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29dfeb6b72f4fe44293139cf91c3433de93390ab626ae0568f6eec414e8afec4 | 2026-02-02T00:00:00-05:00 | RoboStriker: Hierarchical Decision-Making for Autonomous Humanoid Boxing | arXiv:2601.22517v1 Announce Type: new Abstract: Achieving human-level competitive intelligence and physical agility in humanoid robots remains a major challenge, particularly in contact-rich and highly dynamic tasks such as boxing. While Multi-Agent Reinforcement Learning (MARL) offers a principled framework for strate... | https://arxiv.org/abs/2601.22517 | Academic Papers | svg |
e9a05bae7c0fc57942c50a32c269c106110c3586efd67c901a63649b00e3c360 | 2026-02-02T00:00:00-05:00 | Design Perspective on Materials Experience: A CiteSpace-Based Bibliometric and Visual Analysis of Interdisciplinary Research | arXiv:2601.22518v1 Announce Type: new Abstract: Based on a bibliometric analysis of literature from 2005 to 2024, this study reveals that material experience is undergoing a profound transformation characterized by evolving material definitions, methodological advances, and increasing interdisciplinary integration. Mat... | https://arxiv.org/abs/2601.22518 | Academic Papers | svg |
01a9503c7c2ce7944bfe43d815361276f87b311b44f30d9dd82a52f6a342b585 | 2026-02-02T00:00:00-05:00 | One Ring to Rule Them All: Unifying Group-Based RL via Dynamic Power-Mean Geometry | arXiv:2601.22521v1 Announce Type: new Abstract: Group-based reinforcement learning has evolved from the arithmetic mean of GRPO to the geometric mean of GMPO. While GMPO improves stability by constraining a conservative objective, it shares a fundamental limitation with GRPO: reliance on a fixed aggregation geometry th... | https://arxiv.org/abs/2601.22521 | Academic Papers | svg |
fa1718ef73e221d9f2afe4a3de5817863989fd4b8dd4ef15e96e2ce68c184545 | 2026-02-02T00:00:00-05:00 | Can 3D point cloud data improve automated body condition score prediction in dairy cattle? | arXiv:2601.22522v1 Announce Type: new Abstract: Body condition score (BCS) is a widely used indicator of body energy status and is closely associated with metabolic status, reproductive performance, and health in dairy cattle; however, conventional visual scoring is subjective and labor-intensive. Computer vision appro... | https://arxiv.org/abs/2601.22522 | Academic Papers | svg |
584924c5435a2bcee3846fe92193fa3237e259e4be035f97722b3c77b8358c12 | 2026-02-02T00:00:00-05:00 | Variational Bayesian Flow Network for Graph Generation | arXiv:2601.22524v1 Announce Type: new Abstract: Graph generation aims to sample discrete node and edge attributes while satisfying coupled structural constraints. Diffusion models for graphs often adopt largely factorized forward-noising, and many flow-matching methods start from factorized reference noise and coordina... | https://arxiv.org/abs/2601.22524 | Academic Papers | svg |
23e8b3992887d3e5232f8af1ac0103aa65b1398501964b44d756eea7b8ddaa14 | 2026-02-02T00:00:00-05:00 | Flexible FTN-OTFS for High-Mobility LEO Satellite-to-Ground Communication | arXiv:2601.22526v1 Announce Type: new Abstract: In this paper, a lightweight LEO satellite-assisted flexible faster-than-Nyquist (FTN)-orthogonal time frequency space (OTFS) (LEO-FFTN-OTFS) scheme is proposed to address the stringent constraints on onboard power consumption and the severe impact of fast time-varying ch... | https://arxiv.org/abs/2601.22526 | Academic Papers | svg |
63432c576e7b99dfc159c3609df3e8448786d6ec83a71c13382b0667dd4aafc0 | 2026-02-02T00:00:00-05:00 | $\rho$-$\texttt{EOS}$: Training-free Bidirectional Variable-Length Control for Masked Diffusion LLMs | arXiv:2601.22527v1 Announce Type: new Abstract: Beyond parallel generation and global context modeling, current masked diffusion large language models (dLLMs) suffer from a fundamental limitation: they require a predefined, fixed generation length, which lacks flexibility and forces an inevitable trade-off between outp... | https://arxiv.org/abs/2601.22527 | Academic Papers | svg |
3f5e9f3f793f959501f6d4aba71812a592c1548adbe45fb29cd2c43256a162fc | 2026-02-02T00:00:00-05:00 | Darwinian Memory: A Training-Free Self-Regulating Memory System for GUI Agent Evolution | arXiv:2601.22528v1 Announce Type: new Abstract: Multimodal Large Language Model (MLLM) agents facilitate Graphical User Interface (GUI) automation but struggle with long-horizon, cross-application tasks due to limited context windows. While memory systems provide a viable solution, existing paradigms struggle to adapt ... | https://arxiv.org/abs/2601.22528 | Academic Papers | svg |
5df7dd010b0d4ff13e5782e49b87830bf7f6d72b31af33f69af7bbb2a8dd19a4 | 2026-02-02T00:00:00-05:00 | SHED Light on Segmentation for Dense Prediction | arXiv:2601.22529v1 Announce Type: new Abstract: Dense prediction infers per-pixel values from a single image and is fundamental to 3D perception and robotics. Although real-world scenes exhibit strong structure, existing methods treat it as an independent pixel-wise prediction, often resulting in structural inconsisten... | https://arxiv.org/abs/2601.22529 | Academic Papers | svg |
6aba2504029313f380cc53fcf9c4182400b539a453bc445e5f88eb29bb3a4c6f | 2026-02-02T00:00:00-05:00 | Enhancing TableQA through Verifiable Reasoning Trace Reward | arXiv:2601.22530v1 Announce Type: new Abstract: A major challenge in training TableQA agents, compared to standard text- and image-based agents, is that answers cannot be inferred from a static input but must be reasoned through stepwise transformations of the table state, introducing multi-step reasoning complexity an... | https://arxiv.org/abs/2601.22530 | Academic Papers | svg |
0ca15af99d892db10cdde24e523048d6c906c2aa63476a42b37aec14e93d21d2 | 2026-02-02T00:00:00-05:00 | Learn from A Rationalist: Distilling Intermediate Interpretable Rationales | arXiv:2601.22531v1 Announce Type: new Abstract: Because of the pervasive use of deep neural networks (DNNs), especially in high-stakes domains, the interpretability of DNNs has received increased attention. The general idea of rationale extraction (RE) is to provide an interpretable-by-design framework for DNNs via a s... | https://arxiv.org/abs/2601.22531 | Academic Papers | svg |
18d4fe7b9649506807582d1f7268f751d738cf556d680df32516e595c8206202 | 2026-02-02T00:00:00-05:00 | Demystifying Design Choices of Reinforcement Fine-tuning: A Batched Contextual Bandit Learning Perspective | arXiv:2601.22532v1 Announce Type: new Abstract: The reinforcement fine-tuning area is undergoing an explosion papers largely on optimizing design choices. Though performance gains are often claimed, inconsistent conclusions also arise from time to time, making the progress illusive. Reflecting on this illusion, we stil... | https://arxiv.org/abs/2601.22532 | Academic Papers | svg |
