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70e0533314d64cb0a4646d605446ddec8ec6d66495fb736dcdb1bece2e1f3a5c | 2026-02-02T00:00:00-05:00 | On the convergence and efficiency of splitting schemes for the Cahn-Hilliard-Biot model | arXiv:2601.22854v1 Announce Type: new Abstract: In this paper, we present a novel solution strategy for the Cahn-Hilliard-Biot model, a three-way coupled system that features the interplay of solid phase separation, fluid dynamics, and elastic deformations in porous media. It is a phase-field model that combines the Ca... | https://arxiv.org/abs/2601.22854 | Academic Papers | svg |
0b6cf5aaca5d6d27e9d13ee9da853672192366f857bc31785342dd03b7f55c04 | 2026-02-02T00:00:00-05:00 | OptiMAG: Structure-Semantic Alignment via Unbalanced Optimal Transport | arXiv:2601.22856v1 Announce Type: new Abstract: Multimodal Attributed Graphs (MAGs) have been widely adopted for modeling complex systems by integrating multi-modal information, such as text and images, on nodes. However, we identify a discrepancy between the implicit semantic structure induced by different modality em... | https://arxiv.org/abs/2601.22856 | Academic Papers | svg |
cd44945bd918c1e7e042ada3a99efaa0d99b017591dad7710620b0923be250ec | 2026-02-02T00:00:00-05:00 | Learning to Build Shapes by Extrusion | arXiv:2601.22858v1 Announce Type: new Abstract: We introduce Text Encoded Extrusion (TEE), a text-based representation that expresses mesh construction as sequences of face extrusions rather than polygon lists, and a method for generating 3D meshes from TEE using a large language model (LLM). By learning extrusion sequ... | https://arxiv.org/abs/2601.22858 | Academic Papers | svg |
df2ffd83371bcfbf30147d2e160b3e992c02323c43e94aa2f66093028b1ebf39 | 2026-02-02T00:00:00-05:00 | MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering | arXiv:2601.22859v1 Announce Type: new Abstract: The evolution of Large Language Model (LLM) agents for software engineering (SWE) is constrained by the scarcity of verifiable datasets, a bottleneck stemming from the complexity of constructing executable environments across diverse languages. To address this, we introdu... | https://arxiv.org/abs/2601.22859 | Academic Papers | svg |
0ea559fc4952fb1b04c8f191c9d2fa8b1bc8b77a3c8b407228b7f33171c8e5ca | 2026-02-02T00:00:00-05:00 | Bayesian Interpolating Neural Network (B-INN): a scalable and reliable Bayesian model for large-scale physical systems | arXiv:2601.22860v1 Announce Type: new Abstract: Neural networks and machine learning models for uncertainty quantification suffer from limited scalability and poor reliability compared to their deterministic counterparts. In industry-scale active learning settings, where generating a single high-fidelity simulation may... | https://arxiv.org/abs/2601.22860 | Academic Papers | svg |
3bb20934a1a9054c6fa7faedd8e49a6d4367758385a8ec16a5f1882b821eab3b | 2026-02-02T00:00:00-05:00 | Under-Canopy Terrain Reconstruction in Dense Forests Using RGB Imaging and Neural 3D Reconstruction | arXiv:2601.22861v1 Announce Type: new Abstract: Mapping the terrain and understory hidden beneath dense forest canopies is of great interest for numerous applications such as search and rescue, trail mapping, forest inventory tasks, and more. Existing solutions rely on specialized sensors: either heavy, costly airborne... | https://arxiv.org/abs/2601.22861 | Academic Papers | svg |
b32b6859b1af715ebcaaa33e45e48f6845d395a4d41bd9ee855032ccfda3a914 | 2026-02-02T00:00:00-05:00 | Design of a GPU with Heterogeneous Cores for Graphics | arXiv:2601.22862v1 Announce Type: new Abstract: Heterogeneous architectures can deliver higher performance and energy efficiency than symmetric counterparts by using multiple architectures tuned to different types of workloads. While previous works focused on CPUs, this work extends the concept of heterogeneity to GPUs... | https://arxiv.org/abs/2601.22862 | Academic Papers | svg |
8f031129be91438e1be438aecdcdafbc8205ddc07a12cbed661437faedf7a26d | 2026-02-02T00:00:00-05:00 | {\mu}Touch: Enabling Accurate, Lightweight Self-Touch Sensing with Passive Magnets | arXiv:2601.22864v1 Announce Type: new Abstract: Self-touch gestures (e.g., nuanced facial touches and subtle finger scratches) provide rich insights into human behaviors, from hygiene practices to health monitoring. However, existing approaches fall short in detecting such micro gestures due to their diverse movement p... | https://arxiv.org/abs/2601.22864 | Academic Papers | svg |
49a5e8ce2a54201e594b2fdb7e33e7919936fe96c5beffbecb12dd6ef4c7e92b | 2026-02-02T00:00:00-05:00 | Degradation-Aware Frequency Regulation of a Heterogeneous Battery Fleet via Reinforcement Learning | arXiv:2601.22865v1 Announce Type: new Abstract: Battery energy storage systems are increasingly deployed as fast-responding resources for grid balancing services such as frequency regulation and for mitigating renewable generation uncertainty. However, repeated charging and discharging induces cycling degradation and r... | https://arxiv.org/abs/2601.22865 | Academic Papers | svg |
c95ab55e2e95d03cfe53cce359acf77eae9f8f9e9e492a0784e6d39a4dc8cc66 | 2026-02-02T00:00:00-05:00 | Randomized Methods for Kernelized DMD | arXiv:2601.22867v1 Announce Type: new Abstract: Dynamic Mode Decomposition (DMD) is a data-driven method related to Koopman operator theory that extracts information about dominant dynamics from data snapshots. In this paper we examine techniques to accelerate the application of DMD to large-scale data sets with an eye... | https://arxiv.org/abs/2601.22867 | Academic Papers | svg |
70213006c96044f7bc089684d8901b3ee3f967882bb607b0f2aa28abb3b9d36c | 2026-02-02T00:00:00-05:00 | When Anomalies Depend on Context: Learning Conditional Compatibility for Anomaly Detection | arXiv:2601.22868v1 Announce Type: new Abstract: Anomaly detection is often formulated under the assumption that abnormality is an intrinsic property of an observation, independent of context. This assumption breaks down in many real-world settings, where the same object or action may be normal or anomalous depending on... | https://arxiv.org/abs/2601.22868 | Academic Papers | svg |
4813987de54b28fb647834cd35b8d86ef30c88239e48fdc5f065dafd276ad1b8 | 2026-02-02T00:00:00-05:00 | Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild | arXiv:2601.22871v1 Announce Type: new Abstract: As foundation models (FMs) approach human-level fluency, distinguishing synthetic from organic content has become a key challenge for Trustworthy Web Intelligence. This paper presents JudgeGPT and RogueGPT, a dual-axis framework that decouples "authenticity" from "attribu... | https://arxiv.org/abs/2601.22871 | Academic Papers | svg |
