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Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application

arXiv:2606.25362v1 Announce Type: cross Abstract: Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose conse

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Learning Perceptive Platform Adaptive Locomotion Controllers for Quadrupedal Robots

DGX agent

arXiv:2606.25179v1 Announce Type: new Abstract: Universal quadrupedal locomotion remains limited by the difficulty of integrating perception across diverse robot morphologies. State-of-the-art control

researcharxiv-cs-ro

25 Jun 2026

Learning Robot Visual Navigation in Crowds via Intention-Aware Scene Representations

arXiv:2606.26047v1 Announce Type: new Abstract: Robot crowd navigation requires the ability to infer human intentions while accounting for the structural constraints of the environment. Currently, dee

safetyarxiv-cs-ro

Learning Structural Hardness for Combinatorial Auctions: Instance-Dependent Algorithm Selection via Graph Neural Networks

arXiv:2602.14772v2 Announce Type: replace Abstract: The Winner Determination Problem (WDP) in combinatorial auctions is NP-hard, and no existing method reliably predicts which instances will defeat fa

applicationsarxiv-cs-lg

Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts

arXiv:2606.25665v1 Announce Type: new Abstract: Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target da

Learning task-specific subspaces via interventional post-training of speech foundation models

arXiv:2606.17967v2 Announce Type: replace Abstract: Speech foundation models, pre-trained on large corpora of unlabelled speech data, produce general-purpose representations which are useful across ta

tutorialsarxiv-cs-cl

Learning to Adapt: Reptile-D-Learning for Robust and Efficient Control Under Parametric Uncertainty

model-releasesarxiv-cs-ro

arXiv:2606.25659v1 Announce Type: new Abstract: Learning-based Lyapunov Control (LLC) provides formal stability guarantees for nonlinear systems, but its validity relies heavily on accurate system mod

Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models

arXiv:2504.09910v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sen

researcharxiv-cs-cl

Learning with a Single Rollout via Monte Carlo Pass@k Critic

arXiv:2606.25451v1 Announce Type: new Abstract: Estimating token-level advantages in reinforcement learning (RL) for language models remains challenging because scaling up episodic experience collecti

Learning with Monotone Adversarial Corruptions

arXiv:2601.02193v2 Announce Type: replace Abstract: We study the extent to which standard machine learning algorithms rely on exchangeability and independence of data by introducing a monotone adversa

LEVIRDet: A Million-Scale 159-Category Dataset and Foundation Model for Universal Remote Sensing Object Detection

model-releasesarxiv-cs-cv

arXiv:2606.25312v1 Announce Type: new Abstract: Remote sensing object detection has advanced rapidly with the development of large-scale benchmarks and modern detection architectures. However, existin

LibEvoBench: Probing Temporal Knowledge Stratification in Code Generation Models

model-releasesarxiv-cs-ai

arXiv:2606.25402v1 Announce Type: cross Abstract: Large software projects often depend on older versions of libraries, even as APIs continue to evolve across releases. This creates a challenge for LLM

Lifelong In-Context Learning with Transformers Requires Parametric Forms of Attention

arXiv:2606.25342v1 Announce Type: new Abstract: Lifelong continual learning remains an obstacle on the path to human-like intelligence. Modern transformers show sparks of intelligence with in-context

tutorialsarxiv-cs-lg

Lightweight PCGAE-Net: Parallel CrossGate Attention and Bottleneck AutoEncoder for Efficient 5G Channel Prediction

arXiv:2606.25401v1 Announce Type: cross Abstract: Accurate channel state information (CSI) prediction is essential for proactive beamforming and resource management in 5G massive MIMO systems, yet the

safetyarxiv-cs-ai

Limitations of SGD for Multi-Index Models Beyond Statistical Queries

arXiv:2602.05704v2 Announce Type: replace Abstract: Understanding the limitations of gradient methods, and stochastic gradient descent (SGD) in particular, is a central challenge in learning theory. T

