id stringlengths 64 64 | published stringlengths 19 25 | title stringlengths 7 262 | description stringlengths 6 54.4k | link stringlengths 31 227 | category stringclasses 6
values | image stringlengths 3 247 |
|---|---|---|---|---|---|---|
a8a1a5e4de5f69122c4ecddfd8477812cf3de398d6cfd019b7ee738d612e49eb | 2026-02-02T00:00:00-05:00 | VarParser: Unleashing the Neglected Power of Variables for LLM-based Log Parsing | arXiv:2601.22676v1 Announce Type: new Abstract: Logs serve as a primary source of information for engineers to diagnose failures in large-scale online service systems. Log parsing, which extracts structured events from massive unstructured log data, is a critical first step for downstream tasks like anomaly detection a... | https://arxiv.org/abs/2601.22676 | Academic Papers | svg |
92ad69c916412772961b8a49ced0f9e0cb8804380aa19261ffe53699f44f6ebc | 2026-02-02T00:00:00-05:00 | Full-Graph vs. Mini-Batch Training: Comprehensive Analysis from a Batch Size and Fan-Out Size Perspective | arXiv:2601.22678v1 Announce Type: new Abstract: Full-graph and mini-batch Graph Neural Network (GNN) training approaches have distinct system design demands, making it crucial to choose the appropriate approach to develop. A core challenge in comparing these two GNN training approaches lies in characterizing their mode... | https://arxiv.org/abs/2601.22678 | Academic Papers | svg |
120a4ccc8aa98ea6760874e7a7b744d26f43de0d44a7db1288343d615e3fcd27 | 2026-02-02T00:00:00-05:00 | Stabilizing Consistency Training: A Flow Map Analysis and Self-Distillation | arXiv:2601.22679v1 Announce Type: new Abstract: Consistency models have been proposed for fast generative modeling, achieving results competitive with diffusion and flow models. However, these methods exhibit inherent instability and limited reproducibility when training from scratch, motivating subsequent work to expl... | https://arxiv.org/abs/2601.22679 | Academic Papers | svg |
79eab2ba6be8c6c208ff4393d71ca2672bd1e6e4c3d335b69256e14907ee1d5b | 2026-02-02T00:00:00-05:00 | Visual Personalization Turing Test | arXiv:2601.22680v1 Announce Type: new Abstract: We introduce the Visual Personalization Turing Test (VPTT), a new paradigm for evaluating contextual visual personalization based on perceptual indistinguishability, rather than identity replication. A model passes the VPTT if its output (image, video, 3D asset, etc.) is ... | https://arxiv.org/abs/2601.22680 | Academic Papers | svg |
ddc46255012600f361aa22974951ed77856a7ce4131f51356a59cb537c3bfb80 | 2026-02-02T00:00:00-05:00 | OOVDet: Low-Density Prior Learning for Zero-Shot Out-of-Vocabulary Object Detection | arXiv:2601.22685v1 Announce Type: new Abstract: Zero-shot out-of-vocabulary detection (ZS-OOVD) aims to accurately recognize objects of in-vocabulary (IV) categories provided at zero-shot inference, while simultaneously rejecting undefined ones (out-of-vocabulary, OOV) that lack corresponding category prompts. However,... | https://arxiv.org/abs/2601.22685 | Academic Papers | svg |
abde1377910e0ce030ab3eb64c01826281dc76d8f6e6ef08a624187626da5d42 | 2026-02-02T00:00:00-05:00 | FlyAware: Inertia-Aware Aerial Manipulation via Vision-Based Estimation and Post-Grasp Adaptation | arXiv:2601.22686v1 Announce Type: new Abstract: Aerial manipulators (AMs) are gaining increasing attention in automated transportation and emergency services due to their superior dexterity compared to conventional multirotor drones. However, their practical deployment is challenged by the complexity of time-varying in... | https://arxiv.org/abs/2601.22686 | Academic Papers | svg |
cd765513f7e5f98fef5958928e021977ab748d3c5be3685185dfd8553a673dd3 | 2026-02-02T00:00:00-05:00 | A Mathematical Analysis of a Smooth-Convex-Concave Splitting Scheme for the Swift--Hohenberg Equation | arXiv:2601.22687v1 Announce Type: new Abstract: The Swift--Hohenberg equation is a widely studied fourth-order model, originally proposed to describe hydrodynamic fluctuations. It admits an energy-dissipation law and, under suitable assumptions, bounded solutions. Many structure-preserving numerical schemes have been p... | https://arxiv.org/abs/2601.22687 | Academic Papers | svg |
67cf732eac29733aea1f9e290e3eefdfc8a870b976856d615b44ed564837d4df | 2026-02-02T00:00:00-05:00 | TSLM: Tree-Structured Language Modeling for Divergent Thinking | arXiv:2601.22688v1 Announce Type: new Abstract: Language models generate reasoning sequentially, preventing them from decoupling irrelevant exploration paths during search. We introduce Tree-Structured Language Modeling (TSLM), which uses special tokens to encode branching structure, enabling models to generate and sel... | https://arxiv.org/abs/2601.22688 | Academic Papers | svg |
d75f2c401fe36e8f83745ff53afc7e3f6cbe0dfad5043fdb3b3532e366df33c1 | 2026-02-02T00:00:00-05:00 | Assistive Robots and Reasonable Work Assignment Reduce Perceived Stigma toward Persons with Disabilities | arXiv:2601.22689v1 Announce Type: new Abstract: Robots are becoming more prominent in assisting persons with disabilities (PwD). Whilst there is broad consensus that robots can assist in mitigating physical impairments, the extent to which they can facilitate social inclusion remains equivocal. In fact, the exposed sta... | https://arxiv.org/abs/2601.22689 | Academic Papers | svg |
7796cc103d61a227037f1f524c0fc2b23c2ea491e2b7eb5f4e731bb4bc2c037d | 2026-02-02T00:00:00-05:00 | Do Transformers Have the Ability for Periodicity Generalization? | arXiv:2601.22690v1 Announce Type: new Abstract: Large language models (LLMs) based on the Transformer have demonstrated strong performance across diverse tasks. However, current models still exhibit substantial limitations in out-of-distribution (OOD) generalization compared with humans. We investigate this gap through... | https://arxiv.org/abs/2601.22690 | Academic Papers | svg |
d675a765dd7c5ec162bee8ea9ffde46d230a3b688346b5c3bb941cb25c7c5d19 | 2026-02-02T00:00:00-05:00 | Constraint Satisfaction Problems over Finitely Bounded Homogeneous Structures: a Dichotomy between FO and L-hard | arXiv:2601.22691v1 Announce Type: new Abstract: Feder-Vardi conjecture, which proposed that every finite-domain Constraint Satisfaction Problem (CSP) is either in P or it is NP-complete, has been solved independently by Bulatov and Zhuk almost ten years ago. Bodirsky-Pinsker conjecture which states a similar dichotomy ... | https://arxiv.org/abs/2601.22691 | Academic Papers | svg |
9042a8fe3374dc86ab856a91e377941f9f09ffd65358fe51f04c09d8c79c94e8 | 2026-02-02T00:00:00-05:00 | FNF: Functional Network Fingerprint for Large Language Models | arXiv:2601.22692v1 Announce Type: new Abstract: The development of large language models (LLMs) is costly and has significant commercial value. Consequently, preventing unauthorized appropriation of open-source LLMs and protecting developers' intellectual property rights have become critical challenges. In this work, w... | https://arxiv.org/abs/2601.22692 | Academic Papers | svg |
