Search: “models”

An Empirical Analysis of Static Analysis Methods for Detection and Mitigation of Code Library Hallucinations

arXiv:2604.07755v1 Announce Type: new Abstract: Despite extensive research, Large Language Models continue to hallucinate when generating code, particularly when using libraries. On NL-to-code benchma

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Agents

AnomalyAgent: Agentic Industrial Anomaly Synthesis via Tool-Augmented Reinforcement Learning

DGX agent

arXiv:2604.07900v1 Announce Type: new Abstract: Industrial anomaly generation is a crucial method for alleviating the data scarcity problem in anomaly detection tasks. Most existing anomaly synthesis

agentsarxiv-cs-cv

10 Apr 2026

Apple: Toward General Active Perception via Reinforcement Learning

arXiv:2505.06182v5 Announce Type: replace-cross Abstract: Active perception is a fundamental skill that enables us humans to deal with uncertainty in our inherently partially observable environment. F

Are Stochastic Multi-objective Bandits Harder than Single-objective Bandits?

arXiv:2604.07096v1 Announce Type: new Abstract: Multi-objective bandits have attracted increasing attention because of their broad applicability and mathematical elegance, where the reward of each arm

Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries

arXiv:2604.06416v1 Announce Type: cross Abstract: Although LLM context lengths have grown, there is evidence that their ability to integrate information across long-form texts has not kept pace. We ev

AugLift: Depth-Aware Input Reparameterization Improves Domain Generalization in 2D-to-3D Pose Lifting

arXiv:2508.07112v4 Announce Type: replace-cross Abstract: Lifting-based 3D human pose estimation infers 3D joints from 2D keypoints but generalizes poorly because (x,y) coordinates alone are an ill-

researcharxiv-cs-lg

BadImplant: Injection-based Multi-Targeted Graph Backdoor Attack

arXiv:2601.15474v2 Announce Type: replace-cross Abstract: Graph neural network (GNN) have demonstrated exceptional performance in solving critical problems across diverse domains yet remain susceptibl

Bridging Theory and Practice in Crafting Robust Spiking Reservoirs

arXiv:2604.06395v1 Announce Type: new Abstract: Spiking reservoir computing provides an energy-efficient approach to temporal processing, but reliably tuning reservoirs to operate at the edge-of-chaos

BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook

model-releasesarxiv-cs-cv

arXiv:2506.12040v2 Announce Type: replace-cross Abstract: Binary quantization represents the most extreme form of compression, reducing weights to +/-1 for maximal memory and computational efficiency.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency

model-releasesarxiv-cs-ai

arXiv:2604.07286v1 Announce Type: cross Abstract: Autonomous vehicles deployed in remote environments typically rely on embedded processors, compact batteries, and lightweight sensors. These hardware

Compressible Softmax-Attended Language under Incompressible Attention

arXiv:2604.04384v2 Announce Type: replace-cross Abstract: Softmax attention defines an interaction through d_h head dimensions, but not all dimensions carry equal weight once real text passes throug

Tutorials

Conditional flow matching for physics-constrained inverse problems with finite training data

arXiv:2603.14135v3 Announce Type: replace-cross Abstract: This study presents a conditional flow matching framework for solving physics-constrained Bayesian inverse problems. In this setting, samples

tutorialsarxiv-cs-lg

Contextual Earnings-22: A Speech Recognition Benchmark with Custom Vocabulary in the Wild

model-releasesarxiv-cs-cl

arXiv:2604.07354v1 Announce Type: new Abstract: The accuracy frontier of speech-to-text systems has plateaued on academic benchmarks.1 In contrast, industrial benchmarks and adoption in high-stakes do

Contrastive Decoding Mitigates Score Range Bias in LLM-as-a-Judge

arXiv:2510.18196v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are commonly used as evaluators in various applications, but the reliability of the outcomes remains a challenge.

