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e21024c3848eeb2ddecc4bdb71b879db7ad0c1dc2116b7f3a1107960dfb9f6c1 | 2026-01-16T00:00:00-05:00 | On the Need to Rethink Trust in AI Assistants for Software Development: A Critical Review | arXiv:2504.12461v3 Announce Type: replace Abstract: Trust is a fundamental concept in human decision-making and collaboration that has long been studied in philosophy and psychology. However, software engineering (SE) articles often use the term trust informally; providing an explicit definition or embedding results in... | https://arxiv.org/abs/2504.12461 | Academic Papers | svg |
0943c518f039bc26d9dde8ec3c02392b4fe594c1ff192a6b26910c50367e345e | 2026-01-16T00:00:00-05:00 | Pushing the frontiers of subexponential FPT time for Feedback Vertex Set | arXiv:2504.17708v2 Announce Type: replace Abstract: The paper deals with the Feedback Vertex Set problem parameterized by the solution size. Given a graph $G$ and a parameter $k$, one has to decide if there is a set $S$ of at most $k$ vertices such that $G-S$ is acyclic. Assuming the Exponential Time Hypothesis, it is ... | https://arxiv.org/abs/2504.17708 | Academic Papers | svg |
994f9784d74f5730c2f357254f51904f76f299dace8a5b65f30cbfdb72019337 | 2026-01-16T00:00:00-05:00 | Mixed Bernstein-Fourier Approximants for Optimal Trajectory Generation with Periodic Behavior | arXiv:2504.17969v3 Announce Type: replace Abstract: Efficient trajectory generation is crucial for autonomous systems; however, current numerical methods often struggle to handle periodic behaviors effectively, particularly when the onboard sensors require equidistant temporal sampling. This paper introduces a novel mi... | https://arxiv.org/abs/2504.17969 | Academic Papers | svg |
7e8391d061e512c00c18c3b32665dcc854d8d3cea2e806ef651b801f2a2c9a77 | 2026-01-16T00:00:00-05:00 | RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video | arXiv:2505.02064v4 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have made rapid progress in perception, understanding, and reasoning, yet existing benchmarks fall short in evaluating these abilities under continuous and dynamic real-world video streams. Such settings require models to maint... | https://arxiv.org/abs/2505.02064 | Academic Papers | svg |
b257883c466e341914e3c55dd3fe1dbec928bdcfd8556bea11374eb12c3baf46 | 2026-01-16T00:00:00-05:00 | Towards Understanding Deep Learning Model in Image Recognition via Coverage Test | arXiv:2505.08814v2 Announce Type: replace Abstract: Deep neural networks (DNNs) play a crucial role in the field of artificial intelligence, and their security-related testing has been a prominent research focus. By inputting test cases, the behavior of models is examined for anomalies, and coverage metrics are utilize... | https://arxiv.org/abs/2505.08814 | Academic Papers | svg |
dd558162fe166026cbfe371ab4b41ab31adf79502636c69507e481679162bc4b | 2026-01-16T00:00:00-05:00 | On the Failure of Latent State Persistence in Large Language Models | arXiv:2505.10571v4 Announce Type: replace Abstract: While Large Language Models (LLMs) excel in reasoning, whether they can sustain persistent latent states remains under-explored. The capacity to maintain and manipulate unexpressed, internal representations-analogous to human working memory-is a cornerstone of complex... | https://arxiv.org/abs/2505.10571 | Academic Papers | svg |
11d141f8651d59dd6a6a0cb2e2002d387df8fe931195b25dd05bcdb4587fecd3 | 2026-01-16T00:00:00-05:00 | SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training | arXiv:2505.11594v3 Announce Type: replace Abstract: The efficiency of attention is important due to its quadratic time complexity. We enhance the efficiency of attention through two key contributions: First, we leverage the new FP4 Tensor Cores in Blackwell GPUs to accelerate attention computation. Our implementation a... | https://arxiv.org/abs/2505.11594 | Academic Papers | svg |
d04a73dfa01ac425a8cb5195be3a237cde6e9ef708716589b9519494c347cac3 | 2026-01-16T00:00:00-05:00 | Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation | arXiv:2505.13111v3 Announce Type: replace Abstract: Knowledge distillation (KD) is a core component in the training and deployment of modern generative models, particularly large language models (LLMs). While its empirical benefits are well documented -- enabling smaller student models to emulate the performance of muc... | https://arxiv.org/abs/2505.13111 | Academic Papers | svg |
2533a0445f4b74f626c6a154a7b4d19baa64cd76479bf9867d8f3aba6e09b0f2 | 2026-01-16T00:00:00-05:00 | Deep Learning for Continuous-Time Stochastic Control with Jumps | arXiv:2505.15602v3 Announce Type: replace Abstract: In this paper, we introduce a model-based deep-learning approach to solve finite-horizon continuous-time stochastic control problems with jumps. We iteratively train two neural networks: one to represent the optimal policy and the other to approximate the value functi... | https://arxiv.org/abs/2505.15602 | Academic Papers | svg |
13c9199ec3cb4647c7c5c317b53f548a7505dc75aafd811b509b5cc2443f17ff | 2026-01-16T00:00:00-05:00 | LLM-Based Emulation of the Radio Resource Control Layer: Towards AI-Native RAN Protocols | arXiv:2505.16821v5 Announce Type: replace Abstract: Integrating Large AI Models (LAMs) into 6G mobile networks is a key enabler of the AI-Native Air Interface (AI-AI), where protocol intelligence must scale beyond handcrafted logic. This paper presents, to our knowledge, the first standards-compliant emulation of the R... | https://arxiv.org/abs/2505.16821 | Academic Papers | svg |
15b9679b317c32d74de5d811ce00c72fb1eae0311f92f7e7d87e4298bccd5e16 | 2026-01-16T00:00:00-05:00 | PMOA-TTS: Introducing the PubMed Open Access Textual Times Series Corpus | arXiv:2505.20323v2 Announce Type: replace Abstract: Clinical narratives encode temporal dynamics essential for modeling patient trajectories, yet large-scale temporally annotated resources are scarce. We introduce PMOA-TTS, a corpus of 124,699 single-patient PubMed Open Access case reports converted into structured tex... | https://arxiv.org/abs/2505.20323 | Academic Papers | svg |
a74044ee699bd775940361552da3a96fcf50e133efc15175c2a784bbf8178391 | 2026-01-16T00:00:00-05:00 | GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning | arXiv:2505.20355v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) is a popular method for parameter-efficient fine-tuning (PEFT) of generative models, valued for its simplicity and effectiveness. Despite recent enhancements, LoRA still suffers from a fundamental limitation: overfitting when the bottleneck ... | https://arxiv.org/abs/2505.20355 | Academic Papers | svg |
47ff94b8f16b92eb84efdfbb8d7b8b4315d76975759c0903c1ebec7366d12a8a | 2026-01-16T00:00:00-05:00 | AgriFM: A Multi-source Temporal Remote Sensing Foundation Model for Agriculture Mapping | arXiv:2505.21357v3 Announce Type: replace Abstract: Accurate crop mapping fundamentally relies on modeling multi-scale spatiotemporal patterns, where spatial scales range from individual field textures to landscape-level context, and temporal scales capture both short-term phenological transitions and full growing-seas... | https://arxiv.org/abs/2505.21357 | Academic Papers | svg |