2375c497daef2457edb39841455e2e2e6e8e66035771388690671fd08da83902 | 2026-02-02T00:00:00-05:00 | From Labels to Facets: Building a Taxonomically Enriched Turkish Learner Corpus | arXiv:2601.22875v1 Announce Type: new Abstract: In terms of annotation structure, most learner corpora rely on holistic flat label inventories which, even when extensive, do not explicitly separate multiple linguistic dimensions. This makes linguistically deep annotation difficult and complicates fine-grained analyses ... | https://arxiv.org/abs/2601.22875 | Academic Papers | svg |
c80373841d52fd5417e82ee8af489a50bc02494fa87590082b94e63885f28064 | 2026-02-02T00:00:00-05:00 | Matterhorn: Efficient Analog Sparse Spiking Transformer Architecture with Masked Time-To-First-Spike Encoding | arXiv:2601.22876v1 Announce Type: new Abstract: Spiking neural networks (SNNs) have emerged as a promising candidate for energy-efficient LLM inference. However, current energy evaluations for SNNs primarily focus on counting accumulate operations, and fail to account for real-world hardware costs such as data movement... | https://arxiv.org/abs/2601.22876 | Academic Papers | svg |
73be6cc5478baf86ca1b41f7a823b9890adb75946652ce1b876f35361fcef58a | 2026-02-02T00:00:00-05:00 | Synthetic Time Series Generation via Complex Networks | arXiv:2601.22879v1 Announce Type: new Abstract: Time series data are essential for a wide range of applications, particularly in developing robust machine learning models. However, access to high-quality datasets is often limited due to privacy concerns, acquisition costs, and labeling challenges. Synthetic time series... | https://arxiv.org/abs/2601.22879 | Academic Papers | svg |
138d33118d62f7f159568f88fdf3369fb80740d6882a1c2d9041e7e61942d084 | 2026-02-02T00:00:00-05:00 | Reinforcement Learning-Based Co-Design and Operation of Chiller and Thermal Energy Storage for Cost-Optimal HVAC Systems | arXiv:2601.22880v1 Announce Type: new Abstract: We study the joint operation and sizing of cooling infrastructure for commercial HVAC systems using reinforcement learning, with the objective of minimizing life-cycle cost over a 30-year horizon. The cooling system consists of a fixed-capacity electric chiller and a ther... | https://arxiv.org/abs/2601.22880 | Academic Papers | svg |
1e7e3aca99d327411eb448081fd16e7576442a411e1422dd76b2398b188071ed | 2026-02-02T00:00:00-05:00 | AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice Systems | arXiv:2601.22881v1 Announce Type: new Abstract: Microservice systems (MSS) have become a predominant architectural style for cloud services. Yet the community still lacks high-quality, publicly available datasets for anomaly detection (AD) and root cause analysis (RCA) in MSS. Most benchmarks emphasize performance-rela... | https://arxiv.org/abs/2601.22881 | Academic Papers | svg |
8f51ddd32789b43970899505f4150f86ebd5de1c174761f4b490c9f6270c8fba | 2026-02-02T00:00:00-05:00 | Leveraging LLMs For Turkish Skill Extraction | arXiv:2601.22885v1 Announce Type: new Abstract: Skill extraction is a critical component of modern recruitment systems, enabling efficient job matching, personalized recommendations, and labor market analysis. Despite T\"urkiye's significant role in the global workforce, Turkish, a morphologically complex language, lac... | https://arxiv.org/abs/2601.22885 | Academic Papers | svg |
3162e45d3087289f9c94bd95753626d04df32da786dd154d05d51db1588299b0 | 2026-02-02T00:00:00-05:00 | MoVE: Mixture of Value Embeddings -- A New Axis for Scaling Parametric Memory in Autoregressive Models | arXiv:2601.22887v1 Announce Type: new Abstract: Autoregressive sequence modeling stands as the cornerstone of modern Generative AI, powering results across diverse modalities ranging from text generation to image generation. However, a fundamental limitation of this paradigm is the rigid structural coupling of model ca... | https://arxiv.org/abs/2601.22887 | Academic Papers | svg |
4a35e68315af4855be5ccdf1fcc0410529d0cec87563accc098cb79ea54a166b | 2026-02-02T00:00:00-05:00 | Should LLMs, $\textit{like}$, Generate How Users Talk? Building Dialect-Accurate Dialog[ue]s Beyond the American Default with MDial | arXiv:2601.22888v1 Announce Type: new Abstract: More than 80% of the 1.6 billion English speakers do not use Standard American English (SAE) and experience higher failure rates and stereotyped responses when interacting with LLMs as a result. Yet multi-dialectal performance remains underexplored. We introduce $\textbf{... | https://arxiv.org/abs/2601.22888 | Academic Papers | svg |
f24f7f1018ff0ec31041c3c630ef143931f1f2290e8ad2fedd6619e963cdd470 | 2026-02-02T00:00:00-05:00 | DiffuSpeech: Silent Thought, Spoken Answer via Unified Speech-Text Diffusion | arXiv:2601.22889v1 Announce Type: new Abstract: Current speech language models generate responses directly without explicit reasoning, leading to errors that cannot be corrected once audio is produced. We introduce \textbf{``Silent Thought, Spoken Answer''} -- a paradigm where speech LLMs generate internal text reasoni... | https://arxiv.org/abs/2601.22889 | Academic Papers | svg |
471335597d780c31e3d741870970137d8ae1123554aabd7e3dcd128667fdcbc4 | 2026-02-02T00:00:00-05:00 | PlatoLTL: Learning to Generalize Across Symbols in LTL Instructions for Multi-Task RL | arXiv:2601.22891v1 Announce Type: new Abstract: A central challenge in multi-task reinforcement learning (RL) is to train generalist policies capable of performing tasks not seen during training. To facilitate such generalization, linear temporal logic (LTL) has recently emerged as a powerful formalism for specifying s... | https://arxiv.org/abs/2601.22891 | Academic Papers | svg |
8b3bbeae0cd7b6257c033e053b3ffb8b6fbd2bd54871d99e8087ef9b94f3e7bf | 2026-02-02T00:00:00-05:00 | Assessing the Real-World Impact of Post-Quantum Cryptography on WPA-Enterprise Networks | arXiv:2601.22892v1 Announce Type: new Abstract: The advent of large-scale quantum computers poses a significant threat to contemporary network security protocols, including Wi-Fi Protected Access (WPA)-Enterprise authentication. To mitigate this threat, the adoption of Post-Quantum Cryptography (PQC) is critical. In th... | https://arxiv.org/abs/2601.22892 | Academic Papers | svg |
59cb1ab240846dc38951a886a7990676bdab2fb2602dcc55087ffc1afe1cf560 | 2026-02-02T00:00:00-05:00 | When Machines Get It Wrong: Large Language Models Perpetuate Autism Myths More Than Humans Do | arXiv:2601.22893v1 Announce Type: new Abstract: As Large Language Models become ubiquitous sources of health information, understanding their capacity to accurately represent stigmatized conditions is crucial for responsible deployment. This study examines whether leading AI systems perpetuate or challenge misconceptio... | https://arxiv.org/abs/2601.22893 | Academic Papers | svg |
80e2d6fa76370fbde38e3bdfcb80a8c3c723629bc7db45ed9347f92ec1b5e0d5 | 2026-02-02T00:00:00-05:00 | Calibrated Multivariate Distributional Regression with Pre-Rank Regularization | arXiv:2601.22895v1 Announce Type: new Abstract: The goal of probabilistic prediction is to issue predictive distributions that are as informative as possible, subject to being calibrated. Despite substantial progress in the univariate setting, achieving multivariate calibration remains challenging. Recent work has intr... | https://arxiv.org/abs/2601.22895 | Academic Papers | svg |