073e893c134a1c02516f8d54160cb50dc36839299440108827aca025fe48443c | 2026-02-02T00:00:00-05:00 | LEAP -- Live Experiments for Active Pedagogy | arXiv:2601.22534v1 Announce Type: new Abstract: Interactive computational environments can help students explore algorithmic concepts through collaborative hands-on experimentation. However, static and instructor controlled demos in lectures limit engagement. Even when interactive visualizations are used, interactions ... | https://arxiv.org/abs/2601.22534 | Academic Papers | svg |
a445f9807f085c2ad5e5cd36f63a62d825f2db53ea1c2da5c6eb864f62113155 | 2026-02-02T00:00:00-05:00 | High Rate Efficient Local List Decoding from HDX | arXiv:2601.22535v1 Announce Type: new Abstract: We construct the first (locally computable, approximately) locally list decodable codes with rate, efficiency, and error tolerance approaching the information theoretic limit, a core regime of interest for the complexity theoretic task of hardness amplification. Our algor... | https://arxiv.org/abs/2601.22535 | Academic Papers | svg |
6cc6617e97edc4858525bfeaca226d6276b6f3dd0b7e5e633bba840d184c80f0 | 2026-02-02T00:00:00-05:00 | Decoding in Geometry: Alleviating Embedding-Space Crowding for Complex Reasoning | arXiv:2601.22536v1 Announce Type: new Abstract: Sampling-based decoding underlies complex reasoning in large language models (LLMs), where decoding strategies critically shape model behavior. Temperature- and truncation-based methods reshape the next-token distribution through global probability reweighting or threshol... | https://arxiv.org/abs/2601.22536 | Academic Papers | svg |
4a9b6add54722857e70b684974478f70643b47630ba62eec2bd0544591acc2fb | 2026-02-02T00:00:00-05:00 | Learning to Defer in Non-Stationary Time Series via Switching State-Space Models | arXiv:2601.22538v1 Announce Type: new Abstract: We study Learning to Defer for non-stationary time series with partial feedback and time-varying expert availability. At each time step, the router selects an available expert, observes the target, and sees only the queried expert's prediction. We model signed expert resi... | https://arxiv.org/abs/2601.22538 | Academic Papers | svg |
270faf0399291da7cd6459f8fac2debc32b8e6c477cd8cadd9f118cdf0206602 | 2026-02-02T00:00:00-05:00 | Neural-Inspired Posterior Approximation (NIPA) | arXiv:2601.22539v1 Announce Type: new Abstract: Humans learn efficiently from their environment by engaging multiple interacting neural systems that support distinct yet complementary forms of control, including model-based (goal-directed) planning, model-free (habitual) responding, and episodic memory-based learning. ... | https://arxiv.org/abs/2601.22539 | Academic Papers | svg |
2c70d072a6fd7d6f2d50349227a54d74179c0293d760a3b52f61e7656d12fb05 | 2026-02-02T00:00:00-05:00 | Benchmarking Long Roll-outs of Auto-regressive Neural Operators for the Compressible Navier-Stokes Equations with Conserved Quantity Correction | arXiv:2601.22541v1 Announce Type: new Abstract: Deep learning has been proposed as an efficient alternative for the numerical approximation of PDE solutions, offering fast, iterative simulation of PDEs through the approximation of solution operators. However, deep learning solutions have struggle to perform well over l... | https://arxiv.org/abs/2601.22541 | Academic Papers | svg |
11a4f060201c4c598f953409fd87890de0708b68c8b77d962e041ce781d6c823 | 2026-02-02T00:00:00-05:00 | Detect and Act: Automated Dynamic Optimizer through Meta-Black-Box Optimization | arXiv:2601.22542v1 Announce Type: new Abstract: Dynamic Optimization Problems (DOPs) are challenging to address due to their complex nature, i.e., dynamic environment variation. Evolutionary Computation methods are generally advantaged in solving DOPs since they resemble dynamic biological evolution. However, existing ... | https://arxiv.org/abs/2601.22542 | Academic Papers | svg |
778801e16ead80e527ef56f16a58b08f415656225a2b8050d3db4b83c164011f | 2026-02-02T00:00:00-05:00 | SCaLRec: Semantic Calibration for LLM-enabled Cloud-Device Sequential Recommendation | arXiv:2601.22543v1 Announce Type: new Abstract: Cloud-device collaborative recommendation partitions computation across the cloud and user devices: the cloud provides semantic user modeling, while the device leverages recent interactions and cloud semantic signals for privacy-preserving, responsive reranking. With larg... | https://arxiv.org/abs/2601.22543 | Academic Papers | svg |
9566e0788af3a126b146e52f229b04d5ee2312919f29d5fec7c71fd0f270ca2b | 2026-02-02T00:00:00-05:00 | Adapting Reinforcement Learning for Path Planning in Constrained Parking Scenarios | arXiv:2601.22545v1 Announce Type: new Abstract: Real-time path planning in constrained environments remains a fundamental challenge for autonomous systems. Traditional classical planners, while effective under perfect perception assumptions, are often sensitive to real-world perception constraints and rely on online se... | https://arxiv.org/abs/2601.22545 | Academic Papers | svg |
4aed398ee270aa6acc5b873395cc158c382788c66ad5ad2d1be818977b44deb2 | 2026-02-02T00:00:00-05:00 | Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation | arXiv:2601.22546v1 Announce Type: new Abstract: The recent advancements in Large Language Models (LLMs) have attracted interest in exploring their in-context learning abilities and chain-of-thought capabilities. However, there are few studies investigating the specific traits related to the powerful generation capacity... | https://arxiv.org/abs/2601.22546 | Academic Papers | svg |
cf105407e39697ec888c5ef52501ae5a41b0f568100d7c6a524c1a7bc9cc49c8 | 2026-02-02T00:00:00-05:00 | PersonaAct: Simulating Short-Video Users with Personalized Agents for Counterfactual Filter Bubble Auditing | arXiv:2601.22547v1 Announce Type: new Abstract: Short-video platforms rely on personalized recommendation, raising concerns about filter bubbles that narrow content exposure. Auditing such phenomena at scale is challenging because real user studies are costly and privacy-sensitive, and existing simulators fail to repro... | https://arxiv.org/abs/2601.22547 | Academic Papers | svg |
0e3a65376aa7591afb0be5c48c8024c229ea56a84b12ce14e25c6ac2efcd5952 | 2026-02-02T00:00:00-05:00 | Are LLM Evaluators Really Narcissists? Sanity Checking Self-Preference Evaluations | arXiv:2601.22548v1 Announce Type: new Abstract: Recent research has shown that large language models (LLM) favor own outputs when acting as judges, undermining the integrity of automated post-training and evaluation workflows. However, it is difficult to disentangle which evaluation biases are explained by narcissism v... | https://arxiv.org/abs/2601.22548 | Academic Papers | svg |
f1bc6c6c967ea8db2ecac3a2b41eb269889c0a9c9033f7ca75d4f9466c95948e | 2026-02-02T00:00:00-05:00 | Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned Simulation | arXiv:2601.22550v1 Announce Type: new Abstract: Exoskeletons show great promise for enhancing mobility, but providing appropriate assistance remains challenging due to the complexity of human adaptation to external forces. Current state-of-the-art approaches for optimizing exoskeleton controllers require extensive huma... | https://arxiv.org/abs/2601.22550 | Academic Papers | svg |
