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Large Language Models in Resolving Contextual Knowledge Conflicts

arXiv:2609.03148v1 Announce Type: new Abstract: Most prior works focused on conflicts between an LLM's internal parametric knowledge and externally provided context. In contrast, we investigate how LL

safetyarxiv-cs-cl

Last Translation Benchmark

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Clear filters

arXiv:2609.04173v1 Announce Type: new Abstract: For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases.

model-releasesarxiv-cs-cl

4 Sept 2026

Latent Energy Action Planning with World Models

arXiv:2609.03294v1 Announce Type: new Abstract: Latent world models support efficient model predictive control from high-dimensional observations, yet optimizing a single learned latent objective can

LDC: Learning to Generate Research Idea with Dynamic Control

arXiv:2412.14626v3 Announce Type: replace-cross Abstract: Recent advancements in large language models (LLMs) have demonstrated their potential in automating the scientific research ideation. Existing

researcharxiv-cs-ai

LeanGRPO: Eliminating Redundant Recomputation in Diffusion RL

arXiv:2609.03528v1 Announce Type: cross Abstract: Diffusion reinforcement learning (RL) has recently achieved significant success in post-training image and video generative models. However, most diff

safetyarxiv-cs-ai

LeanStream: A Speculate-and-Refine Streaming Framework for Efficient on-Device LLM Inference

arXiv:2609.03079v1 Announce Type: new Abstract: On-device LLM inference is attractive for privacy and responsiveness, but remains challenging on mobile and embedded devices because model weights far e

local-aiarxiv-cs-lg

Learnable composition for neural operators

arXiv:2609.03069v1 Announce Type: new Abstract: Neural operators are fast, differentiable surrogates for physical simulation, but their accuracy often degrades when domain geometry, size, or operating

researcharxiv-cs-lg

Learning-based Adaptive Safety-Critical Control With Evolving Unsafe Regions

arXiv:2503.16551v4 Announce Type: replace Abstract: Control barrier functions (CBFs) provide a principled framework for safety-critical control, but their construction typically requires an explicit a

safetyarxiv-cs-ro

Learning Constraints-Based Adaptive Hypergraph Neural Networks for Solving Vehicle Routing Problems

arXiv:2503.10421v2 Announce Type: replace Abstract: The application of learning based methods to vehicle routing problems has emerged as a pivotal area of research in combinatorial optimization. These

Learning from Scarce Labels: Multi-View Echocardiography for Ejection Fraction Prediction

arXiv:2609.02969v1 Announce Type: cross Abstract: We present, to the best of our knowledge, the first publicly available resource for predicting left ventricular ejection fraction (EF) from parasterna

researcharxiv-cs-cv

Learning Informative Prior with Infinite-Dimensional Continuous Normalizing Flow for Bayesian Inverse Problem

arXiv:2609.03343v1 Announce Type: cross Abstract: This paper addresses infinite-dimensional Bayesian inference for inverse problem of partial differential equations with model parameters in infinite-d

researcharxiv-cs-lg

Agents

Learning to Zoom Efficiently with a Contrastive Curriculum

arXiv:2609.03206v1 Announce Type: cross Abstract: Using a zoom-in tool is an important foundational part of modern visual agents, because it allows to efficiently handle tasks involving high-resolutio

agentsarxiv-cs-cl

Learning What Not to Forget: Long-Horizon Agent Memory from a Few Kilobytes of Learning

arXiv:2606.20954v2 Announce Type: replace-cross Abstract: Long-running language-model systems accumulate interaction history that outgrows the context window, so they must continually evict. When an e

Legibility is Not Interpretability: Comparing Judged and Actual Importance in Chain-Of-Thought Reasoning

arXiv:2609.04194v1 Announce Type: new Abstract: Reasoning traces from chain-of-thought models appear to offer a legible window into how a model arrives at its answer. A growing body of work treats the

researcharxiv-cs-cl

Less Is Moral: A CHARMing Framework for Moral Foundations Detection in Endorsement Behaviour

arXiv:2609.03330v1 Announce Type: new Abstract: Moral language plays a central role in shaping online endorsement and the diffusion of information, yet existing moral foundation detection systems ofte

safetyarxiv-cs-cl

LevelSyn: Physical-Aware Logic Synthesis via Level-Asynchronous Graph Neural Networks

arXiv:2609.03594v1 Announce Type: cross Abstract: As integrated circuit technology scales into the nanometer regime, the traditional disconnect between logic synthesis and physical design has led to s

