Search: “benchmark”

Multi-level Collaborative Distillation Meets Global Workspace Model: A Unified Framework for OCIL

arXiv:2508.08677v2 Announce Type: replace-cross Abstract: Online Class-Incremental Learning (OCIL) enables models to learn continuously from non-i.i.d. data streams. Since samples of the data streams

tutorialsarxiv-cs-cv

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Multi-Turn Adaptive Prompting Attack on Large Vision-Language Models

DGX agent

arXiv:2602.14399v2 Announce Type: replace Abstract: Multi-turn jailbreak attacks have proven effective against text-only large language models (LLMs), where malicious content is gradually introduced t

model-releasesarxiv-cs-cv

29 May 2026

MuPHI: Learning Implicit Multimodal Harm Reasoning via Semantically Grounded Reward Optimization

arXiv:2605.29951v1 Announce Type: new Abstract: Understanding how harm emerges from interaction between otherwise benign image-text pairs requires intent-aware cross-modal reasoning beyond surface-lev

NaRA: Noise-Aware LoRA for Parameter-Efficient Fine-Tuning of Diffusion LLMs

arXiv:2605.29716v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive generative paradigm. Given the prohibitive computational cost of

Offline Multi-agent Reinforcement Learning via Sequential Score Decomposition

arXiv:2505.05968v3 Announce Type: replace Abstract: Offline cooperative multi-agent reinforcement learning (MARL) faces unique challenges due to distributional shifts, particularly stemming from the h

safetyarxiv-cs-lg

Offline Reinforcement Learning with Generative Trajectory Policies

arXiv:2510.11499v2 Announce Type: replace-cross Abstract: Generative models have emerged as a powerful class of policies for offline reinforcement learning (RL) due to their ability to capture complex

safetyarxiv-cs-ai

OmniAID: Decoupling Semantic and Artifacts for Universal AI-Generated Image Detection in the Wild

arXiv:2511.08423v3 Announce Type: replace Abstract: A truly universal AI-Generated Image (AIGI) detector must simultaneously generalize across diverse generative models and varied semantic content. Cu

tutorialsarxiv-cs-cv

OmniCD: A Foundational Framework for Remote Sensing Image Change Detection Guided by Multimodal Semantics

arXiv:2605.30168v1 Announce Type: new Abstract: Change detection (CD) in remote sensing is vital for applications such as urban monitoring and disaster assessment, yet traditional methods struggle wit

researcharxiv-cs-cv

OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources

arXiv:2605.29250v1 Announce Type: cross Abstract: Real-world information needs require access to structurally diverse knowledge sources, from unstructured text and relational tables to knowledge graph

On-Policy Replay for Continual Supervised Fine-Tuning

arXiv:2605.29495v1 Announce Type: new Abstract: Continual supervised fine-tuning (SFT) is the de facto recipe for adapting large language models (LLMs) to a stream of downstream tasks, but it suffers

On the Construction and Implications of Low-Loss Valleys in LoRA-based Bayesian Inference

arXiv:2605.29580v1 Announce Type: new Abstract: While parameter-efficient fine-tuning methods like low-rank adaptation (LoRA) are standard for large language models, principled estimation of epistemic

Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification

arXiv:2605.29556v1 Announce Type: new Abstract: Building mathematical optimization models is critical in operations research (OR), while it requires substantial human expertise. Recent advancements ha

researcharxiv-cs-ai

Optimizing Latent Representations for Robust Building Damage Assessment Onboard Earth Observation Satellites

model-releasesarxiv-cs-cv

arXiv:2605.29575v1 Announce Type: new Abstract: Rapid identification of damaged buildings after natural disasters or on war areas is crucial to support emergency response and prioritize interventions.

