Search: “benchmark”

Quantum Versus Classical Machine Learning for Neuropsychiatric Disorders: A Feature-Matched Multi-Cohort Evaluation

arXiv:2610.21344v1 Announce Type: new Abstract: Resting-state functional connectivity is widely used in machine learning-based classification of neuropsychiatric disorders. Quantum machine learning mo

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Reasoning Error from Known Fact: Step-Level Self-Consistency Group Relative Policy Optimization for LLMs

DGX agent

arXiv:2607.18915v2 Announce Type: replace Abstract: With the rapid advancement of large language models (LLMs), modern systems not only possess strong foundational capabilities and extensive knowledge

safetyarxiv-cs-cl

16 Oct 2026

Tutorials

ReasonUni: Reasoning-Augmented Reconstruction Advances Unified Multimodal Models

arXiv:2610.20781v1 Announce Type: cross Abstract: Image-to-text (I2T) understanding and text-to-image (T2I) generation are dual tasks in unified multimodal models (UMMs). Reconstructive post-training

tutorialsarxiv-cs-ai

RecSoup: Efficient Model Merging Approach for Controllable Multi-Objective Recommendation

arXiv:2610.21220v1 Announce Type: cross Abstract: Recommender systems often need to balance multiple conflicting objectives. Controllable Multi-Objective Recommendation (CMOR) enables dynamic adjustme

reflection/Beam-1.0-501B-A23B · Hugging Face

model-releasesr-localllama

Beam is an open model built by Reflection. Beam takes text as input and generates text as output. This release includes the instruction-tuned model in BF16, FP8, and NVFP4 numerics. Beam uses a sparse

Reinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond

arXiv:2610.21539v1 Announce Type: new Abstract: Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse scenarios, following

agentsarxiv-cs-ai

Resisting Harmful Instructions through Training-Free Weight Editing

arXiv:2610.21189v1 Announce Type: new Abstract: Instruction-following language models can comply with requests that conflict with intended behaviors and safety alignment. We introduce a training-free

safetyarxiv-cs-lg

ReState: Noise-Level-Aware State Correction for Training-Free Object Removal in Few-Step Diffusion Models

arXiv:2610.21287v1 Announce Type: new Abstract: Training-free object removal commonly redirects self-attention away from the target region, but on few-step distilled diffusion models this often leaves

researcharxiv-cs-cv

ReText: Text Boosts Generalization in Image-Based Person Re-identification

arXiv:2602.05785v2 Announce Type: replace-cross Abstract: Generalizable image-based person re-identification (Re-ID) aims to recognize individuals across cameras in unseen domains without retraining.

researcharxiv-cs-ai

Rethinking Learning from Label Proportions via Moment Matching

arXiv:2610.21262v1 Announce Type: new Abstract: Learning from label proportions (LLP) is a weakly supervised setting where training data are grouped into bags with only bag-level label proportions obs

Rethinking Successive Rejects Algorithms

arXiv:2610.22051v1 Announce Type: cross Abstract: In the best-arm identification problem, an algorithm adaptively selects arms to find the arm of the largest mean reward. Successive Rejects (SR) is a

Rethinking the Role of Tool Graph Memory in Multi-Turn Tool Use

arXiv:2610.21571v1 Announce Type: cross Abstract: Large language model (LLM) agents solve complex tasks by calling external tools over multiple turns. Recent work has proposed tool graph memory, which

agentsarxiv-cs-ai

RoboCloth: A Large-Scale Real Cloth Material Dataset for Neural Reflectance Reconstruction

arXiv:2610.21090v1 Announce Type: new Abstract: We present RoboCloth, a dataset of 500 real cloth materials captured under flexible, calibrated camera and lighting configurations, where both are robot

RoLaQ: Joint Rotation and Range Calibration for 2-Bit KV Caches in Multi-Head Latent Attention

arXiv:2610.20686v1 Announce Type: new Abstract: Multi-head latent attention (MLA) compresses the content of a key-value cache into a shared latent per token, making low-bit storage attractive for long

RoPE-Flow: Monocular Vision-based Robot Pose Estimation via 2D Graph-Conditioned 3D Flow Matching

arXiv:2610.21086v1 Announce Type: new Abstract: Camera-to-robot pose estimation from a monocular image is significant for vision-based robot navigation and control. However, recovering 3D structural f

Same Context, Different Agents: A Behavioral Study of Semantic Layers, Orchestration and Compaction in Data Agents

arXiv:2610.20959v1 Announce Type: cross Abstract: Answering questions over heterogeneous data means combining evidence from files, databases, documents and the web. Data agents automate this, but thei

agentsarxiv-cs-ai

Same Target, Different Origins: Aligning Latent Rollout for Consistent PDE Emulation

arXiv:2610.21370v1 Announce Type: new Abstract: Neural PDE solvers commonly generate multi-step predictions through autoregressive rollout, recursively applying a learned evolution operator and feedin

