Search: “benchmark”

arXiv:2605.01865v1 Announce Type: cross Abstract: Cooperative multi-agent reinforcement learning (MARL) requires agents to discover joint strategies in a combinatorially large state-action space, yet

agentsarxiv-cs-ai

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Quantifying the human visual exposome with vision language models

DGX agent

arXiv:2605.03863v1 Announce Type: cross Abstract: The visual environment is a fundamental yet unquantified determinant of mental health. While the concept of the environmental exposome is well establi

agentsarxiv-cs-cv

6 May 2026

Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits

arXiv:2605.03434v1 Announce Type: new Abstract: Reinforcement learning is one of the most challenging learning paradigms where efficacy and efficiency gains are extremely valuable. Hierarchical reinfo

RAG over Thinking Traces Can Improve Reasoning Tasks

arXiv:2605.03344v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is widely believed to offer limited benefit for reasoning

RD-ViT: Recurrent-Depth Vision Transformer for Semantic Segmentation with Reduced Data Dependence Extending the Recurrent-Depth Transformer Architecture to Dense Prediction

arXiv:2605.03999v1 Announce Type: new Abstract: Vision Transformers (ViTs) achieve state-of-the-art segmentation accuracy but require large training datasets because each layer has unique parameters t

Re-Key-Free, Risky-Free: Adaptable Model Usage Control

arXiv:2511.18772v2 Announce Type: replace-cross Abstract: Deep neural networks (DNNs) have become valuable intellectual property of model owners, due to the substantial resources required for their de

applicationsarxiv-cs-ai

Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models

arXiv:2605.02912v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) has traditionally been framed as binary classification or outlier detection, providing neither interpretable reasoning nor

researcharxiv-cs-cv

ReCode: Reinforcing Code Generation with Reasoning-Process Rewards

arXiv:2508.05170v3 Announce Type: replace-cross Abstract: In practice, rigorous reasoning is often a key driver of correct code, while Reinforcement Learning (RL) for code generation often neglects op

RLDX-1 Technical Report

arXiv:2605.03269v1 Announce Type: cross Abstract: While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intellig

safetyarxiv-cs-lg

RoboAlign-R1: Distilled Multimodal Reward Alignment for Robot Video World Models

model-releasesarxiv-cs-ro

arXiv:2605.03821v1 Announce Type: new Abstract: Existing robot video world models are typically trained with low-level objectives such as reconstruction and perceptual similarity, which are poorly ali

Robust Language Identification for Romansh Varieties

arXiv:2603.15969v2 Announce Type: replace Abstract: The Romansh language has several regional varieties, called idioms, which sometimes have limited mutual intelligibility. Despite this linguistic div

Hardware

Robust Visual SLAM for UAV Navigation in GPS-Denied and Degraded Environments: A Multi-Paradigm Evaluation and Deployment Study

arXiv:2605.03678v1 Announce Type: new Abstract: Reliable localization in GPS-denied, visually degraded environments is critical for autonomous UAV opera- tions. This paper presents a systematic compar

hardwarearxiv-cs-ro

Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQL

arXiv:2605.03720v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought have demonstrated remarkable capabilities in code generation and mat

SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition

arXiv:2605.03706v1 Announce Type: new Abstract: Zero-shot Named Entity Recognition (ZS-NER) remains brittle under domain and schema shifts, where unseen label definitions often misalign with a large l

SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning

arXiv:2601.04809v5 Announce Type: replace Abstract: Reinforcement learning (RL) offers a principled way to enhance the reasoning capabilities of large language models, yet its effectiveness hinges on

applicationsarxiv-cs-ai

Should We Still Pretrain Encoders with Masked Language Modeling?

arXiv:2507.00994v4 Announce Type: replace Abstract: Learning high-quality text representations is fundamental to a wide range of NLP tasks. While encoder pretraining has traditionally relied on Masked

Simulated Students in Tutoring Dialogues: Substance or Illusion?

arXiv:2601.04025v2 Announce Type: replace Abstract: Advances in large language models (LLMs) enable many new innovations in education. However, evaluating the effectiveness of new technology requires