Applications

Order-Agnostic Autoregressive Modelling with Missing Data

arXiv:2605.06355v2 Announce Type: replace Abstract: Order-Agnostic autoregressive models have demonstrated strong performance in deep generative modeling, yet their use in settings with incomplete dat

applicationsarxiv-cs-lg

OVA-IB: One vs All Information Bottleneck for Multi-Modal Alignment

arXiv:2605.29900v1 Announce Type: new Abstract: Contrastive learning is effective for aligning paired views or modalities, but alignment beyond two modalities remains non-trivial and comparatively und

Parallax: Parameterized Local Linear Attention for Language Modeling

arXiv:2605.29157v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become the central paradigm in artificial intelligence, yet the core computational primitive of attention has remain

Applications

PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding

arXiv:2605.30126v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) map visual inputs into dense token sequences, imposing a quadratic computational bottleneck for inference. Elasti

applicationsarxiv-cs-ai

Applications

PassNet: Scaling Large Language Models for Graph Compiler Pass Generation

arXiv:2605.29357v1 Announce Type: new Abstract: Modern tensor compilers such as TorchInductor deliver substantial speedups on mainstream models, yet face a systematic performance ceiling on long-tail

applicationsarxiv-cs-ai

PEARL: Training Socratic Tutors with Pedagogically Aligned Reinforcement Learning

arXiv:2605.29582v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promise as educational tutors, yet effective tutoring requires more than solving problems: it must provide pro

safetyarxiv-cs-cl

Pocket-Dentist: On-Device Dental Image Understanding via Efficient Multimodal Large Language Models

arXiv:2605.29299v1 Announce Type: cross Abstract: Evaluations of dental vision-language models remain fragmented across datasets, task definitions and metrics, and often ignore their computational cos

PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solvers

arXiv:2605.30094v1 Announce Type: new Abstract: Poker is a landmark challenge for artificial intelligence. The dominant approach relies on equilibrium solvers built on counterfactual regret minimizati

Predicting Causal Effects from Natural Language Queries using Structured Representations

arXiv:2605.29631v1 Announce Type: cross Abstract: Randomized controlled trials are a cornerstone of medicine and the social sciences as they enable reliable estimates of causal effects. However, they

Prescribe-then-Select: Adaptive Policy Selection for Contextual Stochastic Optimization

arXiv:2509.08194v2 Announce Type: replace Abstract: We address the problem of policy selection in contextual stochastic optimization (CSO), where covariates are available as contextual information and

PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation

arXiv:2605.28867v1 Announce Type: cross Abstract: Generating high-quality time-series data is challenging because real-world signals often exhibit multimodal patterns and multiscale dynamics, includin

tutorialsarxiv-cs-ai

PRO-CUA: Process-Reward Optimization for Computer Use Agents

arXiv:2605.29119v1 Announce Type: new Abstract: Computer use agents (CUAs) have shown strong potential for automating complex digital workflows, yet their training remains constrained by costly live e

safetyarxiv-cs-ai

Probabilistic bias adjustment of seasonal forecasts using generative machine learning: A case study of Arctic sea ice predictions

arXiv:2605.29172v1 Announce Type: new Abstract: Seasonal climate predictions support planning and risk management by offering early information of the most likely-to-occur climate conditions in the co

Local Ai

Prompt-Level Reward Specifications for Open-Ended Post-Training

arXiv:2605.29275v1 Announce Type: new Abstract: Open-ended post-training benefits from rewards that make prompt-specific success conditions explicit, rather than relying only on post-hoc scalar scores

local-aiarxiv-cs-cl

Local Ai

PROTOCOL: Late Interaction Retrieval for Protein Homolog Search

arXiv:2605.29158v1 Announce Type: new Abstract: Protein homology search underlies function annotation, structure prediction, and evolutionary analysis, but remains challenging in the 'twilight zone,'

local-aiarxiv-cs-lg

Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

arXiv:2605.30280v1 Announce Type: cross Abstract: Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented cap

RAISE: RAG Design as an Architecture Search Problem

arXiv:2605.30029v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems expose numerous design choices spanning query rewriting, chunking, retrieval depth, reranking, and context

ReasonBreak: Probing Vulnerabilities in Reasoning-Enabled Vision-Language-Action Models for Autonomous Driving

arXiv:2605.29114v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models with integrated reasoning have been proposed for end-to-end autonomous driving, assuming a tight coupling between

Reasoning and Tool-use Compete in Agentic RL:From Quantifying Interference to Disentangled Tuning

arXiv:2602.00994v2 Announce Type: replace Abstract: Agentic Reinforcement Learning (ARL) trains large language models to interleave reasoning with external tool execution to solve complex tasks. Most

Reasoning-preserved Efficient Distillation of Large Language Models via Activation-aware Initialization

model-releasesarxiv-cs-cl

arXiv:2605.29327v1 Announce Type: new Abstract: Efficient Distillation (EDistill) compresses large language models (LLMs) by structured pruning parameters and tuning lightweight modules with high trai

