Search: “benchmark”

UniSHARP: Universal Sharp Monocular View Synthesis

arXiv:2606.07514v1 Announce Type: new Abstract: In this work, we focus on extending SHARP, the popular photorealistic view synthesis method, for universal monocular rendering across a continuum of cam

8 Jun 2026

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ADK Arena: Evaluating Agent Development Kits via LLM-as-a-Developer

DGX agent

arXiv:2606.05548v1 Announce Type: cross Abstract: The rapid proliferation of Agent Development Kits (ADKs), SDK-level frameworks for building LLM-powered autonomous agents, has outpaced any empirical

model-releasesarxiv-cs-ai

6 Jun 2026

GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection

arXiv:2606.05566v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but they remain vulnerable to Prompt Injection (PI) and Jailbreak (JB) attack

6 Jun 2026

Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin

arXiv:2606.05368v1 Announce Type: new Abstract: Accurate, spatially explicit characterization of tropical forest structure is essential for carbon accounting and ecosystem monitoring, yet most ML pipe

CHALIS: A Challenge Dataset for Language Identification in Difficult Scenarios

arXiv:2606.06088v1 Announce Type: new Abstract: We present CHALIS (Challenging Language Identification Samples), a new benchmark dataset explicitly designed to address difficult cases in language iden

Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments

arXiv:2606.05661v1 Announce Type: cross Abstract: Continual learning, the ability of AI systems to improve through sequential experience, has attracted substantial interest, but no high-quality benchm

Dense Contexts Are Hard Contexts: Lexical Density Limits Effective Context in LLMs

arXiv:2606.06203v1 Announce Type: new Abstract: Input length and the position of relevant information are widely cited as the primary causes of degraded LLM long-context performance. Here, we study le

The Granularity Gap: A Multi-Dimensional Longitudinal Audit of Sycophancy in Gemini Models

arXiv:2606.05183v1 Announce Type: new Abstract: Large language models are increasingly deployed as high-stakes advisors, yet standard alignment benchmarks treat sycophancy as a binary failure mode. We

Unifying Dataset Pruning and Distillation for Efficient Large-scale Compression

arXiv:2502.06434v2 Announce Type: replace Abstract: Dataset pruning (DP) and dataset distillation (DD) fundamentally differ in their outputs: DP selects original image subsets, while DD generates synt

Affordance2Action: Task-Conditioned Scene-level Affordance Grounding for Real-Time Manipulation

model-releasesarxiv-cs-ro

arXiv:2606.04172v1 Announce Type: new Abstract: Task-conditioned manipulation requires grounding instructions to task-relevant functional parts rather than object categories. This setting is scene-dep

Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing Problems

arXiv:2606.04816v1 Announce Type: new Abstract: Large language models (LLMs) increasingly translate natural-language optimization problems into executable solver code. Yet for constraint-dense operati

Can Generalist Agents Automate Data Curation?

arXiv:2606.04261v1 Announce Type: new Abstract: Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement,

CounterFace: A Synthetic Face Dataset for Fine-Grained Counterfactual Evaluation of Face Recognition Systems

arXiv:2407.13922v3 Announce Type: replace-cross Abstract: Face recognition (FR) systems are widely deployed in critical applications, making their reliability and robustness across diverse populations

researcharxiv-cs-ai

Emotion Entanglement and Bayesian Inference for Multi-Dimensional Emotion Understanding

arXiv:2604.00819v2 Announce Type: replace-cross Abstract: Understanding emotions in natural language is inherently a multi-dimensional reasoning problem, where multiple affective signals interact thro

M^3Eval: Multi-Modal Memory Evaluation through Cognitively-Grounded Video Tasks

arXiv:2606.05008v1 Announce Type: cross Abstract: As multi-modal models advance towards long-form video understanding, memory emerges as a critical capability. Despite substantial efforts in developin

Metric-Aware Hybrid Forecasting for the CTF4Science Lorenz Challenge

arXiv:2606.04191v1 Announce Type: cross Abstract: We describe our approach to the CTF4Science Lorenz challenge, a benchmark that mixes short-horizon forecasting, long-time distribution matching, and t

