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PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs

arXiv:2608.02150v1 Announce Type: new Abstract: Embodied intelligence and world models require video understanding systems to go beyond recognizing objects and actions and develop an understanding of

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Model Releases

PhysAgent: A Multi-Agent Framework for Reliable Remote Heart Rate Estimation

DGX agent

arXiv:2608.00066v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) enables non-contact heart-rate estimation from facial videos, but its weak physiological signal is easily corrupted b

model-releasesarxiv-cs-cv

4 Aug 2026

Physical Self-Supervised Learning: IMU Sensing without Manual Labels

arXiv:2607.18361v2 Announce Type: replace Abstract: Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data a

PICTURE: Enhancing Theory-of-Mind in Large Language Models by Revealing, Not Hiding, Characters' Lack of Knowledge

arXiv:2608.01598v1 Announce Type: new Abstract: Simulating human-like Theory of Mind (ToM) has been a longstanding problem in natural language processing (NLP). To address this, existing works introdu

Pixel Ignores, Superpixel Sees: Adverse Weather Image Restoration via Semantic-Center SSM

arXiv:2608.01760v1 Announce Type: new Abstract: Adverse weather image restoration aims to recover clear visibility from degraded images in complex weather conditions. Existing works attempt to address

Local Ai

PixelSR: Efficient Screen Content Super-Resolution via Pixel Classification

arXiv:2608.00646v1 Announce Type: new Abstract: Screen content images are generally composed of texts and graphics. Compared to natural images, these man-made images contain a large quantity of sharp

local-aiarxiv-cs-cv

Tutorials

PixVL: Self-Supervised Training of Pixel-Level MLLMs via a Unified Mask--Text Consistency Cycle

arXiv:2608.01354v1 Announce Type: new Abstract: Recent studies develop pixel-level multimodal large language models (MLLMs) that support both Region Segmentation and Region Understanding, extending mu

tutorialsarxiv-cs-cv

PlainMedScale: A Corpus of Multi-Level Simplified Medical Texts in German and English

arXiv:2608.01158v1 Announce Type: new Abstract: We introduce PlainMedScale, a topic-aligned medical corpus spanning four levels of comprehensibility in German and English, drawn from MSD (professional

PlantRig - From Bones to Branches: Adaptation of Autoregressive Rigging Models for Plant Skeletal Reconstruction

arXiv:2608.01072v1 Announce Type: new Abstract: Autoregressive rigging models such as UniRig and SkinTokens perform well on articulated characters, but their ability to generalize to plant structures

safetyarxiv-cs-cv

Tutorials

Plasticity of Growing and Elastic Neural Networks in Online Continual Learning

arXiv:2608.01475v1 Announce Type: new Abstract: Neural networks that can grow or both grow and shrink during learning, referred to as growing neural networks and elastic neural networks, respectively,

tutorialsarxiv-cs-lg

PNEC-Mamba: Prototype-Guided Positive-Negative Evidence Calibration for Hyperspectral Image Classification

model-releasesarxiv-cs-cv

arXiv:2608.01910v1 Announce Type: new Abstract: In real-world hyperspectral scenes, pixel representations are often ambiguous due to factors such as spectral similarity, mixed pixels, and local contex

Policy Optimality Measurement for Multi-Vehicle Decision-Making: From Extrinsic Indicators to Intrinsic Quality

arXiv:2608.01133v1 Announce Type: new Abstract: Evaluating Multi-Agent Reinforcement Learning (MARL) policies in autonomous driving fundamentally relies on extrinsic statistical indicators (e.g., rewa

safetyarxiv-cs-lg

Polyatomic Complexes: A topologically-informed learning representation for atomistic systems

arXiv:2409.15600v3 Announce Type: replace Abstract: A representation of a molecule or material should be invariant to the symmetries of physics, unique, continuous, efficient and general. These proper

PolymerGPT: Multi-property Optimization with a Decoder-Based GPT Model for Generative Polymer Design

arXiv:2608.01431v1 Announce Type: cross Abstract: Polymer property prediction and inverse generative design targeting desired properties are two crucial tasks in machine learning-assisted polymer desi

Poplar: A Scalable Pipeline for Human-Centric Image Dataset Synthesis

arXiv:2608.00440v1 Announce Type: new Abstract: Recent image generators can synthesize convincing human-centric images, yet producing a useful collection remains different from producing a single succ

