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Semantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems

arXiv:2607.18255v2 Announce Type: replace Abstract: Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple agents, me

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Human DGX agent

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Semantic RGB--Depth Based Surgical Skill Assessment in Microscopic Stereo Videos

DGX agent

arXiv:2610.01205v1 Announce Type: new Abstract: Objective assessment of microsurgical technical skill is essential for competency-based training and quality assurance, yet existing video-based approac

researcharxiv-cs-cv

2 Oct 2026

SemanTok: Predictable Semantic Tokens for Efficient Autoregressive Video Generation

arXiv:2610.00686v1 Announce Type: new Abstract: Recent video-based world models pair the scalability of autoregressive (AR) prediction with the visual quality of diffusion models. The choice of scene

safetyarxiv-cs-cv

Sentence Specificity Scores for Collaborative Technical Documentation: A Domain-Transfer Study

arXiv:2610.01046v1 Announce Type: cross Abstract: Collaboration depends on shared context, and technical documentation is one way that context persists across people and AI teammates. Specificity, the

Sequential Capacity of Quantum Processes with Finite Memory

arXiv:2610.02068v1 Announce Type: cross Abstract: How complex can the responses of a quantum device become as it runs longer with a fixed internal memory? We quantify this complexity through sequentia

Sequential Functional Structured Tucker Compression for Large Language Model Attentions

arXiv:2610.00717v1 Announce Type: cross Abstract: Post-training compression of LLM attention is often formulated as independent matrix approximation, ignoring both the shared structure among attention

Sharp Non-Asymptotic Analysis of the Penalized Challenger in eta-EB-TCI for Bernoulli Bandits

arXiv:2610.01951v1 Announce Type: new Abstract: Top-two algorithms are simple and effective for fixed-confidence best-arm identification, but their sharp non-asymptotic behavior is still not well unde

Sharp Oracle-Regret Tradeoffs for Projection-Free Online Convex Optimization

arXiv:2610.00254v1 Announce Type: new Abstract: We characterize the regret attainable in online convex optimization when access to the feasible set is limited to an exact linear optimization oracle. T

Sharpen Before You Adapt: Data-Free Entry-State Sharpening for Test-Time Reinforcement Learning

arXiv:2610.00903v1 Announce Type: new Abstract: Test-time reinforcement learning (TTRL) adapts language models on unlabeled test problems using supervision derived from their own samples. This makes t

safetyarxiv-cs-lg

Sharpening Tax in Post-Training

arXiv:2610.01509v1 Announce Type: new Abstract: An emerging hypothesis about reinforcement learning (RL) post-training of large language models (LLMs) is that it merely sharpens existing behaviors of

agentsarxiv-cs-ai

SHARPO: Segment-Level Credit Assignment for Agentic Reinforcement Learning

arXiv:2610.00838v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) trains a large language model (LLM) to act over long, multi-step interactions. However, a single localized error c

safetyarxiv-cs-ai

ShatterQuant: Breaking Uniform Precision with Block-Wise Mixed-Precision on a Systolic Transformer Hardware Accelerator

arXiv:2610.00207v1 Announce Type: cross Abstract: Due to limited support for intra-tensor heterogeneous precision in conventional accelerators, neural network quantization remains largely restricted t

ShelfChange3D: Object-Level 3D Change Detection for Retail Shelf Monitoring

arXiv:2610.01283v1 Announce Type: new Abstract: Reliable shelf monitoring is an important capability for retail automation, yet existing out-of-stock detection methods mainly operate in image space an

applicationsarxiv-cs-cv

ShowerFlex: Achieving Pseudo-Static Balancing in a Continuum Shower Hose

arXiv:2610.00936v1 Announce Type: new Abstract: With the global population rapidly aging, maintaining independence in Activities of Daily Living (ADLs), particularly bathing or showering, has become a

applicationsarxiv-cs-ro

SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning

arXiv:2610.01962v1 Announce Type: cross Abstract: The ability of vision-language models (VLMs) to associate visual identities with biographical information creates a need for selective unlearning of p

Signal-Noise Factorization Isolates Nuisance Variation into Removable Subspaces

arXiv:2610.00751v1 Announce Type: cross Abstract: Recent theoretical work identified fundamental properties of representation geometry that shape inference ability of deep neural networks. These inclu