DailyArt: Discovering Articulation from Single Static Images via Latent Dynamics

arXiv:2604.07758v1 Announce Type: new Abstract: Articulated objects are essential for embodied AI and world models, yet inferring their kinematics from a single closed-state image remains challenging

Data Selection for Multi-turn Dialogue Instruction Tuning

arXiv:2604.07892v1 Announce Type: new Abstract: Instruction-tuned language models increasingly rely on large multi-turn dialogue corpora, but these datasets are often noisy and structurally inconsiste

local-aiarxiv-cs-cl

Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook

arXiv:2604.06210v2 Announce Type: cross Abstract: As LLMs are globally deployed, aligning their cultural value orientations is critical for safety and user engagement. However, existing benchmarks fac

Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism

arXiv:2601.05524v2 Announce Type: replace Abstract: Parallel Speculative Decoding (PSD) accelerates traditional Speculative Decoding (SD) by overlapping draft generation with verification. However, it

EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience

arXiv:2604.08063v1 Announce Type: new Abstract: Reconstructing visual stimuli from non-invasive electroencephalography (EEG) remains challenging due to its low spatial resolution and high noise, parti

Efficient Learned Data Compression via Dual-Stream Feature Decoupling

arXiv:2604.07239v1 Announce Type: cross Abstract: While Learned Data Compression (LDC) has achieved superior compression ratios, balancing precise probability modeling with system efficiency remains c

researcharxiv-cs-lg

EviSnap: Faithful Evidence-Cited Explanations for Cold-Start Cross-Domain Recommendation

arXiv:2604.06172v1 Announce Type: cross Abstract: Cold-start cross-domain recommender (CDR) systems predict a user's preferences in a target domain using only their source-domain behavior, yet existin

Tutorials

ExplainFuzz: Explainable and Constraint-Conditioned Test Generation with Probabilistic Circuits

arXiv:2604.06559v1 Announce Type: cross Abstract: Understanding and explaining the structure of generated test inputs is essential for effective software testing and debugging. Existing approaches--in

tutorialsarxiv-cs-lg

Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach

arXiv:2604.06838v1 Announce Type: new Abstract: In this paper, we propose using Learning from Answer Sets to approximate black-box models, such as Neural Networks (NN), in the specific case of learnin

local-aiarxiv-cs-ai

Faithful-First Reasoning, Planning, and Acting for Multimodal LLMs

arXiv:2511.08409v4 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) frequently suffer from unfaithfulness, generating reasoning chains that drift from visual evidence or contr

FedDetox: Robust Federated SLM Alignment via On-Device Data Sanitization

arXiv:2604.06833v1 Announce Type: cross Abstract: As high quality public data becomes scarce, Federated Learning (FL) provides a vital pathway to leverage valuable private user data while preserving p

local-aiarxiv-cs-lg

From Exposure to Internalization: Dual-Stream Calibration for In-context Clinical Reasoning

arXiv:2604.06262v1 Announce Type: cross Abstract: Contextual clinical reasoning demands robust inference grounded in complex, heterogeneous clinical records. While state-of-the-art fine-tuning, in-con

Front-End Ethics for Sensor-Fused Health Conversational Agents: An Ethical Design Space for Biometrics

arXiv:2604.06203v1 Announce Type: cross Abstract: The integration of continuous data from built-in sensors and Large Language Models (LLMs) has fueled a surge of 'Sensor-Fused LLM agents' for personal

Fundus-R1: Training a Fundus-Reading MLLM with Knowledge-Aware Reasoning on Public Data

arXiv:2604.08322v1 Announce Type: new Abstract: Fundus imaging such as CFP, OCT and UWF is crucial for the early detection of retinal anomalies and diseases. Fundus image understanding, due to its kno

Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline

model-releasesarxiv-cs-ai

arXiv:2412.19685v2 Announce Type: replace-cross Abstract: Existing facial forgery detection methods typically focus on binary classification or pixel-level localization, providing little semantic insi

Inference-Time Code Selection via Symbolic Equivalence Partitioning

arXiv:2604.06485v1 Announce Type: cross Abstract: 'Best-of-N' selection is a popular inference-time scaling method for code generation using Large Language Models (LLMs). However, to reliably identify

LAMP: Lift Image-Editing as General 3D Priors for Open-world Manipulation

arXiv:2604.08475v1 Announce Type: new Abstract: Human-like generalization in open-world remains a fundamental challenge for robotic manipulation. Existing learning-based methods, including reinforceme

Learning to Negotiate: Multi-Agent Deliberation for Collective Value Alignment in LLMs

arXiv:2603.10476v2 Announce Type: replace Abstract: LLM alignment has progressed in single-agent settings through paradigms such as RL with human feedback (RLHF), while recent work explores scalable a

safetyarxiv-cs-cl

Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation

arXiv:2511.17844v4 Announce Type: replace-cross Abstract: Fine-tuning large-scale text-to-video diffusion models to add new generative controls, such as those over physical camera parameters (e.g., sh

