Search: “benchmark”

arXiv:2605.04461v1 Announce Type: new Abstract: While Test-Time Scaling (TTS) offers a promising direction to enhance video generation without the surging costs of training, current test-time video ge

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SWAN: Semantic Watermarking with Abstract Meaning Representation

DGX agent

arXiv:2605.04305v1 Announce Type: new Abstract: We introduce SWAN (Semantic Watermarking with Abstract Meaning Representation), a novel framework that embeds watermark signatures into the semantic str

model-releasesarxiv-cs-cl

7 May 2026

Telegraph English: Semantic Prompt Compression via Structured Symbolic Rewriting

arXiv:2605.04426v1 Announce Type: new Abstract: We introduce Telegraph English (TE), a prompt-compression protocol that rewrites natural language into a symbol-rich, formally-structured dialect. Where

The First Token Knows: Single-Decode Confidence for Hallucination Detection

arXiv:2605.05166v1 Announce Type: new Abstract: Self-consistency detects hallucinations by generating multiple sampled answers to a question and measuring agreement, but this requires repeated decodin

researcharxiv-cs-cl

Tightly-Coupled Estimation and Guidance for Robust Low-Thrust Rendezvous via Adaptive Homotopy

model-releasesarxiv-cs-ro

arXiv:2605.04481v1 Announce Type: new Abstract: Minimum-fuel low-thrust rendezvous guidance yields bang-bang control structures highly sensitive to estimation errors, sensor anomalies, and solver regu

To Fuse or to Drop? Dual-Path Learning for Resolving Modality Conflicts in Multimodal Emotion Recognition

arXiv:2605.04877v1 Announce Type: cross Abstract: Multimodal emotion recognition (MER) benefits from combining text, audio, and vision, yet standard fusion often fails when modalities conflict. Crucia

safetyarxiv-cs-lg

Transformed Latent Variable Multi-Output Gaussian Processes

arXiv:2605.05133v1 Announce Type: new Abstract: Multi-Output Gaussian Processes (MOGPs) provide a principled probabilistic framework for modelling correlated outputs but face scalability bottlenecks w

researcharxiv-cs-lg

TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments

arXiv:2605.04107v1 Announce Type: cross Abstract: Production agent frameworks (OpenAI Function Calling, Anthropic Tool Use, MCP) transmit tool schemas as JSON, a format designed for machine parsing, n

UI2Code^N: UI-to-Code Generation as Interactive Visual Optimization

arXiv:2511.08195v3 Announce Type: replace Abstract: UI-to-code aims to translate UI screenshots into executable front-end code. Despite progress with vision-language models (VLMs), most existing metho

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Local Ai

Validity-Calibrated Reasoning Distillation

arXiv:2605.04078v1 Announce Type: new Abstract: Reasoning distillation aims to transfer multi-step reasoning capabilities from large language models to smaller, more efficient ones. While recent metho

local-aiarxiv-cs-lg

Vision-EKIPL: External Knowledge-Infused Policy Learning for Visual Reasoning

arXiv:2506.06856v3 Announce Type: replace Abstract: Visual reasoning is crucial for understanding complex multimodal data and advancing Artificial General Intelligence. Existing methods enhance the re

VL-UniTrack: A Unified Framework with Visual-Language Prompts for UAV-Ground Visual Tracking

arXiv:2605.04574v1 Announce Type: new Abstract: UAV-ground visual tracking (UGVT) aims to simultaneously track the same object from both the UAV and the ground view. However, existing two-stream metho

Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging

arXiv:2605.05161v1 Announce Type: new Abstract: Zero-shot anomaly localisation via vision-language models (VLMs) offers a compelling approach for rare pathology detection, yet its performance is funda

When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning

arXiv:2605.05172v1 Announce Type: new Abstract: Behavior Cloning (BC) has emerged as a highly effective paradigm for robot learning. However, BC lacks a self-guided mechanism for online improvement af

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Applications

When LLMs get significantly worse: A statistical approach to detect model degradations

arXiv:2602.10144v2 Announce Type: replace-cross Abstract: Minimizing the inference cost and latency of foundation models has become a crucial area of research. Optimization approaches include theoreti

applicationsarxiv-cs-lg

12 Angry AI Agents: Evaluating Multi-Agent LLM Decision-Making Through Cinematic Jury Deliberation

arXiv:2605.01986v1 Announce Type: new Abstract: What if the twelve jurors of Sidney Lumet's 12 Angry Men (1957) were not men, but large language models? Would the one juror who disagrees still be able

