Search: “benchmark”

Gradient-Aligned Pair Selection for Personalized Preference Optimization

arXiv:2610.00061v1 Announce Type: new Abstract: Personalizing large language models (LLMs) requires aligning generation behavior with user-specific preferences rather than aggregate quality. While Dir

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Gumbel Straight Flow: Distilling Autoregressive Models into One-step Flow Maps

DGX agent

arXiv:2610.00497v1 Announce Type: cross Abstract: We present Gumbel Straight Flow (GSF), a continuous flow map language model that leverages the noise-data coupling of a pretrained autoregressive lang

researcharxiv-cs-ai

2 Oct 2026

Tutorials

HAWK: Rethinking Multimodal Drafting for Speculative Decoding

arXiv:2610.00623v1 Announce Type: new Abstract: Speculative decoding has achieved substantial lossless speedups for LLMs, but remains less effective for large vision-language models (LVLMs), where lig

tutorialsarxiv-cs-cv

HHR: Hierarchical Hash Retrieval for Efficient LLM Generation

arXiv:2610.01230v1 Announce Type: new Abstract: Efficient long-context inference is essential for large language models (LLMs), yet it poses a severe computational bottleneck. Hash-based retrieval off

Higher-Order Positional Encodings for Graph Representation Learning

arXiv:2610.01903v1 Announce Type: new Abstract: Many real-world systems exhibit higher-order interactions among groups of entities that cannot be captured by pairwise relationships alone. Graph Transf

applicationsarxiv-cs-lg

Homomorphic Advantage Operator: Stabilizing Reinforcement Learning Under Fully Homomorphic Encryption Constraints

arXiv:2610.02074v1 Announce Type: new Abstract: Privacy-preserving machine learning presents significant deployment challenges on the cloud for intelligent systems with confidential data. Fully Homomo

How Much Can Language Models Gain from Test-Time Computation?

arXiv:2610.01110v1 Announce Type: new Abstract: How much can test-time computation improve a language model, and at what cost? Test-time scaling is widely proposed as a substitute for larger models, b

Hardware

ibUMAP: Coherent and Scalable Field Evaluation for UMAP Optimization

arXiv:2610.01445v1 Announce Type: new Abstract: UMAP achieves scalable layout optimization through stochastic negative sampling. However, this stochasticity can lead to unstable embeddings across reru

hardwarearxiv-cs-lg

Improving Math Reasoning through Value-guided Informative Search

arXiv:2610.01080v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved the mathematical reasoning capabilities of large language models. Recen

Improving scoring functions for protein-protein docking with LambdaLoss

arXiv:2610.00191v1 Announce Type: cross Abstract: Modeling protein-protein interactions requires accurate scoring functions that can rank potential poses (conformations) of a protein-protein complex t

In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement

arXiv:2601.22169v2 Announce Type: replace-cross Abstract: Humans are susceptible to undesirable behaviours and privacy leaks under the influence of alcohol. This paper investigates drunk language, i.e

Tutorials

IQS-BO: In-Context Query Selection for Bayesian Optimisation

arXiv:2610.01269v1 Announce Type: new Abstract: Bayesian Optimisation (BO) is a powerful framework for the optimisation of expensive black-box functions, but typically requires refitting a surrogate a

tutorialsarxiv-cs-lg

JEPA-Bisim: Learning Robust Visual Representations for Planning with Joint-Embedding Predictive World Models

arXiv:2602.18639v2 Announce Type: replace Abstract: World models learned from high-dimensional visual observations allow agents to make decisions and plan directly in latent space, avoiding pixel-leve

Jev-IDS: System One Models for Network Intrusion Detection

arXiv:2610.01079v1 Announce Type: cross Abstract: Machine-learning Network Intrusion Detection Systems (IDS) depend on substantial labeled datasets and task-specific training, whereas Large Language M

JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces

arXiv:2610.00437v1 Announce Type: new Abstract: LLM agents generate intermediate reasoning and actions token by token, making extended interactions slow and computationally expensive. Jev-style models

K-Dense BYOK: An Open-Source AI Research Assistant That Runs Locally and Keeps a Hash-Chained Lab Notebook

arXiv:2610.00074v1 Announce Type: new Abstract: K-Dense BYOK (bring your own keys) is a free, open-source AI research assistant for scientists in any field that runs on the researcher's own computer.

