Search: “papers”

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives

arXiv:2510.06096v3 Announce Type: replace-cross Abstract: The objectives that Large Language Models (LLMs) implicitly optimize remain dangerously opaque, making trustworthy alignment and auditing a gr

safetyarxiv-cs-cl

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Industry

The interesting thing about viral narratives is that they don't have to be true to spread. There's now enough evidence pointing to the fact …

DGX agent

The interesting thing about viral narratives is that they don't have to be true to spread. There's now enough evidence pointing to the fact that AI usage does not kill jobs, but that might not matter

industrycristobal-valenzuela--x

30 Jun 2026

The Mirage of Optimizing Training Policies: Monotonic Inference Policies as the Real Objective for LLM Reinforcement Learning

arXiv:2606.29526v1 Announce Type: new Abstract: Reinforcement learning (RL) has gained growing attention in large language model (LLM) post-training, yet RL training remains fragile and can suffer fro

safetyarxiv-cs-lg

Applications

The registrar's function in a hybrid society. AI value chain,smart data and the concept of property

arXiv:2606.28789v1 Announce Type: cross Abstract: Artificial intelligence reaches the land registry not as another tool but as a value chain that turns data into intelligence and intelligence into eco

applicationsarxiv-cs-ai

Theory of Continual Learning Against Data Poisoning Attacks

arXiv:2606.29841v1 Announce Type: new Abstract: Continual learning (CL), where a model is trained on a sequence of data tasks, is increasingly being adopted across key fields such as large language mo

safetyarxiv-cs-lg

To Use or not to Use Muon: How Simplicity Bias in Optimizers Matters

arXiv:2603.00742v2 Announce Type: replace Abstract: While Adam has long been the ubiquitous default optimizer for deep neural networks, Muon has recently seen rapid adoption due to its superior traini

safetyarxiv-cs-lg

Towards Long-Form Spatio-Temporal Video Grounding

arXiv:2602.23294v2 Announce Type: replace Abstract: In real scenarios, videos can span several minutes or even hours. However, existing research on spatio-temporal video grounding (STVG), given a text

local-aiarxiv-cs-cv

Tutorials

Traffic-CBM: A Structurally Interpretable Multimodal Framework for Encrypted Traffic Classification

arXiv:2606.29909v1 Announce Type: new Abstract: Encrypted traffic classification has achieved strong performance, but its decision process remains difficult to interpret. Existing methods usually comb

tutorialsarxiv-cs-cv

TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction

arXiv:2606.28716v1 Announce Type: new Abstract: The robustness of trajectory prediction models is crucial for developing safe autonomous driving systems. Adversarial attacks on trajectory prediction c

Understanding LLM Intervention Explanations in Multi-Party Human-Robot Interaction

arXiv:2606.29460v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly embedded in social robots to support natural group interactions, yet their role in complex multi-party set

researcharxiv-cs-ro

UniCA: Bi-directional Cross-Attention with Positive Similarity Loss for Robust Multi-Modal Retrieval

model-releasesarxiv-cs-cv

arXiv:2606.28350v1 Announce Type: cross Abstract: Multi-modal retrieval has become increasingly critical for handling the growing volume of integrated visual-textual data in real-world applications, b

Unified Complex-valued Neural Network: A Magnitude-Phase Computational Model for Event-Driven Neuromorphic Learning

arXiv:2606.29099v1 Announce Type: cross Abstract: Artificial neural networks (ANN) provide accurate continuous-valued representation, whereas spiking neural networks (SNN) offer event-driven temporal

local-aiarxiv-cs-ai

Unified Enhancement of the Generalization and Robustness of Language Models via Bi-Stage Optimization

model-releasesarxiv-cs-cl

arXiv:2503.16550v2 Announce Type: replace Abstract: Neural network language models (LMs) are confronted with significant challenges in generalization and robustness. Currently, many studies focus on i

UrbanCDNet: Appearance-Robust and Boundary-Aware Bitemporal Change Detection for Korean Urban Building Monitoring

model-releasesarxiv-cs-cv

arXiv:2606.29781v1 Announce Type: new Abstract: Urban building change detection from bi-temporal aerial imagery is important for redevelopment monitoring, infrastructure management, and unauthorized-c