Rectified LpJEPA: Joint-Embedding Predictive Architectures with Sparse and Maximum-Entropy Representations

arXiv:2602.01456v2 Announce Type: replace-cross Abstract: Joint-Embedding Predictive Architectures (JEPA) learn view-invariant representations and admit projection-based distribution matching for coll

tutorialsarxiv-cs-cv

Reinforcement Learning with Robust Rubric Rewards

arXiv:2605.30244v1 Announce Type: cross Abstract: While Reinforcement Learning with Verifiable Rewards (RLVR) is effective for deterministically checkable tasks, many vision-language tasks are partial

researcharxiv-cs-ai

Relevance as a Vulnerability: How Web Retrieval Degrades Safety Alignment in LLM Agents

arXiv:2605.29224v1 Announce Type: cross Abstract: AI agents augment large language models with external tools such as web retrieval, enabling grounded and up-to-date responses. However, incorporating

REPOT: Recoverable Program-of-Thought via Checkpoint Repair

arXiv:2605.30052v1 Announce Type: cross Abstract: One-shot Program-of-Thought (PoT) emits a Python program that prints a primitive-action plan; a single invalid action silently invalidates the traject

Representation Unlearning: Forgetting through Information Compression

arXiv:2601.21564v2 Announce Type: replace Abstract: Machine unlearning seeks to remove the influence of specific training data from a model, a need driven by privacy regulations and robustness concern

Resolution as a Direction: Vector-Panning Feature Alignment for Cross-Resolution Re-Identification

arXiv:2510.00936v2 Announce Type: replace Abstract: Cross-resolution person re-identification (CR-ReID) remains challenging in practical surveillance, where camera quality and capture distance lead to

safetyarxiv-cs-cv

Rethinking FID Through the Geometry of the Reference Dataset

arXiv:2605.29335v1 Announce Type: cross Abstract: Frechet Inception Distance (FID) is widely used to evaluate image generators, yet lower FID does not always correspond to better sample quality. We sh

researcharxiv-cs-ai

Rethinking Literature Search Evaluation: Deep Research Helps, and Human Citation Lists Are Not a Ground Truth

arXiv:2605.29234v1 Announce Type: new Abstract: We study large-scale literature search from two complementary angles: improving the retrieval pipeline, and stress-testing the human reference list as a

Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting

arXiv:2605.29401v1 Announce Type: new Abstract: Time-Series Foundation Models (TSFMs) excel at zero-shot unimodal forecasting using numerical data, but unlike LLMs they cannot consume multimodal, non-

Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective

arXiv:2605.29319v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on table reasoning tasks but incur substantial inference cost due to long reasoning traces. Ste

researcharxiv-cs-cl

Return-to-Go Is More Than a Number: Q-Guided Alignment for Return-Conditioned Supervised Learning

arXiv:2605.29028v1 Announce Type: cross Abstract: Conditioned Sequence Models (CSMs) learn policies by treating return-to-go (RTG) as a control signal. However, existing CSMs often treat the RTGs as s

Agents

Revisiting Observation Reduction for Web Agents: Comprehensive Evaluation with a Lightweight Framework

arXiv:2605.29397v1 Announce Type: new Abstract: HTML observations in LLM-based web agents are extremely long, and while many reduction methods have been proposed, it remains unclear which methods redu

agentsarxiv-cs-cl

Agents

RewardFlow: Topology-Aware Reward Propagation on State Graphs for Agentic RL with Large Language Models

arXiv:2603.18859v2 Announce Type: replace Abstract: Reinforcement learning (RL) shows promise for enhancing LLM agentic reasoning, yet sparse terminal rewards hinder fine-grained optimization. Process

agentsarxiv-cs-ai

RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization

model-releasesarxiv-cs-cv

arXiv:2603.27758v2 Announce Type: replace Abstract: Metric Cross-View Geo-Localization (MCVGL) aims to estimate the 3-DoF camera pose (position and heading) by matching ground and satellite images. In

Risk-averse Fair Multi-class Classification

arXiv:2509.05771v2 Announce Type: replace-cross Abstract: We develop a new classification framework based on the theory of coherent risk measures and systemic risk. The proposed approach is suitable f