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PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch

model-releasesarxiv-cs-cl

arXiv:2510.06670v2 Announce Type: replace Abstract: High-quality instruction data is critical for LLM alignment, yet existing open-source datasets often lack efficiency, requiring hundreds of thousand

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Pistachio: Towards Synthetic, Balanced, and Long-Form Video Anomaly Benchmarks

DGX agent

arXiv:2511.19474v4 Announce Type: replace-cross Abstract: Automatically detecting abnormal events in videos is crucial for modern autonomous systems, yet existing Video Anomaly Detection (VAD) benchma

model-releasesarxiv-cs-ai

10 Apr 2026

Local Ai

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

PlaneCycle: Training-Free 2D-to-3D Lifting of Foundation Models Without Adapters

arXiv:2603.04165v3 Announce Type: replace-cross Abstract: Large-scale 2D foundation models exhibit strong transferable representations, yet extending them to 3D volumetric data typically requires retr

Planning Task Shielding: Detecting and Repairing Flaws in Planning Tasks through Turning them Unsolvable

arXiv:2604.07042v1 Announce Type: new Abstract: Most research in planning focuses on generating a plan to achieve a desired set of goals. However, a goal specification can also be used to encode a pro

Planning with Minimal Disruption

arXiv:2508.15358v2 Announce Type: replace Abstract: In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals. We refer to thi

Plasma GraphRAG: Physics-Grounded Parameter Selection for Gyrokinetic Simulations

arXiv:2604.06279v1 Announce Type: cross Abstract: Accurate parameter selection is fundamental to gyrokinetic plasma simulations, yet current practices rely heavily on manual literature reviews, leadin

Plug-and-Play Logit Fusion for Heterogeneous Pathology Foundation Models

arXiv:2604.07779v1 Announce Type: new Abstract: Pathology foundation models (FMs) have become central to computational histopathology, offering strong transfer performance across a wide range of diagn

safetyarxiv-cs-cv

PLUME: Latent Reasoning Based Universal Multimodal Embedding

arXiv:2604.02073v2 Announce Type: replace Abstract: Universal multimodal embedding (UME) maps heterogeneous inputs into a shared retrieval space with a single model. Recent approaches improve UME by g

PokeGym: A Visually-Driven Long-Horizon Benchmark for Vision-Language Models

arXiv:2604.08340v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) have achieved remarkable progress in static visual understanding, their deployment in complex 3D embodied environmen

PolySLGen: Online Multimodal Speaking-Listening Reaction Generation in Polyadic Interaction

arXiv:2604.08125v1 Announce Type: new Abstract: Human-like multimodal reaction generation is essential for natural group interactions between humans and embodied AI. However, existing approaches are l

safetyarxiv-cs-cv

Predictive Representations for Skill Transfer in Reinforcement Learning

arXiv:2604.07016v1 Announce Type: new Abstract: A key challenge in scaling up Reinforcement Learning is generalizing learned behaviour. Without the ability to carry forward acquired knowledge an agent

agentsarxiv-cs-lg

Preventing Overfitting in Deep Image Prior for Hyperspectral Image Denoising

arXiv:2604.08272v1 Announce Type: new Abstract: Deep image prior (DIP) is an unsupervised deep learning framework that has been successfully applied to a variety of inverse imaging problems. However,

researcharxiv-cs-cv

PriPG-RL: Privileged Planner-Guided Reinforcement Learning for Partially Observable Systems with Anytime-Feasible MPC

arXiv:2604.08036v1 Announce Type: cross Abstract: This paper addresses the problem of training a reinforcement learning (RL) policy under partial observability by exploiting a privileged, anytime-feas

safetyarxiv-cs-ro

Privacy Attacks on Image AutoRegressive Models

arXiv:2502.02514v5 Announce Type: replace Abstract: Image AutoRegressive generation has emerged as a new powerful paradigm with image autoregressive models (IARs) matching state-of-the-art diffusion m

researcharxiv-cs-cv

PrivFedTalk: Privacy-Aware Federated Diffusion with Identity-Stable Adapters for Personalized Talking-Head Generation

arXiv:2604.08037v1 Announce Type: cross Abstract: Talking-head generation has advanced rapidly with diffusion-based generative models, but training usually depends on centralized face-video and speech