bfa902d409d3be003ddbbfe03a5eb27c7297adfa85d4a7ead392542162f6950c | 2026-02-02T00:00:00-05:00 | PEAR: Pixel-aligned Expressive humAn mesh Recovery | arXiv:2601.22693v1 Announce Type: new Abstract: Reconstructing detailed 3D human meshes from a single in-the-wild image remains a fundamental challenge in computer vision. Existing SMPLX-based methods often suffer from slow inference, produce only coarse body poses, and exhibit misalignments or unnatural artifacts in f... | https://arxiv.org/abs/2601.22693 | Academic Papers | svg |
bc41eed4671582b1aca3d0c82cf08b3db6c7ab3b0f0caf70cb1d9b63969fa551 | 2026-02-02T00:00:00-05:00 | Farewell to Item IDs: Unlocking the Scaling Potential of Large Ranking Models via Semantic Tokens | arXiv:2601.22694v1 Announce Type: new Abstract: Recent studies on scaling up ranking models have achieved substantial improvement for recommendation systems and search engines. However, most large-scale ranking systems rely on item IDs, where each item is treated as an independent categorical symbol and mapped to a lea... | https://arxiv.org/abs/2601.22694 | Academic Papers | svg |
db3284e4561c9901b5a80a1f31579c45248508567ca39dd81d568ae7fb023013 | 2026-02-02T00:00:00-05:00 | Bi-MCQ: Reformulating Vision-Language Alignment for Negation Understanding | arXiv:2601.22696v1 Announce Type: new Abstract: Recent vision-language models (VLMs) achieve strong zero-shot performance via large-scale image-text pretraining and have been widely adopted in medical image analysis. However, existing VLMs remain notably weak at understanding negated clinical statements, largely due to... | https://arxiv.org/abs/2601.22696 | Academic Papers | svg |
ff764df66cc2dede5c7a36e6326455bc85a976ec583ef8519f9fdd44e9a310ed | 2026-02-02T00:00:00-05:00 | Models Know Models Best: Evaluation via Model-Preferred Formats | arXiv:2601.22699v1 Announce Type: new Abstract: Performance of Large Language Models (LLMs) on multiple-choice tasks differs markedly between symbol-based and cloze-style evaluation formats. The observed discrepancies are systematically attributable to task characteristics: natural language continuation benefits from l... | https://arxiv.org/abs/2601.22699 | Academic Papers | svg |
1146d7b708ce449e5ead3b23d9af677c700689d848d3519ec800a6c0157e3644 | 2026-02-02T00:00:00-05:00 | Best-of-Q: Improving VLM agents with Q-function Action Ranking at Inference | arXiv:2601.22701v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have become powerful backbones for agents to autonomously operate in digital environments like the web and operating systems. However, these models suffer from inadaptability to fast-changing environments like the web, which can be alleviated... | https://arxiv.org/abs/2601.22701 | Academic Papers | svg |
53648075376e17f85ac4f6c19117b8204c26039ee1178b88cd8027b17eaa4627 | 2026-02-02T00:00:00-05:00 | Metric Hub: A metric library and practical selection workflow for use-case-driven data quality assessment in medical AI | arXiv:2601.22702v1 Announce Type: new Abstract: Machine learning (ML) in medicine has transitioned from research to concrete applications aimed at supporting several medical purposes like therapy selection, monitoring and treatment. Acceptance and effective adoption by clinicians and patients, as well as regulatory app... | https://arxiv.org/abs/2601.22702 | Academic Papers | svg |
b50d1e61a9280d60bb0fd7f2c771516940fdb3560abfdd4bdc508620ebfd600c | 2026-02-02T00:00:00-05:00 | DAVIS: OOD Detection via Dominant Activations and Variance for Increased Separation | arXiv:2601.22703v1 Announce Type: new Abstract: Detecting out-of-distribution (OOD) inputs is a critical safeguard for deploying machine learning models in the real world. However, most post-hoc detection methods operate on penultimate feature representations derived from global average pooling (GAP) -- a lossy operati... | https://arxiv.org/abs/2601.22703 | Academic Papers | svg |
67f2279648d54fbfd9faa4497879ce16e9d6bd8d842d6bce8367ac05d5299931 | 2026-02-02T00:00:00-05:00 | Multi-target DoA estimation with a single Rydberg atomic receiver by spectral analysis of spatially-resolved fluorescence | arXiv:2601.22704v1 Announce Type: new Abstract: Rydberg-based Direction-of-Arrival (DoA) estimation has been hampered by the complexity of receiver arrays and the single-target, narrow-band limitations of existing single-receiver methods. This paper introduces a novel approach that addresses these limitations. We demon... | https://arxiv.org/abs/2601.22704 | Academic Papers | svg |
5c507b67d2f956c1bc4db4414ce1bec3d22468b553896329f4432ac2799b8a8e | 2026-02-02T00:00:00-05:00 | CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control | arXiv:2601.22705v1 Announce Type: new Abstract: Batch inference for agentic workloads stresses the GPU key-value (KV) cache in a sustained and cumulative manner, often causing severe throughput degradation well before memory capacity is exhausted. We identify this phenomenon as middle-phase thrashing, a previously unde... | https://arxiv.org/abs/2601.22705 | Academic Papers | svg |
e241ae49e1bc04a1b76b05fef8e9aa327367272221b757e37c52eebfaeaeba7b | 2026-02-02T00:00:00-05:00 | RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories | arXiv:2601.22706v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area. Existing benchmarks often fall short by relying on synthetic vulnerabilities or evaluating fu... | https://arxiv.org/abs/2601.22706 | Academic Papers | svg |
2e0065a1a816c375ebe03787dcc962c4ed0e4d2ef9a69d8c24a351831e4ea456 | 2026-02-02T00:00:00-05:00 | Deep Learning-Based Early-Stage IR-Drop Estimation via CNN Surrogate Modeling | arXiv:2601.22707v1 Announce Type: new Abstract: IR-drop is a critical power integrity challenge in modern VLSI designs that can cause timing degradation, reliability issues, and functional failures if not detected early in the design flow. Conventional IR-drop analysis relies on physics-based signoff tools, which provi... | https://arxiv.org/abs/2601.22707 | Academic Papers | svg |
a991e68a3da075b27be219bb1331a21c37b231c3c667baae4aa24c188e9663d6 | 2026-02-02T00:00:00-05:00 | A Unified Study of LoRA Variants: Taxonomy, Review, Codebase, and Empirical Evaluation | arXiv:2601.22708v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a fundamental parameter-efficient fine-tuning method that balances efficiency and performance in large-scale neural networks. However, the proliferation of LoRA variants has led to fragmentation in methodology, theory, code, and evaluation. T... | https://arxiv.org/abs/2601.22708 | Academic Papers | svg |
06b0b07ef69a1c2a42f6d51aa4b9f6bf72b67429f484a0a582db08862ffd9431 | 2026-02-02T00:00:00-05:00 | Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs | arXiv:2601.22709v1 Announce Type: new Abstract: Vision-Language Models (VLMs) achieve strong multimodal performance but are costly to deploy, and post-training quantization often causes significant accuracy loss. Despite its potential, quantization-aware training for VLMs remains underexplored. We propose GRACE, a fram... | https://arxiv.org/abs/2601.22709 | Academic Papers | svg |