Soft Tournament Equilibrium

arXiv:2604.04328v3 Announce Type: replace-cross Abstract: The evaluation of general-purpose artificial agents, particularly those based on LLMs, presents a significant challenge due to the non-transit

StateSMix: Online Lossless Compression via Mamba State Space Models and Sparse N-gram Context Mixing

arXiv:2605.02904v1 Announce Type: new Abstract: We present StateSMix, a fully self-contained lossless compressor that couples an online-trained Mamba-style State Space Model (SSM) with sparse n-gram c

Steer Like the LLM: Activation Steering that Mimics Prompting

arXiv:2605.03907v1 Announce Type: new Abstract: Large language models can be steered at inference time through prompting or activation interventions, but activation steering methods often underperform

Strategy-Aware Optimization Modeling with Reasoning LLMs

arXiv:2605.02545v1 Announce Type: new Abstract: Large language models (LLMs) can generate syntactically valid optimization programs, yet often struggle to reliably choose an effective modeling strateg

applicationsarxiv-cs-ai

Stream-R1: Reliability-Perplexity Aware Reward Distillation for Streaming Video Generation

arXiv:2605.03849v1 Announce Type: new Abstract: Distillation-based acceleration has become foundational for making autoregressive streaming video diffusion models practical, with distribution matching

safetyarxiv-cs-cv

Structured Diffusion Bridges: Inductive Bias for Denoising Diffusion Bridges

arXiv:2605.02973v1 Announce Type: new Abstract: Modality translation is inherently under-constrained, as multiple cross-modal mappings may yield the same marginals. Recent work has shown that diffusio

safetyarxiv-cs-lg

SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation

arXiv:2605.03534v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) grounds answers in retrieved passages, but retrieval is not verification: a passage can be topical and still fail t

Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions

arXiv:2605.03221v1 Announce Type: new Abstract: Long-tailed class distributions are pervasive in multi-class medical datasets and pose significant challenges for deep learning models which typically u

applicationsarxiv-cs-cv

Talk is Cheap, Communication is Hard: Dynamic Grounding Failures and Repair in Multi-Agent Negotiation

arXiv:2605.01750v1 Announce Type: cross Abstract: Grounding is the collaborative process of establishing mutual belief sufficient for the current communicative purpose. While static grounding maps lan

safetyarxiv-cs-ai

Task-Aware Scanning Parameter Configuration for Robotic Inspection Using Vision Language Embeddings and Hyperdimensional Computing

arXiv:2605.03909v1 Announce Type: cross Abstract: Robotic laser profiling is widely used for dimensional verification and surface inspection, yet measurement fidelity is often dominated by sensor conf

Tenability and Weak Semantics: Modeling Non-uniform Defense -- Extended Version

arXiv:2605.02024v1 Announce Type: new Abstract: In Dung-style abstract argumentation, various semantics capture notions of acceptability of arguments. The admissibility semantics capture the notion th

The Cost of Consensus: Isolated Self-Correction Prevails Over Unguided Homogeneous Multi-Agent Debate

arXiv:2605.00914v1 Announce Type: cross Abstract: Multi-agent debate, where teams of LLMs iteratively exchange rationales and vote on answers, is widely deployed under the assumption that peer review

The Right Answer, the Wrong Direction: Why Transformers Fail at Counting and How to Fix It

arXiv:2605.03258v1 Announce Type: cross Abstract: Large language models often fail at simple counting tasks, even when the items to count are explicitly present in the prompt. We investigate whether t

Toward a Science of Intent: Closure Gaps and Delegation Envelopes for Open-World AI Agents

arXiv:2604.25000v2 Announce Type: replace Abstract: Recent work has framed intelligence in verifiable tasks as reducing time-to-solution through learned structure and test-time search, while systems w

Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts

arXiv:2605.03348v1 Announce Type: new Abstract: We propose S3 (Specialization, Selection, Sparsification), a framework that rethinks multimodal learning through a structural perspective. Instead of en

researcharxiv-cs-lg

Towards Multi-Agent Autonomous Reasoning in Hydrodynamics

arXiv:2605.01102v1 Announce Type: new Abstract: Single-agent systems (SAS) have become the default pattern for LLM-driven scientific workflows, but routing planning, tool use, and synthesis through a

