Search: “context”

On the Emergence of Syntax by Means of Local Interaction

arXiv:2604.17857v1 Announce Type: new Abstract: Can syntactic processing emerge spontaneously from purely local interaction? We present a concrete instance on a minimal system: an 18,658-parameter two

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On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability

DGX agent

arXiv:2604.16576v1 Announce Type: cross Abstract: Decoder-only large language models (LLMs) are increasingly replacing BERT-style architectures as the backbone for dense retrieval, achieving substanti

researcharxiv-cs-cl

21 Apr 2026

On the Sample Complexity of Learning for Blind Inverse Problems

arXiv:2512.23405v4 Announce Type: replace Abstract: Blind inverse problems arise in many experimental settings where both the signal of interest and the forward operator are (partially) unknown. In th

researcharxiv-cs-lg

Agents

OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models

arXiv:2604.17915v1 Announce Type: new Abstract: Vision-Language Models(VLMs) excel at autoregressive text generation, yet end-to-end autonomous driving requires multi-task learning with structured out

agentsarxiv-cs-cv

Online Conformal Prediction with Adversarial Semi-bandit Feedback via Regret Minimization

arXiv:2604.17984v1 Announce Type: new Abstract: Uncertainty quantification is crucial in safety-critical systems, where decisions must be made under uncertainty. In particular, we consider the problem

OVOD-Agent: A Markov-Bandit Framework for Proactive Visual Reasoning and Self-Evolving Detection

arXiv:2511.21064v2 Announce Type: replace-cross Abstract: Open-Vocabulary Object Detection (OVOD) aims to enable detectors to generalize across categories by leveraging semantic information. Although

safetyarxiv-cs-cv

P-Check: Advancing Personalized Reward Model via Learning to Generate Dynamic Checklist

arXiv:2601.02986v2 Announce Type: replace Abstract: Recent approaches in personalized reward modeling have primarily focused on leveraging user interaction history to align model judgments with indivi

PersonalHomeBench: Evaluating Agents in Personalized Smart Homes

arXiv:2604.16813v1 Announce Type: cross Abstract: Agentic AI systems are rapidly advancing toward real-world applications, yet their readiness in complex and personalized environments remains insuffic

Local Ai

R-FLoRA: Residual-Statistic-Gated Low-Rank Adaptation for Single-Image Face Morphing Attack Detection

arXiv:2604.17321v1 Announce Type: new Abstract: Face morphing attacks pose a substantial risk to the reliability of face recognition systems used in passport issuance, border control, and digital iden

local-aiarxiv-cs-cv

Applications

RAG-DIVE: A Dynamic Approach for Multi-Turn Dialogue Evaluation in Retrieval-Augmented Generation

arXiv:2604.16310v1 Announce Type: cross Abstract: Evaluating Retrieval-Augmented Generation (RAG) systems using static multi-turn datasets fails to capture the dynamic nature of real-world dialogues.

applicationsarxiv-cs-cl

Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning

arXiv:2604.16850v1 Announce Type: new Abstract: Fast execution of contact-rich manipulation is critical for practical deployment, yet providing fast demonstrations for imitation learning (IL) remains

researcharxiv-cs-ro

ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning

arXiv:2603.05863v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have revolutionized code generation, standard ``System 1'' approaches that generate solutions in a single forward

ReGA: Model-Based Safeguard for LLMs via Representation-Guided Abstraction

arXiv:2506.01770v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved tremendous success in various tasks, yet concerns about their safety and security have emerged. In

Revisiting Change VQA in Remote Sensing with Structured and Native Multimodal Qwen Models

arXiv:2604.18429v1 Announce Type: new Abstract: Change visual question answering (Change VQA) addresses the problem of answering natural-language questions about semantic changes between bi-temporal r

Robust Bias Evaluation with FilBBQ: A Filipino Bias Benchmark for Question-Answering Language Models

arXiv:2602.14466v2 Announce Type: replace Abstract: With natural language generation becoming a popular use case for language models, the Bias Benchmark for Question-Answering (BBQ) has grown to be an

RoMathExam: A Longitudinal Dataset of Romanian Math Exams (1895-2025) with a Seven-Decade Core (1957-2025)

arXiv:2604.16392v1 Announce Type: cross Abstract: AI in Education research increasingly relies on authentic, curriculum-grounded assessment data, yet large, well-structured exam corpora remain scarce

RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design

model-releasesarxiv-cs-lg

arXiv:2604.17175v1 Announce Type: new Abstract: We introduce RosettaSearch, an inference-time multi-objective optimization approach for protein sequence optimization. We use large language models (LLM

SaFeR-Steer: Evolving Multi-Turn MLLMs via Synthetic Bootstrapping and Feedback Dynamics

arXiv:2604.16358v1 Announce Type: cross Abstract: MLLMs are increasingly deployed in multi-turn settings, where attackers can escalate unsafe intent through the evolving visual-text history and exploi

safetyarxiv-cs-cl

Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection

arXiv:2601.05403v2 Announce Type: replace Abstract: Large language models (LLMs) have been widely applied across various domains of finance. Since their training data are largely derived from human-au

ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generation

arXiv:2604.17147v1 Announce Type: new Abstract: We introduce ScenarioControl, the first vision-language control mechanism for learned driving scenario generation. Given a text prompt or an input image

researcharxiv-cs-cv

Agents

Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generation

arXiv:2604.16958v1 Announce Type: new Abstract: Narrative-driven product photography has become a prevalent paradigm in modern marketing, as coherent visual storytelling helps convey product value and

agentsarxiv-cs-cv

SemLT3D: Semantic-Guided Expert Distillation for Camera-only Long-Tailed 3D Object Detection

arXiv:2604.18476v1 Announce Type: new Abstract: Camera-only 3D object detection has emerged as a cost-effective and scalable alternative to LiDAR for autonomous driving, yet existing methods primarily

safetyarxiv-cs-cv

Applications

SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe

arXiv:2410.05248v4 Announce Type: replace Abstract: To acquire instruction-following capabilities, large language models (LLMs) undergo instruction tuning, where they are trained on instruction-respon

applicationsarxiv-cs-cl

Sky2Ground: A Benchmark for Site Modeling under Varying Altitude

arXiv:2603.13740v3 Announce Type: replace Abstract: We introduce Sky2Ground, a three-view dataset designed for varying altitude camera localization, correspondence learning, and reconstruction. The da

SpeakerSleuth: Can Large Audio-Language Models Judge Speaker Consistency across Multi-turn Dialogues?

arXiv:2601.04029v2 Announce Type: replace Abstract: Large Audio-Language Models (LALMs) as judges have emerged as a prominent approach for evaluating speech generation quality, yet their ability to as

SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation

arXiv:2512.21204v2 Announce Type: replace Abstract: Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compar

SQL Query Engine: A Self-Healing LLM Pipeline for Natural Language to PostgreSQL Translation

arXiv:2604.16511v1 Announce Type: cross Abstract: We present SQL Query Engine, an open-source, self-hosted service that translates natural language questions into validated PostgreSQL queries through

Stability-Weighted Decoding for Diffusion Language Models

arXiv:2604.17068v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) enable parallel text generation by iteratively denoising a fully masked sequence, unmasking a subset of masked t

StealthGraph: Exposing Domain-Specific Risks in LLMs through Knowledge-Graph-Guided Harmful Prompt Generation

arXiv:2601.04740v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied in specialized domains such as finance and healthcare, where they introduce unique safety risk

safetyarxiv-cs-cl

StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning

arXiv:2604.18401v1 Announce Type: new Abstract: General agents have given rise to phenomenal applications such as OpenClaw and Claude Code. As these agent systems (a.k.a. Harnesses) strive for bolder

Style over Story: Measuring LLM Narrative Preferences via Structured Selection

arXiv:2510.02025v4 Announce Type: replace Abstract: We introduce a constraint-selection-based experiment design for measuring narrative preferences of Large Language Models (LLMs). This design offers

Support Sufficiency as Consequence-Sensitive Compression in Belief Arbitration

arXiv:2604.16434v1 Announce Type: cross Abstract: When a system commits to a hypothesis, much of the evidential structure behind that commitment is lost to compression. Standard accounts assume that s

