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Scaling Video Understanding via Compact Latent Multi-Agent Collaboration

arXiv:2605.00444v1 Announce Type: new Abstract: Multi-modal large language models (MLLMs) advance vision language understanding but face inherent limitations in long-video tasks due to bounded percept

safetyarxiv-cs-cv

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SCAN: Structured Capability Assessment and Navigation for LLMs

DGX agent

arXiv:2505.06698v4 Announce Type: replace Abstract: Evaluating Large Language Models (LLMs) has become increasingly important, with automatic evaluation benchmarks gaining prominence as alternatives t

researcharxiv-cs-cl

4 May 2026

Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models

arXiv:2510.04378v2 Announce Type: replace Abstract: Identifying the structure of a partially observed causal system is essential to various scientific fields. Recent advances have focused on constrain

ScreenParse: Moving Beyond Sparse Grounding with Complete Screen Parsing Supervision

model-releasesarxiv-cs-cv

arXiv:2602.14276v2 Announce Type: replace Abstract: Modern computer-use agents (CUA) must perceive a screen as a structured state, what elements are visible, where they are, and what text they contain

Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification

arXiv:2605.00218v1 Announce Type: cross Abstract: Mobile remote identity verification (RIdV) systems are exposed to attacks that manipulate or replace the facial video stream, including presentation a

Semantic Level of Detail for Knowledge Graphs: Discovering Abstraction Boundaries via Spectral Heat Diffusion

arXiv:2603.08965v2 Announce Type: replace Abstract: Graph-structured knowledge systems -- from knowledge graphs to GraphRAG pipelines -- organize information into hierarchical communities, yet lack a

SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination

arXiv:2605.00176v1 Announce Type: cross Abstract: Double-machine-learning pipelines for the Average Dose-Response Function rely on kernel-weighted local-linear smoothers, which inherit unbounded funct

Short Chains, Deep Thoughts: Balancing Reasoning Efficiency and Intra-Segment Capability via Split-Merge Optimization

arXiv:2602.03141v3 Announce Type: replace Abstract: While Large Reasoning Models (LRMs) have demonstrated impressive capabilities in solving complex tasks through the generation of long reasoning chai

SIMON: Saliency-aware Integrative Multi-view Object-centric Neural Decoding

arXiv:2605.00401v1 Announce Type: new Abstract: Recent EEG-to-image retrieval methods leverage pretrained vision encoders and foveation-inspired priors, but typically assume a fixed, center-focused vi

safetyarxiv-cs-cv

SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening

arXiv:2510.07922v4 Announce Type: replace Abstract: Decentralized Federated Learning (DFL) enables privacy-preserving collaborative training without centralized servers but remains vulnerable to Byzan

Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution

arXiv:2605.00056v1 Announce Type: new Abstract: Groundwater in the Densu Basin is increasingly threatened by heavy metal contamination, but conventional methods fail to capture the statistical complex

Smart Profit-Aware Crop Advisory System: Kisan AI

arXiv:2605.00133v1 Announce Type: new Abstract: Modern crop advisory systems exhibit a critical limitation termed extit{economic blindness}. These systems primarily optimize for biological yield, ofte

Soft Graph Diffusion Transformer for MIMO Detection

arXiv:2605.00449v1 Announce Type: cross Abstract: Learning-based MIMO detection has shown strong empirical performance, yet existing methods typically rely on fixed-depth architectures without explici

Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series

arXiv:2605.00069v1 Announce Type: new Abstract: Elastic distances like dynamic time warping (DTW) are central to time series machine learning because they compare sequences under local temporal misali

Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech

arXiv:2603.15988v2 Announce Type: replace-cross Abstract: Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies. However, subjective evalua

Sparse VideoGen2: Accelerate Video Generation with Sparse Attention via Semantic-Aware Permutation

arXiv:2505.18875v4 Announce Type: replace Abstract: Diffusion Transformers (DiTs) are essential for video generation but suffer from significant latency due to the quadratic complexity of attention. B

Spiking Sequence Machines and Transformers

arXiv:2605.00662v1 Announce Type: cross Abstract: Sequence learning reduces to similarity-based retrieval over a temporally indexed representation space, a constraint on any sequence model, not a prop

SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting

arXiv:2605.00126v1 Announce Type: new Abstract: Generative models for time-series imputation achieve strong reconstruction accuracy, yet provide no finite-sample reliability guarantees, a critical lim

Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance

arXiv:2605.00553v1 Announce Type: new Abstract: Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety. Finding effec

State Stream Transformer (SST) V2: Parallel Training of Nonlinear Recurrence for Latent Space Reasoning

model-releasesarxiv-cs-cl

arXiv:2605.00206v1 Announce Type: cross Abstract: Current transformers discard their rich latent residual stream between positions, reconstructing latent reasoning context at each new position and lea

Static and Dynamic Graph Alignment Network for Temporal Video Grounding

model-releasesarxiv-cs-cv

arXiv:2605.00684v1 Announce Type: new Abstract: Temporal Video Grounding (TVG) aims to localize temporal moments in an untrimmed video that semantically correspond to given natural language queries. R

Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium

arXiv:2503.10990v2 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) with diverse human preferences is critical for ensuring fairness and informed outcomes when deploying th

Statistical Testing Framework for Clustering Pipelines by Selective Inference

arXiv:2603.18413v3 Announce Type: replace-cross Abstract: A data analysis pipeline is a structured sequence of steps that transforms raw data into meaningful insights by integrating multiple analysis

Stepper: Stepwise Immersive Scene Generation with Multiview Panoramas

arXiv:2603.28980v2 Announce Type: replace Abstract: The synthesis of immersive 3D scenes from text is rapidly maturing, driven by novel video generative models and feed-forward 3D reconstruction, with

Stereo Multistage Spatial Attention for Real-Time Mobile Manipulation Under Visual Scale Variation and Disturbances

arXiv:2605.00471v1 Announce Type: new Abstract: Robots operating in open, unstructured real-world environments must rely on onboard visual perception while autonomously moving across different locatio

applicationsarxiv-cs-ro

Structural Prognostic Event Modeling for Multimodal Cancer Survival Analysis

arXiv:2512.01116v3 Announce Type: replace Abstract: The integration of histology images and gene profiles has shown great promise for improving survival prediction in cancer. However, current approach

Structure-Aware Chunking for Tabular Data in Retrieval-Augmented Generation

arXiv:2605.00318v1 Announce Type: new Abstract: Tabular documents such as CSV and Excel files are widely used in enterprise data pipelines, yet existing chunking strategies for retrieval-augmented gen

applicationsarxiv-cs-cl

Structure Liberates: How Constrained Sensemaking Produces More Novel Research Output

arXiv:2605.00557v1 Announce Type: new Abstract: Scientific discovery is an extended process of ideation--surveying prior work, forming hypotheses, and refining reasoning--yet existing approaches treat

Tutorials

Structured In-context Environment Scaling for Large Language Model Reasoning

arXiv:2509.23330v3 Announce Type: replace Abstract: Large language models (LLMs) have achieved significant advancements in reasoning capabilities through reinforcement learning (RL) via environmental

tutorialsarxiv-cs-cl

Sure About That Line? Approaching Confidence-Based, Real-Time Line Assignment in Reading Gaze Data

arXiv:2605.00033v1 Announce Type: cross Abstract: Remote and webcam-based eye tracking in multi-line reading suffers from various noise factors and layout ambiguity, precisely where real-time reading

Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialogue

arXiv:2605.00506v1 Announce Type: new Abstract: We model utterance production as probabilistic cost-sensitive choice over contextual alternatives, using information-theoretic notions of cost. We disti

applicationsarxiv-cs-cl

Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality

arXiv:2507.09001v3 Announce Type: replace-cross Abstract: Machine learning (ML) models for electronic structure typically rely on large datasets generated by computationally expensive Kohn-Sham densit