Tools

very successful first poster sessions day at AIEWF, thanks to @heathercmiller and @ACM_President for the support!! tomorrow: Poaster session…

very successful first poster sessions day at AIEWF, thanks to @heathercmiller and @ACM_President for the support!! tomorrow: Poaster sessions! submit your hottest tweets to @vibhuuuus for printing! sp

toolsswyx--x

Vision-driven Preference Synthesis for Mitigating Hallucinations in VLMs

arXiv:2606.28401v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown strong performance in visual understanding, yet they still suffer from hallucinations, generating content that

safetyarxiv-cs-cv

VISTA-DZ: Visual Semantic Trajectory Adaptation for Personalized Dilemma Zone Prediction

arXiv:2606.29548v1 Announce Type: cross Abstract: Driver decision making in the dilemma zone at signalized intersections is safety critical, as vehicles approaching a yellow signal must decide whether

What Color is the Sky (for a non-human) ?

arXiv:2606.28912v1 Announce Type: new Abstract: The light of the daytime sky contains a mixture of many colors yet is perceived as blue by human observers. This is largely due to the particular respon

researcharxiv-cs-cv

Tutorials

What LLMs explain is not what they believe: Evaluating explanation sufficiency under models' own input beliefs

arXiv:2606.28615v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in high-stakes domains, where free-text explanations such as chain-of-thought and post-hoc rati

tutorialsarxiv-cs-ai

When Does Sparsity Mitigate the Curse of Depth in LLMs

arXiv:2603.15389v2 Announce Type: replace Abstract: Recent work has demonstrated the curse of depth in large language models (LLMs), where later layers contribute less to learning and representation t

researcharxiv-cs-cl

When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling

model-releasesarxiv-cs-ai

arXiv:2606.28661v1 Announce Type: cross Abstract: People overthink; language models over-sample, and the extra effort can talk both into a worse answer. Reasoning systems answer a hard question by sam

When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems

arXiv:2606.29115v1 Announce Type: cross Abstract: Autonomous vehicles (AVs) are increasingly deployed in urban environments, yet their safety frameworks remain primarily designed around collision avoi

Wireless Backdoor Attack and Defense for Semantic Communications over Multiple Access Channel

arXiv:2606.30595v1 Announce Type: cross Abstract: Semantic communication (SemCom) aims to preserve semantic meaning and task-oriented information beyond conventional message recovery over wireless cha

researcharxiv-cs-lg

Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection

arXiv:2606.30587v1 Announce Type: cross Abstract: Researchers and practitioners increasingly apply Large Language Models (LLMs) for automated vulnerability detection. Recent work has shown that LLMs a

Your Data Manifold is Secretly a Reward Model: Shell-LCC for Text-to-Video Generation

arXiv:2606.30248v1 Announce Type: new Abstract: Recent text-to-video (T2V) diffusion models rely heavily on auxiliary reward signals (e.g., via reward models or DPO) to align generated content with hu

local-aiarxiv-cs-cv

Zero-Shot Depth from Defocus

model-releasesarxiv-cs-cv

arXiv:2603.26658v2 Announce Type: replace Abstract: Depth from Defocus (DfD) is the task of estimating a dense metric depth map from a focus stack. Unlike previous works overfitting to a certain datas

A Comparison of Fusion Techniques for Multi-Modal Human Activity Recognition on the HARMES Dataset

model-releasesarxiv-cs-lg

arXiv:2606.27886v1 Announce Type: new Abstract: Recent advances in Human Activity Recognition (HAR) from wearable sensors have shown that multi-modal deep learning models consistently outperform their

A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts

arXiv:2606.27881v1 Announce Type: cross Abstract: Temporal variation poses a unique challenge for named entity recognition (NER) in historical texts, where entities drift in surface form and salience

AI-Generated Image Recognition via Fusion of CNNs and Vision Transformers

arXiv:2606.27637v1 Announce Type: new Abstract: Recent advancements in synthetic data technology have opened a new era where images of remarkable quality are generated, blurring the lines between real

researcharxiv-cs-cv

AI-Model Network: Concept, Current State and Future

arXiv:2606.27382v1 Announce Type: new Abstract: While the primary function of computers lies in computation and processing, the core value of the Internet is rooted in sharing and collaboration. Compu

An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations

arXiv:2407.01014v2 Announce Type: replace Abstract: Diffusion models excel in solving imaging inverse problems due to their ability to model complex image priors. However, their reliance on large, cle

researcharxiv-cs-cv

Applications

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings

arXiv:2606.27672v1 Announce Type: new Abstract: Inspired by advances in natural language processing and computer vision, 'time-series foundation models' (TSFMs) have recently been introduced with the

applicationsarxiv-cs-lg

ATOD: Annealed Turn-aware On-policy Distillation for Multi-turn Autonomous Agents

arXiv:2606.27814v1 Announce Type: new Abstract: Training small language-model agents for long-horizon interactive tasks requires both fast imitation and reward-driven improvement. On-policy distillati

