Search: “benchmark”

Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning

arXiv:2605.08616v1 Announce Type: new Abstract: Collaborative machine learning (CML) enables multiple clients to train a global model jointly in a data-distributed setting. To address data privacy and

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Robust Spectral Watermark for Synthetic Tabular Data

DGX agent

arXiv:2511.21600v2 Announce Type: replace-cross Abstract: The rise of generative AI has enabled the production of high-fidelity synthetic tabular data across fields such as healthcare, finance, and pu

model-releasesarxiv-cs-lg

12 May 2026

Route by State, Recover from Trace: STAR with Failure-Aware Markov Routing for Multi-Agent Spatiotemporal Reasoning

arXiv:2605.10057v1 Announce Type: new Abstract: Compositional spatiotemporal reasoning often requires a system to invoke multiple heterogeneous specialists, such as geometric, temporal, topological, a

RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards

arXiv:2605.10899v1 Announce Type: new Abstract: Training deep research agents, namely systems that plan, search, evaluate evidence, and synthesize long-form reports, pushes reinforcement learning beyo

safetyarxiv-cs-cl

RubricRefine: Improving Tool-Use Agent Reliability with Training-Free Pre-Execution Refinement

arXiv:2605.09730v1 Announce Type: new Abstract: Iterative self-refinement is a popular inference-time reliability technique, but its effectiveness in code-mode tool use depends heavily on the structur

SAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Network

arXiv:2510.03648v2 Announce Type: replace Abstract: Continuous learning of novel classes is crucial for edge devices to preserve data privacy and maintain reliable performance in dynamic environments.

SAMOFT: Robust Multi-Object Tracking via Region and Flow

arXiv:2605.09417v1 Announce Type: new Abstract: Multi-object tracking (MOT) is a fundamental task in computer vision that requires continuously tracking multiple targets while maintaining consistent i

researcharxiv-cs-cv

SARL: Label-Free Reinforcement Learning by Rewarding Reasoning Topology

arXiv:2603.27977v2 Announce Type: replace Abstract: Reinforcement learning is critical to improving large reasoning models, but its success relies heavily on verifiable rewards (RLVR), making it hard

SayNext-Bench: Why Do LLMs Struggle with Next-Utterance Anticipation?

arXiv:2602.00327v2 Announce Type: replace Abstract: We explore the use of large language models (LLMs) for next-utterance anticipation in human dialogue. Despite recent advances in LLMs demonstrating

Scalable Gaussian process inference via neural feature maps

arXiv:2605.10285v1 Announce Type: cross Abstract: We present a theoretically grounded Gaussian process framework that leverages neural feature maps to construct expressive kernels. We show that the le

Applications

Scalable Inspection Planning via Flow-based Mixed Integer Linear Programming

arXiv:2603.16593v2 Announce Type: replace Abstract: Inspection planning is concerned with computing the shortest robot path to inspect a given set of points of interest (POIs) using the robot's sensor

applicationsarxiv-cs-ro

SCALAR: A Neurosymbolic Framework for Automated Conjecture and Reasoning in Quantum Circuit Analysis

arXiv:2605.10327v1 Announce Type: cross Abstract: In this paper, we present SCALAR (Symbolic Conjecture and LLM-Assisted Reasoning), a neurosymbolic framework for automated conjecture generation in qu

Scam2Prompt: A Scalable Framework for Auditing Malicious Scam Endpoints in Production LLMs

arXiv:2509.02372v3 Announce Type: replace-cross Abstract: Large Language Models have become critical to modern software development, but their reliance on uncurated web-scale datasets for training int

SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks

arXiv:2605.09038v1 Announce Type: new Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries. This is especia

SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data

arXiv:2605.08519v1 Announce Type: new Abstract: Learning from scarce labeled data with a larger pool of unlabeled samples, known as semi-supervised few-shot learning (SS-FSL), remains critical for app

Seed Hijacking of LLM Sampling and Quantum Random Number Defense

arXiv:2605.08313v1 Announce Type: cross Abstract: Large language models (LLMs) rely on deterministic pseudorandom number generators (PRNGs) for autoregressive sampling, creating a critical supply-chai