Practical Noise Modeling for SPAD Intensity Imaging

arXiv:2608.00489v1 Announce Type: new Abstract: Single-photon avalanche diode (SPAD) cameras are promising for low-light and high-dynamic-range intensity imaging, but their practical use is limited by

safetyarxiv-cs-cv

Agents

Practical Online KV Cache Compaction for LLM Agents: An Empirical Study

arXiv:2608.00902v1 Announce Type: new Abstract: LLM agents accumulate long trajectories of reasoning steps, tool calls, and environment feedback, making the KV cache a major inference bottleneck. KV c

agentsarxiv-cs-cl

PredAct-Bench: Benchmarking Tool-Augmented Dialogue under Controlled Tool Noise

model-releasesarxiv-cs-cl

arXiv:2608.02372v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in task-oriented dialogue systems that support multi-step decision-making in high-stakes domains

Predicting Startup Exit from Textual Descriptors - A Computational Linguistics Framework

arXiv:2608.00045v1 Announce Type: new Abstract: This study shows that textual descriptors alone can predict early-stage startup success, defined as Exit, without relying on contextual, financial, or h

Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines

arXiv:2608.01819v1 Announce Type: new Abstract: To improve the operational readiness of combat aircraft engines and reduce unplanned maintenance costs, accurately estimating the remaining useful life

Pretrain on Small Synthetic Data, Scale Large for Free: Symmetry-Aware Foundation Model for Logic Rule Induction

arXiv:2608.00383v1 Announce Type: cross Abstract: Logical rule induction seeks interpretable rules that transfer across propositional schemas. This requires respecting symmetries: atom naming, example

Pretraining on Call Graphs: When Binary Analysis Tasks Profit From Context

arXiv:2608.02084v1 Announce Type: cross Abstract: Binary function embedding models are trained to encode the semantics of binary code in such a way that they can be generalized to a variety of reverse

Agents

PRISM: Privileged Probabilistic Latent Supervision for End-to-End Autonomous Driving Motion Planning

arXiv:2608.01201v1 Announce Type: cross Abstract: End-to-end autonomous driving (E2E AD) systems integrate perception, prediction, and planning into a single differentiable architecture. While these m

agentsarxiv-cs-cv

Private Generative Bootstrap via Blocking

model-releasesarxiv-cs-lg

arXiv:2608.02480v1 Announce Type: cross Abstract: With AI systems gaining more access to individuals' information, it is important to protect privacy when reporting statistical answers. Equally import

Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability

arXiv:2608.01864v1 Announce Type: cross Abstract: Predicting drought risk is essential for anticipating impacts on water resources, agriculture, ecosystems, and climate adaptation planning. Yet drough

Probabilistic Reachable-Action Verification of Visuomotor Policies via Set-Based Training

arXiv:2608.02545v1 Announce Type: new Abstract: Reachability analysis for visuomotor policies is difficult because large visual encoders make end-to-end set propagation computationally expensive and e

safetyarxiv-cs-ro

Tutorials

Probing the 3D Object-Level Understanding of Pre-Trained Detection Transformers

arXiv:2608.01495v1 Announce Type: new Abstract: Detection transformer models, including DETR and its extensions, learn to output a set of object-level embeddings that can be simultaneously decoded int

tutorialsarxiv-cs-cv

Agents

Progressive Agent Skill Generation via Reinforcement Learning

arXiv:2608.01678v1 Announce Type: cross Abstract: Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence

agentsarxiv-cs-cl

Progressive^2: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression

arXiv:2608.00129v1 Announce Type: new Abstract: Knowledge distillation (KD) is a widely utilized technique for transferring knowledge from a large model (the teacher) to a smaller model (the student).

Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detection

arXiv:2608.01348v1 Announce Type: new Abstract: Data augmentation, which simulates diverse visual variations to expand the source distribution and induce synthetic domain shifts, is a simple yet effec

Prompt-Induced Waste in Large Reasoning Models: A Preregistered Two-Harness Benchmark of Coding Agents

model-releasesarxiv-cs-cl

arXiv:2608.01347v1 Announce Type: new Abstract: Large reasoning models used as coding agents incur costs from deliberation, tool calls, and repeated agent turns, yet the causal effect of prompt wordin

PromptPath: Prompt-Adaptive Computational Pathways for In-Context Learning

arXiv:2608.02129v1 Announce Type: new Abstract: In-context learning (ICL) has attracted increasing attention for enabling models to perform new tasks using only a few ``input--output'' prompt examples

Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression

arXiv:2608.00687v1 Announce Type: new Abstract: LiDAR point clouds provide explicit, deterministic physical boundaries critical for collaborative safety-critical perception. However, wireless channels

safetyarxiv-cs-cv

ProtoAct: Turning Wet-Lab Protocols into Embodied Robotic Actions

arXiv:2608.01690v1 Announce Type: new Abstract: Biological wet-lab protocols are written for trained researchers and often leave routine operations, state-dependent conditions, and contextual paramete

researcharxiv-cs-ro

Protocol generalisation for brain tissue microstructure estimation via hypernetwork-controlled geometric deep learning

model-releasesarxiv-cs-cv

arXiv:2608.02053v1 Announce Type: cross Abstract: Brain tissue microstructure estimation with machine learning provides higher computational efficiency than conventional fitting. However, machine lear

Provably Safe Generative Sampling with Constricting Barrier Functions

arXiv:2602.21429v3 Announce Type: replace Abstract: Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distribut

safetyarxiv-cs-lg

Proxy Avatar Meets Low-Rank Caching: Real-Time One-Shot Emotion-Controllable Portrait Animation

arXiv:2608.01978v1 Announce Type: new Abstract: Audio-driven portrait animation has advanced rapidly with diffusion-based generative models, yet real-time one-shot generation with expressive emotion c

Pruned BPE: Post-training Visibility Pruning and Token Reallocation for Byte Pair Encoding

arXiv:2608.00837v1 Announce Type: new Abstract: Byte Pair Encoding (BPE) is widely used for subword tokenization, but standard BPE exposes every learned merge token to the downstream model, including

Local Ai

Pseudorandom Streams within Diffusion Models Act as Learnable Inputs That Affect Generation Quality

arXiv:2608.02575v1 Announce Type: new Abstract: Diffusion models rely on stochastic inputs, yet on finite-precision hardware, the 'randomness' they consume is realized as deterministic numerical orbit

local-aiarxiv-cs-lg

Push-Wiper: Toward General-Purpose Robotic Cleaning across Varied Stains and Surfaces with Segmented Pushing Trajectories

arXiv:2608.00730v1 Announce Type: new Abstract: Viscous stains, characterized by high viscosity and complex rheological properties, remain a major challenge for robotic surface cleaning. Conventional

safetyarxiv-cs-ro

Applications

PyDPF: A Python Package for Differentiable Particle Filtering

arXiv:2510.25693v3 Announce Type: replace-cross Abstract: State-space models (SSMs) are a widely used tool in time series analysis. In the complex systems that arise from real-world data, it is common

applicationsarxiv-cs-lg

QR-Erase: Efficient Subspace-Based Machine Unlearning with Layer Localization

arXiv:2608.01422v1 Announce Type: new Abstract: Machine unlearning seeks to remove targeted information from trained models without requiring costly retraining. Existing optimization-based methods oft

Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models

model-releasesarxiv-cs-lg

arXiv:2506.07121v2 Announce Type: replace Abstract: Ensuring the safety and robustness of large language models (LLMs) is a fundamental challenge and a critical prerequisite for the responsible deploy

QuASH: Using Natural-Language Heuristics to Query Visual-Language Robotic Maps

arXiv:2510.14546v2 Announce Type: replace Abstract: Embeddings from Visual-Language Models are increasingly utilized to represent semantics in robotic maps, offering an open-vocabulary scene understan

researcharxiv-cs-ro

Quaternion Tensor Modeling for Joint Color-Polarization Demosaicking

arXiv:2608.02144v1 Announce Type: new Abstract: Division-of-focal-plane (DoFP) color polarization cameras enable snapshot acquisition of color polarization mosaic images, but the inherently sparse sam

QuerySplat: Decoupling Geometry and Appearance Representations in 3DGS Prediction

model-releasesarxiv-cs-cv

arXiv:2608.01186v1 Announce Type: new Abstract: While feed-forward 3D Gaussian Splatting (3DGS) enables efficient 3D reconstruction, achieving high-fidelity rendering remains challenging. Existing pix

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics

arXiv:2608.01522v1 Announce Type: cross Abstract: Teaching a language model a skill it has not mastered is obstructed by three recurring difficulties: training data is scarce, ground-truth reasoning t

Quick on the Uptake: Eliciting Implicit Intents from Human Demonstrations for Personalized Mobile-Use Agents

arXiv:2508.08645v3 Announce Type: replace Abstract: As multimodal large language models advance rapidly, the automation of mobile tasks has become increasingly feasible through the use of mobile-use a

safetyarxiv-cs-cl