e5c4330b0ff117404b7b09ce315a68b435e2a544c8f639ccfc9e4ec8baab3903 | 2026-02-02T00:00:00-05:00 | Hybrid Cross-Device Localization via Neural Metric Learning and Feature Fusion | arXiv:2601.22551v1 Announce Type: new Abstract: We present a hybrid cross-device localization pipeline developed for the CroCoDL 2025 Challenge. Our approach integrates a shared retrieval encoder and two complementary localization branches: a classical geometric branch using feature fusion and PnP, and a neural feed-fo... | https://arxiv.org/abs/2601.22551 | Academic Papers | svg |
f1ce5877cc93989f274f1ade868585fa98a4ea497eccdda5289d88934e1a7dca | 2026-02-02T00:00:00-05:00 | LeanArchitect: Automating Blueprint Generation for Humans and AI | arXiv:2601.22554v1 Announce Type: new Abstract: Large-scale formalization projects in Lean rely on blueprints: structured dependency graphs linking informal mathematical exposition to formal declarations. While blueprints are central to human collaboration, existing tooling treats the informal ($\LaTeX$) and formal (Le... | https://arxiv.org/abs/2601.22554 | Academic Papers | svg |
5f7b2ce11f5c58ae2f8e866e8825fe3f3db6bb6c7d5ee1bb771350d456a7b3a4 | 2026-02-02T00:00:00-05:00 | VocBulwark: Towards Practical Generative Speech Watermarking via Additional-Parameter Injection | arXiv:2601.22556v1 Announce Type: new Abstract: Generated speech achieves human-level naturalness but escalates security risks of misuse. However, existing watermarking methods fail to reconcile fidelity with robustness, as they rely either on simple superposition in the noise space or on intrusive alterations to model... | https://arxiv.org/abs/2601.22556 | Academic Papers | svg |
89d73e4572feed2c78b4c76bf4a828bb65f44c84e22ac7a76b44357be255891a | 2026-02-02T00:00:00-05:00 | Recursive Mutexes in Separation Logic | arXiv:2601.22557v1 Announce Type: new Abstract: Mutexes (i.e., locks) are well understood in separation logic, and can be specified in terms of either protecting an invariant or atomically changing the state of the lock. In this abstract, we develop the same styles of specifications for \emph{recursive} mutexes, a comm... | https://arxiv.org/abs/2601.22557 | Academic Papers | svg |
66fba21a6f09f01dda12911291371aa19535399a237b4bbfa1c2f979518cf483 | 2026-02-02T00:00:00-05:00 | Inverse acoustic scattering for random obstacles with multi-frequency data | arXiv:2601.22560v1 Announce Type: new Abstract: We study an inverse random obstacle scattering problems in $\mathbb{R}^2$ where the scatterer is formulated by a Gaussian process defined on the angular parameter domain. Equipped with a modified covariance function which is mathematically well-defined and physically cons... | https://arxiv.org/abs/2601.22560 | Academic Papers | svg |
4b0a4f1326033fda7b7f36f3ad99d29778fa67134f7dbfd1e7b8cd5ed78a51ad | 2026-02-02T00:00:00-05:00 | Approximately Optimal Multi-Stream Quickest Change Detection for Gaussian Streams | arXiv:2601.22561v1 Announce Type: new Abstract: This paper considers the bandit quickest change detection problem in which one stream contains a change-point that shifts its distribution by an unknown amount in an unknown direction. We consider an agent that can observe only a single stream at each time, and the goal o... | https://arxiv.org/abs/2601.22561 | Academic Papers | svg |
ec04ffa2a2bd41c63b9e67a79d06f72e877d9a502006b7adeff41c0c35f2dbc5 | 2026-02-02T00:00:00-05:00 | EUGens: Efficient, Unified, and General Dense Layers | arXiv:2601.22563v1 Announce Type: new Abstract: Efficient neural networks are essential for scaling machine learning models to real-time applications and resource-constrained environments. Fully-connected feedforward layers (FFLs) introduce computation and parameter count bottlenecks within neural network architectures... | https://arxiv.org/abs/2601.22563 | Academic Papers | svg |
a7e67d9eeec97cdcb1967dbbab6e73a99ba5e145e9989e9e0800f3cec051782e | 2026-02-02T00:00:00-05:00 | Quantum $(r,\delta)$-Locally Recoverable BCH and Homothetic-BCH Codes | arXiv:2601.22567v1 Announce Type: new Abstract: Quantum $(r,\delta)$-locally recoverable codes ($(r,\delta)$-LRCs) are the quantum version of classical $(r,\delta)$-LRCs designed to recover multiple failures in large-scale distributed and cloud storage systems. A quantum $(r,\delta)$-LRC, $Q(C)$, can be constructed fro... | https://arxiv.org/abs/2601.22567 | Academic Papers | svg |
d50b597d13cd8634429a6fffc43cdbe0ab7664c7c1219058b5529a5400a10451 | 2026-02-02T00:00:00-05:00 | Whispers of Wealth: Red-Teaming Google's Agent Payments Protocol via Prompt Injection | arXiv:2601.22569v1 Announce Type: new Abstract: Large language model (LLM) based agents are increasingly used to automate financial transactions, yet their reliance on contextual reasoning exposes payment systems to prompt-driven manipulation. The Agent Payments Protocol (AP2) aims to secure agent-led purchases through... | https://arxiv.org/abs/2601.22569 | Academic Papers | svg |
fe21cf13f8d619a3fb0b6d8c7bf2af198a9fc5616df37fdbd0fd6616d92e3182 | 2026-02-02T00:00:00-05:00 | Leveraging Data to Say No: Memory Augmented Plug-and-Play Selective Prediction | arXiv:2601.22570v1 Announce Type: new Abstract: Selective prediction aims to endow predictors with a reject option, to avoid low confidence predictions. However, existing literature has primarily focused on closed-set tasks, such as visual question answering with predefined options or fixed-category classification. Thi... | https://arxiv.org/abs/2601.22570 | Academic Papers | svg |
3a0d93404894da300ece621cf78a41a493b794288769ca324d60570d7571756a | 2026-02-02T00:00:00-05:00 | PerfGuard: A Performance-Aware Agent for Visual Content Generation | arXiv:2601.22571v1 Announce Type: new Abstract: The advancement of Large Language Model (LLM)-powered agents has enabled automated task processing through reasoning and tool invocation capabilities. However, existing frameworks often operate under the idealized assumption that tool executions are invariably successful,... | https://arxiv.org/abs/2601.22571 | Academic Papers | svg |
e2c782c4c5add64f3c82e6b08efa2c5ab6bc37249add4fc7b3b4f5f71fd7a648 | 2026-02-02T00:00:00-05:00 | DELNet: Continuous All-in-One Weather Removal via Dynamic Expert Library | arXiv:2601.22573v1 Announce Type: new Abstract: All-in-one weather image restoration methods are valuable in practice but depend on pre-collected data and require retraining for unseen degradations, leading to high cost. We propose DELNet, a continual learning framework for weather image restoration. DELNet integrates ... | https://arxiv.org/abs/2601.22573 | Academic Papers | svg |
6406b6ca46cec78f3355f13ce8a27185938fc06a41a0de10fc2daebcc54582ff | 2026-02-02T00:00:00-05:00 | Mitigating Hallucinations in Video Large Language Models via Spatiotemporal-Semantic Contrastive Decoding | arXiv:2601.22574v1 Announce Type: new Abstract: Although Video Large Language Models perform remarkably well across tasks such as video understanding, question answering, and reasoning, they still suffer from the problem of hallucination, which refers to generating outputs that are inconsistent with explicit video cont... | https://arxiv.org/abs/2601.22574 | Academic Papers | svg |
