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ec775776cf608f653f34c7406f0d543ac09a6034877c4079ef68a058ff20b7ff | 2026-02-02T00:00:00-05:00 | DoS Attacks and Defense Technologies in Blockchain Systems: A Hierarchical Analysis | arXiv:2507.22611v2 Announce Type: replace Abstract: Blockchain technology is widely used in various fields due to its ability to provide decentralization and trustless security. This is a fundamental understanding held by many advocates, but it is misunderstood, leading participants to fail to recognize the limitations... | https://arxiv.org/abs/2507.22611 | Academic Papers | svg |
43d6e2e78530faf2b67d4d3baf6c0daa265e0deff2570c6c540955470ddc72c2 | 2026-02-02T00:00:00-05:00 | ElectriQ: A Benchmark for Assessing the Response Capability of Large Language Models in Power Marketing | arXiv:2507.22911v2 Announce Type: replace Abstract: As power systems decarbonise and digitalise, high penetrations of distributed energy resources and flexible tariffs make electric power marketing (EPM) a key interface between regulation, system operation and sustainable-energy deployment. Many utilities still rely on... | https://arxiv.org/abs/2507.22911 | Academic Papers | svg |
09b5e2d89f7c2b248bc75e1b585e0f9bdc62c97ea53decbe940ed9eebf040c17 | 2026-02-02T00:00:00-05:00 | Thinking Machines: Mathematical Reasoning in the Age of LLMs | arXiv:2508.00459v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in structured reasoning and symbolic tasks, with coding emerging as a particularly successful application. This progress has naturally motivated efforts to extend these models to mathematics, both ... | https://arxiv.org/abs/2508.00459 | Academic Papers | svg |
b3661ebbd2a5080da616a18171cc52e8f98ab957bc816c082f846b0e1fe8a9c8 | 2026-02-02T00:00:00-05:00 | Benchmarking Foundation Models for Mitotic Figure Classification | arXiv:2508.04441v2 Announce Type: replace Abstract: The performance of deep learning models is known to scale with data quantity and diversity. In pathology, as in many other medical imaging domains, the availability of labeled images for a specific task is often limited. Self-supervised learning techniques have enable... | https://arxiv.org/abs/2508.04441 | Academic Papers | svg |
a5fed53080868e1ae0ea3f211a3b7826e7c343026a188e8320256080a794ec2e | 2026-02-02T00:00:00-05:00 | Matrix-Driven Identification and Reconstruction of LLM Weight Homology | arXiv:2508.06309v3 Announce Type: replace Abstract: We propose Matrix-Driven Identification and Reconstruction (MDIR), a SOTA large language model homology method that accurately detects weight correspondences between models and provides rigorous $p$-value estimation of the statistical significance of these corresponde... | https://arxiv.org/abs/2508.06309 | Academic Papers | svg |
b5c97ac2c5977d4cad4f6e1f1b67a32d276b09c7478b4df068bad548820a697b | 2026-02-02T00:00:00-05:00 | From Label Error Detection to Correction: A Modular Framework and Benchmark for Object Detection Datasets | arXiv:2508.06556v2 Announce Type: replace Abstract: Object detection has advanced rapidly in recent years, driven by increasingly large and diverse datasets. However, label errors often compromise the quality of these datasets and affect the outcomes of training and benchmark evaluations. Although label error detection... | https://arxiv.org/abs/2508.06556 | Academic Papers | svg |
0718e03f6a96b89679b50f41c7d14b982082278c3ad7db63bbcb6f5073262f71 | 2026-02-02T00:00:00-05:00 | QuiZSF: A Retrieval-Augmented Framework for Zero-Shot Time Series Forecasting | arXiv:2508.06915v2 Announce Type: replace Abstract: Accurate forecasting of sequential data streams is a cornerstone of modern Web services, supporting applications such as traffic management, user behavior modeling, and online anomaly prevention. However, in many Web environments, new domains emerge rapidly and labele... | https://arxiv.org/abs/2508.06915 | Academic Papers | svg |
436b7befcd50c1b19c9a959a48e047b2f5be0cb3d694f94584eef4fee521b047 | 2026-02-02T00:00:00-05:00 | Emergent morphogenesis via planar fabrication enabled by a reduced model of composites | arXiv:2508.08198v2 Announce Type: replace Abstract: The ability to engineer complex three-dimensional shapes from planar sheets with precise, programmable control underpins emerging technologies in soft robotics, reconfigurable devices, and functional materials. Here, we present a reduced-order numerical and experiment... | https://arxiv.org/abs/2508.08198 | Academic Papers | svg |
12481fbf60aa7c9a2af974895afb8d872dcbea625607a55d5aa6315168142521 | 2026-02-02T00:00:00-05:00 | BiasGym: Fantastic LLM Biases and How to Find (and Remove) Them | arXiv:2508.08855v3 Announce Type: replace Abstract: Understanding biases and stereotypes encoded in the weights of Large Language Models (LLMs) is crucial for developing effective mitigation strategies. However, biased behaviour is often subtle and non-trivial to isolate, even when deliberately elicited, making systema... | https://arxiv.org/abs/2508.08855 | Academic Papers | svg |
607d31706a32e0f3521570dd5aceb413a990056873a2587a9ec87027b41c2744 | 2026-02-02T00:00:00-05:00 | A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions | arXiv:2508.09128v3 Announce Type: replace Abstract: Reinforcement learning (RL) has proven to be particularly effective in solving complex decision-making problems for a wide range of applications. From a control theory perspective, RL can be considered as an adaptive optimal control scheme. Lyapunov and barrier functi... | https://arxiv.org/abs/2508.09128 | Academic Papers | svg |
009da03ee0a2e3b68f723d40d3ec1ebfd6c6f0a56f41b5cdea877a1c81dca66e | 2026-02-02T00:00:00-05:00 | Multi-Level Safety Continual Projection for Fine-Tuned Large Language Models without Retraining | arXiv:2508.09190v4 Announce Type: replace Abstract: While fine-tuning services drive the rapid expansion of task capabilities in large language models (LLMs), they are often accompanied by the degradation and reorganization of safety-aligned representations, making models more prone to deviating from human preferences ... | https://arxiv.org/abs/2508.09190 | Academic Papers | svg |
616c3f5067551df31d5a20abc466343103237a9cd5a2ae7948e751255d44a7c6 | 2026-02-02T00:00:00-05:00 | A Generalized Alternating Anderson Acceleration Method | arXiv:2508.10158v2 Announce Type: replace Abstract: In this work, we propose a generalized alternating Anderson acceleration method, a periodic scheme composed of $t$ fixed-point iteration steps, interleaved with $s$ steps of Anderson acceleration with window size $m$, to solve linear and nonlinear problems. This allow... | https://arxiv.org/abs/2508.10158 | Academic Papers | svg |
