Search: “framework”

Prototype-guided Bilateral Alignment Multimodal Federated Learning

arXiv:2609.38925v1 Announce Type: new Abstract: Multimodal federated learning (MFL) has emerged as a pivotal paradigm for leveraging distributed data to enhance model performance. However, existing me

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Prototype-Rule Neurosymbolic Regularization for Rank-Constrained Tensor Neural Networks under Label Scarcity

DGX agent

arXiv:2609.40131v1 Announce Type: cross Abstract: Rank-constrained tensor neural networks reduce the parameterization of high-order inputs, but they do not explicitly constrain class geometry in the l

safetyarxiv-cs-cv

1 Oct 2026

ProtScape: A molecular structure and energy-aware representation for protein conformation generation

arXiv:2410.20317v2 Announce Type: replace Abstract: Molecular dynamics (MD) simulations are a principled but computationally expensive approach for studying protein conformational variability, making

tutorialsarxiv-cs-lg

Provable Test-Time Scaling for Beam Search in LLM Reasoning

arXiv:2609.38672v1 Announce Type: cross Abstract: Beam-search-based test-time methods provide an effective way to improve large language model (LLM) performance on long-horizon generation by pruning i

Pseudo-Label-Triggered Retraining from Forecast Errors for Online Time Series Forecasting

arXiv:2609.39789v1 Announce Type: cross Abstract: Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although re

tutorialsarxiv-cs-ai

Local Ai

RAST: Resolution-Aware Privileged Structure Transfer for Low-Resolution Audio Activity Recognition

arXiv:2609.38780v1 Announce Type: cross Abstract: Audio is increasingly used for human activity recognition (HAR) because it captures object interactions, environmental events, and contextual cues in

local-aiarxiv-cs-ai

RATIO: Reasoning Analysis and Token-level Inference Optimization for Quantized Reasoning Models

arXiv:2609.39801v1 Announce Type: new Abstract: Post-training quantization (PTQ) has become a widely adopted technique for reducing the memory footprint and inference cost of large language models (LL

researcharxiv-cs-lg

Reach Into The CHOIR: Free-List Elicitation Uncovers Distinct Model Voices in LLM Ensembles

arXiv:2609.38448v1 Announce Type: new Abstract: Open-ended LLM homogeneity can create false plurality when several systems appear to offer independent perspectives while returning the same familiar de

Recognition of Urbanized Areas in UAV-Derived Very-High-Resolution Visible-Light Imagery

arXiv:2609.40212v1 Announce Type: new Abstract: This study compared classifiers that differentiate between urbanized and non-urbanized areas based on unmanned aerial vehicle (UAV)-acquired RGB imagery

Applications

Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstruction

arXiv:2609.39960v1 Announce Type: new Abstract: 4D scene reconstruction aims to recover the evolving geometry, appearance, and motion of dynamic environments from visual observations. Despite substant

applicationsarxiv-cs-cv

Agents

Reinforcement Learning-Guided Graph Transformations for SpTRSV Optimization

arXiv:2609.40159v1 Announce Type: cross Abstract: Sparse triangular solve (SpTRSV) is a fundamental kernel in numerous scientific and engineering applications. However, the data dependencies inherent

agentsarxiv-cs-lg

Reliability-Aware Checkpoint Selection for Domain Generalization

arXiv:2609.39934v1 Announce Type: cross Abstract: Checkpoint selection in domain generalization often relies on source-validation accuracy, yet the selected checkpoint need not provide reliable probab

Local Ai

Rep2Skill: Representation-Guided Skill Self-Evolution for LLM Agents

arXiv:2609.39149v1 Announce Type: new Abstract: Textual skills enable large language model (LLM) based agents to accumulate reusable procedural knowledge without updating model parameters. Yet existin

local-aiarxiv-cs-ai

Restoring without Forgetting: Filter-Level Continual Image Restoration via Parameter-Space Integrated Gradients

model-releasesarxiv-cs-cv

arXiv:2609.38591v1 Announce Type: new Abstract: Adapting image restoration models to a stream of new tasks without revisiting past data remains challenging due to catastrophic forgetting. In this work

Rethinking Generative Image Compression at Extremely Low Bitrates

model-releasesarxiv-cs-cv

arXiv:2609.39315v1 Announce Type: new Abstract: Generative image compression produces visually plausible reconstructions at low bitrates, yet their behavior as the rate approaches zero remains largely

