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05518beaa86e2c4b9b72b2496fe5b02d1dafd864a8dd65b973a8f332595e9146 | 2026-01-13T00:00:00-05:00 | Generating readily synthesizable small molecule fluorophore scaffolds with reinforcement learning | arXiv:2601.07145v1 Announce Type: new Abstract: Developing new fluorophores for advanced imaging techniques requires exploring new chemical space. While generative AI approaches have shown promise in designing novel dye scaffolds, prior efforts often produced synthetically intractable candidates due to a lack of reacti... | https://arxiv.org/abs/2601.07145 | Academic Papers | svg |
0b7946dfa025490278acad4d3be032feb1eca3abe79a5805a10dd7cea1263e30 | 2026-01-13T00:00:00-05:00 | PASS-Enabled Covert Communications With Distributed Cooperative Wardens | arXiv:2601.07147v1 Announce Type: new Abstract: This paper investigates PASS-enabled downlink covert communication in the presence of distributed surveillance, where multiple wardens perform signal detection and fuse their local binary decisions via majority-voting rule. We consider a dual-waveguide architecture that s... | https://arxiv.org/abs/2601.07147 | Academic Papers | svg |
8abf910cea2e068e101dd4632aa1ccd1478cf4b1ae41f369bbd7faf5e126d456 | 2026-01-13T00:00:00-05:00 | Measuring Iterative Temporal Reasoning with TimePuzzles | arXiv:2601.07148v1 Announce Type: new Abstract: We introduce TimePuzzles, a constraint-based date inference task for evaluating iterative temporal reasoning. Each puzzle combines factual temporal anchors with (cross-cultural) calendar relations, admits one or multiple valid solution dates, and is algorithmically genera... | https://arxiv.org/abs/2601.07148 | Academic Papers | svg |
0dbb0308cb2050a0fea2be4f4981c9b3927cc7bada4ff04b449a5d77f8c26fd6 | 2026-01-13T00:00:00-05:00 | Rewarding Creativity: A Human-Aligned Generative Reward Model for Reinforcement Learning in Storytelling | arXiv:2601.07149v1 Announce Type: new Abstract: While Large Language Models (LLMs) can generate fluent text, producing high-quality creative stories remains challenging. Reinforcement Learning (RL) offers a promising solution but faces two critical obstacles: designing reliable reward signals for subjective storytellin... | https://arxiv.org/abs/2601.07149 | Academic Papers | svg |
449a52695b5d801e8c9af6e212a684e240b3d755b121def6e292c1d5f400f1d2 | 2026-01-13T00:00:00-05:00 | Fault detection of nonlinear industrial processes based on control theory-informed machine learning methods | arXiv:2601.07150v1 Announce Type: new Abstract: This paper deals with analysis, simultaneous detection of faults and attacks, fault-tolerant control and attack-resilient of cyber-physical control systems. In our recent work, it has been observed that an attack detector driven by an input residual signal is capable of r... | https://arxiv.org/abs/2601.07150 | Academic Papers | svg |
5c2c21072d8fde5cdfb2fa76d187576aec0409156899c0ce6e1ed6c47177f8a6 | 2026-01-13T00:00:00-05:00 | Agents of Diffusion: Enhancing Diffusion Language Models with Multi-Agent Reinforcement Learning for Structured Data Generation (Extended Version) | arXiv:2601.07152v1 Announce Type: new Abstract: Generating high-quality structured data such as JSON records, remains a fundamental challenge for large language models (LLMs), particularly when semantic richness must coexist with strict schema adherence. While autoregressive LLMs offer strong structural consistency, th... | https://arxiv.org/abs/2601.07152 | Academic Papers | svg |
682d5874034607c0d41be6ca0e8faab11387cfbe03425c52714713bf82997733 | 2026-01-13T00:00:00-05:00 | Can Large Language Models Understand, Reason About, and Generate Code-Switched Text? | arXiv:2601.07153v1 Announce Type: new Abstract: Code-switching is a pervasive phenomenon in multilingual communication, yet the robustness of large language models (LLMs) in mixed-language settings remains insufficiently understood. In this work, we present a comprehensive evaluation of LLM capabilities in understandin... | https://arxiv.org/abs/2601.07153 | Academic Papers | svg |
3821319cc5e323f360ab7bf11dc4f1f32305a8c6f5286135de2da1529da93225 | 2026-01-13T00:00:00-05:00 | Motion Focus Recognition in Fast-Moving Egocentric Video | arXiv:2601.07154v1 Announce Type: new Abstract: From Vision-Language-Action (VLA) systems to robotics, existing egocentric datasets primarily focus on action recognition tasks, while largely overlooking the inherent role of motion analysis in sports and other fast-movement scenarios. To bridge this gap, we propose a re... | https://arxiv.org/abs/2601.07154 | Academic Papers | svg |
700534950fb3e5e34435e319aaf93a9179f7685cac9f8467c9f74a46e7032f4f | 2026-01-13T00:00:00-05:00 | Stable On-Policy Distillation through Adaptive Target Reformulation | arXiv:2601.07155v1 Announce Type: new Abstract: Knowledge distillation (KD) is a widely adopted technique for transferring knowledge from large language models to smaller student models; however, conventional supervised KD often suffers from a distribution mismatch between training and inference. While on-policy KD app... | https://arxiv.org/abs/2601.07155 | Academic Papers | svg |
623be2feb0dfd465ad087e9bcac4ac8b4a6333f94733eccdb598e02aa661a6d4 | 2026-01-13T00:00:00-05:00 | Nonlinear Observer Design for Visual-Inertial Odometry | arXiv:2601.07156v1 Announce Type: new Abstract: This paper addresses the problem of Visual-Inertial Odometry (VIO) for rigid body systems evolving in three-dimensional space. We introduce a novel matrix Lie group structure, denoted SE_{3+n}(3), that unifies the pose, gravity, linear velocity, and landmark positions wit... | https://arxiv.org/abs/2601.07156 | Academic Papers | svg |
acb8d061001708f9a998ee441f5f74c3826e02de25fffcf1daaa9939d2a17ab7 | 2026-01-13T00:00:00-05:00 | AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units | arXiv:2601.07160v1 Announce Type: new Abstract: To meet the ever-increasing demand for computational efficiency, Neural Processing Units (NPUs) have become critical in modern AI infrastructure. However, unlocking their full potential requires developing high-performance compute kernels using vendor-specific Domain-Spec... | https://arxiv.org/abs/2601.07160 | Academic Papers | svg |
21af26387153e134c29ef0d8807cacd59c100ff82c49d4810edd38f666f9c3bf | 2026-01-13T00:00:00-05:00 | Test-time Adaptive Hierarchical Co-enhanced Denoising Network for Reliable Multimodal Classification | arXiv:2601.07163v1 Announce Type: new Abstract: Reliable learning on low-quality multimodal data is a widely concerning issue, especially in safety-critical applications. However, multimodal noise poses a major challenge in this domain and leads existing methods to suffer from two key limitations. First, they struggle ... | https://arxiv.org/abs/2601.07163 | Academic Papers | svg |
