Search: “retrieval”

MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale

arXiv:2604.04771v2 Announce Type: replace-cross Abstract: Current document parsing methods advance primarily through model architecture innovation, while systematic engineering of training data remain

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Mitigating Visual Context Degradation in Large Multimodal Models: A Training-Free Decoupled Agentic Framework

DGX agent

arXiv:2509.23322v2 Announce Type: replace Abstract: With the continuous expansion of Large Language Models (LLMs) and advances in reinforcement learning, LLMs have demonstrated exceptional reasoning c

model-releasesarxiv-cs-cv

10 Apr 2026

Applications

MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations

arXiv:2603.28253v2 Announce Type: replace-cross Abstract: Time series forecasting is vital across many domains, yet existing models struggle with fixed-length inputs and inadequate multi-scale modelin

applicationsarxiv-cs-ai

Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search

arXiv:2604.06771v1 Announce Type: cross Abstract: Conversational Query Rewriting (CQR) aims to rewrite ambiguous queries to achieve more efficient conversational search. Early studies have predominant

safetyarxiv-cs-ai

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms

arXiv:2604.04868v2 Announce Type: replace-cross Abstract: Tabular foundation models (TFMs) such as TabPFN (Tabular Prior-Data Fitted Network) are designed to generalize across heterogeneous tabular da

Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehicles

arXiv:2604.08031v1 Announce Type: cross Abstract: Most Human-Machine Interaction (HMI) research overlooks the maneuvering needs of passengers in autonomous driving (AD). Natural language offers an int

Applications

OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence

arXiv:2604.07296v2 Announce Type: replace Abstract: Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses on domain-specif

applicationsarxiv-cs-cl

Orion-Lite: Distilling LLM Reasoning into Efficient Vision-Only Driving Models

arXiv:2604.08266v1 Announce Type: new Abstract: Leveraging the general world knowledge of Large Language Models (LLMs) holds significant promise for improving the ability of autonomous driving systems

PeReGrINE: Evaluating Personalized Review Fidelity with User Item Graph Context

arXiv:2604.07788v1 Announce Type: cross Abstract: We introduce PeReGrINE, a benchmark and evaluation framework for personalized review generation grounded in graph-structured user--item evidence. PeRe

Phantasia: Context-Adaptive Backdoors in Vision Language Models

arXiv:2604.08395v1 Announce Type: new Abstract: Recent advances in Vision-Language Models (VLMs) have greatly enhanced the integration of visual perception and linguistic reasoning, driving rapid prog

researcharxiv-cs-cv

Plasma GraphRAG: Physics-Grounded Parameter Selection for Gyrokinetic Simulations

arXiv:2604.06279v1 Announce Type: cross Abstract: Accurate parameter selection is fundamental to gyrokinetic plasma simulations, yet current practices rely heavily on manual literature reviews, leadin

Rethinking Entropy Allocation in LLM-based ASR: Understanding the Dynamics between Speech Encoders and LLMs

arXiv:2604.08003v1 Announce Type: cross Abstract: Integrating large language models (LLMs) into automatic speech recognition (ASR) has become a dominant paradigm. Although recent LLM-based ASR models

SANDO: Safe Autonomous Trajectory Planning for Dynamic Unknown Environments

model-releasesarxiv-cs-ro

arXiv:2604.07599v1 Announce Type: new Abstract: SANDO is a safe trajectory planner for 3D dynamic unknown environments, where obstacle locations and motions are unknown a priori and a collision-free p

SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models

arXiv:2506.01062v4 Announce Type: replace Abstract: We introduce SealQA, a new challenge benchmark for evaluating SEarch-Augmented Language models on fact-seeking questions where web search yields con

See the Forest for the Trees: Loosely Speculative Decoding via Visual-Semantic Guidance for Efficient Inference of Video LLMs

arXiv:2604.05650v2 Announce Type: replace Abstract: Video Large Language Models (Video-LLMs) excel in video understanding but suffer from high inference latency during autoregressive generation. Specu

researcharxiv-cs-cl

SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses

arXiv:2602.22683v2 Announce Type: replace Abstract: The rapid advancement of AI-powered smart glasses-one of the hottest wearable devices-has unlocked new frontiers for multimodal interaction, with Vi

Applications

TREASURE: The Visa Payment Foundation Model for High-Volume Transaction Understanding

arXiv:2511.19693v3 Announce Type: replace-cross Abstract: Payment networks form the backbone of modern commerce, generating high volumes of transaction records from daily activities. Properly modeling

applicationsarxiv-cs-ai

TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation

arXiv:2604.07894v1 Announce Type: new Abstract: Personalized large language models (PLLMs) have garnered significant attention for their ability to align outputs with individual's needs and preference

Weakly Supervised Distillation of Hallucination Signals into Transformer Representations

arXiv:2604.06277v1 Announce Type: new Abstract: Existing hallucination detection methods for large language models (LLMs) rely on external verification at inference time, requiring gold answers, retri

Why Are We Lonely? Leveraging LLMs to Measure and Understand Loneliness in Caregivers and Non-caregivers

arXiv:2604.07834v1 Announce Type: new Abstract: This paper presents an LLM-driven approach for constructing diverse social media datasets to measure and compare loneliness in the caregiver and non-car

