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arXiv:2609.33515v1 Announce Type: new Abstract: Many probabilistic predictors must satisfy exact structure in every stochastic realization, yet common hard-constraint approaches form predictions in am

applicationsarxiv-cs-lg

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Research

Smoothing the Semantic Landscape: Generalizable AI-Generated Image Detection via Text-Induced Flatness

DGX agent

arXiv:2603.00717v3 Announce Type: replace Abstract: Recent advances in generative models have exposed a fundamental weakness of image fake detectors: they often fail to generalize to unseen synthesis

researcharxiv-cs-cv

29 Sept 2026

Local Ai

Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies

arXiv:2511.03095v2 Announce Type: replace Abstract: Modern artificial intelligence has revolutionized how we extract representations from scientific data, yet the statistical properties of these repre

local-aiarxiv-cs-lg

SpatialSkill: Self-Evolving Skills for Cross-View Spatial Reasoning

arXiv:2609.34124v1 Announce Type: new Abstract: Cross-view spatial reasoning requires a model to align different viewpoints into a coherent spatial representation, yet this ability remains challenging

Local Ai

SpatialSpeak: QA-Native Reconstruction with Local and Global Context for Spatial Chain-of-Thought Reasoning

arXiv:2609.33616v1 Announce Type: new Abstract: Vision-language models (VLMs) can benefit from geometric priors for multi-view spatial reasoning, yet answer-only training does not directly supervise t

local-aiarxiv-cs-cv

Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting

model-releasesarxiv-cs-lg

arXiv:2609.32143v1 Announce Type: new Abstract: Pre-training Graph Neural Networks (GNNs) via self-supervised learning has become a dominant paradigm, yet efficiently adapting frozen encoders remains

Tutorials

SphMind: Towards Robust, Training-Free VLM-based Spatial Reasoning with a 360 Camera

arXiv:2609.33462v1 Announce Type: new Abstract: Omnidirectional or 360 cameras provide embodied AI agents with a holistic, wide field-of-view (FoV) view of their surroundings, motivating the use of Mu

tutorialsarxiv-cs-cv

SPIDER: Multi-Layer Semantic Token Pruning and Adaptive Sub-Layer Skipping in Multimodal Large Language Models

arXiv:2609.34977v1 Announce Type: new Abstract: Multimodal Large Language Models face significant efficiency challenges that stem from two distinct yet coupled sources: data redundancy and computation

SpikeCredit: Temporal Credit Carrier for Reinforcement Learning with Sparse Rewards

arXiv:2609.35268v1 Announce Type: new Abstract: Reinforcement learning (RL) with sparse rewards is challenging because delayed outcomes provide little guidance about which intermediate computations ca

SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecasting

arXiv:2609.35097v1 Announce Type: new Abstract: Spiking neural networks (SNNs) offer an energy-efficient paradigm for time-series forecasting through spike-driven computation. However, recent SNN fore

Sprout: Building Dynamic Memory While Reasoning for Agentic Video Understanding

arXiv:2609.35497v1 Announce Type: new Abstract: Long video understanding relies on video memory to overcome the context limits of multimodal large language models. Existing methods follow a build-then

agentsarxiv-cs-cv

StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling

arXiv:2606.15197v3 Announce Type: replace Abstract: Optimization modeling is inherently hierarchical, requiring a precise sequence of symbolic commitments. Traditional learning-based automated optimiz

Statistical Testing for Multiple Instance Learning via Selective Inference with Applications to Computational Pathology

arXiv:2609.31712v1 Announce Type: new Abstract: Multiple instance learning (MIL) is widely used in computational pathology because it enables weakly supervised analysis of whole-slide images (WSIs) wi

STeP: Signal Temporal Logic for Precise Specifications for Action Generation with Vision Language Models

model-releasesarxiv-cs-ro

arXiv:2607.18580v2 Announce Type: replace Abstract: Natural-language robot instructions often specify more than a coarse task goal: they may impose spatial, temporal, and logical requirements that mus

Sub-Model Short-Term Memory Convolutions for Keyword Spotting Systems on Device

arXiv:2609.35005v1 Announce Type: cross Abstract: Keyword Spotting (KWS) is becoming increasingly important as voice-controlled devices grow more widespread. While voice interaction with smartphones a

Summarize Before Grounding: Query-Guided Chunk Condensation for Long-Video Temporal Grounding

arXiv:2609.34598v1 Announce Type: new Abstract: Video temporal grounding (VTG) aims to localize the video interval corresponding to a language query. Recent large vision-language models (LVLMs) show g

Superquadric Primitive Decomposition of 3D point clouds via Geometric-Aware Inlier Refinement

arXiv:2609.35725v1 Announce Type: new Abstract: The decomposition of 3D point clouds into interpretable geometric primitives remains a longstanding challenge in Computer Vision and Computer Graphics.