57709152d54be698fdbccb472be82b57e58da28b6de74471dfdbda6091423977 | 2026-01-13T00:00:00-05:00 | Offline Meta-Reinforcement Learning with Flow-Based Task Inference and Adaptive Correction of Feature Overgeneralization | arXiv:2601.07164v1 Announce Type: new Abstract: Offline meta-reinforcement learning (OMRL) combines the strengths of learning from diverse datasets in offline RL with the adaptability to new tasks of meta-RL, promising safe and efficient knowledge acquisition by RL agents. However, OMRL still suffers extrapolation erro... | https://arxiv.org/abs/2601.07164 | Academic Papers | svg |
dc43eeb17cf00d25802bf784c4e6df75c83dce2a1b7a12ce7799526c54650b22 | 2026-01-13T00:00:00-05:00 | TranSC: Hardware-Aware Design of Transcendental Functions Using Stochastic Logic | arXiv:2601.07172v1 Announce Type: new Abstract: The hardware-friendly implementation of transcendental functions remains a longstanding challenge in design automation. These functions, which cannot be expressed as finite combinations of algebraic operations, pose significant complexity in digital circuit design. This s... | https://arxiv.org/abs/2601.07172 | Academic Papers | svg |
5869d7ea9a8b13f3858b324b8914b3ecb2b18f351a615d7dabf55040e439ce2b | 2026-01-13T00:00:00-05:00 | The MAC scheme for linear elasticity in displacement-stress formulation on non-uniform staggered grids | arXiv:2601.07174v1 Announce Type: new Abstract: A marker-and-cell finite difference method is developed for solving the two dimensional and three dimensional linear elasticity in the displacement-stress formulation on staggered grids. The method employs a staggered grid arrangement, where the displacement components ar... | https://arxiv.org/abs/2601.07174 | Academic Papers | svg |
53bd36ed443fc2f99bce5a548f91829dde5756eea9a2e8dc013cd1b70a5169c1 | 2026-01-13T00:00:00-05:00 | Safe-FedLLM: Delving into the Safety of Federated Large Language Models | arXiv:2601.07177v1 Announce Type: new Abstract: Federated learning (FL) addresses data privacy and silo issues in large language models (LLMs). Most prior work focuses on improving the training efficiency of federated LLMs. However, security in open environments is overlooked, particularly defenses against malicious cl... | https://arxiv.org/abs/2601.07177 | Academic Papers | svg |
de53f36f6cccc3c3570ffcb2e55b8196cb110fab3b2a1791c61b5f56c73e7c35 | 2026-01-13T00:00:00-05:00 | DIVER: Dynamic Iterative Visual Evidence Reasoning for Multimodal Fake News Detection | arXiv:2601.07178v1 Announce Type: new Abstract: Multimodal fake news detection is crucial for mitigating adversarial misinformation. Existing methods, relying on static fusion or LLMs, face computational redundancy and hallucination risks due to weak visual foundations. To address this, we propose DIVER (Dynamic Iterat... | https://arxiv.org/abs/2601.07178 | Academic Papers | svg |
080efae4e2377f3bc7b1a5c75a619b73e450b7dd86298522e4deb6106de2af95 | 2026-01-13T00:00:00-05:00 | Structured Reasoning for Large Language Models | arXiv:2601.07180v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance by generating long chains of thought, but longer traces always introduce redundant or ineffective reasoning steps. One typical behavior is that they often perform unnecessary verification and revisions even if they h... | https://arxiv.org/abs/2601.07180 | Academic Papers | svg |
210abc55e2089f32aa7ca6d6f9014ce371bd041321aebd2bd0097ff98a14f3e1 | 2026-01-13T00:00:00-05:00 | ShowUI-Aloha: Human-Taught GUI Agent | arXiv:2601.07181v1 Announce Type: new Abstract: Graphical User Interfaces (GUIs) are central to human-computer interaction, yet automating complex GUI tasks remains a major challenge for autonomous agents, largely due to a lack of scalable, high-quality training data. While recordings of human demonstrations offer a ri... | https://arxiv.org/abs/2601.07181 | Academic Papers | svg |
c7a1466bc7968127bd8d0738f871ac1d2b154d52629ed0d0312992f95dfe0065 | 2026-01-13T00:00:00-05:00 | PRPO: Aligning Process Reward with Outcome Reward in Policy Optimization | arXiv:2601.07182v1 Announce Type: new Abstract: Policy optimization for large language models often suffers from sparse reward signals in multi-step reasoning tasks. Critic-free methods like GRPO assign a single normalized outcome reward to all tokens, providing limited guidance for intermediate reasoning . While Proce... | https://arxiv.org/abs/2601.07182 | Academic Papers | svg |
b50989e3ea71b859c1f869dedbc73d9dffcca610bad23052d78674418d7f4b61 | 2026-01-13T00:00:00-05:00 | RAIRS: Optimizing Redundant Assignment and List Layout for IVF-Based ANN Search | arXiv:2601.07183v1 Announce Type: new Abstract: IVF is one of the most widely used ANNS (Approximate Nearest Neighbors Search) methods in vector databases. The idea of redundant assignment is to assign a data vector to more than one IVF lists for reducing the chance of missing true neighbors in IVF search. However, the... | https://arxiv.org/abs/2601.07183 | Academic Papers | svg |
1924375ab1c365cf2f4fd13dbfc50137e893dc16c454b4aa729ac8e6b08f8594 | 2026-01-13T00:00:00-05:00 | Defenses Against Prompt Attacks Learn Surface Heuristics | arXiv:2601.07185v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in security-sensitive applications, where they must follow system- or developer-specified instructions that define the intended task behavior, while completing benign user requests. When adversarial instructions appea... | https://arxiv.org/abs/2601.07185 | Academic Papers | svg |
8047c27f87b4e4ff4fbe1a35d099994e30e14deb1fc3c2a117dd7f08cd402314 | 2026-01-13T00:00:00-05:00 | PROTEA: Securing Robot Task Planning and Execution | arXiv:2601.07186v1 Announce Type: new Abstract: Robots need task planning methods to generate action sequences for complex tasks. Recent work on adversarial attacks has revealed significant vulnerabilities in existing robot task planners, especially those built on foundation models. In this paper, we aim to address the... | https://arxiv.org/abs/2601.07186 | Academic Papers | svg |
e6984da0385dbc45dd16c99270d09d23b2fb62d70111ab55e2258d32b86695c5 | 2026-01-13T00:00:00-05:00 | Standardization of Post-Publication Code Verification by Journals is Possible with the Support of the Community | arXiv:2601.07189v1 Announce Type: new Abstract: Reproducibility remains a challenge in machine learning research. While code and data availability requirements have become increasingly common, post-publication verification in journals is still limited and unformalized. This position paper argues that it is plausible fo... | https://arxiv.org/abs/2601.07189 | Academic Papers | svg |
112d68222d63a21282c96ad92028ded62f984a1d8bec1aead6c0c4e74feb632d | 2026-01-13T00:00:00-05:00 | Active Context Compression: Autonomous Memory Management in LLM Agents | arXiv:2601.07190v1 Announce Type: new Abstract: Large Language Model (LLM) agents struggle with long-horizon software engineering tasks due to "Context Bloat." As interaction history grows, computational costs explode, latency increases, and reasoning capabilities degrade due to distraction by irrelevant past errors. E... | https://arxiv.org/abs/2601.07190 | Academic Papers | svg |