Local Ai

Agent Skill Framework: Perspectives on the Potential of Small to Medium Language Models in Industrial Environments

arXiv:2602.16653v4 Announce Type: replace Abstract: Skills are getting increasingly used in recent agentic frameworks, performing robustly with proprietary LLMs. Yet, their effectiveness for small and

local-aiarxiv-cs-ai

DemoStream: Batched Task-and-Motion-Plan Optimization for Automated Learning from Demonstration

arXiv:2610.22020v1 Announce Type: cross Abstract: Learning from demonstration is an effective strategy for endowing robots with manipulation capabilities but requires training on datasets comprised of

safetyarxiv-cs-ai

DialFork: Going Beyond Factuality to Evaluate Procedural Hallucination in Conversational Agents

arXiv:2610.21561v1 Announce Type: cross Abstract: Large language models (LLMs) deployed as conversational agents are increasingly expected to follow procedural logic. While factual hallucination has r

Fast-VLA: You Only Think Once for Robot Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.21389v1 Announce Type: new Abstract: Vision-language-action (VLA) policies commonly place a multi-billion-parameter model inside the feedback loop, repeatedly recomputing task context whene

GraphACM: Graph-based Action Consistency Measurement for Uncertainty Quantification in LLM Agents

arXiv:2610.22031v1 Announce Type: new Abstract: Behavioral consistency across repeated executions provides an informative signal for assessing the reliability of large language model (LLM) agents. How

KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents

arXiv:2610.22331v1 Announce Type: new Abstract: Deep search agents solve knowledge-intensive tasks through long-horizon, multi-turn interactions, where assigning credit to individual search decisions

safetyarxiv-cs-ai

Language Models Share a Readout Bottleneck in Working Memory

arXiv:2610.21176v1 Announce Type: cross Abstract: Large language models (LLMs) reproduce a human working-memory signature on the N-back task: performance declines as memory load N increases, and error

Tutorials

Learning Speech Attribute Representations via Multi-Teacher Distillation

arXiv:2610.21941v1 Announce Type: new Abstract: Recent post-training approaches have shown that self-supervised speech encoders can be adapted to learn a single utterance-level speech attribute throug

tutorialsarxiv-cs-cl

LLM-as-an-Investigator: Evidence-First Agentic Reasoning for Robust Interactive Problem Diagnosis

arXiv:2606.13220v2 Announce Type: replace Abstract: Large language models (LLMs) are used for technical troubleshooting, where problem descriptions are often incomplete and multiple causes can explain

LLMR: Knowledge Distillation with a Large Language Model-Induced Reward

arXiv:2409.12500v2 Announce Type: replace-cross Abstract: Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) ta

Tutorials

Multi-sourced AI Tutor for Non-Native English Speaker Students

arXiv:2610.22163v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being adopted as teaching tools in computing education, particularly for developing AI tutors capable of a

tutorialsarxiv-cs-ai

PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning

arXiv:2610.21308v1 Announce Type: cross Abstract: Consumer phones and wearables increasingly collect physiological time-series data, yet most mobile systems treat these streams as summaries or text ra

Recursive Reward Improvement via Adversarial Meta-Prompt Evolution

arXiv:2610.21040v1 Announce Type: new Abstract: Recursive Self-Improvement (RSI) has gained wide attention for its ability to work with limited data. In a proposer-solver loop, a proposer model writes

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

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

Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech

arXiv:2610.20766v1 Announce Type: new Abstract: Imagined speech decoding from EEG offers a promising route toward naturalistic brain-computer interfaces. Traditional closed-set classifiers treat seman

safetyarxiv-cs-lg

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

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

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

TRACER: A Multimodal Harness for Patient Care in Hospitals

arXiv:2610.21266v1 Announce Type: cross Abstract: Continuous video and clinical records provide complementary information for understanding how patient behavior evolves over a hospital stay. A central

Writability and the Pathway: A Controlled Study of Knowledge Injection into Addressed Memory

arXiv:2610.21553v1 Announce Type: new Abstract: Addressed conditional memory provides a local surface for post-hoc knowledge injection, but a successful write need not transfer to a new question or pr

ZoomCap: Evidence-Routed Visual Zooming for Recoverable Visual Information Transfer

arXiv:2610.21320v1 Announce Type: cross Abstract: Detailed captions transfer visual information into vision-language model supervision, but quality is easily conflated with length: long single-pass ou

A LoRA is Worth a Thousand Pictures

arXiv:2412.12048v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has become a standard approach for parameter-efficient fine-tuning of image diffusion models, with over 200K models train

A Strong Harness Narrows the Model Gap: A Comparative Evaluation for Legislative Document-to-XML Conversion

arXiv:2610.19999v1 Announce Type: new Abstract: Legal-content reproduction requires matching the source exactly in section hierarchy, verbatim text, citations, and amendment spans; a dropped citation

Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation

arXiv:2610.20571v1 Announce Type: new Abstract: Long-form agentic video generation must turn a screenplay into shots that remain spatially and temporally consistent across nonconsecutive requests. Pas

AssetForge: Asset-Augmented Task Synthesis for Business Automation Agents

arXiv:2610.18330v1 Announce Type: new Abstract: Training business automation agents requires executable tasks that connect a natural-language request to an application state and a verifiable end state

AuthGateBench: Benchmarking LLMs as Authorization Gate

arXiv:2610.16426v1 Announce Type: new Abstract: Automated approval systems let coding agents execute high-risk actions with minimal human oversight by delegating the authorization decision to a classi

Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs

arXiv:2610.18818v2 Announce Type: cross Abstract: Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings. For machine translation of low-resource langua