkable evidence for safety- and compliance-critical domains. ATLAS integrates three pillars: a Unified Meta-Model (UMM) reconciles heterogeneous sche... | https://arxiv.org/abs/2510.25890 | Academic Papers | svg |
bf260f747294d809f5ca172374a70674a088b21572bbbce62609422fea90fc57 | 2026-01-01T00:00:00-05:00 | On the limitation of evaluating machine unlearning using only a single training seed | arXiv:2510.26714v4 Announce Type: replace Abstract: Machine unlearning (MU) aims to remove the influence of certain data points from a trained model without costly retraining. Most practical MU algorithms are only approximate and their performance can only be assessed empirically. Care must therefore be taken to make e... | https://arxiv.org/abs/2510.26714 | Academic Papers | svg |
373e716c68f26ae73efa4fab809a9ed0e4d260d369c061b0738cb73f1b4965d4 | 2026-01-01T00:00:00-05:00 | Deep sequence models tend to memorize geometrically; it is unclear why | arXiv:2510.26745v2 Announce Type: replace Abstract: Deep sequence models are said to store atomic facts predominantly in the form of associative memory: a brute-force lookup of co-occurring entities. We identify a dramatically different form of storage of atomic facts that we term as geometric memory. Here, the model h... | https://arxiv.org/abs/2510.26745 | Academic Papers | svg |
40cbbe3fb18138855058749549c05b7de6365306b2b2542b1ca26c2ea08b6049 | 2026-01-01T00:00:00-05:00 | Can machines think efficiently? | arXiv:2510.26954v2 Announce Type: replace Abstract: The Turing Test is no longer adequate for distinguishing human and machine intelligence. With advanced artificial intelligence systems already passing the original Turing Test and contributing to serious ethical and environmental concerns, we urgently need to update t... | https://arxiv.org/abs/2510.26954 | Academic Papers | svg |
93336f620629f2d7556f5f0d947283258b68ba5a84036ddc69511f623c33a05b | 2026-01-01T00:00:00-05:00 | OpenSIR: Open-Ended Self-Improving Reasoner | arXiv:2511.00602v2 Announce Type: replace Abstract: Recent advances in large language model (LLM) reasoning through reinforcement learning rely on annotated datasets for verifiable rewards, which may limit models' ability to surpass human-level performance. While self-play offers a promising alternative, existing appro... | https://arxiv.org/abs/2511.00602 | Academic Papers | svg |
6785e7a771c06e63895ab113b80bdaa769641aa8db727e0f9c0b57805fb7e9f6 | 2026-01-01T00:00:00-05:00 | Necessary and Sufficient Conditions for Capacity-Achieving Private Information Retrieval with Adversarial Servers | arXiv:2511.06003v3 Announce Type: replace Abstract: Private information retrieval (PIR) is a mechanism for efficiently downloading messages while keeping the index of the desired message secret from the servers. PIR schemes have been extended to various scenarios with adversarial servers: PIR schemes where some servers... | https://arxiv.org/abs/2511.06003 | Academic Papers | svg |
6d1ae32b4bacad884b1a89dbba90a39dec8598a533202599551eec682c09ebbb | 2026-01-01T00:00:00-05:00 | Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practice | arXiv:2511.07017v2 Announce Type: replace Abstract: Code review is a cornerstone of software quality assurance, and recent advances in Large Language Models (LLMs) have shown promise in its automation. However, existing benchmarks for LLM-based code review face three major limitations. Lack of semantic context: most be... | https://arxiv.org/abs/2511.07017 | Academic Papers | svg |
1f6245a680098c4beac2bd7d9c9de342f54c33ac489ca8c2d05a53fd95d2b5ba | 2026-01-01T00:00:00-05:00 | Can ensembles improve evidence recall? A case study | arXiv:2511.07055v2 Announce Type: replace Abstract: Feature attribution methods typically provide minimal sufficient evidence justifying a model decision. However, in many applications, such as compliance and cataloging, the full set of contributing features must be identified: complete evidence. We present a case stud... | https://arxiv.org/abs/2511.07055 | Academic Papers | svg |
1572fca74a59b86c828e3c587c2606a86bd2203ffa9097b921647391f903cda0 | 2026-01-01T00:00:00-05:00 | Feedback Descent: Open-Ended Text Optimization via Pairwise Comparison | arXiv:2511.07919v2 Announce Type: replace Abstract: We introduce \textit{Feedback Descent}, a framework that optimizes text artifacts -- prompts, code, and molecules -- through structured textual feedback, rather than relying solely on scalar rewards. By preserving detailed critiques instead of compressing them to bina... | https://arxiv.org/abs/2511.07919 | Academic Papers | svg |
2cc2320dbdc6a67d2f6381112756d130c2bb5763e7d4b5c3f1efef5cd8d42924 | 2026-01-01T00:00:00-05:00 | Training Language Models to Explain Their Own Computations | arXiv:2511.08579v2 Announce Type: replace Abstract: Can language models (LMs) learn to faithfully describe their internal computations? Are they better able to describe themselves than other models? We study the extent to which LMs' privileged access to their own internals can be leveraged to produce new techniques for... | https://arxiv.org/abs/2511.08579 | Academic Papers | svg |
f8167eb78e8983f461520d493eb9188f2ebe3cab3321ef0bb2c620775d4b76e3 | 2026-01-01T00:00:00-05:00 | Scaling Spatial Intelligence with Multimodal Foundation Models | arXiv:2511.13719v3 Announce Type: replace Abstract: Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation models to cultivate spatial intelligence within the SenseNova-SI family, built upon estab... | https://arxiv.org/abs/2511.13719 | Academic Papers | svg |
1744a3a0a2820db99af735bc96646a35bdf370dab860104881eed0081945e9d4 | 2026-01-01T00:00:00-05:00 | Complex variational autoencoders admit K\"ahler structure | arXiv:2511.15172v4 Announce Type: replace Abstract: It has been discovered that latent-Euclidean variational autoencoders (VAEs) admit, in various capacities, Riemannian structure. We adapt these arguments but for complex VAEs with a complex latent stage. We show that complex VAEs reveal to some level K\"ahler geometri... | https://arxiv.org/abs/2511.15172 | Academic Papers | svg |