NoRA: Evaluating Grounded Reasonableness in Visual First-person Normative Action Reasoning

arXiv:2606.04806v1 Announce Type: cross Abstract: LLMs and agentic systems are increasingly deployed in social environments, making normative competence critical for safe and appropriate behavior. How

Spatial Artifact Coherence Determines Codec Robustness in Patch-Based rPPG

arXiv:2606.04198v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) achieves low heart-rate error on uncompressed benchmarks yet is deployed over compressed video channels in telehealth

researcharxiv-cs-cv

When Clients Stop Following: A Cognitive Conceptualization Diagram-driven Framework for Strategic Counseling

arXiv:2606.04389v1 Announce Type: new Abstract: Large Language Models (LLMs) show promise in psychological counseling, yet existing benchmarks rely heavily on highly cooperative simulated clients. We

researcharxiv-cs-cl

Eliciting Complex Spatial Reasoning in MLLMs through Wide-Baseline Matching

arXiv:2606.03577v1 Announce Type: new Abstract: Wide-baseline matching (WBM) requires integrating geometric understanding, viewpoint changes, fine-grained perception, and occlusion reasoning, making i

Agents

Handoff Debt: The Rediscovery Cost When Coding Agents Take Over Interrupted Tasks

arXiv:2606.02875v1 Announce Type: new Abstract: Coding-agent benchmarks evaluate whether a single uninterrupted agent can resolve a repository issue. Real software work is messier: tasks are interrupt

agentsarxiv-cs-ai

LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks

arXiv:2606.03303v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit strong informal mathematical reasoning but struggle to generate mechanically verifiable proofs in formal languages

Testing LLM Arithmetic Reasoning Generalization with Automatic Numeric-Remapping Attacks

arXiv:2606.03606v1 Announce Type: cross Abstract: Large language models achieve strong performance on arithmetic reasoning benchmarks, and one common response to arithmetic brittleness is to delegate

Local Ai

WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts

arXiv:2606.03220v1 Announce Type: cross Abstract: Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the requirement-induced states and transitions

local-aiarxiv-cs-ai

Agents

When Helping Hurts and How to Fix It: Multi-Agent Debate for Data Cleaning

arXiv:2606.02866v1 Announce Type: new Abstract: When does multi-agent debate help data cleaning, and when does it hurt? Across three benchmarks, four model families, and over 6,000 task-condition pair

agentsarxiv-cs-ai

A Multi-Domain Red Teaming Framework for Safety, Robustness, and Fairness Evaluation of Medical Large Language Models

arXiv:2606.00027v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed across healthcare, yet existing benchmarks fail to capture model behavior under adversarial or

Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture

model-releasesarxiv-cs-lg

arXiv:2606.01110v1 Announce Type: cross Abstract: Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informe

AdaCodec: A Predictive Visual Code for Video MLLMs

arXiv:2606.02569v1 Announce Type: cross Abstract: Video is temporally redundant: adjacent frames usually share most objects, background, and layout. Yet existing video multimodal large language models

researcharxiv-cs-ai

AGENTCL: Toward Rigorous Evaluation of Continual Learning in Language Agents

arXiv:2606.02461v1 Announce Type: new Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future e

AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents

arXiv:2603.14465v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have evolved into tool-using agents, they remain brittle in long-horizon interactions. Unlike mathematical reason

AgentRedBench: Dynamic Redteaming and Integration-Aware Defense for LLM Agents over SaaS Integrations

arXiv:2606.02240v1 Announce Type: cross Abstract: Indirect prompt injection in tool-use agents is a concrete production threat: LLM agents read from integrations (third-party services such as Gmail, S

APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL

arXiv:2602.16720v2 Announce Type: replace-cross Abstract: Text-to-SQL systems powered by Large Language Models have excelled on academic benchmarks but struggle in complex enterprise environments. The

