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The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap

arXiv:2604.11828v1 Announce Type: new Abstract: Science is widely regarded as humanity's most reliable method for uncovering truths about the natural world. Yet the trajectory of scientific discovery

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The role of System 1 and System 2 semantic memory structure in human and LLM biases

DGX agent

arXiv:2604.12816v1 Announce Type: new Abstract: Implicit biases in both humans and large language models (LLMs) pose significant societal risks. Dual process theories propose that biases arise primari

safetyarxiv-cs-cl

15 Apr 2026

The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results

arXiv:2604.11998v1 Announce Type: cross Abstract: Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particu

The Stackelberg Speaker: Optimizing Persuasive Communication in Social Deduction Games

arXiv:2510.09087v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown remarkable progress in social deduction games (SDGs). However, existing approaches primarily focus on i

safetyarxiv-cs-ai

The Verification Tax: Fundamental Limits of AI Auditing in the Rare-Error Regime

model-releasesarxiv-cs-lg

arXiv:2604.12951v1 Announce Type: new Abstract: The most cited calibration result in deep learning -- post-temperature-scaling ECE of 0.012 on CIFAR-100 (Guo et al., 2017) -- is below the statistical

Thermodynamic Liquid Manifold Networks: Physics-Bounded Deep Learning for Solar Forecasting in Autonomous Off-Grid Microgrids

arXiv:2604.11909v1 Announce Type: cross Abstract: The stable operation of autonomous off-grid photovoltaic systems requires solar forecasting algorithms that respect atmospheric thermodynamics. Contem

agentsarxiv-cs-ai

Think Through Uncertainty: Improving Long-Form Generation Factuality via Reasoning Calibration

arXiv:2604.12046v1 Announce Type: new Abstract: Large language models (LLMs) often hallucinate in long-form generation. Existing approaches mainly improve factuality through post-hoc revision or reinf

researcharxiv-cs-cl

Thinking Sparks!: Emergent Attention Heads in Reasoning Models During Post Training

arXiv:2509.25758v2 Announce Type: replace Abstract: The remarkable capabilities of modern large reasoning models are largely unlocked through post-training techniques such as supervised fine-tuning (S

safetyarxiv-cs-ai

Thought-Retriever: Don't Just Retrieve Raw Data, Retrieve Thoughts for Memory-Augmented Agentic Systems

arXiv:2604.12231v1 Announce Type: new Abstract: Large language models (LLMs) have transformed AI research thanks to their powerful internal capabilities and knowledge. However, existing LLMs still fai

Time-reversed Flow Matching with Worst Transport in High-dimensional Latent Space for Image Anomaly Detection

arXiv:2508.05461v3 Announce Type: replace Abstract: Likelihood-based deep generative models have been widely investigated for Image Anomaly Detection (IAD), particularly Normalizing Flows, yet their s

local-aiarxiv-cs-cv

TimeMark: A Trustworthy Time Watermarking Framework for Exact Generation-Time Recovery from AIGC

arXiv:2604.12216v1 Announce Type: cross Abstract: The widespread use of Large Language Models (LLMs) in text generation has raised increasing concerns about intellectual property disputes. Watermarkin

researcharxiv-cs-cl

Tutorials

TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting

arXiv:2604.12648v1 Announce Type: cross Abstract: Despite the recent success of large language models (LLMs) in time-series forecasting, most existing methods still adopt a Deep Synchronous Fusion str

tutorialsarxiv-cs-ai

TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment

model-releasesarxiv-cs-cv

arXiv:2604.12012v1 Announce Type: new Abstract: Recent progress in vision-language pretraining has enabled significant improvements to many downstream computer vision applications, such as classificat

Token Encoding for Semantic Recovery

arXiv:2604.12931v1 Announce Type: cross Abstract: Token-based semantic communication is promising for future wireless networks, as it can compact semantic tokens under very limited channel capacity. H

researcharxiv-cs-lg

Token-Level Policy Optimization: Linking Group-Level Rewards to Token-Level Aggregation via Sequence-Level Likelihood

arXiv:2604.12736v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has significantly advanced the reasoning ability of large language models (LLMs), particularly in their mathem

safetyarxiv-cs-cl

Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting

arXiv:2604.12503v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenari

Toward Autonomous Long-Horizon Engineering for ML Research

arXiv:2604.13018v1 Announce Type: new Abstract: Autonomous AI research has advanced rapidly, but long-horizon ML research engineering remains difficult: agents must sustain coherent progress across ta