2d1eb8228bb5ed1e614a8772c222e650789be41da64f5d6a952a6f1035fb6163 | 2026-01-01T00:00:00-05:00 | How Robot Dogs See the Unseeable: Improving Visual Interpretability via Peering for Exploratory Robots | arXiv:2511.16262v3 Announce Type: replace Abstract: In vegetated environments, such as forests, exploratory robots play a vital role in navigating complex, cluttered environments where human access is limited and traditional equipment struggles. Visual occlusion from obstacles, such as foliage, can severely obstruct a ... | https://arxiv.org/abs/2511.16262 | Academic Papers | svg |
e7a5a102a70fa4310d8e53f229af8e322706204d46d0c93dc6d417f2e03a07c6 | 2026-01-01T00:00:00-05:00 | BCWildfire: A Long-term Multi-factor Dataset and Deep Learning Benchmark for Boreal Wildfire Risk Prediction | arXiv:2511.17597v2 Announce Type: replace Abstract: Wildfire risk prediction remains a critical yet challenging task due to the complex interactions among fuel conditions, meteorology, topography, and human activity. Despite growing interest in data-driven approaches, publicly available benchmark datasets that support ... | https://arxiv.org/abs/2511.17597 | Academic Papers | svg |
fa8ddea77e30fdcd0ed175665038aa09b859f6bafe8ab47fe7adac189230ef40 | 2026-01-01T00:00:00-05:00 | Hear: Hierarchically Enhanced Aesthetic Representations For Multidimensional Music Evaluation | arXiv:2511.18869v2 Announce Type: replace Abstract: Evaluating song aesthetics is challenging due to the multidimensional nature of musical perception and the scarcity of labeled data. We propose HEAR, a robust music aesthetic evaluation framework that combines: (1) a multi-source multi-scale representations module to ... | https://arxiv.org/abs/2511.18869 | Academic Papers | svg |
fd27894cb27a8db189c36b4ebce4443b8544c9d3c36fa0300f9df83999fd051e | 2026-01-01T00:00:00-05:00 | Energy-Efficient Routing Protocol in Vehicular Opportunistic Networks: A Dynamic Cluster-based Routing Using Deep Reinforcement Learning | arXiv:2511.19026v3 Announce Type: replace Abstract: Opportunistic Networks (OppNets) employ the Store-Carry-Forward (SCF) paradigm to maintain communication during intermittent connectivity. However, routing performance suffers due to dynamic topology changes, unpredictable contact patterns, and resource constraints in... | https://arxiv.org/abs/2511.19026 | Academic Papers | svg |
8c3f70b16f8792c8fab68bdcda5cf1f3c407fca7aece5d1393b1d30c5d22202b | 2026-01-01T00:00:00-05:00 | SEDA: A Self-Adapted Entity-Centric Data Augmentation for Boosting Gird-based Discontinuous NER Models | arXiv:2511.20143v2 Announce Type: replace Abstract: Named Entity Recognition (NER) is a critical task in natural language processing, yet it remains particularly challenging for discontinuous entities. The primary difficulty lies in text segmentation, as traditional methods often missegment or entirely miss cross-sente... | https://arxiv.org/abs/2511.20143 | Academic Papers | svg |
a2c9eb08ec147589f5cda8ac888eac286741486a8d75d8d64d2afa28c9aa5015 | 2026-01-01T00:00:00-05:00 | APT-CGLP: Advanced Persistent Threat Hunting via Contrastive Graph-Language Pre-Training | arXiv:2511.20290v2 Announce Type: replace Abstract: Provenance-based threat hunting identifies Advanced Persistent Threats (APTs) on endpoints by correlating attack patterns described in Cyber Threat Intelligence (CTI) with provenance graphs derived from system audit logs. A fundamental challenge in this paradigm lies ... | https://arxiv.org/abs/2511.20290 | Academic Papers | svg |
d5b4215141fa927fd36104a9d35e0641195491d6a5d920b7c6b7ec77f3c717cc | 2026-01-01T00:00:00-05:00 | Infinity-RoPE: Action-Controllable Infinite Video Generation Emerges From Autoregressive Self-Rollout | arXiv:2511.20649v2 Announce Type: replace Abstract: Current autoregressive video diffusion models are constrained by three core bottlenecks: (i) the finite temporal horizon imposed by the base model's 3D Rotary Positional Embedding (3D-RoPE), (ii) slow prompt responsiveness in maintaining fine-grained action control du... | https://arxiv.org/abs/2511.20649 | Academic Papers | svg |
9e39e129d8e09a6f6ccc878be8a4c642fd91f8e8d70ee65e2ec26ebd152b44b3 | 2026-01-01T00:00:00-05:00 | Large Language Models for Unit Test Generation: Achievements, Challenges, and Opportunities | arXiv:2511.21382v2 Announce Type: replace Abstract: Automated unit test generation is critical for software quality but traditional structure-driven methods often lack the semantic understanding required to produce realistic inputs and oracles. Large language models (LLMs) address this limitation by leveraging their ex... | https://arxiv.org/abs/2511.21382 | Academic Papers | svg |
f2f445babaa831c3e6b18c8395392ef4f7c51f938229d4deded79275de4e878e | 2026-01-01T00:00:00-05:00 | TIM-PRM: Verifying multimodal reasoning with Tool-Integrated PRM | arXiv:2511.22998v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved impressive performances in mathematical reasoning, yet they remain vulnerable to visual hallucinations and logical inconsistencies that standard outcome-based supervision fails to mitigate. While Process Reward Mo... | https://arxiv.org/abs/2511.22998 | Academic Papers | svg |
352bb1a3659da640af691fd43af5a5ea386a55d7fed8104fa6bab546754ae190 | 2026-01-01T00:00:00-05:00 | Dual LoRA: Enhancing LoRA with Magnitude and Direction Updates | arXiv:2512.03402v3 Announce Type: replace Abstract: Low-rank adaptation (LoRA) is one of the most popular methods among parameter-efficient fine-tuning (PEFT) methods to adapt pre-trained large language models (LLMs) to specific downstream tasks. However, the model trained based on LoRA often has an unsatisfactory perf... | https://arxiv.org/abs/2512.03402 | Academic Papers | svg |
28f4155df279dc0fc42609ac5dc903999cbc8411d4a56704bab0e2bfdf8c0cf6 | 2026-01-01T00:00:00-05:00 | Cross-embodied Co-design for Dexterous Hands | arXiv:2512.03743v2 Announce Type: replace Abstract: Dexterous manipulation is limited by both control and design, without consensus as to what makes manipulators best for performing dexterous tasks. This raises a fundamental challenge: how should we design and control robot manipulators that are optimized for dexterity... | https://arxiv.org/abs/2512.03743 | Academic Papers | svg |