SATOR: Backpropagation-Free 3D Test-Time Adaptation via Sample-Adaptive Token Routing

arXiv:2610.21367v1 Announce Type: new Abstract: Test-time adaptation (TTA) mitigates the performance degradation of source-pretrained models on unlabeled test data under distribution shifts. Recent ba

researcharxiv-cs-cv

SCCL-DT: Semantic-Conflict-Aware Contrastive Learning with Dual-Rule Teacher Distillation for Zero-Shot Chest X-Ray Diagnosis

arXiv:2610.22094v1 Announce Type: new Abstract: Contrastive vision-language pre-training supports zero-shot chest X-ray diagnosis, but standard objectives treat every off-diagonal image-report pair in

researcharxiv-cs-cv

SCMerge: Spatial-Consistency-Aware Token Pair Merging for Efficient Semantic Segmentation

arXiv:2610.21642v1 Announce Type: new Abstract: Vision Transformer architectures have showcased impressive performance on semantic segmentation, yet their quadratic attention complexity hinders practi

🆕 show: HIGH TASTE TESTERS We convene top Al power users to compare what actually works-and what falls apart once you start using it.@davis…

🆕 show: HIGH TASTE TESTERS We convene top Al power users to compare what actually works-and what falls apart once you start using it.@davis7, @benhylak, @dexhorthy, @allisonology, @altryne and @swyx g

agentsswyx--x

SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations

arXiv:2610.20866v1 Announce Type: cross Abstract: Realistic traffic simulation and autonomous planning both depend on modeling interactions among heterogeneous road users whose dynamics, constraints,

Sim-to-Real Reinforcement Learning for Path-Safe Target Reaching in Robotic Harvesting of Hydroponic Lettuce

model-releasesarxiv-cs-ro

arXiv:2610.22337v1 Announce Type: new Abstract: Robotic harvesting requires precise manipulation to ensure accurate positioning of the end-effector, especially in a constraint workspace. This study in

SITAR: A Situational Distortion Metric for Automatic Speech Recognition Evaluation

model-releasesarxiv-cs-cl

arXiv:2610.21440v1 Announce Type: new Abstract: In automatic speech recognition (ASR), transcription errors with similar lexical or semantic discrepancies can have markedly different consequences for

Skill-Decoupled On-Policy Expert Distillation for Unified Vision-Language Models

arXiv:2610.22217v1 Announce Type: cross Abstract: Reinforcement learning (RL) can elicit specialized visual capabilities in vision-language models (VLMs), yet jointly optimizing heterogeneous objectiv

safetyarxiv-cs-ai

Sliced Wasserstein Sketches with Error Certificates

arXiv:2610.20788v1 Announce Type: new Abstract: Compact representations that support geometrically meaningful comparisons are valuable for learning from probability distributions. We study optimal, fr

Soft Sensor for Bottom-Hole Pressure Estimation in Petroleum Wells Using Long Short-Term Memory and Transfer Learning

arXiv:2602.03737v2 Announce Type: replace Abstract: Monitoring bottom-hole variables in petroleum wells is essential for production optimization, safety, and emissions reduction. Permanent Downhole Ga

safetyarxiv-cs-lg

SOMA: Stack-of-Objects Manipulation using a Quadruped Robot

arXiv:2610.20968v1 Announce Type: cross Abstract: Executing heavy-duty physical manipulation in unpredictable environments remains a major challenge for quadrupedal robots lacking dedicated manipulato

safetyarxiv-cs-ai

SpaceCache: Intermediate Feature Reuse for Efficient Semantic Segmentation on Point Cloud Streams

arXiv:2610.21207v1 Announce Type: new Abstract: Point-cloud streams are central to autonomous driving and robotics, yet existing segmentation networks process every frame from scratch, although consec

agentsarxiv-cs-cv

Spark-0: Scaling Spatially Aligned Tactile Pretraining for Robot Dexterous Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.22342v1 Announce Type: new Abstract: Generalist robot policies benefit from large-scale visual pretraining but often struggle with contact-rich dexterous manipulation, where force, complian

Applications

SPEAR: Selective Preference Elicitation with Adaptive Rewards for VLM-Guided Robotic Manipulation

arXiv:2610.21621v1 Announce Type: new Abstract: Recent Vision-Language Models (VLMs) enable automated preference labeling for robotic manipulation, yet their deployment remains impractical since each

applicationsarxiv-cs-ro

Spectrum Is Not Enough: A Random-Matrix Account of Data-Free Preconditioning for Differentially Private Learning

arXiv:2610.21312v1 Announce Type: new Abstract: Data-free preconditioning builds the Kronecker factors of KFAC from synthetic probes instead of private data. DP-KFC validates a probe by matching the e