e0bbf5279755c8c74dd8b68475d6c1bc8157c7b529399bda1f99bfdb72a0ed03 | 2026-02-02T00:00:00-05:00 | PhoStream: Benchmarking Real-World Streaming for Omnimodal Assistants in Mobile Scenarios | arXiv:2601.22575v1 Announce Type: new Abstract: Multimodal Large Language Models excel at offline audio-visual understanding, but their ability to serve as mobile assistants in continuous real-world streams remains underexplored. In daily phone use, mobile assistants must track streaming audio-visual inputs and respond... | https://arxiv.org/abs/2601.22575 | Academic Papers | svg |
714f603026ad42726fdd4d1cad02920d8714c62aa53660235d8f77be21588f32 | 2026-02-02T00:00:00-05:00 | FedDis: A Causal Disentanglement Framework for Federated Traffic Prediction | arXiv:2601.22578v1 Announce Type: new Abstract: Federated learning offers a promising paradigm for privacy-preserving traffic prediction, yet its performance is often challenged by the non-identically and independently distributed (non-IID) nature of decentralized traffic data. Existing federated methods frequently str... | https://arxiv.org/abs/2601.22578 | Academic Papers | svg |
4f92ce4314539c70a6e3a1daafa07a9f43eb698e3b0886b9a870f1784d11f8b8 | 2026-02-02T00:00:00-05:00 | Non-Intrusive Graph-Based Bot Detection for E-Commerce Using Inductive Graph Neural Networks | arXiv:2601.22579v1 Announce Type: new Abstract: Malicious bots pose a growing threat to e-commerce platforms by scraping data, hoarding inventory, and perpetrating fraud. Traditional bot mitigation techniques, including IP blacklists and CAPTCHA-based challenges, are increasingly ineffective or intrusive, as modern bot... | https://arxiv.org/abs/2601.22579 | Academic Papers | svg |
ac5244c58e80ebcf4209745c4a535d9630f9dad3cd8883b0ea4072d18b2bcbb5 | 2026-02-02T00:00:00-05:00 | SpanNorm: Reconciling Training Stability and Performance in Deep Transformers | arXiv:2601.22580v1 Announce Type: new Abstract: The success of Large Language Models (LLMs) hinges on the stable training of deep Transformer architectures. A critical design choice is the placement of normalization layers, leading to a fundamental trade-off: the ``PreNorm'' architecture ensures training stability at t... | https://arxiv.org/abs/2601.22580 | Academic Papers | svg |
cc4c1fddab6955afccbd4bd38e2ed120a058adadf8766d45cd2810279028ed97 | 2026-02-02T00:00:00-05:00 | Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model | arXiv:2601.22581v1 Announce Type: new Abstract: Although cross-domain few-shot learning (CDFSL) for hyper-spectral image (HSI) classification has attracted significant research interest, existing works often rely on an unrealistic data augmentation procedure in the form of external noise to enlarge the sample size, thu... | https://arxiv.org/abs/2601.22581 | Academic Papers | svg |
a6a0698432551e62d7bc75db7f310a0b01713b065066249cfbd5cacce8b747bc | 2026-02-02T00:00:00-05:00 | MC-GRPO: Median-Centered Group Relative Policy Optimization for Small-Rollout Reinforcement Learning | arXiv:2601.22582v1 Announce Type: new Abstract: Group-relative policy optimization methods train language models by generating multiple rollouts per prompt and normalizing rewards with a shared mean reward baseline. In resource-constrained settings where the rollout budget is small, accuracy often degrades. We find tha... | https://arxiv.org/abs/2601.22582 | Academic Papers | svg |
7dec15012bcdcc86388f5cf2826de9e21b0834ff77f2119e7ee8b49b9906b927 | 2026-02-02T00:00:00-05:00 | Scalable Fair Influence Blocking Maximization via Approximately Monotonic Submodular Optimization | arXiv:2601.22584v1 Announce Type: new Abstract: Influence Blocking Maximization (IBM) aims to select a positive seed set to suppress the spread of negative influence. However, existing IBM methods focus solely on maximizing blocking effectiveness, overlooking fairness across communities. To address this issue, we forma... | https://arxiv.org/abs/2601.22584 | Academic Papers | svg |
e9735ec3b0e5a1699da6b5327c952f04819aa513a718a1d6553d7f7426a6fda0 | 2026-02-02T00:00:00-05:00 | HetCCL: Accelerating LLM Training with Heterogeneous GPUs | arXiv:2601.22585v1 Announce Type: new Abstract: The rapid growth of large language models is driving organizations to expand their GPU clusters, often with GPUs from multiple vendors. However, current deep learning frameworks lack support for collective communication across heterogeneous GPUs, leading to inefficiency a... | https://arxiv.org/abs/2601.22585 | Academic Papers | svg |
3d4ce788667b0530063287210a5395fc8d6fc53934863901a9dc47392869cf2d | 2026-02-02T00:00:00-05:00 | WED-Net: A Weather-Effect Disentanglement Network with Causal Augmentation for Urban Flow Prediction | arXiv:2601.22586v1 Announce Type: new Abstract: Urban spatio-temporal prediction under extreme conditions (e.g., heavy rain) is challenging due to event rarity and dynamics. Existing data-driven approaches that incorporate weather as auxiliary input often rely on coarse-grained descriptors and lack dedicated mechanisms... | https://arxiv.org/abs/2601.22586 | Academic Papers | svg |
203469beb213edaf582c108a877ab5e9589cef412d5ff3ed16402d4f4be3c001 | 2026-02-02T00:00:00-05:00 | An ultra-weak three-field finite element formulation for the biharmonic and extended Fisher--Kolmogorov equations | arXiv:2601.22587v1 Announce Type: new Abstract: This paper discusses a so-called ultra-weak three-field formulation of the biharmonic problem where the solution, its gradient, and an additional Lagrange multiplier are the three unknowns. We establish the well-posedness of the problem using the abstract theory for saddl... | https://arxiv.org/abs/2601.22587 | Academic Papers | svg |
325eec8fe0f2d1cd6afcff10a2675726b4a3ca458a65ba0eac35bb03fb5c2a57 | 2026-02-02T00:00:00-05:00 | Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry | arXiv:2601.22588v1 Announce Type: new Abstract: Large language models (LLMs) are widely used as reference-free evaluators via prompting, but this "LLM-as-a-Judge" paradigm is costly, opaque, and sensitive to prompt design. In this work, we investigate whether smaller models can serve as efficient evaluators by leveragi... | https://arxiv.org/abs/2601.22588 | Academic Papers | svg |
6b739cb0ad2c4d8b54ebe7ac3f8b9098d16b2932e9fad51b5d9b0daff9cbbe37 | 2026-02-02T00:00:00-05:00 | FedCARE: Federated Unlearning with Conflict-Aware Projection and Relearning-Resistant Recovery | arXiv:2601.22589v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training without centralizing raw data, but privacy regulations such as the right to be forgotten require FL systems to remove the influence of previously used training data upon request. Retraining a federated model fro... | https://arxiv.org/abs/2601.22589 | Academic Papers | svg |
