Search: “inference”

FastVLA: Task-Conditioned and Temporally Adaptive Visual Token Compression for Efficient Vision-Language-Action Models

arXiv:2610.22146v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models present a promising paradigm for general-purpose robot manipulation, but closed-loop deployment is often limited by

researcharxiv-cs-ro

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Research

Feature-preserving Latent-EnKF for Data Assimilation of Flows with Shocks

DGX agent

arXiv:2606.12559v2 Announce Type: replace-cross Abstract: The ensemble Kalman filter (EnKF) is widely adopted for sequential data assimilation, but fails for solutions with discontinuities, such as sh

researcharxiv-cs-lg

16 Oct 2026

FedSumUp: User-Auditable Privacy-Aware Representation Sharing for Resource-Constrained Federated Intelligence

arXiv:2610.21194v1 Announce Type: cross Abstract: Existing federated data condensation methods face privacy risks, as uploaded synthetic data may preserve sensitive information and remain vulnerable t

researcharxiv-cs-ai

FIRM: Fisher-Informed Retention Merging for Federated LLM Unlearning

arXiv:2610.21720v1 Announce Type: cross Abstract: Federated LLM unlearning addresses targeted knowledge removal when forget and retain data remain distributed across private clients and are inaccessib

From Documents to Spans: Span-Centric Learning for LLM-based Explainable Medical Coding

arXiv:2603.15270v3 Announce Type: replace-cross Abstract: Medical coding, which assigns standardized ICD codes to clinical documents, is a fundamental yet challenging task in modern healthcare. It req

FuseAdapt: Parameter-Efficient Multimodal Fusion for Semantic Segmentation with Missing Modalities

model-releasesarxiv-cs-cv

arXiv:2610.21779v1 Announce Type: new Abstract: Multi-modal semantic segmentation benefits from complementary sensors, but this benefit is fragile when some modalities are degraded or missing at test

Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards

arXiv:2610.20814v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards often trains language models on responses generated by a different, possibly stale behavior policy. Cor

GSM: Efficient Language Modeling with Shared Global State

arXiv:2609.33465v3 Announce Type: replace Abstract: Improving language model efficiency requires not only cheaper operations but also eliminating redundant computation. We argue that effective modelin

Hybrid Generative-Tagging Decoding for Efficient Product Attribute-Value Extraction

arXiv:2610.21329v1 Announce Type: new Abstract: Attribute-value extraction is important for building high-quality product catalogs in e-commerce. Existing systems either rely on generative or tagging

researcharxiv-cs-cl

Improving Environment-Agnostic Coding Agents: The Role of Scaffolding

arXiv:2610.22289v1 Announce Type: new Abstract: Stronger language models may increasingly construct useful procedures for themselves, reducing the advantage of developer-supplied scaffolding. We inves

Hardware

Introducing Reflection Beam: an open-weight model for agentic coding on AWS

Reflection AI's Beam is an open-weight model for agentic coding and long-horizon tasks. This post shows how to deploy Beam on AWS: start with an Amazon SageMaker JumpStart real-time endpoint, or use A

hardwareaws-ml-blog

ISW-Agent: Integrating LLM-Driven Dialogue with n8n Workflow for Internal Solitary Wave Remote Sensing Image Processing

arXiv:2610.21502v1 Announce Type: new Abstract: Internal solitary wave (ISW) remote sensing requires task parameters and spatial information to be transferred across image acquisition, detection, stri

KNOU-Flood: Advancing global streamflow forecasting with lead-aware probabilistic machine learning

arXiv:2610.20640v1 Announce Type: new Abstract: Global streamflow forecasting supports flood preparedness, yet data-driven models trained on reanalysis lose skill when driven by operational forecasts,

researcharxiv-cs-lg

Learning from Unverified Peers via Shared Residual Distillation

arXiv:2610.22080v1 Announce Type: new Abstract: A language model can generate several solutions to the same problem, some leading to correct answers and others to incorrect ones. Many post-training me

Learning Not To Interfere: Adapting Experts For Model Merging

arXiv:2603.13467v2 Announce Type: replace-cross Abstract: Model merging has shown that multitask models can be created by directly combining the parameters of different models that are each specialize

researcharxiv-cs-ai

Learning Rewards in Strategic Games

arXiv:2610.21912v1 Announce Type: cross Abstract: Understanding the objectives that drive agents' behavior is crucial in strategic environments, where multiple agents interact and adapt to one another

Local Ai

Learning Self-Orchestration in Decentralized Agent Swarms

arXiv:2610.22322v1 Announce Type: cross Abstract: The capabilities of an agent swarm depend on both its individual members and how they coordinate. Yet existing multi-agent systems often train agents

local-aiarxiv-cs-ai

Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval

arXiv:2610.21030v1 Announce Type: cross Abstract: Reasoning-conditioned embeddings can improve retrieval by progressively refining semantic constraints. However, additional reasoning may introduce ove

Learning When to Withdraw: Curriculum Coverage and Rule-Specific Failures in Fine-Tuned Language Agents

arXiv:2610.21045v1 Announce Type: new Abstract: Tool-using language agents must revise prepared actions when evidence changes or authority is revoked. Selective withdrawal means suspending affected ac

LEASH: Uncertainty-Aware Weak-to-Strong Escalation and Oversight for Computer-Use Agents

arXiv:2610.22280v1 Announce Type: new Abstract: Computer-use agents increasingly tackle long-horizon tasks, where running a frontier model at every step is expensive and a small open-weight model alon

