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Social Reasoning in Machines: Investigating Collective Truth-Seeking Dynamics in Large Language Model Debate

arXiv:2605.30391v1 Announce Type: cross Abstract: Human reasoning has long been theorised to operate socially, not through isolated individual cognition, but through collective adversarial discourse,

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Human DGX agent

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Social welfare optimisation under institutional reward and punishment

DGX agent

arXiv:2605.31330v1 Announce Type: cross Abstract: Institutional incentives are widely used to promote cooperation among autonomous, self-regarding agents, from human societies to multi-agent and AI sy

model-releasesarxiv-cs-ai

1 Jun 2026

SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models

model-releasesarxiv-cs-cv

arXiv:2605.31597v1 Announce Type: new Abstract: Measuring structured object understanding in vision foundation models remains challenging due to inconsistent evaluation protocols and limited part-leve

Softly Constrained Denoisers for Diffusion Models Applied to Partial Differential Equations

arXiv:2512.14980v4 Announce Type: replace Abstract: Diffusion models have become a powerful generative prior for solutions of partial differential equations (PDEs). Existing approaches enforce physica

Softsign: Smooth Sign in Your Optimizer For Better Parameter Heterogeneity Handling

model-releasesarxiv-cs-lg

arXiv:2605.31371v1 Announce Type: new Abstract: Sign-based and LMO-inspired optimizers have recently attracted substantial attention in deep learning due to their strong performance and low memory foo

Sophrosyne: Agentic Exploration of Relational Data Systems Needs Moderation

arXiv:2605.30862v1 Announce Type: cross Abstract: Text2SQL agents powered by LLMs translate natural language intent into SQL by exploring the data system through tool calls before formulating the quer

SpatialAct: Probing Spatial Reasoning-to-Action Capabilities of VLM Agents in 3D Scenes

arXiv:2605.31148v1 Announce Type: cross Abstract: Humans can effortlessly perceive spatial layouts, form cognitive representations, reason about spatial relations, and translate such reasoning into ac

Applications

Spatio-temporal stochastic graph-based learning for infectious disease forecasting

arXiv:2605.30662v1 Announce Type: new Abstract: Spatio-temporal graph-based models have typically been used to forecast new cases of infectious diseases such as COVID-19 and chickenpox outbreaks. Howe

applicationsarxiv-cs-lg

SpecDB: LLM-Generated Customized Databases via Feature-Oriented Decomposition

arXiv:2605.31097v1 Announce Type: cross Abstract: Mainstream relational databases ship a uniform feature set across deployments, although individual workloads exercise only a fraction of the available

SPECTRA: Synthetic IR Test Collections with Relevance Oracles and Controlled Distractor Diagnostics

arXiv:2605.31575v1 Announce Type: cross Abstract: Scalable information retrieval testing needs corpora that are large enough to stress index construction, ranking latency, query routing, and evaluatio

researcharxiv-cs-ai

Spectral Anatomy of Quantum Gaussian Process Kernels

arXiv:2605.30952v1 Announce Type: new Abstract: Two recent results have reshaped quantum Gaussian processes (QGPs). On the one hand, itet{lowe2025assessing} rule out the exponential speedups claimed b

researcharxiv-cs-lg

Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning

arXiv:2509.22335v3 Announce Type: replace-cross Abstract: We investigate why deep neural networks suffer from loss of plasticity in continual learning, and thus fail to learn new tasks without reiniti

tutorialsarxiv-cs-ai

Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail

arXiv:2605.31244v1 Announce Type: new Abstract: Neural scaling laws describe predictable power-law relationships between model size, dataset size, compute, and performance. While these laws guide the

tutorialsarxiv-cs-lg

Speculative Decoding Across Languages

arXiv:2605.30580v1 Announce Type: new Abstract: Speculative decoding has become a crucial component of large language model (LLM) inference, enabling faster generation by drafting multiple tokens and

researcharxiv-cs-cl

Speculative Pipeline Decoding: Higher-Accruacy and Zero-Bubble Speculation via Pipeline Parallelism

arXiv:2605.30852v1 Announce Type: new Abstract: Speculative Decoding (SD) accelerates low-concurrency LLM inference by employing a draft-then-verify paradigm. However, mainstream methods typically rel

researcharxiv-cs-cl

SPM-Bench: Benchmarking Large Language Models for Scanning Probe Microscopy

arXiv:2602.22971v2 Announce Type: replace Abstract: As LLMs achieved breakthroughs in general reasoning, their proficiency in specialized scientific domains reveals pronounced gaps in existing benchma