ed2c64e89c51b29ea6b02cf3737e68192f28ceade430ce58c7d05ad77d3ea487 | 2026-02-02T00:00:00-05:00 | Small is Beautiful: A Practical and Efficient Log Parsing Framework | arXiv:2601.22590v1 Announce Type: new Abstract: Log parsing is a fundamental step in log analysis, partitioning raw logs into constant templates and dynamic variables. While recent semantic-based parsers leveraging Large Language Models (LLMs) exhibit superior generalizability over traditional syntax-based methods, the... | https://arxiv.org/abs/2601.22590 | Academic Papers | svg |
5390d71686405556ca30b3ae818bc76abace83ece776d7c31b3209853415512c | 2026-02-02T00:00:00-05:00 | Heterogeneous Graph Alignment for Joint Reasoning and Interpretability | arXiv:2601.22593v1 Announce Type: new Abstract: Multi-graph learning is crucial for extracting meaningful signals from collections of heterogeneous graphs. However, effectively integrating information across graphs with differing topologies, scales, and semantics, often in the absence of shared node identities, remains... | https://arxiv.org/abs/2601.22593 | Academic Papers | svg |
e17dcb7020a91d37d03545f2369bd700deed10bde0a87a30bc7f038b544d38bc | 2026-02-02T00:00:00-05:00 | Language Model Circuits Are Sparse in the Neuron Basis | arXiv:2601.22594v1 Announce Type: new Abstract: The high-level concepts that a neural network uses to perform computation need not be aligned to individual neurons (Smolensky, 1986). Language model interpretability research has thus turned to techniques such as \textit{sparse autoencoders} (SAEs) to decompose the neuro... | https://arxiv.org/abs/2601.22594 | Academic Papers | svg |
266b6e201053d3ad7d10fc7dbc61347bd13ce3185a3332a0272f8b746243fec8 | 2026-02-02T00:00:00-05:00 | Learn More with Less: Uncertainty Consistency Guided Query Selection for RLVR | arXiv:2601.22595v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently improved mathematical reasoning through Reinforcement Learning with Verifiable Reward (RLVR). However, existing RLVR algorithms require large query budgets, making annotation costly. We investigate whether fewer but more informat... | https://arxiv.org/abs/2601.22595 | Academic Papers | svg |
05fd4a9ec6a3182b80776e8cfe1394bf9bd6c7d013a265cb6b25f88b7ca9213b | 2026-02-02T00:00:00-05:00 | FOTBCD: A Large-Scale Building Change Detection Benchmark from French Orthophotos and Topographic Data | arXiv:2601.22596v1 Announce Type: new Abstract: We introduce FOTBCD, a large-scale building change detection dataset derived from authoritative French orthophotos and topographic building data provided by IGN France. Unlike existing benchmarks that are geographically constrained to single cities or limited regions, FOT... | https://arxiv.org/abs/2601.22596 | Academic Papers | svg |
c49917c40261247beeadf1a3a3e2690ed7a18bcca34f0db6a2a3e56840d4d0ef | 2026-02-02T00:00:00-05:00 | TimeMachine-bench: A Benchmark for Evaluating Model Capabilities in Repository-Level Migration Tasks | arXiv:2601.22597v1 Announce Type: new Abstract: With the advancement of automated software engineering, research focus is increasingly shifting toward practical tasks reflecting the day-to-day work of software engineers. Among these tasks, software migration, a critical process of adapting code to evolving environments... | https://arxiv.org/abs/2601.22597 | Academic Papers | svg |
88ba5bb49a90372c780c45b9f750784885bd4676b89748453c40f7464b08b6dc | 2026-02-02T00:00:00-05:00 | A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation | arXiv:2601.22599v1 Announce Type: new Abstract: Query-based universal sound separation is fundamental to intelligent auditory systems, aiming to isolate specific sources from mixtures. Despite recent advances, existing methods continue to suffer from residual interference in complex acoustic scenes. This performance li... | https://arxiv.org/abs/2601.22599 | Academic Papers | svg |
619978c08758650b5ee33ce4bcfc7878b712af9a56ec98c7539fbad5c3a474ef | 2026-02-02T00:00:00-05:00 | Lethe:Adapter-Augmented Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning | arXiv:2601.22601v1 Announce Type: new Abstract: Federated unlearning (FU) aims to erase designated client-level, class-level, or sample-level knowledge from a global model. Existing studies commonly assume that the collaboration ends up with the unlearning operation, overlooking the follow-up situation where the federa... | https://arxiv.org/abs/2601.22601 | Academic Papers | svg |
1c97effe479ac0874cc769cfda0d596fda513c1b2159a3fc1ea031e34e99077e | 2026-02-02T00:00:00-05:00 | An inertial minimal-deformation-rate framework for shape optimization | arXiv:2601.22605v1 Announce Type: new Abstract: We propose a robust numerical framework for PDE-constrained shape optimization and Willmore-driven surface hole filling. To address two central challenges -- slow progress in flat energy landscapes, which can trigger premature stagnation at suboptimal configurations, and ... | https://arxiv.org/abs/2601.22605 | Academic Papers | svg |
3908026e7f04c23b166defb402fa73b1b89619dfb029e37fceb72e7734e92aba | 2026-02-02T00:00:00-05:00 | From Self-Evolving Synthetic Data to Verifiable-Reward RL: Post-Training Multi-turn Interactive Tool-Using Agents | arXiv:2601.22607v1 Announce Type: new Abstract: Interactive tool-using agents must solve real-world tasks via multi-turn interaction with both humans and external environments, requiring dialogue state tracking, multi-step tool execution, while following complex instructions. Post-training such agents is challenging be... | https://arxiv.org/abs/2601.22607 | Academic Papers | svg |
728a32b1382216d1c12aaeafb289daaafa7917435736e6a562298998db0e4f20 | 2026-02-02T00:00:00-05:00 | Computing Dominating Sets in Disk Graphs with Centers in Convex Position | arXiv:2601.22609v1 Announce Type: new Abstract: Given a set $P$ of $n$ points in the plane and a collection of disks centered at these points, the disk graph $G(P)$ has vertex set $P$, with an edge between two vertices if their corresponding disks intersect. We study the dominating set problem in $G(P)$ under the speci... | https://arxiv.org/abs/2601.22609 | Academic Papers | svg |
1b0af543ad197e3a5c0a2fd7385db6777571514e5793b80a5e92468bd529673f | 2026-02-02T00:00:00-05:00 | Local-Global Multimodal Contrastive Learning for Molecular Property Prediction | arXiv:2601.22610v1 Announce Type: new Abstract: Accurate molecular property prediction requires integrating complementary information from molecular structure and chemical semantics. In this work, we propose LGM-CL, a local-global multimodal contrastive learning framework that jointly models molecular graphs and textua... | https://arxiv.org/abs/2601.22610 | Academic Papers | svg |
ca9f192ce15f029edbd3f1c82a9f4ff0c66e3bc58086fb2f6afddf2c559b34ff | 2026-02-02T00:00:00-05:00 | Stabilizing Transformer Training Through Consensus | arXiv:2601.22614v1 Announce Type: new Abstract: Standard attention-based transformers are known to exhibit instability under learning rate overspecification during training, particularly at high learning rates. While various methods have been proposed to improve resilience to such overspecification by modifying the opt... | https://arxiv.org/abs/2601.22614 | Academic Papers | svg |