90866432757f5f54a34d18f5e748f1ad9ce1fefcf0fca6afc8b17175697e3316 | 2026-01-16T00:00:00-05:00 | Optimal kernel regression bounds under energy-bounded noise | arXiv:2505.22235v3 Announce Type: replace Abstract: Non-conservative uncertainty bounds are key for both assessing an estimation algorithm's accuracy and in view of downstream tasks, such as its deployment in safety-critical contexts. In this paper, we derive a tight, non-asymptotic uncertainty bound for kernel-based e... | https://arxiv.org/abs/2505.22235 | Academic Papers | svg |
2418e728803732ec4f809bc60ed27403d1456d9974256b07ce3f364c346a539d | 2026-01-16T00:00:00-05:00 | From Dormant to Deleted: Tamper-Resistant Unlearning Through Weight-Space Regularization | arXiv:2505.22310v2 Announce Type: replace Abstract: Recent unlearning methods for LLMs are vulnerable to relearning attacks: knowledge believed-to-be-unlearned re-emerges by fine-tuning on a small set of (even seemingly-unrelated) examples. We study this phenomenon in a controlled setting for example-level unlearning i... | https://arxiv.org/abs/2505.22310 | Academic Papers | svg |
5bed3f1bb899e61d6142e8968822932738e717f6194f0ae73d14169d6c6e69fd | 2026-01-16T00:00:00-05:00 | MathArena: Evaluating LLMs on Uncontaminated Math Competitions | arXiv:2505.23281v3 Announce Type: replace Abstract: The rapid advancement of reasoning capabilities in large language models (LLMs) has led to notable improvements on mathematical benchmarks. However, many of the most commonly used evaluation datasets (e.g., AIME 2024) are widely available online, making it difficult t... | https://arxiv.org/abs/2505.23281 | Academic Papers | svg |
811cbb3b34c94c52fd2d37aa8c833afeea6487d2adf59adecb6e71c4d5aab992 | 2026-01-16T00:00:00-05:00 | Exploiting Euclidean Distance Field Properties for Fast and Safe 3D planning with a modified Lazy Theta* | arXiv:2505.24024v2 Announce Type: replace Abstract: This paper presents the FS-Planner, a fast graph-search planner based on a modified Lazy Theta* algorithm that exploits the analytical properties of Euclidean Distance Fields (EDFs). We introduce a new cost function that integrates an EDF-based term proven to satisfy ... | https://arxiv.org/abs/2505.24024 | Academic Papers | svg |
d64d7fe5278807948c702d712aa0291d6f7ec1a5c484ea3da01e904e6602f971 | 2026-01-16T00:00:00-05:00 | Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in Robotics | arXiv:2506.00070v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) have recently shown great promise in advancing robotics by combining embodied reasoning with robot control. A common approach involves training on embodied reasoning tasks related to robot control using Supervised Fine-Tuning (SFT)... | https://arxiv.org/abs/2506.00070 | Academic Papers | svg |
dba62e2a80c87349526a312d2510f866e955772d54e290084207496d77bf8c0d | 2026-01-16T00:00:00-05:00 | NestedFP: High-Performance, Memory-Efficient Dual-Precision Floating Point Support for LLMs | arXiv:2506.02024v3 Announce Type: replace Abstract: Meeting service-level objectives (SLOs) in Large Language Models (LLMs) serving is critical, but managing the high variability in load presents a significant challenge. Recent advancements in FP8 inference, backed by native hardware support, offer a potential solution... | https://arxiv.org/abs/2506.02024 | Academic Papers | svg |
3fd72f1da12bd89274a89754eb9b523cc009ec9c75b15f60519fb7ad46ac9622 | 2026-01-16T00:00:00-05:00 | APEX: Asynchronous Parallel CPU-GPU Execution for Online LLM Inference on Constrained GPUs | arXiv:2506.03296v4 Announce Type: replace Abstract: Deploying large language models (LLMs) for online inference is often constrained by limited GPU memory, particularly due to the growing KV cache during auto-regressive decoding. Hybrid GPU-CPU execution has emerged as a promising solution by offloading KV cache manage... | https://arxiv.org/abs/2506.03296 | Academic Papers | svg |
6163bd0e9e679764362c5e16b546884f4a79f38972c9a215c213f052223c9601 | 2026-01-16T00:00:00-05:00 | Normalize Filters! Classical Wisdom for Deep Vision | arXiv:2506.04401v5 Announce Type: replace Abstract: Classical image filters, such as those for averaging or differencing, are carefully normalized to ensure consistency, interpretability, and to avoid artifacts like intensity shifts, halos, or ringing. In contrast, convolutional filters learned end-to-end in deep netwo... | https://arxiv.org/abs/2506.04401 | Academic Papers | svg |
3bc657295258fbdaf002a6666c5f7c79bf7604cede36fd5d33b5523e2adb0e68 | 2026-01-16T00:00:00-05:00 | Learning normalized image densities via dual score matching | arXiv:2506.05310v3 Announce Type: replace Abstract: Learning probability models from data is at the heart of many machine learning endeavors, but is notoriously difficult due to the curse of dimensionality. We introduce a new framework for learning \emph{normalized} energy (log probability) models that is inspired by d... | https://arxiv.org/abs/2506.05310 | Academic Papers | svg |
87836c096a5a8af5515586ff8a27f380eb165a233d82abf1886a6748d7f60c0b | 2026-01-16T00:00:00-05:00 | The State-of-the-Art in Lifelog Retrieval: A Review of Progress at the ACM Lifelog Search Challenge Workshop 2022-24 | arXiv:2506.06743v2 Announce Type: replace Abstract: The ACM Lifelog Search Challenge (LSC) is a venue that welcomes and compares systems that support the exploration of lifelog data, and in particular the retrieval of specific information, through an interactive competition format. This paper reviews the recent advance... | https://arxiv.org/abs/2506.06743 | Academic Papers | svg |
8d04c2ab4ca3b258c9974ad43a1deb3712a2c5920a609f802280d824e64a1dc0 | 2026-01-16T00:00:00-05:00 | Audio Generation Through Score-Based Generative Modeling: Design Principles and Implementation | arXiv:2506.08457v2 Announce Type: replace Abstract: Diffusion models have emerged as powerful deep generative techniques, producing high-quality and diverse samples in applications in various domains including audio. While existing reviews provide overviews, there remains limited in-depth discussion of these specific d... | https://arxiv.org/abs/2506.08457 | Academic Papers | svg |
6cf6f0d58c2d3f58718a0f9ae32df9e3c0eb0aeabfc31a1143137c47a36c07f9 | 2026-01-16T00:00:00-05:00 | Semi-Tensor-Product Based Convolutional Neural Networks | arXiv:2506.10407v3 Announce Type: replace Abstract: The semi-tensor product of vectors generalizes the conventional inner product, enabling algebraic operations between vectors of different dimensions. Building upon this foundation, we introduce a domain-based convolutional product and integrate it with the STP to form... | https://arxiv.org/abs/2506.10407 | Academic Papers | svg |