5bc89aec79be2cc38d846c9e7468c79ff2f7ff9531cdde1b105f6c61cfe975b1 | 2026-02-02T00:00:00-05:00 | AlienLM: Alienization of Language for API-Boundary Privacy in Black-Box LLMs | arXiv:2601.22710v1 Announce Type: new Abstract: Modern LLMs are increasingly accessed via black-box APIs, requiring users to transmit sensitive prompts, outputs, and fine-tuning data to external providers, creating a critical privacy risk at the API boundary. We introduce AlienLM, a deployable API-only privacy layer th... | https://arxiv.org/abs/2601.22710 | Academic Papers | svg |
5f941b7f155e3b10fabbd784461b197b7a8d338867980ee454e55e9a40d6fcd5 | 2026-02-02T00:00:00-05:00 | SQUAD: Scalable Quorum Adaptive Decisions via ensemble of early exit neural networks | arXiv:2601.22711v1 Announce Type: new Abstract: Early-exit neural networks have become popular for reducing inference latency by allowing intermediate predictions when sufficient confidence is achieved. However, standard approaches typically rely on single-model confidence thresholds, which are frequently unreliable du... | https://arxiv.org/abs/2601.22711 | Academic Papers | svg |
e04a8605a6569b06aceaed2d68ca1c414c7e4c036bef50fb3fcd0aa8a531c5f5 | 2026-02-02T00:00:00-05:00 | Vision-Language Models Unlock Task-Centric Latent Actions | arXiv:2601.22714v1 Announce Type: new Abstract: Latent Action Models (LAMs) have rapidly gained traction as an important component in the pre-training pipelines of leading Vision-Language-Action models. However, they fail when observations contain action-correlated distractors, often encoding noise instead of meaningfu... | https://arxiv.org/abs/2601.22714 | Academic Papers | svg |
09c0e13bcde99ac6358dbf874c30ab242a70bc986144a08904c49b4b1f6f9797 | 2026-02-02T00:00:00-05:00 | Breaking the Blocks: Continuous Low-Rank Decomposed Scaling for Unified LLM Quantization and Adaptation | arXiv:2601.22716v1 Announce Type: new Abstract: Current quantization methods for LLMs predominantly rely on block-wise structures to maintain efficiency, often at the cost of representational flexibility. In this work, we demonstrate that element-wise quantization can be made as efficient as block-wise scaling while pr... | https://arxiv.org/abs/2601.22716 | Academic Papers | svg |
d77d48fb0898074c6ebd24c29e32c81a0ae9f880c8b16dc2102e9b0f0d658dfb | 2026-02-02T00:00:00-05:00 | A Step Back: Prefix Importance Ratio Stabilizes Policy Optimization | arXiv:2601.22718v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training has increasingly demonstrated strong ability to elicit reasoning behaviors in large language models (LLMs). For training efficiency, rollouts are typically generated in an off-policy manner using an older sampling policy and then ... | https://arxiv.org/abs/2601.22718 | Academic Papers | svg |
7b67a687a0ecc8538b8d0e853350f680eb992a7894c3a848dba03dc30932960a | 2026-02-02T00:00:00-05:00 | AEGIS: White-Box Attack Path Generation using LLMs and Training Effectiveness Evaluation for Large-Scale Cyber Defence Exercises | arXiv:2601.22720v1 Announce Type: new Abstract: Creating attack paths for cyber defence exercises requires substantial expert effort. Existing automation requires vulnerability graphs or exploit sets curated in advance, limiting where it can be applied. We present AEGIS, a system that generates attack paths using LLMs,... | https://arxiv.org/abs/2601.22720 | Academic Papers | svg |
ac3bf6e6e34fd32a84fe84cd0ac16bc6351296579f82fe2af9a53df89485e923 | 2026-02-02T00:00:00-05:00 | Local Intrinsic Dimension of Representations Predicts Alignment and Generalization in AI Models and Human Brain | arXiv:2601.22722v1 Announce Type: new Abstract: Recent work has found that neural networks with stronger generalization tend to exhibit higher representational alignment with one another across architectures and training paradigms. In this work, we show that models with stronger generalization also align more strongly ... | https://arxiv.org/abs/2601.22722 | Academic Papers | svg |
96deb82a02b17b927ee0c1959229bf674939d52d5dad11ea500be1e4939ebcb5 | 2026-02-02T00:00:00-05:00 | OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation | arXiv:2601.22725v1 Announce Type: new Abstract: Recent advances in diffusion models have significantly elevated the visual fidelity of Virtual Try-On (VTON) systems, yet reliable evaluation remains a persistent bottleneck. Traditional metrics struggle to quantify fine-grained texture details and semantic consistency, w... | https://arxiv.org/abs/2601.22725 | Academic Papers | svg |
763c25a902d0b52cc07f41af818b5110cdd39ba83d8ea08442483259fb7ba514 | 2026-02-02T00:00:00-05:00 | On Small Pair Decompositions for Point Sets | arXiv:2601.22728v1 Announce Type: new Abstract: $\newcommand{\Re}{\mathbb{R}}$We study the minWSPD problem of computing the minimum-size well-separated pairs decomposition of a set of points, and show constant approximation algorithms in low-dimensional Euclidean space and doubling metrics. This problem is computationa... | https://arxiv.org/abs/2601.22728 | Academic Papers | svg |
7b4b9f65af12fb880e2eaa3a019a3664546a2e080f4930840f1b424da9fa15a2 | 2026-02-02T00:00:00-05:00 | GaussianOcc3D: A Gaussian-Based Adaptive Multi-modal 3D Occupancy Prediction | arXiv:2601.22729v1 Announce Type: new Abstract: 3D semantic occupancy prediction is a pivotal task in autonomous driving, providing a dense and fine-grained understanding of the surrounding environment, yet single-modality methods face trade-offs between camera semantics and LiDAR geometry. Existing multi-modal framewo... | https://arxiv.org/abs/2601.22729 | Academic Papers | svg |
fc01c6d48381e5dd29efe0b3ecbe67dbfe616fee27fb1123f7ab62714f4c4112 | 2026-02-02T00:00:00-05:00 | ImgCoT: Compressing Long Chain of Thought into Compact Visual Tokens for Efficient Reasoning of Large Language Model | arXiv:2601.22730v1 Announce Type: new Abstract: Compressing long chains of thought (CoT) into compact latent tokens is crucial for efficient reasoning with large language models (LLMs). Recent studies employ autoencoders to achieve this by reconstructing textual CoT from latent tokens, thus encoding CoT semantics. Howe... | https://arxiv.org/abs/2601.22730 | Academic Papers | svg |
7220a6902287e9d1ec5ae6687cbae9602cd995bd52384c8a23f7812f319a9467 | 2026-02-02T00:00:00-05:00 | MM-THEBench: Do Reasoning MLLMs Think Reasonably? | arXiv:2601.22735v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) mark a shift from non-thinking models to post-trained reasoning models capable of solving complex problems through thinking. However, whether such thinking mitigates hallucinations in multimodal perception and re... | https://arxiv.org/abs/2601.22735 | Academic Papers | svg |
9635f791e016765d6bcdfa9f363357338f55a072054a0b71986c2d01e187cab6 | 2026-02-02T00:00:00-05:00 | Decomposing Epistemic Uncertainty for Causal Decision Making | arXiv:2601.22736v1 Announce Type: new Abstract: Causal inference from observational data provides strong evidence for the best action in decision-making without performing expensive randomized trials. The effect of an action is usually not identifiable under unobserved confounding, even with an infinite amount of data.... | https://arxiv.org/abs/2601.22736 | Academic Papers | svg |