Revisiting On-policy Adversarial Black-Box Distillation: Calibrating Groupwise Reward Geometry for Effective Advantage Construction

arXiv:2609.39757v1 Announce Type: cross Abstract: Black-box distillation is a practical route for transferring capabilities from API-accessible large language models that expose only text outputs into

safetyarxiv-cs-cv

RiboUnmix: Learning Shared Translational Dynamics from Biased and Noisy Ribo-seq Measurements

model-releasesarxiv-cs-lg

arXiv:2609.39644v1 Announce Type: new Abstract: Ribosome profiling (Ribo-seq) measures ribosome distributions along mRNAs, but observed occupancy profiles also contain experiment-specific distortions

Right Answers, Costly Models: The Efficiency Gap in LLM-based Optimization Modeling

model-releasesarxiv-cs-ai

arXiv:2609.38884v1 Announce Type: cross Abstract: Optimization modeling formulates real-world decision problems as mathematical programs that solvers can use to find optimal decisions. Large language

RL-Guided PAC-NMPC for Probabilistically-Safe Perception-Based Navigation in Unknown Environments

arXiv:2609.39854v1 Announce Type: new Abstract: In this paper, we present an approach for combining stochastic nonlinear model predictive control (SNMPC) and reinforcement learning (RL) to enable prob

safetyarxiv-cs-ro

RoboCoach: World Models as Active Coaches for Compositional Robot Skills

arXiv:2609.39685v1 Announce Type: cross Abstract: Long-horizon robot manipulation reuses skills across many task compositions, but improving these compositions with additional end-to-end demonstration

Robust LassoNet: Enhancing Feature Selection in Neural Networks via Robust Loss Functions

arXiv:2609.38263v1 Announce Type: cross Abstract: Feature selection in neural networks remains a challenging problem, particularly in the presence of noisy or contaminated data. LassoNet is a recent a

researcharxiv-cs-lg

Applications

Robust Transfer Learning for Paper ECG Recognition

arXiv:2609.39581v1 Announce Type: cross Abstract: Paper ECG recognition is challenging because real-world ECG images vary in layout, physical artifacts, and label availability. We introduce RobECG-CL,

applicationsarxiv-cs-ai

RSIGame: Autonomous Agentic Game Development with Recursive Self-improvement

model-releasesarxiv-cs-cl

arXiv:2609.39045v1 Announce Type: new Abstract: Recent advances in large language models have made automatic game generation increasingly feasible, yet reliably improving generated games beyond a play

SCB: SpeechConversationBench for Evaluating Multi-Turn Reasoning in Speech-to-Speech Models

arXiv:2609.40198v1 Announce Type: cross Abstract: Speech-to-speech systems must solve tasks whose requirements emerge across conversational turns. We introduce SpeechConversationBench (SCB), a focused

SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision

arXiv:2609.39679v1 Announce Type: cross Abstract: Audio deepfake detection (ADD) must remain effective when new spoofing attacks emerge after deployment. Emerging audio language model (ALM)-based ADD

researcharxiv-cs-lg

Seeing as Humans Do: Learning from Motion to Segment Anything Without Supervision

arXiv:2609.39785v1 Announce Type: new Abstract: The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised m

tutorialsarxiv-cs-cv

Self-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization

model-releasesarxiv-cs-ai

arXiv:2609.38757v1 Announce Type: new Abstract: Large language models are increasingly participating in complex real-world tasks in the form of algorithm-design agents, designing and refining algorith

Self-Evolving Harness on Multiple Tasks with the Agent as Its Own Optimizer

model-releasesarxiv-cs-ai

arXiv:2609.38372v1 Announce Type: new Abstract: A harness is the code around a language-model agent that organizes prompts, calls tools, manages context, and controls execution. As models grow stronge

Semantic Chunking and the Entropy of Natural Language

arXiv:2602.13194v3 Announce Type: replace-cross Abstract: Humans and large language models can predict next letter or word from its prior context much better than random guessing, indicating strong re

ShieldCLIP: Selective Safety Alignment for Harmful Content Mitigation in Multimodal Foundation Models

arXiv:2609.39688v1 Announce Type: cross Abstract: Multimodal encoders such as CLIP underlie many downstream systems, but their web-scale training data embed harmful associations that safety alignment

SimEX: Simulation-Integrated Robotics AutoResearch

arXiv:2609.38982v1 Announce Type: cross Abstract: Coding agents powered by large language models (LLMs) have shown remarkable abilities to autonomously reason about and achieve goals in the digital wo