ea1e398e077e199fe2823e529809f1868dadf58763398bab2294afa24a2d144b | 2026-02-02T00:00:00-05:00 | A Unified Evaluation Framework for Multi-Annotator Tendency Learning | arXiv:2508.10393v2 Announce Type: replace Abstract: Recent works have emerged in multi-annotator learning that shift focus from Consensus-oriented Learning (CoL), which aggregates multiple annotations into a single ground-truth prediction, to Individual Tendency Learning (ITL), which models annotator-specific labeling ... | https://arxiv.org/abs/2508.10393 | Academic Papers | svg |
2e672eed9fdcb52253cdf9690db14f5d92834679aa7f043e40059e7a17801dbb | 2026-02-02T00:00:00-05:00 | Spirals and Beyond: Competitive Plane Search with Multi-Speed Agents | arXiv:2508.10793v2 Announce Type: replace Abstract: We consider the problem of minimizing the worst-case search time for a hidden point target in the plane using multiple mobile agents of differing speeds, all starting from a common origin. The search time is normalized by the target's distance to the origin, following... | https://arxiv.org/abs/2508.10793 | Academic Papers | svg |
92cb60ef47526c6c686dbb94b8d7442c69d39e46154128f9609b45fa5659c795 | 2026-02-02T00:00:00-05:00 | DREAMS: Preserving both Local and Global Structure in Dimensionality Reduction | arXiv:2508.13747v2 Announce Type: replace Abstract: Dimensionality reduction techniques are widely used for visualizing high-dimensional data in two dimensions. Existing methods are typically designed to preserve either local (e.g., $t$-SNE, UMAP) or global (e.g., MDS, PCA) structure of the data, but none of the establ... | https://arxiv.org/abs/2508.13747 | Academic Papers | svg |
feb635b3dc70cc9e610a9d296cc23d0cb72390a8ced7c9441ef3a9ae6c00dcdc | 2026-02-02T00:00:00-05:00 | GMOR: A Lightweight Robust Point Cloud Registration Framework via Geometric Maximum Overlapping | arXiv:2508.17427v2 Announce Type: replace Abstract: Point cloud registration based on correspondences computes the rigid transformation that maximizes the number of inliers constrained within the noise threshold. Current state-of-the-art (SOTA) methods employing spatial compatibility graphs or branch-and-bound (BnB) se... | https://arxiv.org/abs/2508.17427 | Academic Papers | svg |
fc601f612bbc6a73074455b402a7d81b2203e74c957460ebe4d28a8e91a2cbd1 | 2026-02-02T00:00:00-05:00 | MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation | arXiv:2508.19236v2 Announce Type: replace Abstract: Temporal context is essential for robotic manipulation because such tasks are inherently non-Markovian, yet mainstream VLA models typically overlook it and struggle with long-horizon, temporally dependent tasks. Cognitive science suggests that humans rely on working m... | https://arxiv.org/abs/2508.19236 | Academic Papers | svg |
a9f303dc6403d3e4a125094ebd64d1cf98c5b3c58593e3a13465ca37e08aa674 | 2026-02-02T00:00:00-05:00 | Quantum latent distributions in deep generative models | arXiv:2508.19857v2 Announce Type: replace Abstract: Many successful families of generative models leverage a low-dimensional latent distribution that is mapped to a data distribution. Though simple latent distributions are often used, the choice of distribution has a strong impact on model performance. Recent experimen... | https://arxiv.org/abs/2508.19857 | Academic Papers | svg |
7e1b4a6851856ebed9e420854ff4e90ee48787d20b32f9d63b0cf65e7bedbb49 | 2026-02-02T00:00:00-05:00 | Automatic Reviewers Fail to Detect Faulty Reasoning in Research Papers: A New Counterfactual Evaluation Framework | arXiv:2508.21422v2 Announce Type: replace Abstract: Large Language Models (LLMs) have great potential to accelerate and support scholarly peer review and are increasingly used as fully automatic review generators (ARGs). However, potential biases and systematic errors may pose significant risks to scientific integrity;... | https://arxiv.org/abs/2508.21422 | Academic Papers | svg |
a9ea3d982e954fb44f7c62fc21c6851007f9eb93cac2abbba98db50e66b1caba | 2026-02-02T00:00:00-05:00 | Social World Models | arXiv:2509.00559v2 Announce Type: replace Abstract: Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited information. In contrast, AI systems struggle to structure and reason about implicit social contexts, as they lack explicit repr... | https://arxiv.org/abs/2509.00559 | Academic Papers | svg |
f3d18d9adabd1e72a80f6713d2935c6bbc943b29d6b7b10fa51e1507735506f8 | 2026-02-02T00:00:00-05:00 | FLM-Audio: Natural Monologues Improves Native Full-Duplex Chatbots via Dual Training | arXiv:2509.02521v3 Announce Type: replace Abstract: Full-duplex dialog models aim to listen and speak simultaneously, delivering rapid responses to dynamic user input. Among different solutions to full-duplexity, a native solution merges multiple channels in each time step, achieving the lowest latency. However, prevai... | https://arxiv.org/abs/2509.02521 | Academic Papers | svg |
bf53040413310ed58d8d7e9d03aa2b1b3dfab740c331ae070896c89dd6fe9862 | 2026-02-02T00:00:00-05:00 | TRACE: Unlocking Effective CXL Bandwidth via Lossless Compression and Precision Scaling | arXiv:2509.03377v3 Announce Type: replace Abstract: LLM inference is increasingly limited by memory bandwidth, and the bottleneck worsens at long context as the KV cache grows. CXL memory adds capacity to offload weights and KV, but its link and device-side DDR bandwidth are far below HBM, so decoding stalls once traff... | https://arxiv.org/abs/2509.03377 | Academic Papers | svg |
c6553e3f4dd6a40301b98c0c27de210fb87e58104daa19e0b027dcc0e9eba06b | 2026-02-02T00:00:00-05:00 | SpiderNets: Vision Models Predict Human Fear From Aversive Images | arXiv:2509.04889v2 Announce Type: replace Abstract: Phobias are common and impairing, and exposure therapy, which involves confronting patients with fear-provoking visual stimuli, is the most effective treatment. Scalable computerized exposure therapy requires automated prediction of fear directly from image content to... | https://arxiv.org/abs/2509.04889 | Academic Papers | svg |
28731e8b020f0d9384b42889b04312defcc32926397228dee7e3a6998c6140de | 2026-02-02T00:00:00-05:00 | AI for Scientific Discovery is a Social Problem | arXiv:2509.06580v5 Announce Type: replace Abstract: Artificial intelligence (AI) is being increasingly applied to scientific research, but its benefits remain unevenly distributed across different communities and disciplines. While technical challenges such as limited data, fragmented standards, and unequal access to c... | https://arxiv.org/abs/2509.06580 | Academic Papers | svg |
701ffb600215de1673501aa5771fb9bcfb96b94fad4cc018d75e6f26503a56e3 | 2026-02-02T00:00:00-05:00 | RAFFLES: Reasoning-based Attribution of Faults for LLM Systems | arXiv:2509.06822v3 Announce Type: replace Abstract: The advent of complex, interconnected long-horizon LLM systems has made it incredibly tricky to identify where and when these systems break down. Evaluation capabilities that currently exist today are limited in that they often focus on simple metrics, end-to-end outc... | https://arxiv.org/abs/2509.06822 | Academic Papers | svg |