0b2be84c7a1d27b1f1f07acd141cc52d96caa8f5fc944391e481e15e672d0bb7 | 2026-01-01T00:00:00-05:00 | GRASP: GRouped Activation Shared Parameterization for Parameter-Efficient Fine-Tuning and Robust Inference of Transformers | arXiv:2512.04296v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) provides a scalable alternative to full-model adaptation by updating only a small subset of parameters in large pre-trained models. We introduce GRASP - GRouped Activation Shared Parameterization - a lightweight PEFT framework th... | https://arxiv.org/abs/2512.04296 | Academic Papers | svg |
2569d6a4baef3092d64e08fe490a7f72f633faacc9635fdf9fb1cf9b843141bc | 2026-01-01T00:00:00-05:00 | Minimization-based embedded boundary methods as polynomial corrections: a stability study of discontinuous Galerkin for hyperbolic equations | arXiv:2512.05278v2 Announce Type: replace Abstract: This work establishes a novel, unified theoretical framework for a class of high order embedded boundary methods, revealing that the Reconstruction for Off-site Data (ROD) treatment shares a fundamental structure with the recently developed shifted boundary polynomial... | https://arxiv.org/abs/2512.05278 | Academic Papers | svg |
85dfd35b7454ce694703b57c129f51fdf12d934d5c9144a8383c9fe7baf9afb1 | 2026-01-01T00:00:00-05:00 | Randomized Algorithms for Low-Rank Matrix and Tensor Decompositions | arXiv:2512.05286v2 Announce Type: replace Abstract: This paper surveys randomized algorithms in numerical linear algebra for low-rank decompositions of matrices and tensors. The survey begins with a review of classical matrix algorithms that can be accelerated by randomized dimensionality reduction, such as the singula... | https://arxiv.org/abs/2512.05286 | Academic Papers | svg |
8929c80adeb8b165744f9ec041e9768b00db5a672281be7cc1f9e3ddb4348afa | 2026-01-01T00:00:00-05:00 | The Complexity of One or Many Faces in the Overlay of Many Arrangements | arXiv:2512.11445v2 Announce Type: replace Abstract: We present an extension of the Combination Lemma of [GSS89] that expresses the complexity of one or several faces in the overlay of many arrangements, as a function of the number of arrangements, the number of faces, and the complexities of these faces in the separate... | https://arxiv.org/abs/2512.11445 | Academic Papers | svg |
d8594cf753abc6cbd96b42686feb04b09c34bd46ce6eade298453c3ce512a482 | 2026-01-01T00:00:00-05:00 | Spiking Manifesto | arXiv:2512.11843v2 Announce Type: replace Abstract: Practically everything computers do is better, faster, and more power-efficient than the brain. For example, a calculator performs numerical computations more energy-efficiently than any human. Yet modern AI models are a thousand times less efficient than the brain. T... | https://arxiv.org/abs/2512.11843 | Academic Papers | svg |
eba143d8529875a1f37b33010f8c7d3b793272ac1cb7a4d8a74fc71bb58cd9eb | 2026-01-01T00:00:00-05:00 | A Geometric Theory of Cognition | arXiv:2512.12225v2 Announce Type: replace Abstract: Human cognition spans perception, memory, intuitive judgment, deliberative reasoning, action selection, and social inference, yet these capacities are often explained through distinct computational theories. Here we present a unified mathematical framework in which di... | https://arxiv.org/abs/2512.12225 | Academic Papers | svg |
e7e85e89f7d7b2aa0daa26240c7e23c32d02b6eb9db31e09974cc1d728a3fcf7 | 2026-01-01T00:00:00-05:00 | Reveal Hidden Pitfalls and Navigate Next Generation of Vector Similarity Search from Task-Centric Views | arXiv:2512.12980v2 Announce Type: replace Abstract: Vector Similarity Search (VSS) in high-dimensional spaces is rapidly emerging as core functionality in next-generation database systems for numerous data-intensive services -- from embedding lookups in large language models (LLMs), to semantic information retrieval an... | https://arxiv.org/abs/2512.12980 | Academic Papers | svg |
f714034099456923e4608db47abf6d9ed3e8bd9841f52d072fb17ff327bd2904 | 2026-01-01T00:00:00-05:00 | DiRe: Diversity-promoting Regularization for Dataset Condensation | arXiv:2512.13083v2 Announce Type: replace Abstract: In Dataset Condensation, the goal is to synthesize a small dataset that replicates the training utility of a large original dataset. Existing condensation methods synthesize datasets with significant redundancy, so there is a dire need to reduce redundancy and improve... | https://arxiv.org/abs/2512.13083 | Academic Papers | svg |
63c5182066c3123913bedb614b2ad2e81e2cc84ea77ad1e2cc9737bde9c7bd3d | 2026-01-01T00:00:00-05:00 | OPTIMA: Optimal One-shot Pruning for LLMs via Quadratic Programming Reconstruction | arXiv:2512.13886v2 Announce Type: replace Abstract: Post-training model pruning is a promising solution, yet it faces a trade-off: simple heuristics that zero weights are fast but degrade accuracy, while principled joint optimization methods recover accuracy but are computationally infeasible at modern scale. One-shot ... | https://arxiv.org/abs/2512.13886 | Academic Papers | svg |
989ddc9056840c42eec6f02f05ed0edbfbff1f64a02016052a2e3e18047f8e9d | 2026-01-01T00:00:00-05:00 | Probabilistic Inclusion Depth for Fuzzy Contour Ensemble Visualization | arXiv:2512.15187v2 Announce Type: replace Abstract: We propose Probabilistic Inclusion Depth (PID) for the ensemble visualization of scalar fields. By introducing a probabilistic inclusion operator $\subset_{\!p}$, our method is a general data depth model supporting ensembles of fuzzy contours, such as soft masks from ... | https://arxiv.org/abs/2512.15187 | Academic Papers | svg |
bcd16a87ed63f871e2dfb096828463f00b099eaf1645ce57e4598020c7da3acd | 2026-01-01T00:00:00-05:00 | Risk-Aware GPU-Assisted Cardinality Estimation for Cost-Based Query Optimizers | arXiv:2512.19750v2 Announce Type: replace Abstract: Cardinality estimation is a cornerstone of cost-based optimizers (CBOs), yet real-world workloads often violate the assumptions behind static statistics, degrading decision stability and increasing plan flip rates. We empirically characterize failures caused by stale ... | https://arxiv.org/abs/2512.19750 | Academic Papers | svg |