Long-Context Drafting Does Not Require Long-Context Drafters

model-releasesarxiv-cs-lg

arXiv:2610.20865v1 Announce Type: new Abstract: Speculative decoding pairs a cheap drafter with parallel verification against the target. At ultra-long context it stops paying, because a drafter that

Agents

LOOP-MAC: Learning-Based Multi-Agent Coordination with Guaranteed Local Feasibility in a Virtual Power Plant

arXiv:2310.17882v3 Announce Type: replace Abstract: By aggregating distributed energy resources (DERs) into a single dispatchable resource, virtual power plants (VPPs) enable the grid to use the flexi

agentsarxiv-cs-lg

Love for Believable AI: Artificial Partiality and Relationship Persistence as an Engineerable Stance

arXiv:2610.09895v1 Announce Type: cross Abstract: We study a behavioral mechanism for artificial partiality in conversational agents. The paper reports no human-subjects data and makes no claim about

researcharxiv-cs-cl

Agents

MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environments

arXiv:2610.20939v1 Announce Type: new Abstract: Online reinforcement learning (RL) with verifiable rewards has driven rapid progress in mathematics, coding, and tool use, but scaling this success to G

agentsarxiv-cs-ai

Hardware

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

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

Agents

Mosaic: Multi-Session Agentic Reinforcement Learning

arXiv:2610.22174v1 Announce Type: cross Abstract: Most existing agentic reinforcement learning methods assume single-session trajectories, where interaction proceeds within one continuously accumulate

agentsarxiv-cs-ai

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

Applications

Multi-task Neural Diffusion Processes

arXiv:2510.03419v3 Announce Type: replace-cross Abstract: Neural diffusion processes provide a scalable, non-Gaussian approach to modelling distributions over functions, but existing formulations are

applicationsarxiv-cs-ai

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

OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering

arXiv:2610.21345v1 Announce Type: new Abstract: Agentic knowledge graph question answering(KGQA) relies on sequences of executable operations, but trajectory-level rewards obscure their individual con

safetyarxiv-cs-cl

Open-world AI: a Tale of Three Requirements

arXiv:2610.20782v1 Announce Type: new Abstract: The closed world asks AI for specialized competence and lets it manufacture the iid data its theory assumes: a fixed criterion licenses collecting examp

researcharxiv-cs-ai

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

model-releasesarxiv-cs-cv

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

Plans Are Not Executions: Adapting Frozen World-Model Planners to Controller-Induced Execution Shift

arXiv:2610.22041v1 Announce Type: new Abstract: World-model planners evaluate candidate trajectories by predicting their future consequences, implicitly relying on the plan-to-motion relationship lear

researcharxiv-cs-ro

Tutorials

Poisoning the corpora of protein design systems during pre-training and retrieval

arXiv:2610.21200v1 Announce Type: new Abstract: Protein foundation models increasingly support the generation of novel biological sequences, yet their reliance on large public datasets creates opportu

tutorialsarxiv-cs-lg

Local Ai

Purify Before You Align: Improving Representation Alignment for Diffusion Models

arXiv:2610.20761v1 Announce Type: new Abstract: Representation alignment improves and accelerates diffusion training by encouraging intermediate features to match a pretrained visual encoder. However,

local-aiarxiv-cs-cv

Qwen-Audio-Jev: A Unified Interface for Audio Decisions

arXiv:2610.21614v1 Announce Type: cross Abstract: Large audio language models (LALMs) have made substantial progress in audio understanding. However, in high-throughput applications requiring frequent

Qwen Flash Next potential SOTA Q2 nobody talks about

model-releasesr-localllama

Whenever it comes to Strata and low bit QFN discussion, everyone is talking about Unsloth, ISTA-lab and nobody mentions Agantai with his GYRO series of quants - https://huggingface.co/agentionai/Qwen3

RDL-Bench: A Benchmark for Task-Aware Question Answering over Relational Databases

model-releasesarxiv-cs-lg

arXiv:2610.21382v1 Announce Type: new Abstract: Relational databases (RDBs) remain the primary infrastructure for storing and managing structured data across enterprise and scientific domains. Natural

Reasoning without Gold Standards: A Proxy-Judge Theory of Autoformalization

arXiv:2606.09449v2 Announce Type: replace Abstract: Autoformalization (AF) requires both prover acceptance and fidelity to an informal source, but prover acceptance alone cannot establish fidelity, an

researcharxiv-cs-cl

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

Robust Covariance Estimation for Entry-wise Contaminated and Partially Observed Data via Expectation Maximization Algorithm

arXiv:2610.21709v1 Announce Type: cross Abstract: Robust covariance estimation under entry-wise contamination and missing data is challenging in various signal processing applications. Under the Stude

researcharxiv-cs-lg

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

Routing Policy Optimization for Online Diffusion Reinforcement Learning

arXiv:2610.22282v1 Announce Type: new Abstract: Reinforcement learning can align visual generators with reward signals beyond their pretraining objectives, but existing score-function methods typicall

safetyarxiv-cs-cv

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

Score Mismatch and Set-Constrained Sampling in Zero-Shot Conditional Discrete Diffusion

arXiv:2610.21061v1 Announce Type: cross Abstract: Discrete diffusion models have become a powerful framework for modeling complex discrete distributions across a range of applications. While theoretic

safetyarxiv-cs-lg

Applications

Self-Refining Vision Language Model for Robotic Failure Detection and Reasoning

arXiv:2602.12405v2 Announce Type: replace-cross Abstract: Reasoning about failures is crucial for building reliable and trustworthy robotic systems. Prior approaches either treat failure reasoning as

applicationsarxiv-cs-lg