Autoencoder Architectures for Athlete Performance Scoring from Wearable Telemetry

arXiv:2606.28145v1 Announce Type: new Abstract: Wearable devices produce large, high dimensional training logs for everyday runners, and interpretation rather than data collection is now the limiting

researcharxiv-cs-lg

Bifocal Diffusion Language Models: Asymmetric Bidirectional Context for Parallel Generation

arXiv:2606.27732v1 Announce Type: cross Abstract: Discrete diffusion language models (dLLMs) recover masked tokens in parallel, offering significant speedups over autoregressive (AR) generation. Howev

CacheMPC: Certified Cached Model Predictive Control for Quadruped Locomotion

arXiv:2606.28300v1 Announce Type: new Abstract: Model Predictive Control (MPC) is the standard predictive layer in hierarchical quadruped controllers, but the per-cycle QP solve limits the update rate

safetyarxiv-cs-ro

CalBrief: A Pilot Diagnostic Benchmark for Evidence-Calibrated Scientific Briefing with Large Language Models

model-releasesarxiv-cs-ai

arXiv:2606.27383v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as research assistants, yet it remains unclear whether they can calibrate research takeaways to the

Contrastive Language-Colored Pointmap Pretraining for Unified 3D Scene Understanding

arXiv:2604.02546v2 Announce Type: replace Abstract: Pretraining 3D encoders by aligning with Contrastive Language Image Pretraining (CLIP) has emerged as a promising direction to learn generalizable r

safetyarxiv-cs-cv

CSD: Content-aware Speculative Decoding for Efficient Image Generation

arXiv:2606.27829v1 Announce Type: new Abstract: Speculative decoding (SD) has emerged as a key solution to accelerate the inference of autoregressive models. However, in the field of image generation,

safetyarxiv-cs-cv

Data Scaling Laws in Imitation Learning for Robotic Manipulation

arXiv:2410.18647v4 Announce Type: replace Abstract: Data scaling has revolutionized fields like natural language processing and computer vision, providing models with remarkable generalization capabil

safetyarxiv-cs-ro

Hardware

DataStates-LLM: Scalable Checkpointing for Transformer Models Using Composable State Providers

arXiv:2601.16956v1 Announce Type: cross Abstract: The rapid growth of Large Transformer-based models, specifically Large Language Models (LLMs), now scaling to trillions of parameters, has necessitate

hardwarearxiv-cs-ai

DDSA: Dual-Domain Strategic Attack for Spatial-Temporal Efficiency in Adversarial Robustness Testing

arXiv:2601.14302v2 Announce Type: replace-cross Abstract: Image transmission and processing systems in resource-critical applications face significant challenges from adversarial perturbations that co

Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case

arXiv:2603.12001v2 Announce Type: replace-cross Abstract: Distributed AI and IoT applications increasingly execute across heterogeneous resources spanning end devices, edge/fog infrastructure, and clo

local-aiarxiv-cs-lg

Deep Neural Networks Inspired by Differential Equations

arXiv:2510.09685v2 Announce Type: replace-cross Abstract: Deep learning has become a pivotal technology in fields such as computer vision, scientific computing, and dynamical systems, significantly ad

Tutorials

DeLux: Cross-Modal Local Artifact Restoration in Video Using Neuromorphic Data

arXiv:2606.27576v1 Announce Type: new Abstract: Conventional RGB cameras suffer from lighting artifacts such as flare, glare, flicker, and overexposure, leading to irrecoverable information loss that

tutorialsarxiv-cs-cv

DG^VoiC: Speaker Clustering for Fraud Investigation under Real Call-Centre Conditions

arXiv:2606.28048v1 Announce Type: cross Abstract: Insurance fraud remains costly and operationally difficult, particularly in call-centre workflows where many customer interactions begin at FNOL. Whil

Drifting in the Future: Stabilizing Path Following Drifting on High-Latency Vehicle Systems

arXiv:2606.27914v1 Announce Type: new Abstract: Autonomously controlling and handling a vehicle at and beyond its stability limit is a mathematically and computationally demanding task. Prior demonstr

safetyarxiv-cs-ro

DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forums

arXiv:2606.27619v1 Announce Type: new Abstract: Dyslexic learners increasingly use artificial intelligence (AI) tools to support reading, writing, organisation, and study-related tasks. However, their