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Learning On The Job: Zero-Shot Task Execution under Parametric Uncertainty via Trajectory-Parametrized Dual Control

model-releasesarxiv-cs-ro

arXiv:2510.20483v2 Announce Type: replace Abstract: Model-based control can achieve reliable task performance, but its effectiveness depends on the accuracy of the underlying model. Robots operating u

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Human DGX agent

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Learning Samples Importance: Parameterizing Dual Variables in Everywhere Learning

DGX agent

arXiv:2609.36310v1 Announce Type: new Abstract: Everywhere learning provides a principled framework for training AI models under constraints that must hold throughout the data distribution. In the dua

tutorialsarxiv-cs-lg

30 Sept 2026

Learning Social Navigation from Internet Videos in the Policy State Space

model-releasesarxiv-cs-cv

arXiv:2609.37476v1 Announce Type: cross Abstract: Training robust social-navigation policies requires simulators with diverse scene layouts, terrain, and human motion, but constructing such environmen

Learning the Structure of Triangular Transport Maps

arXiv:2609.37122v1 Announce Type: new Abstract: Triangular transport maps provide a flexible approach to sampling-based probabilistic modeling, including density estimation, generative modeling, and B

tutorialsarxiv-cs-lg

Learning to Explore Hidden Kinematics for Articulated Object Manipulation

model-releasesarxiv-cs-ro

arXiv:2609.36553v1 Announce Type: new Abstract: The kinematics of an articulated object is often ambiguous from vision alone. Interaction resolves the ambiguity, and active perception methods exploit

Learning to Harvest Without Collapse in a Regenerative Commons: A Lagrangian Framework

arXiv:2609.36478v1 Announce Type: new Abstract: The tragedy of the commons poses a multi-agent safety problem: reward-seeking agents can deplete a shared resource, and cooperation among its users does

safetyarxiv-cs-ai

Learning to Prove, Not Just to Answer: Reinforcement Learning from Formal Verification for Natural-Language Logical Reasoning

arXiv:2609.37203v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed for natural-language logical reasoning, where the final answer is easy to check but the proof beh

Applications

Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoising

arXiv:2603.25869v2 Announce Type: replace-cross Abstract: Self-supervised image denoising methods have traditionally relied on architectural constraints, pseudo-pair constructions, or specialized loss

applicationsarxiv-cs-cv

Learning to Retrieve Missing Evidence for Long-Term Memory QA

arXiv:2609.37443v1 Announce Type: cross Abstract: Long-term memory enables language models to use past interactions in future conversations. However, evidence needed to answer a question may be scatte

Learning via Self-Consistency for Diffusion-based Video Reasoning

arXiv:2609.36826v1 Announce Type: new Abstract: Video generation models have demonstrated emerging zero-shot capabilities for visual reasoning, perception, and other vision tasks. However, diffusion-b

researcharxiv-cs-cv

Learning What to Remember: Long-horizon Counterfactual Memory Optimization

arXiv:2609.37930v1 Announce Type: new Abstract: Persistent textual memory allows language models to carry information across long interactions, but learning what to remember is fundamentally a credit-

safetyarxiv-cs-cl

Learning When to Update: A Near-Optimal Timing Bandit Approach

arXiv:2609.37932v1 Announce Type: new Abstract: Systems operating in dynamic environments require timely updates to sustain performance. For resource-intensive systems such as machine learning models

researcharxiv-cs-lg

LEGO-Anything: Coding Agents for 3D Scene Reconstruction

model-releasesarxiv-cs-cv

arXiv:2609.36380v1 Announce Type: new Abstract: A 3D scene reconstructed from a single image is most useful when represented not as a rendering or a fixed 3D output, but as an explicit scene program w

LEMON-ZEST: Evolution-Informed Tokenization for Efficient Protein Language Modeling

model-releasesarxiv-cs-lg

arXiv:2609.37675v1 Announce Type: new Abstract: Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural language processing across sequence- and stru

LeRF: Learning Reference Coordinate Frames for Perspective Taking Reasoning

arXiv:2609.36219v1 Announce Type: new Abstract: Perspective taking is a fundamental component of spatial intelligence, requiring models interpret spatial relations from a specified viewpoint, such as

tutorialsarxiv-cs-cv

Local Ai

Less Supervision, Better Generalization: Weakly Supervised Fake Region Localization in Diffusion-Edited Images

arXiv:2609.36882v1 Announce Type: new Abstract: Localizing AI-edited regions is essential for interpretable forensic analysis, but remains challenging due to subtle and spatially distributed artifacts

local-aiarxiv-cs-cv

Less Uniform Discrete Diffusion is More Powerful and Scalable

arXiv:2609.35817v1 Announce Type: cross Abstract: Although uniform diffusion language models (UDLMs) represent a promising diffusion paradigm, scaling them remains challenging. We identify the core ob