5637c932207d27a0b1e89e00487841c9a121533c90da2ed6aedf90d22105573e | 2026-02-02T00:00:00-05:00 | Lingua-SafetyBench: A Benchmark for Safety Evaluation of Multilingual Vision-Language Models | arXiv:2601.22737v1 Announce Type: new Abstract: Robust safety of vision-language large models (VLLMs) under joint multilingual and multimodal inputs remains underexplored. Existing benchmarks are typically multilingual but text-only, or multimodal but monolingual. Recent multilingual multimodal red-teaming efforts rend... | https://arxiv.org/abs/2601.22737 | Academic Papers | svg |
57d0a0998a972e13b62466a3ede460935dc9ee3ef6acdc65c2088c2733034463 | 2026-02-02T00:00:00-05:00 | StreamSense: Streaming Social Task Detection with Selective Vision-Language Model Routing | arXiv:2601.22738v1 Announce Type: new Abstract: Live streaming platforms require real-time monitoring and reaction to social signals, utilizing partial and asynchronous evidence from video, text, and audio. We propose StreamSense, a streaming detector that couples a lightweight streaming encoder with selective routing ... | https://arxiv.org/abs/2601.22738 | Academic Papers | svg |
0582de19eee8feedeedd9cf62fd086097243f434aa41631c543dd5027426dcca | 2026-02-02T00:00:00-05:00 | AR-BENCH: Benchmarking Legal Reasoning with Judgment Error Detection, Classification and Correction | arXiv:2601.22742v1 Announce Type: new Abstract: Legal judgments may contain errors due to the complexity of case circumstances and the abstract nature of legal concepts, while existing appellate review mechanisms face efficiency pressures from a surge in case volumes. Although current legal AI research focuses on tasks... | https://arxiv.org/abs/2601.22742 | Academic Papers | svg |
5e409ba88f77b4dfc87f38d7112a861177ba79aef0a02698a3dfa76872809d8f | 2026-02-02T00:00:00-05:00 | Beauty and the Beast: Imperceptible Perturbations Against Diffusion-Based Face Swapping via Directional Attribute Editing | arXiv:2601.22744v1 Announce Type: new Abstract: Diffusion-based face swapping achieves state-of-the-art performance, yet it also exacerbates the potential harm of malicious face swapping to violate portraiture right or undermine personal reputation. This has spurred the development of proactive defense methods. However... | https://arxiv.org/abs/2601.22744 | Academic Papers | svg |
fba9b4d196949b9a0876da915f240dd3f799a2f480441453fbd48b3d6081a8a7 | 2026-02-02T00:00:00-05:00 | Is Softmax Loss All You Need? A Principled Analysis of Softmax-family Loss | arXiv:2601.22745v1 Announce Type: new Abstract: The Softmax loss is one of the most widely employed surrogate objectives for classification and ranking tasks. To elucidate its theoretical properties, the Fenchel-Young framework situates it as a canonical instance within a broad family of surrogates. Concurrently, anoth... | https://arxiv.org/abs/2601.22745 | Academic Papers | svg |
6c55749d73375918b1b9db27df1842efefc9f9b329a21308f33af4e6062a734f | 2026-02-02T00:00:00-05:00 | UrbanMoE: A Sparse Multi-Modal Mixture-of-Experts Framework for Multi-Task Urban Region Profiling | arXiv:2601.22746v1 Announce Type: new Abstract: Urban region profiling, the task of characterizing geographical areas, is crucial for urban planning and resource allocation. However, existing research in this domain faces two significant limitations. First, most methods are confined to single-task prediction, failing t... | https://arxiv.org/abs/2601.22746 | Academic Papers | svg |
6cfb5fee45981d7f4eca8d8e20c3ab5267c57fd77444c3abb9528f2ced24f07d | 2026-02-02T00:00:00-05:00 | AutoMerge: Search-Based Model Merging Framework for Effective Model Reuse | arXiv:2601.22748v1 Announce Type: new Abstract: Software reuse has long been recognized as a critical and widely studied topic in software engineering, offering substantial benefits in reducing development costs, improving software quality, and enhancing operational efficiency. This paradigm extends into deep learning ... | https://arxiv.org/abs/2601.22748 | Academic Papers | svg |
db2a5f98646769193cfceb9bdabb222f5fcea1449b38feb0c78975ce9524b3b1 | 2026-02-02T00:00:00-05:00 | Discovering Scaling Exponents with Physics-Informed M\"untz-Sz\'asz Networks | arXiv:2601.22751v1 Announce Type: new Abstract: Physical systems near singularities, interfaces, and critical points exhibit power-law scaling, yet standard neural networks leave the governing exponents implicit. We introduce physics-informed M"untz-Sz'asz Networks (MSN-PINN), a power-law basis network that treats scal... | https://arxiv.org/abs/2601.22751 | Academic Papers | svg |
e4614adcccdee51dc603bbba145e6fa5deb5aca4fa566f8e6b3e7b16dad53599 | 2026-02-02T00:00:00-05:00 | OSNIP: Breaking the Privacy-Utility-Efficiency Trilemma in LLM Inference via Obfuscated Semantic Null Space | arXiv:2601.22752v1 Announce Type: new Abstract: We propose Obfuscated Semantic Null space Injection for Privacy (OSNIP), a lightweight client-side encryption framework for privacy-preserving LLM inference. Generalizing the geometric intuition of linear kernels to the high-dimensional latent space of LLMs, we formally d... | https://arxiv.org/abs/2601.22752 | Academic Papers | svg |
7e226d83c1909e10c391323dc28787216666ace3ce62a3f34df345f60490e24e | 2026-02-02T00:00:00-05:00 | Procedural Knowledge Extraction from Industrial Troubleshooting Guides Using Vision Language Models | arXiv:2601.22754v1 Announce Type: new Abstract: Industrial troubleshooting guides encode diagnostic procedures in flowchart-like diagrams where spatial layout and technical language jointly convey meaning. To integrate this knowledge into operator support systems, which assist shop-floor personnel in diagnosing and res... | https://arxiv.org/abs/2601.22754 | Academic Papers | svg |
e542429eb66c7ede798eea276429bae83565a729a976ab08fe13fd85ff100649 | 2026-02-02T00:00:00-05:00 | Understanding Generalization from Embedding Dimension and Distributional Convergence | arXiv:2601.22756v1 Announce Type: new Abstract: Deep neural networks often generalize well despite heavy over-parameterization, challenging classical parameter-based analyses. We study generalization from a representation-centric perspective and analyze how the geometry of learned embeddings controls predictive perform... | https://arxiv.org/abs/2601.22756 | Academic Papers | svg |
2ee2d259dbfe8320923be67631b89ad338eb9505a7ef7b7ded910b89a2e81265 | 2026-02-02T00:00:00-05:00 | Unveiling Scaling Behaviors in Molecular Language Models: Effects of Model Size, Data, and Representation | arXiv:2601.22757v1 Announce Type: new Abstract: Molecular generative models, often employing GPT-style language modeling on molecular string representations, have shown promising capabilities when scaled to large datasets and model sizes. However, it remains unclear and subject to debate whether these models adhere to ... | https://arxiv.org/abs/2601.22757 | Academic Papers | svg |
