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Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency

arXiv:2605.27946v1 Announce Type: cross Abstract: Backpropagation is the default learning rule for artificial neural networks and is often treated as the settled approach whenever differentiability is

Isometry pursuit

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arXiv:2411.18502v2 Announce Type: replace-cross Abstract: Isometry pursuit is a convex algorithm for identifying orthonormal column-submatrices of wide matrices. It consists of a novel normalization m

researcharxiv-cs-ai

28 May 2026

Iterative Causal Discovery: Per-Edge Impossibility Certificates, Tier-Aware Oracle Queries, and the 1+K Lower Bound

arXiv:2605.27477v1 Announce Type: cross Abstract: Causal-discovery algorithms return a directed graph, yet provide no principled means of distinguishing edge directions identified by the data from tho

Janus-LoRA: A Balanced Low-Rank Adaptation for Continual Learning

model-releasesarxiv-cs-cv

arXiv:2605.28495v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has emerged as a promising paradigm for Continual Learning. It independently updates its low-rank factors (A and B), creating

JECA^2: Judgment-Explanation Consistent Adversarial Attack against Forensic Vision-Language Models

arXiv:2605.28609v1 Announce Type: new Abstract: Forensic vision-language models (VLMs) have recently been developed to detect image tampering and provide natural-language explanations. However, their

safetyarxiv-cs-cv

JMedEthicBench: A Multi-Turn Conversational Benchmark for Evaluating Medical Safety in Japanese Large Language Models

arXiv:2601.01627v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) are increasingly deployed in healthcare field, it becomes essential to carefully evaluate their medical safety

Joint Training of Multi-Token Prediction in Reinforcement Learning via Optimal Coefficient Calibration

arXiv:2605.28184v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as the standard paradigm for improving reasoning capability of large language models,

Keyphrase Generative Representation of Youth Crisis Conversations Beyond Static Taxonomies

arXiv:2605.27546v1 Announce Type: new Abstract: Crisis Responders (CRs) rapidly assess thousands of youth SMS conversations each year to identify mental health concerns and guide support. Yet youth di

tutorialsarxiv-cs-cl

Knowing When to Ask: Segment-Level Credit Assignment for LLM Tool Use

arXiv:2605.27788v1 Announce Type: cross Abstract: Humans know when to reach for help e.g. 347 imes 28 warrants a calculator while 2+2 does not. Language models do not. Prompt-based approaches can inst

agentsarxiv-cs-cl

Knowledge Dependency Estimation for Reliable Question Answering

arXiv:2605.28047v1 Announce Type: new Abstract: Reliable question answering requires identifying not only whether an answer is correct, but also which available knowledge the prediction depends on. In

researcharxiv-cs-cl

KSAFE-MM: A Multimodal Safety Benchmark via Localized Contextualization for Korean Cultural Risks

model-releasesarxiv-cs-cl

arXiv:2605.28013v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) exacerbate safety risks by introducing vulnerabilities across multiple modalities, such as language and vision.

KVoiceBench, KOpenAudioBench, and KMMAU: Agent-Driven Korean Speech Benchmarks for Evaluating SpeechLMs

arXiv:2605.27984v1 Announce Type: cross Abstract: Speech language models (SpeechLMs) have achieved substantial progress by extending large language models (LLMs) to the speech modality. However, Speec

La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching

arXiv:2507.09466v2 Announce Type: replace Abstract: Recently, many generative models for de novo protein structure design have emerged. Yet, only few tackle the difficult task of directly generating f

LACUNA: Safe Agents as Recursive Program Holes

arXiv:2605.28617v1 Announce Type: new Abstract: LLM agents increasingly act by writing code, yet a split persists between the runtime that drives the agent and the code the model writes. The runtime o

safetyarxiv-cs-ai

Laguna M.1/XS.2 Technical Report

arXiv:2605.27605v1 Announce Type: new Abstract: We present Laguna M.1 and Laguna XS.2, two Mixture-of-Experts foundation models built for long-horizon, agentic coding: M.1 has 225.8B total parameters