Simon-SR: Spatially Adaptive Modulation and Visual Prompt Adaptation for Text-Reinforced Super-Resolution

arXiv:2607.09351v2 Announce Type: replace Abstract: Severe downsampling makes single image super-resolution (SISR) an ill-posed problem, in which the language-guided multi-modal methods are especially

Agents

SimTrace: Grounded Multimodal User Trajectories Generation for Online User Modeling

arXiv:2609.38397v1 Announce Type: new Abstract: Virtual clients offer a cost-effective approach to support applications such as A/B testing, recommender system development, and interface evaluation. H

agentsarxiv-cs-ai

Social-WM: Safety-Aware Latent World Models for Robot Social Navigation

arXiv:2609.40177v1 Announce Type: new Abstract: Safe social navigation requires a robot to anticipate not only the future consequences of its actions, but also whether a nominal action can actually be

safetyarxiv-cs-ro

Soft Spatial Reasoning

arXiv:2609.38717v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) commonly perform spatial reasoning through chain-of-thought (CoT), encoding intermediate reasoning as autoregress

Hardware

Sparse-WAM: Accelerating World Action Models via Action-Guided Sparse Imagination

arXiv:2609.38984v1 Announce Type: cross Abstract: World-action models (WAMs) leverage pretrained video models to improve generalization in robot control by jointly predicting future visual states and

hardwarearxiv-cs-ai

Spatial-Temporal Multi-scale Network for Screen Content Video Quality Enhancement

arXiv:2609.39894v1 Announce Type: new Abstract: Different from natural videos, Screen Content Videos (SCVs) are characterized by abrupt motion, scene switches, and high-frequency details such as text

Local Ai

SpatialCORE: Confidence-Aware Grounded Spatial Reasoning in Large Vision--Language Models

arXiv:2609.38716v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have made remarkable progress across visual perception tasks, yet spatial reasoning remains a persistent weakness

local-aiarxiv-cs-ai

SPOON: Towards Coherent Compositional 3D Scene Generation from Uncalibrated Multi-view Images

arXiv:2609.39590v1 Announce Type: new Abstract: Compositional 3D scene generation aims to recover complete 3D object shapes and their spatial arrangement from visual observations. Recent image-conditi

tutorialsarxiv-cs-cv

Steepest Guidance: A Practical and Principled Approach to Inference-Time Alignment of Flow and Diffusion-based Models

arXiv:2609.39091v1 Announce Type: cross Abstract: Inference-time alignment of flow and diffusion-based models is critical for achieving flexible generative modeling. Theoretically, Doob's h-transform

safetyarxiv-cs-lg

SteerQuant: Steering Quantization Error with Action-Guided Scaling in World-Action Models

model-releasesarxiv-cs-ro

arXiv:2609.39056v1 Announce Type: new Abstract: World-action models (WAMs) jointly generate future world states and actions through iterative denoising, using shared weights to process heterogeneous s

StreamRig: Exploiting Intra-Rig Geometry for Streaming Multi-Camera Odometry

model-releasesarxiv-cs-cv

arXiv:2609.40244v1 Announce Type: new Abstract: Mobile robots and vehicles carry synchronized multi-camera rigs, yet many streaming 3D foundation models are designed for monocular input, leaving effic

Structure-aware Reinforcement Learning for Protein Directed Evolution

arXiv:2609.39048v1 Announce Type: cross Abstract: Protein optimization remains a longstanding goal in life sciences. Existing machine learning-assisted directed evolution (MLDE) methods primarily rely

Structure of Basic Human Values in Russian Social Media

model-releasesarxiv-cs-cl

arXiv:2603.18822v2 Announce Type: replace Abstract: What is known about the values of national populations rests almost entirely on questionnaires, which prompt respondents to rate researcher-supplied

Switching Linear Attention

arXiv:2609.39034v1 Announce Type: cross Abstract: Designing expressive sequence layers with efficient inference remains a central challenge in modern machine learning. Standard softmax attention achie

SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets

arXiv:2609.38225v1 Announce Type: cross Abstract: Imitation learning enables robots to acquire complex skills directly from massive demonstration datasets, but its performance degrades severely when d

T-ARC: Topology-Aware Randomized Clustering via Distributionally Robust Stochastic Block Models

model-releasesarxiv-cs-lg

arXiv:2609.39466v1 Announce Type: new Abstract: In this work, we introduce a new clustering method, namely T-ARC (Topology-Aware Randomized Clustering), that corrects the geometric bias of K-means by

TACIT: Optimization Models that Learn from Their Mistakes

arXiv:2609.38434v1 Announce Type: cross Abstract: Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge,

tutorialsarxiv-cs-ai