LabBook: Harnessing Experimental History for Efficient LLM-Driven Discovery

arXiv:2610.00675v1 Announce Type: cross Abstract: Evolutionary approaches to LLM-driven discovery often generate new programs from a small set of selected ancestors. This keeps contexts manageable but

Lang3DSeg: Annotation-Free Open-Vocabulary 3D Segmentation with Point Transformers

arXiv:2610.00855v1 Announce Type: new Abstract: Accurate 3D semantic perception is critical for safe autonomous navigation. However, supervised LiDAR segmentation remains tied to closed taxonomies and

agentsarxiv-cs-cv

Learning Commute-Time-Preserving World Models for Planning

arXiv:2610.01373v1 Announce Type: new Abstract: World models allow agents to plan in latent space by choosing a sequence of actions that most reduces the distance to a given goal state. Thus, planning

Learning from Failure: Leveraging Unreliable Predictions in Semi-Supervised Real-World Adverse Weather Removal

arXiv:2610.02051v1 Announce Type: new Abstract: Adverse weather image restoration aims to recover images degraded by rain, haze, snow, and other weather-induced artifacts, thereby improving the robust

applicationsarxiv-cs-cv

LineupRL: Verifiable Reinforcement Learning for Time Series Captioning via Caption-to-Series Identification

arXiv:2610.01800v1 Announce Type: new Abstract: Time series captioning is a fundamental step in time series understanding and can also serve as the bridge between signal and natural language. Supervis

applicationsarxiv-cs-ai

Low-Budget Active Learning through Entropic Optimal Transport

arXiv:2610.01199v1 Announce Type: new Abstract: We consider low-budget active learning, which consists of selecting a limited number of points, the coreset, such that a model can be trained to high ac

Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning

arXiv:2610.01936v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable fluency across many tasks but remain limited by their static, parameter bound knowledge and th

MatrixReward: Reward from Rubric Matrix for Open-Ended Generation

arXiv:2610.00389v1 Announce Type: cross Abstract: Open-ended query generation lacks standard answers, thus necessitating an effective reward mechanism. Pointwise scoring rubrics provide limited inform

researcharxiv-cs-ai

MCIR: A Feature Dependence-Aware Explainability Method with Reliability Guarantees

arXiv:2610.01641v1 Announce Type: cross Abstract: Modern machine-learning models often contain strongly dependent or redundant features, making feature attribution difficult because shared predictive

Mean Spatial Frequency Decoupling for Learning-Based Uplink-to-Downlink Covariance Conversion in FDD Massive MIMO

arXiv:2610.00596v1 Announce Type: cross Abstract: In frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, the uplink (UL)-to-downlink (DL) channel covariance matri

MEGA: Object-Level Mesh Extraction from 3D Gaussian Splatting via Spatial Visual Distillation

arXiv:2610.01707v1 Announce Type: new Abstract: Mesh extraction from 3D Gaussian Splatting (3DGS) aims to endow 3D Gaussians with accurate geometric structures, enabling explicit and precise 3D occupa

researcharxiv-cs-cv

MemFit: Efficient Long-Term Agentic Memory

arXiv:2610.00872v1 Announce Type: new Abstract: Long-term memory systems for large language models (LLMs) have gained popularity for extending reasoning capabilities across applications. Current memor

agentsarxiv-cs-ai

Model Merging via Data-Free Covariance Estimation

arXiv:2604.01329v2 Announce Type: replace Abstract: Model merging provides a way of cheaply combining individual models to produce a model that inherits each individual's capabilities. While some merg

MoLE: Mixture of Latent Experts for Complementary Visual Reasoning

arXiv:2610.01917v1 Announce Type: cross Abstract: Latent visual reasoning equips vision--language models with continuous intermediate states that can process visual evidence without explicit textual r

researcharxiv-cs-ai

MOMAT: Mixture of Multiple Atlases for Low-Power Jailbreak Defense of Quantized LLMs

arXiv:2610.01058v1 Announce Type: cross Abstract: Quantized large language models are increasingly deployed on edge devices for their low latency and energy efficiency. However, model quantization wea

MoRA: MoE Pruning via Router Bias Learning and Expert Approximation

arXiv:2610.00367v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models enable parameter scaling with limited per-token computation by activating only a small subset of experts for each token,