91ccd4686adb276925cbf461dd489690a435447d3b30267691aae8367900eca0 | 2026-02-02T00:00:00-05:00 | Leveraging AI Agents for Autonomous Networks: A Reference Architecture and Empirical Studies | arXiv:2509.08312v2 Announce Type: replace Abstract: The evolution toward Level 4 (L4) Autonomous Networks (AN) represents a strategic inflection point in telecommunications, where networks must transcend reactive automation to achieve genuine cognitive capabilities--fulfilling TM Forum's vision of self-configuring, sel... | https://arxiv.org/abs/2509.08312 | Academic Papers | svg |
196cb361fdb61cb9af41545bd0fd801fb99feb0e16e1c93aad4027ec5362648a | 2026-02-02T00:00:00-05:00 | HyperMOOC: Augmenting MOOC Videos with Concept-based Embedded Visualizations | arXiv:2509.08404v3 Announce Type: replace Abstract: Massive Open Online Courses (MOOCs) have become increasingly popular worldwide. However, learners primarily rely on watching videos, easily losing knowledge context and reducing learning effectiveness. We propose HyperMOOC, a novel approach augmenting MOOC videos with... | https://arxiv.org/abs/2509.08404 | Academic Papers | svg |
ea8ee2428017a614d75b484783020499745d694ecfcdad6d968428b7b5ea80bb | 2026-02-02T00:00:00-05:00 | Feature Space Topology Control via Hopkins Loss | arXiv:2509.11154v2 Announce Type: replace Abstract: Feature space topology refers to the organization of samples within the feature space. Modifying this topology can be beneficial in machine learning applications, including dimensionality reduction, generative modeling, transfer learning, and robustness to adversarial... | https://arxiv.org/abs/2509.11154 | Academic Papers | svg |
98904e1fde571caa8b7d20fa7e638b48d566235f10159059f11f527d354b554c | 2026-02-02T00:00:00-05:00 | EgoMem: Lifelong Memory Agent for Full-duplex Omnimodal Models | arXiv:2509.11914v2 Announce Type: replace Abstract: We introduce EgoMem, the first lifelong memory agent tailored for full-duplex models that process real-time omnimodal streams. EgoMem enables real-time models to recognize multiple users directly from raw audiovisual streams, to provide personalized response, and to m... | https://arxiv.org/abs/2509.11914 | Academic Papers | svg |
bbc876baf691bb65624400463325de3bb82148bfea08f351202b3cff735b9ee9 | 2026-02-02T00:00:00-05:00 | Information Loss and Disparate Effects in Network Embeddings | arXiv:2509.12396v2 Announce Type: replace Abstract: An extensive line of work studies fairness interventions for network embeddings, but less is known about their baseline behavior. In this work, we ask: how do baseline embeddings (without fairness interventions) produce disparate effects at the representation level? W... | https://arxiv.org/abs/2509.12396 | Academic Papers | svg |
27571b1343060ba33dfbf5fa3f12115594949c7159f2c987febee1eeb6c5f34a | 2026-02-02T00:00:00-05:00 | Linear Complexity Computation of Code Distance and Minimum Size of Trapping Sets for LDPC Codes with Bounded Treewidth | arXiv:2509.13040v2 Announce Type: replace Abstract: It is well known that, given \(b\ge 0\), finding an $(a,b)$-trapping set with the minimum \(a\) in a binary linear code is NP-hard. In this paper, we demonstrate that this problem can be solved with linear complexity with respect to the code length for codes with boun... | https://arxiv.org/abs/2509.13040 | Academic Papers | svg |
1f02777b43cf44561aeda511e19396587e8f573e7dc21589a30f33e1012096d3 | 2026-02-02T00:00:00-05:00 | Optimal Learning from Label Proportions with General Loss Functions | arXiv:2509.15145v2 Announce Type: replace Abstract: Motivated by problems in online advertising, we address the task of Learning from Label Proportions (LLP). We introduce a novel and versatile low-variance debiasing methodology to learn from aggregate label information, significantly advancing the state of the art in ... | https://arxiv.org/abs/2509.15145 | Academic Papers | svg |
77435f12445f986197960c3518199098d389c4b11ef4afad6005d58be224ec27 | 2026-02-02T00:00:00-05:00 | Self-Improvement of Language Models by Post-Training on Multi-Agent Debate | arXiv:2509.15172v3 Announce Type: replace Abstract: Self-improvement, where models improve beyond their current performance without external supervision, remains a challenge. The core difficulty is sourcing a training signal stronger than what the model itself can currently produce. Majority voting has been shown to pr... | https://arxiv.org/abs/2509.15172 | Academic Papers | svg |
06eef4782afadb5cc726154b42711637e82e7981700684d4f487c810bfe5abea | 2026-02-02T00:00:00-05:00 | Impact of Phonetics on Speaker Identity in Adversarial Voice Attack | arXiv:2509.15437v2 Announce Type: replace Abstract: Adversarial perturbations in speech pose a serious threat to automatic speech recognition (ASR) and speaker verification by introducing subtle waveform modifications that remain imperceptible to humans but can significantly alter system outputs. While targeted attacks... | https://arxiv.org/abs/2509.15437 | Academic Papers | svg |
33900d50afa79c5b16e6169f41e16c78650cf284671d31a696a860b1108d5bc9 | 2026-02-02T00:00:00-05:00 | Thinking in cocktail party: Chain-of-Thought and reinforcement learning for target speaker automatic speech recognition | arXiv:2509.15612v2 Announce Type: replace Abstract: Target Speaker Automatic Speech Recognition (TS-ASR) aims to transcribe the speech of a specified target speaker from multi-speaker mixtures in cocktail party scenarios. Recent advancement of Large Audio-Language Models (LALMs) has already brought some new insights to... | https://arxiv.org/abs/2509.15612 | Academic Papers | svg |
5dc07865443a551c18b95b0857af517266a474e940cf7f399e3e64f06eeb8b97 | 2026-02-02T00:00:00-05:00 | CompSpoof: A Dataset and Joint Learning Framework for Component-Level Audio Anti-spoofing Countermeasures | arXiv:2509.15804v2 Announce Type: replace Abstract: Component-level audio Spoofing (Comp-Spoof) targets a new form of audio manipulation where only specific components of a signal, such as speech or environmental sound, are forged or substituted while other components remain genuine. Existing anti-spoofing datasets and... | https://arxiv.org/abs/2509.15804 | Academic Papers | svg |
56d6da56d23e01b187a6a3306e2751cbe05abf74a8853bee17aa49e186f6f981 | 2026-02-02T00:00:00-05:00 | FESTA: Functionally Equivalent Sampling for Trust Assessment of Multimodal LLMs | arXiv:2509.16648v4 Announce Type: replace Abstract: The accurate trust assessment of multimodal large language models (MLLMs) generated predictions, which can enable selective prediction and improve user confidence, is challenging due to the diverse multi-modal input paradigms. We propose Functionally Equivalent Sampli... | https://arxiv.org/abs/2509.16648 | Academic Papers | svg |