SurgFlow: 3D Object-Centric Contact Flow for Surgical Robot Manipulation

arXiv:2609.33237v1 Announce Type: new Abstract: Paired video-action demonstrations enable autonomous surgical behavior, but such data is scarce: robots perform roughly 1% of surgeries, while video-onl

agentsarxiv-cs-ro

SwingRL: Adaptive Observation Reinforcement Learning with World-Model Prediction for Cable-Suspended Hoisting Control

model-releasesarxiv-cs-ro

arXiv:2609.33053v1 Announce Type: new Abstract: Cable-suspended hoisting is widely used to move heavy or bulky payloads that cannot be handled conveniently by rigid pick-and-place systems, for example

SWT: Self-Supervised Video Object Segmentation via Sliding, Wavelet and Transportation

arXiv:2609.31725v1 Announce Type: new Abstract: Video Object Segmentation (VOS) aims to accurately segment target objects from consecutive video frames and track the changes of the objects in each fra

SymbolicArena: A Unified Infrastructure for Benchmark Distillation and Dynamic Evaluation in Symbolic Regression

model-releasesarxiv-cs-lg

arXiv:2609.35113v1 Announce Type: new Abstract: Symbolic regression (SR) seeks concise and interpretable mathematical expressions from data for scientific equation discovery. Existing SR benchmarks fa

SymDrift: One-Shot Generative Modeling under Symmetries

arXiv:2605.06140v2 Announce Type: replace Abstract: Generative modeling of physical systems, such as molecules, requires learning distributions that are invariant under global symmetries, such as rota

SynDORBench: Evaluating LVLM Perceptual Robustness Under Physically Constrained Visibility Conditions

arXiv:2609.31823v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have demonstrated remarkable performance on multimodal reasoning benchmarks, yet their perceptual reliability under

Synergizing Discriminative Exemplars and Self-Refined Experience for MLLM-based In-Context Learning in Medical Diagnosis

arXiv:2603.27737v2 Announce Type: replace Abstract: General Multimodal Large Language Models (MLLMs) often underperform in capturing domain-specific nuances in medical diagnosis, trailing behind fully

Hardware

Systematic Exploration of Multi-core Architectures for Efficient LLM Serving using WaferAI-SIM

arXiv:2510.05632v2 Announce Type: replace-cross Abstract: With the widespread adoption of Large Language Models (LLMs), the demand for high-performance LLM inference services continues to grow. Multi-

hardwarearxiv-cs-lg

T-SNN: Temporal Simplicial Neural Network for EEG Decoding

arXiv:2609.34002v1 Announce Type: new Abstract: Decoding brain states requires models that capture both the evolution of neural activity and interactions among groups of brain regions. Existing EEG me

Teach to Learn: Hint Annealing for Self-improving LLM Reasoning

model-releasesarxiv-cs-lg

arXiv:2609.34975v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) improves language-model reasoning by comparing verified rewards among multiple solution rollouts for each quer

Temporal Heterogeneous Graph Pretraining for Relational Deep Learning

arXiv:2609.35219v1 Announce Type: new Abstract: Relational deep learning models database rows and foreign-key links as a heterogeneous graph for prediction from record attributes and relational contex

Hardware

Terrain-Aware Autonomous Planetary Exploration for Exteroceptive-Proprioceptive Mapping with Quadruped Scouts

arXiv:2609.35493v1 Announce Type: new Abstract: Autonomous planetary exploration requires robots to navigate unknown, uneven terrain while assessing risk, traversability, and energetic cost. Quadruped

hardwarearxiv-cs-ro

Applications

Test-Time Generalized Category Discovery

arXiv:2609.33937v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) and Generalized Category Discovery (GCD) are traditionally treated as disjoint problems: the former adapts models to domain s

applicationsarxiv-cs-cv

The Earth in One Gaze: Training-Free Active Focus for UHR Remote Sensing Understanding

arXiv:2609.31747v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) must balance local detail against scene context when interpreting ultra-high-resolution (UHR) remote sensing (R

Think Fast, Plan Selectively: Adaptive Deliberation for Efficient Data-Driven MPC

arXiv:2609.32591v1 Announce Type: cross Abstract: Data-driven model predictive control (MPC) combines learned world models with online trajectory optimization, achieving strong performance in continuo