Target Parameterization in Diffusion Models for Nonlinear Spatiotemporal System Identification

arXiv:2604.17566v1 Announce Type: cross Abstract: Machine learning is becoming increasingly important for nonlinear system identification, including dynamical systems with spatially distributed output

researcharxiv-cs-lg

Test-Time Alignment via Hypothesis Reweighting

arXiv:2412.08812v2 Announce Type: replace Abstract: Reward models trained on aggregate preferences often fail to capture individual users' values, but existing adaptation methods such as fine-tuning o

Local Ai

The Consensus Trap: Rescuing Multi-Agent LLMs from Adversarial Majorities via Token-Level Collaboration

arXiv:2604.17139v1 Announce Type: new Abstract: Multi-agent large language model (LLM) architectures increasingly rely on response-level aggregation, such as Majority Voting (MAJ), to raise reasoning

local-aiarxiv-cs-cl

The Impact of Off-Policy Training Data on Probe Generalisation

arXiv:2511.17408v4 Announce Type: replace-cross Abstract: Probing has emerged as a promising method for monitoring large language models (LLMs), enabling cheap inference-time detection of concerning b

Towards a Data-Parameter Correspondence for LLMs: A Preliminary Discussion

model-releasesarxiv-cs-lg

arXiv:2604.17384v1 Announce Type: new Abstract: Large language model optimization has historically bifurcated into isolated data-centric and model-centric paradigms: the former manipulates involved sa

Tutorials

Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design

arXiv:2604.18062v1 Announce Type: new Abstract: Accurate machine-learning models for aerodynamic prediction are essential for accelerating shape optimization, yet remain challenging to develop for com

tutorialsarxiv-cs-lg

Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles

arXiv:2604.18089v1 Announce Type: new Abstract: Bayesian Deep Ensembles (BDEs) represent a powerful approach for uncertainty quantification in deep learning, combining the robustness of Deep Ensembles

researcharxiv-cs-lg

TriTS: Time Series Forecasting from a Multimodal Perspective

arXiv:2604.16748v1 Announce Type: new Abstract: Time series forecasting plays a pivotal role in critical sectors such as finance, energy, transportation, and meteorology. However, Long-term Time Serie

Understanding Counting Mechanisms in Large Language and Vision-Language Models

arXiv:2511.17699v2 Announce Type: replace Abstract: Counting is one of the fundamental abilities of large language models (LLMs) and large vision-language models (LVLMs). This paper examines how these

researcharxiv-cs-cv

UniGeo: Unifying Geometric Guidance for Camera-Controllable Image Editing via Video Models

arXiv:2604.17565v1 Announce Type: new Abstract: Camera-controllable image editing aims to synthesize novel views of a given scene under varying camera poses while strictly preserving cross-view geomet

researcharxiv-cs-cv

Tutorials

UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules

arXiv:2506.03157v4 Announce Type: replace-cross Abstract: Molecular Dynamics (MD) simulations are essential for understanding the atomic-level behavior of molecular systems, giving insights into their

tutorialsarxiv-cs-lg

VC-Inspector: Advancing Reference-free Evaluation of Video Captions with Factual Analysis

arXiv:2509.16538v3 Announce Type: replace-cross Abstract: We propose VC-Inspector, a lightweight, open-source large multimodal model (LMM) for reference-free evaluation of video captions, with a focus

Vocab Diet: Reshaping the Vocabulary of LLMs via Vector Arithmetic

arXiv:2510.17001v2 Announce Type: replace Abstract: Large language models (LLMs) often encode word-form variation (e.g., walk vs. walked) as linear directions in the embedding space. However, standard

WeatherArchive-Bench: Benchmarking Retrieval-Augmented Reasoning for Historical Weather Archives

arXiv:2510.05336v2 Announce Type: replace Abstract: Historical archives on weather events are collections of enduring primary source records that offer rich, untapped narratives of how societies have

When Earth Foundation Models Meet Diffusion: An Application to Land Surface Temperature Super-Resolution

arXiv:2604.16841v1 Announce Type: new Abstract: Land surface temperature (LST) super-resolution is important for environmental monitoring. However, it remains challenging as coarse thermal observation

Local Ai

Where to Focus: Query-Modulated Multimodal Keyframe Selection for Long Video Understanding

arXiv:2604.17422v1 Announce Type: new Abstract: Long video understanding remains a formidable challenge for Multimodal Large Language Models (MLLMs) due to the prohibitive computational cost of proces

local-aiarxiv-cs-cv