c56cc60a35f9076dbbdd0a5cf6741cbc6e8d80e47d12ae9f907c2ff6f7b60962 | 2026-01-16T00:00:00-05:00 | HP2C-DT: High-Precision High-Performance Computer-enabled Digital Twin | arXiv:2506.10523v2 Announce Type: replace Abstract: Digital twins are transforming the way we monitor, analyze, and control physical systems, but designing architectures that balance real-time responsiveness with heavy computational demands remains a challenge. Cloud-based solutions often struggle with latency and reso... | https://arxiv.org/abs/2506.10523 | Academic Papers | svg |
6de42b6b2369893ae86ddf50dabfaab5ebed56505af4f33031f0353d4f6c7c0b | 2026-01-16T00:00:00-05:00 | Approximations for Fault-Tolerant Total and Partial Positive Influence Domination | arXiv:2506.12828v3 Announce Type: replace Abstract: In $\textit{total domination}$, given a graph $G=(V,E)$, we seek a minimum-size set of nodes $S\subseteq V$, such that every node in $V$ has at least one neighbor in $S$. We define a $\textit{fault-tolerant}$ version of total domination, where we require any node in $... | https://arxiv.org/abs/2506.12828 | Academic Papers | svg |
5f4af74399adfedd25b65b1c76939c8a549e059a4b6f34c7d6953156d29377e1 | 2026-01-16T00:00:00-05:00 | LittleBit: Ultra Low-Bit Quantization via Latent Factorization | arXiv:2506.13771v4 Announce Type: replace Abstract: Deploying large language models (LLMs) often faces challenges from substantial memory and computational costs. Quantization offers a solution, yet performance degradation in the sub-1-bit regime remains particularly difficult. This paper introduces LittleBit, a novel ... | https://arxiv.org/abs/2506.13771 | Academic Papers | svg |
56302ede176c1b69cfe9ce42184b0bfadc352a9709ed3043473659788a3ca0b8 | 2026-01-16T00:00:00-05:00 | Advancing Safe Mechanical Ventilation Using Offline RL With Hybrid Actions and Clinically Aligned Rewards | arXiv:2506.14375v2 Announce Type: replace Abstract: Invasive mechanical ventilation (MV) is a life-sustaining therapy commonly used in the intensive care unit (ICU) for patients with severe and acute conditions. These patients frequently rely on MV for breathing. Given the high risk of death in such cases, optimal MV s... | https://arxiv.org/abs/2506.14375 | Academic Papers | svg |
f06133d7aeaa23af6c9eda9984c7dbb3236a11b5d2ac9e73fd9b21824cbce32c | 2026-01-16T00:00:00-05:00 | Curating art exhibitions using machine learning | arXiv:2506.19813v3 Announce Type: replace Abstract: Here we present a series of artificial models - a total of four related models - based on machine learning techniques that attempt to learn from existing exhibitions which have been curated by human experts, in order to be able to do similar curatorship work. Out of o... | https://arxiv.org/abs/2506.19813 | Academic Papers | svg |
f39e3109fa394aaf069335855d2d12232d18662c667ac7d92097e12dada5743e | 2026-01-16T00:00:00-05:00 | The Open Proof Corpus: A Large-Scale Study of LLM-Generated Mathematical Proofs | arXiv:2506.21621v2 Announce Type: replace Abstract: In recent months, large language models (LLMs) have made significant progress in mathematical proof generation, but further advancement is hindered by the lack of a large-scale, high-quality dataset of human-evaluated proofs. While expensive to create, such a dataset ... | https://arxiv.org/abs/2506.21621 | Academic Papers | svg |
af7d6e48db11f09faba71f738285890b8bb7d0ca30dd9ffffed8e93408dc8aea | 2026-01-16T00:00:00-05:00 | Uncovering Systemic and Environment Errors in Autonomous Systems Using Differential Testing | arXiv:2507.03870v2 Announce Type: replace Abstract: When an autonomous agent behaves undesirably, including failure to complete a task, it can be difficult to determine whether the behavior is due to a systemic agent error, such as flaws in the model or policy, or an environment error, where a task is inherently infeas... | https://arxiv.org/abs/2507.03870 | Academic Papers | svg |
ece58d51b30160fd9e88039a878263aa5bf3a0d5639fc98d6d3933d0d7b7df16 | 2026-01-16T00:00:00-05:00 | COALA: Numerically Stable and Efficient Framework for Context-Aware Low-Rank Approximation | arXiv:2507.07580v2 Announce Type: replace Abstract: Recent studies suggest that context-aware low-rank approximation is a useful tool for compression and fine-tuning of modern large-scale neural networks. In this type of approximation, a norm is weighted by a matrix of input activations, significantly improving metrics... | https://arxiv.org/abs/2507.07580 | Academic Papers | svg |
1c19b6e4a81c0a7321f3156e37c807f945a297054fa30e7ccaeebf391c34bd00 | 2026-01-16T00:00:00-05:00 | A simple formalization of alpha-equivalence | arXiv:2507.10181v2 Announce Type: replace Abstract: While teaching untyped $\lambda$-calculus to undergraduate students, we were wondering why $\alpha$-equivalence is not directly inductively defined. In this paper, we demonstrate that this is indeed feasible. Specifically, we provide a grounded, inductive definition f... | https://arxiv.org/abs/2507.10181 | Academic Papers | svg |
8f42fbcdfd0bb6327b1a0feb917146a1b20c06416a69090e8906a9fde96a71b2 | 2026-01-16T00:00:00-05:00 | CodeAssistBench (CAB): Dataset & Benchmarking for Multi-turn Chat-Based Code Assistance | arXiv:2507.10646v5 Announce Type: replace Abstract: Programming assistants powered by large language models have improved dramatically, yet existing benchmarks still evaluate them in narrow code-generation settings. Recent efforts such as InfiBench and StackEval rely on Stack Overflow questions and remain limited to si... | https://arxiv.org/abs/2507.10646 | Academic Papers | svg |
9a192b758751fedacb8545d0e169c639a41c7f25a9a37d769b269eb76ffa9d7f | 2026-01-16T00:00:00-05:00 | Keep the beat going: Automatic drum transcription with momentum | arXiv:2507.12596v2 Announce Type: replace Abstract: How can we process a piece of recorded music to detect and visualize the onset of each instrument? A simple, interpretable approach is based on partially fixed nonnegative matrix factorization (NMF). Yet despite the method's simplicity, partially fixed NMF is challeng... | https://arxiv.org/abs/2507.12596 | Academic Papers | svg |
eb4490e7084542cb87b2fb95b5a371d49f01de314f90018ba21793ae2ab2316f | 2026-01-16T00:00:00-05:00 | Approximation algorithms for scheduling with rejection in green manufacturing | arXiv:2507.12635v3 Announce Type: replace Abstract: Motivated by green manufacturing, this paper investigates a scheduling with rejection problem subject to an energy consumption constraint. Machines are associated with non-uniform energy consumption rates, defined as the energy consumed per unit time. Each job is eith... | https://arxiv.org/abs/2507.12635 | Academic Papers | svg |