Probabilistic Language Tries: A Unified Framework for Compression, Decision Policies, and Execution Reuse

arXiv:2604.06228v1 Announce Type: cross Abstract: We introduce probabilistic language tries (PLTs), a unified representation that makes explicit the prefix structure implicitly defined by any generati

safetyarxiv-cs-ai

Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network

arXiv:2512.13746v4 Announce Type: replace-cross Abstract: Fiber reinforcement and polymer matrix respond differently to manufacturing conditions due to mismatch in coefficient of thermal expansion and

applicationsarxiv-cs-lg

Production-Ready Automated ECU Calibration using Residual Reinforcement Learning

arXiv:2604.07059v1 Announce Type: new Abstract: Electronic Control Units (ECUs) have played a pivotal role in transforming motorcars of yore into the modern vehicles we see on our roads today. They ac

applicationsarxiv-cs-lg

Prompt reinforcing for long-term planning of large language models

model-releasesarxiv-cs-cl

arXiv:2510.05921v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable success in a wide range of natural language processing tasks and can be adapted through prompt

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

Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression

model-releasesarxiv-cs-cl

arXiv:2604.04988v1 Announce Type: cross Abstract: Modern deployment often requires trading accuracy for efficiency under tight CPU and memory constraints, yet common compression proxies such as parame

Pseudo-Expert Regularized Offline RL for End-to-End Autonomous Driving in Photorealistic Closed-Loop Environments

arXiv:2512.18662v2 Announce Type: replace-cross Abstract: End-to-end (E2E) autonomous driving models that take only camera images as input and directly predict a future trajectory are appealing for th

agentsarxiv-cs-cv

PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards

arXiv:2512.01236v2 Announce Type: replace Abstract: Personalized generation models for a single subject have demonstrated remarkable effectiveness, highlighting their significant potential. However, w

PULSE: Privileged Knowledge Transfer from Rich to Deployable Sensors for Embodied Multi-Sensory Learning

arXiv:2510.24058v2 Announce Type: replace-cross Abstract: Multi-sensory systems for embodied intelligence, from wearable body-sensor networks to instrumented robotic platforms, routinely face a sensor

PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents

arXiv:2512.14735v2 Announce Type: replace-cross Abstract: This paper proposes PyFi, a novel framework for pyramid-like financial image understanding that enables vision language models (VLMs) to reaso

agentsarxiv-cs-ai

Q-Probe: Scaling Image Quality Assessment to High Resolution via Context-Aware Agentic Probing

arXiv:2601.15356v4 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has empowered Multimodal Large Language Models (MLLMs) to achieve superior human preference alignment in Image Qua

Local Ai

Q-Zoom: Query-Aware Adaptive Perception for Efficient Multimodal Large Language Models

arXiv:2604.06912v1 Announce Type: cross Abstract: MLLMs require high-resolution visual inputs for fine-grained tasks like document understanding and dense scene perception. However, current global res

local-aiarxiv-cs-ai

QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis

arXiv:2604.05704v2 Announce Type: replace Abstract: Multimodal Sentiment Analysis (MSA) aims to infer human sentiment from textual, acoustic, and visual signals. In real-world scenarios, however, mult

applicationsarxiv-cs-ai

QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks

model-releasesarxiv-cs-lg

arXiv:2604.07013v1 Announce Type: cross Abstract: Designing quantum neural networks (QNNs) that are both accurate and deployable on NISQ hardware is challenging. Handcrafted ansatze must balance expre

Quality-preserving Model for Electronics Production Quality Tests Reduction

arXiv:2604.06451v1 Announce Type: new Abstract: Manufacturing test flows in high-volume electronics production are typically fixed during product development and executed unchanged on every unit, even

safetyarxiv-cs-lg

Qualixar OS: A Universal Operating System for AI Agent Orchestration

arXiv:2604.06392v1 Announce Type: new Abstract: We present Qualixar OS, the first application-layer operating system for universal AI agent orchestration. Unlike kernel-level approaches (AIOS) or sing

safetyarxiv-cs-ai

Local Ai

Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification

arXiv:2604.08502v1 Announce Type: new Abstract: Class Activation Mapping (CAM) methods are widely used to generate visual explanations for deep learning classifiers in medical imaging. However, existi

local-aiarxiv-cs-cv

Quantitative Estimation of Target Task Performance from Unsupervised Pretext Task in Semi/Self-Supervised Learning

arXiv:2508.07299v2 Announce Type: replace-cross Abstract: The effectiveness of unlabeled data in Semi/Self-Supervised Learning (SSL) depends on appropriate assumptions for specific scenarios, thereby

Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach

model-releasesarxiv-cs-lg

arXiv:2604.06541v1 Announce Type: cross Abstract: We investigate whether a multiscale tensor-network architecture can provide a useful inductive bias for reconstruction-based anomaly detection in coll

Quantum Vision Theory Applied to Audio Classification for Deepfake Speech Detection

arXiv:2604.08104v1 Announce Type: new Abstract: We propose Quantum Vision (QV) theory as a new perspective for deep learning-based audio classification, applied to deepfake speech detection. Inspired

researcharxiv-cs-cl

Rag Performance Prediction for Question Answering

arXiv:2604.07985v1 Announce Type: new Abstract: We address the task of predicting the gain of using RAG (retrieval augmented generation) for question answering with respect to not using it. We study t

researcharxiv-cs-cl

RAGE-XY: RADAR-Aided Longitudinal and Lateral Forces Estimation For Autonomous Race Cars

arXiv:2604.07939v1 Announce Type: new Abstract: In this work, we present RAGE-XY, an extended version of RAGE, a real-time estimation framework that simultaneously infers vehicle velocity, tire slip a

agentsarxiv-cs-ro

RAGEN-2: Reasoning Collapse in Agentic RL

arXiv:2604.06268v1 Announce Type: new Abstract: RL training of multi-turn LLM agents is inherently unstable, and reasoning quality directly determines task performance. Entropy is widely used to track

agentsarxiv-cs-lg

Hardware

RDSplat: Robust Watermarking for 3D Gaussian Splatting Against 2D and 3D Diffusion Editing

arXiv:2512.06774v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has become a leading representation for high-fidelity 3D assets, yet protecting these assets via digital watermarking r

hardwarearxiv-cs-cv

Tutorials

Reading Recognition in the Wild

arXiv:2505.24848v4 Announce Type: replace Abstract: To enable egocentric contextual AI in always-on smart glasses, it is crucial to be able to keep a record of the user's interactions with the world,

tutorialsarxiv-cs-cv

Reason in Chains, Learn in Trees: Self-Rectification and Grafting for Multi-turn Agent Policy Optimization

arXiv:2604.07165v1 Announce Type: new Abstract: Reinforcement learning for Large Language Model agents is often hindered by sparse rewards in multi-step reasoning tasks. Existing approaches like Group

safetyarxiv-cs-ai

Reason-SVG: Enhancing Structured Reasoning for Vector Graphics Generation with Reinforcement Learning

arXiv:2505.24499v2 Announce Type: replace Abstract: Generating high-quality Scalable Vector Graphics (SVGs) is challenging for Large Language Models (LLMs), as it requires advanced reasoning for struc

safetyarxiv-cs-cv

Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs

arXiv:2604.07562v1 Announce Type: new Abstract: Unsupervised methods are widely used to induce latent semantic structure from large text collections, yet their outputs often contain incoherent, redund

applicationsarxiv-cs-cl

Reasoning Fails Where Step Flow Breaks

arXiv:2604.06695v1 Announce Type: new Abstract: Large reasoning models (LRMs) that generate long chains of thought now perform well on multi-step math, science, and coding tasks. However, their behavi

Reasoning Graphs: Deterministic Agent Accuracy through Evidence-Centric Chain-of-Thought Feedback

arXiv:2604.07595v1 Announce Type: cross Abstract: Language model agents reason from scratch on every query: each time an agent retrieves evidence and deliberates, the chain of thought is discarded and

agentsarxiv-cs-cl

Reasoning Within the Mind: Dynamic Multimodal Interleaving in Latent Space

arXiv:2512.12623v3 Announce Type: replace-cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced cross-modal understanding and reasoning by incorpo

safetyarxiv-cs-cl

ReCellTy: Domain-Specific Knowledge Graph Retrieval-Augmented LLMs Reasoning Workflow for Single-Cell Annotation

arXiv:2505.00017v2 Announce Type: replace Abstract: With the rapid development of large language models (LLMs), their application to cell type annotation has drawn increasing attention. However, gener

researcharxiv-cs-cl