69d06cded02e9eef4aaf6e3bd71e70c83ddf50d2fa324a10493d99472b092e84 | 2026-02-02T00:00:00-05:00 | Game-Theoretic Co-Evolution for LLM-Based Heuristic Discovery | arXiv:2601.22896v1 Announce Type: new Abstract: Large language models (LLMs) have enabled rapid progress in automatic heuristic discovery (AHD), yet most existing methods are predominantly limited by static evaluation against fixed instance distributions, leading to potential overfitting and poor generalization under d... | https://arxiv.org/abs/2601.22896 | Academic Papers | svg |
a4e2bceb79e7693589f3f555ca1a3814d663997558229b6d8b0a9be97815f231 | 2026-02-02T00:00:00-05:00 | Uncertainty-Aware Extrapolation in Bayesian Oblique Trees | arXiv:2601.22899v1 Announce Type: new Abstract: Decision trees are widely used due to their interpretability and efficiency, but they struggle in regression tasks that require reliable extrapolation and well-calibrated uncertainty. Piecewise-constant leaf predictions are bounded by the training targets and often become... | https://arxiv.org/abs/2601.22899 | Academic Papers | svg |
4afd0a2fda5a8a84486e81e15e201f1178ba3904e6f58ec311a276450552107f | 2026-02-02T00:00:00-05:00 | MulFeRL: Enhancing Reinforcement Learning with Verbal Feedback in a Multi-turn Loop | arXiv:2601.22900v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is widely used to improve reasoning in multiple domains, yet outcome-only scalar rewards are often sparse and uninformative, especially on failed samples, where they merely indicate failure and provide no insight into ... | https://arxiv.org/abs/2601.22900 | Academic Papers | svg |
798f9b9c4d167da764f0ffa69865e2e0c1e7bba4ddc79722432eda3489a7b33b | 2026-02-02T00:00:00-05:00 | Status Updating via Integrated Sensing and Communication: Freshness Optimisation | arXiv:2601.22901v1 Announce Type: new Abstract: This paper studies strategic design in an integrated sensing and communication (ISAC) architecture for status updating of remotely navigating agents. We consider an ISAC-enabled base station that can sense the state of a remote source and communicate this information back... | https://arxiv.org/abs/2601.22901 | Academic Papers | svg |
ce4960604df8de455cade8562631a019ad5198dc15c453ae9e0df7559970aeaa | 2026-02-02T00:00:00-05:00 | DINO-SAE: DINO Spherical Autoencoder for High-Fidelity Image Reconstruction and Generation | arXiv:2601.22904v1 Announce Type: new Abstract: Recent studies have explored using pretrained Vision Foundation Models (VFMs) such as DINO for generative autoencoders, showing strong generative performance. Unfortunately, existing approaches often suffer from limited reconstruction fidelity due to the loss of high-freq... | https://arxiv.org/abs/2601.22904 | Academic Papers | svg |
9188953788724ebb49f89ef7d945a073f5d194d4168e0f76fea0e39c78d8331a | 2026-02-02T00:00:00-05:00 | FlexLoRA: Entropy-Guided Flexible Low-Rank Adaptation | arXiv:2601.22905v1 Announce Type: new Abstract: Large pre-trained models achieve remarkable success across diverse domains, yet fully fine-tuning incurs prohibitive computational and memory costs. Parameter-efficient fine-tuning (PEFT) has thus become a mainstream paradigm. Among them, Low-Rank Adaptation (LoRA) introd... | https://arxiv.org/abs/2601.22905 | Academic Papers | svg |
d55af531935ca73a1d1839b74ce995d09098ed07563a622d596d5019cdc782ef | 2026-02-02T00:00:00-05:00 | Feedback Control via Integrated Sensing and Communication: Uncertainty Optimisation | arXiv:2601.22912v1 Announce Type: new Abstract: This paper studies strategic design in an integrated sensing and communication (ISAC) architecture for feedback control of cyber-physical systems. We focus on a setting in which the regulation of a physical process (i.e., remote source) is performed via an ISAC-enabled ba... | https://arxiv.org/abs/2601.22912 | Academic Papers | svg |
d17e0ad63ef34dd47d3e6d93f3980fa0734b14c77075a3568604ca4080fd8291 | 2026-02-02T00:00:00-05:00 | Multi-Cue Anomaly Detection and Localization under Data Contamination | arXiv:2601.22913v1 Announce Type: new Abstract: Visual anomaly detection in real-world industrial settings faces two major limitations. First, most existing methods are trained on purely normal data or on unlabeled datasets assumed to be predominantly normal, presuming the absence of contamination, an assumption that i... | https://arxiv.org/abs/2601.22913 | Academic Papers | svg |
473bada8a5ec644c60e108dadaf5ea0dd85d2f014606b6bee841deeacc99e2d1 | 2026-02-02T00:00:00-05:00 | LLMDR: Large language model driven framework for missing data recovery in mixed data under low resource regime | arXiv:2601.22916v1 Announce Type: new Abstract: The missing data problem is one of the important issues to address for achieving data quality. While imputation-based methods are designed to achieve data completeness, their efficacy is observed to be diminishing as and when there is increasing in the missingness percent... | https://arxiv.org/abs/2601.22916 | Academic Papers | svg |
fa80dc72847edc12c727c4e62a45e9a1bf26d90fcacaec3c677b87c037594acf | 2026-02-02T00:00:00-05:00 | Deep in the Jungle: Towards Automating Chimpanzee Population Estimation | arXiv:2601.22917v1 Announce Type: new Abstract: The estimation of abundance and density in unmarked populations of great apes relies on statistical frameworks that require animal-to-camera distance measurements. In practice, acquiring these distances depends on labour-intensive manual interpretation of animal observati... | https://arxiv.org/abs/2601.22917 | Academic Papers | svg |
0926889d1a0182cb09f430a757be52830368e0d60061989aa0aea619f01cd8e0 | 2026-02-02T00:00:00-05:00 | A Serverless Edge-Native Data Processing Architecture for Autonomous Driving Training | arXiv:2601.22919v1 Announce Type: new Abstract: Data is both the key enabler and a major bottleneck for machine learning in autonomous driving. Effective model training requires not only large quantities of sensor data but also balanced coverage that includes rare yet safety-critical scenarios. Capturing such events de... | https://arxiv.org/abs/2601.22919 | Academic Papers | svg |
97bf0573e0a5bc9e94fcb318d4d022c0aebacfbba238f8768e9af8fe1718a3b9 | 2026-02-02T00:00:00-05:00 | Q-Hawkeye: Reliable Visual Policy Optimization for Image Quality Assessment | arXiv:2601.22920v1 Announce Type: new Abstract: Image Quality Assessment (IQA) predicts perceptual quality scores consistent with human judgments. Recent RL-based IQA methods built on MLLMs focus on generating visual quality descriptions and scores, ignoring two key reliability limitations: (i) although the model's pre... | https://arxiv.org/abs/2601.22920 | Academic Papers | svg |