e0cad7fd49174fcaa7e96d5965138ea7a490f8a15e69644766f204a4023ab677 | 2026-02-02T00:00:00-05:00 | AutoRefine: From Trajectories to Reusable Expertise for Continual LLM Agent Refinement | arXiv:2601.22758v1 Announce Type: new Abstract: Large language model agents often fail to accumulate knowledge from experience, treating each task as an independent challenge. Recent methods extract experience as flattened textual knowledge, which cannot capture procedural logic of complex subtasks. They also lack main... | https://arxiv.org/abs/2601.22758 | Academic Papers | svg |
4d2b5e2028ef6fb92d830f124da3954acadc1ef597ecccc15c02bc45246f7286 | 2026-02-02T00:00:00-05:00 | Qualitative Evaluation of LLM-Designed GUI | arXiv:2601.22759v1 Announce Type: new Abstract: As generative artificial intelligence advances, Large Language Models (LLMs) are being explored for automated graphical user interface (GUI) design. This study investigates the usability and adaptability of LLM-generated interfaces by analysing their ability to meet diver... | https://arxiv.org/abs/2601.22759 | Academic Papers | svg |
a3cabf3eedac0cbe15b3a5cb8b9f72c284e628c17584f208170ed947d03f8bd1 | 2026-02-02T00:00:00-05:00 | AscendCraft: Automatic Ascend NPU Kernel Generation via DSL-Guided Transcompilation | arXiv:2601.22760v1 Announce Type: new Abstract: The performance of deep learning models critically depends on efficient kernel implementations, yet developing high-performance kernels for specialized accelerators remains time-consuming and expertise-intensive. While recent work demonstrates that large language models (... | https://arxiv.org/abs/2601.22760 | Academic Papers | svg |
353794c26eebbd7fbbbf73ceb2ba2ac5a901aac6c0c4ceb59b0800c2c5fcf67d | 2026-02-02T00:00:00-05:00 | Numerical Differentiation of Functions of Two Variables Using Chebyshev Polynomials | arXiv:2601.22762v1 Announce Type: new Abstract: We investigate the problem of numerical differentiation of bivariate functions from weighted Wiener classes using Chebyshev polynomial expansions. We develop and analyze a new version of the truncation method based on Chebyshev polynomials and the idea of hyperbolic cross... | https://arxiv.org/abs/2601.22762 | Academic Papers | svg |
6764269fe42a3d96fe8ea99caead77951ffc6497f31fec7c1d8631593f063339 | 2026-02-02T00:00:00-05:00 | Is Training Necessary for Anomaly Detection? | arXiv:2601.22763v1 Announce Type: new Abstract: Current state-of-the-art multi-class unsupervised anomaly detection (MUAD) methods rely on training encoder-decoder models to reconstruct anomaly-free features. We first show these approaches have an inherent fidelity-stability dilemma in how they detect anomalies via rec... | https://arxiv.org/abs/2601.22763 | Academic Papers | svg |
732bd569f6a4b2a59af3d859068f958f85c68969d29fa920c6b6e6ddf4e76123 | 2026-02-02T00:00:00-05:00 | How Far Can Pretrained LLMs Go in Symbolic Music? Controlled Comparisons of Supervised and Preference-based Adaptation | arXiv:2601.22764v1 Announce Type: new Abstract: Music often shares notable parallels with language, motivating the use of pretrained large language models (LLMs) for symbolic music understanding and generation. Despite growing interest, the practical effectiveness of adapting instruction-tuned LLMs to symbolic music re... | https://arxiv.org/abs/2601.22764 | Academic Papers | svg |
a8709da246bd904bc1bbe658f0b42b0819c380fd43d6c21420e885ef07430d2e | 2026-02-02T00:00:00-05:00 | Sparse Attention as Compact Kernel Regression | arXiv:2601.22766v1 Announce Type: new Abstract: Recent work has revealed a link between self-attention mechanisms in transformers and test-time kernel regression via the Nadaraya-Watson estimator, with standard softmax attention corresponding to a Gaussian kernel. However, a kernel-theoretic understanding of sparse att... | https://arxiv.org/abs/2601.22766 | Academic Papers | svg |
48469bd0d15b6726c090c83feaa3df01a5c7547b5e55ca14523611f3bb03abda | 2026-02-02T00:00:00-05:00 | Beyond Abstract Compliance: Operationalising trust in AI as a moral relationship | arXiv:2601.22769v1 Announce Type: new Abstract: Dominant approaches, e.g. the EU's "Trustworthy AI framework", treat trust as a property that can be designed for, evaluated, and governed according to normative and technical criteria. They do not address how trust is subjectively cultivated and experienced, culturally e... | https://arxiv.org/abs/2601.22769 | Academic Papers | svg |
36d0f0d3254d7759cdd6db69a54b600652ca9d43c59c31a1b1e4d83db7360691 | 2026-02-02T00:00:00-05:00 | Okara: Detection and Attribution of TLS Man-in-the-Middle Vulnerabilities in Android Apps with Foundation Models | arXiv:2601.22770v1 Announce Type: new Abstract: Transport Layer Security (TLS) is fundamental to secure online communication, yet vulnerabilities in certificate validation that enable Man-in-the-Middle (MitM) attacks remain a pervasive threat in Android apps. Existing detection tools are hampered by low-coverage UI int... | https://arxiv.org/abs/2601.22770 | Academic Papers | svg |
220843b7d7d04493ac6fcf68ef0d28cc6b753ac75d93863a82266f94e852fd98 | 2026-02-02T00:00:00-05:00 | Rust and Go directed fuzzing with LibAFL-DiFuzz | arXiv:2601.22772v1 Announce Type: new Abstract: In modern SSDLC, program analysis and automated testing are essential for minimizing vulnerabilities before software release, with fuzzing being a fast and widely used dynamic testing method. However, traditional coverage-guided fuzzing may be less effective in specific t... | https://arxiv.org/abs/2601.22772 | Academic Papers | svg |
8c9043643e0444031d549704902081ccf7b11eccd1e8be2dad530bb00ec608cc | 2026-02-02T00:00:00-05:00 | Constructing Safety Cases for AI Systems: A Reusable Template Framework | arXiv:2601.22773v1 Announce Type: new Abstract: Safety cases, structured arguments that a system is acceptably safe, are becoming central to the governance of AI systems. Yet, traditional safety-case practices from aviation or nuclear engineering rely on well-specified system boundaries, stable architectures, and known... | https://arxiv.org/abs/2601.22773 | Academic Papers | svg |
a560f0398a4d913543abb75d6dcebc4ce2e515f272b1a793ea27561a9dd9aa86 | 2026-02-02T00:00:00-05:00 | TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization | arXiv:2601.22776v1 Announce Type: new Abstract: Multi-turn tool-integrated reasoning enables Large Language Models (LLMs) to solve complex tasks through iterative information retrieval. However, current reinforcement learning (RL) frameworks for search-augmented reasoning predominantly rely on sparse outcome-level rewa... | https://arxiv.org/abs/2601.22776 | Academic Papers | svg |
457caf4600b46c708872d38f24ace678b00bce8b9f4865afdc5a6f07ecc71f5a | 2026-02-02T00:00:00-05:00 | RASST: Fast Cross-modal Retrieval-Augmented Simultaneous Speech Translation | arXiv:2601.22777v1 Announce Type: new Abstract: Simultaneous speech translation (SST) produces target text incrementally from partial speech input. Recent speech large language models (Speech LLMs) have substantially improved SST quality, yet they still struggle to correctly translate rare and domain-specific terminolo... | https://arxiv.org/abs/2601.22777 | Academic Papers | svg |