LaneRoPE: Positional Encoding for Collaborative Parallel Reasoning and Generation

arXiv:2605.27570v1 Announce Type: new Abstract: Parallel LLM test-time scaling techniques (e.g., best-of-N) require drawing N>1 sequences conditioned on the same input prompt. These methods boost accu

researcharxiv-cs-ai

Large Language Models Approach Expert Pedagogical Quality in Math Tutoring but Differ in Instructional and Linguistic Profiles

arXiv:2512.20780v3 Announce Type: replace Abstract: Recent work has explored the use of large language models (LLMs) to generate tutoring responses in mathematics, yet it remains unclear how closely t

agentsarxiv-cs-cl

Large Language Models as Automatic Annotators and Annotation Adjudicators for Fine-Grained Opinion Analysis

arXiv:2601.16800v3 Announce Type: replace Abstract: Fine-grained opinion analysis of text provides a detailed understanding of expressed sentiments, including the addressed entity. Although this level

agentsarxiv-cs-cl

Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States

arXiv:2605.28690v1 Announce Type: cross Abstract: Many applications in quantum simulation, quantum chemistry, and quantum machine learning require not a single quantum state but an ensemble of states

Latent Diffusion for Missing Data

arXiv:2605.28427v1 Announce Type: new Abstract: Diffusion models have emerged as powerful generative approaches for missing-data imputation, yet most existing methods operate directly in data space an

Law of Neural Interaction: Depth-Width Shape, Interaction Efficiency, and Generalization

model-releasesarxiv-cs-lg

arXiv:2605.27989v1 Announce Type: new Abstract: The guidance of scaling laws has increased the resource demands of modern large language models (LLMs), yet it remains questionable whether these models

LCO: LLM-based Constraint Optimization for Safer Agentic LLMs in Real-world Tasks

model-releasesarxiv-cs-cl

arXiv:2605.27375v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly acting as autonomous agents, but their continuous interaction with the environment can lead to in-context

Learn from Weaknesses: Automated Domain Specialization for Small Computer-Use Agents

arXiv:2605.28775v1 Announce Type: cross Abstract: Computer-use agents (CUAs) have recently made substantial progress, but deploying a separate large expert for each software domain remains expensive.

agentsarxiv-cs-ai

Learn from your own latents and not from tokens: A sample-complexity theory

arXiv:2605.27734v1 Announce Type: new Abstract: Generative models, from diffusion models to large language models, achieve remarkable performance but at a cost in training data orders of magnitude lar

tutorialsarxiv-cs-lg

Learning a Kinodynamic Trajectory Manifold for Impact-Aware Compliant Catching of Fast-Moving Objects

arXiv:2605.28462v1 Announce Type: new Abstract: Fast catching of free-flying objects is difficult because of short reaction time, impact uncertainty, and kinodynamic constraints. We use reinforcement

tutorialsarxiv-cs-ro

Applications

Learning after COVID-19 and the ICT career aspirations: Are students entering the AI era with weaker skills?

arXiv:2605.27391v1 Announce Type: cross Abstract: This paper examines whether students are entering the generative AI era with sufficiently strong educational foundations, focusing on the relationship

applicationsarxiv-cs-ai

Learning Compositional Latent Structure with Vector Networks

arXiv:2605.28007v1 Announce Type: cross Abstract: Deep networks are powerful function approximators, but they typically store many different computations in shared weight matrices, making it difficult

Learning Correlated Reward Models: Statistical Barriers and Opportunities

arXiv:2510.15839v2 Announce Type: replace Abstract: Random Utility Models (RUMs) are a classical framework for modeling user preferences and play a key role in reward modeling for Reinforcement Learni

tutorialsarxiv-cs-lg

Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs

arXiv:2507.06999v2 Announce Type: replace-cross Abstract: Reasoning is essential for large language models (LLMs), especially in complex tasks such as mathematical problem solving. However, multimodal

safetyarxiv-cs-cl

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent

arXiv:2605.28612v1 Announce Type: new Abstract: Parity functions are fundamental Boolean operations with critical applications across machine learning, cryptography, and error correction. Yet, learnin

Learning Logical Operations for Arbitrary Quantum Error Correction Codes

arXiv:2605.28162v1 Announce Type: cross Abstract: Logical operations are essential for quantum computation within quantum error-correcting codes. However, discovering their physical realizations is ch