ca80f721810dc9bc29ac8422f739e9bc419129785fc7637131b5fb805398e964 | 2026-02-02T00:00:00-05:00 | Accurate and Efficient Low-Rank Model Merging in Core Space | arXiv:2509.17786v4 Announce Type: replace Abstract: In this paper, we address the challenges associated with merging low-rank adaptations of large neural networks. With the rise of parameter-efficient adaptation techniques, such as Low-Rank Adaptation (LoRA), model fine-tuning has become more accessible. While fine-tun... | https://arxiv.org/abs/2509.17786 | Academic Papers | svg |
143931fd523f3d0b848858058fd2db04e822524f733ca1566dc70788bfc5e8d8 | 2026-02-02T00:00:00-05:00 | A Scalable Lift-and-Project Differentiable Approach For the Maximum Cut Problem | arXiv:2509.18612v2 Announce Type: replace Abstract: We propose a scalable framework for solving the Maximum Cut (MaxCut) problem in large graphs using projected gradient ascent on quadratic objectives. Our approach is differentiable and leverages GPUs for gradient-based optimization. It is not a machine learning method... | https://arxiv.org/abs/2509.18612 | Academic Papers | svg |
052844e4eb8c206e898192cd526fd80efa489a2c69e2b7527f18432175c8917b | 2026-02-02T00:00:00-05:00 | Latent Iterative Refinement Flow: A Geometric Constrained Approach for Few-Shot Generation | arXiv:2509.19903v2 Announce Type: replace Abstract: Diffusion and flow-matching models trained with limited data often tend to memorize the training data instead of generalization, leading to severely reduced diversity. In this paper, we provide a dynamical perspective and identify this ``collapse-to-memorization'' phe... | https://arxiv.org/abs/2509.19903 | Academic Papers | svg |
3efa3b029ed9782310f9a31b7cb90ca14081394a12ca82b7e06f6f60508a2a83 | 2026-02-02T00:00:00-05:00 | Towards Atoms of Large Language Models | arXiv:2509.20784v2 Announce Type: replace Abstract: The fundamental representational units (FRUs) of large language models (LLMs) remain undefined, limiting further understanding of their underlying mechanisms. In this paper, we introduce Atom Theory to systematically define, evaluate, and identify such FRUs, which we ... | https://arxiv.org/abs/2509.20784 | Academic Papers | svg |
660904941339b13e5409732622822979ba208a9d968ee211e7abfcab2d87e594 | 2026-02-02T00:00:00-05:00 | LAVA: Explainability for Unsupervised Latent Embeddings | arXiv:2509.21149v2 Announce Type: replace Abstract: Unsupervised black-box models are drivers of scientific discovery, yet are difficult to interpret, as their output is often a multidimensional embedding rather than a well-defined target. While explainability for supervised learning uncovers how input features contrib... | https://arxiv.org/abs/2509.21149 | Academic Papers | svg |
4312ca9941ceed5a5124c47438724701ce7082b5d6cbd13a4b0167cc547fcbe4 | 2026-02-02T00:00:00-05:00 | It's Not You, It's Clipping: A Soft Trust-Region via Probability Smoothing for LLM RL | arXiv:2509.21282v2 Announce Type: replace Abstract: Training large language models (LLMs) with reinforcement learning (RL) methods such as PPO and GRPO commonly relies on ratio clipping to stabilise updates. While effective at preventing instability, clipping discards information, introduces gradient discontinuities an... | https://arxiv.org/abs/2509.21282 | Academic Papers | svg |
5d687456588fe4ae5019b9f1e8e1ea3e7bc535745dcc22d1978abb5f345418ad | 2026-02-02T00:00:00-05:00 | Filtering with Confidence: When Data Augmentation Meets Conformal Prediction | arXiv:2509.21479v2 Announce Type: replace Abstract: With promising empirical performance across a wide range of applications, synthetic data augmentation appears a viable solution to data scarcity and the demands of increasingly data-intensive models. Its effectiveness lies in expanding the training set in a way that r... | https://arxiv.org/abs/2509.21479 | Academic Papers | svg |
0e62eaae0089441fc02513448347d107049c49a1174dbcccba8517bb2232ed89 | 2026-02-02T00:00:00-05:00 | Incentives in Federated Learning with Heterogeneous Agents | arXiv:2509.21612v2 Announce Type: replace Abstract: Federated learning promises significant sample-efficiency gains by pooling data across multiple agents, yet incentive misalignment is an obstacle: each update is costly to the contributor but boosts every participant. We introduce a game-theoretic framework that captu... | https://arxiv.org/abs/2509.21612 | Academic Papers | svg |
8876590e9379745701157eb1242e3713895e0ef61d00aed8db5f40c5a55ce57f | 2026-02-02T00:00:00-05:00 | Lifelong Learning with Behavior Consolidation for Vehicle Routing | arXiv:2509.21765v3 Announce Type: replace Abstract: Recent neural solvers have demonstrated promising performance in learning to solve routing problems. However, existing studies are primarily based on one-off training on one or a set of predefined problem distributions and scales, i.e., tasks. When a new task arises, ... | https://arxiv.org/abs/2509.21765 | Academic Papers | svg |
c2f05b8f7b2f8c962949ddda41390428dd7e403aece4073cce868e13b7f6e548 | 2026-02-02T00:00:00-05:00 | SimulSense: Sense-Driven Interpreting for Efficient Simultaneous Speech Translation | arXiv:2509.21932v2 Announce Type: replace Abstract: How to make human-interpreter-like read/write decisions for simultaneous speech translation (SimulST) systems? Current state-of-the-art systems formulate SimulST as a multi-turn dialogue task, requiring specialized interleaved training data and relying on computationa... | https://arxiv.org/abs/2509.21932 | Academic Papers | svg |
d6b8e838a99318a38e95f27f1b93c496ff2035af2eb47ade0770a3822e3a53d3 | 2026-02-02T00:00:00-05:00 | Collaborative Belief Reasoning with LLMs for Efficient Multi-Agent Collaboration | arXiv:2509.21981v3 Announce Type: replace Abstract: Effective real-world multi-agent collaboration requires not only accurate planning but also the ability to reason about collaborators' intents--a crucial capability for avoiding miscoordination and redundant communication under partial observable environments. Due to ... | https://arxiv.org/abs/2509.21981 | Academic Papers | svg |
48a2f1a559f8dfb71e4fc994bf642239d7031d773dcf2e47a0c15333a6259ac6 | 2026-02-02T00:00:00-05:00 | Towards a more realistic evaluation of machine learning models for bearing fault diagnosis | arXiv:2509.22267v2 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery. While recent advances in machine learning (ML), particularly deep learning, have shown strong performance in controlled settings, many studie... | https://arxiv.org/abs/2509.22267 | Academic Papers | svg |
9f40d1e013de18c15a62e3e6defd51c5c2e9c6764d58cc2b064f5495003d395a | 2026-02-02T00:00:00-05:00 | ChatInject: Abusing Chat Templates for Prompt Injection in LLM Agents | arXiv:2509.22830v2 Announce Type: replace Abstract: The growing deployment of large language model (LLM) based agents that interact with external environments has created new attack surfaces for adversarial manipulation. One major threat is indirect prompt injection, where attackers embed malicious instructions in exte... | https://arxiv.org/abs/2509.22830 | Academic Papers | svg |