ccb1420f3c6b947b9662a4d2cc95b4d0871b4e0f7c2fb5bc15fe38aae2a0f452 | 2026-01-01T00:00:00-05:00 | Few-Shot-Based Modular Image-to-Video Adapter for Diffusion Models | arXiv:2512.20000v2 Announce Type: replace Abstract: Diffusion models (DMs) have recently achieved impressive photorealism in image and video generation. However, their application to image animation remains limited, even when trained on large-scale datasets. Two primary challenges contribute to this: the high dimension... | https://arxiv.org/abs/2512.20000 | Academic Papers | svg |
52f48f2c05db21aab55b7aa6bd2119072286f7db324abb9535e45ece6f64dfab | 2026-01-01T00:00:00-05:00 | Fun-Audio-Chat Technical Report | arXiv:2512.20156v3 Announce Type: replace Abstract: Recent advancements in joint speech-text models show great potential for seamless voice interactions. However, existing models face critical challenges: temporal resolution mismatch between speech tokens (25Hz) and text tokens (~3Hz) dilutes semantic information, incu... | https://arxiv.org/abs/2512.20156 | Academic Papers | svg |
803727dce2d0a958fc1bacb679a72fd63270a9b3acb5edc4b5d6f030565de59f | 2026-01-01T00:00:00-05:00 | AUDRON: A Deep Learning Framework with Fused Acoustic Signatures for Drone Type Recognition | arXiv:2512.20407v2 Announce Type: replace Abstract: Unmanned aerial vehicles (UAVs), commonly known as drones, are increasingly used across diverse domains, including logistics, agriculture, surveillance, and defense. While these systems provide numerous benefits, their misuse raises safety and security concerns, makin... | https://arxiv.org/abs/2512.20407 | Academic Papers | svg |
06e43f60e2bc5d2df4720392354902188abd875f91e809bda7b4d8c8b184f4d5 | 2026-01-01T00:00:00-05:00 | Leveraging High-Fidelity Digital Models and Reinforcement Learning for Mission Engineering: A Case Study of Aerial Firefighting Under Perfect Information | arXiv:2512.20589v2 Announce Type: replace Abstract: As systems engineering (SE) objectives evolve from design and operation of monolithic systems to complex System of Systems (SoS), the discipline of Mission Engineering (ME) has emerged which is increasingly being accepted as a new line of thinking for the SE community... | https://arxiv.org/abs/2512.20589 | Academic Papers | svg |
2161b6a8171bfb84ddca9c08ee582990ac157eb3399b8203fe3280ef19cf8a0f | 2026-01-01T00:00:00-05:00 | Mixed Precision General Alternating-Direction Implicit Method for Solving Large Sparse Linear Systems | arXiv:2512.21164v3 Announce Type: replace Abstract: In this article, we introduce a three-precision formulation of the General Alternating-Direction Implicit method (GADI) designed to accelerate the solution of large-scale sparse linear systems $Ax=b$. GADI is a framework that can represent many existing Alternating-Di... | https://arxiv.org/abs/2512.21164 | Academic Papers | svg |
053ad3a182a2f04702824ac92e49d1c32cda61534610ad3d9ecd041c1f95a2f8 | 2026-01-01T00:00:00-05:00 | Heaven-Sent or Hell-Bent? Benchmarking the Intelligence and Defectiveness of LLM Hallucinations | arXiv:2512.21635v2 Announce Type: replace Abstract: Hallucinations in large language models (LLMs) are commonly regarded as errors to be minimized. However, recent perspectives suggest that some hallucinations may encode creative or epistemically valuable content, a dimension that remains underquantified in current lit... | https://arxiv.org/abs/2512.21635 | Academic Papers | svg |
2a012887ba250747d963955b4bd0aca9848338415f4d7750a40f8682a9b51e34 | 2026-01-01T00:00:00-05:00 | SlideChain: Semantic Provenance for Lecture Understanding via Blockchain Registration | arXiv:2512.21684v2 Announce Type: replace Abstract: Modern vision--language models (VLMs) are increasingly used to interpret and generate educational content, yet their semantic outputs remain challenging to verify, reproduce, and audit over time. Inconsistencies across model families, inference settings, and computing... | https://arxiv.org/abs/2512.21684 | Academic Papers | svg |
82efdd54f989667c0b18c1bd755681b0fabb9ba694130addc977635347663b28 | 2026-01-01T00:00:00-05:00 | SyncAnyone: Implicit Disentanglement via Progressive Self-Correction for Lip-Syncing in the wild | arXiv:2512.21736v2 Announce Type: replace Abstract: High-quality AI-powered video dubbing demands precise audio-lip synchronization, high-fidelity visual generation, and faithful preservation of identity and background. Most existing methods rely on a mask-based training strategy, where the mouth region is masked in ta... | https://arxiv.org/abs/2512.21736 | Academic Papers | svg |
15d4523caa07faeb75b6c30053a03d9422b2d8b186eef518fc78c7ebf18faaf5 | 2026-01-01T00:00:00-05:00 | Inference-based GAN Video Generation | arXiv:2512.21776v2 Announce Type: replace Abstract: Video generation has seen remarkable progress thanks to advancements in generative deep learning. However, generating long sequences remains a significant challenge. Generated videos should not only display coherent and continuous movement but also meaningful movement... | https://arxiv.org/abs/2512.21776 | Academic Papers | svg |
f789c3bdc7b563213050b61ad2c1730a06ada2ad2ca8d074dc6be6441cb5b699 | 2026-01-01T00:00:00-05:00 | KG20C & KG20C-QA: Scholarly Knowledge Graph Benchmarks for Link Prediction and Question Answering | arXiv:2512.21799v2 Announce Type: replace Abstract: In this paper, we present KG20C and KG20C-QA, two curated datasets for advancing question answering (QA) research on scholarly data. KG20C is a high-quality scholarly knowledge graph constructed from the Microsoft Academic Graph through targeted selection of venues, q... | https://arxiv.org/abs/2512.21799 | Academic Papers | svg |
785cec328687f8182533a4cda3da6fcee21a3a6f0982e3c6c86ee860388423c0 | 2026-01-01T00:00:00-05:00 | Interpretable Perturbation Modeling Through Biomedical Knowledge Graphs | arXiv:2512.22251v2 Announce Type: replace Abstract: Understanding how small molecules perturb gene expression is essential for uncovering drug mechanisms, predicting off-target effects, and identifying repurposing opportunities. While prior deep learning frameworks have integrated multimodal embeddings into biomedical ... | https://arxiv.org/abs/2512.22251 | Academic Papers | svg |