Selective LoRA for Visual Tokens and Attention Heads

arXiv:2512.19219v2 Announce Type: replace-cross Abstract: Low-rank adaptation (LoRA) is widely used for parameter-efficient fine-tuning, but its standard all-token, all-head design ignores the heterog

Self-ReSET: Learning to Self-Recover from Unsafe Reasoning Trajectories

arXiv:2605.08936v1 Announce Type: new Abstract: Large Reasoning Models possess remarkable capabilities for self-correction in general domain; however, they frequently struggle to recover from unsafe r

Semantic Alignment in Hyperbolic Space for Open-Vocabulary Semantic Segmentation

arXiv:2605.08874v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation requires adapting image-level vision-language models such as CLIP to dense pixel-level prediction, which is challe

safetyarxiv-cs-cv

Semantic Voting: Execution-Grounded Consensus for LLM Code Generation

arXiv:2605.08680v1 Announce Type: cross Abstract: LLM code-generation pipelines often sample multiple candidates and select one final answer without access to a complete oracle. Existing pipelines mix

Semi-Supervised Neural Super-Resolution for Mesh-Based Simulations

arXiv:2605.09284v1 Announce Type: cross Abstract: Mesh-based simulations provide high-fidelity solutions to partial differential equations (PDEs), but achieving such accuracy typically requires fine m

Sequential Feature Selection for Efficient Landslide Segmentation from Multi-Spectral Data

arXiv:2605.09746v1 Announce Type: cross Abstract: Landslide detection from satellite imagery has advanced through deep learning, yet most models rely on large, highly correlated spectral-topographic i

Shepherd: A Runtime Substrate Empowering Meta-Agents with a Formalized Execution Trace

arXiv:2605.10913v1 Announce Type: new Abstract: We introduce Shepherd, a functional programming model that formalizes meta-agent operations on target agents as functions, with core operations mechaniz

simpleposter: a simple baseline for product poster generation

model-releasesarxiv-cs-cv

arXiv:2605.08784v1 Announce Type: new Abstract: Product poster generation poses distinct challenges beyond general poster design, requiring both faithful preservation of product appearance and precise

SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization

arXiv:2605.08809v1 Announce Type: cross Abstract: Pretraining large language models (LLMs) with next-token prediction has led to remarkable advances, yet the context-dependent nature of token embeddin

Simulating Complex Multi-Turn Tool Calling Interactions in Stateless Execution Environments

arXiv:2601.19914v2 Announce Type: replace-cross Abstract: Synthetic data has proven itself to be a valuable resource for tuning smaller, cost-effective language models to handle the complexities of mu

Simultaneous Long-tailed Recognition and Multi-modal Fusion for Highly Imbalanced Multi-modal Data

arXiv:2605.10498v1 Announce Type: cross Abstract: Long-tailed distributions in class-imbalanced data present a fundamental challenge for deep learning models, which tend to be biased toward majority c

Simulus: Combining Improvements in Sample-Efficient World Model Agents

arXiv:2502.11537v4 Announce Type: replace-cross Abstract: World models (WMs) represent the frontier of sample-efficient reinforcement learning, but their complexity leaves many promising improvements

SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning

arXiv:2605.09423v1 Announce Type: new Abstract: LLM/VLM-based digital agents have advanced rapidly thanks to scalable sandboxes for coding, web navigation, and computer use, which provide rich interac

Single-Configuration Attack Success Rate Is Not Enough: Jailbreak Evaluations Should Report Distributional Attack Success

arXiv:2605.09070v1 Announce Type: cross Abstract: Many jailbreak attack research papers report attack success rates for a limited number of parameter settings, even though there are many combinations

SKG-VLA: Scene Knowledge Graph Priors for Structured Scene Semantics and Multimodal Reasoning for Decision Making

arXiv:2605.09343v1 Announce Type: new Abstract: Decision making in large-scale complaint handling systems increasingly relies on heterogeneous evidence, including complaint narratives, screenshots, or

Skill-R1: Agent Skill Evolution via Reinforcement Learning

arXiv:2605.09359v1 Announce Type: cross Abstract: Agentic large language models often rely on skills, reusable natural language procedures that guide planning, action, and tool use. In practice, skill

