Search: “benchmark”

MATCHA: Matching Text via Contrastive Semantic Alignment

arXiv:2605.27345v1 Announce Type: new Abstract: Reliable evaluation is essential for understanding large language model (LLM) performance, yet today's go-to metrics, namely token-overlap scores (e.g.,

safetyarxiv-cs-cl

Measuring Prediction Uncertainty in Neural Cellular Automata

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DGX agent

arXiv:2605.26726v1 Announce Type: cross Abstract: Neural cellular automata (NCA) provide a lightweight alternative to encoder-decoder segmentation networks. However, it can be difficult to decide when

safetyarxiv-cs-ai

27 May 2026

MedGuideX: Internalizing Decision Logic from Executable Guidelines into Large Language Models for Clinical Reasoning

arXiv:2605.26567v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) encode evidence-based decision logic that clinicians apply by evaluating patient variables, conditional criteria, an

MedVol-R1: Reward-Driven Evidence Grounding for Volumetric Reasoning Segmentation

arXiv:2605.26621v1 Announce Type: cross Abstract: Volumetric Reasoning Segmentation (VRS) aims to segment a target region in a 3D medical scan from a free-form clinical query, where the referent is of

MemMorph: Tool Hijacking in LLM Agents via Memory Poisoning

arXiv:2605.26154v1 Announce Type: cross Abstract: LLM-driven agents are capable of selecting external tools to complete users' tasks. However, attackers could compromise such process, steering agents

safetyarxiv-cs-ai

Memory-Distilled Selection for Noise-Robust Anomaly Detection

arXiv:2605.26676v1 Announce Type: new Abstract: Anomaly detection (AD) under data contamination is critical for deploying unsupervised defect detection in industrial environments, where curating perfe

MerLean-Prover: A Recursive Looping Harness for End-to-End Lean 4 Theorem Proving

arXiv:2605.26959v1 Announce Type: cross Abstract: MerLean-Prover is an end-to-end Lean4 theorem prover that replaces sorry declarations with kernel-checkable proofs. It is built from three agent types

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling

arXiv:2505.18728v2 Announce Type: replace-cross Abstract: The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph St

MicroSpec: Accelerating Speculative Decoding with Lightweight In-Context Vocabularies

arXiv:2605.26444v1 Announce Type: new Abstract: Large language models typically employ vocabularies of over 100k tokens, which creates a major computational bottleneck at the final linear projection l

local-aiarxiv-cs-cl

Agents

Mind the Tool Failures: Achieving Synergistic Tool Gains for Medical Agents

arXiv:2605.26691v1 Announce Type: new Abstract: Medical AI agents increasingly use external tools for diagnosis, treatment recommendation, and evidence retrieval, yet most existing approaches assume t

agentsarxiv-cs-ai

MobileExplorer: Accelerating On-Device Inference for Mobile GUI Agents via Online Exploration

arXiv:2605.26546v1 Announce Type: new Abstract: Mobile graphical user interface (GUI) agents enable AI models to autonomously operate smartphones on behalf of users. However, most existing systems foc

MobileMoE: Scaling On-Device Mixture of Experts

arXiv:2605.27358v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) has become the de facto architecture for hundred-billion-parameter language models, yet its advantages at sub-billion scales

Model discovery for dynamical systems with complex-valued product units

model-releasesarxiv-cs-cv

arXiv:2605.27158v1 Announce Type: new Abstract: Discovering the governing equations of a dynamical system from observed trajectories provides deeper insight into its structure than mere prediction of

MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training

arXiv:2605.26842v1 Announce Type: cross Abstract: The Muon optimizer has recently offered a promising alternative to AdamW for large language model training, leveraging matrix orthogonalization to pro

local-aiarxiv-cs-cl

MRT: Masked Region Transformer for Layered Image Generation and Editing at Scale

model-releasesarxiv-cs-cv

arXiv:2605.27235v1 Announce Type: new Abstract: Layered image generation and editing is a fundamental capability that enables layer-wise reuse, editing, and composition of generated visual content, an

Multi-Agent Causal Discovery Using Large Language Models

arXiv:2407.15073v4 Announce Type: replace Abstract: Causal discovery aims to identify causal relationships between variables and is a fundamental problem across the sciences. Traditional statistical c

MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding

arXiv:2605.26320v1 Announce Type: cross Abstract: The application of generalist multimodal models (GMMs) to specialized scientific domains remains limited due to the scarcity of comprehensive domain-s

Natural Human Motion Recovery by Aligning High-Order Temporal Dynamics from Monocular Videos

arXiv:2605.26879v1 Announce Type: new Abstract: Human motion recovered from monocular videos often appears overly smooth or dynamically inconsistent, even when joint positions are numerically accurate

Neural Autoregressive Control Variates for the Quantum Monte Carlo Sign Problem

model-releasesarxiv-cs-lg

arXiv:2605.26814v1 Announce Type: cross Abstract: We train a pair of autoregressive models to construct zero-mean control variates to mitigate the sign problem in quantum Monte Carlo simulations. The

Not All Modalities Are Equal: Instruction-Aware Gating for Multimodal Videos

arXiv:2605.26232v1 Announce Type: new Abstract: Pre-trained video large language models excel at visual reasoning. However, they struggle when videos arrive with auxiliary streams, such as audio, dept

OMD-GraphRAG: Enhancing GraphRAG with Ontology-Guided Extraction, Multi-Dimensional Clustering and Dual-Channel Fusion

arXiv:2603.25152v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems face significant challenges in complex reasoning, multi-hop queries, and domain-specific QA. While exis

OmniGF: A Dual-Branch Vision-Language Framework for Unified Gaze Following

arXiv:2605.26399v1 Announce Type: new Abstract: Understanding human gaze behavior is essential for complex scene comprehension and human-computer interaction. Traditional gaze following models are typ

local-aiarxiv-cs-cv

On the Hidden Costs of Counterfactual Knowledge Training in LLM Unlearning

arXiv:2605.27083v1 Announce Type: new Abstract: Counterfactual tuning (CFT) has emerged as a promising paradigm for Large Language Model (LLM) unlearning by training models to generate alternative fic

Once-For-All: A Train-Once and Select-Anytime Framework for Multimodal Instruction Tuning

arXiv:2605.26761v1 Announce Type: new Abstract: Multimodal instruction tuning is the de facto recipe for adapting vision language models (VLMs), yet instruction data are highly redundant, making data

Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks

arXiv:2605.26526v1 Announce Type: new Abstract: Recent defenses for safeguarding open-weight large language models (LLMs) are intended to prevent adversarial usage. Underlying these defenses is an ass

safetyarxiv-cs-lg

PashtoTTS-Bench: automated screening for low-resource non-Latin-script text-to-speech

arXiv:2605.26978v1 Announce Type: new Abstract: Text-to-speech (TTS) evaluation for low-resource non-Latin-script languages can fail when it relies on a single ASR round-trip word error rate (WER). A

PATE-TabTransGAN: Differentially Private Synthetic Tabular Data Generation via Transformer-Based Student Discrimination

arXiv:2605.26802v1 Announce Type: new Abstract: Generating high-fidelity synthetic tabular data under formal differential privacy guarantees remains an open challenge. Methods that provide strong theo

researcharxiv-cs-lg

Periodic Topological Deep Learning for Polymer Design and Discovery

arXiv:2605.26833v1 Announce Type: cross Abstract: Polymers underpin applications across energy, healthcare, and materials science, yet their vast chemical space makes systematic discovery challenging.

Applications

Phase-Type Variational Autoencoders for Heavy-Tailed Data

arXiv:2603.01800v2 Announce Type: replace-cross Abstract: Heavy-tailed distributions are ubiquitous in real-world data, where rare but extreme events dominate risk and variability. However, standard V

applicationsarxiv-cs-ai

PilotTTS: A Disciplined Modular Recipe for Competitive Speech Synthesis

arXiv:2605.27258v1 Announce Type: cross Abstract: Building state-of-the-art text-to-speech (TTS) systems typically demands millions of hours of proprietary data and complex multi-stage architectures,

