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Sample Where You Struggle: Sharpening Base Model Reasoning via Entropy-Guided Power Sampling

arXiv:2606.09926v1 Announce Type: cross Abstract: Sampling from the sequence-level power distribution p^alpha elicits RL-level reasoning from base language models without any parameter updates, but th

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Hardware

Sampling Triangulations and Calabi-Yau Threefolds with Autoregressive GNNs

DGX agent

arXiv:2605.27770v2 Announce Type: cross Abstract: We introduce `dualGNN', an autoregressive message-passing GNN for sampling fine, regular triangulations (FRTs) of convex polytopes. dualGNN operates o

hardwarearxiv-cs-lg

10 Jun 2026

SARM2: Multi-Task Stage Aware Reward Modeling for Self Improving Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2606.10305v1 Announce Type: new Abstract: Fine-tuning vision-language-action (VLA) policies for long-horizon manipulation still relies heavily on behavior cloning, which requires costly high-qua

SCAIL-2: Unifying Controlled Character Animation with End-to-end In-Context Conditioning

model-releasesarxiv-cs-cv

arXiv:2606.10804v1 Announce Type: new Abstract: Controlled character animation requires transferring motion from a driving sequence to a reference character. Prior works heavily rely on intermediate r

Scaling Self-Supervised Speech Models Uncovers Deep Linguistic Relationships: Evidence from the Pacific Cluster

arXiv:2603.07238v2 Announce Type: replace Abstract: Similarities between language representations derived from Self-Supervised Speech Models (S3Ms) have been observed to primarily reflect geographic p

researcharxiv-cs-cl

Schmidt Decomposition-Based Methods for Efficient Quantum Image Encoding

arXiv:2606.10874v1 Announce Type: new Abstract: In quantum image processing, a fundamental step is encoding classical image data into quantum states. This can be achieved using methods such as Flexibl

researcharxiv-cs-cv

SCOPE: Sequential Causal Optimization of Process Interventions

arXiv:2512.17629v4 Announce Type: replace-cross Abstract: Prescriptive Process Monitoring (PresPM) recommends interventions during running business processes to optimize key performance indicators (KP

SD-GRPO: Verifiable Segment Decomposition for Long-Form Vision-Language Generation

arXiv:2606.09871v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) and its variants, originally developed for Large Language Models (LLMs), have recently been applied to Multi

Seal-Robust KCR: A Robust Kuzushiji Character Recognition Framework under Seal Interference

arXiv:2602.19086v2 Announce Type: replace Abstract: Kuzushiji was one of the most widely used cursive writing systems in pre-modern Japan. Due to its highly cursive forms and extensive glyph variation

researcharxiv-cs-cv

Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning

arXiv:2606.10780v1 Announce Type: cross Abstract: Secure aggregation is a vital component for mitigating gradient leakage in federated learning, but its communication cost conventionally scales with t

Segment and Select: Vision-Language Segmentation in 3D Scenarios

arXiv:2606.10594v1 Announce Type: new Abstract: 3D vision-language segmentation aims to segment target objects in 3D scenarios according to the linguistic instructions and visual observations. Prior a

researcharxiv-cs-cv

Applications

Segmentation-Driven Monocular Shape from Polarization based on Physical Model

arXiv:2601.04776v2 Announce Type: replace Abstract: Monocular shape-from-polarization (SfP) leverages the intrinsic relationship between light polarization properties and surface geometry to recover s

applicationsarxiv-cs-cv

Selection, Not Salience: The Shape and Limits of Personalization in Social Highlighting

arXiv:2606.10398v1 Announce Type: cross Abstract: Does personalizing what a reader sees pay off, and where does it stop? Using a social web highlighter and a co-readership identity control (the same d

Selective Disk Bispectrum: A Complete and Rotation Invariant Image Descriptor

arXiv:2511.19706v2 Announce Type: replace-cross Abstract: Rotation invariance is a fundamental requirement across many computer vision tasks. Historically, this inductive bias has been encoded through

safetyarxiv-cs-cv

Self-Distillation Policy Optimization via Visual Feedback: Bridging Code and Visual Artifacts

arXiv:2606.10334v1 Announce Type: new Abstract: Code-generating large language models (LLMs) increasingly produce visual artifacts such as charts, web pages, and slides by writing programs that are ex