0bd6330b48b7efd7b67f2d3e54717e6ad25ad235cf0001fdec3c4866dce43d92 | 2026-02-02T00:00:00-05:00 | Evaluating Large Language Models for Security Bug Report Prediction | arXiv:2601.22921v1 Announce Type: new Abstract: Early detection of security bug reports (SBRs) is critical for timely vulnerability mitigation. We present an evaluation of prompt-based engineering and fine-tuning approaches for predicting SBRs using Large Language Models (LLMs). Our findings reveal a distinct trade-off... | https://arxiv.org/abs/2601.22921 | Academic Papers | svg |
2343c5b8f2d4fc6fd88c7c4ce95a9a6753bea21672602d9a63af5a007b90d913 | 2026-02-02T00:00:00-05:00 | BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models | arXiv:2601.22925v1 Announce Type: new Abstract: Recent years have witnessed a rapid surge in research leveraging Large Language Models (LLMs) for recommendation. These methods typically employ supervised fine-tuning (SFT) to adapt LLMs to recommendation scenarios, and utilize beam search during inference to efficiently... | https://arxiv.org/abs/2601.22925 | Academic Papers | svg |
345d5bb5b7f0bbb5f4bdcd1bed7681849a2fe9c2822feafd23cc0c26c81f894f | 2026-02-02T00:00:00-05:00 | Toward Fully Autonomous Driving: AI, Challenges, Opportunities, and Needs | arXiv:2601.22927v1 Announce Type: new Abstract: Automated driving (AD) is promising, but the transition to fully autonomous driving is, among other things, subject to the real, ever-changing open world and the resulting challenges. However, research in the field of AD demonstrates the ability of artificial intelligence... | https://arxiv.org/abs/2601.22927 | Academic Papers | svg |
aa59bfa77b7ded7eae2cbfdf2548529ec50a38c1292926ff39e457509b367c9d | 2026-02-02T00:00:00-05:00 | LLMs Explain't: A Post-Mortem on Semantic Interpretability in Transformer Models | arXiv:2601.22928v1 Announce Type: new Abstract: Large Language Models (LLMs) are becoming increasingly popular in pervasive computing due to their versatility and strong performance. However, despite their ubiquitous use, the exact mechanisms underlying their outstanding performance remain unclear. Different methods fo... | https://arxiv.org/abs/2601.22928 | Academic Papers | svg |
7b42ebad1cfac184177a07c5ad9b784d2914dff530ab4442cc25e2ce4dca7f37 | 2026-02-02T00:00:00-05:00 | Semantic Leakage from Image Embeddings | arXiv:2601.22929v1 Announce Type: new Abstract: Image embeddings are generally assumed to pose limited privacy risk. We challenge this assumption by formalizing semantic leakage as the ability to recover semantic structures from compressed image embeddings. Surprisingly, we show that semantic leakage does not require e... | https://arxiv.org/abs/2601.22929 | Academic Papers | svg |
7f638ce33e6b104e78e1bed5f63fd9cbd4872a4a615e2832e864b89445d3cbff | 2026-02-02T00:00:00-05:00 | MTDrive: Multi-turn Interactive Reinforcement Learning for Autonomous Driving | arXiv:2601.22930v1 Announce Type: new Abstract: Trajectory planning is a core task in autonomous driving, requiring the prediction of safe and comfortable paths across diverse scenarios. Integrating Multi-modal Large Language Models (MLLMs) with Reinforcement Learning (RL) has shown promise in addressing "long-tail" sc... | https://arxiv.org/abs/2601.22930 | Academic Papers | svg |
d1d7429da063c66a201d52c9d0de98fc7e77bfa46506c98c4b5baba274d0f7f5 | 2026-02-02T00:00:00-05:00 | Benchmarking Machine Translation on Chinese Social Media Texts | arXiv:2601.22931v1 Announce Type: new Abstract: The prevalence of rapidly evolving slang, neologisms, and highly stylized expressions in informal user-generated text, particularly on Chinese social media, poses significant challenges for Machine Translation (MT) benchmarking. Specifically, we identify two primary obsta... | https://arxiv.org/abs/2601.22931 | Academic Papers | svg |
c77577c0a43819693a714507b0bfacedfaa6924df277c19166b172d7e1711b51 | 2026-02-02T00:00:00-05:00 | DC-LA: Difference-of-Convex Langevin Algorithm | arXiv:2601.22932v1 Announce Type: new Abstract: We study a sampling problem whose target distribution is $\pi \propto \exp(-f-r)$ where the data fidelity term $f$ is Lipschitz smooth while the regularizer term $r=r_1-r_2$ is a non-smooth difference-of-convex (DC) function, i.e., $r_1,r_2$ are convex. By leveraging the ... | https://arxiv.org/abs/2601.22932 | Academic Papers | svg |
a9c9e2d86f9468666da8093b7ccda6945abdb0aab92c74a4d9bf9c9f2548a699 | 2026-02-02T00:00:00-05:00 | Protecting Private Code in IDE Autocomplete using Differential Privacy | arXiv:2601.22935v1 Announce Type: new Abstract: Modern Integrated Development Environments (IDEs) increasingly leverage Large Language Models (LLMs) to provide advanced features like code autocomplete. While powerful, training these models on user-written code introduces significant privacy risks, making the models the... | https://arxiv.org/abs/2601.22935 | Academic Papers | svg |
03d7350565e9102697c108c6172eafe9083142a28535131e55ca250b519d1eb3 | 2026-02-02T00:00:00-05:00 | A Real-Time Privacy-Preserving Behavior Recognition System via Edge-Cloud Collaboration | arXiv:2601.22938v1 Announce Type: new Abstract: As intelligent sensing expands into high-privacy environments such as restrooms and changing rooms, the field faces a critical privacy-security paradox. Traditional RGB surveillance raises significant concerns regarding visual recording and storage, while existing privacy... | https://arxiv.org/abs/2601.22938 | Academic Papers | svg |
7fc2f733477c474211d394ff9208ef99f30702272d20c1cdc1ee0dc95948a537 | 2026-02-02T00:00:00-05:00 | FNWoS: Fractional Neural Walk-on-Spheres Methods for High-Dimensional PDEs Driven by $\alpha$-stable L\'{e}vy Process on Irregular Domains | arXiv:2601.22942v1 Announce Type: new Abstract: In this paper, we develop a highly parallel and derivative-free fractional neural walk-on-spheres method (FNWoS) for solving high-dimensional fractional Poisson equations on irregular domains. We first propose a simplified fractional walk-on-spheres (FWoS) scheme that rep... | https://arxiv.org/abs/2601.22942 | Academic Papers | svg |
a19bbd8d732dff4ea4d8884dc9b035b0c61d0350447f8ff07d679676ddae9935 | 2026-02-02T00:00:00-05:00 | Scalable Topology-Preserving Graph Coarsening with Graph Collapse | arXiv:2601.22943v1 Announce Type: new Abstract: Graph coarsening reduces the size of a graph while preserving certain properties. Most existing methods preserve either spectral or spatial characteristics. Recent research has shown that preserving topological features helps maintain the predictive performance of graph n... | https://arxiv.org/abs/2601.22943 | Academic Papers | svg |
5a66453773a526677ad1db769832da0f48f24aee4df632c52ce42fb9406f1011 | 2026-02-02T00:00:00-05:00 | Environment-Conditioned Tail Reweighting for Total Variation Invariant Risk Minimization | arXiv:2601.22944v1 Announce Type: new Abstract: Out-of-distribution (OOD) generalization remains challenging when models simultaneously encounter correlation shifts across environments and diversity shifts driven by rare or hard samples. Existing invariant risk minimization (IRM) methods primarily address spurious corr... | https://arxiv.org/abs/2601.22944 | Academic Papers | svg |