Plan Then Action:High-Level Planning Guidance Reinforcement Learning for LLM Reasoning

arXiv:2510.01833v2 Announce Type: replace Abstract: Large language models (LLMs) demonstrate strong reasoning abilities via Chain-of-Thought (CoT), but their token-level generation encourages local de

local-aiarxiv-cs-ai

Position: AI Safety Requires Effective Controllability

arXiv:2605.27117v1 Announce Type: new Abstract: AI safety is still largely framed as alignment: training models to follow human preferences, safety policies, and normative constraints. That framing ha

Probabilistic Smoothing with Ratio-Monotone Transforms for Global Optimization

arXiv:2605.27316v1 Announce Type: new Abstract: Probabilistic smoothing is a standard tool for global optimization, but existing methods rely on Gaussian kernels and specific transforms, often resulti

researcharxiv-cs-lg

Probing Cultural Awareness in LLMs: A Case Study of Cross-Culture Aesthetic Stylistics

arXiv:2605.27296v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in diverse cultural contexts, yet their ability to master aesthetic stylistics, i.e., the strateg

Agents

Probing the Knowledge Boundary: An Interactive Agentic Framework for Deep Knowledge Extraction

arXiv:2602.00959v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can be seen as compressed knowledge bases, but it remains unclear what knowledge they truly contain and how far t

agentsarxiv-cs-cl

Prototyping an End-to-End Multi-Modal Tiny-CNN for Cardiovascular Sensor Patches

arXiv:2510.18668v2 Announce Type: replace-cross Abstract: The vast majority of cardiovascular diseases may be preventable if early signs and risk factors are detected. Cardiovascular monitoring with b

local-aiarxiv-cs-cv

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks

model-releasesarxiv-cs-lg

arXiv:2605.26243v1 Announce Type: new Abstract: Graph neural networks (GNNs) achieve strong performance on relational data, but real-world graphs are often distributed across organizations that cannot

Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering

arXiv:2605.27164v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems for question answering typically retrieve evidence by semantic similarity between the query and document ch

Ratio-Variance Regularized Policy Optimization

arXiv:2605.26784v1 Announce Type: cross Abstract: Standard on-policy reinforcement learning relies on heuristic clipping to enforce trust regions, but this mechanism imposes a severe cost by indiscrim

local-aiarxiv-cs-ai

RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation

model-releasesarxiv-cs-ro

arXiv:2605.26348v1 Announce Type: new Abstract: Mobile robots can fail before they collide: a velocity that is safe now may commit the robot to a passage that moving obstacles will soon close. We stud

ReCA: Multi-Shot Long Video Extrapolation via Recursive Context Allocation

arXiv:2605.26525v1 Announce Type: cross Abstract: Minute-scale cinematic video generation is a central challenge for generative video models. Existing paradigms address only fragments of this challeng

Recursive Flow Matching

arXiv:2605.26535v1 Announce Type: cross Abstract: Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving hig

Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline

arXiv:2605.26560v1 Announce Type: cross Abstract: Objective. Outpatient notes carry follow-up instructions pairing actions with future times ('MRI brain in two weeks'). Extracting (action, date) pairs

ReVEL: Multi-Turn Reflective LLM-Guided Heuristic Evolution via Structured Performance Feedback

arXiv:2604.04940v2 Announce Type: replace Abstract: Designing effective heuristics for NP-hard combinatorial optimization problems remains challenging and often requires substantial domain expertise.

local-aiarxiv-cs-ai

Robust Koopman Control Barrier Filters for Safe Actor-Critic Reinforcement Learning

arXiv:2605.26452v1 Announce Type: cross Abstract: Safe reinforcement learning (RL) for robotic systems requires policies that improve task performance while satisfying state and input constraints duri

safetyarxiv-cs-lg

Applications

RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning

arXiv:2601.22476v2 Announce Type: replace-cross Abstract: Floorplanning determines the coordinate and shape of each module in Integrated Circuits. With the scaling of technology nodes, in floorplannin

applicationsarxiv-cs-ai

Scaling GraphLLM with Bilevel-Optimized Sparse Querying

arXiv:2602.09038v2 Announce Type: replace-cross Abstract: LLMs have recently shown strong potential in enhancing node-level tasks on text-attributed graphs (TAGs) by providing explanation features. Ho

SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs

arXiv:2512.04868v2 Announce Type: replace-cross Abstract: Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual depend