LexiconVLA: Learning Reusable Atomic Action Codebooks for Unseen Tasks

model-releasesarxiv-cs-ro

arXiv:2609.36774v1 Announce Type: new Abstract: Vision-language-action (VLA) models struggle to reuse recurring interactions in unseen tasks. Our diagnostic study reveals that reliable task completion

LIBERO-MAX: Do Robot Policies Adapt When the World Changes?

arXiv:2609.36518v1 Announce Type: cross Abstract: Robots must often continue a task after a target moves, the viewpoint shifts, or an obstacle appears, even though their earlier observations and commi

LIFT: Layout-In-Future Video Generation under Large Viewpoint Change via On-Policy Self-Distillation

arXiv:2609.38146v1 Announce Type: new Abstract: We introduce LIFT, a unified image-to-video generation framework that complements camera control with Layout-In-FuTure control, enabling users to specif

safetyarxiv-cs-cv

LLM Judge Validation Under Sparse Overlap: From Inference to Design

arXiv:2609.31857v2 Announce Type: replace Abstract: Validating an LLM-as-a-judge requires estimating its agreement with humans, yet annotation budgets rarely allow every item to be multiply labeled. W

LLM unbranding: Erasing Commercial Identity while Preserving Generic Utility

model-releasesarxiv-cs-cl

arXiv:2609.37127v1 Announce Type: new Abstract: Establishing unbranding as a critical practice to prevent visual logos from acquiring negative connotations is standard in image generation. Large Langu

LLMs are not stochastic parrots: Evidence for meaning-mediated abstraction from conlang-like tasks

arXiv:2609.34187v2 Announce Type: replace-cross Abstract: The strong version of the stochastic parrot argument claims that, although large language models (LLMs) may exceed rote regurgitation, they ca

LLMs learn different forms of metacognition when trained to predict their own accuracy

arXiv:2609.33886v2 Announce Type: replace Abstract: Large language models are trained to always produce an answer, regardless of whether they possess the relevant knowledge, which leads them to fabric

tutorialsarxiv-cs-cl

Agents

LLMs Learn to Evade Latent Monitors from Prior Feedback Alone

arXiv:2609.36490v1 Announce Type: cross Abstract: Latent space monitors aim to detect undesired behaviors in LLM agents by inspecting an agent's internal activations rather than its outputs. However,

agentsarxiv-cs-ai

Local Predictability and Collective Fidelity in LLM-Agent Societies

arXiv:2609.35813v1 Announce Type: cross Abstract: Compact surrogates could reduce the cost of simulating large language model societies, but must reproduce collective behavior. We compare individual p

Local Search with Correlated Randomness

arXiv:2607.17469v2 Announce Type: replace-cross Abstract: How much does an algorithm's running-time distribution under independent randomness reveal about its behavior when independence is no longer g

researcharxiv-cs-lg

Locating Answer-Correctness Signals in Frozen Large Language Models

arXiv:2609.37700v1 Announce Type: new Abstract: Language models expose internal signals that predict whether an answer is correct, readable from a single forward pass of a frozen model without additio

LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction

arXiv:2609.36396v1 Announce Type: cross Abstract: As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical chal

researcharxiv-cs-lg

LoLBench: Evaluating Coding Agents with Long-Horizon Proposals on Large Software Systems

model-releasesarxiv-cs-cl

arXiv:2609.37143v1 Announce Type: cross Abstract: Modern coding agents can deliver increasingly large repository-level changes, and recent benchmarks reflect this by emphasizing long-horizon tasks wit

Applications

Long-Term Memory-Guided Enhancement for Target Perception in Audio-Language Models

arXiv:2609.36577v1 Announce Type: cross Abstract: Audio large language models (ALLMs) can reason about the content of audio recordings to perform complex tasks. However, these capabilities usually col

applicationsarxiv-cs-ai

LongCat-DeepResearch Technical Report

arXiv:2609.36071v1 Announce Type: new Abstract: We present LongCat-DeepResearch, a deep research system that combines an enhanced LongCat model with a multi-agent workflow for producing comprehensive,