SkillRAE: Agent Skill-Based Context Compilation for Retrieval-Augmented Execution

arXiv:2605.10114v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents (e.g., OpenClaw) increasingly rely on reusable skill libraries to solve artifact-rich tasks such as document-cen

agentsarxiv-cs-cl

SLIM: Sparse Latent Steering for Interpretable and Property-Directed LLM-Based Molecular Editing

arXiv:2605.10831v1 Announce Type: cross Abstract: Large language models possess strong chemical reasoning capabilities, making them effective molecular editors. However, property-relevant information

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training

arXiv:2605.08738v1 Announce Type: cross Abstract: Structured pruning and knowledge distillation (KD) are typical techniques for compressing large language models, but it remains unclear how they shoul

SlimSpec: Low-Rank Draft LM-Head for Accelerated Speculative Decoding

arXiv:2605.10453v1 Announce Type: cross Abstract: Speculative decoding speeds up autoregressive generation in Large Language Models (LLMs) through a two-step procedure, where a lightweight draft model

researcharxiv-cs-cl

SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization

arXiv:2601.22131v2 Announce Type: replace Abstract: Multi-objective optimization aims to solve problems with competing objectives. Evaluating such problems is often slow or expensive, limiting the bud

researcharxiv-cs-lg

SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints

arXiv:2602.09317v2 Announce Type: replace-cross Abstract: Neural networks are increasingly used as fast surrogate models across various domains, but unconstrained predictions can violate physical, ope

Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse

arXiv:2601.11042v2 Announce Type: replace-cross Abstract: Sequential knowledge editing in large language models often causes catastrophic collapse of the model's general abilities, especially for para

SpectraLLM: Uncovering the Ability of LLMs for Molecular Structure Elucidation from Multi-Spectral Data

arXiv:2508.08441v3 Announce Type: replace-cross Abstract: Automated molecular structure elucidation remains challenging, as existing approaches often depend on pre-compiled databases or restrict thems

Applications

SPECTRE: Hybrid Ordinary-Parallel Speculative Serving for Resource-Efficient LLM Inference

arXiv:2605.08151v1 Announce Type: cross Abstract: LLM serving platforms are increasingly deployed as multi-model cloud systems, where user demand is often long-tailed: a few popular large models recei

applicationsarxiv-cs-ai

SSA: Improving Performance With a Better Scoring Function

arXiv:2508.14685v4 Announce Type: replace Abstract: While transformer models exhibit strong in-context learning (ICL) abilities, they often fail to generalize under simple distribution shifts. We anal

researcharxiv-cs-cl

StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception

arXiv:2605.09989v1 Announce Type: cross Abstract: Recent advances in robot imitation learning have yielded powerful visuomotor policies capable of manipulating a wide variety of objects directly from

safetyarxiv-cs-cv

STRIVE: Structured Spatiotemporal Exploration for Reinforcement Learning in Video Question Answering

arXiv:2604.01824v2 Announce Type: replace Abstract: We introduce STRIVE (SpatioTemporal Reinforcement with Importance-aware Variant Exploration), a structured reinforcement learning framework for vide

safetyarxiv-cs-cv

Applications

Sundial: A Family of Highly Capable Time Series Foundation Models

arXiv:2502.00816v4 Announce Type: replace Abstract: We introduce Sundial, a family of native, flexible, and scalable time series foundation models. To predict the next-patch's distribution, we propose

applicationsarxiv-cs-lg

Local Ai

SymTorch: Symbolic Distillation of Neural Networks

arXiv:2602.21307v2 Announce Type: replace Abstract: What mathematical functions do neural network components learn? Symbolic distillation addresses this question by expressing neural network component

local-aiarxiv-cs-lg

SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference

arXiv:2605.08835v1 Announce Type: new Abstract: The expansion of Artificial Intelligence-generated content service requires diffusion model serving to simultaneously achieve high throughput and low ta

Tabular Foundation Model for Generative Modelling

arXiv:2605.09424v1 Announce Type: new Abstract: Generative modelling is a demanding test of foundation models, because it requires robust, holistic representation learning for a given data modality, r