fa335b29339e979a6e81978862fdfa31cc39a569b6962f51dbc5899856af5cc1 | 2026-02-02T00:00:00-05:00 | From Data Leak to Secret Misses: The Impact of Data Leakage on Secret Detection Models | arXiv:2601.22946v1 Announce Type: new Abstract: Machine learning models are increasingly used for software security tasks. These models are commonly trained and evaluated on large Internet-derived datasets, which often contain duplicated or highly similar samples. When such samples are split across training and test se... | https://arxiv.org/abs/2601.22946 | Academic Papers | svg |
97eaa59b1f7c5c4e2dbb8b40b0869d6255ce7d79e0630af963bb8a8468243cbb | 2026-02-02T00:00:00-05:00 | Relaxing Positional Alignment in Masked Diffusion Language Models | arXiv:2601.22947v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) have emerged as a promising alternative to dominant autoregressive approaches. Although they achieve competitive performance on several tasks, a substantial gap remains in open-ended text generation. We hypothesize that one cause o... | https://arxiv.org/abs/2601.22947 | Academic Papers | svg |
86fe35b47c200ef38fc0687f3114f39ccf2933df1e637fffff44819f6da0dec9 | 2026-02-02T00:00:00-05:00 | Alignment among Language, Vision and Action Representations | arXiv:2601.22948v1 Announce Type: new Abstract: A fundamental question in cognitive science and AI concerns whether different learning modalities: language, vision, and action, give rise to distinct or shared internal representations. Traditional views assume that models trained on different data types develop speciali... | https://arxiv.org/abs/2601.22948 | Academic Papers | svg |
459298c616aa538fde77b677a3b9cabe4b486452a0315f4d988c2ef3eed35d95 | 2026-02-02T00:00:00-05:00 | Autonomous Chain-of-Thought Distillation for Graph-Based Fraud Detection | arXiv:2601.22949v1 Announce Type: new Abstract: Graph-based fraud detection on text-attributed graphs (TAGs) requires jointly modeling rich textual semantics and relational dependencies. However, existing LLM-enhanced GNN approaches are constrained by predefined prompting and decoupled training pipelines, limiting reas... | https://arxiv.org/abs/2601.22949 | Academic Papers | svg |
fabf5abca8cfb683a48c568e192ef9be19b6035dc5f1db9497e9b5f052de157f | 2026-02-02T00:00:00-05:00 | Perplexity Cannot Always Tell Right from Wrong | arXiv:2601.22950v1 Announce Type: new Abstract: Perplexity -- a function measuring a model's overall level of "surprise" when encountering a particular output -- has gained significant traction in recent years, both as a loss function and as a simple-to-compute metric of model quality. Prior studies have pointed out se... | https://arxiv.org/abs/2601.22950 | Academic Papers | svg |
b48ecb12f47e7af47285abd45c45dc2a84e98938cbc460696b0897a197950291 | 2026-02-02T00:00:00-05:00 | Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filtering | arXiv:2601.22952v1 Announce Type: new Abstract: Static Application Security Testing (SAST) tools are essential for identifying software vulnerabilities, but they often produce a high volume of false positives (FPs), imposing a substantial manual triage burden on developers. Recent advances in Large Language Model (LLM)... | https://arxiv.org/abs/2601.22952 | Academic Papers | svg |
554307864dc8da7c4d50855dd2a8eeb270d9a26398eec4cca4c3805b13144675 | 2026-02-02T00:00:00-05:00 | Residual Context Diffusion Language Models | arXiv:2601.22954v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode multiple tokens in parallel. However, state-of-the-art block-wise dLLMs rely on a "remasking" mechanism that decodes only the m... | https://arxiv.org/abs/2601.22954 | Academic Papers | svg |
cd33e8403464bac7bd5302f714ac969e436bac114c1cdcf0c26fa97769786ae0 | 2026-02-02T00:00:00-05:00 | SWE-Manager: Selecting and Synthesizing Golden Proposals Before Coding | arXiv:2601.22956v1 Announce Type: new Abstract: Large language model (LLM) research in software engineering has largely focused on tasks such as code generation and bug repair. In practice, teams often draft multiple candidate proposals for fixing an issue and then deliberate on one golden proposal for implementation. ... | https://arxiv.org/abs/2601.22956 | Academic Papers | svg |
67d59e5476ce531d56c61bc5d624e8c01241af3f83e6b810dc4a78ef99ee24eb | 2026-02-02T00:00:00-05:00 | Triage: Hierarchical Visual Budgeting for Efficient Video Reasoning in Vision-Language Models | arXiv:2601.22959v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face significant computational challenges in video processing due to massive data redundancy, which creates prohibitively long token sequences. To address this, we introduce Triage, a training-free, plug-and-play framework that reframes video... | https://arxiv.org/abs/2601.22959 | Academic Papers | svg |
8b37fa63a85e5d87e955c10cdb56441e1a3aaf218a49849d7e463bce78c2a5b7 | 2026-02-02T00:00:00-05:00 | Improving Supervised Machine Learning Performance in Optical Quality Control via Generative AI for Dataset Expansion | arXiv:2601.22961v1 Announce Type: new Abstract: Supervised machine learning algorithms play a crucial role in optical quality control within industrial production. These approaches require representative datasets for effective model training. However, while non-defective components are frequent, defective parts are rar... | https://arxiv.org/abs/2601.22961 | Academic Papers | svg |
35bda6274c082482e1c317327856932c8142ea7c6bff0ae402d055e0443a2a22 | 2026-02-02T00:00:00-05:00 | ERA: Epoch-Resolved Arbitration for Duelling Admins in Group Management CRDTs | arXiv:2601.22963v1 Announce Type: new Abstract: Conflict-Free Replicated Data Types (CRDTs) are used in a range of fields for their coordination-free replication with strong eventual consistency. By prioritising availability over consistency under partition, nodes accumulate events in different orders, and rely on an a... | https://arxiv.org/abs/2601.22963 | Academic Papers | svg |
525833a1c8a89e4146d74d67b77beff566b4311d9fcdaf444da4950fea868ae2 | 2026-02-02T00:00:00-05:00 | EvoClinician: A Self-Evolving Agent for Multi-Turn Medical Diagnosis via Test-Time Evolutionary Learning | arXiv:2601.22964v1 Announce Type: new Abstract: Prevailing medical AI operates on an unrealistic ''one-shot'' model, diagnosing from a complete patient file. However, real-world diagnosis is an iterative inquiry where Clinicians sequentially ask questions and order tests to strategically gather information while managi... | https://arxiv.org/abs/2601.22964 | Academic Papers | svg |