1c79f72dbbc91341ee76d5cd6856701cec46920d0ce3f489cee0ba9b58dbac15 | 2026-01-16T00:00:00-05:00 | Enhancing Smart Grid Information Exchanges: A Three-Phase Method for Evaluating Information and Data Models during their Development Process | arXiv:2507.12649v2 Announce Type: replace Abstract: The ongoing process of smart grid digitalisation is increasing the volume of automated information exchange across distributed energy systems. This has driven the development of new information and data models when existing models fail to offer an optimal description ... | https://arxiv.org/abs/2507.12649 | Academic Papers | svg |
437a58669403035b6783a15856b0da1b61029f302d1e894dc20463ead7e216a5 | 2026-01-16T00:00:00-05:00 | A Framework of Distributed Source Encryption using Mutual Information Security Criterion and the Strong Converse Theorem | arXiv:2507.13294v4 Announce Type: replace Abstract: We reinvestigate the general distributed secure source coding based on the common key cryptosystem proposed by Oohama and Santoso (ITW 2021). They proposed a framework of distributed source encryption and derived the necessary and sufficient conditions to have reliabl... | https://arxiv.org/abs/2507.13294 | Academic Papers | svg |
2fc221bc1f4b8afa2711712f683120ea7529643bdb0e0488719c6e527c30ea51 | 2026-01-16T00:00:00-05:00 | 1/2 order convergence rate of Euler-type methods for time-changed stochastic differential equations with super-linearly growing drift and diffusion coefficients | arXiv:2507.14562v4 Announce Type: replace Abstract: This paper investigates the strong convergence properties of two Euler-type methods for a class of time-changed stochastic differential equations (TCSDEs) with super-linearly growing drift and diffusion coefficients. Building upon existing research, we propose a backw... | https://arxiv.org/abs/2507.14562 | Academic Papers | svg |
11ae998b8f7a53d0468d26ce058367672a075c4a490635667ca5cf502384a036 | 2026-01-16T00:00:00-05:00 | An intelligent agent-based simulation of human mobility in extreme urban morphologies | arXiv:2507.15143v2 Announce Type: replace Abstract: This paper investigates the feasibility of human mobility in extreme urban morphologies, characterized by high-density vertical structures and linear city layouts. To assess whether agents can navigate efficiently within such unprecedented topologies, we develop a hyb... | https://arxiv.org/abs/2507.15143 | Academic Papers | svg |
d57ecb5c8158ed1554d2b5cd9a3f4e14f0417cdd1cb581444e096e40a5950e06 | 2026-01-16T00:00:00-05:00 | Pareto-Grid-Guided Large Language Models for Fast and High-Quality Heuristics Design in Multi-Objective Combinatorial Optimization | arXiv:2507.20923v3 Announce Type: replace Abstract: Multi-objective combinatorial optimization problems (MOCOP) frequently arise in practical applications that require the simultaneous optimization of conflicting objectives. Although traditional evolutionary algorithms can be effective, they typically depend on domain ... | https://arxiv.org/abs/2507.20923 | Academic Papers | svg |
446a64d2ffa4d6b5825cc987f5b5197bae6de5f360856e15cf4f2f28ed932b5d | 2026-01-16T00:00:00-05:00 | Out of Distribution, Out of Luck: How Well Can LLMs Trained on Vulnerability Datasets Detect Top 25 CWE Weaknesses? | arXiv:2507.21817v4 Announce Type: replace Abstract: Automated vulnerability detection research has made substantial progress, yet its real-world impact remains limited. Prior work found that current vulnerability datasets suffer from issues including label inaccuracy rates of 20%-71%, extensive duplication, and poor co... | https://arxiv.org/abs/2507.21817 | Academic Papers | svg |
c225985e1b38bd369b7d4eb65724418a0d669825e7a45f9e8fb8919606545055 | 2026-01-16T00:00:00-05:00 | UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysis | arXiv:2508.01343v2 Announce Type: replace Abstract: The increasing number of attacks on the contract layer of DApps has resulted in economic losses amounting to $66 billion. Vulnerabilities arise when contracts interact with external protocols without verifying the results of the calls, leading to exploit entry points ... | https://arxiv.org/abs/2508.01343 | Academic Papers | svg |
0f0b73a2e4bd1f468c8d36d7ecd882d419aa33457ab15476dfe2b687beacb2bb | 2026-01-16T00:00:00-05:00 | MultiCFV: Detecting Control Flow Vulnerabilities in Smart Contracts Leveraging Multimodal Deep Learning | arXiv:2508.01346v2 Announce Type: replace Abstract: The introduction of smart contract functionality marks the advent of the blockchain 2.0 era, enabling blockchain technology to support digital currency transactions and complex distributed applications. However, many smart contracts have been found to contain vulnerab... | https://arxiv.org/abs/2508.01346 | Academic Papers | svg |
ab1d55670f41cef9d14a4a9f79a041b166675dcd9b844dd582ab986e46c0dfdd | 2026-01-16T00:00:00-05:00 | NATLM: Detecting Defects in NFT Smart Contracts Leveraging LLM | arXiv:2508.01351v2 Announce Type: replace Abstract: Security issues are becoming increasingly significant with the rapid evolution of Non-fungible Tokens (NFTs). As NFTs are traded as digital assets, they have emerged as prime targets for cyber attackers. In the development of NFT smart contracts, there may exist undis... | https://arxiv.org/abs/2508.01351 | Academic Papers | svg |
eae41be8a3b280e7356b9ee61459e87fafa35b6d1712d841b45d7aa46608455e | 2026-01-16T00:00:00-05:00 | A Study of Commonsense Reasoning over Visual Object Properties | arXiv:2508.10956v2 Announce Type: replace Abstract: Inspired by human categorization, object property reasoning involves identifying and recognizing low-level details and higher-level abstractions. While current visual question answering (VQA) studies consider multiple object properties, such as size, they typically bl... | https://arxiv.org/abs/2508.10956 | Academic Papers | svg |
d89f770fb47320ef1f94cac5d9f862f1c8765dca66a87e436731721e81a0e07c | 2026-01-16T00:00:00-05:00 | Adaptive Model-Predictive Control of a Soft Continuum Robot Using a Physics-Informed Neural Network Based on Cosserat Rod Theory | arXiv:2508.12681v2 Announce Type: replace Abstract: Dynamic control of soft continuum robots (SCRs) holds great potential for expanding their applications, but remains a challenging problem due to the high computational demands of accurate dynamic models. While data-driven approaches like Koopman-operator-based methods... | https://arxiv.org/abs/2508.12681 | Academic Papers | svg |
94eb80687fd88c4b67fc69ccdf75f2f0f253390f95d0964e6b308a5df404cb77 | 2026-01-16T00:00:00-05:00 | Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement | arXiv:2508.12851v3 Announce Type: replace Abstract: The emergence of Mixture-of-Experts (MoE) has transformed the scaling of large language models by enabling vast model capacity through sparse activation. Yet, converting these performance gains into practical edge deployment remains difficult, as the massive memory fo... | https://arxiv.org/abs/2508.12851 | Academic Papers | svg |
