Search: “benchmark”

Context Over Content: Exposing Evaluation Faking in Automated Judges

arXiv:2604.15224v1 Announce Type: cross Abstract: The extit{LLM-as-a-judge} paradigm has become the operational backbone of automated AI evaluation pipelines, yet rests on an unverified assumption: th

safetyarxiv-cs-cl

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Contract-Coding: Towards Repo-Level Generation via Structured Symbolic Paradigm

DGX agent

arXiv:2604.13100v1 Announce Type: cross Abstract: The shift toward intent-driven software engineering (often termed 'Vibe Coding') exposes a critical Context-Fidelity Trade-off: vague user intents ove

model-releasesarxiv-cs-ai

17 Apr 2026

Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge

arXiv:2604.14488v1 Announce Type: cross Abstract: In any domain where knowledge accumulates under formal authority -- law, drug regulation, software security -- a later document can formally void an e

Cross Paradigm Representation and Alignment Transformer for Image Deraining

arXiv:2504.16455v2 Announce Type: replace Abstract: Transformer-based networks have achieved strong performance in low-level vision tasks like image deraining by utilizing spatial or channel-wise self

Hardware

cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots

arXiv:2603.05493v2 Announce Type: replace Abstract: Effective robot autonomy requires motion generation that is safe, feasible, and reactive. Current methods are fragmented: fast planners output physi

hardwarearxiv-cs-ro

Applications

De-Anonymization at Scale via Tournament-Style Attribution

arXiv:2601.12407v2 Announce Type: replace-cross Abstract: As LLMs rapidly advance and enter real-world use, their privacy implications are increasingly important. We study an authorship de-anonymizati

applicationsarxiv-cs-cl

Deepfake Detection Generalization with Diffusion Noise

arXiv:2604.14570v1 Announce Type: new Abstract: Deepfake detectors face growing challenges in generalization as new image synthesis techniques emerge. In particular, deepfakes generated by diffusion m

local-aiarxiv-cs-cv

DeepPrune: Parallel Scaling without Inter-trace Redundancy

arXiv:2510.08483v2 Announce Type: replace Abstract: Parallel scaling has emerged as a powerful paradigm to enhance reasoning capabilities in large language models (LLMs) by generating multiple Chain-o

DeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos

arXiv:2601.09240v2 Announce Type: replace Abstract: Satellite videos provide continuous observations of surface dynamics but pose significant challenges for multi-object tracking (MOT), especially und

Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach

arXiv:2411.00361v4 Announce Type: replace Abstract: Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However

safetyarxiv-cs-lg

Dissecting Failure Dynamics in Large Language Model Reasoning

arXiv:2604.14528v1 Announce Type: cross Abstract: Large Language Models (LLMs) achieve strong performance through extended inference-time deliberation, yet how their reasoning failures arise remains p

local-aiarxiv-cs-cl

DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models

arXiv:2604.15016v1 Announce Type: new Abstract: EEG foundation models (FMs) achieve strong cross-subject and cross-task generalization but impose substantial computational and memory costs that hinder

researcharxiv-cs-lg

Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error

arXiv:2510.26109v4 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has significantly boosted the reasoning capability of language models (LMs). However, existing

safetyarxiv-cs-lg

DocVAL: Validated Chain-of-Thought Distillation for Grounded Document VQA

arXiv:2511.22521v2 Announce Type: replace Abstract: Document visual question answering requires models not only to answer questions correctly, but also to precisely localize answers within complex doc

local-aiarxiv-cs-cv

Domain-Adaptive Model Merging Across Disconnected Modes

arXiv:2603.05957v2 Announce Type: replace-cross Abstract: Learning across domains is challenging when data cannot be centralized due to privacy or heterogeneity, which limits the ability to train a si

researcharxiv-cs-ai

Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG

arXiv:2604.14572v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds LLM responses in external evidence but treats the model as a passive consumer of search results: it never

DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule

arXiv:2602.22699v2 Announce Type: replace-cross Abstract: SQL is the de facto interface for exploratory data analysis; however, releasing exact query results can expose sensitive information through m

Applications

DVFace: Spatio-Temporal Dual-Prior Diffusion for Video Face Restoration

arXiv:2604.14560v1 Announce Type: new Abstract: Video face restoration aims to enhance degraded face videos into high-quality results with realistic facial details, stable identity, and temporal coher

applicationsarxiv-cs-cv

DySCO: Dynamic Attention-Scaling Decoding for Long-Context Language Models

arXiv:2602.22175v2 Announce Type: replace Abstract: Understanding and reasoning over long contexts is a crucial capability for language models (LMs). Although recent models support increasingly long c

Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks

arXiv:2601.03448v2 Announce Type: replace Abstract: Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. While this approach facilitates the learning o

Enhancing LLM-Based Neural Network Generation: Few-Shot Prompting and Efficient Validation for Automated Architecture Design

arXiv:2512.24120v2 Announce Type: replace Abstract: Automated neural network architecture design remains a significant challenge in computer vision. Task diversity and computational constraints requir

researcharxiv-cs-cv

Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization

arXiv:2604.14267v1 Announce Type: new Abstract: Search agents extend Large Language Models (LLMs) beyond static parametric knowledge by enabling access to up-to-date and long-tail information unavaila

safetyarxiv-cs-lg

EuropeMedQA Study Protocol: A Multilingual, Multimodal Medical Examination Dataset for Language Model Evaluation

arXiv:2604.14306v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated high proficiency on English-centric medical examinations, their performance often declines when fac

EviSearch: A Human in the Loop System for Extracting and Auditing Clinical Evidence for Systematic Reviews

arXiv:2604.14165v1 Announce Type: new Abstract: We present EviSearch, a multi-agent extraction system that automates the creation of ontology-aligned clinical evidence tables directly from native tria

Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting

arXiv:2602.12389v3 Announce Type: replace-cross Abstract: Temporal knowledge graph (TKG) forecasting requires predicting future facts by jointly modeling structural dependencies within each snapshot a

Faithfulness Serum: Mitigating the Faithfulness Gap in Textual Explanations of LLM Decisions via Attribution Guidance

arXiv:2604.14325v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance and have revolutionized NLP, but their lack of explainability keeps them treated as black boxes,

Fall into a Pit, Gain in a Wit: Cognitive-Guided Harmful Meme Detection via Misjudgment Risk Pattern Retrieval

arXiv:2510.15946v3 Announce Type: replace Abstract: Internet memes have emerged as a popular multimodal medium, yet they are increasingly weaponized to convey harmful opinions through subtle rhetorica

FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching

arXiv:2604.15115v1 Announce Type: new Abstract: Most existing Byzantine-robust federated learning (FL) methods suffer from slow and unstable convergence. Moreover, when handling a substantial proporti

FreqTrack: Frequency Learning based Vision Transformer for RGB-Event Object Tracking

arXiv:2604.14526v1 Announce Type: new Abstract: Existing single-modal RGB trackers often face performance bottlenecks in complex dynamic scenes, while the introduction of event sensors offers new pote

Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification

arXiv:2604.14958v1 Announce Type: new Abstract: Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples.

From Memorization to Creativity: LLM as a Designer of Novel Neural Architectures

arXiv:2601.02997v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in program synthesis, yet their capacity for neural architecture design -- balancing syntactic reliability,

From Reactive to Proactive: Assessing the Proactivity of Voice Agents via ProVoice-Bench

arXiv:2604.15037v1 Announce Type: cross Abstract: Recent advancements in LLM agents are gradually shifting from reactive, text-based paradigms toward proactive, multimodal interaction. However, existi

From Tokens to Steps: Verification-Aware Speculative Decoding for Efficient Multi-Step Reasoning

arXiv:2604.15244v1 Announce Type: new Abstract: Speculative decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose outputs that a stronger target mod

G-MIXER: Geodesic Mixup-based Implicit Semantic Expansion and Explicit Semantic Re-ranking for Zero-Shot Composed Image Retrieval

arXiv:2604.14710v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) aims to retrieve target images by integrating a reference image with a corresponding modification text. CIR requires join

researcharxiv-cs-cv

Generative Augmented Inference

arXiv:2604.14575v1 Announce Type: new Abstract: Data-driven operations management often relies on parameters estimated from costly human-generated labels. Recent advances in large language models (LLM

researcharxiv-cs-lg

H2VLR: Heterogeneous Hypergraph Vision-Language Reasoning for Few-Shot Anomaly Detection

arXiv:2604.14507v1 Announce Type: new Abstract: As a classic vision task, anomaly detection has been widely applied in industrial inspection and medical imaging. In this task, data scarcity is often a

Hardware

HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding

arXiv:2601.14724v3 Announce Type: replace-cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated significant improvement in offline video understanding. Howe

hardwarearxiv-cs-cl

HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps

arXiv:2604.14944v1 Announce Type: cross Abstract: We present HRDexDB, a large-scale, multi-modal dataset of high-fidelity dexterous grasping sequences featuring both human and diverse robotic hands. U

HY-World 2.0: A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds

arXiv:2604.14268v1 Announce Type: new Abstract: We introduce HY-World 2.0, a multi-modal world model framework that advances our prior project HY-World 1.0. HY-World 2.0 accommodates diverse input mod

researcharxiv-cs-cv

Agents

IE as Cache: Information Extraction Enhanced Agentic Reasoning

arXiv:2604.14930v1 Announce Type: new Abstract: Information Extraction aims to distill structured, decision-relevant information from unstructured text, serving as a foundation for downstream understa

agentsarxiv-cs-cl

Improving Clean Accuracy via a Tangent-Space Perspective on Adversarial Training

arXiv:2408.14728v2 Announce Type: replace Abstract: Adversarial training has proven effective in improving the robustness of deep neural networks against adversarial attacks. However, this enhanced ro

Improving Language Models with Intentional Analysis

arXiv:2502.04689v4 Announce Type: replace Abstract: Intent, a critical cognitive notion and mental state, is ubiquitous in human communication and problem-solving. Accurately understanding the underly

Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning

arXiv:2407.00809v3 Announce Type: replace Abstract: This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kern

researcharxiv-cs-lg

Label-efficient underwater species classification with logistic regression on frozen foundation model embeddings

arXiv:2604.00313v2 Announce Type: replace Abstract: Automated species classification from underwater imagery is bottlenecked by the cost of expert annotation, and supervised models trained on one data

Lazy or Efficient? Towards Accessible Eye-Tracking Event Detection Using LLMs

arXiv:2604.13243v1 Announce Type: cross Abstract: Gaze event detection is fundamental to vision science, human-computer interaction, and applied analytics. However, current workflows often require spe

researcharxiv-cs-ai

Learning Adaptive Reasoning Paths for Efficient Visual Reasoning

arXiv:2604.14568v1 Announce Type: cross Abstract: Visual reasoning models (VRMs) have recently shown strong cross-modal reasoning capabilities by integrating visual perception with language reasoning.

safetyarxiv-cs-cl

Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection

arXiv:2604.15065v1 Announce Type: new Abstract: Transformer-based detectors have advanced small-object detection, but they often remain inefficient and vulnerable to background-induced query noise, wh

Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting

arXiv:2505.11017v2 Announce Type: replace Abstract: Time series forecasting is critical across multiple domains, where time series data exhibit both local patterns and global dependencies. While Trans

local-aiarxiv-cs-lg