8dafcba1c475c1a8e8b12a58b7daa09d769bc9cc88e4a726bc6f7bf4bf29d508 | 2026-01-01T00:00:00-05:00 | SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence | arXiv:2512.22334v2 Announce Type: replace Abstract: We introduce SciEvalKit, a unified benchmarking toolkit designed to evaluate AI models for science across a broad range of scientific disciplines and task capabilities. Unlike general-purpose evaluation platforms, SciEvalKit focuses on the core competencies of scienti... | https://arxiv.org/abs/2512.22334 | Academic Papers | svg |
aaf01e714f95efe1fbf914ef854968c685af7a629fd81a37b34e3111bf04fb8c | 2026-01-01T00:00:00-05:00 | VL-LN Bench: Towards Long-horizon Goal-oriented Navigation with Active Dialogs | arXiv:2512.22342v2 Announce Type: replace Abstract: In most existing embodied navigation tasks, instructions are well-defined and unambiguous, such as instruction following and object searching. Under this idealized setting, agents are required solely to produce effective navigation outputs conditioned on vision and la... | https://arxiv.org/abs/2512.22342 | Academic Papers | svg |
246365541f4ac94b162ea0df5815d8b5f80ca814b19c39e86acbab7a35300958 | 2026-01-01T00:00:00-05:00 | OxygenREC: An Instruction-Following Generative Framework for E-commerce Recommendation | arXiv:2512.22386v2 Announce Type: replace Abstract: Traditional recommendation systems suffer from inconsistency in multi-stage optimization objectives. Generative Recommendation (GR) mitigates them through an end-to-end framework; however, existing methods still rely on matching mechanisms based on inductive patterns.... | https://arxiv.org/abs/2512.22386 | Academic Papers | svg |
027f4bbeca05c747f75098fc1920ad13599b8e24029e3027fda8f940a0919c01 | 2026-01-01T00:00:00-05:00 | Building Software by Rolling the Dice: A Qualitative Study of Vibe Coding | arXiv:2512.22418v2 Announce Type: replace Abstract: Large language models (LLMs) are reshaping software engineering by enabling "vibe coding," in which developers build software primarily through prompts rather than writing code. Although widely publicized as a productivity breakthrough, little is known about how pract... | https://arxiv.org/abs/2512.22418 | Academic Papers | svg |
1124a56fd626854d9de21b46a8cc861caf5cb64853c40b340e6bc75a593e334d | 2026-01-01T00:00:00-05:00 | SuperiorGAT: Graph Attention Networks for Sparse LiDAR Point Cloud Reconstruction in Autonomous Systems | arXiv:2512.22439v2 Announce Type: replace Abstract: LiDAR-based perception in autonomous systems is constrained by fixed vertical beam resolution and further compromised by beam dropout resulting from environmental occlusions. This paper introduces SuperiorGAT, a graph attention-based framework designed to reconstruct ... | https://arxiv.org/abs/2512.22439 | Academic Papers | svg |
cc9e9b979a86d83df2ba164d0b4e8153e92a5c2b038b9dece2f155b7c7c14509 | 2026-01-01T00:00:00-05:00 | Tracking by Predicting 3-D Gaussians Over Time | arXiv:2512.22489v2 Announce Type: replace Abstract: We propose Video Gaussian Masked Autoencoders (Video-GMAE), a self-supervised approach for representation learning that encodes a sequence of images into a set of Gaussian splats moving over time. Representing a video as a set of Gaussians enforces a reasonable induct... | https://arxiv.org/abs/2512.22489 | Academic Papers | svg |
97e8973b71d427d7c9bb2f09a976b16ea2100a6a7c763c285e9a5487e86bdb40 | 2026-01-01T00:00:00-05:00 | A Lightweight Coordinate-Conditioned Diffusion Approach for 6G C-V2X Radio Environment Maps | arXiv:2512.22535v2 Announce Type: replace Abstract: Transmitter vehicles that broadcast 6G Cellular Vehicle-to-Everything (C-V2X)-based messages, e.g., Basic Safety Messages (BSMs), are prone to be impacted by PHY issues due to the lack of dynamic high-fidelity Radio Environment Map (REM) with dynamic location variatio... | https://arxiv.org/abs/2512.22535 | Academic Papers | svg |
296dc77790c5f6b476225405cae51f3690a4f3d35146cb4aa112fc717941b9d3 | 2026-01-01T00:00:00-05:00 | CritiFusion: Semantic Critique and Spectral Alignment for Faithful Text-to-Image Generation | arXiv:2512.22681v2 Announce Type: replace Abstract: Recent text-to-image diffusion models have achieved remarkable visual fidelity but often struggle with semantic alignment to complex prompts. We introduce CritiFusion, a novel inference-time framework that integrates a multimodal semantic critique mechanism with frequ... | https://arxiv.org/abs/2512.22681 | Academic Papers | svg |
c4ce3dda287e201d08e5f5c1fc8293daaafbce50fb2c7b533ef5c5c30de02488 | 2026-01-01T00:00:00-05:00 | Protonic Nickelate Device Networks for Spatiotemporal Neuromorphic Computing | arXiv:2512.22722v2 Announce Type: replace Abstract: Computation in biological neural circuits arises from the interplay of nonlinear temporal responses and spatially distributed dynamic network interactions. Replicating this richness in hardware has remained challenging, as most neuromorphic devices emulate only isolat... | https://arxiv.org/abs/2512.22722 | Academic Papers | svg |
1826596e6afbd48a7dfb52dadc5227c7f9a6e0e25f11304653f87839909bf87b | 2026-01-01T00:00:00-05:00 | TrimTokenator-LC: Towards Adaptive Visual Token Pruning for Large Multimodal Models with Long Contexts | arXiv:2512.22748v2 Announce Type: replace Abstract: Large Multimodal Models (LMMs) have proven effective on various tasks. They typically encode visual inputs into Original Model sequences of tokens, which are then concatenated with textual tokens and jointly processed by the language model. However, the growing number... | https://arxiv.org/abs/2512.22748 | Academic Papers | svg |
8bbb27dd2bb7d4df187bd37b817906ecef1d083674993148b219a601bb5c2786 | 2026-01-01T00:00:00-05:00 | Federated Multi-Task Clustering | arXiv:2512.22897v2 Announce Type: replace Abstract: Spectral clustering has emerged as one of the most effective clustering algorithms due to its superior performance. However, most existing models are designed for centralized settings, rendering them inapplicable in modern decentralized environments. Moreover, current... | https://arxiv.org/abs/2512.22897 | Academic Papers | svg |
