Search: “benchmark”

Mitigating Memorization Where It Happens

arXiv:2602.08901v2 Announce Type: replace Abstract: Large language models (LLMs) can memorize and reproduce training sequences verbatim, raising privacy and copyright concerns. Existing mitigation met

Hardware

MJev-Spatial: System One Spatial Intelligence via Native Attention Matching

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DGX agent

arXiv:2610.22276v1 Announce Type: new Abstract: Spatial intelligence lets an agent perceive and act in the physical world, and many of its judgments must be made at interaction rates: which spatial re

hardwarearxiv-cs-cv

16 Oct 2026

Tutorials

Motion- and Gravity-Aware Feedforward 3D Reconstruction

arXiv:2610.22177v1 Announce Type: cross Abstract: Feed-forward transformer models have recently achieved strong performance in multi-view 3D reconstruction, but they still rely primarily on appearance

tutorialsarxiv-cs-ai

MSP: Modality Self-Play for Training Multimodal LLMs Without Paired Cross-Modal Data

arXiv:2610.20965v1 Announce Type: new Abstract: Large multimodal models have achieved remarkable progress by scaling model capacity and training on massive paired multimodal data. However, this paradi

safetyarxiv-cs-ai

Multi-SFT: Permissively-Licensed Multilingual Instruction-Following

arXiv:2610.22086v1 Announce Type: cross Abstract: Supervised fine-tuning datasets remain overwhelmingly English-centric and commercially restricted, as their content is frequently generated by proprie

safetyarxiv-cs-ai

Tutorials

Multi-Target Few-Shot Prompting with Diffusion Language Models

arXiv:2610.22294v1 Announce Type: new Abstract: Few-shot prompting is useful when instructions and rewards are hard to specify. One such application is editing by example, where a demonstrated formatt

tutorialsarxiv-cs-cl

MYOPIA: Understanding the Limits of Tree-Based Reasoning in Vision-Language Models

arXiv:2610.21573v1 Announce Type: new Abstract: Decision trees can make vision-language model (VLM) classification interpretable by replacing a single prediction with a sequence of root-to-leaf questi

NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness

arXiv:2609.08183v2 Announce Type: replace Abstract: Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into

safetyarxiv-cs-cl

No Single Tokenizer Feature Reliably Predicts Downstream Language Model Performance

arXiv:2610.21679v1 Announce Type: new Abstract: Tokenization is a critical component of the language modeling pipeline, but it is poorly understood. Tokenizers can impact downstream performance, but t

researcharxiv-cs-cl

One Bias Score, Two Factors: How Mitigations Affect Option Bias in Multiple-Choice Evaluation

arXiv:2610.20993v1 Announce Type: new Abstract: Reordering multiple-choice options can change a model's answer, an effect studied as selection bias (a preference for certain option letters) and as ord

safetyarxiv-cs-cl

OntoAct: Hypothesis-Based Neuro-Symbolic Agents through Ontological Abstraction

arXiv:2610.21983v1 Announce Type: new Abstract: Learning from experience requires recovering reusable knowledge and determining when it applies to a new situation. We introduce OntoAct, a hypothesis-b

OpenGCUA: Toward a Fully Open Stack for Generalist Computer Use

arXiv:2610.22200v1 Announce Type: new Abstract: Computer use has reached a turning point: the focus is expanding from GUI-only interaction to systems that complete tasks on users' computers through vi

OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents

arXiv:2610.21496v1 Announce Type: new Abstract: Reliable repository-level coding requires agents not only to generate correct code but also to preserve constraints and respond to tool feedback across

Out-of-Distribution Object Detection in Street Scenes via Synthetic Outlier Exposure and Transfer Learning

arXiv:2603.16122v2 Announce Type: replace Abstract: Out-of-distribution (OOD) objects are often silently overlooked by object detectors, which produce no detection for them, leaving downstream modules

Local Ai

P2TPO: Structure-Aligned Page-to-Token Credit Assignment for Full-Page Document Parsing

arXiv:2610.21979v1 Announce Type: cross Abstract: End-to-end full-page document parsing must preserve the content and structure of heterogeneous elements, yet otherwise accurate outputs can contain lo

local-aiarxiv-cs-cl

PathFM: Fast Generative Forecasting of Multivariate Trajectories

arXiv:2610.22358v1 Announce Type: new Abstract: Time series foundation models are becoming increasingly popular for general-purpose forecasting. However, most leading models predict marginal quantiles

PEDBench: Can Speech Models Detect Phonetic Errors?

arXiv:2610.20830v1 Announce Type: new Abstract: Phonetic error detection (PED) aims to identify phoneme-level substitutions, deletions, and insertions and is essential for educational and clinical spe

Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble

arXiv:2610.21134v1 Announce Type: new Abstract: Predicting live birth before fresh embryo transfer could support shared decision-making in IVF/ICSI, but routine clinical data are small, heterogeneous,

PremiseCheck: Efficient Fact-Checking of Human and AI Peer Reviews

arXiv:2610.21046v1 Announce Type: cross Abstract: Peer review is under strain from the growth of submissions, and review comments written by humans or AI can misstate what a paper says. We introduce R

PremiseSQL: Evaluating Premise Critique and Abstention in Text-to-SQL Systems

arXiv:2610.22158v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have substantially improved Text-to-SQL performance, yet existing NL2SQL systems largely assume that use

Priming Base Models for Agentic Post-Training: An Open Midtraining Recipe

arXiv:2610.22019v1 Announce Type: cross Abstract: Frontier models, general-purpose and domain-specific alike, now include a midtraining stage between pretraining and post-training, but little has been

PRISM: PRIvileged Sibling-guided Multi-Agent Topology Design

arXiv:2610.20628v1 Announce Type: new Abstract: Multi-agent systems (MAS) based on large language models (LLMs) have become a powerful way to address complex problems, and their effectiveness depends

agentsarxiv-cs-cl

Privacy-Preserving Text Inference for Health-Misinformation Classification and Semantic Retrieval

model-releasesarxiv-cs-lg

arXiv:2610.20796v1 Announce Type: cross Abstract: Health-misinformation analysis presents a dual challenge: misleading claims can affect public well-being, while systems designed to detect them may ex

Q(gamma_H): Entropy-Guided Horizon Mixing in Multi-Discount Q-Learning

arXiv:2610.21764v1 Announce Type: new Abstract: A fixed discount factor in Q-learning imposes a single planning horizon throughout training, despite the differing demands of early exploration and long

safetyarxiv-cs-lg

Applications

QREF: Improving DeepSearch Agents by Aligning Search with Reasoning via Query Refinement and Evidence Filtering

arXiv:2610.21512v1 Announce Type: new Abstract: DeepSearch agents achieve strong performance on complex information-seeking tasks through iterative search and reasoning. However, they often issue inte

applicationsarxiv-cs-ai

Quantile-Guided Diffusion for Probabilistic Time-Series Forecasting

arXiv:2610.20739v1 Announce Type: new Abstract: Quantile forecasts summarize uncertainty at each future time step, while diffusion forecasters represent it through sampled future trajectories. We stud

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

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

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

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

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

model-releasesarxiv-cs-lg

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

model-releasesarxiv-cs-lg

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

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

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