1a70cc0458a283e8178293e0bb9756ab8d7a83372bab6ce09883c428caec11f3 | 2026-01-13T00:00:00-05:00 | Relink: Constructing Query-Driven Evidence Graph On-the-Fly for GraphRAG | arXiv:2601.07192v1 Announce Type: new Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) mitigates hallucinations in Large Language Models (LLMs) by grounding them in structured knowledge. However, current GraphRAG methods are constrained by a prevailing \textit{build-then-reason} paradigm, which relies on... | https://arxiv.org/abs/2601.07192 | Academic Papers | svg |
b5173a860d2848b9526544ffff96c91e06738c9e203b1d1937aaade0f701a6e5 | 2026-01-13T00:00:00-05:00 | Beyond Variance: Knowledge-Aware LLM Compression via Fisher-Aligned Subspace Diagnostics | arXiv:2601.07197v1 Announce Type: new Abstract: Post-training activation compression is essential for deploying Large Language Models (LLMs) on resource-constrained hardware. However, standard methods like Singular Value Decomposition (SVD) are gradient-blind: they preserve high-variance dimensions regardless of their ... | https://arxiv.org/abs/2601.07197 | Academic Papers | svg |
79c48ebd9a2abb11c13538ccee55567ad6877e14723aff3c09184d702fb3d270 | 2026-01-13T00:00:00-05:00 | Forward versus Backward: Comparing Reasoning Objectives in Direct Preference Optimization | arXiv:2601.07199v1 Announce Type: new Abstract: Large language models exhibit impressive reasoning capabilities yet frequently generate plausible but incorrect solutions, a phenomenon commonly termed hallucination. This paper investigates the effect of training objective composition on reasoning reliability through Dir... | https://arxiv.org/abs/2601.07199 | Academic Papers | svg |
ae2258313903700fec68af19839685b2616e8b6ff3e1b22145857a4c4fd085ed | 2026-01-13T00:00:00-05:00 | Safeguarding LLM Fine-tuning via Push-Pull Distributional Alignment | arXiv:2601.07200v1 Announce Type: new Abstract: The inherent safety alignment of Large Language Models (LLMs) is prone to erosion during fine-tuning, even when using seemingly innocuous datasets. While existing defenses attempt to mitigate this via data selection, they typically rely on heuristic, instance-level assess... | https://arxiv.org/abs/2601.07200 | Academic Papers | svg |
593aacab0a10bb51bc38203826edb6072d95afe81f9bf22a22da2a4d71a610a5 | 2026-01-13T00:00:00-05:00 | CalPro: Prior-Aware Evidential--Conformal Prediction with Structure-Aware Guarantees for Protein Structures | arXiv:2601.07201v1 Announce Type: new Abstract: Deep protein structure predictors such as AlphaFold provide confidence estimates (e.g., pLDDT) that are often miscalibrated and degrade under distribution shifts across experimental modalities, temporal changes, and intrinsically disordered regions. We introduce CalPro, a... | https://arxiv.org/abs/2601.07201 | Academic Papers | svg |
d02ad6ab50acee037a4e24b2e5d8eb6b1318b24866a3c4cc274bb386a8691392 | 2026-01-13T00:00:00-05:00 | Intercultural Communication Strategies of a Technology Brand: A Comparative Quantitative Analysis of Xiaomi's Digital Marketing in China and Russia | arXiv:2601.07204v1 Announce Type: new Abstract: In the 21st century, the era of globalization, consumers are dispersed across the globe, and brands compete for their attention and loyalty, largely within the digital realm. This reality elevates the importance of effective communication and the transmission of product v... | https://arxiv.org/abs/2601.07204 | Academic Papers | svg |
3871aa1a25212b5b9228c24c89df3c6dba11ef2d908abd95dc12bc9725b547b0 | 2026-01-13T00:00:00-05:00 | LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing | arXiv:2601.07206v1 Announce Type: new Abstract: Large language model (LLM) routing assigns each query to the most suitable model from an ensemble. We introduce LLMRouterBench, a large-scale benchmark and unified framework for LLM routing. It comprises over 400K instances from 21 datasets and 33 models. Moreover, it pro... | https://arxiv.org/abs/2601.07206 | Academic Papers | svg |
44c1db4c2b685ede7179e6b4e84627b0dd2b798f56dfcbcedd333fb48a628241 | 2026-01-13T00:00:00-05:00 | MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization | arXiv:2601.07208v1 Announce Type: new Abstract: Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), yet its efficacy is primarily confined to domains with verifiable ground truths. Extending GRPO to open-domain settings remains a critical challenge, ... | https://arxiv.org/abs/2601.07208 | Academic Papers | svg |
c1e4c21a11070bfe40eeaf0a76c777d44a5b7268c44db9a1f42de2d5efd52cd1 | 2026-01-13T00:00:00-05:00 | SIRR-LMM: Single-image Reflection Removal via Large Multimodal Model | arXiv:2601.07209v1 Announce Type: new Abstract: Glass surfaces create complex interactions of reflected and transmitted light, making single-image reflection removal (SIRR) challenging. Existing datasets suffer from limited physical realism in synthetic data or insufficient scale in real captures. We introduce a synthe... | https://arxiv.org/abs/2601.07209 | Academic Papers | svg |
08200310ee9c18e3765ea60c5214f58d75b9b790e7c10d62e687536cb15afa74 | 2026-01-13T00:00:00-05:00 | MI-PRUN: Optimize Large Language Model Pruning via Mutual Information | arXiv:2601.07212v1 Announce Type: new Abstract: Large Language Models (LLMs) have become indispensable across various domains, but this comes at the cost of substantial computational and memory resources. Model pruning addresses this by removing redundant components from models. In particular, block pruning can achieve... | https://arxiv.org/abs/2601.07212 | Academic Papers | svg |
d7aae4f3766469ae293dc097b251e80cd4183c1ede1a43c6742ca2903b393086 | 2026-01-13T00:00:00-05:00 | BlindU: Blind Machine Unlearning without Revealing Erasing Data | arXiv:2601.07214v1 Announce Type: new Abstract: Machine unlearning enables data holders to remove the contribution of their specified samples from trained models to protect their privacy. However, it is paradoxical that most unlearning methods require the unlearning requesters to firstly upload their data to the server... | https://arxiv.org/abs/2601.07214 | Academic Papers | svg |
91d3863888d5b49e5da4b54b3c3d5c06d1f8bb1653e5189d660466e327772fed | 2026-01-13T00:00:00-05:00 | SceneNAT: Masked Generative Modeling for Language-Guided Indoor Scene Synthesis | arXiv:2601.07218v1 Announce Type: new Abstract: We present SceneNAT, a single-stage masked non-autoregressive Transformer that synthesizes complete 3D indoor scenes from natural language instructions through only a few parallel decoding passes, offering improved performance and efficiency compared to prior state-of-the... | https://arxiv.org/abs/2601.07218 | Academic Papers | svg |
bd19c70d1a8e158715c81a090b816c8e731d217028b8fa42e751d6fd24145983 | 2026-01-13T00:00:00-05:00 | VENUS: Visual Editing with Noise Inversion Using Scene Graphs | arXiv:2601.07219v1 Announce Type: new Abstract: State-of-the-art text-based image editing models often struggle to balance background preservation with semantic consistency, frequently resulting either in the synthesis of entirely new images or in outputs that fail to realize the intended edits. In contrast, scene grap... | https://arxiv.org/abs/2601.07219 | Academic Papers | svg |