f466a1bfe831e80397b1c5e97c2b141596cc05a23d4d1842cf7b8a530deb273b | 2026-01-01T00:00:00-05:00 | Multimodal Fact-Checking: An Agent-based Approach | arXiv:2512.22933v2 Announce Type: replace Abstract: The rapid spread of multimodal misinformation poses a growing challenge for automated fact-checking systems. Existing approaches, including large vision language models (LVLMs) and deep multimodal fusion methods, often fall short due to limited reasoning and shallow e... | https://arxiv.org/abs/2512.22933 | Academic Papers | svg |
7096797ae380f98db10a785b6eb4c266740a5d00024fd04c832f2b71d5947f2f | 2026-01-01T00:00:00-05:00 | ColaVLA: Leveraging Cognitive Latent Reasoning for Hierarchical Parallel Trajectory Planning in Autonomous Driving | arXiv:2512.22939v2 Announce Type: replace Abstract: Autonomous driving requires generating safe and reliable trajectories from complex multimodal inputs. Traditional modular pipelines separate perception, prediction, and planning, while recent end-to-end (E2E) systems learn them jointly. Vision-language models (VLMs) f... | https://arxiv.org/abs/2512.22939 | Academic Papers | svg |
4e0db68a7a47a99d33b8aee5a867ae2b399415682bb64b002025c03cb7162b7a | 2026-01-01T00:00:00-05:00 | A Context-Aware Temporal Modeling through Unified Multi-Scale Temporal Encoding and Hierarchical Sequence Learning for Single-Channel EEG Sleep Staging | arXiv:2512.22976v2 Announce Type: replace Abstract: Automatic sleep staging is a critical task in healthcare due to the global prevalence of sleep disorders. This study focuses on single-channel electroencephalography (EEG), a practical and widely available signal for automatic sleep staging. Existing approaches face c... | https://arxiv.org/abs/2512.22976 | Academic Papers | svg |
482e7f85a51f7a1d2d33b5a0a35093467d47423d685a862ddca833e9f25c7044 | 2026-01-01T00:00:00-05:00 | PoseStreamer: A Multi-modal Framework for 6DoF Pose Estimation of Unseen Moving Objects | arXiv:2512.22979v2 Announce Type: replace Abstract: Six degree of freedom (6DoF) pose estimation for novel objects is a critical task in computer vision, yet it faces significant challenges in high-speed and low-light scenarios where standard RGB cameras suffer from motion blur. While event cameras offer a promising so... | https://arxiv.org/abs/2512.22979 | Academic Papers | svg |
a2a0dd5e11d4925d275379edc5bb5bd3ac7a94ac3412984a3b63cafb0d047e8c | 2026-01-01T00:00:00-05:00 | OpenGround: Active Cognition-based Reasoning for Open-World 3D Visual Grounding | arXiv:2512.23020v2 Announce Type: replace Abstract: 3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes. Existing methods rely on a pre-defined Object Lookup Table (OLT) to query Visual Language Models (VLMs) for reasoning about object locations, which limits the applications ... | https://arxiv.org/abs/2512.23020 | Academic Papers | svg |
5a9e3419d9d1614e40e771903ec45bc8b28b416f155a12522525929346088e43 | 2026-01-01T00:00:00-05:00 | Differentiable Physics-Driven Human Representation for Millimeter-Wave Based Pose Estimation | arXiv:2512.23054v2 Announce Type: replace Abstract: While millimeter-wave (mmWave) presents advantages for Human Pose Estimation (HPE) through its non-intrusive sensing capabilities, current mmWave-based HPE methods face limitations in two predominant input paradigms: Heatmap and Point Cloud (PC). Heatmap represents de... | https://arxiv.org/abs/2512.23054 | Academic Papers | svg |
586db1c82fd4eb25682fffc3a68c4811a590ba7affca2cc5a6d5f0ba622761e4 | 2026-01-01T00:00:00-05:00 | Artificial Intelligence and Employment Exposure: A Territorial and Gender Perspective | arXiv:2512.23059v2 Announce Type: replace Abstract: The diffusion of artificial intelligence, particularly generative models, is expected to transform labor markets in uneven ways across sectors, territories, and social groups. This paper proposes a methodological framework to estimate the potential exposure of employm... | https://arxiv.org/abs/2512.23059 | Academic Papers | svg |
ba9aac0c563a28fe77fd507f96b70a10df45054f6eeb62c088682b36025806d5 | 2026-01-01T00:00:00-05:00 | APOLLO Blender: A Robotics Library for Visualization and Animation in Blender | arXiv:2512.23103v2 Announce Type: replace Abstract: High-quality visualizations are an essential part of robotics research, enabling clear communication of results through figures, animations, and demonstration videos. While Blender is a powerful and freely available 3D graphics platform, its steep learning curve and l... | https://arxiv.org/abs/2512.23103 | Academic Papers | svg |
9e50148f3d313fb409fa8d7a97898114eabe127ce65fd1bb593a27b568723e5c | 2026-01-01T00:00:00-05:00 | InSPO: Unlocking Intrinsic Self-Reflection for LLM Preference Optimization | arXiv:2512.23126v2 Announce Type: replace Abstract: Direct Preference Optimization (DPO) and its variants have become standard for aligning Large Language Models due to their simplicity and offline stability. However, we identify two fundamental limitations. First, the optimal policy depends on arbitrary modeling choic... | https://arxiv.org/abs/2512.23126 | Academic Papers | svg |
e2c433c0fba97e1935cb79c8f51455c8eb3584ddc242de2edb8f3dc59b36c7e4 | 2026-01-01T00:00:00-05:00 | Beyond URDF: The Universal Robot Description Directory for Shared, Extensible, and Standardized Robot Models | arXiv:2512.23135v2 Announce Type: replace Abstract: Robots are typically described in software by specification files (e.g., URDF, SDF, MJCF, USD) that encode only basic kinematic, dynamic, and geometric information. As a result, downstream applications such as simulation, planning, and control must repeatedly re-deriv... | https://arxiv.org/abs/2512.23135 | Academic Papers | svg |
239f4fd8a660f1f17ae4aca785327cd39741b27de3e85a9cfb2494e3953b5996 | 2026-01-01T00:00:00-05:00 | A New Software Tool for Generating and Visualizing Robot Self-Collision Matrices | arXiv:2512.23140v2 Announce Type: replace Abstract: In robotics, it is common to check whether a given robot state results in self-intersection (i.e., a self-collision query) or to assess its distance from such an intersection (i.e., a self-proximity query). These checks are typically performed between pairs of shapes ... | https://arxiv.org/abs/2512.23140 | Academic Papers | svg |