d0e1c58f1ab59bf32faf5dd6dc4448653df101b35013d76b34220d10a12047ac | 2026-02-02T00:00:00-05:00 | Self-Imitated Diffusion Policy for Efficient and Robust Visual Navigation | arXiv:2601.22965v1 Announce Type: new Abstract: Diffusion policies (DP) have demonstrated significant potential in visual navigation by capturing diverse multi-modal trajectory distributions. However, standard imitation learning (IL), which most DP methods rely on for training, often inherits sub-optimality and redunda... | https://arxiv.org/abs/2601.22965 | Academic Papers | svg |
8aeedf6c928182e9ac02142cc2ab3d5378a986d135b098669846cce7b7fbf83d | 2026-02-02T00:00:00-05:00 | A Unified View of Attention and Residual Sinks: Outlier-Driven Rescaling is Essential for Transformer Training | arXiv:2601.22966v1 Announce Type: new Abstract: We investigate the functional role of emergent outliers in large language models, specifically attention sinks (a few tokens that consistently receive large attention logits) and residual sinks (a few fixed dimensions with persistently large activations across most tokens... | https://arxiv.org/abs/2601.22966 | Academic Papers | svg |
ca9e1ffe36a841f38df0c79bafa6e4d8d103468901783a16ad3e0a4e8df70703 | 2026-02-02T00:00:00-05:00 | Improved Algorithms for Nash Welfare in Linear Bandits | arXiv:2601.22969v1 Announce Type: new Abstract: Nash regret has recently emerged as a principled fairness-aware performance metric for stochastic multi-armed bandits, motivated by the Nash Social Welfare objective. Although this notion has been extended to linear bandits, existing results suffer from suboptimality in a... | https://arxiv.org/abs/2601.22969 | Academic Papers | svg |
86eb0c322e923a837750155782a916bafea9421996cb6c290d262ab204de6e42 | 2026-02-02T00:00:00-05:00 | Stabilizing the Q-Gradient Field for Policy Smoothness in Actor-Critic | arXiv:2601.22970v1 Announce Type: new Abstract: Policies learned via continuous actor-critic methods often exhibit erratic, high-frequency oscillations, making them unsuitable for physical deployment. Current approaches attempt to enforce smoothness by directly regularizing the policy's output. We argue that this appro... | https://arxiv.org/abs/2601.22970 | Academic Papers | svg |
bbcb9860ed72e3c0d8b698a9c88af86bc72e3fedbbdadf449b7a003d4b467ab5 | 2026-02-02T00:00:00-05:00 | MiTa: A Hierarchical Multi-Agent Collaboration Framework with Memory-integrated and Task Allocation | arXiv:2601.22974v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have substantially accelerated the development of embodied agents. LLM-based multi-agent systems mitigate the inefficiency of single agents in complex tasks. However, they still suffer from issues such as memory inconsistenc... | https://arxiv.org/abs/2601.22974 | Academic Papers | svg |
575a1e93c64f999a2223a90b59032a2ec87921c9439b959346a7e8a549f88b9e | 2026-02-02T00:00:00-05:00 | Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text | arXiv:2601.22975v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a cornerstone for unlocking complex reasoning in Large Language Models (LLMs). Yet, scaling up RL is bottlenecked by limited existing verifiable data, where improvements increasingly saturate over prolonged ... | https://arxiv.org/abs/2601.22975 | Academic Papers | svg |
b64f5ffae810293c072add9bed988dc4a3c3ae51652cde42bdb66a219798f896 | 2026-02-02T00:00:00-05:00 | Quantifying Model Uniqueness in Heterogeneous AI Ecosystems | arXiv:2601.22977v1 Announce Type: new Abstract: As AI systems evolve from isolated predictors into complex, heterogeneous ecosystems of foundation models and specialized adapters, distinguishing genuine behavioral novelty from functional redundancy becomes a critical governance challenge. Here, we introduce a statistic... | https://arxiv.org/abs/2601.22977 | Academic Papers | svg |
db89fe82c1696cdcbfe3adba73f22f33cee3aebc4933734349c4b0fc5ee8376b | 2026-02-02T00:00:00-05:00 | SpecIBT: Formally Verified Protection Against Speculative Control-Flow Hijacking | arXiv:2601.22978v1 Announce Type: new Abstract: This paper introduces SpecIBT, a formally verified defense against Spectre BTB, RSB, and PHT that combines CET-style hardware-assisted control-flow integrity with compiler-inserted speculative load hardening (SLH). SpecIBT is based on the novel observation that in the pre... | https://arxiv.org/abs/2601.22978 | Academic Papers | svg |
35a5eb4982af4dbe47df5acb0d390d0b73cb811586a53b00c8ba07b0c5867bba | 2026-02-02T00:00:00-05:00 | Learnable Permutation for Structured Sparsity on Transformer Models | arXiv:2601.22980v1 Announce Type: new Abstract: Structured sparsity has emerged as a popular model pruning technique, widely adopted in various architectures, including CNNs, Transformer models, and especially large language models (LLMs) in recent years. A promising direction to further improve post-pruning performanc... | https://arxiv.org/abs/2601.22980 | Academic Papers | svg |
e73297f4295a2b170710168df5b0a79d0523a4f25adf115bd1251a23538efea1 | 2026-02-02T00:00:00-05:00 | About an Automating Annotation Method for Robot Markers | arXiv:2601.22982v1 Announce Type: new Abstract: Factory automation has become increasingly important due to labor shortages, leading to the introduction of autonomous mobile robots for tasks such as material transportation. Markers are commonly used for robot self-localization and object identification. In the RoboCup ... | https://arxiv.org/abs/2601.22982 | Academic Papers | svg |
901ac667134ce5941ce1e1e33616be9cbd616d53a60505deedf1006d93cc5e2b | 2026-02-02T00:00:00-05:00 | PIDSMaker: Building and Evaluating Provenance-based Intrusion Detection Systems | arXiv:2601.22983v1 Announce Type: new Abstract: Recent provenance-based intrusion detection systems (PIDSs) have demonstrated strong potential for detecting advanced persistent threats (APTs) by applying machine learning to system provenance graphs. However, evaluating and comparing PIDSs remains difficult: prior work ... | https://arxiv.org/abs/2601.22983 | Academic Papers | svg |
db95fc504b9ebf2e3618834fc470c860d7d41f2161ef3f11fc2e40ee4ced06a7 | 2026-02-02T00:00:00-05:00 | Why Your Deep Research Agent Fails? On Hallucination Evaluation in Full Research Trajectory | arXiv:2601.22984v1 Announce Type: new Abstract: Diagnosing the failure mechanisms of Deep Research Agents (DRAs) remains a critical challenge. Existing benchmarks predominantly rely on end-to-end evaluation, obscuring critical intermediate hallucinations, such as flawed planning, that accumulate throughout the research... | https://arxiv.org/abs/2601.22984 | Academic Papers | svg |
f06284cef77dac121be017d9341edcef3498db84933b7c9b5c75f776b1ca1dea | 2026-02-02T00:00:00-05:00 | dgMARK: Decoding-Guided Watermarking for Diffusion Language Models | arXiv:2601.22985v1 Announce Type: new Abstract: We propose dgMARK, a decoding-guided watermarking method for discrete diffusion language models (dLLMs). Unlike autoregressive models, dLLMs can generate tokens in arbitrary order. While an ideal conditional predictor would be invariant to this order, practical dLLMs exhi... | https://arxiv.org/abs/2601.22985 | Academic Papers | svg |