6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence

arXiv:2605.01546v1 Announce Type: cross Abstract: Sixth-generation (6G) networks are increasingly envisioned as AI-native infrastructures integrating communication, sensing, and computing into a unifi

Adaptive Data Compression and Reconstruction for Memory-Bounded EEG Continual Learning

arXiv:2605.03085v1 Announce Type: new Abstract: Electroencephalography (EEG) signals provide millisecond-level temporal resolution but their analysis is limited by remarkable noise and inter-subject v

researcharxiv-cs-lg

Adaptive Negative Scheduling for Graph Contrastive Learning

arXiv:2605.03076v1 Announce Type: new Abstract: Graph contrastive learning (GCL) has become a central paradigm for self-supervised representation learning in computational intelligence, with applicati

ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms

arXiv:2605.03212v1 Announce Type: cross Abstract: Modeling latent clinical constructs from unconstrained clinical interactions is a unique challenge in affective computing. We present ADAPTS (Agentic

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AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages

arXiv:2601.06395v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly multilingual, yet open models continue to underperform relative to proprietary systems, with the gap m

Agentic-imodels: Evolving agentic interpretability tools via autoresearch

arXiv:2605.03808v1 Announce Type: cross Abstract: Agentic data science (ADS) systems are rapidly improving their capability to autonomously analyze, fit, and interpret data, potentially moving towards

Aligning Inductive Bias for Data-Efficient Generalization in State Space Models

arXiv:2509.20789v4 Announce Type: replace Abstract: The remarkable success of modern AI has been closely tied to scaling laws, yet the finite supply of high-quality data makes data efficiency--learnin

safetyarxiv-cs-lg

Amortized Variational Inference for Joint Posterior and Predictive Distributions in Bayesian Uncertainty Quantification

arXiv:2605.03710v1 Announce Type: cross Abstract: Bayesian predictive inference propagates parameter uncertainty to quantities of interest through the posterior-predictive distribution. In practice, t

Local Ai

An Empirical Study of Agent Skills for Healthcare: Practice, Gaps, and Governance

arXiv:2605.02709v1 Announce Type: new Abstract: Healthcare automation is shaped by local procedures and organizational constraints, so agent capabilities rarely transfer unchanged across settings. Age

local-aiarxiv-cs-ai

Audio-Visual Intelligence in Large Foundation Models

arXiv:2605.04045v1 Announce Type: new Abstract: Audio-Visual Intelligence (AVI) has emerged as a central frontier in artificial intelligence, bridging auditory and visual modalities to enable machines

safetyarxiv-cs-cv

Beyond Scalars: Evaluating and Understanding LLM Reasoning via Geometric Progress and Stability

arXiv:2603.10384v2 Announce Type: replace Abstract: Evaluating LLM reliability via scalar probabilities often fails to capture the structural dynamics of reasoning. We introduce TRACED, a framework th

BFORE: Butterfly-Firefly Optimized Retinex Enhancement for Low-Light Image Quality Improvement

arXiv:2605.03509v1 Announce Type: new Abstract: Low-light image enhancement is a fundamental challenge in computer vision and multimedia applications, as images captured under insufficient illuminatio

Causal Software Engineering: A Vision and Roadmap

arXiv:2605.02454v1 Announce Type: cross Abstract: Software engineering increasingly involves making high-stakes decisions under uncertainty, using signals from code, field data, and socio-technical pr

CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation

arXiv:2605.00930v1 Announce Type: cross Abstract: In this work, we introduce CellxPert, a scalable multimodal foundation model that unifies single-cell and spatial multi-omics within a common represen