Applications

TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra

arXiv:2609.32384v1 Announce Type: new Abstract: Real-world time series are inherently non-stationary, with trends, periodic patterns, and uncertainty evolving over time. While the Fourier domain offer

applicationsarxiv-cs-lg

TimelyDAgger: Timing-Aware Expert Querying for VLA Policy Improvement

arXiv:2609.33157v1 Announce Type: new Abstract: DAgger improves robot policies by aggregating expert supervision from states visited during policy execution. Robot-gated DAgger automates expert querie

safetyarxiv-cs-ro

TMCS: Tool-Grounded Multi-Agent Reasoning for Compositional Chemical Problem Solving

arXiv:2609.35336v1 Announce Type: cross Abstract: Despite the promise of Large Language Models (LLMs) in computational chemistry, rigorous combinatorial chemistry problems remain difficult because the

agentsarxiv-cs-cv

Token-Disentangled Latent Test-Time Scaling for Vision-Language Reasoning

arXiv:2609.35228v1 Announce Type: new Abstract: Latent test-time scaling improves reasoning by refining hidden states during inference, but existing methods typically apply a single scalar reward to a

Towards Autonomous and Auditable Medical Imaging Model Development

arXiv:2607.10522v2 Announce Type: replace Abstract: Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and

agentsarxiv-cs-cv

Towards Generalizable 3D Anomaly Detection via Relational Inconsistency Modeling

arXiv:2609.35059v1 Announce Type: new Abstract: 3D anomaly detection (3DAD) aims to identify defective regions in point cloud data, serving as a critical component in industrial inspection systems. Ex

Towards Identifiable Representations under Misspecified Structure

arXiv:2609.33273v1 Announce Type: new Abstract: The presence of noise that depends on the latent variables poses a fundamental challenge to identifiability. Existing results rely on conditional indepe

Hardware

Towards Kinematic Actionable Infeasibility Detection in Motion Planning

arXiv:2609.32806v1 Announce Type: new Abstract: Motion planning in robotics requires not only computing collision-free paths but also certifying infeasibility when no such path exists. Complete method

hardwarearxiv-cs-ro

Transferable Mass Spectrum Prediction via Reference-Guided Test-time Specialization

arXiv:2609.35649v1 Announce Type: new Abstract: Tandem mass spectrum prediction supports compound identification across metabolomics, natural-product discovery, and environmental analysis. However, pr

TransforMARS: Fault-Tolerant Self-Reconfiguration for Arbitrarily Shaped Modular Aerial Robot Systems

arXiv:2509.14025v2 Announce Type: replace Abstract: Modular Aerial Robot Systems (MARS) consist of multiple drone modules that are physically bound together to form a single structure for flight. Expl

researcharxiv-cs-ro

TreeRef-BFN: Equivariance-Free De Novo Molecule Generation based on 2D Topology and Internal 3D Geometry

arXiv:2609.32502v1 Announce Type: cross Abstract: De novo 3D molecular generation jointly models molecular size, topology, and geometry. Most methods pre-sample molecular size and generate Cartesian c

TriDrive: Joint Driver, Vehicle, and Road Modeling for Forecasting and Driver Monitoring

arXiv:2609.33000v1 Announce Type: cross Abstract: Predicting how drivers, vehicles, and road scenes interact and evolve together is central to driver monitoring. Prior work models in-cabin activity or

TTRSD: Test-Time Reinforcement Learning with Self-Distillation for Vision-Language Models

arXiv:2609.33414v1 Announce Type: new Abstract: Test-time reinforcement learning enables vision-language models (VLMs) to adapt using unlabeled inputs. However, repeated sampling under fixed visual co

safetyarxiv-cs-cv

Two-Stage Multi-View Gait Recognition with a Re-Embedding Network

arXiv:2609.32244v1 Announce Type: new Abstract: Gait recognition always remains challenging due to severe overfitting and the rigid view constraints common in single-stage approaches. We propose a two

Uncertainty Quantification of Next Generation Reservoir Computing with Applications to Memory-Driven Dynamical Systems

arXiv:2609.32169v1 Announce Type: cross Abstract: Nonlinear dynamical systems with memory arise across science and engineering, yet uncertainty quantification for efficient forecasting methods such as

Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective

arXiv:2601.06597v3 Announce Type: replace Abstract: Can we design a model such that its stochastic training favours a desired class of solutions without enforcing an explicit penalty? Under suitable c