66146c0e928e28286ee4132eb7e1d6ea50ea7ed61c1a6bd472ab65fde81a38a5 | 2026-02-02T00:00:00-05:00 | On the Separability of Information in Diffusion Models | arXiv:2509.23937v4 Announce Type: replace Abstract: Diffusion models transform noise into data by injecting information that was captured in their neural network during the training phase. In this paper, we ask: \textit{what} is this information? We find that, in pixel-space diffusion models, (1) a large fraction of th... | https://arxiv.org/abs/2509.23937 | Academic Papers | svg |
14e295079d58416d6321866becf07ee0d4ef200885e6fa8563f9e87a87ee1e5a | 2026-02-02T00:00:00-05:00 | Fidel-TS: A High-Fidelity Multimodal Benchmark for Time Series Forecasting | arXiv:2509.24789v3 Announce Type: replace Abstract: The evaluation of time series forecasting models is hindered by a critical lack of high-quality benchmarks, leading to a potential illusion of progress. Existing datasets suffer from issues ranging from pre-training data contamination in the age of LLMs to the tempora... | https://arxiv.org/abs/2509.24789 | Academic Papers | svg |
e0daec9cececf9fb189d841988d85527ae998abde0af4ca573ca1023c3580ccd | 2026-02-02T00:00:00-05:00 | Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation | arXiv:2509.24798v4 Announce Type: replace Abstract: We present Causal-Adapter, a modular framework that adapts frozen text-to-image diffusion backbones for counterfactual image generation. Our method enables causal interventions on target attributes, consistently propagating their effects to causal dependents without a... | https://arxiv.org/abs/2509.24798 | Academic Papers | svg |
43e3bfb7fc1b7ae955f197f66d3b2608c56e02b79c430b7e4dcc7de7f7b5cead | 2026-02-02T00:00:00-05:00 | IRIS: Intrinsic Reward Image Synthesis | arXiv:2509.25562v2 Announce Type: replace Abstract: Despite the success of Reinforcement Learning from Human Feedback (RLHF) in language reasoning, its application to autoregressive Text-to-Image (T2I) generation is often constrained by the limited availability of human preference data. This paper explores how an autor... | https://arxiv.org/abs/2509.25562 | Academic Papers | svg |
2ef4e590319cae63740077976d992ae413655b2bb2f8f0c877cc6fe1e7217d44 | 2026-02-02T00:00:00-05:00 | Think Less, Label Better: Multi-Stage Domain-Grounded Synthetic Data Generation for Fine-Tuning Large Language Models in Telecommunications | arXiv:2509.25736v2 Announce Type: replace Abstract: The success of large language models (LLMs) depends heavily on large-scale, high-quality instruction-following and reinforcement datasets. However, generating such data through human annotation is prohibitively time-consuming particularly for domain-specific tasks lik... | https://arxiv.org/abs/2509.25736 | Academic Papers | svg |
6cd361a8b5254e7c7bbbb745d33af4cac2b6c492258e1d00b31eb7d30621446a | 2026-02-02T00:00:00-05:00 | A Generalized Information Bottleneck Theory of Deep Learning | arXiv:2509.26327v3 Announce Type: replace Abstract: The Information Bottleneck (IB) principle offers a compelling theoretical framework to understand how neural networks (NNs) learn. However, its practical utility has been constrained by unresolved theoretical ambiguities and significant challenges in accurate estimati... | https://arxiv.org/abs/2509.26327 | Academic Papers | svg |
3c55846900e7a2da92fa837313f616ccde4298a34f65a5fc9de6eda5ab415914 | 2026-02-02T00:00:00-05:00 | TAP: Two-Stage Adaptive Personalization of Multi-Task and Multi-Modal Foundation Models in Federated Learning | arXiv:2509.26524v2 Announce Type: replace Abstract: In federated learning (FL), local personalization of models has received significant attention, yet personalized fine-tuning of foundation models remains a significant challenge. In particular, there is a lack of understanding in the literature on how to fine-tune and... | https://arxiv.org/abs/2509.26524 | Academic Papers | svg |
c6876cbe23103b20d89cdf23d54f4423a0e38a6da8127828a37871f8848d1f97 | 2026-02-02T00:00:00-05:00 | Efficient Approximation Algorithms for Fair Influence Maximization under Maximin Constraint | arXiv:2509.26579v2 Announce Type: replace Abstract: Fair Influence Maximization (FIM) seeks to mitigate disparities in influence across different groups and has recently garnered increasing attention. A widely adopted notion of fairness in FIM is the maximin constraint, which directly requires maximizing the utility (i... | https://arxiv.org/abs/2509.26579 | Academic Papers | svg |
a7e1ad4f83075244d790f47741c64cc36405a576f9915ecb5103ea27d9d4a8c9 | 2026-02-02T00:00:00-05:00 | FedLLM-Align: Feature Extraction From Heterogeneous Clients | arXiv:2510.00065v2 Announce Type: replace Abstract: Federated learning (FL) enables collaborative model training without sharing raw data, making it attractive for privacy-sensitive domains, e.g., healthcare, finance, and IoT. A major obstacle, however, is the potential heterogeneity of tabular data across clients, in ... | https://arxiv.org/abs/2510.00065 | Academic Papers | svg |
b1ffbc0f16be02206226caa3abc448ce0694110b1620af2daf69d4315f2fc24b | 2026-02-02T00:00:00-05:00 | Thoughtbubbles: an Unsupervised Method for Parallel Thinking in Latent Space | arXiv:2510.00219v2 Announce Type: replace Abstract: Current approaches for scaling inference-time compute in transformers train them to emit explicit chain-of-thought tokens before producing an answer. While these methods are powerful, they are limited because they cannot be applied during pretraining and rely solely o... | https://arxiv.org/abs/2510.00219 | Academic Papers | svg |
ace5d4a822e4b7bfaf0a946b196cf19f2325e1dfc4a639a72f0d619c7bc82061 | 2026-02-02T00:00:00-05:00 | It Takes Two: Your GRPO Is Secretly DPO | arXiv:2510.00977v2 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) has emerged as a prominent reinforcement learning algorithm for post-training Large Language Models. Different from critic-based methods such as PPO, GRPO estimates the advantage function using group-level statistics to reduce... | https://arxiv.org/abs/2510.00977 | Academic Papers | svg |
f20e858ca872bcead753680fc0a6b5142c4c7651f710d2f0171b896c99f79e3b | 2026-02-02T00:00:00-05:00 | How Well Can Preference Optimization Generalize Under Noisy Feedback? | arXiv:2510.01458v3 Announce Type: replace Abstract: As large language models (LLMs) advance their capabilities, aligning these models with human preferences has become crucial. Preference optimization, which trains models to distinguish between preferred and non-preferred responses based on human feedback, has become a... | https://arxiv.org/abs/2510.01458 | Academic Papers | svg |
e83a3786fd5926072b1bf98fff40593b53d316e31c9421c377fbecc7de570dfb | 2026-02-02T00:00:00-05:00 | InvThink: Towards AI Safety via Inverse Reasoning | arXiv:2510.01569v2 Announce Type: replace Abstract: We present InvThink, a simple yet powerful approach that gives language models the capability of inverse thinking: reasoning through failure modes before generating responses. Unlike existing safety alignment methods that optimize directly for safe response, InvThink ... | https://arxiv.org/abs/2510.01569 | Academic Papers | svg |