b65c146b91be13f56ca626100414154120bd4bd22d7d736f2a39fbc548bcb65f | 2026-01-13T00:00:00-05:00 | The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices? | arXiv:2601.07220v1 Announce Type: new Abstract: Multilingual language models (LMs) promise broader NLP access, yet current systems deliver uneven performance across the world's languages. This survey examines why these gaps persist and whether they reflect intrinsic linguistic difficulty or modeling artifacts. We organ... | https://arxiv.org/abs/2601.07220 | Academic Papers | svg |
3969e636c0c1fe4667cda1ca7c459c2d35685ef8ce371cfc27fc50f4564b70e7 | 2026-01-13T00:00:00-05:00 | Language-Grounded Multi-Domain Image Translation via Semantic Difference Guidance | arXiv:2601.07221v1 Announce Type: new Abstract: Multi-domain image-to-image translation re quires grounding semantic differences ex pressed in natural language prompts into corresponding visual transformations, while preserving unrelated structural and seman tic content. Existing methods struggle to maintain structural... | https://arxiv.org/abs/2601.07221 | Academic Papers | svg |
e84258f876121162b3bc634d4b6972fd7f628d05fc3dddd7bb741093b87481a5 | 2026-01-13T00:00:00-05:00 | Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration | arXiv:2601.07224v1 Announce Type: new Abstract: While Hybrid Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become the standard paradigm for training LLM agents, effective mechanisms for data allocation between these stages remain largely underexplored. Current data arbitration strategies ofte... | https://arxiv.org/abs/2601.07224 | Academic Papers | svg |
12761f743f7b50ba323f84bb313f536fc0731691de9a70e05fb4e9e823963a35 | 2026-01-13T00:00:00-05:00 | Lost in the Noise: How Reasoning Models Fail with Contextual Distractors | arXiv:2601.07226v1 Announce Type: new Abstract: Recent advances in reasoning models and agentic AI systems have led to an increased reliance on diverse external information. However, this shift introduces input contexts that are inherently noisy, a reality that current sanitized benchmarks fail to capture. We introduce... | https://arxiv.org/abs/2601.07226 | Academic Papers | svg |
bf529396e1a7729ec83f8a3279b0e4d5a22755581c172c0b448f4bea1719d36f | 2026-01-13T00:00:00-05:00 | DiSCo: Making Absence Visible in Intelligent Summarization Interfaces | arXiv:2601.07229v1 Announce Type: new Abstract: Intelligent interfaces increasingly use large language models to summarize user-generated content, yet these summaries emphasize what is mentioned while overlooking what is missing. This presence bias can mislead users who rely on summaries to make decisions. We present D... | https://arxiv.org/abs/2601.07229 | Academic Papers | svg |
e4b49b55263f1686867b8b6acf848e619a47a71ec1ab35cac4081e9273be86a8 | 2026-01-13T00:00:00-05:00 | Yes FLoReNce, I Will Do Better Next Time! Agentic Feedback Reasoning for Humorous Meme Detection | arXiv:2601.07232v1 Announce Type: new Abstract: Humorous memes blend visual and textual cues to convey irony, satire, or social commentary, posing unique challenges for AI systems that must interpret intent rather than surface correlations. Existing multimodal or prompting-based models generate explanations for humor b... | https://arxiv.org/abs/2601.07232 | Academic Papers | svg |
3d3e368bbbd6ebb2916a13f6c9c679c031b59d8c1ecc6c32b70c64e39ffcd094 | 2026-01-13T00:00:00-05:00 | From "Thinking" to "Justifying": Aligning High-Stakes Explainability with Professional Communication Standards | arXiv:2601.07233v1 Announce Type: new Abstract: Explainable AI (XAI) in high-stakes domains should help stakeholders trust and verify system outputs. Yet Chain-of-Thought methods reason before concluding, and logical gaps or hallucinations can yield conclusions that do not reliably align with their rationale. Thus, we ... | https://arxiv.org/abs/2601.07233 | Academic Papers | svg |
6c9cfb3e46455186fea606789af63223cf97b9fb7dd4a623cd1205b055df042c | 2026-01-13T00:00:00-05:00 | Making Absence Visible: The Roles of Reference and Prompting in Recognizing Missing Information | arXiv:2601.07234v1 Announce Type: new Abstract: Interactive systems that explain data, or support decision making often emphasize what is present while overlooking what is expected but missing. This presence bias limits users' ability to form complete mental models of a dataset or situation. Detecting absence depends o... | https://arxiv.org/abs/2601.07234 | Academic Papers | svg |
d5d8ea9634a5f8da43d7398f9fbf8015c53d78da5cdb7136ec602f2f293e92bd | 2026-01-13T00:00:00-05:00 | Sentiment Analysis on Movie Reviews: A Deep Dive into Modern Techniques and Open Challenges | arXiv:2601.07235v1 Announce Type: new Abstract: This paper presents a comprehensive survey of sentiment analysis methods for movie reviews, a benchmark task that has played a central role in advancing natural language processing. We review the evolution of techniques from early lexicon-based and classical machine learn... | https://arxiv.org/abs/2601.07235 | Academic Papers | svg |
3583fd6de8dbee87b6fb00a7112013970f3eb8aa38ad847143290c784c937cd3 | 2026-01-13T00:00:00-05:00 | Group Pattern Selection Optimization: Let LRMs Pick the Right Pattern for Reasoning | arXiv:2601.07238v1 Announce Type: new Abstract: Large reasoning models (LRMs) exhibit diverse high-level reasoning patterns (e.g., direct solution, reflection-and-verification, and exploring multiple solutions), yet prevailing training recipes implicitly bias models toward a limited set of dominant patterns. Through a ... | https://arxiv.org/abs/2601.07238 | Academic Papers | svg |
648131ea51c1a6e2aca6850166e4ea031e07254f57174d13744ff0b41d5c6e98 | 2026-01-13T00:00:00-05:00 | Stochastic CHAOS: Why Deterministic Inference Kills, and Distributional Variability Is the Heartbeat of Artifical Cognition | arXiv:2601.07239v1 Announce Type: new Abstract: Deterministic inference is a comforting ideal in classical software: the same program on the same input should always produce the same output. As large language models move into real-world deployment, this ideal has been imported wholesale into inference stacks. Recent wo... | https://arxiv.org/abs/2601.07239 | Academic Papers | svg |
77803ecc149bc17cd1cfd465e8ba335fd40aa592dc4f9dc218ad84389142a408 | 2026-01-13T00:00:00-05:00 | Bias-Aware BP Decoding of Quantum Codes via Directional Degeneracy | arXiv:2601.07240v1 Announce Type: new Abstract: We study directionally informed belief propagation (BP) decoding for quantum CSS codes, where anisotropic Tanner-graph structure and biased noise concentrate degeneracy along preferred directions. We formalize this by placing orientation weights on Tanner-graph edges, agg... | https://arxiv.org/abs/2601.07240 | Academic Papers | svg |
dd876cd0f4cd5e25e3b37ab4209376d727e8e400e7d07826b9825dd021813175 | 2026-01-13T00:00:00-05:00 | HERE: Hierarchical Active Exploration of Radiance Field with Epistemic Uncertainty Minimization | arXiv:2601.07242v1 Announce Type: new Abstract: We present HERE, an active 3D scene reconstruction framework based on neural radiance fields, enabling high-fidelity implicit mapping. Our approach centers around an active learning strategy for camera trajectory generation, driven by accurate identification of unseen reg... | https://arxiv.org/abs/2601.07242 | Academic Papers | svg |