Stabilizing the Modality Gap: Null-Space Projected Continual Learning for Vision-Language Models

arXiv:2507.19839v2 Announce Type: replace-cross Abstract: The modality gap is intrinsic to multimodal alignment, where embeddings from different modalities remain partially separated in a shared repre

StepSafe: Learning Timely Intervention with Context-Aware Safety Reasoning

arXiv:2610.20960v1 Announce Type: new Abstract: Safety in agentic systems is inherently sequential: whether an action is safe depends on prior actions, tool outputs, information disclosures, and inter

safetyarxiv-cs-ai

Stop Training Against Soft Users: Mentalized Adversarial Self-Play for Proactive Dialogue

arXiv:2610.21156v1 Announce Type: new Abstract: Proactive dialogue agents must guide multi-turn interactions toward task goals while adapting to users whose intentions and attitudes evolve. Existing p

safetyarxiv-cs-cl

SurvICL: a Foundation Model for in-context Survival Analysis

arXiv:2610.22242v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) now outperform gradient-boosted trees in classification and regression. Yet in survival analysis, which predicts when

System1-Arena: Which Decisions Can Be Left to System 1?

arXiv:2610.21244v1 Announce Type: cross Abstract: Large language models (LLMs) often make discrete decisions, such as flagging phishing or escalating to a human. System-1 models, such as Jev and the O

TASTE: Task-Aware Out-of-Distribution Detection via Stein Operators

arXiv:2602.07640v2 Announce Type: replace Abstract: Out-of-distribution detection methods are often either data-centric, detecting deviations from the training input distribution irrespective of their

Applications

Teaching DINOv3 About Partial 3D Geometry: A Self-Supervised Geometry-Aware Approach

arXiv:2610.21802v1 Announce Type: new Abstract: Partial shape matching is a crucial yet underexplored problem in 3D vision, with significant relevance to real-world scenarios where shapes are often on

applicationsarxiv-cs-cv

Temporal Specification Guidance for Text-to-Video Generation

arXiv:2610.20822v1 Announce Type: new Abstract: Text-to-video models often generate the visual concepts requested in natural language prompts but fail to satisfy their intended ordering, timing, and p

The Lagrangian Structure of Cost-Penalized Active Feature Acquisition

arXiv:2610.21919v1 Announce Type: new Abstract: Active Feature Acquisition (AFA) sequentially selects features to maximize predictive accuracy under an acquisition budget. A natural heuristic greedily

safetyarxiv-cs-lg

Thinking-Free Policy Initialization Makes Distilled Reasoning Models More Effective and Efficient Reasoners

arXiv:2509.26226v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Reward (RLVR) effectively solves complex tasks but demands extremely long context lengths during traini

safetyarxiv-cs-cl

Thinking with Images as Continuous Actions: Numerical Visual Chain-of-Thought

arXiv:2602.23959v2 Announce Type: replace Abstract: Recent MLLMs increasingly rely on visual chain-of-thought to perform region-grounded reasoning over images. However, existing approaches ground regi

safetyarxiv-cs-cv

TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data

model-releasesarxiv-cs-cl

arXiv:2610.21274v1 Announce Type: cross Abstract: Time-series data generated continuously across industrial, environmental, and especially health domains produce multimodal signals whose scale and het

Tool-Augmented Large Language Models for Reliable Medical Calculation from Patient Notes

arXiv:2610.21477v1 Announce Type: new Abstract: Medical calculators translate raw patient observations into clinically actionable values and scores. However, applying these calculators directly to fre

Topology-Inspired Fine-Tuning for Wind-Field Super-Resolution

arXiv:2610.22262v1 Announce Type: new Abstract: Scientific super-resolution (SR) models are typically optimized with pointwise reconstruction losses, yet scientific interpretation often hinges on extr

Applications

Tourist arrival forecasting with game-theoretic social influence weighting and deep learning

arXiv:2507.03411v2 Announce Type: replace Abstract: In the era of Industry 5.0, data-driven decision-making has become indispensable for planning in service systems such as tourism. This paper propose

applicationsarxiv-cs-lg

Towards Reliable Long-Context Reasoning: Detecting Unanswerable Questions via FactGuard

arXiv:2504.05607v2 Announce Type: replace-cross Abstract: Despite the success of Large Language Models (LLMs) in long-context tasks, their reliability in recognizing unanswerable questions remains a c