agentsarxiv-cs-cl

Toward Efficient and Robust Behavior Models for Multi-Agent Driving Simulation

arXiv:2512.05812v5 Announce Type: replace-cross Abstract: Scalable multi-agent driving simulation requires behavior models that are both realistic and computationally efficient. We address this by opt

local-aiarxiv-cs-cv

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector

arXiv:2509.07177v3 Announce Type: replace Abstract: Large language models have demonstrated impressive capabilities across various domains. However, their general-purpose nature often limits their eff

Towards Generalized Certified Robustness with Multi-Norm Training

arXiv:2410.03000v3 Announce Type: replace Abstract: Existing certified training methods can only train models to be robust against a certain perturbation type (e.g. l_infty or l_2). However, an l_inft

safetyarxiv-cs-lg

Towards grounded autonomous research: an end-to-end LLM mini research loop on published computational physics

arXiv:2604.12198v1 Announce Type: cross Abstract: Recent autonomous LLM agents have demonstrated end-to-end automation of machine-learning research. Real-world physical science is intrinsically harder

agentsarxiv-cs-ai

Towards Interpretable Foundation Models for Retinal Fundus Images

arXiv:2603.18846v2 Announce Type: replace Abstract: Foundation models are used to extract transferable representations from large amounts of unlabeled data, typically via self-supervised learning (SSL

local-aiarxiv-cs-cv

Towards Long-horizon Agentic Multimodal Search

arXiv:2604.12890v1 Announce Type: cross Abstract: Multimodal deep search agents have shown great potential in solving complex tasks by iteratively collecting textual and visual evidence. However, mana

Towards Platonic Representation for Table Reasoning: A Foundation for Permutation-Invariant Retrieval

arXiv:2604.12133v1 Announce Type: new Abstract: Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We argu

safetyarxiv-cs-ai

Towards Realistic and Consistent Orbital Video Generation via 3D Foundation Priors

arXiv:2604.12309v1 Announce Type: new Abstract: We present a novel method for generating geometrically realistic and consistent orbital videos from a single image of an object. Existing video generati

researcharxiv-cs-cv

Towards Robust Real-World Spreadsheet Understanding with Multi-Agent Multi-Format Reasoning

arXiv:2604.12282v1 Announce Type: new Abstract: Spreadsheets are central to real-world applications such as enterprise reporting, auditing, and scientific data management. Despite their ubiquity, exis

agentsarxiv-cs-cl

ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection

arXiv:2604.12321v1 Announce Type: new Abstract: Existing Chinese toxic content detection methods mainly target sentence-level classification but often fail to provide readable and contiguous toxic evi

Training single-electron and single-photon stochastic physical neural networks

arXiv:2604.10861v1 Announce Type: cross Abstract: The computational demands of deep learning motivate the investigation of alternative approaches to computation. One alternative is physical neural net

researcharxiv-cs-lg

Transferable Expertise for Autonomous Agents via Real-World Case-Based Learning

arXiv:2604.12717v1 Announce Type: new Abstract: LLM-based autonomous agents perform well on general reasoning tasks but still struggle to reliably use task structure, key constraints, and prior experi

Transformer Based Machine Fault Detection From Audio Input

model-releasesarxiv-cs-lg

arXiv:2604.12733v1 Announce Type: cross Abstract: In recent years, Sound AI is being increasingly used to predict machine failures. By attaching a microphone to the machine of interest, one can get re

Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs

arXiv:2604.12610v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation. Howe

Tree Learning: A Multi-Skill Continual Learning Framework for Humanoid Robots

model-releasesarxiv-cs-ro

arXiv:2604.12909v1 Announce Type: new Abstract: As reinforcement learning for humanoid robots evolves from single-task to multi-skill paradigms, efficiently expanding new skills while avoiding catastr

TriFit: Trimodal Fusion with Protein Dynamics for Mutation Fitness Prediction

model-releasesarxiv-cs-lg

arXiv:2604.12026v1 Announce Type: new Abstract: Predicting the functional impact of single amino acid substitutions (SAVs) is central to understanding genetic disease and engineering therapeutic prote