afc166cd569b749be2a219888047806f51ec3b36fe00fdc267ebd097214275cc | 2026-02-02T00:00:00-05:00 | TTSA3R: Training-Free Temporal-Spatial Adaptive Persistent State for Streaming 3D Reconstruction | arXiv:2601.22615v1 Announce Type: new Abstract: Streaming recurrent models enable efficient 3D reconstruction by maintaining persistent state representations. However, they suffer from catastrophic memory forgetting over long sequences due to balancing historical information with new observations. Recent methods allevi... | https://arxiv.org/abs/2601.22615 | Academic Papers | svg |
441c0be6f8b9155a58c5ce4be23907112567732b8000bd58eedd96b58c66f7bf | 2026-02-02T00:00:00-05:00 | UniGeo: A Unified 3D Indoor Object Detection Framework Integrating Geometry-Aware Learning and Dynamic Channel Gating | arXiv:2601.22616v1 Announce Type: new Abstract: The growing adoption of robotics and augmented reality in real-world applications has driven considerable research interest in 3D object detection based on point clouds. While previous methods address unified training across multiple datasets, they fail to model geometric... | https://arxiv.org/abs/2601.22616 | Academic Papers | svg |
b81eb61dae4d9f406f60cc80a38e55ecb6034c2851b9cae68cb58885ab08c0c0 | 2026-02-02T00:00:00-05:00 | EntroCut: Entropy-Guided Adaptive Truncation for Efficient Chain-of-Thought Reasoning in Small-scale Large Reasoning Models | arXiv:2601.22617v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) excel at complex reasoning tasks through extended chain-of-thought generation, but their reliance on lengthy intermediate steps incurs substantial computational cost. We find that the entropy of the model's output distribution in early reason... | https://arxiv.org/abs/2601.22617 | Academic Papers | svg |
420991a900dde42c44360e1aa4442779d995f3af6d3ac99ef8d3d28a3b81568a | 2026-02-02T00:00:00-05:00 | Layer-wise Swapping for Generalizable Multilingual Safety | arXiv:2601.22620v1 Announce Type: new Abstract: Despite the rapid advancements of Large Language Models (LLMs), safety risks remain a critical challenge for low-resource languages. Existing safety datasets are predominantly English centric, limiting progress in multilingual safety alignment. As a result, low resource e... | https://arxiv.org/abs/2601.22620 | Academic Papers | svg |
4ca5ba8233ba792a2214947e04edd4b837322116cc3b7d667f6613103a4eceb4 | 2026-02-02T00:00:00-05:00 | Ethical Risks of Large Language Models in Medical Consultation: An Assessment Based on Reproductive Ethics | arXiv:2601.22621v1 Announce Type: new Abstract: Background: As large language models (LLMs) are increasingly used in healthcare and medical consultation settings, a growing concern is whether these models can respond to medical inquiries in a manner that is ethically compliant--particularly in accordance with local eth... | https://arxiv.org/abs/2601.22621 | Academic Papers | svg |
438a67a290c38cb32413dd577798d0010efe847cbc84382ef93f51511a5f0514 | 2026-02-02T00:00:00-05:00 | SYMPHONY: Synergistic Multi-agent Planning with Heterogeneous Language Model Assembly | arXiv:2601.22623v1 Announce Type: new Abstract: Recent advancements have increasingly focused on leveraging large language models (LLMs) to construct autonomous agents for complex problem-solving tasks. However, existing approaches predominantly employ a single-agent framework to generate search branches and estimate r... | https://arxiv.org/abs/2601.22623 | Academic Papers | svg |
c78a5b12f11a93a375b35bf77708a849db6094f4718cb06d4b32e7d3131ffb2c | 2026-02-02T00:00:00-05:00 | COBRA++: Enhanced COBRA Optimizer with Augmented Surrogate Pool and Reinforced Surrogate Selection | arXiv:2601.22624v1 Announce Type: new Abstract: The optimization problems in realistic world present significant challenges onto optimization algorithms, such as the expensive evaluation issue and complex constraint conditions. COBRA optimizer (including its up-to-date variants) is a representative and effective tool f... | https://arxiv.org/abs/2601.22624 | Academic Papers | svg |
8efef3f5fca78824258db0fd565252c33f6ebd28404e6b77ca0c1d5a4efb3439 | 2026-02-02T00:00:00-05:00 | Elderly HealthMag: Systematic Building and Calibrating a Tool for Identifying and Evaluating Senior User Digital Health Software | arXiv:2601.22627v1 Announce Type: new Abstract: Digital health (DH) software is increasingly deployed to populations where many end users live with one or more health conditions. Yet, DH software development teams frequently operate using implicit, incorrect assumptions about these users, resulting in products that und... | https://arxiv.org/abs/2601.22627 | Academic Papers | svg |
2ee68a54750e761798db056853e11819d24b0edbc1c964cc3786d0ba8cfa186e | 2026-02-02T00:00:00-05:00 | TTCS: Test-Time Curriculum Synthesis for Self-Evolving | arXiv:2601.22628v1 Announce Type: new Abstract: Test-Time Training offers a promising way to improve the reasoning ability of large language models (LLMs) by adapting the model using only the test questions. However, existing methods struggle with difficult reasoning problems for two reasons: raw test questions are oft... | https://arxiv.org/abs/2601.22628 | Academic Papers | svg |
98b6add2f69a50ec7c54487d80b25b65dbbc597f04cb61f82d16e3aedfd949e4 | 2026-02-02T00:00:00-05:00 | Time-Annealed Perturbation Sampling: Diverse Generation for Diffusion Language Models | arXiv:2601.22629v1 Announce Type: new Abstract: Diffusion language models (Diffusion-LMs) introduce an explicit temporal dimension into text generation, yet how this structure can be leveraged to control generation diversity for exploring multiple valid semantic or reasoning paths remains underexplored. In this paper, ... | https://arxiv.org/abs/2601.22629 | Academic Papers | svg |
59aa034762ef660ccc81526e58aeb92c1e4c4d841afceb4730f3a42193975470 | 2026-02-02T00:00:00-05:00 | LINA: Linear Autoregressive Image Generative Models with Continuous Tokens | arXiv:2601.22630v1 Announce Type: new Abstract: Autoregressive models with continuous tokens form a promising paradigm for visual generation, especially for text-to-image (T2I) synthesis, but they suffer from high computational cost. We study how to design compute-efficient linear attention within this framework. Speci... | https://arxiv.org/abs/2601.22630 | Academic Papers | svg |
9250228494596f65205801608afd79ac1cb09776b219e1f51731a2f5e6df67b9 | 2026-02-02T00:00:00-05:00 | PEFT-MuTS: A Multivariate Parameter-Efficient Fine-Tuning Framework for Remaining Useful Life Prediction based on Cross-domain Time Series Representation Model | arXiv:2601.22631v1 Announce Type: new Abstract: The application of data-driven remaining useful life (RUL) prediction has long been constrained by the availability of large amount of degradation data. Mainstream solutions such as domain adaptation and meta-learning still rely on large amounts of historical degradation ... | https://arxiv.org/abs/2601.22631 | Academic Papers | svg |