Training-Free Probabilistic Time-Series Forecasting with Conformal Seasonal Pools

arXiv:2605.03789v1 Announce Type: cross Abstract: We propose Conformal Seasonal Pools (CSP), a training-free probabilistic time-series forecaster that mixes same-season empirical draws with signed res

Transformers with Selective Access to Early Representations

arXiv:2605.03953v1 Announce Type: cross Abstract: Several recent Transformer architectures expose later layers to representations computed in the earliest layers, motivated by the observation that low

Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration

arXiv:2605.01970v2 Announce Type: cross Abstract: Memory systems enable otherwise-stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. We characte

UnAC: Adaptive Visual Prompting with Abstraction and Stepwise Checking for Complex Multimodal Reasoning

arXiv:2605.03950v1 Announce Type: new Abstract: Although recent LMMs have become much stronger at visual perception, they remain unreliable on problems that require multi-step reasoning over visual ev

Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe

arXiv:2605.03677v1 Announce Type: new Abstract: On-policy distillation (OPD) has recently emerged as an effective post-training paradigm for consolidating the capabilities of specialized expert models

safetyarxiv-cs-lg

Unified Multimodal Visual Tracking with Dual Mixture-of-Experts

arXiv:2605.03716v1 Announce Type: new Abstract: Multimodal visual object tracking can be divided into to several kinds of tasks (e.g. RGB and RGB+X tracking), based on the input modality. Existing met

researcharxiv-cs-cv

Universal Smoothness via Bernstein Polynomials: A Constructive Approximation Approach for Activation Functions

arXiv:2605.02591v1 Announce Type: new Abstract: The efficacy of deep neural networks is heavily reliant on the design of non-linear activation functions, yet existing approaches often struggle to bala

researcharxiv-cs-ai

Vanishing L2 regularization for the softmax Multi Armed Bandit

arXiv:2605.03752v1 Announce Type: new Abstract: Multi Armed Bandit (MAB) algorithms are a cornerstone of reinforcement learning and have been studied both theoretically and numerically. One of the mos

When Prompts Interact: Assessing Prompt Arithmetic for Deconfounding under Distribution Shift

arXiv:2605.03096v1 Announce Type: cross Abstract: In classification tasks, models may rely on confounding variables to achieve strong in-distribution performance, capturing spurious features that fail

When Stress Becomes Signal: Detecting Antifragility-Compatible Regimes in Multi-Agent LLM Systems

arXiv:2605.02463v1 Announce Type: cross Abstract: Multi-agent LLM systems are increasingly used to solve complex tasks through decomposition, debate, specialization, and ensemble reasoning. However, t

When to Think, When to Speak: Learning Disclosure Policies for LLM Reasoning

arXiv:2605.03314v1 Announce Type: new Abstract: In single-stream autoregressive interfaces, the same tokens both update the model state and constitute an irreversible public commitment. This coupling

safetyarxiv-cs-cl

Where to Bind Matters: Hebbian Fast Weights in Vision Transformers for Few-Shot Character Recognition

arXiv:2605.02920v1 Announce Type: cross Abstract: Standard transformer architectures learn fixed slow-weight representations during training and lack mechanisms for rapid adaptation within an episode.

A decoupled diffusion planner that adapts to changing cost limits by using cost-conditioned generation for safety and reward gradients for performance

arXiv:2605.02777v1 Announce Type: new Abstract: Offline safe reinforcement learning often requires policies to adapt at deployment time to safety budgets that vary across episodes or change within a s

5 May 2026

A hybrid solution approach for the Integrated Healthcare Timetabling Competition 2024

arXiv:2511.04685v2 Announce Type: replace Abstract: In this work, we present the solution approach for the Integrated Healthcare Timetabling Competition 2024 submitted by Team Twente, which ultimately

5 May 2026

A Light Weight Multi-Features-View Convolution Neural Network For Plant Disease Identification

arXiv:2605.00903v1 Announce Type: new Abstract: Agriculture is a key sector of the economies of developing countries. It serves as a primary source of income and employment for rural populations. Howe