Signed Lexical Confidence for Risk-Calibrated Intent Routing

arXiv:2610.00262v1 Announce Type: cross Abstract: Selective intent routing allows an assistant to act on reliable predictions while deferring uncertain requests. Standard confidence scores primarily r

safetyarxiv-cs-ai

Local Ai

SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resolution 3D Generation

arXiv:2610.02201v1 Announce Type: cross Abstract: High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize l

local-aiarxiv-cs-ai

SimplexUQ: An Evaluation Framework and Benchmark for Conformal Uncertainty on Simplex-Valued Predictions

arXiv:2610.00523v1 Announce Type: new Abstract: Conformal prediction guarantees marginal coverage, but a single calibration threshold can still spread that coverage unevenly, over-covering easy region

Sim+Real: Joint Simulation - Experiment Training Improves Balanced Prediction in Physical Systems

arXiv:2610.01974v1 Announce Type: new Abstract: Simulation and experimental measurements provide complementary data for learning spatiotemporal physical systems, but standard simulation-to-experiment

Skeleton-and-Strategy Prompting: Training-Free Negation Understanding for Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2610.01180v1 Announce Type: new Abstract: Despite the strong performance of Vision-Language Models (VLMs) on a wide range of visual question answering (VQA) tasks, these models consistently stru

SkeleWAM: Skeleton World-Action Modeling for Efficient Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.02120v1 Announce Type: new Abstract: World action models (WAMs) combine robot action generation with future state prediction. Existing WAMs typically predict videos or learned visual latent

SkillEvoLean: Mutation-enhanced skill evolution for Lean provers

arXiv:2610.01799v1 Announce Type: new Abstract: Skill evolution offers a promising way to improve large language model agents without updating their parameters, but its use in formal theorem proving r

SkillSpec: Consensus-Gated Agent Skill Evolution via Representation Specialization

arXiv:2610.00704v1 Announce Type: new Abstract: Natural-language skills are textual procedural memories through which large language model (LLM) agents retain reusable task knowledge without updating

agentsarxiv-cs-lg

Skin-Deep: A Geometric Diagnostic for Alignment Fragility in Large Language Model Representations

arXiv:2606.22676v2 Announce Type: replace Abstract: Refusal on a safety benchmark does not reveal how stable that behavior will remain after model updates. Benign downstream fine-tuning can weaken ref

SLIM: Simplex-Lattice Interpolation Merging

arXiv:2610.01037v1 Announce Type: new Abstract: Optimizing merging coefficients for large language models can require many costly benchmark evaluations. We propose extbf{Simplex-Lattice Interpolation

Local Ai

Smoother Flow Matching via Contrastive Trajectory Repulsion

arXiv:2610.01408v1 Announce Type: new Abstract: Trajectory crossing remains a critical bottleneck in Flow Matching (FM), and previous works typically view these crossings from a theoretical optimizati

local-aiarxiv-cs-cv

SmoothOperator: Enhancing Representations for Fine-grained Open-set Recognition via Modulated Label Smoothing

model-releasesarxiv-cs-cv

arXiv:2610.00851v1 Announce Type: new Abstract: Open Set Recognition (OSR) aims to enable models to accurately classify known classes while rejecting samples from unseen classes. A key challenge in OS

SoftServe: A Scalable Quasi-Newton Method for Deep Learning

arXiv:2610.02182v1 Announce Type: cross Abstract: Quasi-Newton (QN) methods have long been among the most effective methods for large-scale unconstrained convex optimization. Two obstacles have limite

SoK: Decentralized Agent Economic Infrastructure

arXiv:2610.01756v1 Announce Type: cross Abstract: Decentralized agent economies increasingly build a single task from protocols that were designed and secured separately. This creates a simple problem

agentsarxiv-cs-ai

SonarVoxNet: Diver Detection in 3D Bounding Box using 3D Sonar

arXiv:2610.01644v1 Announce Type: new Abstract: Autonomous underwater vehicles (AUVs) assisting human divers must continuously track not only the diver's 3D position but also their full-body orientati

agentsarxiv-cs-ro

Soundwich: Video Generation with Layered and Controllable Audio

arXiv:2610.00691v1 Announce Type: new Abstract: Recent joint audio-video generative models can synthesize realistic videos with synchronized sound, but typically generate audio as a single mixed track