d599f2ec0ae8ecd685bf249ca37000928058e2cd738b3448e5ed16ab15af68bb | 2026-02-02T00:00:00-05:00 | PENEX: AdaBoost-Inspired Neural Network Regularization | arXiv:2510.02107v3 Announce Type: replace Abstract: AdaBoost sequentially fits so-called weak learners to minimize an exponential loss, which penalizes misclassified data points more severely than other loss functions like cross-entropy. Paradoxically, AdaBoost generalizes well in practice as the number of weak learner... | https://arxiv.org/abs/2510.02107 | Academic Papers | svg |
e812b5f236351a92ad4bbd1a4b1d8949d1aab4e186f5eedc1058c531d8d01aef | 2026-02-02T00:00:00-05:00 | Test-Time Anchoring for Discrete Diffusion Posterior Sampling | arXiv:2510.02291v2 Announce Type: replace Abstract: While continuous diffusion models have achieved remarkable success, discrete diffusion offers a unified framework for jointly modeling text and images. Beyond unification, discrete diffusion provides faster inference, finer control, and principled training-free guidan... | https://arxiv.org/abs/2510.02291 | Academic Papers | svg |
9b4df8c03a84bfdac0bd9daaa623e5549bc9a955a5ec14384d7542d359c871d1 | 2026-02-02T00:00:00-05:00 | VideoNSA: Native Sparse Attention Scales Video Understanding | arXiv:2510.02295v2 Announce Type: replace Abstract: Video understanding in multimodal language models remains limited by context length: models often miss key transition frames and struggle to maintain coherence across long time scales. To address this, we adapt Native Sparse Attention (NSA) to video-language models. O... | https://arxiv.org/abs/2510.02295 | Academic Papers | svg |
f49b7c322731f85016ba3467541fd9770060fce6989c88c6ee8fa2879caf2459 | 2026-02-02T00:00:00-05:00 | ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data | arXiv:2510.02952v2 Announce Type: replace Abstract: Inferring trajectories from longitudinal spatially-resolved omics data is fundamental to understanding the dynamics of structural and functional tissue changes in development, regeneration and repair, disease progression, and response to treatment. We propose ContextF... | https://arxiv.org/abs/2510.02952 | Academic Papers | svg |
932b6e31aa772a448bf332c3b1d309450f8c8496eefcedddc19de05f5fb7362a | 2026-02-02T00:00:00-05:00 | PT$^2$-LLM: Post-Training Ternarization for Large Language Models | arXiv:2510.03267v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown impressive capabilities across diverse tasks, but their large memory and compute demands hinder deployment. Ternarization has gained attention as a promising compression technique, delivering substantial size reduction and high ... | https://arxiv.org/abs/2510.03267 | Academic Papers | svg |
ec79ce08d54b0be315ede1d4b47d8773e63b76df6d4a85614a41867d84cba37d | 2026-02-02T00:00:00-05:00 | FrameOracle: Learning What to See and How Much to See in Videos | arXiv:2510.03584v2 Announce Type: replace Abstract: Vision-language models (VLMs) advance video understanding but operate under tight computational budgets, making performance dependent on selecting a small, high-quality subset of frames. Existing frame sampling strategies, such as uniform or fixed-budget selection, fa... | https://arxiv.org/abs/2510.03584 | Academic Papers | svg |
ca2784f791d4cc6863f2c640e1426838473f4a9eaf8a4a6e2ac8f60412aa97da | 2026-02-02T00:00:00-05:00 | Security Analysis of Ponzi Schemes in Ethereum Smart Contracts | arXiv:2510.03819v2 Announce Type: replace Abstract: The rapid advancement of blockchain technology has precipitated the widespread adoption of Ethereum and smart contracts across a variety of sectors. However, this has also given rise to numerous fraudulent activities, with many speculators embedding Ponzi schemes with... | https://arxiv.org/abs/2510.03819 | Academic Papers | svg |
c28f998d84da658e2d4531eda973dccc84e7656d64fcde0807e386c1b4b3a837 | 2026-02-02T00:00:00-05:00 | Unmasking Backdoors: An Explainable Defense via Gradient-Attention Anomaly Scoring for Pre-trained Language Models | arXiv:2510.04347v2 Announce Type: replace Abstract: Pre-trained language models have achieved remarkable success across a wide range of natural language processing (NLP) tasks, particularly when fine-tuned on large, domain-relevant datasets. However, they remain vulnerable to backdoor attacks, where adversaries embed m... | https://arxiv.org/abs/2510.04347 | Academic Papers | svg |
f789bce664fcaa515c509200c13c1b1a2c7ed8ef5af78e0f0d720ecb2770db86 | 2026-02-02T00:00:00-05:00 | Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models | arXiv:2510.04618v2 Announce Type: replace Abstract: Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation -- modifying inputs with instructions, strategies, or evidence, rather than weight updates. Prior approaches improve usability but often suffer... | https://arxiv.org/abs/2510.04618 | Academic Papers | svg |
f25a71e052bced36c3be5e1788ce1a12d6d8739332d95b039765436b7a9130e9 | 2026-02-02T00:00:00-05:00 | Training Dynamics Impact Post-Training Quantization Robustness | arXiv:2510.06213v2 Announce Type: replace Abstract: While post-training quantization is widely adopted for efficient deployment of large language models, the mechanisms underlying quantization robustness remain unclear. We conduct a comprehensive analysis of quantization degradation across open-source language model tr... | https://arxiv.org/abs/2510.06213 | Academic Papers | svg |
1ef0d03eb015f4ff6fd423b79134d47158de1bbaa27fef6fd19ae6eb55810f2c | 2026-02-02T00:00:00-05:00 | Quantifying Data Contamination in Psychometric Evaluations of LLMs | arXiv:2510.07175v2 Announce Type: replace Abstract: Recent studies apply psychometric questionnaires to Large Language Models (LLMs) to assess high-level psychological constructs such as values, personality, moral foundations, and dark traits. Although prior work has raised concerns about possible data contamination fr... | https://arxiv.org/abs/2510.07175 | Academic Papers | svg |
893e97c473442a2c6e38097c6c6727d44cc58edef193727022d3b177a16c7a38 | 2026-02-02T00:00:00-05:00 | The Unintended Trade-off of AI Alignment:Balancing Hallucination Mitigation and Safety in LLMs | arXiv:2510.07775v2 Announce Type: replace Abstract: Hallucination in large language models (LLMs) has been widely studied in recent years, with progress in both detection and mitigation aimed at improving truthfulness. Yet, a critical side effect remains largely overlooked: enhancing truthfulness can negatively impact ... | https://arxiv.org/abs/2510.07775 | Academic Papers | svg |