SpongeBob: Sync-Aware Harmonious Audio-Visual Generative Editing

arXiv:2605.25193v2 Announce Type: replace Abstract: Visual and acoustic events in the physical world are inherently coupled, yet existing video editing methods typically adopt decoupled pipelines, lac

safetyarxiv-cs-cv

SSR: Scaling Surefooted and Symmetric Humanoid Traversal to the Open World

arXiv:2605.30770v1 Announce Type: new Abstract: Extending humanoid traversal to the open world is key to practical deployment in human environments, but remains challenging. The robot must use vision

tutorialsarxiv-cs-ro

Stateful Online Monitoring Catches Distributed Agent Attacks

arXiv:2605.31593v1 Announce Type: cross Abstract: Language models can find thousands of severe software vulnerabilities, and agents are increasingly being misused for cyberattacks. To avoid detection,

safetyarxiv-cs-ai

SteerFace: Debiasing Synthetic Face Generation via Adaptive Residue Perturbation

arXiv:2605.30894v1 Announce Type: new Abstract: The shortage of legally compliant data for face recognition training has sparked growing interest in using synthetic data as an alternative. While recen

researcharxiv-cs-cv

Steering LLMs? Actually, Sparse Autoencoders can outperform simple baselines

arXiv:2605.31183v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) have been seen as a promising avenue for exploring the internals of Large Language Models (LLMs) and for steering model out

STEP: Learning STructured Embeddings for Progressive Time Series

arXiv:2605.31061v1 Announce Type: cross Abstract: We present a novel method for learning interpretable representations of progressive time series, that is, data capturing irreversible state transition

tutorialsarxiv-cs-ai

Stochastic Gradients under Nuisances

arXiv:2508.20326v2 Announce Type: replace-cross Abstract: Stochastic gradient optimization is the dominant learning paradigm for a variety of scenarios, from classical supervised learning to modern se

researcharxiv-cs-lg

Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding

arXiv:2602.06161v2 Announce Type: replace-cross Abstract: Parallel diffusion decoding can accelerate diffusion language model inference by unmasking multiple tokens per step, but aggressive parallelis

researcharxiv-cs-ai

Structural Bias Beyond Homophily: A Study of Fairness in Link Prediction

arXiv:2602.11802v2 Announce Type: replace Abstract: Graph link prediction (LP) plays a critical role in socially impactful applications such as job recommendation and friendship formation, making fair

Structure-Induced Information for Rerooting Levin Tree Search

arXiv:2605.30664v1 Announce Type: new Abstract: Subgoal-based policy tree search, which uses a policy to guide search, is effective for complex single-agent deterministic problems but often relies on

safetyarxiv-cs-ai

Structured interactions improve distributed coordination beyond model scaling in a real-world multi-robot system

arXiv:2605.30383v1 Announce Type: cross Abstract: Scaling individual robot capabilities is common but costly. Here we investigate a system-level design question in real-world multi-robot coordination:

Student Capacity Moderates Knowledge Distillation Effectiveness: A Systematic Study Across ResNet Teacher-Student Pairs on CIFAR-10

arXiv:2605.31191v1 Announce Type: cross Abstract: We investigate how teacher-student capacity relationships modulate knowledge distillation (KD) effectiveness in ResNet-based image classification on C

researcharxiv-cs-cv

Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models

arXiv:2605.31111v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) learn compact latent world models by predicting future embeddings, but no single coordinate of the late

agentsarxiv-cs-lg

Hardware

SubsurfaceGen: Procedural Generation of Field-Scale Earth Models and Seismic Data

arXiv:2605.30541v1 Announce Type: new Abstract: Full waveform inversion (FWI) is the gold standard for subsurface imaging, with applications from carbon sequestration to energy and mineral exploration

hardwarearxiv-cs-lg

Supervised Learning as Lossy Compression: Characterizing Generalization and Sample Complexity via Finite Blocklength Analysis

arXiv:2602.04107v2 Announce Type: replace Abstract: This paper presents a novel information-theoretic perspective on generalization in machine learning by framing the learning problem within the conte

Supervised Training Rapidly Degrades Early Visual Cortex Alignment Across Biologically Plausible Learning Rules

arXiv:2605.30556v1 Announce Type: new Abstract: Random, untrained neural networks consistently match or exceed trained networks in representational similarity to early visual cortex. This puzzling fin

