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Computer Science & AI
Much of the new work in computer science and AI appears first as a preprint on arXiv. ReadSearch reads those listings every day, with papers OpenAlex files under the same subjects, and this page shows the newest it has, from machine learning and language to systems, theory and security.
The newest 1,000 papers, from 4 November 2026 to 5 November 2026: 1,000 arXiv preprints.
Newest papers, page 35
ProcessPrefBench: Evaluating Cross-Task Transfer of Individual Process Preferences from Naturalistic Human-AI Interaction
In natural human-AI dialogue, users' corrections, requests, and self-descriptions carry reusable signals about how they want work to be done.PRISM-AMC: Replay-Free Continual Learning for Automatic Modulation Classification
Automatic modulation classification (AMC) is a key component of cognitive wireless systems, enabling spectrum awareness and adaptive communication.Offline Learning of Reserve Prices from Censored Bids
We study how a seller can learn a reserve price for a second-price auction from censored historical bid data.Fluidic Body: Swarm Robots as a Fluid Extension of the Hand
Swarm robots have attracted growing interest as a physical interface for human--computer interaction, since a swarm can reshape its formation to fit the objects and surroundings it works with.SynMOE: A Syntax-Aware MOE-Guided Approach for PEFT-Based Code Intelligence Learning
Parameter-Efficient Fine-Tuning (PEFT) has emerged as an important approach for adapting Language Models (LMs) in downstream tasks.RouTex: Multi-reference 3D Texture Generation by Reprogramming Rotary Position Encoding
While single-image 3D texture generation has advanced significantly, conditioning texture synthesis on multi-view reference images remains largely under-explored, despite its necessity for resolving…Which One Is Sounding? Binaural Sounding Instance Disambiguation for Audio-Visual Segmentation
Existing audio-visual segmentation methods mainly predict sound-related regions or category masks and often fail to identify the sounding instance when multiple same-class candidates coexist.UniCompress: From Multi-LoRA Experts to One Compact Model for Edge Intelligence
Embodied and edge systems increasingly rely on vision-language models (VLMs) to support heterogeneous skills, yet deploying a separate task adapter for each skill while retaining a high-precision…SysPilot: Cross-Library Performance Autotuning via Library Performance Contracts
Modern scientific and machine-learning applications compose many independently developed libraries.Catastrophic Overfitting is Weight Drift the Training Loss Cannot See
Fast adversarial training (FAT) makes robust training affordable by replacing the multi-step attack with a single-step one, but it suffers from catastrophic overfitting (CO): partway through…SleuthVLM: Do We Really Need Audio-in-Video for Omni Video Understanding?
Omni models answer questions about videos by jointly processing sampled frames and audio in one turn, with progress driven largely by scaling model size and omni training data.Shallow Encoding Emits Deep Memories
Hybrid language models interleave a few softmax-attention layers with many linear-attention or state-space layers, so every layer keeps a memory of earlier tokens: a key-value cache or a fixed-size…GeoBiaset: A Counterfactual Benchmark for Demographic Bias in World-Level Geolocalization
World-level image geolocalization is an increasingly important task, yet the biases of current models remain poorly understood.STARE: Scene Text-Aware multimodal Retrieval for Everyday objects
In this study, we focus on service robots that retrieve and transport everyday objects based on free-form instructions that may refer to visual features and scene text.Overview of the NTCIR-19 SciClaimEval Task: Cross-modal Claim Verification in Scientific Papers
This paper presents an overview of \emph{SciClaimEval}, a pilot task at NTCIR-19 on verifying scientific claims against the tables and figures of the papers in which they appear.Constant-Round Massively Parallel Algorithms for Approximate MSTs in Metric Spaces
We develop a unified algorithmic framework for computing approximate minimum spanning trees (MSTs) in metric spaces in the fully scalable Massively Parallel Computation (MPC) model.Drift-adaptive Representation Preservation for Domain-Incremental Learning
Pretrained vision models provide strong general-purpose representations, but in Domain-Incremental Learning (DIL), where the input distribution changes sequentially, adapting to new domains often…Reasoning-Trace Overflow in Code Generation: An Empirical Study of Explicit Reasoning Models
Explicit reasoning can help language models generate code, but it also introduces a practical risk: the model may spend so much of its output budget reasoning that it never produces usable code.Many Pixels, One Composition: Self-Supervised Spectra for Specimen-Level Prediction
Can hyperspectral (HS) imaging be used to predict the elemental composition of physical specimens found on the Earth's surface?Towards Insight-Centric Data Spaces: Integrating Visual Analytics and Decentralized Data Ecosystems
Visual analytics and dataspaces address complementary challenges in modern data ecosystems: visual analytics enables interactive exploration and human-centered reasoning over complex data…
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