Learning Tangent Bundles and Characteristic Classes with Autoencoder Atlases

arXiv:2602.22873v2 Announce Type: replace-cross Abstract: We introduce a theoretical framework that connects multi-chart autoencoders in manifold learning with the classical theory of vector bundles a

researcharxiv-cs-ai

Learning the Error Patterns of Language Models

arXiv:2605.28328v1 Announce Type: cross Abstract: When generating outputs for domains with specific validity constraints (e.g., a program should compile), LLMs often fail in a small number of focused

tutorialsarxiv-cs-ai

Learning Theory of the SVRG: Generalization and Convergence Analysis

arXiv:2605.28513v1 Announce Type: cross Abstract: Variance reduction (VR) methods employ stochastic gradients with decreasing variance, and they have been widely applied to solve large-scale optimizat

researcharxiv-cs-ai

Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization

arXiv:2605.28309v1 Announce Type: new Abstract: In large-scale benchmarking of stochastic optimization algorithms, the key challenge is no longer whether repeated runs are needed for reliability, but

tutorialsarxiv-cs-lg

Learning to Assign Prediction Tasks to Agents with Capacity Constraints

arXiv:2605.27999v1 Announce Type: cross Abstract: We address the problem of learning to assign prediction tasks to one agent from a set of available human or AI agents. In particular, we focus on the

safetyarxiv-cs-ai

Learning to Bid in Repeated Second-Price Auctions with Dynamic Values and Aggregated Feedback

arXiv:2605.28133v1 Announce Type: new Abstract: We study the problem of learning to bid when the bidder's value is dynamic, i.e., when the current value depends on past outcomes. Specifically, we cons

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation

arXiv:2605.28239v1 Announce Type: new Abstract: Semi-supervised referring expression segmentation (SS-RES) aims to achieve precise pixel-level language grounding under limited annotation, yet suffers

researcharxiv-cs-cv

Applications

Learning to target with network interference

arXiv:2605.27794v1 Announce Type: cross Abstract: This paper studies adaptive targeting under network interference in a bandit setting, where treatments applied to one individual may affect others thr

applicationsarxiv-cs-lg

Learning to Translate from Soft to Hard LLM Prompts

model-releasesarxiv-cs-cl

arXiv:2605.27642v1 Announce Type: new Abstract: Soft prompt tuning is a parameter-efficient method for adapting LLMs to specific tasks, but suffers from a lack of interpretability. Building on recent

Learning What to Recommend: Minimax Optimal Simple Regret in Logistic Bandits

arXiv:2601.21167v2 Announce Type: replace Abstract: We study stochastic logistic bandits with d-dimensional action features under the simple-regret objective, where a learner uses T rounds of explorat

Hardware

Learning When to Optimize: Verified Optimization Skills from Expert GPU-Kernel Lineages

arXiv:2605.28213v1 Announce Type: new Abstract: LLM-based agents are increasingly used to generate GPU kernels, but they often know what optimizations to try without knowing when those optimizations a

hardwarearxiv-cs-ai

Learning with Importance Weighted Variational Inference

arXiv:2410.12035v2 Announce Type: replace-cross Abstract: Several variational bounds involving importance weighting ideas generalize the Evidence Lower BOund (ELBO) for marginal likelihood optimizatio

LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning

arXiv:2605.28120v1 Announce Type: cross Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) advances flat document retrieval by structuring knowledge as relational graphs, enabling more co

agentsarxiv-cs-ai

LEIA: Learned Environment for Interactive Architected Materials

model-releasesarxiv-cs-lg

arXiv:2605.28368v1 Announce Type: new Abstract: World models have enabled interactive exploration of game environments and robotic manipulation, but physical engineering remains beyond their reach: re

LESA: Learnable Stage-Aware Predictors for Diffusion Model Acceleration

arXiv:2602.20497v3 Announce Type: replace-cross Abstract: Diffusion models have achieved remarkable success in image and video generation tasks. However, the high computational demands of Diffusion Tr

Let Relations Speak: An End-to-End LLM-GNN Soft Prompt Framework for Fraud Detection

arXiv:2605.28524v1 Announce Type: new Abstract: In recent years, Large Language Models (LLMs) have shown great capability in processing graph tasks such as fraud detection. However, most existing meth

safetyarxiv-cs-ai

Let the Results Speak: A Replication-First Paradigm for LLM Behavioral Benchmarking

arXiv:2605.27914v1 Announce Type: cross Abstract: Subjective evaluation of LLM behavior -- empathy, restraint, calibrated emotional tone -- is hard. Human inter-rater agreement on such qualities satur