Surface Constraint Policy for Learning Surface-Constrained and Dynamically Feasible Robot Skills

arXiv:2605.31321v1 Announce Type: new Abstract: Diffusion-based imitation learning methods have driven rapid progress in robot dexterous manipulation tasks. However, they have limitations when applied

safetyarxiv-cs-ro

Local Ai

SurGe: Improved Surface Geometry in Point Maps

arXiv:2605.31577v1 Announce Type: new Abstract: Recent feedforward 3D reconstruction methods predict point maps and estimate global 3D geometry remarkably well. However, their predictions still exhibi

local-aiarxiv-cs-cv

Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection

arXiv:2603.12916v3 Announce Type: replace-cross Abstract: Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonomo

Survival Reinforcement Learning: Toward Scalable Self-Supervised RL

arXiv:2605.31273v1 Announce Type: new Abstract: While self-supervised Contrastive Reinforcement Learning (CRL) has shown remarkable depth-scaling capabilities, successfully using networks over 64 laye

agentsarxiv-cs-lg

SVI-Bench: A Dynamic Microworld for Strategic Video Intelligence

model-releasesarxiv-cs-cv

arXiv:2605.31529v1 Announce Type: new Abstract: True video intelligence demands more than recognizing what is visible: it requires reasoning about why events unfold, predicting what would change under

SWIM: Single-Instance Whole-Body Imitation for swiMming

arXiv:2605.31120v1 Announce Type: cross Abstract: We propose a new method for synthesizing physically-based swimming motions. Physically-based character animation aims to generate physically valid, co

tutorialsarxiv-cs-ai

Symbolic Intermediaries as a Linguistic-Numerical Interface for LLM-Driven Geometric Reasoning

arXiv:2505.17607v3 Announce Type: replace Abstract: Large Language Models (LLMs) display reasoning capabilities over linguistic and symbolic objects but have limited capabilities to directly interpret

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation

arXiv:2511.11440v3 Announce Type: replace-cross Abstract: Performance gains of Vision Language Models (VLMs) obtained by fine-tuning are generally based on ad hoc data collection and annotation of rea

safetyarxiv-cs-cl

TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery

model-releasesarxiv-cs-lg

arXiv:2605.31156v1 Announce Type: new Abstract: Causal discovery aims to recover directed causal relations from observational and interventional data, providing a basis for mechanistic understanding a

TAGA: A Tangent-Based Reactive Approach for Socially Compliant Robot Navigation Around Human Groups

model-releasesarxiv-cs-ro

arXiv:2503.21168v3 Announce Type: replace Abstract: Robots navigating human-populated environments must avoid collisions while respecting the social structure of crowds, particularly the implicit boun

TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation

arXiv:2605.31217v1 Announce Type: new Abstract: Monocular 6-DoF spacecraft pose estimation methods predominantly process individual frames, discarding the temporal information present in an image sequ

safetyarxiv-cs-cv

Target-Agnostic Calibration under Distribution Shift with Frequency-Aware Gradient Rectification

arXiv:2508.19830v2 Announce Type: replace-cross Abstract: Real-world model deployments inevitably encounter distribution shifts, rendering the confidence estimates of deep neural networks highly unrel

Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models

arXiv:2605.31393v1 Announce Type: cross Abstract: Sign language translation (SLT) remains constrained by limited paired sign-video/text corpora and heavy-tailed target vocabularies. We study target-si

researcharxiv-cs-ai

Targeted Speaker Poisoning Framework in Zero-Shot Text-to-Speech

arXiv:2603.07551v2 Announce Type: replace-cross Abstract: Zero-shot Text-to-Speech (TTS) voice cloning poses severe privacy risks, demanding the removal of specific speaker identities from trained TTS

TARIC: Memory-Augmented Traversability-Aware Outdoor VLN under Interrupted Semantic Cues

arXiv:2605.31121v1 Announce Type: cross Abstract: Outdoor vision-language navigation (VLN) in long-range, open-world environments is frequently disrupted by semantic-cue interruptions, where informati

safetyarxiv-cs-ai

TASER: Task-Aware Stein Regularisation for Geometry-Driven Robustness

arXiv:2605.30601v1 Announce Type: new Abstract: Modern deep networks remain fragile under distribution shift and adversarial perturbations, often due to excessive or poorly structured input sensitivit

researcharxiv-cs-lg