2a1641679262f9d370f81a87e65fb3b310802279f49fbbc3562e2f910b8d40f5 | 2026-01-16T00:00:00-05:00 | CASPER: Concept-integrated Sparse Representation for Scientific Retrieval | arXiv:2508.13394v2 Announce Type: replace Abstract: Identifying relevant research concepts is crucial for effective scientific search. However, primary sparse retrieval methods often lack concept-aware representations. To address this, we propose CASPER, a sparse retrieval model for scientific search that utilizes both... | https://arxiv.org/abs/2508.13394 | Academic Papers | svg |
945ebba025f05e0abe4a0c16c3d2e87e2c799aa5937664af64cdde9b517fbade | 2026-01-16T00:00:00-05:00 | Unleashing Semantic and Geometric Priors for 3D Scene Completion | arXiv:2508.13601v2 Announce Type: replace Abstract: Camera-based 3D semantic scene completion (SSC) provides dense geometric and semantic perception for autonomous driving and robotic navigation. However, existing methods rely on a coupled encoder to deliver both semantic and geometric priors, which forces the model to... | https://arxiv.org/abs/2508.13601 | Academic Papers | svg |
3db3d4a4d4fd0f97780197c58a18b86af708c746c9c6a47bb82ac6427e41d196 | 2026-01-16T00:00:00-05:00 | OMHBench: Benchmarking Balanced and Grounded Omni-Modal Multi-Hop Reasoning | arXiv:2508.16198v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have increasingly supported omni-modal processing across text, vision, and speech. However, existing evaluation frameworks for such models suffer from critical limitations, including modality shortcuts and biased reasoning path... | https://arxiv.org/abs/2508.16198 | Academic Papers | svg |
397b24edc65a9b3560be8defcdf673037906cb3af050829eab8fee26ebcd369c | 2026-01-16T00:00:00-05:00 | BASIL: Bayesian Assessment of Sycophancy in LLMs | arXiv:2508.16846v3 Announce Type: replace Abstract: Sycophancy (overly agreeable or flattering behavior) poses a fundamental challenge for human-AI collaboration, particularly in high-stakes decision-making domains such as health, law, and education. A central difficulty in studying sycophancy in large language models ... | https://arxiv.org/abs/2508.16846 | Academic Papers | svg |
133e682fd97498ff055b1221f3bd63dbb6e6594863e897f8b4f0d2f3cdcd35c9 | 2026-01-16T00:00:00-05:00 | Some new properties of the PamPa scheme | arXiv:2508.17147v2 Announce Type: replace Abstract: In this paper, we provide a few new properties of Active Flux (AF)/Point-Average-Moment PolynomiAl-interpreted (\pampa) schemes. First, we show, in full generality, that the AF/pampa schemes can be interpreted in such a way that the discontinuous Galerkin (dG) scheme ... | https://arxiv.org/abs/2508.17147 | Academic Papers | svg |
4b0f032bef6d3b13c7057f228b7872815b3092092b50979a866438b8d5158961 | 2026-01-16T00:00:00-05:00 | How Quantization Shapes Bias in Large Language Models | arXiv:2508.18088v2 Announce Type: replace Abstract: This work presents a comprehensive evaluation of how quantization affects model bias, with particular attention to its impact on individual demographic subgroups. We focus on weight and activation quantization strategies and examine their effects across a broad range ... | https://arxiv.org/abs/2508.18088 | Academic Papers | svg |
7ea0a1e0819312b46af33b02eb2eb4d00faf885d13f1e624830062a3d1526f54 | 2026-01-16T00:00:00-05:00 | FastMesh: Efficient Artistic Mesh Generation via Component Decoupling | arXiv:2508.19188v3 Announce Type: replace Abstract: Recent mesh generation approaches typically tokenize triangle meshes into sequences of tokens and train autoregressive models to generate these tokens sequentially. Despite substantial progress, such token sequences inevitably reuse vertices multiple times to fully re... | https://arxiv.org/abs/2508.19188 | Academic Papers | svg |
9f5c1013ad7fb23832a4713a0fa2dc8612d0c7bcf0b539dfb727a3995b6e34c4 | 2026-01-16T00:00:00-05:00 | Network-Level Prompt and Trait Leakage in Local Research Agents | arXiv:2508.20282v3 Announce Type: replace Abstract: We show that Web and Research Agents (WRAs) -- language-model-based systems that investigate complex topics on the Internet -- are vulnerable to inference attacks by passive network observers. Deployment of WRAs \emph{locally} by organizations and individuals for priv... | https://arxiv.org/abs/2508.20282 | Academic Papers | svg |
6f3cb4044b8f2ccc73b58da326a080c00a5b6ebd16effc270c1eb9478d764480 | 2026-01-16T00:00:00-05:00 | MindGuard: Intrinsic Decision Inspection for Securing LLM Agents Against Metadata Poisoning | arXiv:2508.20412v3 Announce Type: replace Abstract: The Model Context Protocol (MCP) is increasingly adopted to standardize the interaction between LLM agents and external tools. However, this trend introduces a new threat: Tool Poisoning Attacks (TPA), where tool metadata is poisoned to induce the agent to perform una... | https://arxiv.org/abs/2508.20412 | Academic Papers | svg |
edc5fe9735207128cd821b52cf3a39023492e92d17c8b6a642a592870bb4ecc6 | 2026-01-16T00:00:00-05:00 | Encoder-Only Image Registration | arXiv:2509.00451v3 Announce Type: replace Abstract: Learning-based techniques have significantly improved the accuracy and speed of deformable image registration. However, challenges such as reducing computational complexity and handling large deformations persist. To address these challenges, we analyze how convolutio... | https://arxiv.org/abs/2509.00451 | Academic Papers | svg |
7bdbbdde88be6f71ce282de1e82809db263b9a75eb4ab75c26cb80cd9f62deec | 2026-01-16T00:00:00-05:00 | Morse sequences on stacks and flooding sequences | arXiv:2509.01384v2 Announce Type: replace Abstract: This paper builds upon the framework of \emph{Morse sequences}, a simple and effective approach to discrete Morse theory. A Morse sequence on a simplicial complex consists of a sequence of nested subcomplexes generated by expansions and fillings-two operations origina... | https://arxiv.org/abs/2509.01384 | Academic Papers | svg |
daabcce116a737c1592d4ad08a2c2628f1cdade73da6a5780dffd3427782ff1e | 2026-01-16T00:00:00-05:00 | JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation | arXiv:2509.02097v4 Announce Type: replace Abstract: Current evaluation methods for large language models (LLMs) primarily rely on static benchmarks, presenting two major challenges: limited knowledge coverage and fixed difficulties that mismatch with the evaluated LLMs. These limitations lead to superficial assessments... | https://arxiv.org/abs/2509.02097 | Academic Papers | svg |
6a60501e0e2e431f797498dba17afad15b538d1e44e1899f29090b3fc9c52b3f | 2026-01-16T00:00:00-05:00 | Small Open Models Achieve Near Parity with Large Models in Low Resource Literary Translation at a Fraction of the Cost | arXiv:2509.07829v2 Announce Type: replace Abstract: Literary translation has recently gained attention as a distinct and complex task in machine translation research. However, the translation by small open models remains an open problem. We contribute to this ongoing research by introducing TinyFabulist Translation Fra... | https://arxiv.org/abs/2509.07829 | Academic Papers | svg |