fb8bdb36304ae36458d9fdb163fefa7b6e782cd008b71ffe87dfec77c37c3f8b | 2026-01-13T00:00:00-05:00 | Learning to Trust the Crowd: A Multi-Model Consensus Reasoning Engine for Large Language Models | arXiv:2601.07245v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong aver- age performance yet remain unreliable at the instance level, with frequent hallucinations, brittle failures, and poorly calibrated confidence. We study reliability through the lens of multi-model consensus: given responses... | https://arxiv.org/abs/2601.07245 | Academic Papers | svg |
692bf74daed8c52791519622b9f6c9a690a03ffb6b2ea67b79e455c45f09dbab | 2026-01-13T00:00:00-05:00 | Rate-distortion Theory on Non-compact Spaces: A Concentration-compactness Approach | arXiv:2601.07246v1 Announce Type: new Abstract: In this paper, we study rate-distortion theory for general sources with an emphasis on the existence of optimal reconstruction distributions. Classical existence results rely on compactness assumptions that are often violated in non-compact settings. By introducing the co... | https://arxiv.org/abs/2601.07246 | Academic Papers | svg |
f6060f4c65c034ed5dcc471eae19028ff61d68692e3c05ca66e0697ade5fee31 | 2026-01-13T00:00:00-05:00 | DarwinTOD: LLM Driven Lifelong Self Evolution for Task Oriented Dialog Systems | arXiv:2601.07248v1 Announce Type: new Abstract: Traditional task-oriented dialog systems are unable to evolve from ongoing interactions or adapt to new domains after deployment, that is a critical limitation in real-world dynamic environments. Continual learning approaches depend on episodic retraining with human curat... | https://arxiv.org/abs/2601.07248 | Academic Papers | svg |
ed3079dbf5fbc6e9e0b214fdd6a5161b089b59dd48d25f29037338ef8bdf19f8 | 2026-01-13T00:00:00-05:00 | DDT: A Dual-Masking Dual-Expert Transformer for Energy Time-Series Forecasting | arXiv:2601.07250v1 Announce Type: new Abstract: Accurate energy time-series forecasting is crucial for ensuring grid stability and promoting the integration of renewable energy, yet it faces significant challenges from complex temporal dependencies and the heterogeneity of multi-source data. To address these issues, we... | https://arxiv.org/abs/2601.07250 | Academic Papers | svg |
d542ee3a30f58fc8f3cb641ae484e87fa0ebba41cd80ea8cfda35d14b07e74df | 2026-01-13T00:00:00-05:00 | MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences | arXiv:2601.07251v1 Announce Type: new Abstract: Recent advancements have expanded the role of Large Language Models in board games from playing agents to creative co-designers. However, a critical gap remains: current systems lack the capacity to offer constructive critique grounded in the emergent user experience. Bri... | https://arxiv.org/abs/2601.07251 | Academic Papers | svg |
bd5f05be1a1580dedd235fb5b33327dd976073b17f2215318f28c508b1aec00a | 2026-01-13T00:00:00-05:00 | SwarmFoam: An OpenFOAM Multi-Agent System Based on Multiple Types of Large Language Models | arXiv:2601.07252v1 Announce Type: new Abstract: Numerical simulation is one of the mainstream methods in scientific research, typically performed by professional engineers. With the advancement of multi-agent technology, using collaborating agents to replicate human behavior shows immense potential for intelligent Comp... | https://arxiv.org/abs/2601.07252 | Academic Papers | svg |
5257630e65d61f6e8a4f31b9583c6054d04b3e0b8b5a35344f6dce3da0e9ce58 | 2026-01-13T00:00:00-05:00 | Universal Adversarial Purification with DDIM Metric Loss for Stable Diffusion | arXiv:2601.07253v1 Announce Type: new Abstract: Stable Diffusion (SD) often produces degraded outputs when the training dataset contains adversarial noise. Adversarial purification offers a promising solution by removing adversarial noise from contaminated data. However, existing purification methods are primarily desi... | https://arxiv.org/abs/2601.07253 | Academic Papers | svg |
5650d8d3e6fd8cc4361536ef4fca5611f76dca00f248f800494585b96a42de2c | 2026-01-13T00:00:00-05:00 | Innovation Capacity of Dynamical Learning Systems | arXiv:2601.07257v1 Announce Type: new Abstract: In noisy physical reservoirs, the classical information-processing capacity $C_{\mathrm{ip}}$ quantifies how well a linear readout can realize tasks measurable from the input history, yet $C_{\mathrm{ip}}$ can be far smaller than the observed rank of the readout covarianc... | https://arxiv.org/abs/2601.07257 | Academic Papers | svg |
d1b2bace51307282f8ce3e24f278fab8756de65d133b793abd34bb3789716df0 | 2026-01-13T00:00:00-05:00 | Simulated Annealing-based Candidate Optimization for Batch Acquisition Functions | arXiv:2601.07258v1 Announce Type: new Abstract: Bayesian Optimization with multi-objective acquisition functions such as q-Expected Hypervolume Improvement (qEHVI) requires efficient candidate optimization to maximize acquisition function values. Traditional approaches rely on continuous optimization methods like Seque... | https://arxiv.org/abs/2601.07258 | Academic Papers | svg |
a6812e8e43f498a5f3f9ac69f26ff2627097483c22387b624ef1e1bd0103b9d0 | 2026-01-13T00:00:00-05:00 | ActiShade: Activating Overshadowed Knowledge to Guide Multi-Hop Reasoning in Large Language Models | arXiv:2601.07260v1 Announce Type: new Abstract: In multi-hop reasoning, multi-round retrieval-augmented generation (RAG) methods typically rely on LLM-generated content as the retrieval query. However, these approaches are inherently vulnerable to knowledge overshadowing - a phenomenon where critical information is ove... | https://arxiv.org/abs/2601.07260 | Academic Papers | svg |
412c307d1cb4314c4676fe806e1df14f03d50e4461334a82c653f10618a7c68a | 2026-01-13T00:00:00-05:00 | Pseudodata-guided Invariant Representation Learning Boosts the Out-of-Distribution Generalization in Enzymatic Kinetic Parameter Prediction | arXiv:2601.07261v1 Announce Type: new Abstract: Accurate prediction of enzyme kinetic parameters is essential for understanding catalytic mechanisms and guiding enzyme engineering.However, existing deep learning-based enzyme-substrate interaction (ESI) predictors often exhibit performance degradation on sequence-diverg... | https://arxiv.org/abs/2601.07261 | Academic Papers | svg |
4b8d90b2d5382e4a5082199693167b5407dda099551283c7787ecc4a6061dc73 | 2026-01-13T00:00:00-05:00 | ColorBrowserAgent: An Intelligent GUI Agent for Complex Long-Horizon Web Automation | arXiv:2601.07262v1 Announce Type: new Abstract: The web browser serves as a primary interface for daily human activities, making its automation a critical frontier for Human-Centred AI. While Large Language Models (LLMs) have enabled autonomous agents to interact with web GUIs, their reliability in real-world scenarios... | https://arxiv.org/abs/2601.07262 | Academic Papers | svg |