d13e0a0e4a2eb8a6e80bf031fc138040da0d94366d7745226366e614c4a2dec8 | 2026-02-02T00:00:00-05:00 | ArabicDialectHub: A Cross-Dialectal Arabic Learning Resource and Platform | arXiv:2601.22987v1 Announce Type: new Abstract: We present ArabicDialectHub, a cross-dialectal Arabic learning resource comprising 552 phrases across six varieties (Moroccan Darija, Lebanese, Syrian, Emirati, Saudi, and MSA) and an interactive web platform. Phrases were generated using LLMs and validated by five native... | https://arxiv.org/abs/2601.22987 | Academic Papers | svg |
8c575dccc62d0d7e4cafc6a505d408a37d359ec6baecffbdd98c149023be8a0a | 2026-02-02T00:00:00-05:00 | Learning Geometrically-Grounded 3D Visual Representations for View-Generalizable Robotic Manipulation | arXiv:2601.22988v1 Announce Type: new Abstract: Real-world robotic manipulation demands visuomotor policies capable of robust spatial scene understanding and strong generalization across diverse camera viewpoints. While recent advances in 3D-aware visual representations have shown promise, they still suffer from severa... | https://arxiv.org/abs/2601.22988 | Academic Papers | svg |
17ec96cfa32388b6469baa2ed5204104c266198cf960c3fe351a9f762c7db668 | 2026-02-02T00:00:00-05:00 | Self-Supervised Slice-to-Volume Reconstruction with Gaussian Representations for Fetal MRI | arXiv:2601.22990v1 Announce Type: new Abstract: Reconstructing 3D fetal MR volumes from motion-corrupted stacks of 2D slices is a crucial and challenging task. Conventional slice-to-volume reconstruction (SVR) methods are time-consuming and require multiple orthogonal stacks for reconstruction. While learning-based SVR... | https://arxiv.org/abs/2601.22990 | Academic Papers | svg |
1f039a8249f2d32907ab3d9069f72032dc29c325df317a98bfff35f2e4b4cecc | 2026-02-02T00:00:00-05:00 | Value-at-Risk Constrained Policy Optimization | arXiv:2601.22993v1 Announce Type: new Abstract: We introduce the Value-at-Risk Constrained Policy Optimization algorithm (VaR-CPO), a sample efficient and conservative method designed to optimize Value-at-Risk (VaR) constraints directly. Empirically, we demonstrate that VaR-CPO is capable of safe exploration, achieving... | https://arxiv.org/abs/2601.22993 | Academic Papers | svg |
0747c0a13328df018ba9b4732f5ccd7a0e3f665e4f2f7a746f24fd300e10e572 | 2026-02-02T00:00:00-05:00 | Competitive Non-Clairvoyant KV-Cache Scheduling for LLM Inference | arXiv:2601.22996v1 Announce Type: new Abstract: Large Language Model (LLM) inference presents a unique scheduling challenge due to the Key-Value (KV) cache, where a job's memory footprint grows linearly with the number of decoded tokens. This growth couples scheduling decisions with feasibility: a scheduler must minimi... | https://arxiv.org/abs/2601.22996 | Academic Papers | svg |
87c0fecd753b42be5995f3bc5307b194a1c2c7ed8b07af4035c26434cbf4f9ce | 2026-02-02T00:00:00-05:00 | TriCEGAR: A Trace-Driven Abstraction Mechanism for Agentic AI | arXiv:2601.22997v1 Announce Type: new Abstract: Agentic AI systems act through tools and evolve their behavior over long, stochastic interaction traces. This setting complicates assurance, because behavior depends on nondeterministic environments and probabilistic model outputs. Prior work introduced runtime verificati... | https://arxiv.org/abs/2601.22997 | Academic Papers | svg |
039ae1f27370f6fe5f7264ed893cdd5b1c8aae721b10c7285d0d1cccfec91a94 | 2026-02-02T00:00:00-05:00 | Mano: Restriking Manifold Optimization for LLM Training | arXiv:2601.23000v1 Announce Type: new Abstract: While large language models (LLMs) have emerged as a significant advancement in artificial intelligence, the hardware and computational costs for training LLMs are also significantly burdensome. Among the state-of-the-art optimizers, AdamW relies on diagonal curvature est... | https://arxiv.org/abs/2601.23000 | Academic Papers | svg |
1c6c0f917bde1a81c911def8a2c9432df684c821b78d5d0e5cef97ef03687e1d | 2026-02-02T00:00:00-05:00 | Bias Beyond Borders: Political Ideology Evaluation and Steering in Multilingual LLMs | arXiv:2601.23001v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly shape global discourse, making fairness and ideological neutrality essential for responsible AI deployment. Despite growing attention to political bias in LLMs, prior work largely focuses on high-resource, Western languages or nar... | https://arxiv.org/abs/2601.23001 | Academic Papers | svg |
43821f32ce4aa6989755a0fa80c3ec8defaf8aa5ba44fcbf932f9eaab693f362 | 2026-02-02T00:00:00-05:00 | InstructDiff: Domain-Adaptive Data Selection via Differential Entropy for Efficient LLM Fine-Tuning | arXiv:2601.23006v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is fundamental to adapting large language models, yet training on complete datasets incurs prohibitive costs with diminishing returns. Existing data selection methods suffer from severe domain specificity: techniques optimized for general inst... | https://arxiv.org/abs/2601.23006 | Academic Papers | svg |
0f0cb1221ff77adfb5bc85a8130c278d69cedce2f90fe084b913f41c44542163 | 2026-02-02T00:00:00-05:00 | Leveraging Multi-Rater Annotations to Calibrate Object Detectors in Microscopy Imaging | arXiv:2601.23007v1 Announce Type: new Abstract: Deep learning-based object detectors have achieved impressive performance in microscopy imaging, yet their confidence estimates often lack calibration, limiting their reliability for biomedical applications. In this work, we introduce a new approach to improve model calib... | https://arxiv.org/abs/2601.23007 | Academic Papers | svg |
d5ad266c344045516ac6560f8a2179352b9846f0245feaa24614713c1058b90a | 2026-02-02T00:00:00-05:00 | SolAgent: A Specialized Multi-Agent Framework for Solidity Code Generation | arXiv:2601.23009v1 Announce Type: new Abstract: Smart contracts are the backbone of the decentralized web, yet ensuring their functional correctness and security remains a critical challenge. While Large Language Models (LLMs) have shown promise in code generation, they often struggle with the rigorous requirements of ... | https://arxiv.org/abs/2601.23009 | Academic Papers | svg |
2a7bd0a346f4d73341c6a70404f1890bf07ab3378fb6b52e5929d573c730f5e4 | 2026-02-02T00:00:00-05:00 | Automatic Constraint Policy Optimization based on Continuous Constraint Interpolation Framework for Offline Reinforcement Learning | arXiv:2601.23010v1 Announce Type: new Abstract: Offline Reinforcement Learning (RL) relies on policy constraints to mitigate extrapolation error, where both the constraint form and constraint strength critically shape performance. However, most existing methods commit to a single constraint family: weighted behavior cl... | https://arxiv.org/abs/2601.23010 | Academic Papers | svg |
3a3addac26f21448ab03c05f2ff3b26d24fb5b21d9b3a4e40742c00ff4a19e6f | 2026-02-02T00:00:00-05:00 | Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG | arXiv:2601.23011v1 Announce Type: new Abstract: Reliable control of myoelectric prostheses is often hindered by high inter-subject variability and the clinical impracticality of high-density sensor arrays. This study proposes a deep learning framework for accurate gesture recognition using only two surface electromyogr... | https://arxiv.org/abs/2601.23011 | Academic Papers | svg |