29c2269e7efc97fdc3248fb2b62361ad393e2557bac027035a9c0948784f79ed | 2026-02-02T00:00:00-05:00 | DART-ing Through the Drift: Dynamic Tracing of Knowledge Neurons for Adaptive Inference-Time Pruning | arXiv:2601.22632v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit substantial parameter redundancy, particularly in Feed-Forward Networks (FFNs). Existing pruning methods suffer from two primary limitations. First, reliance on dataset-specific calibration introduces significant data dependency and co... | https://arxiv.org/abs/2601.22632 | Academic Papers | svg |
fe21255fd20e2c8e4f55e527a410b56bf95a4252f86a4aa6429b34db194734f4 | 2026-02-02T00:00:00-05:00 | MCP-Diag: A Deterministic, Protocol-Driven Architecture for AI-Native Network Diagnostics | arXiv:2601.22633v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into network operations (AIOps) is hindered by two fundamental challenges: the stochastic grounding problem, where LLMs struggle to reliably parse unstructured, vendor-specific CLI output, and the security gap of granting au... | https://arxiv.org/abs/2601.22633 | Academic Papers | svg |
2025538d79beb826f239eae4dfa77e17a584f7c77fc0b16bc433b29f98b38c3f | 2026-02-02T00:00:00-05:00 | What can Computer Vision learn from Ranganathan? | arXiv:2601.22634v1 Announce Type: new Abstract: The Semantic Gap Problem (SGP) in Computer Vision (CV) arises from the misalignment between visual and lexical semantics leading to flawed CV dataset design and CV benchmarks. This paper proposes that classification principles of S.R. Ranganathan can offer a principled st... | https://arxiv.org/abs/2601.22634 | Academic Papers | svg |
840875c75703d150406f3382ad2ea7ab4a2cbbe5a5b8d456936590feb11d32e2 | 2026-02-02T00:00:00-05:00 | Statistical Estimation of Adversarial Risk in Large Language Models under Best-of-N Sampling | arXiv:2601.22636v1 Announce Type: new Abstract: Large Language Models (LLMs) are typically evaluated for safety under single-shot or low-budget adversarial prompting, which underestimates real-world risk. In practice, attackers can exploit large-scale parallel sampling to repeatedly probe a model until a harmful respon... | https://arxiv.org/abs/2601.22636 | Academic Papers | svg |
5e1a4f43d2c278db38c3dbdb1a7af29267dbea44bf13e1247bf2a53b387bfd0a | 2026-02-02T00:00:00-05:00 | ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review | arXiv:2601.22638v1 Announce Type: new Abstract: Automated peer review has evolved from simple text classification to structured feedback generation. However, current state-of-the-art systems still struggle with "surface-level" critiques: they excel at summarizing content but often fail to accurately assess novelty and ... | https://arxiv.org/abs/2601.22638 | Academic Papers | svg |
5ec80844e797b68c4faa7d9b66f199871e9e336ec63ccf56029a7b4eaf665ec1 | 2026-02-02T00:00:00-05:00 | Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification | arXiv:2601.22642v1 Announce Type: new Abstract: Large Language Models (LLMs) show remarkable capabilities, yet their stochastic next-token prediction creates logical inconsistencies and reward hacking that formal symbolic systems avoid. To bridge this gap, we introduce a formal logic verification-guided framework that ... | https://arxiv.org/abs/2601.22642 | Academic Papers | svg |
3efb7aac7b0a048b77f19b96151e01959aac839ef3fbbdf73cc87a0189c581af | 2026-02-02T00:00:00-05:00 | Beyond Medical Chatbots: Meddollina and the Rise of Continuous Clinical Intelligence | arXiv:2601.22645v1 Announce Type: new Abstract: Generative medical AI now appears fluent and knowledgeable enough to resemble clinical intelligence, encouraging the belief that scaling will make it safe. But clinical reasoning is not text generation. It is a responsibility-bound process under ambiguity, incomplete evid... | https://arxiv.org/abs/2601.22645 | Academic Papers | svg |
4abdb58ad2172b1c040e25b37a2134453286ccf1b16a4a659a0923f99ff5e069 | 2026-02-02T00:00:00-05:00 | Test-Time Mixture of World Models for Embodied Agents in Dynamic Environments | arXiv:2601.22647v1 Announce Type: new Abstract: Language model (LM)-based embodied agents are increasingly deployed in real-world settings. Yet, their adaptability remains limited in dynamic environments, where constructing accurate and flexible world models is crucial for effective reasoning and decision-making. To ad... | https://arxiv.org/abs/2601.22647 | Academic Papers | svg |
44ae5abc109a715d7658847b77acc51d1bd47c5ea5a2d2f3dc86b397b11ff102 | 2026-02-02T00:00:00-05:00 | UCPO: Uncertainty-Aware Policy Optimization | arXiv:2601.22648v1 Announce Type: new Abstract: The key to building trustworthy Large Language Models (LLMs) lies in endowing them with inherent uncertainty expression capabilities to mitigate the hallucinations that restrict their high-stakes applications. However, existing RL paradigms such as GRPO often suffer from ... | https://arxiv.org/abs/2601.22648 | Academic Papers | svg |
00d6793931f19a07c97dccade2c094767328a9669e811fccb5ad0d6eb6a9e4fe | 2026-02-02T00:00:00-05:00 | GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning | arXiv:2601.22651v1 Announce Type: new Abstract: Training-data attribution for vision generative models aims to identify which training data influenced a given output. While most methods score individual examples, practitioners often need group-level answers (e.g., artistic styles or object classes). Group-wise attribut... | https://arxiv.org/abs/2601.22651 | Academic Papers | svg |
54e2d8f4ad5e23882303ca6797f70b5e3cf3a94d8bfb6b25d5f52cdd83812f6d | 2026-02-02T00:00:00-05:00 | Human-Centered Explainability in AI-Enhanced UI Security Interfaces: Designing Trustworthy Copilots for Cybersecurity Analysts | arXiv:2601.22653v1 Announce Type: new Abstract: Artificial intelligence (AI) copilots are increasingly integrated into enterprise cybersecurity platforms to assist analysts in threat detection, triage, and remediation. However, the effectiveness of these systems depends not only on the accuracy of underlying models but... | https://arxiv.org/abs/2601.22653 | Academic Papers | svg |
cf0dbe7183a02b767d81461a1aa6e0476aa650191f7cc41666c6245e25434fd5 | 2026-02-02T00:00:00-05:00 | Parameter conditioned interpretable U-Net surrogate model for data-driven predictions of convection-diffusion-reaction processes | arXiv:2601.22654v1 Announce Type: new Abstract: We present a combined numerical and data-driven workflow for efficient prediction of nonlinear, instationary convection-diffusion-reaction dynamics on a two-dimensional phenotypic domain, motivated by macroscopic modeling of cancer cell plasticity. A finite-difference sol... | https://arxiv.org/abs/2601.22654 | Academic Papers | svg |
57fd5593bd6bd6abe2ce9e4daef8d0b7f298c179dabf56051db19dafd5319f0b | 2026-02-02T00:00:00-05:00 | The Semantic Trap: Do Fine-tuned LLMs Learn Vulnerability Root Cause or Just Functional Pattern? | arXiv:2601.22655v1 Announce Type: new Abstract: LLMs demonstrate promising performance in software vulnerability detection after fine-tuning. However, it remains unclear whether these gains reflect a genuine understanding of vulnerability root causes or merely an exploitation of functional patterns. In this paper, we i... | https://arxiv.org/abs/2601.22655 | Academic Papers | svg |