3e41d467ea6656a6ec18d2e67845940607c08c401a12427ad43f1c52b0386451 | 2026-01-01T00:00:00-05:00 | SurgWorld: Learning Surgical Robot Policies from Videos via World Modeling | arXiv:2512.23162v2 Announce Type: replace Abstract: Data scarcity remains a fundamental barrier to achieving fully autonomous surgical robots. While large scale vision language action (VLA) models have shown impressive generalization in household and industrial manipulation by leveraging paired video action data from d... | https://arxiv.org/abs/2512.23162 | Academic Papers | svg |
cb28a7cb91986861dce08f485897135e1c22f6e5f1f8f88aa96be7c6b95eadfc | 2026-01-01T00:00:00-05:00 | Evaluating Parameter Efficient Methods for RLVR | arXiv:2512.23165v2 Announce Type: replace Abstract: We systematically evaluate Parameter-Efficient Fine-Tuning (PEFT) methods under the paradigm of Reinforcement Learning with Verifiable Rewards (RLVR). RLVR incentivizes language models to enhance their reasoning capabilities through verifiable feedback; however, while... | https://arxiv.org/abs/2512.23165 | Academic Papers | svg |
4b3e24d8e0ee62e711528a62e5343e1ea72dd9a1553582a45f13b5d8722b72ae | 2026-01-01T00:00:00-05:00 | A New Family of Binary Sequences via Elliptic Function Fields over Finite Fields of Odd Characteristics | arXiv:2512.23194v2 Announce Type: replace Abstract: Motivated by the constructions of binary sequences by utilizing the cyclic elliptic function fields over the finite field $\mathbb{F}_{2^{n}}$ by Jin \textit{et al.} in [IEEE Trans. Inf. Theory 71(8), 2025], we extend the construction to the cyclic elliptic function f... | https://arxiv.org/abs/2512.23194 | Academic Papers | svg |
d0bf862e6f874c68df79ae0a2b9012ed18d5ddbc07eace576746e707fcc0a64d | 2026-01-01T00:00:00-05:00 | KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta | arXiv:2512.23236v2 Announce Type: replace Abstract: Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key system challenges - model architecture diversity, kernel primitive diversity, and hardware generation and architecture heterogenei... | https://arxiv.org/abs/2512.23236 | Academic Papers | svg |
a2d31f6e7cdfe6cbc81fd2f297dd325356e10bf75d74af8aca0cd3374729ec03 | 2026-01-01T00:00:00-05:00 | YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection | arXiv:2512.23273v2 Announce Type: replace Abstract: Existing Real-Time Object Detection (RTOD) methods commonly adopt YOLO-like architectures for their favorable trade-off between accuracy and speed. However, these models rely on static dense computation that applies uniform processing to all inputs, misallocating repr... | https://arxiv.org/abs/2512.23273 | Academic Papers | svg |
d1b1acbfb39c4aa3be69a6a1ea74e72af87685bef73fb81d06c9e5eeb0ea3356 | 2026-01-01T00:00:00-05:00 | CubeBench: Diagnosing Interactive, Long-Horizon Spatial Reasoning Under Partial Observations | arXiv:2512.23328v2 Announce Type: replace Abstract: Large Language Model (LLM) agents, while proficient in the digital realm, face a significant gap in physical-world deployment due to the challenge of forming and maintaining a robust spatial mental model. We identify three core cognitive challenges hindering this tran... | https://arxiv.org/abs/2512.23328 | Academic Papers | svg |
78f50822df9c43bef3278649a97dd7f12c2a9559d6d394a7d04a8c452b86f9b1 | 2026-01-01T00:00:00-05:00 | Visual Language Hypothesis | arXiv:2512.23335v2 Announce Type: replace Abstract: We study visual representation learning from a structural and topological perspective. We begin from a single hypothesis: that visual understanding presupposes a semantic language for vision, in which many perceptual observations correspond to a small number of discre... | https://arxiv.org/abs/2512.23335 | Academic Papers | svg |
0465fe8b506f944f1d552f1e99519eb61bb924a7a52f35a3aec204d4772d2b27 | 2026-01-01T00:00:00-05:00 | ISOPO: Proximal policy gradients without pi-old | arXiv:2512.23353v2 Announce Type: replace Abstract: This note introduces Isometric Policy Optimization (ISOPO), an efficient method to approximate the natural policy gradient in a single gradient step. In comparison, existing proximal policy methods such as GRPO or CISPO use multiple gradient steps with variants of imp... | https://arxiv.org/abs/2512.23353 | Academic Papers | svg |
8bf04d323e6e7ca1237af91c27daac2b1cf4ecca52f1e622cd67f8ec31a6f45b | 2026-01-01T00:00:00-05:00 | SoulX-LiveTalk: Real-Time Infinite Streaming of Audio-Driven Avatars via Self-Correcting Bidirectional Distillation | arXiv:2512.23379v2 Announce Type: replace Abstract: Deploying massive diffusion models for real-time, infinite-duration, audio-driven avatar generation presents a significant engineering challenge, primarily due to the conflict between computational load and strict latency constraints. Existing approaches often comprom... | https://arxiv.org/abs/2512.23379 | Academic Papers | svg |
e91f819cd347181793c7f4167f3a4abee35aedd096be862d198ca0e0407c9b0c | 2026-01-01T00:00:00-05:00 | DriveLaW:Unifying Planning and Video Generation in a Latent Driving World | arXiv:2512.23421v2 Announce Type: replace Abstract: World models have become crucial for autonomous driving, as they learn how scenarios evolve over time to address the long-tail challenges of the real world. However, current approaches relegate world models to limited roles: they operate within ostensibly unified arch... | https://arxiv.org/abs/2512.23421 | Academic Papers | svg |
c2f2e96340e185d38d7b937b679ea8647f2e0182ae3e66bc53c33d4020ea084d | 2026-01-01T00:00:00-05:00 | Distilled HuBERT for Mobile Speech Emotion Recognition: A Cross-Corpus Validation Study | arXiv:2512.23435v2 Announce Type: replace Abstract: Speech Emotion Recognition (SER) has significant potential for mobile applications, yet deployment remains constrained by the computational demands of state-of-the-art transformer architectures. This paper presents a mobile-efficient SER system based on DistilHuBERT, ... | https://arxiv.org/abs/2512.23435 | Academic Papers | svg |