a214352cb5c71a16b752e8a1fd07bee9ff0337ec413b341a00dfd86ee30344e2 | 2026-01-16T00:00:00-05:00 | Compartmentalised Agentic Reasoning for Clinical NLI | arXiv:2509.10222v2 Announce Type: replace Abstract: Large language models can produce fluent judgments for clinical natural language inference, yet they frequently fail when the decision requires the correct inferential schema rather than surface matching. We introduce CARENLI, a compartmentalised agentic framework tha... | https://arxiv.org/abs/2509.10222 | Academic Papers | svg |
cc6d034ee0ff325144951d764fdc1e58e297a529d1b8eb2c3b4d89ff1086067d | 2026-01-16T00:00:00-05:00 | Judge Q: Trainable Queries for Optimized Information Retention in KV Cache Eviction | arXiv:2509.10798v2 Announce Type: replace Abstract: Large language models (LLMs) utilize key-value (KV) cache to store historical information during sequence processing. The size of KV cache grows linearly as the length of the sequence extends, which seriously affects memory usage and decoding efficiency. Current metho... | https://arxiv.org/abs/2509.10798 | Academic Papers | svg |
37c6e8b9f3001c6370e62a165e4d6b66bbdec70bfdfe8913dc4d0fd81324b676 | 2026-01-16T00:00:00-05:00 | Graph Algorithm Unrolling with Douglas-Rachford Iterations for Image Interpolation with Guaranteed Initialization | arXiv:2509.11926v3 Announce Type: replace Abstract: Conventional deep neural nets (DNNs) initialize network parameters at random and then optimize each one via stochastic gradient descent (SGD), resulting in substantial risk of poor-performing local minima.Focusing on the image interpolation problem and leveraging a re... | https://arxiv.org/abs/2509.11926 | Academic Papers | svg |
d67b046ccfcd22b1bc5e6a864fa091af304afb436a8dda5bdd131c5ed2120c9d | 2026-01-16T00:00:00-05:00 | Multi-Threaded Software Model Checking via Parallel Trace Abstraction Refinement | arXiv:2509.13699v2 Announce Type: replace Abstract: Automatic software verification is a valuable means for software quality assurance. However, automatic verification and in particular software model checking can be time-consuming, which hinders their practical applicability e.g., the use in continuous integration. On... | https://arxiv.org/abs/2509.13699 | Academic Papers | svg |
3c9e83bda93d1ff19d8eede227344aa4869d0ff7d1c9db0116daa85f49f90e21 | 2026-01-16T00:00:00-05:00 | SPATIALGEN: Layout-guided 3D Indoor Scene Generation | arXiv:2509.14981v4 Announce Type: replace Abstract: Creating high-fidelity 3D models of indoor environments is essential for applications in design, virtual reality, and robotics. However, manual 3D modeling remains time-consuming and labor-intensive. While recent advances in generative AI have enabled automated scene ... | https://arxiv.org/abs/2509.14981 | Academic Papers | svg |
268cfa608fe44b9fd78380505f55c4721f255faa128b3b72f7112b4dc5458313 | 2026-01-16T00:00:00-05:00 | Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph | arXiv:2509.16180v2 Announce Type: replace Abstract: We propose an algorithm with improved query-complexity for the problem of hypothesis selection under local differential privacy constraints. Given a set of $k$ probability distributions $Q$, we describe an algorithm that satisfies local differential privacy, performs ... | https://arxiv.org/abs/2509.16180 | Academic Papers | svg |
1691b83460238ac392c423a19b60f1a332e65d089c3f0209d868592eae5f4299 | 2026-01-16T00:00:00-05:00 | Filling in the Clinical Gaps in Benchmark: Case for HealthBench for the Japanese medical system | arXiv:2509.17444v3 Announce Type: replace Abstract: This study investigates the applicability of HealthBench, a large-scale, rubric-based medical benchmark, to the Japanese context. Although robust evaluation frameworks are essential for the safe development of medical LLMs, resources in Japanese are scarce and often c... | https://arxiv.org/abs/2509.17444 | Academic Papers | svg |
d465b8bcaac0cc9a764f17039dcca83781f783f8ff9907e8cf319d3419b26976 | 2026-01-16T00:00:00-05:00 | Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation | arXiv:2509.17702v2 Announce Type: replace Abstract: Autonomous robotic systems applied to new domains require an abundance of expensive, pixel-level dense labels to train robust semantic segmentation models under full supervision. This study proposes a model-agnostic Depth Edge Alignment Loss to improve Weakly Supervis... | https://arxiv.org/abs/2509.17702 | Academic Papers | svg |
03cef9ebeae86ab5d6c1271ff1bd2cf5411f92d66c5506f4f2ad452da65c4149 | 2026-01-16T00:00:00-05:00 | Unveiling m-Sharpness Through the Structure of Stochastic Gradient Noise | arXiv:2509.18001v3 Announce Type: replace Abstract: Sharpness-aware minimization (SAM) has emerged as a highly effective technique to improve model generalization, but its underlying principles are not fully understood. We investigate m-sharpness, where SAM performance improves monotonically as the micro-batch size for... | https://arxiv.org/abs/2509.18001 | Academic Papers | svg |
39581d24f02658ce9cb61a496bab4c121bd1f26db7225b0c6304bc0d5e1e3933 | 2026-01-16T00:00:00-05:00 | Governing Together: Toward Infrastructure for Community-Run Social Media | arXiv:2509.19653v2 Announce Type: replace Abstract: Decentralizing the governance of social computing systems to communities promises to empower them to make independent decisions, with nuance and in accordance with their values. Yet, communities do not govern in isolation. Many problems communities face are common, or... | https://arxiv.org/abs/2509.19653 | Academic Papers | svg |
caa6a5dc5ec19c5ee93ce48ba6c5541367d23e94fa6ae8e62d903346cb7f6e7c | 2026-01-16T00:00:00-05:00 | Functional Critics Are Essential in Off-Policy Actor-Critic: Provable Convergence and Efficient Exploration | arXiv:2509.22964v3 Announce Type: replace Abstract: Off-policy reinforcement learning (RL) with function approximation offers an effective way to improve sample efficiency by reusing past experience. Within this setting, the actor-critic (AC) framework has achieved strong empirical success but suffers from the "moving ... | https://arxiv.org/abs/2509.22964 | Academic Papers | svg |
f51fd99265bfc5c8b0f1751a9a11870b1086307a19dca6f968a2e867c2938cfc | 2026-01-16T00:00:00-05:00 | Knowledge Homophily in Large Language Models | arXiv:2509.23773v2 Announce Type: replace Abstract: Large Language Models (LLMs) have been increasingly studied as neural knowledge bases for supporting knowledge-intensive applications such as question answering and fact checking. However, the structural organization of their knowledge remains unexplored. Inspired by ... | https://arxiv.org/abs/2509.23773 | Academic Papers | svg |
44440b5248f7602ceb53494af947534d833531a39a719312bcb4723d50196ae1 | 2026-01-16T00:00:00-05:00 | YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection | arXiv:2509.25164v3 Announce Type: replace Abstract: This study presents a comprehensive analysis of Ultralytics YOLO26(also called as YOLOv26), highlighting its key architectural enhancements and performance benchmarking for real-time object detection. YOLO26, released in September 2025, stands as the newest and most a... | https://arxiv.org/abs/2509.25164 | Academic Papers | svg |