LinStereo: Linear-Complexity Global Attention for Multi-Scale Iterative Stereo Matching

arXiv:2606.25437v1 Announce Type: new Abstract: Existing Vision Foundation Model (VFM)-based iterative stereo pipelines under-exploit three information pathways: multi-scale backbone features are coll

applicationsarxiv-cs-cv

LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search

arXiv:2606.25039v1 Announce Type: cross Abstract: Recovering governing Ordinary Differential Equations (ODEs) from data is a central challenge in modeling dynamical systems across scientific domains.

researcharxiv-cs-cl

LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges

arXiv:2606.25057v1 Announce Type: new Abstract: The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large languag

safetyarxiv-cs-cl

LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning

arXiv:2606.24901v1 Announce Type: new Abstract: Continual learning capability is critical for Industrial LLMs, as deployed models must be continuously updated to meet evolving requirements and environ

applicationsarxiv-cs-lg

LLM Performance on a Real, Double-Marked GCSE Benchmark

model-releasesarxiv-cs-cl

arXiv:2606.24973v1 Announce Type: new Abstract: We introduce a dataset of 32,534 double-marked real student responses to GCSE mock exams (GCSEs are the UK's national exams, taken at age ~16), spanning

LLM Program Optimization via Retrieval Augmented Search

arXiv:2501.18916v2 Announce Type: replace Abstract: Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We pr

tutorialsarxiv-cs-lg

Logit Distance Bounds Representational Similarity

arXiv:2602.15438v3 Announce Type: replace Abstract: For a broad family of discriminative models that includes autoregressive language models, identifiability results imply that if two models induce th

Long-Term Simulation Exposes Cognitive-Developmental Risks in AI Companions

arXiv:2606.25396v1 Announce Type: new Abstract: AI companions powered by large language models increasingly interact with cognition-developing users, including children and adolescents, creating risks

safetyarxiv-cs-ai

Low-Complexity Policy Tessellations in Structured Markov Decision Processes

arXiv:2606.25593v1 Announce Type: new Abstract: We study optimal-policy geometry in structured Markov decision processes. While approximate dynamic programming and reinforcement learning typically app

Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applications

arXiv:2606.24989v1 Announce Type: new Abstract: Urban flow and air-quality simulations generate high-dimensional datasets describing velocity and pollutant transport across multiple spatial, temporal,

Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning

arXiv:2606.25526v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning assumes each agent shares the same reward function and can be trained effectively using the Trust Region

MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios

arXiv:2606.24950v1 Announce Type: new Abstract: Financial decision-making is contextual: forecasting prices, valuing companies, and assessing event exposure weigh price history, accounting fundamental

MANGO: Automated Multi-Agent Test Oracle Generation for Vision-Language-Action Models

arXiv:2606.24815v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are emerging robotic control systems that integrate perception, language understanding, and action generation in a

agentsarxiv-cs-ro

MAPL: Multi-Objective Preference Learning for Robot Locomotion

arXiv:2606.25398v1 Announce Type: new Abstract: Reward design remains a major bottleneck in reinforcement learning for robot locomotion, where successful policies often depend on carefully tuned, task

safetyarxiv-cs-ro

Margin in Abstract Spaces

arXiv:2603.07221v2 Announce Type: replace Abstract: Margin-based learning, exemplified by linear and kernel methods, is one of the few classical settings where generalization guarantees are independen

applicationsarxiv-cs-lg

Measurable Majorities Are Not Finitely Axiomatizable

arXiv:2606.25954v1 Announce Type: cross Abstract: This theoretical note studies the finite axiomatizability of strict majority reasoning in finite social decision frames. Moss and Pedersen (2026) intr

researcharxiv-cs-ai

Measuring Research Difficulty of Academic Papers: A Case Study in Natural Language Processing

arXiv:2606.25307v1 Announce Type: cross Abstract: With the rapid growth of the number of academic papers, systematically evaluating the difficulty of research and its relationship to academic impact o

applicationsarxiv-cs-cl

MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction

arXiv:2606.25651v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in healthcare settings, accurate error detection and correction in generated or existing text

safetyarxiv-cs-cl

MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models

model-releasesarxiv-cs-cl

arXiv:2604.05738v2 Announce Type: replace Abstract: Medical Vision-Language Models (Med-VLMs) have achieved expert-level proficiency in interpreting diagnostic imaging. However, current models are pre

Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents

arXiv:2601.03785v3 Announce Type: replace Abstract: Long-term human-agent dialogues are organized by topic continuity: adjacent turns often develop the same goal, plan, problem, or event, while relate

agentsarxiv-cs-cl

Memory-Efficient Policy Libraries with Low-Rank Adaptation in Reinforcement Learning

arXiv:2606.25700v1 Announce Type: new Abstract: When fine-tuning Large Language Models (LLMs), there has been success in minimizing both memory usage and computation with Parameter-Efficient Fine-Tuni

Memory Makes the Difference: Evaluating How Different Memory Roles Shape Conversational Agents

arXiv:2606.25361v1 Announce Type: new Abstract: Prior research on memory mechanism in RAG-based conversational system has emphasized how memory is stored and retrieved. However, far less is known abou

agentsarxiv-cs-cl

Memory Retrieval in Visuomotor Policies for Long-Horizon Robot Control

arXiv:2606.25136v1 Announce Type: new Abstract: General-purpose robots operating in partially observable environments, such as homes, require memory to support autonomy. They must recall diverse infor

safetyarxiv-cs-ro

MIL-LC: A Robust Magnetometer-Inertial-LiDAR Fusion Multimodal Localization Framework

arXiv:2606.25796v1 Announce Type: new Abstract: Localization in challenging environments, such as GNSS-denied, geometrically repetitive, or textureless scenes commonly found in offices, hotels, and un

agentsarxiv-cs-ro

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation

arXiv:2606.26016v1 Announce Type: new Abstract: Normalizing Flows (NFs) are powerful generative models capable of exact density estimation and sampling. However, their strict invertibility often force

researcharxiv-cs-cv

MINIF2F-DAFNY: LLM-Guided Mathematical Theorem Proving via Auto-Active Verification

arXiv:2512.10187v3 Announce Type: replace Abstract: LLMs excel at reasoning, but validating their steps remains challenging. Formal verification offers a solution through mechanically checkable proofs

Minimalist Preprocessing Approach for Image Synthesis Detection

arXiv:2606.25297v1 Announce Type: new Abstract: Generative models have significantly advanced image generation, resulting in synthesized images that are increasingly indistinguishable from authentic o

researcharxiv-cs-cv

Minimax PAC Bounds for Learning in Exogenous Contextual MDPs

arXiv:2606.25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.i.d. contexts, with discount factor gamma, finite state space m

MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources

arXiv:2606.25832v1 Announce Type: new Abstract: Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging probl

Mixture-of-Experts RL for Fault-Tolerant Legged Locomotion

arXiv:2606.25965v1 Announce Type: new Abstract: Legged robots deployed in planetary exploration and other remote environments must maintain reliable locomotion despite actuator failures and challengin

researcharxiv-cs-ro

MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning

arXiv:2606.25225v1 Announce Type: new Abstract: Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning. Since audio and visual strea

tutorialsarxiv-cs-cv

Model-agnostic Mitigation Strategies of Data Imbalance for Regression

arXiv:2506.01486v2 Announce Type: replace Abstract: Data imbalance persists as a pervasive challenge in regression tasks, introducing bias in model performance and undermining predictive reliability.

Model Forensics: Investigating Whether Concerning Behavior Reflects Misalignment

arXiv:2606.26071v1 Announce Type: new Abstract: A central goal of safety research is determining whether a model is misaligned. Prior work has largely focused on detecting concerning behavior. But beh