Search: “inference”

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

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Model Releases

Improving Environment-Agnostic Coding Agents: The Role of Scaffolding

DGX agent

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

model-releasesarxiv-cs-ai

16 Oct 2026

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

model-releasesarxiv-cs-cl

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

model-releasesarxiv-cs-cl

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

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

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

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

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

SSR: Structural Signal Retriever for Scalable Agent Discovery in LLM-Orchestrated Multi-Agent Systems

arXiv:2610.21113v1 Announce Type: cross Abstract: Large-scale multi-agent ecosystems require mechanisms for discovering suitable agents without exposing entire registries to a large language model (LL

Stochastic Gradient Descent: A Nonlinear Time Series Perspective

arXiv:2610.20894v1 Announce Type: cross Abstract: This paper investigates the statistical behavior of Stochastic Gradient Descent (SGD) with constant step size under the framework of iterated random f

safetyarxiv-cs-lg

StochLOB: Heavy-Tailed Score Matching for Robust Limit Order Book Trend Prediction

arXiv:2610.21081v1 Announce Type: new Abstract: Score-based diffusion methods usually perturb data with Brownian Gaussian noise, corresponding to a light-tailed forward stochastic differential equatio

researcharxiv-cs-lg

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 World Won't Wait: Evaluating and Teaching Agents Under a Live Clock

arXiv:2610.20665v1 Announce Type: new Abstract: Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls take time. In continuously changing environments, an

researcharxiv-cs-ai

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

UniFGVC: Universal Training-Free Few-Shot Fine-Grained Vision Classification via Attribute-Aware Multimodal Retrieval

arXiv:2508.04136v4 Announce Type: replace-cross Abstract: Few-shot fine-grained visual classification (FGVC) aims to discriminate between subtly different categories with very limited data. While Mult

When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence

arXiv:2610.20764v1 Announce Type: new Abstract: This paper challenges the understanding capabilities of existing AI paradigms and emerging world models by asking a fundamental question: How quickly ca