714d38e77317ca0df22ec92b088f682b9841add14358e153ffae6e32c6b9944f | 2026-01-16T00:00:00-05:00 | A Geometric Unification of Generative AI with Manifold-Probabilistic Projection Models | arXiv:2510.00666v2 Announce Type: replace Abstract: Most models of generative AI for images assume that images are inherently low-dimensional objects embedded within a high-dimensional space. Additionally, it is often implicitly assumed that thematic image datasets form smooth or piecewise smooth manifolds. Common appr... | https://arxiv.org/abs/2510.00666 | Academic Papers | svg |
36b192b2ebe1e6280bc2c88850478109945266463b7bec19e9e4b658ac37297a | 2026-01-16T00:00:00-05:00 | Dual-Uncertainty Guided Policy Learning for Multimodal Reasoning | arXiv:2510.01444v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has advanced reasoning capabilities in multimodal large language models. However, existing methods typically treat visual inputs as deterministic, overlooking the perceptual ambiguity inherent to the visual modalit... | https://arxiv.org/abs/2510.01444 | Academic Papers | svg |
f8592f73042fe009d0d4e61bcbd9f8d18f6dcf3653539a7bfb4b2f64de88ba41 | 2026-01-16T00:00:00-05:00 | Data selection: at the interface of PDE-based inverse problem and randomized linear algebra | arXiv:2510.01567v2 Announce Type: replace Abstract: All inverse problems rely on data to recover unknown parameters, yet not all data are equally informative. This raises the central question of data selection. A distinctive challenge in PDE-based inverse problems is their inherently infinite-dimensional nature: both t... | https://arxiv.org/abs/2510.01567 | Academic Papers | svg |
87571cc5299ea2d4947c6174f02e49f92d47df4109f3052b5f11597891f6d657 | 2026-01-16T00:00:00-05:00 | Learning Regularization Functionals for Inverse Problems: A Comparative Study | arXiv:2510.01755v2 Announce Type: replace Abstract: In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insights. The proposed methods differ in their architectural design and training strategies, ... | https://arxiv.org/abs/2510.01755 | Academic Papers | svg |
f7bc340c03c73af84677f6cee2fbd44cbd863225f27064480ae6243eeb0d5513 | 2026-01-16T00:00:00-05:00 | Fine-Tuning Diffusion Models via Intermediate Distribution Shaping | arXiv:2510.02692v2 Announce Type: replace Abstract: Diffusion models are widely used for generative tasks across domains. While pre-trained diffusion models effectively capture the training data distribution, it is often desirable to shape these distributions using reward functions to align with downstream applications... | https://arxiv.org/abs/2510.02692 | Academic Papers | svg |
bba8c6176576cf97d3b85c1f02c9a2645adb876270c211ddf6706dbb0402a03a | 2026-01-16T00:00:00-05:00 | Distributionally Robust Causal Abstractions | arXiv:2510.04842v2 Announce Type: replace Abstract: Causal Abstraction (CA) theory provides a principled framework for relating causal models that describe the same system at different levels of granularity while ensuring interventional consistency between them. Recently, several approaches for learning CAs have been p... | https://arxiv.org/abs/2510.04842 | Academic Papers | svg |
3e48df477e15682386f96e84ee4ae447fe15898ad9cc2277a8dabf6ed15bc61b | 2026-01-16T00:00:00-05:00 | VAL-Bench: Belief Consistency as a measure for Value Alignment in Language Models | arXiv:2510.05465v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly being used for tasks where outputs shape human decisions, so it is critical to verify that their responses consistently reflect desired human values. Humans, as individuals or groups, don't agree on a universal set of valu... | https://arxiv.org/abs/2510.05465 | Academic Papers | svg |
00e1b344bb70fb2bdd292b6263a112bdc2f0ab38addbe3acf56fa3aede611e16 | 2026-01-16T00:00:00-05:00 | A recursive approach to the construction and enumeration of self-orthogonal and self-dual codes over finite commutative chain rings of even characteristic | arXiv:2510.06069v2 Announce Type: replace Abstract: Let $\mathcal{R}_{e,m}$ be a finite commutative chain ring of even characteristic with maximal ideal $\langle u \rangle$ of nilpotency index $e \geq 2,$ Teichm$\ddot{u}$ller set $\mathcal{T}_{m},$ and residue field $\mathcal{R}_{e,m}/\langle u \rangle$ of order $2^m.$... | https://arxiv.org/abs/2510.06069 | Academic Papers | svg |
384b5dfeca08a4457d7a91a8eaa54e375861b8ff8371c70b190fd051bfb5013e | 2026-01-16T00:00:00-05:00 | Recursive construction and enumeration of self-orthogonal and self-dual codes over finite commutative chain rings of even characteristic | arXiv:2510.06082v2 Announce Type: replace Abstract: Let $\mathscr{R}_{e,m}$ denote a finite commutative chain ring of even characteristic with maximal ideal $\langle u \rangle$ of nilpotency index $e \geq 3,$ Teichm$\ddot{u}$ller set $\mathcal{T}_{m},$ and residue field $\mathscr{R}_{e,m}/\langle u \rangle$ of order $2... | https://arxiv.org/abs/2510.06082 | Academic Papers | svg |
69fa6fe20b70be79d6469246e1aa217e4c627205eb1161e7737ca3f263c53cc7 | 2026-01-16T00:00:00-05:00 | Textual Entailment is not a Better Bias Metric than Token Probability | arXiv:2510.07662v2 Announce Type: replace Abstract: Measurement of social bias in language models is typically by token probability (TP) metrics, which are broadly applicable but have been criticized for their distance from real-world language model use cases and harms. In this work, we test natural language inference ... | https://arxiv.org/abs/2510.07662 | Academic Papers | svg |
22825f41d6ce85091f062400cb5f1e29546924a7a96cdac37ce3d660783840e5 | 2026-01-16T00:00:00-05:00 | Parallel Test-Time Scaling for Latent Reasoning Models | arXiv:2510.07745v3 Announce Type: replace Abstract: Parallel test-time scaling (TTS) is a pivotal approach for enhancing large language models (LLMs), typically by sampling multiple token-based chains-of-thought in parallel and aggregating outcomes through voting or search. Recent advances in latent reasoning, where in... | https://arxiv.org/abs/2510.07745 | Academic Papers | svg |
1911b176a1470f592ec024f88fd26d687bc631a4f80c34c04f80bfb7225bf714 | 2026-01-16T00:00:00-05:00 | One Sentence, Two Embeddings: Contrastive Learning of Explicit and Implicit Semantic Representations | arXiv:2510.09293v2 Announce Type: replace Abstract: Sentence embedding methods have made remarkable progress, yet they still struggle to capture the implicit semantics within sentences. This can be attributed to the inherent limitations of conventional sentence embedding methods that assign only a single vector per sen... | https://arxiv.org/abs/2510.09293 | Academic Papers | svg |
