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Load-Aware Quadruped Navigation with Online Contact-based Geometry Estimation

arXiv:2610.14596v1 Announce Type: new Abstract: Navigating cluttered environments while carrying objects remains a challenging problem for quadruped robots. Compared with works that focused on load-aw

Localized Neural learning vs backpropagation [D]

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DGX agent

I’ve recently started exploring machine learning by building neural networks from scratch and studying the underlying mathematics. I come from a physics and mathematics background, and as I’ve become

researchr-machinelearning

14 Oct 2026

LocalTrain: Integer-Only Local Learning for Energy-Efficient CNN Training on Edge FPGAs

arXiv:2610.17099v1 Announce Type: cross Abstract: Local learning reduces cross-layer training dependencies, but an efficient accelerator must still retain operands, accumulate gradients, and select qu

Locomotion with Online Awareness of Dynamics for Precision Footholds

arXiv:2610.17930v1 Announce Type: new Abstract: Unknown payloads alter robot dynamics and make precise contact execution difficult when foothold margins are small. We present LOAD, a perceptive locomo

LoComposition: Terrain-Adaptive Energy-Efficient Quadruped Locomotion without Gait Priors

arXiv:2606.15896v2 Announce Type: replace-cross Abstract: Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, ga

LongForcing: Scaling Temporal Manifold Matching for Long-Horizon World Rollout

arXiv:2610.18289v1 Announce Type: new Abstract: Autoregressive video diffusion models have emerged as a foundation for long-horizon world rollout. However, when extended far beyond their training wind

Longitudinal Learner Participation Profiling in Online Learning: Heterogeneous Consensus Clustering under Unequal Observation Windows

arXiv:2610.17494v1 Announce Type: new Abstract: Learning management system traces are clustered to identify participation profiles, but unequal observation windows may cause profiles to reflect durati

tutorialsarxiv-cs-lg

Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation

arXiv:2610.16224v1 Announce Type: new Abstract: Text-to-image models have made remarkable progress in visual fidelity and instruction following, yet complex compositional generation remains challengin

Look Before You Move: Agent-Interface-Aligned Supervision for Open-World Mobile Manipulation

arXiv:2610.15572v1 Announce Type: new Abstract: Open-world mobile manipulation requires a robot to find an object, approach it, and move it to a target receptacle from a language instruction. Training

Looped Reasoning Finishes Earlier Than You Think

arXiv:2610.18174v1 Announce Type: new Abstract: Looped reasoning with dynamic depth can be viewed as relaxation toward solution attractors in a learned dynamical landscape. In practice, however, laten

LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers

arXiv:2610.16368v1 Announce Type: new Abstract: Looped Language Models (LoopLMs) improve parameter efficiency by recursively reusing shared Transformer modules, yet their repeated computation over evo

Local Ai

LoopREG: Representation Supervision Enables Test-Time Recurrent Depth in Diffusion Transformers

arXiv:2610.17877v1 Announce Type: new Abstract: Reusing Transformer blocks offers a way to increase diffusion-model computation at test time, but naive recurrence can degrade generation beyond its tra

local-aiarxiv-cs-cv

LoOPSD: On-Policy Self-Distillation with Looped Teachers

arXiv:2610.18162v1 Announce Type: new Abstract: On-policy self-distillation improves a model using guidance from the model itself, but it still requires a teacher that provides more useful predictions

LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts

arXiv:2610.18873v1 Announce Type: cross Abstract: Scaling expert demonstrations alone does not enable generalist robot policies to improve from deployment. Real-world rollouts contain successes, subop

LoRAtorio: An intrinsic approach to LoRA Skill Composition

arXiv:2508.11624v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has become a widely adopted technique in text-to-image diffusion models, enabling the personalisation of visual concepts

Lost in Contrast: Instruction-Preserving Decoding for Reliable Visual Question Answering

arXiv:2610.16722v1 Announce Type: cross Abstract: Visual contrastive decoding corrects answers using the prediction difference between the original image and a reference condition, yet the same contra

Hardware

LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs

arXiv:2610.18393v1 Announce Type: cross Abstract: Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of quantum-mechanical calcula

hardwarearxiv-cs-lg

LynxScenes: A Large-Scale Real-World Benchmark for Outdoor Wheel-Legged Robot Perception, Navigation, Imagination and Reconstruction

model-releasesarxiv-cs-ro

arXiv:2610.18093v1 Announce Type: new Abstract: Wheel-legged robots operating in outdoor spaces shared with humans require accurate perception, socially compliant navigation, future-scene reasoning, a

