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OccamToken: Efficient VLM Inference with Training-Free and Budget-Adaptive Token Pruning

arXiv:2605.29657v1 Announce Type: cross Abstract: Vision-language models (VLMs) rely on long visual token sequences for visual understanding, making the prefill stage expensive in both computation and

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Offline Multi-agent Reinforcement Learning via Sequential Score Decomposition

DGX agent

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

29 May 2026

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

Agents

Offloading Score: Measuring AI Reliance Through Counterfactual Workflows

arXiv:2605.29392v1 Announce Type: cross Abstract: AI tools are increasingly integrated into real-world workflows. However, existing measures of reliance on these tools focus on AI output adoption or o

agentsarxiv-cs-cl

OISD: On-Policy Internal Self-Distillation of Language Models

arXiv:2605.29089v1 Announce Type: cross Abstract: Recent reinforcement learning (RL) post-training approaches primarily optimize the final output policy using sparse outcome-level rewards, while large

Tutorials

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

OmniCustom: Sync Audio-Video Customization Via Joint Audio-Video Generation Model

arXiv:2602.12304v4 Announce Type: replace-cross Abstract: Existing mainstream video customization methods focus on generating identity-consistent videos based on given reference images and textual pro

OmniMatBench: A Human-Calibrated Multimodal Reasoning Benchmark Across 19 Materials Science Subfields

arXiv:2605.29833v1 Announce Type: new Abstract: As multimodal language models play an increasingly important role in scientific research, materials science offers a critical testbed due to its interdi

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 Asymmetric Optimization of Reasoning and Perception in Vision-Language Model Post-Training

arXiv:2605.29496v1 Announce Type: new Abstract: Post-training has greatly improved reasoning in frontier vision-language models, yet its gains for perception remain comparatively limited, creating a b

researcharxiv-cs-cl

Agents

On Distributional Reinforcement Learning in Chaotic Dynamical Systems

arXiv:2605.30160v1 Announce Type: cross Abstract: Chaotic dynamical systems pose a fundamental challenge for Reinforcement Learning (RL): exponential sensitivity to initial conditions induces high-var

agentsarxiv-cs-ai

On Language Generation in the Limit with Bounded Memory

arXiv:2605.30324v1 Announce Type: cross Abstract: We study language generation in the limit under bounded memory. In this task, a learner observes examples from an unknown target language one at a tim

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

On the Geometry of Games and their Solvers

arXiv:2605.29919v1 Announce Type: new Abstract: A central challenge in game theory and learning systems such as GANs is understanding which algorithms can efficiently compute equilibria across the het

On the Optimizer Dependence of Neural Scaling Laws

arXiv:2605.29387v1 Announce Type: cross Abstract: The scaling exponent alpha in neural scaling laws L(N) propto N^{-alpha} is commonly treated as a fixed constant set by architecture and data. We pres

One Click per Cell Type Suffices: Training-free Group Interaction for Cell Instance Segmentation

arXiv:2605.29429v1 Announce Type: new Abstract: Cell instance segmentation models trained on cell-specific datasets suffer severe performance drops on out-of-distribution cell types, while interactive

researcharxiv-cs-cv

Tutorials

One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Them

arXiv:2605.28839v1 Announce Type: new Abstract: Knowledge editing methods such as ROME and MEMIT update factual associations in transformer models by modifying MLP weights. While evaluated mainly by o

tutorialsarxiv-cs-lg

Online Fair Division with Additional Information

arXiv:2505.24503v3 Announce Type: replace-cross Abstract: We study the problem of fairly allocating indivisible goods to agents in an online setting, where goods arrive sequentially and must be alloca

OOD-GraphLLM: Graph Large Language Model for Out-of-Distribution Generalized Drug Synergy Prediction

arXiv:2605.30247v1 Announce Type: new Abstract: Drug synergy prediction (DSP) aims to identify efficacious drug combinations under various cellular contexts with different targets. However, the contin

Open Problem: Separating Geometric and Algorithmic Compression via Cayley-Table Completion

arXiv:2605.29885v1 Announce Type: new Abstract: Modern statistical learning theory and deep learning characterize generalization primarily in terms of continuous capacity control (e.g., norm-based reg

safetyarxiv-cs-lg

Open World Autoencoding Drift Detection with Novel Class Recognition in Tabular Non-stationary Data Streams

arXiv:2605.29834v1 Announce Type: new Abstract: Data stream processing has become a landmark in modern machine learning applications, with concept drifts and novel class appearances posing the primary

OpenClawBench: Benchmarking Process-side Anomalies in Real-world Agent Execution Trajectories

arXiv:2605.29253v1 Announce Type: new Abstract: Task success can hide process anomalies in real-world agent executions. An agent may pass the final task oracle while still accumulating unresolved ambi

