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Learning to Plan by Looking Back: Hindsight Hierarchies for Training Reasoning Models

arXiv:2610.12168v1 Announce Type: new Abstract: We introduce a self-improvement loop for reasoning models based on the following observation: Even when the difficulty of a problem exceeds the model's

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Research

Learning to Retrieve: Internalizing Memory Retrieval for Video World Models

DGX agent

arXiv:2610.11444v1 Announce Type: new Abstract: Video world models aim to generate explorable, 3D-consistent scene videos conditioned on camera trajectories. Existing approaches often rely on external

researcharxiv-cs-cv

9 Oct 2026

Learning What to Trust in Multimodal Learning under Noisy Supervision

arXiv:2610.11057v1 Announce Type: new Abstract: Multimodal classification processes and relates information from multiple modalities to achieve more accurate predictions. However, existing methods typ

safetyarxiv-cs-cv

Learning Which Correspondences to Trust: Confidence-Weighted Event-Camera Localization in LiDAR Maps

arXiv:2610.11967v1 Announce Type: new Abstract: Localizing an event camera against a pre-built LiDAR map can be cast as dense optical-flow estimation between a rendered depth view and an event image,

local-aiarxiv-cs-cv

LEGO: A Lifting-Free Approach for Exocentric-to-Egocentric Video Generation

arXiv:2610.12442v1 Announce Type: new Abstract: Generating an egocentric video from a single exocentric recording is a challenging case of novel view synthesis, as the two cameras share little overlap

safetyarxiv-cs-cv

Less from More: Reinforcing Sparse Video Reasoning from Dense References

arXiv:2610.10893v1 Announce Type: new Abstract: Video-language models commonly assume that more temporal observations lead to more reliable reasoning. We question this assumption and argue that the ke

LEVER: Adaptive Cost-Aware Proof Search Over AND/OR Graphs

arXiv:2610.11862v1 Announce Type: new Abstract: Mathematicians value proofs for more than correctness: among correct proofs, simplicity, purity and the computational cost of finding them vary widely.

agentsarxiv-cs-ai

Leveraging Human-In-The-Loop Demonstrations in Reinforcement Learning for Digital Twin-Driven Robot Flexibility

arXiv:2610.12140v1 Announce Type: new Abstract: Growing automation makes collaborative robots work in more variable environments, increasing the need for adaptation. We propose a human-in-the-loop onl

safetyarxiv-cs-ro

LeWAM: A JEPA World Action Model with Diffusion-Steering-Based MPC

arXiv:2610.12407v1 Announce Type: cross Abstract: World action models (WAMs) predict actions and future observations, typically from a reconstruction-based representation that carries noisy, redundant

safetyarxiv-cs-ai

LIME: Link-based User-item Interaction Modeling with Decoupled XOR Attention for Efficient Test Time Scaling

arXiv:2510.18239v4 Announce Type: replace-cross Abstract: Scaling large recommendation systems requires advancing three major frontiers: processing longer user histories, expanding candidate sets, and

Limited Stereotype Control Through Routing Reweighting in MoE Language Models

arXiv:2603.27141v2 Announce Type: replace Abstract: Demographic prompts are routed differently from neutral prompts in Mixture-of-Experts (MoE) language models, motivating tests of routing-level stere

safetyarxiv-cs-cl

LinSlot: Exploiting Linear Representation hypothesis for unsupervised attribute discovery from slot based object representation

arXiv:2610.10722v1 Announce Type: cross Abstract: This paper studies the problem of learning disentangled representations of objects and their attributes from raw, unstructured image data. Slot-based

LiteNWM: Efficient Latent World Models for Onboard Visual Navigation in the Wild

arXiv:2610.12368v1 Announce Type: new Abstract: Direct visual navigation policies generate trajectories efficiently but do not explicitly evaluate their future consequences. Generative navigation worl

researcharxiv-cs-ro

LIVIN: Benchmarking Spatial and Embodied Intelligence in Digital Twins of Lived-In Homes

model-releasesarxiv-cs-cv

arXiv:2610.12069v1 Announce Type: new Abstract: Realistic household simulation must capture not only diverse environments but also the lived-in object arrangements and spatial constraints that shape r

LlamaTale v0.43.0 - MCP Server for story creation

In the dawn of the LLM craze, when wild Llama2's roamed free and 4k tokens was considered a fairly long context for local models, I forked an interactive fiction and MUD library called Tale to experim

local-air-localllama

LLM-IDEA: Identifiability-Driven Experimental Agent for Autonomous Discovery of Mechanistic World Models

model-releasesarxiv-cs-ai

arXiv:2610.11253v1 Announce Type: new Abstract: Large language model agents are being increasingly deployed as autonomous scientists, designing experiments and inferring mechanistic world models with