a8372c3fe899308c662a036c66b8b0e18fadd7eb49750d177feed7a1f74d59ac | 2026-01-13T00:00:00-05:00 | When Bots Take the Bait: Exposing and Mitigating the Emerging Social Engineering Attack in Web Automation Agent | arXiv:2601.07263v1 Announce Type: new Abstract: Web agents, powered by large language models (LLMs), are increasingly deployed to automate complex web interactions. The rise of open-source frameworks (e.g., Browser Use, Skyvern-AI) has accelerated adoption, but also broadened the attack surface. While prior research ha... | https://arxiv.org/abs/2601.07263 | Academic Papers | svg |
c88ad021bd6332dcfc6c639b0b730b1da00bf2338b67d6f799f875e2db31daa4 | 2026-01-13T00:00:00-05:00 | The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents | arXiv:2601.07264v1 Announce Type: new Abstract: Autonomous agents based on large language models (LLMs) are rapidly evolving to handle multi-turn tasks, but ensuring their trustworthiness remains a critical challenge. A fundamental pillar of this trustworthiness is calibration, which refers to an agent's ability to exp... | https://arxiv.org/abs/2601.07264 | Academic Papers | svg |
1f3d52e615511612b78085a587a66addcf8c20b6e40ad4b803aec04f910ce722 | 2026-01-13T00:00:00-05:00 | From Landslide Conditioning Factors to Satellite Embeddings: Evaluating the Utilisation of Google AlphaEarth for Landslide Susceptibility Mapping using Deep Learning | arXiv:2601.07268v1 Announce Type: new Abstract: Data-driven landslide susceptibility mapping (LSM) typically relies on landslide conditioning factors (LCFs), whose availability, heterogeneity, and preprocessing-related uncertainties can constrain mapping reliability. Recently, Google AlphaEarth (AE) embeddings, derived... | https://arxiv.org/abs/2601.07268 | Academic Papers | svg |
08581961904381716443389a9a1c4098c08ce54e01b17b792144a935cce8a037 | 2026-01-13T00:00:00-05:00 | Document-Level Zero-Shot Relation Extraction with Entity Side Information | arXiv:2601.07271v1 Announce Type: new Abstract: Document-Level Zero-Shot Relation Extraction (DocZSRE) aims to predict unseen relation labels in text documents without prior training on specific relations. Existing approaches rely on Large Language Models (LLMs) to generate synthetic data for unseen labels, which poses... | https://arxiv.org/abs/2601.07271 | Academic Papers | svg |
03a829de4af01ee5a53a8e274642bcd4af8287155e10a093465a206f6aafd980 | 2026-01-13T00:00:00-05:00 | PALUM: Part-based Attention Learning for Unified Motion Retargeting | arXiv:2601.07272v1 Announce Type: new Abstract: Retargeting motion between characters with different skeleton structures is a fundamental challenge in computer animation. When source and target characters have vastly different bone arrangements, maintaining the original motion's semantics and quality becomes increasing... | https://arxiv.org/abs/2601.07272 | Academic Papers | svg |
7d5bd67b23a0d44b519df0fc693d0ca1de924d8865824665791c3e8a057726c5 | 2026-01-13T00:00:00-05:00 | GenDet: Painting Colored Bounding Boxes on Images via Diffusion Model for Object Detection | arXiv:2601.07273v1 Announce Type: new Abstract: This paper presents GenDet, a novel framework that redefines object detection as an image generation task. In contrast to traditional approaches, GenDet adopts a pioneering approach by leveraging generative modeling: it conditions on the input image and directly generates... | https://arxiv.org/abs/2601.07273 | Academic Papers | svg |
bddf551a4afbf8f0a335a6da37a9ca2cecb5cedc19b419f00359cb3c27d81ec5 | 2026-01-13T00:00:00-05:00 | Towards Comprehensive Semantic Speech Embeddings for Chinese Dialects | arXiv:2601.07274v1 Announce Type: new Abstract: Despite having hundreds of millions of speakers, Chinese dialects lag behind Mandarin in speech and language technologies. Most varieties are primarily spoken, making dialect-to-Mandarin speech-LLMs (large language models) more practical than dialect LLMs. Building dialec... | https://arxiv.org/abs/2601.07274 | Academic Papers | svg |
b6af1e67baf6aeeda2517b880a2fb65130f5164589a1bc24a97ad3a9e5ee500c | 2026-01-13T00:00:00-05:00 | A High-Recall Cost-Sensitive Machine Learning Framework for Real-Time Online Banking Transaction Fraud Detection | arXiv:2601.07276v1 Announce Type: new Abstract: Fraudulent activities on digital banking services are becoming more intricate by the day, challenging existing defenses. While older rule driven methods struggle to keep pace, even precision focused algorithms fall short when new scams are introduced. These tools typicall... | https://arxiv.org/abs/2601.07276 | Academic Papers | svg |
163965fcfae1bb54a90c2d6cddd5a9e4e788ec4863559b6d0fdd08b962fc8ad1 | 2026-01-13T00:00:00-05:00 | Parametric Probabilistic Manifold Decomposition for Nonlinear Model Reduction | arXiv:2601.07278v1 Announce Type: new Abstract: Probabilistic Manifold Decomposition (PMD)\cite{doi:10.1137/25M1738863}, developed in our earlier work, provides a nonlinear model reduction by embedding high-dimensional dynamics onto low-dimensional probabilistic manifolds. The PMD has demonstrated strong performance fo... | https://arxiv.org/abs/2601.07278 | Academic Papers | svg |
a931f29b5cf4fc0515496888020961ea683ea998ac0f55d0c14175237462e550 | 2026-01-13T00:00:00-05:00 | Coalition Tactics: Bribery and Control in Parliamentary Elections | arXiv:2601.07279v1 Announce Type: new Abstract: Strategic manipulation of elections is typically studied in the context of promoting individual candidates. In parliamentary elections, however, the focus shifts: voters may care more about the overall governing coalition than the individual parties' seat counts. This pap... | https://arxiv.org/abs/2601.07279 | Academic Papers | svg |
428faaafbfdf5be89a3b0a2923aed7f45a103fdd76f133469fde4a6b5c0e79c0 | 2026-01-13T00:00:00-05:00 | ReasonTabQA: A Comprehensive Benchmark for Table Question Answering from Real World Industrial Scenarios | arXiv:2601.07280v1 Announce Type: new Abstract: Recent advancements in Large Language Models (LLMs) have significantly catalyzed table-based question answering (TableQA). However, existing TableQA benchmarks often overlook the intricacies of industrial scenarios, which are characterized by multi-table structures, neste... | https://arxiv.org/abs/2601.07280 | Academic Papers | svg |
a978b04b10a01303d2c595e69ecaa455535cb377d97bb5a6a5df25c12db5cfc6 | 2026-01-13T00:00:00-05:00 | AdaMorph: Unified Motion Retargeting via Embodiment-Aware Adaptive Transformers | arXiv:2601.07284v1 Announce Type: new Abstract: Retargeting human motion to heterogeneous robots is a fundamental challenge in robotics, primarily due to the severe kinematic and dynamic discrepancies between varying embodiments. Existing solutions typically resort to training embodiment-specific models, which scales p... | https://arxiv.org/abs/2601.07284 | Academic Papers | svg |
8759ff3ac45c9e8dfb46ae3844628ac282ad00ca18dd5cc7e455e3bba48e4ea8 | 2026-01-13T00:00:00-05:00 | Focal Guidance: Unlocking Controllability from Semantic-Weak Layers in Video Diffusion Models | arXiv:2601.07287v1 Announce Type: new Abstract: The task of Image-to-Video (I2V) generation aims to synthesize a video from a reference image and a text prompt. This requires diffusion models to reconcile high-frequency visual constraints and low-frequency textual guidance during the denoising process. However, while e... | https://arxiv.org/abs/2601.07287 | Academic Papers | svg |