Lexical Tone is Hard to Quantize: Probing Discrete Speech Units in Mandarin and Yoruba

arXiv:2604.07467v1 Announce Type: new Abstract: Discrete speech units (DSUs) are derived by quantising representations from models trained using self-supervised learning (SSL). They are a popular repr

Matrix Profile for Anomaly Detection on Multidimensional Time Series

model-releasesarxiv-cs-ai

arXiv:2409.09298v2 Announce Type: replace-cross Abstract: The Matrix Profile (MP), a versatile tool for time series data mining, has been shown effective in time series anomaly detection (TSAD). This

MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping

arXiv:2604.08364v1 Announce Type: new Abstract: In this paper, we introduce MegaStyle, a novel and scalable data curation pipeline that constructs an intra-style consistent, inter-style diverse and hi

MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization

arXiv:2604.06798v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) based large language models (LLMs) offer strong performance but suffer from high memory and computation costs. Weight binariz

local-aiarxiv-cs-ai

Tutorials

MoRight: Motion Control Done Right

arXiv:2604.07348v1 Announce Type: cross Abstract: Generating motion-controlled videos--where user-specified actions drive physically plausible scene dynamics under freely chosen viewpoints--demands tw

tutorialsarxiv-cs-ai

NativeTernary: A Self-Delimiting Binary Encoding with Unary Run-Length Hierarchy Markers for Ternary Neural Network Weights, Structured Data, and General Computing Infrastructure

arXiv:2604.03336v2 Announce Type: replace Abstract: BitNet b1.58 (Ma et al., 2024) demonstrates that large language models can operate entirely on ternary weights {-1, 0, +1}, yet no native binary wir

local-aiarxiv-cs-lg

Applications

Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection

arXiv:2405.11619v2 Announce Type: replace-cross Abstract: Phishing emails continue to pose a significant threat, causing financial losses and security breaches. This study addresses limitations in exi

applicationsarxiv-cs-ai

NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results

model-releasesarxiv-cs-cv

arXiv:2604.06945v2 Announce Type: replace Abstract: This paper reports on the NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration (BSCVR). The challenge aims to advance research on recoverin

Optimal Decay Spectra for Linear Recurrences

arXiv:2604.07658v1 Announce Type: cross Abstract: Linear recurrent models offer linear-time sequence processing but often suffer from suboptimal long-range memory. We trace this to the decay spectrum:

Optimal Rates for Pure {arepsilon}-Differentially Private Stochastic Convex Optimization with Heavy Tails

arXiv:2604.06492v1 Announce Type: new Abstract: We study stochastic convex optimization (SCO) with heavy-tailed gradients under pure epsilon-differential privacy (DP). Instead of assuming a bound on t

PanoSAM2: Lightweight Distortion- and Memory-aware Adaptions of SAM2 for 360 Video Object Segmentation

arXiv:2604.07901v1 Announce Type: new Abstract: 360 video object segmentation (360VOS) aims to predict temporally-consistent masks in 360 videos, offering full-scene coverage, benefiting applications,

PEER: Unified Process-Outcome Reinforcement Learning for Structured Empathetic Reasoning

arXiv:2508.09521v2 Announce Type: replace Abstract: Emotional support conversations require more than fluent responses. Supporters need to understand the seeker's situation and emotions, adopt an appr

safetyarxiv-cs-cl

Physics-Informed Functional Link Constrained Framework with Domain Mapping for Solving Bending Analysis of an Exponentially Loaded Perforated Beam

arXiv:2604.07025v1 Announce Type: cross Abstract: This article presents a novel and comprehensive approach for analyzing bending behavior of the tapered perforated beam under an exponential load. The

PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization

arXiv:2503.24135v3 Announce Type: replace Abstract: Weakly supervised object localization (WSOL) methods allow training models to classify images and localize ROIs. WSOL only requires low-cost image-c

local-aiarxiv-cs-cv

ProofSketcher: Hybrid LLM + Lightweight Proof Checker for Reliable Math/Logic Reasoning

arXiv:2604.06401v1 Announce Type: new Abstract: The large language models (LLMs) might produce a persuasive argument within mathematical and logical fields, although such argument often includes some