cb3932af8038b3aea9920a7ef7827e6c529ea8678c8acbc1409e5df2445578f5 | 2026-01-16T00:00:00-05:00 | Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems | arXiv:2510.13975v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) is a prevalent approach for building LLM-based question-answering systems that can take advantage of external knowledge databases. Due to the complexity of real-world RAG systems, there are many potential causes for erroneous outpu... | https://arxiv.org/abs/2510.13975 | Academic Papers | svg |
080bc809e70ab25fd78a935583a639b36481b7ce5be1ebb1bb9b03d5f39230c1 | 2026-01-16T00:00:00-05:00 | Decorrelation Speeds Up Vision Transformers | arXiv:2510.14657v3 Announce Type: replace Abstract: Masked Autoencoder (MAE) pre-training of vision transformers (ViTs) yields strong performance in low-label data regimes but comes with substantial computational costs, making it impractical in time- and resource-constrained industrial settings. We address this by inte... | https://arxiv.org/abs/2510.14657 | Academic Papers | svg |
36789083ca10ee5d1105a92c7668ea84cd0fd06a0eb2e2e7d35b6897c49556a1 | 2026-01-16T00:00:00-05:00 | Attn-JGNN: Attention Enhanced Join-Graph Neural Networks | arXiv:2510.15583v2 Announce Type: replace Abstract: We propose an Attention Enhanced Join-Graph Neural Networks(Attn-JGNN) model for solving #SAT problems, which significantly improves the solving accuracy. Inspired by the Iterative Join Graph Propagation (IJGP) algorithm, Attn-JGNN uses tree decomposition to encode th... | https://arxiv.org/abs/2510.15583 | Academic Papers | svg |
2ebbc691d83b1c121cfe63ec9fcef6d932e9d6a153fa1b903f6b431076f13600 | 2026-01-16T00:00:00-05:00 | Investigating LLM Capabilities on Long Context Comprehension for Medical Question Answering | arXiv:2510.18691v2 Announce Type: replace Abstract: This study is the first to investigate LLM comprehension capabilities over long-context (LC), clinically relevant medical Question Answering (QA) beyond MCQA. Our comprehensive approach considers a range of settings based on content inclusion of varying size and relev... | https://arxiv.org/abs/2510.18691 | Academic Papers | svg |
04159755038dce8e51b67de8a2df5245733b5c7b9d5845b0eda8dd225dc87793 | 2026-01-16T00:00:00-05:00 | CoRECT: A Framework for Evaluating Embedding Compression Techniques at Scale | arXiv:2510.19340v3 Announce Type: replace Abstract: Dense retrieval systems have proven to be effective across various benchmarks, but require substantial memory to store large search indices. Recent advances in embedding compression show that index sizes can be greatly reduced with minimal loss in ranking quality. How... | https://arxiv.org/abs/2510.19340 | Academic Papers | svg |
28cdd9060d1922b219242a20783b9931e251ff95c238d3c29a89820a95d146de | 2026-01-16T00:00:00-05:00 | User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios | arXiv:2510.20721v3 Announce Type: replace Abstract: Large language models (LLMs) are rapidly being adopted for tasks like drafting emails, summarizing meetings, and answering health questions. In these settings, users may need to share private information (e.g., contact details, health records). To evaluate LLMs' abili... | https://arxiv.org/abs/2510.20721 | Academic Papers | svg |
b1b0397d4d9f0a9feddf9d7c5492d122c5696189d5a99bb4cc2f08db37e7d1f5 | 2026-01-16T00:00:00-05:00 | Universal Maximum Likelihood (List) Decoding via Fast Vector-Matrix Multiplication | arXiv:2510.21414v2 Announce Type: replace Abstract: Maximum-likelihood (ML) decoding for arbitrary block codes remains fundamentally hard, with worst-case time complexity-measured by the total number of multiplications-being no better than straightforward exhaustive search, which requires $q^{k} n$ operations for an $[... | https://arxiv.org/abs/2510.21414 | Academic Papers | svg |
7eb0802fea1546b96e98aabf3efdb8e2b569849ef3846a2abe9f82348942140e | 2026-01-16T00:00:00-05:00 | Deep Jump Gaussian Processes for Surrogate Modeling of High-Dimensional Piecewise Continuous Functions | arXiv:2510.21974v2 Announce Type: replace Abstract: We introduce Deep Jump Gaussian Processes (DJGP), a novel method for surrogate modeling of a piecewise continuous function on a high-dimensional domain. DJGP addresses the limitations of conventional Jump Gaussian Processes (JGP) in high-dimensional input spaces by in... | https://arxiv.org/abs/2510.21974 | Academic Papers | svg |
a5a7febee09ce2d7a28c647a953b48822d0ca3a1e262f62385de6f632b1fddca | 2026-01-16T00:00:00-05:00 | Learning Without Augmenting: Unsupervised Time Series Representation Learning via Frame Projections | arXiv:2510.22655v2 Announce Type: replace Abstract: Self-supervised learning (SSL) has emerged as a powerful paradigm for learning representations without labeled data. Most SSL approaches rely on strong, well-established, handcrafted data augmentations to generate diverse views for representation learning. However, de... | https://arxiv.org/abs/2510.22655 | Academic Papers | svg |
c790200f1ff1d99ae2f641e75e79fe1d34bb2490a1489c8202da05b16da1b930 | 2026-01-16T00:00:00-05:00 | Differential Privacy as a Perk: Federated Learning over Multiple-Access Fading Channels with a Multi-Antenna Base Station | arXiv:2510.23463v3 Announce Type: replace Abstract: Federated Learning (FL) is a distributed learning paradigm that preserves privacy by eliminating the need to exchange raw data during training. In its prototypical edge instantiation with underlying wireless transmissions enabled by analog over-the-air computing (AirC... | https://arxiv.org/abs/2510.23463 | Academic Papers | svg |
faa91142f638ac03ab17ced6f4ca5ed3110a2740e6dda8bd94c1207f00f5b7b0 | 2026-01-16T00:00:00-05:00 | Geometric Algorithms for Neural Combinatorial Optimization with Constraints | arXiv:2510.24039v3 Announce Type: replace Abstract: Self-Supervised Learning (SSL) for Combinatorial Optimization (CO) is an emerging paradigm for solving combinatorial problems using neural networks. In this paper, we address a central challenge of SSL for CO: solving problems with discrete constraints. We design an e... | https://arxiv.org/abs/2510.24039 | Academic Papers | svg |
b02698dbbfb38c73422205a0f9a5c8ef41ff585e4fe8fc016af43b03d4b1127d | 2026-01-16T00:00:00-05:00 | Pinwheel Scheduling with Real Periods | arXiv:2510.24068v3 Announce Type: replace Abstract: For a sequence of tasks, each with a positive integer period, the pinwheel scheduling problem involves finding a valid schedule in the sense that the schedule performs one task per day and each task is performed at least once every consecutive days of its period. It h... | https://arxiv.org/abs/2510.24068 | Academic Papers | svg |
ce567857ee07857fb02db4f2e18c96131cb9d18fb0ad1977522c8fbabed7f47b | 2026-01-16T00:00:00-05:00 | Relative Scaling Laws for LLMs | arXiv:2510.24626v2 Announce Type: replace Abstract: Scaling laws describe how language models improve with additional data, parameters, and compute. While widely used, they are typically measured on aggregate test sets. Aggregate evaluations yield clean trends but average over heterogeneous subpopulations, obscuring pe... | https://arxiv.org/abs/2510.24626 | Academic Papers | svg |
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