TRUST Agents: A Collaborative Multi-Agent Framework for Fake News Detection, Explainable Verification, and Logic-Aware Claim Reasoning

arXiv:2604.12184v1 Announce Type: new Abstract: TRUST Agents is a collaborative multi-agent framework for explainable fact verification and fake news detection. Rather than treating verification as a

Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression

arXiv:2511.06424v2 Announce Type: replace-cross Abstract: While zero-shot diffusion-based compression methods have seen significant progress in recent years, they remain notoriously slow and computati

UCS: Estimating Unseen Coverage for Improved In-Context Learning

arXiv:2604.12015v1 Announce Type: cross Abstract: In-context learning (ICL) performance depends critically on which demonstrations are placed in the prompt, yet most existing selectors prioritize heur

researcharxiv-cs-cl

Tutorials

Ultra-low-light computer vision using trained photon correlations

arXiv:2604.11993v1 Announce Type: new Abstract: Illumination using correlated photon sources has been established as an approach to allowing high-fidelity images to be reconstructed from noisy camera

tutorialsarxiv-cs-cv

Uncertainty-Aware Image Classification In Biomedical Imaging Using Spectral-normalized Neural Gaussian Processes

arXiv:2602.02370v2 Announce Type: replace Abstract: Accurate histopathologic interpretation is key for clinical decision-making; however, current deep learning models for digital pathology are often o

safetyarxiv-cs-cv

Uncertainty Guided Exploratory Trajectory Optimization for Sampling-Based Model Predictive Control

arXiv:2604.12149v1 Announce Type: new Abstract: Trajectory optimization depends heavily on initialization. In particular, sampling-based approaches are highly sensitive to initial solutions, and limit

local-aiarxiv-cs-ro

Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks

arXiv:2604.11833v1 Announce Type: new Abstract: Despite the popularity of Convolutional Neural Networks (CNN), the problem of uncertainty quantification (UQ) of CNN has been largely overlooked. Lack o

researcharxiv-cs-lg

Applications

Uncertainty Quantification on Graph Learning: A Survey

arXiv:2404.14642v4 Announce Type: replace Abstract: Graphical models have demonstrated their exceptional capabilities across numerous applications. However, their performance, confidence, and trustwor

applicationsarxiv-cs-lg

Unconventional Hexacopters via Evolution and Learning: Performance Gains and New Insights

arXiv:2505.14129v2 Announce Type: replace Abstract: Evolution and learning have historically been interrelated topics, and their interplay is attracting increased interest lately. The emerging new fac

researcharxiv-cs-ro

Understanding and Improving Continuous Adversarial Training for LLMs via In-context Learning Theory

arXiv:2604.12817v1 Announce Type: new Abstract: Adversarial training (AT) is an effective defense for large language models (LLMs) against jailbreak attacks, but performing AT on LLMs is costly. To im

safetyarxiv-cs-lg

Understanding or Memorizing? A Case Study of German Definite Articles in Language Models

arXiv:2601.09313v2 Announce Type: replace-cross Abstract: Language models perform well on grammatical agreement, but it is unclear whether this reflects rule-based generalization or memorization. We s

UniMark: Unified Adaptive Multi-bit Watermarking for Autoregressive Image Generators

arXiv:2604.11843v1 Announce Type: new Abstract: Invisible watermarking for autoregressive (AR) image generation has recently gained attention as a means of protecting image ownership and tracing AI-ge

researcharxiv-cs-cv

Universal NER v2: Towards a Massively Multilingual Named Entity Recognition Benchmark

arXiv:2604.12744v1 Announce Type: new Abstract: While multilingual language models promise to bring the benefits of LLMs to speakers of many languages, gold-standard evaluation benchmarks in most lang

Unlocking the Potential of Grounding DINO in Videos: Parameter-Efficient Adaptation for Limited-Data Spatial-Temporal Localization

model-releasesarxiv-cs-cv

arXiv:2604.12346v1 Announce Type: new Abstract: Spatio-temporal video grounding (STVG) aims to localize queried objects within dynamic video segments. Prevailing fully-trained approaches are notorious

Unveiling the Surprising Efficacy of Navigation Understanding in End-to-End Autonomous Driving

arXiv:2604.12208v1 Announce Type: cross Abstract: Global navigation information and local scene understanding are two crucial components of autonomous driving systems. However, our experimental result

local-aiarxiv-cs-ai