LexIssue: Benchmarking Legal Issue Identification in Chinese Civil Litigation

arXiv:2609.02954v1 Announce Type: new Abstract: Identifying the issues disputed between litigating parties is a crucial component of real-world litigation. However, legal issues remain comparatively u

LightEMMA: A Longitudinal Evaluation of Vision-Language Models for Autonomous Driving

arXiv:2505.00284v3 Announce Type: replace-cross Abstract: Rapid advances in vision-language models (VLMs) have generated growing interest in their application to autonomous driving. A prevailing assum

Linearized subspace refinement framework to expose hidden accuracy in trained neural networks

arXiv:2601.13989v2 Announce Type: replace Abstract: Neural networks trained by gradient-based methods often exhibit optimization-induced accuracy plateaus in scientific machine learning tasks. We pres

local-aiarxiv-cs-lg

Tutorials

Listen to the Latents: Self-Correcting Speech Recognition in Large Audio Language Models Through Hidden-State Interactions

arXiv:2609.02940v1 Announce Type: cross Abstract: Recent automatic speech recognition (ASR) systems increasingly integrate large language models (LLMs) to leverage their semantic knowledge, either ext

tutorialsarxiv-cs-ai

LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes

arXiv:2609.03796v1 Announce Type: cross Abstract: We introduce LLaDA-Image, a unified framework that pairs a 6B Diffusion Transformer (DiT) trained from scratch with a frozen vision-language understan

LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models

arXiv:2503.03313v4 Announce Type: replace-cross Abstract: Text-Attributed Graphs (TAGs), where each node is associated with text descriptions, are ubiquitous in real-world scenarios. They typically ex

safetyarxiv-cs-cl

LLM Evaluation as Tensor Completion: Low Rank Structure and Semiparametric Efficiency

arXiv:2604.05460v2 Announce Type: replace-cross Abstract: Large language model (LLM) evaluation platforms increasingly rely on pairwise human judgments. These data are noisy, sparse, and non-uniform,

local-aiarxiv-cs-ai

LLM-Guided Reinforcement Learning for Adaptive NPC Behavior in Multi-Agent Combat Games

arXiv:2609.02931v1 Announce Type: cross Abstract: Scripted and rule-based non-player characters (NPCs) in combat video games often exhibit predictable behaviors that experienced players can exploit, w

Applications

LLM4CKD: Large Language Models for Early Stage Chronic Kidney Disease Screening

arXiv:2609.04013v1 Announce Type: new Abstract: Early screening of chronic kidney disease (CKD) is critical for timely intervention, yet most machine learning (ML) and deep learning (DL) approaches re

applicationsarxiv-cs-ai

Tutorials

LLMs Learn Better In-Context from Rules than from Examples

arXiv:2609.03213v1 Announce Type: new Abstract: Large language models (LLMs) exhibit in-context learning capabilities, where they can learn new tasks from prompt contexts without weight updates. We co

tutorialsarxiv-cs-cl

LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents

arXiv:2606.18388v2 Announce Type: replace-cross Abstract: RL post-training strategies are dataset-dependent and reveal a recurring empirical pattern: capacity parameters accumulate monotonically acros

Lngram v2: Latent N-Gram Memory with Interpretable Discrete Representations

arXiv:2609.03426v1 Announce Type: new Abstract: Transformers lack a native lookup mechanism, requiring repeated dense computation to recognize and reuse local static patterns. Lngram v1 introduces tok

Agents

Local Path Planning and Obstacle Avoidance for an Omnicopter Platform

arXiv:2609.03630v1 Announce Type: new Abstract: Autonomous unmanned aerial vehicles (UAVs) increasingly operate in cluttered environments where global planners such as RRT* are not directly deployable

agentsarxiv-cs-ro

Local Updates, Global Learning (LUGL): Playing Games with non-incremental Learners

arXiv:2609.03660v1 Announce Type: cross Abstract: The dominance of Neural Networks (NNs) in RL is partially due to their incremental learning capability, which naturally suits the online, non-stationa

local-aiarxiv-cs-ai

Long-Horizon Consistent and Interaction-Aware World Models for Multi-Style End-to-End Driving

model-releasesarxiv-cs-ro

arXiv:2609.03225v1 Announce Type: new Abstract: End-to-end autonomous driving has increasingly adopted world model-based reinforcement learning frameworks to improve learning efficiency through extit{

LongCounsel-8: A Benchmark Suite for Longitudinal Depression Tracking from Multi-Session Counseling Dialogues

arXiv:2609.03507v1 Announce Type: cross Abstract: Tracking depression from multi-session counseling dialogues requires estimating both current symptom severity and how it changes across sessions. Yet