MorphoBranch: A Fine-Structure-Preserving Workbench for Morphometric Analysis of Branched Cellular Structures

model-releasesarxiv-cs-cv

arXiv:2610.00860v1 Announce Type: cross Abstract: Background and Objectives: Fluorescence-labeled cellular arbors provide readouts of neuronal and microglial morphology, but fine and weakly labeled pr

Multi-Resolution Feature Fusion U-Net for Magnetic Resonance Imaging Segmentation

model-releasesarxiv-cs-cv

arXiv:2610.00279v1 Announce Type: new Abstract: The segmentation of anatomical structures in medical images and particularly in MRI scans, is essential for clinical diagnosis and monitoring disease pr

MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs

arXiv:2610.01434v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) incur substantial inference costs when processing long visual-textual sequences. While existing operation com

researcharxiv-cs-ai

Neural Fourier Surrogates for Data Reuploading Quantum Neural Networks

arXiv:2610.00841v1 Announce Type: cross Abstract: For quantum machine learning, the exact boundary between classical and quantum advantage is still poorly understood. Direct comparison between quantum

Neural scaling laws and evolution of learnable activation functions of Kolmogorov-Arnold networks

arXiv:2610.00985v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) represent a compelling alternative to traditional Multi-Layer Perceptron (MLP)-based neural networks. By employing act

New paper from Microsoft on compressing agent context at test time. If your agent gets worse as its interaction history grows, this method i…

New paper from Microsoft on compressing agent context at test time. If your agent gets worse as its interaction history grows, this method is worth a look. FOCUS asks which past interactions the agent

agentsdair-ai--x

New Snake-in-the-Box Records via Snakepit Surgery and Learned Construction

arXiv:2607.15270v3 Announce Type: cross Abstract: The snake-in-the-box problem asks for a longest induced path in the hypercube graph Q_n. We find a length-191 snake in dimension n=9, the lowest dimen

Not All Error Yields to Scale: Where Scaling Stops in Vision-Language Inference

arXiv:2610.01640v1 Announce Type: cross Abstract: Vision-language models (VLMs) face a fixed-budget trade-off between processing more visual information for fine-grained perception and using a larger

applicationsarxiv-cs-ai

OmniSeek: Native Tool Integration for Multi-turn Audio-Visual Reasoning

arXiv:2610.02181v1 Announce Type: new Abstract: We present OmniSeek, an agentic framework that transforms an Omni Large Language Model (Omni-LLM) into an active, multi-turn reasoning agent with native

safetyarxiv-cs-cv

On-Device Named-Entity Recognition: A Deployability Study of Accuracy, Cost, Reliability, and Confidence

arXiv:2610.00007v1 Announce Type: cross Abstract: Named-entity recognition (NER) is increasingly wanted on-device (no API, low latency, data kept local). The practitioner's question is not the leaderb

Oneira: From Open-Ended Generation to Open-World Interaction in Video World Models

arXiv:2610.01614v1 Announce Type: new Abstract: Generative video world models can now synthesize open-ended environments that agents can navigate and interact with in simple ways. Yet open-ended gener

agentsarxiv-cs-cv

Local Ai

OneStreamer: Unifying Perception, Memory, and Proactive Response in Streaming Video Interaction

arXiv:2610.01762v1 Announce Type: new Abstract: Streaming video LLMs must retain evidence before its relevance to future tasks is known and respond when sufficient evidence becomes available. The chal

local-aiarxiv-cs-cv

Optimal Transport Reweighting for Robust Learning under Spurious Correlations and Label Noise

arXiv:2610.01028v1 Announce Type: cross Abstract: Machine learning models often suffer performance degradation under subpopulation shift, particularly when spurious correlations cause models to rely o

OR for AI That Does OR: Routing LLMs up the Escalator inside the OSCAR Framework

arXiv:2610.00912v1 Announce Type: new Abstract: Large language models can translate business descriptions into optimization models, but executable code may misrepresent constraints or objectives. A so

Parameter-Efficient Distributionally Robust Adaptation of Tabular Foundation Models under Subpopulation Shift

arXiv:2610.01143v1 Announce Type: cross Abstract: Despite strong mean accuracy, tabular foundation models (TFMs) can perform poorly on underrepresented groups under subpopulation shift, where group pr

Poincare Meets Bellman: Revisable Memory, Operational Quotients, and Evidence-Supported Learning in Changing Environments

arXiv:2512.05990v2 Announce Type: replace Abstract: Memory consolidation determines both what a learner can do now and which changes remain implementable later. We develop a finite-model synthesis of