Self-EmoQ: Plutchik-Guided Value-based Planning to Drive Streaming Emotional TTS

arXiv:2606.09837v1 Announce Type: cross Abstract: Emotional interaction is increasingly crucial for conversational AI, yet current systems lack a self-emotion determination mechanism to drive the stre

Self-Supervised Relevance Modelling in Autonomous Driving via Counterfactual Analysis

arXiv:2606.10688v1 Announce Type: new Abstract: Autonomous driving relies on computationally intensive perception pipelines to continuously detect and track objects in the surrounding environment. Whi

safetyarxiv-cs-ro

SHAPE: Coalition-Aware Expert Pruning for Sparse Mixture-of-Experts LLMs

arXiv:2606.09886v1 Announce Type: cross Abstract: Sparse Mixture-of-Experts (MoE) large language models achieve strong quality with low per-token compute, yet their deployment is often limited by the

SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration

arXiv:2606.10228v1 Announce Type: cross Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains. In this paper, we approach safe explor

Sigma-Branch: Hierarchical Single-Path Network Reconstruction for Dynamic Inference with Reduced Active Parameters

arXiv:2606.09924v1 Announce Type: cross Abstract: Deploying deep neural networks on memory-constrained edge accelerators is bottlenecked by per-inference off-chip weight transfer rather than computati

Sim2Schedule: A Simulator-Guided LLM Framework for Autonomous Open-Pit Mine Scheduling

arXiv:2606.10286v1 Announce Type: new Abstract: Open-pit mine scheduling is a critical process for maximizing economic return under complex geotechnical and operational constraints. While Mixed-Intege

SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks

arXiv:2606.09888v1 Announce Type: new Abstract: Linear attention provides an efficient backbone for long-sequence recommendation by avoiding the quadratic cost of standard Transformers, but its compre

safetyarxiv-cs-lg

Sketch-to-Layout: A Human-Centric Computational Agent for Constraint-Aware Synthesis of Modular Photobioreactors

arXiv:2606.09849v1 Announce Type: cross Abstract: Building-integrated photobioreactors (PBRs) offer a pathway for carbon-neutral architecture, yet deployment is hindered by configuration complexity an

safetyarxiv-cs-cv

SkillResolve-Bench: Measuring and Resolving Same-Capability Ambiguity in Agent Skill Retrieval

arXiv:2606.10388v1 Announce Type: cross Abstract: Agent skill libraries are becoming routable software assets: a retrieved skill can contribute instructions, scripts, resource bindings, and execution

Sleep EEG Signal Criticality as a Non-Invasive Predictor of Cognitive Decline in Dementia

arXiv:2606.10889v1 Announce Type: cross Abstract: Early detection of neurodegeneration remains a critical clinical challenge. This study investigates whether sleep EEG signal criticality, quantified v

Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning

model-releasesarxiv-cs-cl

arXiv:2606.10610v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) has become essential for adapting foundation models to downstream NLP tasks. However, current PEFT methods often

SocraticPO: Policy Optimization via Interactive Guidance

arXiv:2606.09887v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language models usually supervises reasoning with scalar outcome rewards, such as binary correctness. Such rewar

SoK: Colluding Adversaries in Machine Learning Pipelines

arXiv:2606.10091v1 Announce Type: cross Abstract: Machine learning (ML) models are susceptible to various security, privacy, and fairness risks. Adversaries with different characteristics (i.e., objec

safetyarxiv-cs-lg

Soul Computing: A Theoretical Framework and Technical Architecture for Intelligent Agents with Independent Consciousness

arXiv:2606.10413v1 Announce Type: new Abstract: Breakthroughs in large language models and multimodal generation technologies have propelled the digital reconstruction of human mental traits, emotiona

researcharxiv-cs-ai

SPACE: Source-free Proxy Anchor Concept Erasure for MLLMs

arXiv:2606.09868v1 Announce Type: cross Abstract: As Multimodal Large Language Models (MLLMs) face growing privacy risks and regulatory constraints, machine unlearning (MU) has emerged as a crucial so

SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors

arXiv:2606.10734v1 Announce Type: new Abstract: Conformal Prediction (CP) provides robust uncertainty guarantees for predictive models, but is typically applied post hoc, which misaligns model trainin