Lose the Order, Keep the Hierarchy: Deordering HTN Plans

arXiv:2609.03912v1 Announce Type: new Abstract: Hierarchical Task Network (HTN) planning is a powerful planning formalism based on task decomposition. Although most of the literature studied plan gene

researcharxiv-cs-ai

Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories

arXiv:2602.15154v2 Announce Type: replace Abstract: Reliable video understanding requires high-quality video datasets that can provide both precise semantic labels and temporally consistent annotation

researcharxiv-cs-cv

Lost in Reordering: Structural Sensitivity of Multilingual LLMs under Semantics-Preserving Perturbations

arXiv:2609.03511v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate strong multilingual reasoning performance, yet their robustness to semantics-preserving structural variation re

LRConv-NeRV: Low Rank Convolution for Efficient Neural Video Compression

arXiv:2603.18261v2 Announce Type: replace-cross Abstract: Neural Representations for Videos (NeRV) encode entire video sequences within neural network parameters, offering an alternative paradigm to c

Applications

M3T: Discrete Multi-Modal Motion Tokens for Sign Language Production

arXiv:2603.23617v2 Announce Type: replace Abstract: Sign language production requires more than hand motion generation. Non-manual features, including mouthings, eyebrow raises, gaze, and head movemen

applicationsarxiv-cs-cv

Making Every Tool Call Count: Necessary Tool-Evidence Path Rewards for Agentic Vision-Language Models

arXiv:2609.03493v1 Announce Type: new Abstract: Modern vision-language models (VLMs) can directly answer many image-grounded questions, yet they often struggle with complex queries requiring fine-grai

Margins, Not Windows: Training-Free Per-Step Lossy Speculative Decoding

arXiv:2609.02897v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by drafting candidate tokens and verifying them in parallel. Tree-attention drafters such as EAGLE-3 are

Masked Autoregressive Speech Enhancement with Continuous Neural Audio Codec Representations

arXiv:2609.03940v1 Announce Type: cross Abstract: Most previous work on speech enhancement (SE) based on masked generative modeling relied on discrete token representations of audio signals, obtained

researcharxiv-cs-ai

MasterControl Seventeen Every Time

arXiv:2609.03209v1 Announce Type: new Abstract: We study a governed approach to enterprise analytics: a language model interprets the question, while deterministic policy selects and runs a pre-approv

safetyarxiv-cs-ai

Measuring Harmfulness of Computer-Using Agents

arXiv:2508.00935v3 Announce Type: replace-cross Abstract: Computer-using agents (CUAs), which can autonomously control computers to perform multi-step actions, might pose significant safety risks if m

Agents

Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications

arXiv:2508.00669v2 Announce Type: replace-cross Abstract: The proliferation of Large Language Models (LLMs) in medicine has enabled impressive capabilities, yet a critical gap remains in their ability

agentsarxiv-cs-ai

MedQA-MM: Shortcuts Behind Medical Visual Reasoning

arXiv:2609.03261v1 Announce Type: cross Abstract: A benchmark score credits final answers, but not the route by which an item can be answered. In medical multimodal multiple-choice questions (MCQs), t

MemoryLACE: Memory Lifecycle-Aware Consolidation and Evidence Retrieval

arXiv:2609.03201v1 Announce Type: new Abstract: Long-term LLM agents must preserve information across interactions while distinguishing repeated evidence, historical states, updates, and unresolved co

researcharxiv-cs-cl

Hardware

Mesh-Native Physics-Informed Graph Surrogates for TCAD-in-the-Loop Design Space Exploration

arXiv:2609.02988v1 Announce Type: new Abstract: High-fidelity TCAD simulation of drift-diffusion transport remains the workhorse of emerging FinFET device design, but it is computationally expensive,

hardwarearxiv-cs-lg

MetaStructAtlas: A Grounded 3D Vision-Language Dataset and Benchmark for Functional and Structural Reasoning in Whole-Body PET/CT

model-releasesarxiv-cs-cv

arXiv:2609.03690v1 Announce Type: new Abstract: The joint interpretation of metabolic function and anatomical structure is essential for clinical diagnosis in whole-body PET/CT. Although recent advanc

Mind the Gap: Robustness Risks in PII Detection Systems

arXiv:2609.03464v1 Announce Type: new Abstract: Personally Identifiable Information (PII) detection is a foundational component of data protection infrastructure where missed entities constitute direc