SynQuE: Estimating Synthetic Dataset Quality Without Annotations

arXiv:2511.03928v5 Announce Type: replace Abstract: We introduce and formalize the Synthetic Dataset Quality Estimation (SynQuE) problem: ranking synthetic datasets by their expected real-world task p

applicationsarxiv-cs-lg

Task-Conditioned Uncertainty Costmaps for Legged Locomotion

arXiv:2605.00261v1 Announce Type: new Abstract: Legged robots maintain dynamic feasibility through multicontact interactions with terrain. Learned foothold prediction can provide feasibility-aware cos

applicationsarxiv-cs-ro

Technical Report: Activation Residual Hessian Quantization (ARHQ) for Low-Bit LLM Quantization

arXiv:2605.00140v1 Announce Type: cross Abstract: We present Activation Residual Hessian Quantization (ARHQ), a post-training weight splitting method designed to mitigate error propagation in low-bit

Temporal Data Requirement for Predicting Unplanned Hospital Readmissions

arXiv:2605.00738v1 Announce Type: new Abstract: With the proliferation of Electronic Health Records (EHRs), a critical challenge in building predictive models is determining the optimal historical dat

Local Ai

Tempus: A Temporally Scalable Resource-Invariant GEMM Streaming Framework for Versal AI Edge

arXiv:2605.00536v1 Announce Type: cross Abstract: Scaling laws for Large Language Models (LLMs) establish that model quality improves with computational scale, yet edge deployment imposes strict const

local-aiarxiv-cs-lg

The Algorithmic Gaze of Image Quality Assessment: An Audit and Trace Ethnography of the LAION-Aesthetics Predictor

arXiv:2601.09896v4 Announce Type: replace-cross Abstract: Visual generative AI models are trained using a one-size-fits-all measure of aesthetic appeal. However, what is deemed 'aesthetic' is inextric

The Determinism of Randomness: Latent Space Degeneracy in Diffusion Model

arXiv:2511.07756v4 Announce Type: replace Abstract: Diffusion models initialize generation from an isotropic Gaussian latent, yet changing only the random seed can substantially alter prompt faithfuln

safetyarxiv-cs-cv

The Power of Order: Fooling LLMs with Adversarial Table Permutations

arXiv:2605.00445v1 Announce Type: new Abstract: Large Language Models have achieved remarkable success and are increasingly deployed in critical applications involving tabular data, such as Table Ques

applicationsarxiv-cs-lg

The Seismic Wavefield Common Task Framework

arXiv:2512.19927v2 Announce Type: replace Abstract: Seismology faces fundamental challenges in state forecasting and reconstruction (e.g., earthquake early warning and ground motion prediction) and ma

The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning

arXiv:2603.17837v3 Announce Type: replace-cross Abstract: During conversational interactions, humans subconsciously engage in concurrent thinking while listening to a speaker. Although this internal c

Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring

arXiv:2605.00754v1 Announce Type: cross Abstract: Reward models (RMs) have become an indispensable fixture of the language model (LM) post-training playbook, enabling policy alignment and test-time sc

Local Ai

Thinking in Text and Images: Interleaved Vision--Language Reasoning Traces for Long-Horizon Robot Manipulation

arXiv:2605.00438v1 Announce Type: cross Abstract: Long-horizon robotic manipulation requires plans that are both logically coherent and geometrically grounded. Existing Vision-Language-Action policies

local-aiarxiv-cs-ro

Agents

Thinking with Geometry: Active Geometry Integration for Spatial Reasoning

arXiv:2602.06037v4 Announce Type: replace Abstract: Recent progress in spatial reasoning with Multimodal Large Language Models (MLLMs) increasingly leverages geometric priors from 3D encoders. However

agentsarxiv-cs-cv

Thought Graph Traversal for Test-time Scaling in Chest X-ray VLLMs

arXiv:2506.11989v3 Announce Type: replace Abstract: Test-time scaling offers a promising way to improve the reasoning performance of vision-language large models (VLLMs) without additional training. I

Agents

Time-series Meets Complex Motion Modeling: Robust and Computational-effective Motion Predictor for Multi-object Tracking

arXiv:2605.00362v1 Announce Type: new Abstract: Multi-object tracking (MOT) is critical in numerous real-world applications, including surveillance, autonomous driving, and robotics. Accurately predic

agentsarxiv-cs-cv

TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning

arXiv:2605.00015v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) advance generalization and data efficiency in time series forecasting by unified large-scale pretraining. But TS

applicationsarxiv-cs-cv