Tutorials

Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding

arXiv:2512.17817v3 Announce Type: replace Abstract: While 3DGS has emerged as a high-fidelity scene representation, encoding rich, general-purpose features directly from its primitives remains under-e

tutorialsarxiv-cs-cv

Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents

arXiv:2604.06132v2 Announce Type: replace Abstract: Large language models are increasingly deployed as autonomous agents for multi-step workflows in real-world software environments. However, existing

safetyarxiv-cs-ai

Coherent Hierarchical Multi-Label Learning to Defer for Medical Imaging

arXiv:2605.02734v1 Announce Type: new Abstract: Learning to Defer (L2D) enables a model to predict autonomously or defer to an expert, but prior work largely assumes flat label spaces. We study the fi

safetyarxiv-cs-ai

Complex Equation Learner: Rational Symbolic Regression with Gradient Descent in Complex Domain

arXiv:2605.03841v1 Announce Type: new Abstract: Symbolic regression aims to discover interpretable equations from data, yet modern gradient-based methods fail for operators that introduce singularitie

researcharxiv-cs-lg

Applications

Conformalized Percentile Interval: Finite Sample Validity and Improved Conditional Performance

arXiv:2605.03233v1 Announce Type: cross Abstract: Conformal prediction provides distribution-free predictive intervals with finite-sample marginal coverage. However, achieving conditional validity and

applicationsarxiv-cs-lg

Conservative quantum offline model-based optimization

arXiv:2506.19714v2 Announce Type: replace-cross Abstract: Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-o

Context- and Pixel-aware Large Language Model for Video Quality Assessment

arXiv:2505.16025v3 Announce Type: replace Abstract: Video quality assessment (VQA) is a challenging research topic with broad applications. Traditional hand-crafted and discriminative learning-based V

researcharxiv-cs-cv

Contrastive Regularization for Accent-Robust ASR

arXiv:2605.03297v1 Announce Type: cross Abstract: ASR systems based on self-supervised acoustic pretraining and CTC fine-tuning achieve strong performance on native speech but remain sensitive to acce

Controllable and Verifiable Process Data Synthesis for Process Reward Models

arXiv:2605.02395v1 Announce Type: new Abstract: Process reward models (PRMs) rely on high-quality process supervision data, yet existing construction methods often provide limited control over error l

Local Ai

Correct Is Not Enough: Training Reasoning Planners with Executor-Grounded Rewards

arXiv:2605.03862v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards has become a common way to improve explicit reasoning in large language models, but final-answer correc

local-aiarxiv-cs-cl

CoVSpec: Efficient Device-Edge Co-Inference for Vision-Language Models via Speculative Decoding

arXiv:2605.02218v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated strong capabilities in multimodal perception and reasoning. However, deploying large VLMs on mobile devi

CreativityBench: Evaluating Agent Creative Reasoning via Affordance-Based Tool Repurposing

arXiv:2605.02910v1 Announce Type: cross Abstract: Recent advances in large language models have led to strong performance on reasoning and environment-interaction tasks, yet their ability for creative

CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis

arXiv:2605.03259v1 Announce Type: new Abstract: High-throughput plant phenotyping, the quantitative measurement of observable plant traits, is critical for modern breeding but remains constrained by a

CuraView: A Multi-Agent Framework for Medical Hallucination Detection with GraphRAG-Enhanced Knowledge Verification

arXiv:2605.03476v1 Announce Type: new Abstract: Discharge summaries require extracting critical information from lengthy electronic health records (EHRs), a process that is labor-intensive when perfor

Detecting Stealth Sycophancy in Mental-Health Dialogue with Dynamic Emotional Signature Graphs

arXiv:2605.03472v1 Announce Type: new Abstract: As conversational AI therapists are increasingly used in psychological support settings, reliable offline evaluation of therapeutic response quality rem

DiagramNet: An End-to-End Recognition Framework and Dataset for Non-Standard System-Level Diagrams

arXiv:2605.01338v1 Announce Type: new Abstract: System-level diagrams encode the architectural blueprint of chip design, specifying module functions, dataflows, and interface protocols. However, non-s

DINO Soars: DINOv3 for Open-Vocabulary Semantic Segmentation of Remote Sensing Imagery

arXiv:2605.03175v1 Announce Type: new Abstract: The remote sensing (RS) domain suffers from a lack of densely labeled datasets, which are costly to obtain. Thus, models that can segment RS imagery wel

Distributed Deep Variational Approach for Privacy-preserving Data Release

arXiv:2605.03069v1 Announce Type: cross Abstract: Federated learning (FL) lets distributed nodes train a shared model without exchanging their raw data, but in privacy-sensitive deployments medical se