ada90e17a38c9ed97c201aa3547d918e5682b043b6a80254a4a811af623d51e1 | 2026-01-13T00:00:00-05:00 | Kernel Alignment-based Multi-view Unsupervised Feature Selection with Sample-level Adaptive Graph Learning | arXiv:2601.07288v1 Announce Type: new Abstract: Although multi-view unsupervised feature selection (MUFS) has demonstrated success in dimensionality reduction for unlabeled multi-view data, most existing methods reduce feature redundancy by focusing on linear correlations among features but often overlook complex nonli... | https://arxiv.org/abs/2601.07288 | Academic Papers | svg |
5dbfdcee7f41abacae155a4deb11f691e0f3a134341a74010e1b482cb57c4731 | 2026-01-13T00:00:00-05:00 | VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding | arXiv:2601.07290v1 Announce Type: new Abstract: This paper presents VideoLoom, a unified Video Large Language Model (Video LLM) for joint spatial-temporal understanding. To facilitate the development of fine-grained spatial and temporal localization capabilities, we curate LoomData-8.7k, a human-centric video dataset w... | https://arxiv.org/abs/2601.07290 | Academic Papers | svg |
c40d8f0b2119a6052bb6f5c6e6fe07b088a7aa8bfed30859ba5c7d58520e292e | 2026-01-13T00:00:00-05:00 | A Visual Semantic Adaptive Watermark grounded by Prefix-Tuning for Large Vision-Language Model | arXiv:2601.07291v1 Announce Type: new Abstract: Watermarking has emerged as a pivotal solution for content traceability and intellectual property protection in Large Vision-Language Models (LVLMs). However, vision-agnostic watermarks introduce visually irrelevant tokens and disrupt visual grounding by enforcing indiscr... | https://arxiv.org/abs/2601.07291 | Academic Papers | svg |
d83f587aae0756e5a062bc0d4e8065b2d4e4bdf51421c3f7d2b0d836c76094b7 | 2026-01-13T00:00:00-05:00 | Inference-Time Scaling for Visual AutoRegressive modeling by Searching Representative Samples | arXiv:2601.07293v1 Announce Type: new Abstract: While inference-time scaling has significantly enhanced generative quality in large language and diffusion models, its application to vector-quantized (VQ) visual autoregressive modeling (VAR) remains unexplored. We introduce VAR-Scaling, the first general framework for i... | https://arxiv.org/abs/2601.07293 | Academic Papers | svg |
2f0c362b43a7f53d019ed4c067b20613e65b38d9874e45445de978de59edb450 | 2026-01-13T00:00:00-05:00 | Towards Multi-Behavior Multi-Task Recommendation via Behavior-informed Graph Embedding Learning | arXiv:2601.07294v1 Announce Type: new Abstract: Multi-behavior recommendation (MBR) aims to improve the performance w.r.t. the target behavior (i.e., purchase) by leveraging auxiliary behaviors (e.g., click, favourite). However, in real-world scenarios, a recommendation method often needs to process different types of ... | https://arxiv.org/abs/2601.07294 | Academic Papers | svg |
20196ca73d5562829f078725f86db809d1ddbcb95294a6319405910c60218985 | 2026-01-13T00:00:00-05:00 | Stochastic Power-Water Coordination: Unlocking Flexibility in Hybrid RO Desalination Plants via Variable-Speed Pumps and Tank Mixing | arXiv:2601.07295v1 Announce Type: new Abstract: Water desalination plants (DPs) are among the most critical infrastructures and largest electricity loads in water-scarce regions worldwide. Although reverse osmosis (RO) desalination is the most energy-efficient and dominant technology, it remains energy-intensive but ca... | https://arxiv.org/abs/2601.07295 | Academic Papers | svg |
048cb82e2e65ae3bae6f05149182fdedc5d857985326865ea7993d9865404dcd | 2026-01-13T00:00:00-05:00 | LRAS: Advanced Legal Reasoning with Agentic Search | arXiv:2601.07296v1 Announce Type: new Abstract: While Large Reasoning Models (LRMs) have demonstrated exceptional logical capabilities in mathematical domains, their application to the legal field remains hindered by the strict requirements for procedural rigor and adherence to legal logic. Existing legal LLMs, which r... | https://arxiv.org/abs/2601.07296 | Academic Papers | svg |
f3b2c99010012a290f42559b9ad6c14bdfa54c327efaf482b431aacd6db072d4 | 2026-01-13T00:00:00-05:00 | Mimic Human Cognition, Master Multi-Image Reasoning: A Meta-Action Framework for Enhanced Visual Understanding | arXiv:2601.07298v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) excel at single-image understanding, they exhibit significantly degraded performance in multi-image reasoning scenarios. Multi-image reasoning presents fundamental challenges including complex inter-relationships between imag... | https://arxiv.org/abs/2601.07298 | Academic Papers | svg |
9b3da63360906117f591f9aad1ff2eab5fc2223268391f4dc28025064899bcb4 | 2026-01-13T00:00:00-05:00 | Engineering Decisions in MBSE: Insights for a Decision Capture Framework Development | arXiv:2601.07301v1 Announce Type: new Abstract: Decision-making is a core engineering design activity that conveys the engineer's knowledge and translates it into courses of action. Capturing this form of knowledge can reap potential benefits for the engineering teams and enhance development efficiency. Despite its cle... | https://arxiv.org/abs/2601.07301 | Academic Papers | svg |
1a223c850767024e284b96e28245b28288ef07101dd7455cf24a942a0d28609e | 2026-01-13T00:00:00-05:00 | ESDD2: Environment-Aware Speech and Sound Deepfake Detection Challenge Evaluation Plan | arXiv:2601.07303v1 Announce Type: new Abstract: Audio recorded in real-world environments often contains a mixture of foreground speech and background environmental sounds. With rapid advances in text-to-speech, voice conversion, and other generation models, either component can now be modified independently. Such comp... | https://arxiv.org/abs/2601.07303 | Academic Papers | svg |
011ee68c8de99ca819f7c50ed473779f5d267b7f128030e79fbf6d5dae418758 | 2026-01-13T00:00:00-05:00 | Heterogeneous Multi-Expert Reinforcement Learning for Long-Horizon Multi-Goal Tasks in Autonomous Forklifts | arXiv:2601.07304v1 Announce Type: new Abstract: Autonomous mobile manipulation in unstructured warehouses requires a balance between efficient large-scale navigation and high-precision object interaction. Traditional end-to-end learning approaches often struggle to handle the conflicting demands of these distinct phase... | https://arxiv.org/abs/2601.07304 | Academic Papers | svg |
27035d6657760ef66b3fc50d312a0db5d02c9560d9468c52e3c097098966b22b | 2026-01-13T00:00:00-05:00 | Memory-Based Malware Detection under Limited Data Conditions: A Comparative Evaluation of TabPFN and Ensemble Models | arXiv:2601.07305v1 Announce Type: new Abstract: Artificial intelligence and machine learning have significantly advanced malware research by enabling automated threat detection and behavior analysis. However, the availability of exploitable data is limited, due to the absence of large datasets with real-world data. Des... | https://arxiv.org/abs/2601.07305 | Academic Papers | svg |