7068c2cd1aeddc5661fa6feb978f68bae033e70dfddddb480344d9737acbe743 | 2026-02-02T00:00:00-05:00 | Mem-T: Densifying Rewards for Long-Horizon Memory Agents | arXiv:2601.23014v1 Announce Type: new Abstract: Memory agents, which depart from predefined memory-processing pipelines by endogenously managing the processing, storage, and retrieval of memories, have garnered increasing attention for their autonomy and adaptability. However, existing training paradigms remain constra... | https://arxiv.org/abs/2601.23014 | Academic Papers | svg |
243573adcb34f9ef110dca9fe3f8684ceae6e0162e75bb2f965e447192120a03 | 2026-02-02T00:00:00-05:00 | Integrating Multi-Label Classification and Generative AI for Scalable Analysis of User Feedback | arXiv:2601.23018v1 Announce Type: new Abstract: In highly competitive software markets, user experience (UX) evaluation is crucial for ensuring software quality and fostering long-term product success. Such UX evaluations typically combine quantitative metrics from standardized questionnaires with qualitative feedback ... | https://arxiv.org/abs/2601.23018 | Academic Papers | svg |
d7bffc6ef85a824fae8213558943d3c5d627f1ba471ccf12e87913f7b5524a25 | 2026-02-02T00:00:00-05:00 | Uncovering Hidden Inclusions of Vulnerable Dependencies in Real-World Java Projects | arXiv:2601.23020v1 Announce Type: new Abstract: Open-source software (OSS) dependencies are a dominant component of modern software code bases. Using proven and well-tested OSS components lets developers reduce development time and cost while improving quality. However, heavy reliance on open-source software also intro... | https://arxiv.org/abs/2601.23020 | Academic Papers | svg |
75831f69c1cfb0a8ba8d637f122a3388be7ca30890397443fd876cbc889d61c4 | 2026-02-02T00:00:00-05:00 | DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis | arXiv:2601.23022v1 Announce Type: new Abstract: Aspect-Based Sentiment Analysis (ABSA) focuses on extracting sentiment at a fine-grained aspect level and has been widely applied across real-world domains. However, existing ABSA research relies on coarse-grained categorical labels (e.g., positive, negative), which limit... | https://arxiv.org/abs/2601.23022 | Academic Papers | svg |
4c616c8531bd48e7905a9e04508f131b11cbc01351b55bf640dfdf0f3b52c283 | 2026-02-02T00:00:00-05:00 | Causal Characterization of Measurement and Mechanistic Anomalies | arXiv:2601.23026v1 Announce Type: new Abstract: Root cause analysis of anomalies aims to identify those features that cause the deviation from the normal process. Existing methods ignore, however, that anomalies can arise through two fundamentally different processes: measurement errors, where data was generated normal... | https://arxiv.org/abs/2601.23026 | Academic Papers | svg |
ca4ad2708770aa3c7c2cb5bffb940117a5378697cae0acf40d0278a59b7ec1b7 | 2026-02-02T00:00:00-05:00 | Divide-and-Conquer CoT: RL for Reducing Latency via Parallel Reasoning | arXiv:2601.23027v1 Announce Type: new Abstract: Long chain-of-thought reasoning (Long CoT) is now fundamental to state-of-the-art LLMs, especially in mathematical reasoning. However, LLM generation is highly sequential, and long CoTs lead to a high latency. We propose to train Divide-and-Conquer CoT (DC-CoT) to reduce ... | https://arxiv.org/abs/2601.23027 | Academic Papers | svg |
338b14f4db699b0ec3b68b03036789c2ddfd8b658b68515754e3653904585cbd | 2026-02-02T00:00:00-05:00 | Guided by Trajectories: Repairing and Rewarding Tool-Use Trajectories for Tool-Integrated Reasoning | arXiv:2601.23032v1 Announce Type: new Abstract: Tool-Integrated Reasoning (TIR) enables large language models (LLMs) to solve complex tasks by interacting with external tools, yet existing approaches depend on high-quality synthesized trajectories selected by scoring functions and sparse outcome-based rewards, providin... | https://arxiv.org/abs/2601.23032 | Academic Papers | svg |
69db85e52fd46429fcab76625e30884f73f49e87eb666de3087268ba9e180026 | 2026-02-02T00:00:00-05:00 | MOSAIC: Modular Scalable Autonomy for Intelligent Coordination of Heterogeneous Robotic Teams | arXiv:2601.23038v1 Announce Type: new Abstract: Mobile robots have become indispensable for exploring hostile environments, such as in space or disaster relief scenarios, but often remain limited to teleoperation by a human operator. This restricts the deployment scale and requires near-continuous low-latency communica... | https://arxiv.org/abs/2601.23038 | Academic Papers | svg |
d765130c35e8f05a9d95b6a8d328f9a8cff4f26299943b2d2e5028b9b80ef521 | 2026-02-02T00:00:00-05:00 | Avoiding Premature Collapse: Adaptive Annealing for Entropy-Regularized Structural Inference | arXiv:2601.23039v1 Announce Type: new Abstract: Differentiable matching layers, often implemented via entropy-regularized Optimal Transport, serve as a critical approximate inference mechanism in structural prediction. However, recovering discrete permutations via annealing $\epsilon \to 0$ is notoriously unstable. We ... | https://arxiv.org/abs/2601.23039 | Academic Papers | svg |
da7233765240e84fad283ba621392ba70e835c803e6340b761df617e323f7911 | 2026-02-02T00:00:00-05:00 | One-shot Optimized Steering Vector for Hallucination Mitigation for VLMs | arXiv:2601.23041v1 Announce Type: new Abstract: Vision Language Models (VLMs) achieve strong performance on multimodal tasks but still suffer from hallucination and safety-related failures that persist even at scale. Steering offers a lightweight technique to improve model performance. However, steering, whether input-... | https://arxiv.org/abs/2601.23041 | Academic Papers | svg |
5849a56ab769be5d532a6ed24858b6639330afda7a3584e28a47a55dfa33219d | 2026-02-02T00:00:00-05:00 | The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? | arXiv:2601.23045v1 Announce Type: new Abstract: As AI becomes more capable, we entrust it with more general and consequential tasks. The risks from failure grow more severe with increasing task scope. It is therefore important to understand how extremely capable AI models will fail: Will they fail by systematically pur... | https://arxiv.org/abs/2601.23045 | Academic Papers | svg |
c2a2328c801ac453607dc2ba5b5d864e567e523f2747aa7a0c39ad22bce60c33 | 2026-02-02T00:00:00-05:00 | From Abstract to Contextual: What LLMs Still Cannot Do in Mathematics | arXiv:2601.23048v1 Announce Type: new Abstract: Large language models now solve many benchmark math problems at near-expert levels, yet this progress has not fully translated into reliable performance in real-world applications. We study this gap through contextual mathematical reasoning, where the mathematical core mu... | https://arxiv.org/abs/2601.23048 | Academic Papers | svg |
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