Opir: Efficient Multi-Task Safety Classification for Toxicity, Jailbreaks, Hate Speech, and Harmful Content

arXiv:2605.29659v1 Announce Type: cross Abstract: Real-time safety filtering for large language model (LLM) applications requires classifiers that can detect unsafe prompts, toxic language, jailbreak

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

Optimal Gap-Dependent Regret for Private Stochastic Decision-Theoretic Online Learning

arXiv:2605.29148v1 Announce Type: new Abstract: We study stochastic decision-theoretic online learning with full information and event-level pure differential privacy. A COLT open problem of Hu and Me

Optimal Rates for Differentially Private Hypothesis Testing with E-values

arXiv:2605.28952v1 Announce Type: cross Abstract: E-values have attracted considerable interest in recent years as flexible tools for enabling anytime-valid and adaptive data analysis. Hypothesis test

Optimization and Generation in Aerodynamics Inverse Design

arXiv:2602.03582v3 Announce Type: replace Abstract: Aerodynamic inverse design can improve vehicle and aircraft efficiency, but practical design rarely seeks performance alone: vehicle refinement must

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.

OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation

arXiv:2605.29829v1 Announce Type: new Abstract: Leveraging Large Language Models (LLMs) to automatically formulate and solve optimization problems from natural language has emerged as an efficient par

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

Orthogonal Concept Erasure for Diffusion Models

arXiv:2605.28902v1 Announce Type: new Abstract: Concept erasure has emerged as a promising approach to mitigate undesired or unsafe content in diffusion models, yet existing methods still face signifi

Orthogonal Negative Guidance in Attention Feature Space for Text-to-Image Generation

arXiv:2605.29390v1 Announce Type: new Abstract: Text-to-image (T2I) models have become increasingly capable of generating high-quality images. Yet, enforcing the explicit absence of a specified object

safetyarxiv-cs-cv

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

Overcoming Forgetting in LLM Fine-Tuning with Evolution Strategies

arXiv:2605.30148v1 Announce Type: cross Abstract: Evolution Strategies (ES) has recently emerged as a competitive alternative to reinforcement learning (RL) for large language model (LLM) fine-tuning,

P^2RAG: Efficient Privacy-Preserving RAG Service Supporting Arbitrary Top-k Retrieval

arXiv:2603.14778v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enables large language models to use external knowledge, but outsourcing the RAG service raises privacy c

applicationsarxiv-cs-ai

Paper Agents, Paper Gains: An Empirical Analysis of DeFi Investment Agents

arXiv:2605.29174v1 Announce Type: new Abstract: DeFi investment agents, systems that use AI for autonomous on-chain trading, have attained over USD 3 billion in combined token valuations since late 20

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

Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data

arXiv:2605.29058v1 Announce Type: new Abstract: Bayesian Networks (BNs) are of interest from an explainable AI viewpoint, offering transparent probabilistic models for decision support. Baymex is a re

applicationsarxiv-cs-lg

Parameter-Efficient Subspace Decoupling ViT for Mitigating Multi-Task Negative Transfer in Histological Scoring

model-releasesarxiv-cs-cv

arXiv:2605.29852v1 Announce Type: new Abstract: Histological scoring is essential for diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD), yet its automation remains challenging due to the high annot

ParaTool: Shifting Tool Representations from Context to Parameters

arXiv:2605.29561v1 Announce Type: new Abstract: Tool calling extends large language models (LLMs) by enabling grounded interaction with external executable interfaces, thereby supporting environment-c

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

ParCo-SDF: Learning Prior-Free Partial-to-Complete Signed Distance Fields of Deformable Objects

arXiv:2605.29417v1 Announce Type: new Abstract: This study addresses the partial-to-complete geometry reconstruction of deformable objects (DOs) from point-cloud observations toward precise DO manipul

researcharxiv-cs-cv

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

Agents

PatchBoard: Schema-Grounded State Mutation for Reliable and Auditable LLM Multi-Agent Collaboration

arXiv:2605.29313v1 Announce Type: new Abstract: LLM multi-agent systems often coordinate through natural-language dialogue or loosely structured shared memory, making intermediate state difficult to v

agentsarxiv-cs-cl

Path-Space Mirror Descent for On-Policy Reinforcement Learning under the Generalized Schrodinger Bridge

arXiv:2603.21621v2 Announce Type: replace Abstract: Classical on-policy algorithms such as PPO and mirror descent policy optimization provide stable proximal policy updates through tractable action li

safetyarxiv-cs-lg

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