Safety

Automated Essay Scoring and Language Certification: Assessing Generalizability, Agreement and Validity for French

arXiv:2606.02009v1 Announce Type: new Abstract: In Automated Essay Scoring (AES), benchmarking practices have fostered minimalist evaluation practices, in contrast with the broader-view recommendation

safetyarxiv-cs-cl

Applications

CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations

arXiv:2606.00123v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance on public medical benchmarks, yet existing evaluations often remain weak proxie

applicationsarxiv-cs-ai

ClinEnv: An Interactive Multi-Stage Long Horizon EHR Environment for Agents

arXiv:2606.02568v1 Announce Type: new Abstract: Clinical practice is not the selection of an answer from enumerated options: a physician gathers heterogeneous information incrementally and commits to

ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?

arXiv:2606.01849v1 Announce Type: cross Abstract: Differentially private (DP) text synthesis promises to unlock sensitive corpora for model training, but it remains unclear whether DP synthetic data t

Do Multimodal Agents Really Benefit from Tool Use? A Systematic Study of Capability Gains

arXiv:2606.02357v1 Announce Type: cross Abstract: Tool-augmented multimodal agents show strong benchmark gains, often taken as evidence that agents have learned to use tools. We argue that this interp

FOAM: Frequency and Operator Error-Based Adaptive Damping Method for Reducing Staleness-Oriented Error for Shampoo

arXiv:2606.02365v1 Announce Type: cross Abstract: Shampoo is attracting considerable attention for its superior performance on large-scale optimization benchmarks; yet it faces a significant practical

researcharxiv-cs-ai

From Unfamiliar to Familiar: Detecting Pre-training Data via Gradient Deviations in Large Language Models

arXiv:2603.04828v2 Announce Type: replace Abstract: Pre-training data detection for LLMs is essential for addressing copyright concerns and mitigating benchmark contamination. Existing methods mainly

Tutorials

Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs

arXiv:2606.01660v1 Announce Type: new Abstract: Pre-propagation graph neural networks (PPGNNs) push all graph-dependent computation into a preprocessing step and train only on the resulting dense hop

tutorialsarxiv-cs-lg

HERO'S JOURNEY: Testing Complex Rule Induction with Text Games

arXiv:2606.02556v1 Announce Type: new Abstract: We introduce HERO'S JOURNEY, a benchmark for rule induction in goal-directed episodic tasks, where agents must infer hidden rules from demonstrations an

JenBridge: Adaptive Long-Form Video Soundtracking across Scene Transitions

arXiv:2606.01703v1 Announce Type: cross Abstract: We address the challenge of generating high-fidelity, long-form soundtracks that remain coherent across scene transitions. Existing AI music systems a

Learning from Saturated Data: Signals Beyond Correctness for LLM Training

arXiv:2606.01436v1 Announce Type: new Abstract: The growing capabilities of large language models (LLMs) have led to the saturation of many benchmarks and training datasets used to improve them. Motiv

researcharxiv-cs-cl

Make a Video Call with LLM: A Measurement Campaign over Six Mainstream Apps

arXiv:2510.00481v2 Announce Type: replace-cross Abstract: In 2025, Large Language Model (LLM) services have launched a new feature -- AI video chat -- allowing users to interact with AI agents via rea

Measurement-Driven Early Warning of Reliability Breakdown in 5G NSA Railway Networks

model-releasesarxiv-cs-lg

arXiv:2511.08851v5 Announce Type: replace-cross Abstract: This paper presents a measurement-driven study of early warning for reliability breakdown events in 5G non-standalone (NSA) railway networks.

MM-Snowball: Evaluating and Mitigating Hallucination Snowballing in Multimodal Multi-Turn Dialogue

arXiv:2606.00622v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) demonstrate remarkable visual understanding, yet their reliability in interactive settings is severely undermin

Princeton365: A Diverse Dataset with Accurate Camera Pose

arXiv:2506.09035v2 Announce Type: replace Abstract: We introduce Princeton365, a large-scale diverse dataset of 365 videos with accurate camera pose. Our dataset bridges the gap between accuracy and d

Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data

arXiv:2606.00404v1 Announce Type: new Abstract: Implicit neural representations (INRs) model a signal as a continuous coordinate-to-value function. For terrain elevation data, this supports analytic d