4c52b93c9483f8676bb1d5f56d2fffcecff5ec067857a2a821c3388171f8dcd5 | 2026-02-02T00:00:00-05:00 | Post-Norm can Resharpen Attention | arXiv:2510.08341v2 Announce Type: replace Abstract: Length Generalization is the essential capacity of autonomous agents to perform tasks in longer contexts than those encountered during training. To systematically study this feat, we test how well models can approximate the next token distributions in algorithmic task... | https://arxiv.org/abs/2510.08341 | Academic Papers | svg |
14e108be8ce8b966938aee014f484ec3ae95b05aef9569c9305a459c47591741 | 2026-02-02T00:00:00-05:00 | Which Heads Matter for Reasoning? RL-Guided KV Cache Compression | arXiv:2510.08525v2 Announce Type: replace Abstract: Reasoning large language models exhibit complex reasoning behaviors via extended chain-of-thought generation that are highly fragile to information loss during decoding, creating critical challenges for KV cache compression. Existing token-dropping methods directly di... | https://arxiv.org/abs/2510.08525 | Academic Papers | svg |
e3ce87150f265c1cd103a8b4713238922f98ddcd01a8f2d8a01ea55549397fc7 | 2026-02-02T00:00:00-05:00 | GraphGhost: Tracing Structures Behind Large Language Models | arXiv:2510.08613v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit strong reasoning capabilities on structured tasks, yet the internal mechanisms underlying such behaviors remain poorly understood. Existing interpretation methods mainly focus on token-level attributions, which provide limited insi... | https://arxiv.org/abs/2510.08613 | Academic Papers | svg |
15f283b591125637f76ff81aaa1062aa8afc4e84439fdb1ce839abc88c3cffc6 | 2026-02-02T00:00:00-05:00 | On the Provable Performance Guarantee of Efficient Reasoning Models | arXiv:2510.09133v2 Announce Type: replace Abstract: Large reasoning models (LRMs) have achieved remarkable progress in complex problem-solving tasks. Despite this success, LRMs typically suffer from high computational costs during deployment, highlighting a need for efficient inference. A practical direction of efficie... | https://arxiv.org/abs/2510.09133 | Academic Papers | svg |
302be08bf4a2a7f178329d33acbbd03dd81ac2eafd9178cb606dc2060ab4ffc9 | 2026-02-02T00:00:00-05:00 | Herb.jl: A Unifying Program Synthesis Library | arXiv:2510.09726v2 Announce Type: replace Abstract: Program synthesis -- the automatic generation of code given a specification -- is one of the most fundamental tasks in artificial intelligence (AI) and the dream of many programmers. Numerous synthesizers have been developed for program synthesis, offering different a... | https://arxiv.org/abs/2510.09726 | Academic Papers | svg |
e44405dca51a10a783af745f57238fa2405d37d18f32f89b76239f2d548ee9d7 | 2026-02-02T00:00:00-05:00 | GOLD PANNING: Iterative Bayesian Signal Anchoring for Many-Document Needle-in-Haystack Reasoning | arXiv:2510.09770v2 Announce Type: replace Abstract: Large language models (LLMs) exhibit pronounced position bias in long-context needle-in-haystack problems, systematically prioritizing the location of information over its relevance. While current mitigations rely on white-box access, this is effectively impossible fo... | https://arxiv.org/abs/2510.09770 | Academic Papers | svg |
8003667b430c01e82089fd3002bd91adbc7c6b4daa34e7d3b30a8743012a83aa | 2026-02-02T00:00:00-05:00 | Don't Just Fine-tune the Agent, Tune the Environment | arXiv:2510.10197v2 Announce Type: replace Abstract: Large Language Model (LLM) agents show great promise for complex, multi-turn tool-use tasks, but their development is often hampered by the extreme scarcity of high-quality training data. Supervised fine-tuning (SFT) on synthetic data leads to overfitting, whereas sta... | https://arxiv.org/abs/2510.10197 | Academic Papers | svg |
fa7abcbdf32138fdeadc88879d73b0f7e8815c91239d3811d23f1a344c785c14 | 2026-02-02T00:00:00-05:00 | Understanding and Bridging the Planner-Coder Gap: A Systematic Study on the Robustness of Multi-Agent Systems for Code Generation | arXiv:2510.10460v2 Announce Type: replace Abstract: Multi-agent systems (MASs) have emerged as a promising paradigm for automated code generation, demonstrating impressive performance on established benchmarks. Despite their prosperous development, the fundamental mechanisms underlying their robustness remain poorly un... | https://arxiv.org/abs/2510.10460 | Academic Papers | svg |
226c377b437a081915f49111ed63927e1a27b43fcd276bb319e0493d7348ce5e | 2026-02-02T00:00:00-05:00 | DUAL-Bench: Measuring Over-Refusal and Robustness in Vision-Language Models | arXiv:2510.10846v2 Announce Type: replace Abstract: As vision-language models become increasingly capable, maintaining a balance between safety and usefulness remains a central challenge. Safety mechanisms, while essential, can backfire, causing over-refusal, where models decline benign requests out of excessive cautio... | https://arxiv.org/abs/2510.10846 | Academic Papers | svg |
36ad7879feff9091b63bb9886493d40637e98bef3219c19d17ef27cc54e825d2 | 2026-02-02T00:00:00-05:00 | PaperArena: An Evaluation Benchmark for Tool-Augmented Agentic Reasoning on Scientific Literature | arXiv:2510.10909v4 Announce Type: replace Abstract: Understanding and reasoning on the large-scale scientific literature is a crucial touchstone for large language model (LLM) based agents. However, existing works are mainly restricted to tool-free tasks within single papers, largely due to the lack of a benchmark that... | https://arxiv.org/abs/2510.10909 | Academic Papers | svg |
00d1dccdb206f68862c642322983da1bf8bcfcb6d41637fb3b7c00ac20fe8665 | 2026-02-02T00:00:00-05:00 | Stronger-MAS: Multi-Agent Reinforcement Learning for Collaborative LLMs | arXiv:2510.11062v5 Announce Type: replace Abstract: Multi-agent systems (MAS) and reinforcement learning (RL) are widely used to enhance the agentic capabilities of large language models (LLMs). MAS improves task performance through role-based orchestration, while RL uses environmental rewards to learn stronger policie... | https://arxiv.org/abs/2510.11062 | Academic Papers | svg |
7ea555cf4db2128467b58ef7a775b976727950ead594461c549752dee08cb2f1 | 2026-02-02T00:00:00-05:00 | Thompson Sampling via Fine-Tuning of LLMs | arXiv:2510.13328v3 Announce Type: replace Abstract: Bayesian optimization in large unstructured discrete spaces is often hindered by the computational cost of maximizing acquisition functions due to the absence of gradients. We propose a scalable alternative based on Thompson sampling that eliminates the need for acqui... | https://arxiv.org/abs/2510.13328 | Academic Papers | svg |
2c7191d959c4d409b86db940de4af084939c03278b457acaad127cf0b811ab7a | 2026-02-02T00:00:00-05:00 | On Your Own: Pro-level Autonomous Drone Racing in Uninstrumented Arenas | arXiv:2510.13644v2 Announce Type: replace Abstract: Drone technology is proliferating in many industries, including agriculture, logistics, defense, infrastructure, and environmental monitoring. Vision-based autonomy is one of its key enablers, particularly for real-world applications. This is essential for operating i... | https://arxiv.org/abs/2510.13644 | Academic Papers | svg |