1037ff5b8061cfa92d6aeef12aeaa7f0503bcdd9a9aa2174aeaeff351edd6ec3 | 2026-01-01T00:00:00-05:00 | Theory of Mind for Explainable Human-Robot Interaction | arXiv:2512.23482v2 Announce Type: replace Abstract: Within the context of human-robot interaction (HRI), Theory of Mind (ToM) is intended to serve as a user-friendly backend to the interface of robotic systems, enabling robots to infer and respond to human mental states. When integrated into robots, ToM allows them to ... | https://arxiv.org/abs/2512.23482 | Academic Papers | svg |
6072df67e72dd09c9697e4862eee0c01ab3565a282c7f3658d51d0be1862862e | 2026-01-01T00:00:00-05:00 | Circle graphs can be recognized in linear time | arXiv:2512.23492v2 Announce Type: replace Abstract: To date, the best circle graph recognition algorithm runs in almost linear time as it relies on a split decomposition algorithm that uses the union-find data-structure. We show that in the case of circle graphs, the PC-tree data-structure allows one to avoid the union... | https://arxiv.org/abs/2512.23492 | Academic Papers | svg |
36f86ae2b5e254a496220a3a569f4e04a8c2b27296267f6242836dd06d00a014 | 2026-01-01T00:00:00-05:00 | UniHetero: Could Generation Enhance Understanding for Vision-Language-Model at Large Data Scale? | arXiv:2512.23512v2 Announce Type: replace Abstract: Vision-language large models are moving toward the unification of visual understanding and visual generation tasks. However, whether generation can enhance understanding is still under-explored on large data scale. In this work, we analysis the unified structure with ... | https://arxiv.org/abs/2512.23512 | Academic Papers | svg |
68bac842ecd90aac9bb585468a7f8b96d20fd3faf136fc6a8c2737d9468d4c94 | 2026-01-01T00:00:00-05:00 | RxnBench: A Multimodal Benchmark for Evaluating Large Language Models on Chemical Reaction Understanding from Scientific Literature | arXiv:2512.23565v2 Announce Type: replace Abstract: The integration of Multimodal Large Language Models (MLLMs) into chemistry promises to revolutionize scientific discovery, yet their ability to comprehend the dense, graphical language of reactions within authentic literature remains underexplored. Here, we introduce ... | https://arxiv.org/abs/2512.23565 | Academic Papers | svg |
e95c75d46758318c032487c47dfb86f2481fde129c51a808990d44a2cca340d5 | 2026-01-01T00:00:00-05:00 | Algorithms for Distance Sensitivity Oracles and other Graph Problems on the PRAM | arXiv:2512.23604v2 Announce Type: replace Abstract: The distance sensitivity oracle (DSO) problem asks us to preprocess a given graph $G=(V,E)$ in order to answer queries of the form $d(x,y,e)$, which denotes the shortest path distance in $G$ from vertex $x$ to vertex $y$ when edge $e$ is removed. This is an important ... | https://arxiv.org/abs/2512.23604 | Academic Papers | svg |
28a2dae6b49d9a8cd815a956aa11047d544197afb35054edb23731e7d7088d31 | 2026-01-01T00:00:00-05:00 | Physics-Informed Neural Networks for Device and Circuit Modeling: A Case Study of NeuroSPICE | arXiv:2512.23624v2 Announce Type: replace Abstract: We present NeuroSPICE, a physics-informed neural network (PINN) framework for device and circuit simulation. Unlike conventional SPICE, which relies on time-discretized numerical solvers, NeuroSPICE leverages PINNs to solve circuit differential-algebraic equations (DA... | https://arxiv.org/abs/2512.23624 | Academic Papers | svg |
056a7c1eb625dc13365c7702a6e5b0717c6225d2f4627c9fdcf04fb31a5e62b2 | 2026-01-01T00:00:00-05:00 | RoboMirror: Understand Before You Imitate for Video to Humanoid Locomotion | arXiv:2512.23649v2 Announce Type: replace Abstract: Humans learn locomotion through visual observation, interpreting visual content first before imitating actions. However, state-of-the-art humanoid locomotion systems rely on either curated motion capture trajectories or sparse text commands, leaving a critical gap bet... | https://arxiv.org/abs/2512.23649 | Academic Papers | svg |
39d76924ca357e823653c47df8edc8cf453d8ea47921cd0ab5d20c5060dd3fe3 | 2026-01-01T00:00:00-05:00 | IDT: A Physically Grounded Transformer for Feed-Forward Multi-View Intrinsic Decomposition | arXiv:2512.23667v2 Announce Type: replace Abstract: Intrinsic image decomposition is fundamental for visual understanding, as RGB images entangle material properties, illumination, and view-dependent effects. Recent diffusion-based methods have achieved strong results for single-view intrinsic decomposition; however, e... | https://arxiv.org/abs/2512.23667 | Academic Papers | svg |
f950f996e53a34367611d4e0e0f2ddd2b754cc9456c9a4ecfd5d185c75c33b50 | 2026-01-01T00:00:00-05:00 | End-to-End Test-Time Training for Long Context | arXiv:2512.23675v2 Announce Type: replace Abstract: We formulate long-context language modeling as a problem in continual learning rather than architecture design. Under this formulation, we only use a standard architecture -- a Transformer with sliding-window attention. However, our model continues learning at test ti... | https://arxiv.org/abs/2512.23675 | Academic Papers | svg |
711ffc0ebfd7e2c93e235671b1eb26d1418676cf50a0cf6b203706846adda99b | 2026-01-01T00:00:00-05:00 | The Simultaneous Triple Product Property and Group-theoretic Results for the Exponent of Matrix Multiplication | arXiv:cs/0703145v5 Announce Type: replace Abstract: We describe certain special consequences of certain elementary methods from group theory for studying the algebraic complexity of matrix multiplication, as developed by H. Cohn, C. Umans et. al. in 2003 and 2005. The measure of complexity here is the exponent of matri... | https://arxiv.org/abs/cs/0703145 | Academic Papers | svg |
18a5f26f12c3bebd9afbc3b14e62fdc9953582fc99ab8bf00f0fc44ad5919b76 | 2026-01-01T00:00:00-05:00 | Efficient Active Learning with Abstention | arXiv:2204.00043v3 Announce Type: replace-cross Abstract: The goal of active learning is to achieve the same accuracy achievable by passive learning, while using much fewer labels. Exponential savings in terms of label complexity have been proved in very special cases, but fundamental lower bounds show that such improv... | https://arxiv.org/abs/2204.00043 | Academic Papers | svg |
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