987b2333d2f3b632e303e16a441bfab3ca932e3c90bb7e393b6fa0cc57870182 | 2026-02-02T00:00:00-05:00 | NAG: A Unified Native Architecture for Encoder-free Text-Graph Modeling in Language Models | arXiv:2601.22657v1 Announce Type: new Abstract: Prevailing methods for integrating graphs into Language Models (LMs) typically rely on a segregated architecture: external Graph Neural Networks (GNNs) encode structural topology, while LMs process textual semantics. We argue this approach is suboptimal for text-graphs: i... | https://arxiv.org/abs/2601.22657 | Academic Papers | svg |
1d4b0829b8f0fac623188838fde1ee95a11723de4854857bca28fb0dacb254bb | 2026-02-02T00:00:00-05:00 | Layerwise Progressive Freezing Enables STE-Free Training of Deep Binary Neural Networks | arXiv:2601.22660v1 Announce Type: new Abstract: We investigate progressive freezing as an alternative to straight-through estimators (STE) for training binary networks from scratch. Under controlled training conditions, we find that while global progressive freezing works for binary-weight networks, it fails for full b... | https://arxiv.org/abs/2601.22660 | Academic Papers | svg |
2ca2a9c0d8d8d39998abe1d5c6737cef6979825ef5987358a5bebe5217eb5334 | 2026-02-02T00:00:00-05:00 | Evaluating and Rewarding LALMs for Expressive Role-Play TTS via Mean Continuation Log-Probability | arXiv:2601.22661v1 Announce Type: new Abstract: Recent advances in Large Audio Language Models (LALMs) have extended Text-to-Speech (TTS) to interactive role-play scenarios, which demand high expressiveness and strict adherence to role-play instructions. However, existing models struggle to maintain stylistic consisten... | https://arxiv.org/abs/2601.22661 | Academic Papers | svg |
b70b632c7e7c09b3a6d584a0e1f48a3ed6a7574cc9303b175d3c81a196eea0bd | 2026-02-02T00:00:00-05:00 | Task-Aware LLM Council with Adaptive Decision Pathways for Decision Support | arXiv:2601.22662v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong capabilities across diverse decision-making tasks. However, existing approaches often overlook the specialization differences among available models, treating all LLMs as uniformly applicable regardless of task characteristic... | https://arxiv.org/abs/2601.22662 | Academic Papers | svg |
087f38177a639b5032926bc9e33c4b4dbc6af1d74a4da07419eff95662316e64 | 2026-02-02T00:00:00-05:00 | Unsupervised Synthetic Image Attribution: Alignment and Disentanglement | arXiv:2601.22663v1 Announce Type: new Abstract: As the quality of synthetic images improves, identifying the underlying concepts of model-generated images is becoming increasingly crucial for copyright protection and ensuring model transparency. Existing methods achieve this attribution goal by training models using an... | https://arxiv.org/abs/2601.22663 | Academic Papers | svg |
4913eb11134edd21010b8f68df0b6410aaac86d99853aee90795051448db8a90 | 2026-02-02T00:00:00-05:00 | Real-Time Aligned Reward Model beyond Semantics | arXiv:2601.22664v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is a pivotal technique for aligning large language models (LLMs) with human preferences, yet it is susceptible to reward overoptimization, in which policy models overfit to the reward model, exploit spurious reward pattern... | https://arxiv.org/abs/2601.22664 | Academic Papers | svg |
d691568f84ef1f43ff9dfb88b5d1d23405f21e03e95e21279b532404069f1ef9 | 2026-02-02T00:00:00-05:00 | ExpAlign: Expectation-Guided Vision-Language Alignment for Open-Vocabulary Grounding | arXiv:2601.22666v1 Announce Type: new Abstract: Open-vocabulary grounding requires accurate vision-language alignment under weak supervision, yet existing methods either rely on global sentence embeddings that lack fine-grained expressiveness or introduce token-level alignment with explicit supervision or heavy cross-a... | https://arxiv.org/abs/2601.22666 | Academic Papers | svg |
f70a226e75a9ec7edfc6969e430bcfaa86f6ec1332a9661e2f7630d7a8aa2a9d | 2026-02-02T00:00:00-05:00 | From Horizontal Layering to Vertical Integration: A Comparative Study of the AI-Driven Software Development Paradigm | arXiv:2601.22667v1 Announce Type: new Abstract: This paper examines the organizational implications of Generative AI adoption in software engineering through a multiple-case comparative study. We contrast two development environments: a traditional enterprise (brownfield) and an AI-native startup (greenfield). Our anal... | https://arxiv.org/abs/2601.22667 | Academic Papers | svg |
b4887fcce7a3e13cb78eafd4c7313f2698bfcb5d09995cda6b85e74543640f65 | 2026-02-02T00:00:00-05:00 | Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learning | arXiv:2601.22669v1 Announce Type: new Abstract: Federated Learning (FL) facilitates decentralized collaborative learning without transmitting raw data. However, reliance on fixed global rounds or validation data for hyperparameter tuning hinders practical deployment by incurring high computational costs and privacy ris... | https://arxiv.org/abs/2601.22669 | Academic Papers | svg |
a3e0b7abd49a9a2ca6fcde8c732c43ea7b27ac317f1fcec26b05744d03574ba3 | 2026-02-02T00:00:00-05:00 | Postural Virtual Fixtures for Ergonomic Physical Interactions with Supernumerary Robotic Bodies | arXiv:2601.22672v1 Announce Type: new Abstract: Conjoined collaborative robots, functioning as supernumerary robotic bodies (SRBs), can enhance human load tolerance abilities. However, in tasks involving physical interaction with humans, users may still adopt awkward, non-ergonomic postures, which can lead to discomfor... | https://arxiv.org/abs/2601.22672 | Academic Papers | svg |
fef2c932791f7b26a23c129bbddc5bd85100ed3ea3215c38e0645f5d44c1f919 | 2026-02-02T00:00:00-05:00 | VisionTrim: Unified Vision Token Compression for Training-Free MLLM Acceleration | arXiv:2601.22674v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) suffer from high computational costs due to excessive visual tokens, particularly in high-resolution and video-based scenarios. Existing token reduction methods typically focus on isolated pipeline components and often neglect text... | https://arxiv.org/abs/2601.22674 | Academic Papers | svg |
a4e97820052104034ef9b1deaccf16aa5b9c9dc7c71d02d511ed17783cb6793c | 2026-02-02T00:00:00-05:00 | Fire on Motion: Optimizing Video Pass-bands for Efficient Spiking Action Recognition | arXiv:2601.22675v1 Announce Type: new Abstract: Spiking neural networks (SNNs) have gained traction in vision due to their energy efficiency, bio-plausibility, and inherent temporal processing. Yet, despite this temporal capacity, most progress concentrates on static image benchmarks, and SNNs still underperform on dyn... | https://arxiv.org/abs/2601.22675 | Academic Papers | svg |
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