02be157b4ab4ed910dd1fc505422fe8c7e356f751e1f1158fac21af1596171ba | 2026-02-02T00:00:00-05:00 | Color Matters: Demosaicing-Guided Color Correlation Training for Generalizable AI-Generated Image Detection | arXiv:2601.22778v1 Announce Type: new Abstract: As realistic AI-generated images threaten digital authenticity, we address the generalization failure of generative artifact-based detectors by exploiting the intrinsic properties of the camera imaging pipeline. Concretely, we investigate color correlations induced by the... | https://arxiv.org/abs/2601.22778 | Academic Papers | svg |
bb8d758a2b63fa2689281a467e303d9aa322ce8675e4296a36e3cc65024d0bb5 | 2026-02-02T00:00:00-05:00 | Learning with Challenges: Adaptive Difficulty-Aware Data Generation for Mobile GUI Agent Training | arXiv:2601.22781v1 Announce Type: new Abstract: Large-scale, high-quality interaction trajectories are essential for advancing mobile Graphical User Interface (GUI) agents. While existing methods typically rely on labor-intensive human demonstrations or automated model exploration to generate GUI trajectories, they lac... | https://arxiv.org/abs/2601.22781 | Academic Papers | svg |
ac0f9802e50c2d9aa80a756d69cecc39bbde6389d08810396829e2b66e2705ef | 2026-02-02T00:00:00-05:00 | Compact Hypercube Embeddings for Fast Text-based Wildlife Observation Retrieval | arXiv:2601.22783v1 Announce Type: new Abstract: Large-scale biodiversity monitoring platforms increasingly rely on multimodal wildlife observations. While recent foundation models enable rich semantic representations across vision, audio, and language, retrieving relevant observations from massive archives remains chal... | https://arxiv.org/abs/2601.22783 | Academic Papers | svg |
649003f2e68703845c0d0363d63ad211f64a8d35703136cd606ffd83d90ebe87 | 2026-02-02T00:00:00-05:00 | Toward IIT-Inspired Consciousness in LLMs: A Reward-Based Learning Framework | arXiv:2601.22786v1 Announce Type: new Abstract: The pursuit of Artificial General Intelligence (AGI) is a central goal in language model development, in which consciousness-like processing could serve as a key facilitator. While current language models are not conscious, they exhibit behaviors analogous to certain aspe... | https://arxiv.org/abs/2601.22786 | Academic Papers | svg |
44d3cee05e6be136a89b0f66d8227e3564d090dfddaec68fc137e83a6196f60e | 2026-02-02T00:00:00-05:00 | Float8@2bits: Entropy Coding Enables Data-Free Model Compression | arXiv:2601.22787v1 Announce Type: new Abstract: Post-training compression is currently divided into two contrasting regimes. On the one hand, fast, data-free, and model-agnostic methods (e.g., NF4 or HQQ) offer maximum accessibility but suffer from functional collapse at extreme bit-rates below 4 bits. On the other han... | https://arxiv.org/abs/2601.22787 | Academic Papers | svg |
c0e8b4ab3b0b0490a6b998ced3adf758a48d19a8f4469e53584ccf1a50eafb6b | 2026-02-02T00:00:00-05:00 | FACET: Multi-Agent AI Supporting Teachers in Scaling Differentiated Learning for Diverse Students | arXiv:2601.22788v1 Announce Type: new Abstract: Classrooms are becoming increasingly heterogeneous, comprising learners with diverse performance and motivation levels, language proficiencies, and learning differences such as dyslexia and ADHD. While teachers recognize the need for differentiated instruction, growing wo... | https://arxiv.org/abs/2601.22788 | Academic Papers | svg |
f62f967565724aad3922ab8930c75bb36b3199e08e8438df0f77cc7441f0b1b4 | 2026-02-02T00:00:00-05:00 | Conditional Performance Guarantee for Large Reasoning Models | arXiv:2601.22790v1 Announce Type: new Abstract: Large reasoning models have shown strong performance through extended chain-of-thought reasoning, yet their computational cost remains significant. Probably approximately correct (PAC) reasoning provides statistical guarantees for efficient reasoning by adaptively switchi... | https://arxiv.org/abs/2601.22790 | Academic Papers | svg |
ca703b14090c53a14c24144266efe4345f76df4aaaa03128d149bdaca79e489c | 2026-02-02T00:00:00-05:00 | Understanding on the Edge: LLM-generated Boundary Test Explanations | arXiv:2601.22791v1 Announce Type: new Abstract: Boundary value analysis and testing (BVT) is fundamental in software quality assurance because faults tend to cluster at input extremes, yet testers often struggle to understand and justify why certain input-output pairs represent meaningful behavioral boundaries. Large L... | https://arxiv.org/abs/2601.22791 | Academic Papers | svg |
0ddfea66ddff5e067f3d317325db2e982538748ca9538d5afe20ae3f0cbc740b | 2026-02-02T00:00:00-05:00 | Sparse or Dense? A Mechanistic Estimation of Computation Density in Transformer-based LLMs | arXiv:2601.22795v1 Announce Type: new Abstract: Transformer-based large language models (LLMs) are comprised of billions of parameters arranged in deep and wide computational graphs. Several studies on LLM efficiency optimization argue that it is possible to prune a significant portion of the parameters, while only mar... | https://arxiv.org/abs/2601.22795 | Academic Papers | svg |
98b8e1f4b4eedde58ff1debf1d2b09d50a4cc409ab08d5c16d45202fb8b5cfe4 | 2026-02-02T00:00:00-05:00 | HeatMat: Simulation of City Material Impact on Urban Heat Island Effect | arXiv:2601.22796v1 Announce Type: new Abstract: The Urban Heat Island (UHI) effect, defined as a significant increase in temperature in urban environments compared to surrounding areas, is difficult to study in real cities using sensor data (satellites or in-situ stations) due to their coarse spatial and temporal resol... | https://arxiv.org/abs/2601.22796 | Academic Papers | svg |
f51aa8017cdf405a2f69a7962c72029ec6000fa2391efe3da22fa7df2bd7eb39 | 2026-02-02T00:00:00-05:00 | Trackly: A Unified SaaS Platform for User Behavior Analytics and Real Time Rule Based Anomaly Detection | arXiv:2601.22800v1 Announce Type: new Abstract: Understanding user behavior is essential for improving digital experiences, optimizing business conversions, and mitigating threats like account takeovers, fraud, and bot attacks. Most platforms separate product analytics and security, creating fragmented visibility and d... | https://arxiv.org/abs/2601.22800 | Academic Papers | svg |
6cb0a25f08c97bd5aee671ad9faef6097c4d00fb413428d3a40e8aa6249b906b | 2026-02-02T00:00:00-05:00 | Clipping-Free Policy Optimization for Large Language Models | arXiv:2601.22801v1 Announce Type: new Abstract: Reinforcement learning has become central to post-training large language models, yet dominant algorithms rely on clipping mechanisms that introduce optimization issues at scale, including zero-gradient regions, reward hacking, and training instability. We propose Clippin... | https://arxiv.org/abs/2601.22801 | Academic Papers | svg |