Source Identification Is Not Fitness Testing: Measuring the Limits of Synthetic-Data Attribution

arXiv:2610.00417v1 Announce Type: new Abstract: Repeated training on model-generated data can degrade later models. One possible response is to use provenance when deciding which generated examples to

Spatial Lifting for Dense Prediction

arXiv:2610.00017v1 Announce Type: cross Abstract: We present Spatial Lifting (SL), a novel methodology for dense prediction tasks. SL operates by lifting standard inputs, such as 2D images, into a hig

Spatial Strategies, Not Actions: Vector-Quantized Geodesics as Tools for LLM-Driven Agents

arXiv:2610.00613v1 Announce Type: new Abstract: Large language model (LLM) based agents are often criticized for lacking spatial understanding and mainly exploiting statistical text patterns. We inves

agentsarxiv-cs-ai

Spatially Gated Diffusion for Localized Counterfactual Chest Radiograph Editing

arXiv:2610.00805v1 Announce Type: cross Abstract: Editing a chest radiograph requires completing the requested change while preserving unrelated content. We study a latent diffusion editor with an ins

Specificity-Aware Diffusion Steering via Variance-Reduced Sequential Monte Carlo

arXiv:2610.00395v1 Announce Type: new Abstract: Inference-time steering enables pretrained diffusion models to satisfy new constraints without full retraining. However, specificity-aware generation is

Local Ai

SPHERE: Adaptive VR Indoor Scene Generation via LLM-Enhanced Spatial Preference Learning and Human-in-the-Loop RL

arXiv:2610.02023v1 Announce Type: new Abstract: While Large Language Models (LLMs) advance 3D indoor scene synthesis, current pipelines fail to retain user-specific preferences across sessions, making

local-aiarxiv-cs-ai

Sphere Encoder 2

model-releasesarxiv-cs-cv

arXiv:2610.02208v1 Announce Type: new Abstract: Sphere Encoder is an autoencoder that generates images by decoding random points from a high-dimensional latent sphere. We identify two limitations of t

SpikeMoE: Brain-Inspired Competitive Routing for Flexible Spiking Mixture-of-Experts

arXiv:2610.01418v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) enable event-driven computation through biologically inspired dynamics at the neuronal scale, while Mixture-of-Experts (M

SPIRAL: Learning to Search and Aggregate

arXiv:2606.23595v2 Announce Type: replace Abstract: Language model reasoning can be substantially improved at test time via scaffolds that scale inference compute across different primitives -- sequen

StabilityArc: Decoding Protein Sequence Embeddings into Generalizable Stability Landscapes

arXiv:2610.00742v1 Announce Type: cross Abstract: Every protein has a unique stability landscape, but the physical consequences of mutation are governed by recurring biochemical constraints. We test w

Stable and Counterfactually Robust Physical World Models from Imposed Structure and Learned Physics

arXiv:2610.00280v1 Announce Type: new Abstract: A world model learns to forecast how a physical system evolves from recorded trajectories, yet the systems it imitates obey physical laws that are neith

STAGE: Subspace-Targeted Affine Generative Erasure for Text-to-3D Models

arXiv:2610.01302v1 Announce Type: new Abstract: Concept erasure suppresses a target concept while preserving behavior on unrelated inputs. Existing closed-form methods were designed for 2D image diffu

STATERA: Hidden Mass Estimation via Zero-Shot Sim-to-Real Kinematics using Frozen Temporal Tubelets

arXiv:2610.00003v1 Announce Type: cross Abstract: Vision models pretrained for frame-level appearance often struggle to infer hidden physical properties from motion. We study center-of-mass (CoM) loca

STCFormer: Adaptive Spatio-Temporal Modeling with Dynamic Cluster Transformer for Station-based Weather Forecasting

arXiv:2610.00377v1 Announce Type: new Abstract: Station-based weather forecasting supports daily life and economic activity, yet accurate forecasts require modeling complex spatial dependencies among

applicationsarxiv-cs-lg

STEER: Reducing Inference Cost in Relational Foundation Models through Semantically Informed Sampling

arXiv:2610.00907v1 Announce Type: cross Abstract: Relational foundation models (RFMs) are pretrained once on a collection of relational databases and prediction tasks, and then applied zero-shot to pr

StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participation

arXiv:2303.00897v2 Announce Type: replace Abstract: Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices. In real-world federat

applicationsarxiv-cs-lg