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Iterative Compositional Data Generation for Robot Control

arXiv:2512.10891v4 Announce Type: replace-cross Abstract: Collecting robotic manipulation data is expensive, making it impractical to acquire demonstrations for the combinatorially large space of task

tutorialsarxiv-cs-lg

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Human DGX agent

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JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence

DGX agent

arXiv:2510.23538v2 Announce Type: replace Abstract: The scope of neural code intelligence is rapidly expanding beyond text-based source code to encompass the rich visual outputs that programs generate

researcharxiv-cs-ai

15 Apr 2026

Joint Flashback Adaptation for Forgetting-Resistant Instruction Tuning

arXiv:2505.15467v2 Announce Type: replace-cross Abstract: Large language models have achieved remarkable success in various tasks. However, it is challenging for them to learn new tasks incrementally

tutorialsarxiv-cs-ai

Joint Multi-Target Detection-Tracking in Cognitive Massive MIMO Radar via POMCP

arXiv:2507.17506v3 Announce Type: replace-cross Abstract: This work presents a cognitive radar (CR) framework to enhance remote sensing performance, specifically focusing on tracking multiple targets

researcharxiv-cs-lg

KCLarity at SemEval-2026 Task 6: Encoder and Zero-Shot Approaches to Political Evasion Detection

model-releasesarxiv-cs-cl

arXiv:2603.06552v2 Announce Type: replace Abstract: This paper describes the KCLarity team's participation in CLARITY, a shared task at SemEval 2026 on classifying ambiguity and evasion techniques in

KG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning

arXiv:2603.21440v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) demonstrate impressive natural language capabilities but often struggle with knowledge-intensive reasoning tasks.

KG-Reasoner: A Reinforced Model for End-to-End Multi-Hop Knowledge Graph Reasoning

arXiv:2604.12487v1 Announce Type: cross Abstract: Large Language Models (LLMs) exhibit strong abilities in natural language understanding and generation, yet they struggle with knowledge-intensive rea

Knowledge Is Not Static: Order-Aware Hypergraph RAG for Language Models

arXiv:2604.12185v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enhances large language models by grounding outputs in retrieved knowledge. However, existing RAG methods including

applicationsarxiv-cs-cl

KnowRL: Boosting LLM Reasoning via Reinforcement Learning with Minimal-Sufficient Knowledge Guidance

arXiv:2604.12627v1 Announce Type: new Abstract: RLVR improves reasoning in large language models, but its effectiveness is often limited by severe reward sparsity on hard problems. Recent hint-based R

KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates

arXiv:2604.12397v1 Announce Type: new Abstract: Standard Large Language Model (LLM) pre-training typically treats corpora as flattened token sequences, often overlooking the real-world context that hu

tutorialsarxiv-cs-cl

KumoRFM-2: Scaling Foundation Models for Relational Learning

arXiv:2604.12596v1 Announce Type: cross Abstract: We introduce KumoRFM-2, the next iteration of a pre-trained foundation model for relational data. KumoRFM-2 supports in-context learning as well as fi

Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI

arXiv:2510.04705v4 Announce Type: replace Abstract: Accurate liver segmentation in multi-phase MRI is vital for liver fibrosis assessment, yet labeled data is often scarce and unevenly distributed acr

applicationsarxiv-cs-cv

Labeled TrustSet Guided: Batch Active Learning with Reinforcement Learning

arXiv:2604.12303v1 Announce Type: new Abstract: Batch active learning (BAL) is a crucial technique for reducing labeling costs and improving data efficiency in training large-scale deep learning model

safetyarxiv-cs-lg

Large Language Models are Powerful Electronic Health Record Encoders

arXiv:2502.17403v5 Announce Type: replace-cross Abstract: Electronic Health Records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity challenge tradit

LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety

arXiv:2604.12710v1 Announce Type: cross Abstract: Large language models (LLMs) often demonstrate strong safety performance in high-resource languages, yet exhibit severe vulnerabilities when queried i

Latent Chain-of-Thought World Modeling for End-to-End Driving

model-releasesarxiv-cs-cv

arXiv:2512.10226v2 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models for autonomous driving explore inference-time reasoning as a way to improve driving performance and safet