d4d66a7b09a1cacfe6518118ece56b465bac828da75fcbb956a870d57342d622 | 2026-02-02T00:00:00-05:00 | CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learning | arXiv:2601.22803v1 Announce Type: new Abstract: Code verifiers play a critical role in post-verification for LLM-based code generation, yet existing supervised fine-tuning methods suffer from data scarcity, high failure rates, and poor inference efficiency. While reinforcement learning (RL) offers a promising alternati... | https://arxiv.org/abs/2601.22803 | Academic Papers | svg |
e468859b068839e39f1a15c9926cb10908d4bb737c1ecdd10b884414b4b8a6cb | 2026-02-02T00:00:00-05:00 | Trojan-Resilient NTT: Protecting Against Control Flow and Timing Faults on Reconfigurable Platforms | arXiv:2601.22804v1 Announce Type: new Abstract: Number Theoretic Transform (NTT) is the most essential component for polynomial multiplications used in lattice-based Post-Quantum Cryptography (PQC) algorithms such as Kyber, Dilithium, NTRU etc. However, side-channel attacks (SCA) and hardware vulnerabilities in the for... | https://arxiv.org/abs/2601.22804 | Academic Papers | svg |
71890cd2f2f6c695e2705a57c3e4947852c78c778ca5bd44a19cf16e7df7175f | 2026-02-02T00:00:00-05:00 | SOMBRERO: Measuring and Steering Boundary Placement in End-to-End Hierarchical Sequence Models | arXiv:2601.22805v1 Announce Type: new Abstract: Hierarchical sequence models replace fixed tokenization with learned segmentations that compress long byte sequences for efficient autoregressive modeling. While recent end-to-end methods can learn meaningful boundaries from the language-modeling objective alone, it remai... | https://arxiv.org/abs/2601.22805 | Academic Papers | svg |
0f31303a42344bfc6b7e18ca80c5271342c45cd4b5851bcd501b6bdf32a54471 | 2026-02-02T00:00:00-05:00 | Aligning the Unseen in Attributed Graphs: Interplay between Graph Geometry and Node Attributes Manifold | arXiv:2601.22806v1 Announce Type: new Abstract: The standard approach to representation learning on attributed graphs -- i.e., simultaneously reconstructing node attributes and graph structure -- is geometrically flawed, as it merges two potentially incompatible metric spaces. This forces a destructive alignment that e... | https://arxiv.org/abs/2601.22806 | Academic Papers | svg |
1248ac7a346f738449303430359c894811fbfe734afb88224d931bd6de2d1c53 | 2026-02-02T00:00:00-05:00 | Diachronic Stereo Matching for Multi-Date Satellite Imagery | arXiv:2601.22808v1 Announce Type: new Abstract: Recent advances in image-based satellite 3D reconstruction have progressed along two complementary directions. On one hand, multi-date approaches using NeRF or Gaussian-splatting jointly model appearance and geometry across many acquisitions, achieving accurate reconstruc... | https://arxiv.org/abs/2601.22808 | Academic Papers | svg |
303df500b72fd0262fbbdfe19cab0da33516cf7f18552b21839f0d82c7e67a54 | 2026-02-02T00:00:00-05:00 | FarmMind: Reasoning-Query-Driven Dynamic Segmentation for Farmland Remote Sensing Images | arXiv:2601.22809v1 Announce Type: new Abstract: Existing methods for farmland remote sensing image (FRSI) segmentation generally follow a static segmentation paradigm, where analysis relies solely on the limited information contained within a single input patch. Consequently, their reasoning capability is limited when ... | https://arxiv.org/abs/2601.22809 | Academic Papers | svg |
69703b2061205089aed03892c10289357eb22b91a83bb0ce5113bd14a10cd9f6 | 2026-02-02T00:00:00-05:00 | Stable Personas: Dual-Assessment of Temporal Stability in LLM-Based Human Simulation | arXiv:2601.22812v1 Announce Type: new Abstract: Large Language Models (LLMs) acting as artificial agents offer the potential for scalable behavioral research, yet their validity depends on whether LLMs can maintain stable personas across extended conversations. We address this point using a dual-assessment framework me... | https://arxiv.org/abs/2601.22812 | Academic Papers | svg |
bf85dd037bb64654e04babd0c0985d5036df8b1d689c38f7bdd431de7ed7aacb | 2026-02-02T00:00:00-05:00 | Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation | arXiv:2601.22813v1 Announce Type: new Abstract: The NVFP4 lower-precision format, supported in hardware by NVIDIA Blackwell GPUs, promises to allow, for the first time, end-to-end fully-quantized pre-training of massive models such as LLMs. Yet, existing quantized training methods still sacrifice some of the representa... | https://arxiv.org/abs/2601.22813 | Academic Papers | svg |
4babd7f261c355a7c78e99b0448485d4983287fcaf47719a60e9a566ed53624e | 2026-02-02T00:00:00-05:00 | Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Features | arXiv:2601.22816v1 Announce Type: new Abstract: Advances in generative modeling have recently been adapted to tabular data containing discrete and continuous features. However, generating mixed-type features that combine discrete states with an otherwise continuous distribution in a single feature remains challenging. ... | https://arxiv.org/abs/2601.22816 | Academic Papers | svg |
39dac08e299ad21bf57cf91cf4e8bf233a8c941740511d68197fa85d90b6ccbd | 2026-02-02T00:00:00-05:00 | Hide and Seek in Embedding Space: Geometry-based Steganography and Detection in Large Language Models | arXiv:2601.22818v1 Announce Type: new Abstract: Fine-tuned LLMs can covertly encode prompt secrets into outputs via steganographic channels. Prior work demonstrated this threat but relied on trivially recoverable encodings. We formalize payload recoverability via classifier accuracy and show previous schemes achieve 10... | https://arxiv.org/abs/2601.22818 | Academic Papers | svg |
ee037d2b033461ad4cc1e7f95e56b8e6068fbdd17c7e96ce97d0ef8769a99e57 | 2026-02-02T00:00:00-05:00 | User-Adaptive Meta-Learning for Cold-Start Medication Recommendation with Uncertainty Filtering | arXiv:2601.22820v1 Announce Type: new Abstract: Large-scale Electronic Health Record (EHR) databases have become indispensable in supporting clinical decision-making through data-driven treatment recommendations. However, existing medication recommender methods often struggle with a user (i.e., patient) cold-start prob... | https://arxiv.org/abs/2601.22820 | Academic Papers | svg |
e8fd52b06758cf8f0e18e9a618ec539e09cd3fc86462b7ae53146d205fac8f25 | 2026-02-02T00:00:00-05:00 | Offline Reinforcement Learning of High-Quality Behaviors Under Robust Style Alignment | arXiv:2601.22823v1 Announce Type: new Abstract: We study offline reinforcement learning of style-conditioned policies using explicit style supervision via subtrajectory labeling functions. In this setting, aligning style with high task performance is particularly challenging due to distribution shift and inherent confl... | https://arxiv.org/abs/2601.22823 | Academic Papers | svg |
abf19a97a8c1ddeab57967ed845c7f796f7599c10d2680a2a7d86c8141836899 | 2026-02-02T00:00:00-05:00 | Approximation of PDE solution manifolds: Sparse-grid interpolation and quadrature | arXiv:2601.22825v1 Announce Type: new Abstract: We study fully-discrete approximations and quadratures of infinite-variate functions in abstract Bochner spaces associated with a Hilbert space $X$ and an infinite-tensor-product Jacobi measure. For target infinite-variate functions taking values in $X$ which admit absolu... | https://arxiv.org/abs/2601.22825 | Academic Papers | svg |