Local Ai

SPARX: Secure and Privacy-Aware Approximate CNN Acceleration with Edge RISC-V SoC

arXiv:2606.09946v1 Announce Type: cross Abstract: Edge-AI systems increasingly require real-time CNN inference under strict energy, performance, security, and privacy constraints. Approximate computin

local-aiarxiv-cs-cv

Local Ai

Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding

arXiv:2606.10738v1 Announce Type: cross Abstract: Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for s

local-aiarxiv-cs-ai

Tutorials

Spatially Selective Self-Training for Unsupervised Building Change Detection

arXiv:2606.10775v1 Announce Type: new Abstract: Unsupervised building change detection aims to learn building-change masks from unlabeled bi-temporal remote sensing images. Existing label-free methods

tutorialsarxiv-cs-cv

Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention

arXiv:2603.10676v2 Announce Type: replace Abstract: Industrial Control Systems (ICS) underpin critical infrastructure and face growing cyber-physical threats due to the convergence of operational tech

Tutorials

Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing

arXiv:2606.10227v1 Announce Type: new Abstract: Metal additive manufacturing enables the fabrication of complex parts, but achieving consistent build quality remains challenging due to interactions in

tutorialsarxiv-cs-lg

Local Ai

Spatiotemporal Seismic Hazard Assessment Using VQ-VAE and Seismic Statistical Features

arXiv:2606.10069v1 Announce Type: new Abstract: In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 6

local-aiarxiv-cs-lg

SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting

model-releasesarxiv-cs-lg

arXiv:2606.09917v1 Announce Type: new Abstract: Multivariate time series forecasting requires capturing the continuously evolving correlation structure among interacting variables. Existing state-spac

Speaker Group Encoding in Self-supervised Speech Recognition Models

arXiv:2606.10654v1 Announce Type: new Abstract: We investigate what self-supervised speech recognition models (S3Ms) learn about speaker groups (SGs). We examine several states of S3Ms: pretrained, fi

Speech Meets ELF: Audio Conditional Continuous-Target Diffusion for Speech Recognition and Translation

arXiv:2606.10368v1 Announce Type: cross Abstract: Speech-to-text (S2T) systems for recognition (ASR) and translation (S2TT) typically generate discrete text tokens. In contrast, continuous-target lang

researcharxiv-cs-ai

SpeechJBB: Probing Safety Alignment and Comprehension in Large Audio Language Models under Code-Switched Speech

arXiv:2606.06037v2 Announce Type: cross Abstract: Large audio language models (LALMs) are increasingly deployed in real-world applications, yet their safety alignment is still primarily evaluated on m

Hardware

SpenseGPT: Practical One-shot Pruning Enabling Sparse and Dense GEMMs for LLM Inference

arXiv:2606.10445v1 Announce Type: cross Abstract: Semi-structured 2:4 sparsity is widely supported by modern accelerators, providing up to a 2x theoretical speedup. However, its strict 50% sparsity co

hardwarearxiv-cs-cl

Spiking Neural Network inference on FPGAs with hls4ml

arXiv:2606.10008v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) provide a naturally temporal machine-learning framework. Their neurons maintain an internal state and propagate informa

SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI

model-releasesarxiv-cs-cv

arXiv:2606.10021v1 Announce Type: new Abstract: Lumbar spine conditions are a leading cause of disability worldwide, yet reliable quantification of degeneration from MRI remains challenging. In clinic

SSR-Merge: Subspace Signal Routing for Training-Free LoRA Merging in Diffusion Models

model-releasesarxiv-cs-cv

arXiv:2606.10617v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) merging can efficiently combine diverse generative capabilities from multiple trained LoRAs for a diffusion model. However, e

STAGE-Claw: Automated State-based Agent Benchmarking for Realistic Scenarios

arXiv:2606.10394v1 Announce Type: new Abstract: Large language models are increasingly used to power personal agents for everyday applications, but evaluating these agents remains a challenge. Existin

Standard Language Ideology in AI-Generated Language

arXiv:2406.08726v3 Announce Type: replace Abstract: Large language models (LLMs) generate text that reinforces standard language ideology: a bias towards certain language varieties that are granted mo

STEDiff: Strengthening Text Embedding for Text-to-Image Alignment in Diffusion Model

arXiv:2606.10653v1 Announce Type: new Abstract: Although pretrained text-to-image (T2I) generation models can produce high-quality images, they often fail to faithfully reflect the semantic intent of

safetyarxiv-cs-cv