896517a19d7b1f85ad301b1fc48a51bbe3b3ad3422b2dfbe4417802f84c5cf5d | 2026-02-02T00:00:00-05:00 | Identity-GRPO: Optimizing Multi-Human Identity-preserving Video Generation via Reinforcement Learning | arXiv:2510.14256v3 Announce Type: replace Abstract: While advanced methods like VACE and Phantom have advanced video generation for specific subjects in diverse scenarios, they struggle with multi-human identity preservation in dynamic interactions, where consistent identities across multiple characters are critical. T... | https://arxiv.org/abs/2510.14256 | Academic Papers | svg |
e461410787c2eb0e8cd9fef3894f66733902617033c335baabf193e9c894cc79 | 2026-02-02T00:00:00-05:00 | DialectGen: Benchmarking and Improving Dialect Robustness in Multimodal Generation | arXiv:2510.14949v2 Announce Type: replace Abstract: Contact languages like English exhibit rich regional variations in the form of dialects, which are often used by dialect speakers interacting with generative models. However, can multimodal generative models effectively produce content given dialectal textual input? I... | https://arxiv.org/abs/2510.14949 | Academic Papers | svg |
9485441713b317164e5fb7ef6e5570250eb35ef30f2eb58419dc048b6e69deeb | 2026-02-02T00:00:00-05:00 | LLM Latent Reasoning as Chain of Superposition | arXiv:2510.15522v2 Announce Type: replace Abstract: Latent reasoning offers a computation-efficient alternative to Chain-of-Thought but often suffers from performance degradation due to distributional misalignment and ambiguous chain definitions. Ideally, latent reasoning should function as a superposition of multiple ... | https://arxiv.org/abs/2510.15522 | Academic Papers | svg |
b9aa18247092b71d3fd429b8ecc377d884118f0e85fad2c40d53e9dba38b472b | 2026-02-02T00:00:00-05:00 | Open Shouldn't Mean Exempt: Open-Source Exceptionalism and Generative AI | arXiv:2510.16048v2 Announce Type: replace Abstract: Open-source status should not shield generative artificial intelligence systems from ethical or legal accountability. Through a rigorous analysis of regulatory, legal, and policy frameworks, this Article contends that open-source GenAI must be held to the same standar... | https://arxiv.org/abs/2510.16048 | Academic Papers | svg |
eb9ea120efc89e123eb7545a9b09ef1e5bbf28b3659534484d7922a4a6c0c6be | 2026-02-02T00:00:00-05:00 | In the Mood to Exclude: Revitalizing Trespass to Chattels in the Era of GenAI Scraping | arXiv:2510.16049v2 Announce Type: replace Abstract: GenAI companies are strip-mining the web. Their scraping bots harvest content at an unprecedented scale, circumventing technical barriers to fuel billion-dollar models while creators receive nothing. Courts have enabled this exploitation by misunderstanding what prope... | https://arxiv.org/abs/2510.16049 | Academic Papers | svg |
f6beb65baa2a2524a440060eb22f03d85355704df5e9163562e94246f7bf20d5 | 2026-02-02T00:00:00-05:00 | DDSC: Dynamic Dual-Signal Curriculum for Data-Efficient Acoustic Scene Classification under Domain Shift | arXiv:2510.17345v2 Announce Type: replace Abstract: Acoustic scene classification (ASC) suffers from device-induced domain shift, especially when labels are limited. Prior work focuses on curriculum-based training schedules that structure data presentation by ordering or reweighting training examples from easy-to-hard ... | https://arxiv.org/abs/2510.17345 | Academic Papers | svg |
9f4eacc1912bf0f70544710358956b73d4bf92c1aaf87b21249a9a6ebaf6ec18 | 2026-02-02T00:00:00-05:00 | TopSeg: A Multi-Scale Topological Framework for Data-Efficient Heart Sound Segmentation | arXiv:2510.17346v2 Announce Type: replace Abstract: Deep learning approaches for heart-sound (PCG) segmentation built on time-frequency features can be accurate but often rely on large expert-labeled datasets, limiting robustness and deployment. We present TopSeg, a topological representation-centric framework that enc... | https://arxiv.org/abs/2510.17346 | Academic Papers | svg |
0ed64f4434424b52346e1664976f0bbdc2399b32bfbc3793f0ec3348baa8fe45 | 2026-02-02T00:00:00-05:00 | Evaluating LLMs for Career Guidance: Comparative Analysis of Computing Competency Recommendations Across Ten African Countries | arXiv:2510.18902v2 Announce Type: replace Abstract: Employers increasingly expect graduates to utilize large language models (LLMs) in the workplace, yet the competencies needed for computing roles across Africa remain unclear given varying national contexts. This study examined how six LLMs, namely ChatGPT 4, DeepSeek... | https://arxiv.org/abs/2510.18902 | Academic Papers | svg |
47695968470dab1039c69394393ff31d1f0663847017767fdf034cf26a750cb4 | 2026-02-02T00:00:00-05:00 | Context-aware Fairness Evaluation and Mitigation in LLMs | arXiv:2510.18914v2 Announce Type: replace Abstract: Large language models often display undesirable behaviors embedded in their internal representations, undermining fairness, inconsistency drift, amplification of harmful content, and the propagation of unwanted patterns during extended dialogue and conversations. Alth... | https://arxiv.org/abs/2510.18914 | Academic Papers | svg |
d684f4e7c55c15b35b4e587ae592acba29997b858212395504538bd2a13176ed | 2026-02-02T00:00:00-05:00 | Serverless GPU Architecture for Enterprise HR Analytics: A Production-Scale BDaaS Implementation | arXiv:2510.19689v2 Announce Type: replace Abstract: Industrial and government organizations increasingly depend on data-driven analytics for workforce, finance, and regulated decision processes, where timeliness, cost efficiency, and compliance are critical. Distributed frameworks such as Spark and Flink remain effecti... | https://arxiv.org/abs/2510.19689 | Academic Papers | svg |
21871a934f825fc6aa473ad9644803a637548ade4b5c85c8c2ad218c21a84e20 | 2026-02-02T00:00:00-05:00 | MARS-M: When Variance Reduction Meets Matrices | arXiv:2510.21800v3 Announce Type: replace Abstract: Matrix-based preconditioned optimizers, such as Muon, have recently been shown to be more efficient than scalar-based optimizers for training large-scale neural networks, including large language models (LLMs). Recent benchmark studies of LLM pretraining optimizers ha... | https://arxiv.org/abs/2510.21800 | Academic Papers | svg |
ce90fabc97754ed9e8bb4602a702ff7ea5cae086654eb14db76103e51a81641a | 2026-02-02T00:00:00-05:00 | TOM-SWE: User Mental Modeling For Software Engineering Agents | arXiv:2510.21903v2 Announce Type: replace Abstract: Recent advances in coding agents have made them capable of planning, editing, running, and testing complex code bases. Despite their growing ability in coding tasks, these systems still struggle to infer and track user intent, especially when instructions are underspe... | https://arxiv.org/abs/2510.21903 | Academic Papers | svg |
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