Longer Records, Broader Invariance: The Hidden Scaling Problem in Longitudinal Contrastive Learning

arXiv:2609.36409v1 Announce Type: cross Abstract: Longitudinal data are valuable because people change. Yet the objectives used to learn from these data can inadvertently erase that change. In person-

tutorialsarxiv-cs-ai

LongHarness Bench: Stress-Testing Language Model Harnesses for Long-Context Reasoning

model-releasesarxiv-cs-cl

arXiv:2609.38137v1 Announce Type: new Abstract: Language-model (LM) harnesses enable LMs to operate effectively over long contexts using additional compute. However, existing long-context evaluations

LongLive-Plug: Once-for-All Distillation for Video Generation

arXiv:2609.38154v1 Announce Type: new Abstract: Video diffusion models are increasingly developed into specialized models for diverse downstream tasks, and this development often includes a distillati

researcharxiv-cs-cv

LongSpark: Efficient speculative decoding with a fixed-cost parallel drafter

arXiv:2609.37029v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive inference by verifying multiple draft tokens in a single target forward pass. However, as the context

Local Ai

Look Closer: Patch-wise Supervision for AI-Generated Image Detection

arXiv:2609.37937v1 Announce Type: new Abstract: How much of an image does a detector need to see? Small RGB regions can retain useful evidence of image synthesis even when they reveal little of the fu

local-aiarxiv-cs-cv

Look What You Made Us Cluster: Hate Narrative Extraction from Reddit Discourse

arXiv:2609.37408v1 Announce Type: new Abstract: Narrative extraction allows us to identify online hate narratives, supporting the construction of rigorous detection systems. Existing computational app

researcharxiv-cs-cl

Lookahead-R: Budget-Aware Tool Retrieval via Execution-Centric Planning

arXiv:2609.35811v1 Announce Type: cross Abstract: Tool retrieval is a critical bottleneck for LLM-based agents operating over large, heterogeneous API ecosystems. Existing approaches face an inherent

Looped Actor: Depth-Recurrent Reasoning Models for Reinforcement Learning

arXiv:2609.37432v1 Announce Type: new Abstract: Looped reasoning models repeatedly apply a shared set of parameters, enabling more computation without increasing the model size. These models also supp

safetyarxiv-cs-lg

Looped Transformers as Optimizers

model-releasesarxiv-cs-lg

arXiv:2609.37379v1 Announce Type: new Abstract: Looped Transformers provide a parameter-efficient approach to depth scaling by repeatedly applying shared Transformer blocks. Recent reasoning models ha

LoopICL: Looping a single transformer block to solve tabular tasks

arXiv:2609.36108v1 Announce Type: cross Abstract: Tabular foundation models using in-context learning have recently surpassed gradient-boosted trees on predictive tabular tasks. However, recent mechan

Losing the name before the box: measuring and repairing what narrow fine-tuning costs a detector outside its deployment vocabulary

arXiv:2609.36426v1 Announce Type: cross Abstract: A detector pretrained on a broad corpus is fine-tuned on a narrow domain, its in-domain accuracy improves, and it ships. We ask what happens meanwhile

researcharxiv-cs-cv

Local Ai

Loss-Guided Pretraining Data Selection for Time-Series Foundation Models

arXiv:2609.37255v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are pretrained on heterogeneous collections containing billions of observations, yet their training windows are

local-aiarxiv-cs-ai

Lost in Conversation or Lost in Translation? Diagnosing Multi-Turn Degradation in RAG

arXiv:2609.36700v1 Announce Type: cross Abstract: When conversing with large language models (LLMs), users often begin with a simple question and build towards a multi-hop question through follow-up t

Lost in Translation: Measuring the Effect of Non-Native English on End User Performance of Large Language Models

arXiv:2609.36214v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used by people whose first language is not English, yet these users have been shown to receive systematica

researcharxiv-cs-cl

Lucid Dreaming for World Models: Learning to Doubt Imagination and Decide by Trust

arXiv:2609.37156v1 Announce Type: cross Abstract: World models enable agents to learn and plan in imagination, but predictions beyond their experience can become unreliable and mislead decisions. Exis

safetyarxiv-cs-ai

MAADBench: The Refreshable Paradigm for Anomaly Detection in Multi-Agent Systems

arXiv:2609.36556v1 Announce Type: new Abstract: Recent studies report that LLM-based multi-agent systems (MAS) fail at rates of 41%-87%, yet to our knowledge, no benchmark to date supports systematic