Latent-Condensed Transformer for Efficient Long Context Modeling

arXiv:2604.12452v1 Announce Type: new Abstract: Large language models (LLMs) face significant challenges in processing long contexts due to the linear growth of the key-value (KV) cache and quadratic

researcharxiv-cs-cl

Latent patterns of urban mixing in mobility analysis across five global cities

arXiv:2604.12202v1 Announce Type: new Abstract: This study leverages large-scale travel surveys for over 200,000 residents across Boston, Chicago, Hong Kong, London, and Sao Paulo. With rich individua

Latent Planning Emerges with Scale

arXiv:2604.12493v1 Announce Type: cross Abstract: LLMs can perform seemingly planning-intensive tasks, like writing coherent stories or functioning code, without explicitly verbalizing a plan; however

LatentRefusal: Latent-Signal Refusal for Unanswerable Text-to-SQL Queries

arXiv:2601.10398v3 Announce Type: replace Abstract: In LLM-based text-to-SQL systems, unanswerable and underspecified user queries may generate not only incorrect text but also executable programs tha

safetyarxiv-cs-ai

LaV-CoT: Language-Aware Visual CoT with Multi-Aspect Reward Optimization for Real-World Multilingual VQA

model-releasesarxiv-cs-cv

arXiv:2509.10026v4 Announce Type: replace Abstract: As large vision language models (VLMs) advance, their capabilities in multilingual visual question answering (mVQA) have significantly improved. Cha

League of LLMs: A Benchmark-Free Paradigm for Mutual Evaluation of Large Language Models

arXiv:2507.22359v4 Announce Type: replace Abstract: Although large language models (LLMs) have shown exceptional capabilities across a wide range of tasks, reliable evaluation remains a critical chall

Learned Incremental Nonlinear Dynamic Inversion for Quadrotors with and without Slung Payloads

arXiv:2503.09441v2 Announce Type: replace Abstract: The increasing complexity of multirotor applications demands flight controllers that can accurately account for all forces acting on the vehicle. Co

researcharxiv-cs-ro

Learning Chain Of Thoughts Prompts for Predicting Entities, Relations, and even Literals on Knowledge Graphs

arXiv:2604.12651v1 Announce Type: cross Abstract: Knowledge graph embedding (KGE) models perform well on link prediction but struggle with unseen entities, relations, and especially literals, limiting

Learning step-level dynamic soaring in shear flow

arXiv:2604.12413v1 Announce Type: cross Abstract: Dynamic soaring enables sustained flight by extracting energy from wind shear, yet it is commonly understood as a cycle-level maneuver that assumes st

safetyarxiv-cs-ro

Agents

Learning the Value of Value Learning

arXiv:2511.17714v5 Announce Type: replace Abstract: Standard decision frameworks address uncertainty about facts but assume fixed options and values. We extend the Jeffrey-Bolker framework to model re

agentsarxiv-cs-ai

Learning to accelerate distributed ADMM using graph neural networks

arXiv:2509.05288v2 Announce Type: replace Abstract: Distributed optimization is fundamental to large-scale machine learning and control applications. Among existing methods, the alternating direction

researcharxiv-cs-lg

Learning Versatile Humanoid Manipulation with Touch Dreaming

arXiv:2604.13015v1 Announce Type: new Abstract: Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stabili

safetyarxiv-cs-ro

Leveraging Weighted Syntactic and Semantic Context Assessment Summary (wSSAS) Towards Text Categorization Using LLMs

arXiv:2604.12049v1 Announce Type: cross Abstract: The use of Large Language Models (LLMs) for reliable, enterprise-grade analytics such as text categorization is often hindered by the stochastic natur

Agents

LIFE -- an energy efficient advanced continual learning agentic AI framework for frontier systems

arXiv:2604.12874v1 Announce Type: new Abstract: The rapid advancement of AI has changed the character of HPC usage such as dimensioning, provisioning, and execution. Not only has energy demand been am

agentsarxiv-cs-ai

Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation

arXiv:2604.13010v1 Announce Type: cross Abstract: On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, standard OPD requires a live teach

safetyarxiv-cs-ai

Listening Deepfake Detection: A New Perspective Beyond Speaking-Centric Forgery Analysis

arXiv:2604.12650v1 Announce Type: new Abstract: Existing deepfake detection research has primarily focused on scenarios where the manipulated subject is actively speaking, i.e., generating fabricated

tutorialsarxiv-cs-cv

Lit2Vec: A Reproducible Workflow for Building a Legally Screened Chemistry Corpus from S2ORC for Downstream Retrieval and Text Mining

arXiv:2604.12498v1 Announce Type: cross Abstract: We present Lit2Vec, a reproducible workflow for constructing and validating a chemistry corpus from the Semantic Scholar Open Research Corpus using co