LLM-powered Query Expansion for Enhancing Boundary Prediction in Language-driven Action Localization

arXiv:2505.24282v2 Announce Type: replace Abstract: Language-driven action localization in videos requires not only semantic alignment between language query and video segment, but also prediction of

local-aiarxiv-cs-cv

LM Studio 0.4.26 is out now! • ⚡️ `/v1/systemone` and `/v1/decisions` APIs • 👾 Coexistence with Bionic • 🎨 UI updates and bug fixes Yes, y…

LM Studio 0.4.26 is out now! • ⚡️ `/v1/systemone` and `/v1/decisions` APIs • 👾 Coexistence with Bionic • 🎨 UI updates and bug fixes Yes, you can now point your Bionic agent to models loaded in your LM

agentslm-studio--x

LMSpell: Spell Correction with Pre-Trained Language Models

model-releasesarxiv-cs-cl

arXiv:2512.05414v4 Announce Type: replace Abstract: Spell correction is still a challenging problem for many languages, especially low-resource languages (LRLs). While pre-trained language models (PLM

Local Prototype Reconstruction for Text-Compatible Speech-to-LLM Bridge Pretraining

arXiv:2610.11159v1 Announce Type: new Abstract: Speech-to-LLM systems often connect a frozen speech encoder to a frozen large language model (LLM) through a small trainable bridge. The bridge is usual

local-aiarxiv-cs-cl

Local Timescale Gates for Timescale-Robust Continual Spiking Neural Networks

model-releasesarxiv-cs-lg

arXiv:2510.12843v2 Announce Type: replace Abstract: Spiking neural networks (SNNs) promise energy-efficient artificial intelligence on neuromorphic hardware but struggle with tasks requiring both fast

Long Text to Predictive Features: LLM-Guided Blockwise Feature Engineering via Executable Program Search

arXiv:2610.12390v1 Announce Type: cross Abstract: Industrial risk-control systems typically rely on structured-data models for efficient prediction, yet substantial valuable information remains embedd

researcharxiv-cs-cl

Look Back, Think Ahead: Visual Memory on Demand for Efficient Multimodal Reasoning

arXiv:2610.12060v1 Announce Type: new Abstract: Processing long visual token sequences from high-resolution images makes multi-step reasoning computationally expensive for multimodal Large Language Mo

Look Where You Can: Active View Selection for CAD Reconstruction under Occlusion

model-releasesarxiv-cs-cv

arXiv:2610.11954v1 Announce Type: new Abstract: CAD reconstruction methods assume a luxury reality rarely grants: unrestricted visual access to the object, photographed from any desired angle. Real ob

Looking Inside LLMs: Small-World Connectivity as a Signature of Reasoning Performance

arXiv:2610.12304v1 Announce Type: new Abstract: Understanding large language model (LLM) reasoning requires looking beyond behavioral performance to examine how reasoning ability is reflected in inter

local-aiarxiv-cs-ai

Lossy Compressive Text Autoencoders

arXiv:2610.10738v1 Announce Type: new Abstract: Our work explores learning a compressed latent representation of text, at the intersection of data compression and representation learning. We propose a

researcharxiv-cs-cl

Low-Cost Sensor Calibration for Indoor Air Quality Monitoring: A Dataset, Evaluation Scenarios, and a Lightweight Model

arXiv:2610.11236v1 Announce Type: new Abstract: Low-cost sensors enable scalable indoor air quality monitoring but require calibration because of nonlinear distortions, noise, and temporal drift. The

Low-rank tensor structure of precipitation and its application to satellite-reference merging

arXiv:2610.11000v1 Announce Type: new Abstract: The intermittent and variable nature of precipitation makes its accurate estimation over extended domains difficult, yet its spatiotemporal structure su

safetyarxiv-cs-lg

LR-V2X: Loss-Resilient Collaborative Perception under Low-Bandwidth Communication

model-releasesarxiv-cs-cv

arXiv:2610.11264v1 Announce Type: new Abstract: Given the inherent unpredictability of packet loss in vehicular wireless communications, V2X collaborative perception can yield practical benefits only

LTBD: Learnable Trust-Boundary Delimiters for Prompt Injection Defense

arXiv:2610.11634v1 Announce Type: cross Abstract: Large language models (LLMs) perform remarkably well on complex tasks, yet remain highly vulnerable to prompt injection attacks, where malicious instr