5f695a5c3a701c403a9e9a0ac574c1b233299d4a07d557940fc709a47e964a92 | 2026-02-02T00:00:00-05:00 | Decomposing and Composing: Towards Efficient Vision-Language Continual Learning via Rank-1 Expert Pool in a Single LoRA | arXiv:2601.22828v1 Announce Type: new Abstract: Continual learning (CL) in vision-language models (VLMs) faces significant challenges in improving task adaptation and avoiding catastrophic forgetting. Existing methods usually have heavy inference burden or rely on external knowledge, while Low-Rank Adaptation (LoRA) ha... | https://arxiv.org/abs/2601.22828 | Academic Papers | svg |
0f0271d2efb3b683afe5ed06efaa9b94301e0f89c147666120339fd8867d0515 | 2026-02-02T00:00:00-05:00 | A Comparative Evaluation of Large Vision-Language Models for 2D Object Detection under SOTIF Conditions | arXiv:2601.22830v1 Announce Type: new Abstract: Reliable environmental perception remains one of the main obstacles for safe operation of automated vehicles. Safety of the Intended Functionality (SOTIF) concerns safety risks from perception insufficiencies, particularly under adverse conditions where conventional detec... | https://arxiv.org/abs/2601.22830 | Academic Papers | svg |
fcf1870a020353d5718cc1c2c3fb95be7adeed9ffa0f6d27450784340d42d44d | 2026-02-02T00:00:00-05:00 | Toward Pluralizing Reflection in HCI through Daoism | arXiv:2601.22831v1 Announce Type: new Abstract: Reflection is fundamental to how people make sense of everyday life, helping them navigate moments of growth, uncertainty, and change. Yet in HCI, existing frameworks of designing technologies to support reflection remain narrow, emphasizing cognitive, rational problem-so... | https://arxiv.org/abs/2601.22831 | Academic Papers | svg |
569468a2cdeea4b1ea6216820edfca338fdce06bbf6ee98ed320c36430a8c034 | 2026-02-02T00:00:00-05:00 | Just-in-Time Catching Test Generation at Meta | arXiv:2601.22832v1 Announce Type: new Abstract: We report on Just-in-Time catching test generation at Meta, designed to prevent bugs in large scale backend systems of hundreds of millions of line of code. Unlike traditional hardening tests, which pass at generation time, catching tests are meant to fail, surfacing bugs... | https://arxiv.org/abs/2601.22832 | Academic Papers | svg |
908d3a52728c71fe4788ee02b24be3cd90660120c2c6f28e5289d643b4a514d3 | 2026-02-02T00:00:00-05:00 | NativeTok: Native Visual Tokenization for Improved Image Generation | arXiv:2601.22837v1 Announce Type: new Abstract: VQ-based image generation typically follows a two-stage pipeline: a tokenizer encodes images into discrete tokens, and a generative model learns their dependencies for reconstruction. However, improved tokenization in the first stage does not necessarily enhance the secon... | https://arxiv.org/abs/2601.22837 | Academic Papers | svg |
a842668784f907a49fdfa217c062156d33026c3215d230a0dcdb5234e27a0936 | 2026-02-02T00:00:00-05:00 | Neural Clothing Tryer: Customized Virtual Try-On via Semantic Enhancement and Controlling Diffusion Model | arXiv:2601.22838v1 Announce Type: new Abstract: This work aims to address a novel Customized Virtual Try-ON (Cu-VTON) task, enabling the superimposition of a specified garment onto a model that can be customized in terms of appearance, posture, and additional attributes. Compared with traditional VTON task, it enables ... | https://arxiv.org/abs/2601.22838 | Academic Papers | svg |
b57324d3329c6159308ca2ebe601275450024d94cf2d9d5e1ae078783f016916 | 2026-02-02T00:00:00-05:00 | How Much of a Model Do We Need? Redundancy and Slimmability in Remote Sensing Foundation Models | arXiv:2601.22841v1 Announce Type: new Abstract: Large-scale foundation models (FMs) in remote sensing (RS) are developed based on the paradigms established in computer vision (CV) and have shown promise for various Earth observation applications. However, the direct transfer of scaling assumptions from CV to RS has not... | https://arxiv.org/abs/2601.22841 | Academic Papers | svg |
d7353b7145fb75cd7d3f945b7c42ea94ce3ed67ca7bcab5494ce394b30a76e42 | 2026-02-02T00:00:00-05:00 | Unconditional flow-based time series generation with equivariance-regularised latent spaces | arXiv:2601.22848v1 Announce Type: new Abstract: Flow-based models have proven successful for time-series generation, particularly when defined in lower-dimensional latent spaces that enable efficient sampling. However, how to design latent representations with desirable equivariance properties for time-series generativ... | https://arxiv.org/abs/2601.22848 | Academic Papers | svg |
b64d2eb2d78b7756b710214e2c42757d185aeb8183004fe5318977a16de406d4 | 2026-02-02T00:00:00-05:00 | Robust Rigid Body Assembly via Contact-Implicit Optimal Control with Exact Second-Order Derivatives | arXiv:2601.22849v1 Announce Type: new Abstract: Efficient planning of assembly motions is a long standing challenge in the field of robotics that has been primarily tackled with reinforcement learning and sampling-based methods by using extensive physics simulations. This paper proposes a sample-efficient robust optima... | https://arxiv.org/abs/2601.22849 | Academic Papers | svg |
6dbec70ca992580d5dd1040380803f54b3dba4f6b19512984b12d22f34fb69bd | 2026-02-02T00:00:00-05:00 | When Meanings Meet: Investigating the Emergence and Quality of Shared Concept Spaces during Multilingual Language Model Training | arXiv:2601.22851v1 Announce Type: new Abstract: Training Large Language Models (LLMs) with high multilingual coverage is becoming increasingly important -- especially when monolingual resources are scarce. Recent studies have found that LLMs process multilingual inputs in shared concept spaces, thought to support gener... | https://arxiv.org/abs/2601.22851 | Academic Papers | svg |
b21ea4171ce1d554027c85e445047f0ad3b0c2734caeb9a47b6d823b4c37b416 | 2026-02-02T00:00:00-05:00 | Hierarchical Shift Mixing -- Beyond Dense Attention in Transformers | arXiv:2601.22852v1 Announce Type: new Abstract: Since the introduction of the Transformer architecture for large language models, the softmax-based attention layer has faced increasing scrutinity due to its quadratic-time computational complexity. Attempts have been made to replace it with less complex methods, at the ... | https://arxiv.org/abs/2601.22852 | Academic Papers | svg |
44ff70447c2665eb41ad8eee8b5fda2a20a9c2fc002fd8ceb6efae3ace9e4dce | 2026-02-02T00:00:00-05:00 | Inference-Time Dynamic Modality Selection for Incomplete Multimodal Classification | arXiv:2601.22853v1 Announce Type: new Abstract: Multimodal deep learning (MDL) has achieved remarkable success across various domains, yet its practical deployment is often hindered by incomplete multimodal data. Existing incomplete MDL methods either discard missing modalities, risking the loss of valuable task-releva... | https://arxiv.org/abs/2601.22853 | Academic Papers | svg |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.