LiveMoments: Reselected Key Photo Restoration in Live Photos via Reference-guided Diffusion

arXiv:2604.12286v1 Announce Type: new Abstract: Live Photo captures both a high-quality key photo and a short video clip to preserve the precious dynamics around the captured moment. While users may c

safetyarxiv-cs-cv

LLM as Attention-Informed NTM and Topic Modeling as long-input Generation: Interpretability and long-Context Capability

arXiv:2510.03174v2 Announce Type: replace-cross Abstract: Topic modeling aims to produce interpretable topic representations and topic--document correspondences from corpora, but classical neural topi

LLM-Based Automated Diagnosis Of Integration Test Failures At Google

arXiv:2604.12108v1 Announce Type: cross Abstract: Integration testing is critical for the quality and reliability of complex software systems. However, diagnosing their failures presents significant c

applicationsarxiv-cs-ai

LLM-Enhanced Log Anomaly Detection: A Comprehensive Benchmark of Large Language Models for Automated System Diagnostics

model-releasesarxiv-cs-lg

arXiv:2604.12218v1 Announce Type: new Abstract: System log anomaly detection is critical for maintaining the reliability of large-scale software systems, yet traditional methods struggle with the hete

LLM-Guided Prompt Evolution for Password Guessing

arXiv:2604.12601v1 Announce Type: cross Abstract: Passwords still remain a dominant authentication method, yet their security is routinely subverted by predictable user choices and large-scale credent

LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines

arXiv:2604.12223v1 Announce Type: cross Abstract: Pretrained language models (PLMs) like BERT provide strong semantic representations but are costly and opaque, while symbolic models such as the Tsetl

tutorialsarxiv-cs-ai

LLM-Guided Task- and Affordance-Level Exploration in Reinforcement Learning

arXiv:2509.16615v2 Announce Type: replace Abstract: Reinforcement learning (RL) is a promising approach for robotic manipulation, but it can suffer from low sample efficiency and requires extensive ex

tutorialsarxiv-cs-ro

LLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks

arXiv:2604.12096v1 Announce Type: new Abstract: On online advertising platforms, newly introduced promotional ads face the cold-start problem, as they lack sufficient user feedback for model training.

applicationsarxiv-cs-ai

LLMs Struggle with Abstract Meaning Comprehension More Than Expected

arXiv:2604.12018v1 Announce Type: cross Abstract: Understanding abstract meanings is crucial for advanced language comprehension. Despite extensive research, abstract words remain challenging due to t

Local Ai

Local-Splitter: A Measurement Study of Seven Tactics for Reducing Cloud LLM Token Usage on Coding-Agent Workloads

arXiv:2604.12301v1 Announce Type: cross Abstract: We present a systematic measurement study of seven tactics for reducing cloud LLM token usage when a small local model can act as a triage layer in fr

local-aiarxiv-cs-ai

Local Ai

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models

arXiv:2601.14004v4 Announce Type: replace Abstract: Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However,

local-aiarxiv-cs-cl

LogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Software

arXiv:2604.12994v1 Announce Type: cross Abstract: Logical vulnerabilities in software stem from flaws in program logic rather than memory safety, which can lead to critical security failures. Although

safetyarxiv-cs-ai

Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching

arXiv:2604.12126v1 Announce Type: new Abstract: Large Language Models (LLMs) have significantly advanced tool-augmented agents, enabling autonomous reasoning via API interactions. However, executing m

Loop Corrections to the Training and Generalization Errors of Random Feature Models

arXiv:2604.12827v1 Announce Type: cross Abstract: We investigate random feature models in which neural networks sampled from a prescribed initialization ensemble are frozen and used as random features

LoSA: Locality Aware Sparse Attention for Block-Wise Diffusion Language Models

arXiv:2604.12056v1 Announce Type: new Abstract: Block-wise diffusion language models (DLMs) generate multiple tokens in any order, offering a promising alternative to the autoregressive decoding pipel

researcharxiv-cs-cl