35fe0839c2b811a1075235946f966197272bd1486ea9fc37e1a5533b65e96c20 | 2026-01-13T00:00:00-05:00 | Low-Altitude Satellite-AAV Collaborative Joint Mobile Edge Computing and Data Collection via Diffusion-based Deep Reinforcement Learning | arXiv:2601.07307v1 Announce Type: new Abstract: The integration of satellite and autonomous aerial vehicle (AAV) communications has become essential for the scenarios requiring both wide coverage and rapid deployment, particularly in remote or disaster-stricken areas where the terrestrial infrastructure is unavailable.... | https://arxiv.org/abs/2601.07307 | Academic Papers | svg |
b7cd45960144f695a1efc0c2d9ab6dad098c6f2ea18a6f83acdb376c73d70a98 | 2026-01-13T00:00:00-05:00 | Bringing Computation to the data: Interoperable serverless function execution for astrophysical data analysis in the SRCNet | arXiv:2601.07308v1 Announce Type: new Abstract: Serverless computing is a paradigm in which the underlying infrastructure is fully managed by the provider, enabling applications and services to be executed with elastic resource provisioning and minimal operational overhead. A core model within this paradigm is Function... | https://arxiv.org/abs/2601.07308 | Academic Papers | svg |
14af22124a4521ef24b6946fe0155d0b0d9f067120fb9aed4ed252eb84c1f99f | 2026-01-13T00:00:00-05:00 | ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging | arXiv:2601.07309v1 Announce Type: new Abstract: Interactive large language model agents have advanced rapidly, but most remain specialized to a single environment and fail to adapt robustly to other environments. Model merging offers a training-free alternative by integrating multiple experts into a single model. In th... | https://arxiv.org/abs/2601.07309 | Academic Papers | svg |
015db61b4c6e16c39f2ef3447c4c2d8097c486b7da0886601afcaa6c59be6823 | 2026-01-13T00:00:00-05:00 | Revisiting the Ordering of Channel and Spatial Attention: A Comprehensive Study on Sequential and Parallel Designs | arXiv:2601.07310v1 Announce Type: new Abstract: Attention mechanisms have become a core component of deep learning models, with Channel Attention and Spatial Attention being the two most representative architectures. Current research on their fusion strategies primarily bifurcates into sequential and parallel paradigms... | https://arxiv.org/abs/2601.07310 | Academic Papers | svg |
81bfb3b173bd4e97940d8efabd7c407212e4992bc53762db0aeea340f43e961d | 2026-01-13T00:00:00-05:00 | PsyCLIENT: Client Simulation via Conversational Trajectory Modeling for Trainee Practice and Model Evaluation in Mental Health Counseling | arXiv:2601.07312v1 Announce Type: new Abstract: LLM-based client simulation has emerged as a promising tool for training novice counselors and evaluating automated counseling systems. However, existing client simulation approaches face three key challenges: (1) limited diversity and realism in client profiles, (2) the ... | https://arxiv.org/abs/2601.07312 | Academic Papers | svg |
dfd9e657a77573d262408d8d95a70be3bf0965e8fe3bfbbd2fe6240f9b113aa1 | 2026-01-13T00:00:00-05:00 | Explaining Machine Learning Predictive Models through Conditional Expectation Methods | arXiv:2601.07313v1 Announce Type: new Abstract: The rapid adoption of complex Artificial Intelligence (AI) and Machine Learning (ML) models has led to their characterization as black boxes due to the difficulty of explaining their internal decision-making processes. This lack of transparency hinders users' ability to u... | https://arxiv.org/abs/2601.07313 | Academic Papers | svg |
9ca81575831238194bbee111ae0ccc1f18b928ad1c8190a941e9321d77ea6395 | 2026-01-13T00:00:00-05:00 | Mitrasamgraha: A Comprehensive Classical Sanskrit Machine Translation Dataset | arXiv:2601.07314v1 Announce Type: new Abstract: While machine translation is regarded as a "solved problem" for many high-resource languages, close analysis quickly reveals that this is not the case for content that shows challenges such as poetic language, philosophical concepts, multi-layered metaphorical expressions... | https://arxiv.org/abs/2601.07314 | Academic Papers | svg |
241b97ef1810eb8d7dc4b083381d110d197c8f595cc8e5b5b1dc17460f97b1e0 | 2026-01-13T00:00:00-05:00 | VLM-CAD: VLM-Optimized Collaborative Agent Design Workflow for Analog Circuit Sizing | arXiv:2601.07315v1 Announce Type: new Abstract: Analog mixed-signal circuit sizing involves complex trade-offs within high-dimensional design spaces. Existing automatic analog circuit sizing approaches often underutilize circuit schematics and lack the explainability required for industry adoption. To tackle these chal... | https://arxiv.org/abs/2601.07315 | Academic Papers | svg |
45a72ba2a017dbaf2046fb756198390cd42b5533428b9fabcd8ee687f19baac4 | 2026-01-13T00:00:00-05:00 | BEAT-Net: Injecting Biomimetic Spatio-Temporal Priors for Interpretable ECG Classification | arXiv:2601.07316v1 Announce Type: new Abstract: Although deep learning has advanced automated electrocardiogram (ECG) diagnosis, prevalent supervised methods typically treat recordings as undifferentiated one-dimensional (1D) signals or two-dimensional (2D) images. This formulation compels models to learn physiological... | https://arxiv.org/abs/2601.07316 | Academic Papers | svg |
61a3b583704efd8a26581fab8ab66eb1248d82ccb21404a83fa9637913d4145c | 2026-01-13T00:00:00-05:00 | Engineering Favorable Propagation: Near-Field IRS Deployment for Spatial Multiplexing | arXiv:2601.07317v1 Announce Type: new Abstract: In intelligent reflecting surface IRS assisted multiple input multiple output MIMO systems, a strong line of sight LoS link is required to compensate for the severe cascaded path loss. However, such a link renders the effective channel highly rank deficient and fundamenta... | https://arxiv.org/abs/2601.07317 | Academic Papers | svg |
9052142238198902e2060c88afb2badbaeb263bcd4a523d738ddaed3682b07d8 | 2026-01-13T00:00:00-05:00 | Segmental Advantage Estimation: Enhancing PPO for Long-Context LLM Training | arXiv:2601.07320v1 Announce Type: new Abstract: Training Large Language Models (LLMs) for reasoning tasks is increasingly driven by Reinforcement Learning with Verifiable Rewards (RLVR), where Proximal Policy Optimization (PPO) provides a principled framework for stable policy updates. However, the practical applicatio... | https://arxiv.org/abs/2601.07320 | Academic Papers | svg |
6f9f9d43870cbb3282b01380b52fae76f27d36171f447c42df398cdc63705c51 | 2026-01-13T00:00:00-05:00 | Performance Bounds of Joint Detection with Kalman Filtering and Channel Decoding for Wireless Networked Control Systems | arXiv:2601.07322v1 Announce Type: new Abstract: The joint detection uses Kalman filtering (KF) to estimate the prior probability of control outputs to assist channel decoding. In this paper, we regard the joint detection as maximum a posteriori (MAP) decoding and derive the lower and upper bounds based on the pairwise ... | https://arxiv.org/abs/2601.07322 | Academic Papers | svg |
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