LVS: Local View Synthesis from Relative Camera Pose by Reusing Previous Views

arXiv:2610.12127v1 Announce Type: cross Abstract: Interactive scene exploration requires frequent view updates, although small camera motions preserve much of the visible content. Conventional 3D Gaus

LVSPM: Long Sequence View Synthesis and Pose Estimation Model

arXiv:2610.10960v1 Announce Type: new Abstract: We present LVSPM, a generalizable model that jointly estimates camera poses and synthesizes novel views from uncalibrated image collections. Trained wit

Tutorials

Machine learning and information theory concepts towards an AI Mathematician

arXiv:2403.04571v2 Announce Type: replace Abstract: The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathe

tutorialsarxiv-cs-ai

Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery

arXiv:2610.12293v1 Announce Type: cross Abstract: Machine learning (ML) in high-energy nuclear physics (HENP) is entering a new stage in which physical knowledge is incorporated more directly into dat

Machine Learning Optimization for Enhanced OS Fingerprinting

arXiv:2610.11133v1 Announce Type: new Abstract: Operating System (OS) Fingerprinting is a technique that can be used to identify a network's operating systems by evaluating network traffic in the form

MAMHOI: Factorizing Scene-Aware Human-Object Interaction through Affordances

arXiv:2610.12416v1 Announce Type: cross Abstract: Generating realistic human-object interactions (HOI) in complex 3D scenes requires two complementary capabilities: reasoning about interaction feasibi

ManiUnit: A Manipulation Skill Dataset and Benchmark for Long-Horizon Tasks

model-releasesarxiv-cs-ro

arXiv:2610.12089v1 Announce Type: new Abstract: Long-horizon mobile manipulation requires a robot to navigate multi-room environments and execute a sequence of manipulation skills under a single natur

MAP2: Model- and Acceleration-Based Pursuit with MPC and Gaussian Process Residual Learning for Autonomous Racing

arXiv:2610.12196v1 Announce Type: new Abstract: Autonomous racing requires accurate trajectory tracking near the handling limits of a vehicle while maintaining low computational latency. Geometric con

agentsarxiv-cs-ro

MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning

arXiv:2610.11727v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become a cornerstone in software engineering for enhancing Large Language Models (LLMs) with domain-specific

MAPF-World: Action World Model for Multi-Agent Path Finding

arXiv:2508.12087v3 Announce Type: replace Abstract: Multi-agent path finding (MAPF) studies the problem of planning conflict-free paths for multiple agents from given start locations to designated goa

agentsarxiv-cs-ai

Marformer: A Transformer for Predicting Missing Data Distributions

arXiv:2610.12379v1 Announce Type: new Abstract: Real decisions are made under incomplete information. If we observe only some of the random variables we need, we can predict the others. The extbf{cond

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL

model-releasesarxiv-cs-ai

arXiv:2607.16204v2 Announce Type: replace Abstract: Recent growth in reinforcement learning (RL) has surfaced a need for diverse, specialized training environments. Hand-curated environments with fixe

Applications

Masked Generative Motion Planning with Geometry-Guided Token Search

arXiv:2610.10646v1 Announce Type: cross Abstract: Generative motion planners typically use learned trajectory priors for initial generation, while leaving test-time repair to local continuous refineme

applicationsarxiv-cs-ai

MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation

model-releasesarxiv-cs-ai

arXiv:2610.12067v1 Announce Type: cross Abstract: Elucidating molecular structures from spectra is a foundational problem in chemical and materials characterization, yet remains challenging due to spe

MATE4D: Matrix-Guided Editable 4D Generation from a Single Image

arXiv:2610.11181v1 Announce Type: new Abstract: Generative models have rapidly pushed content creation be-yond 2D imagery toward dynamic 3D and 4D scene synthesis. Yet pro-ducing realistic and tempora

MatLat: Material Latent Space for PBR Texture Generation

arXiv:2512.17302v2 Announce Type: replace Abstract: We propose a generative framework for producing high-quality PBR textures on a given 3D mesh. As large-scale PBR texture datasets are scarce, our ap

mAVE: A Watermark for Joint Audio-Visual Generation Models

arXiv:2603.07090v2 Announce Type: replace-cross Abstract: Watermarking joint audio-visual generation supports vendor copyright protection and content provenance. However, independently valid audio and

MC-TRCM: Observation-Aware Recursive Fusion for Incomplete Mobile and Wearable Mental-Health Feature Views

arXiv:2610.11408v1 Announce Type: new Abstract: Public mobile and wearable mental-health datasets often provide summarized feature tables rather than synchronized raw sensor streams. In these releases