Machine Learning-Based Prediction of Dengue Infection Using Clinical and Hematological Data from Bangladesh: A Comparative Analysis of Benchmark and Extended Prediction Models

arXiv:2610.14813v1 Announce Type: new Abstract: Dengue is a viral infection caused by the dengue virus (DENV) and a major public health concern in Bangladesh, where symptom overlap with other febrile

MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation

arXiv:2610.17014v1 Announce Type: cross Abstract: Functional brain connectome represents neural connectivity as a matrix of pairwise interactions between brain regions. Generation of functional connec

MajorTOM:Elliot Pretrain, A Multi-Sensor, Multi-Temporal Pretraining Dataset for Earth Observation

arXiv:2610.18624v1 Announce Type: new Abstract: Pretraining generalist Earth observation foundation models requires data that combine several sensors, varied temporal sampling and, increasingly, text,

Make Every Expansion Count: Learning to Search Efficiently in LLM Code Repair

arXiv:2610.15894v1 Announce Type: new Abstract: Large language model (LLM) code-repair systems fix faulty programs by repeatedly generating and testing revisions. Each revision is costly, making the c

Make {pi}0.5 Learn to Call Astra for Reliable Robot Manipulation

arXiv:2610.14117v1 Announce Type: new Abstract: GPT-6 Astra provides robust embodied reasoning, but frequent external queries increase inference time and token costs. We equip a frozen pi_{0.5} policy

Making LoRA Identifiable: Orthogonal Alignment for Continual Learning

arXiv:2610.16664v1 Announce Type: new Abstract: There is growing interest in applying Parameter-Efficient Fine-Tuning (PEFT) to Continual Learning, as it enables faster training, mitigates catastrophi

Making Tensor Decoupling More Robust and Less Parametric

arXiv:2610.16937v1 Announce Type: new Abstract: Tensor decoupling algorithms recover compact representations of multivariate functions by combining linear factor matrices with univariate internal nonl

Manifold Navigation Learning Using Recurrent Networks: Efficient Training Techniques

arXiv:2610.14325v1 Announce Type: new Abstract: Position tracking is a well-studied problem in Euclidean navigation, yet comparatively little attention has been given to navigation on general manifold

tutorialsarxiv-cs-cv

ManipShield: A Unified Framework for Image Manipulation Detection, Localization and Explanation

arXiv:2511.14259v4 Announce Type: replace Abstract: With the rapid advancement of generative models, powerful image editing methods now enable diverse and highly realistic image manipulations that far

Mapping the Post-Transformer Architecture Landscape in 2026 [D]

model-releasesr-machinelearning

Went down the rabbit hole to map the different Post-Transformer architectures. Various directions include Dragon Hatchling (BDH), Mamba, RWKV, xLSTM, Titans, and a bunch of hybrid models coming from d

Mapping Tire-Road Friction Coefficients Along Driven Trajectories

model-releasesarxiv-cs-ro

arXiv:2610.14235v1 Announce Type: new Abstract: Information of the tire-road friction coefficient is critical for intelligent vehicle systems, as friction establishes the limits at which the vehicle c

MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL

arXiv:2610.14883v1 Announce Type: cross Abstract: We present MARL-BattleGrounds (MARL-BGs), an easy-to-use JAX-native benchmark for heterogeneous and competitive multi-agent reinforcement learning (MA

MARS: Systematic Multi-Level Acceleration for World Action Models

arXiv:2610.17141v1 Announce Type: new Abstract: World Action Models (WAMs) have emerged as a promising alternative for embodied intelligence by jointly predicting future world states and robot actions

Local Ai

MARTS: A Modular Agricultural Robot for Targeted Spraying of Diseased Tomato Plants in Greenhouses

arXiv:2610.18123v1 Announce Type: new Abstract: Broadcast spraying remains the dominant method of disease control in greenhouse tomato production, yet it treats healthy and diseased tissue alike, expo

local-aiarxiv-cs-ro

Masked Attention Pooling Is Invariant to Padding and Permutation: A Formal Proof in Lean

arXiv:2610.17132v1 Announce Type: new Abstract: Attention-based multiple instance learning (MIL) pools a variable-size bag of instance embeddings into one vector. In batched training, bags are padded

Masked Diffusion for Travel Diaries Synthesis

arXiv:2610.17109v1 Announce Type: new Abstract: Because household travel surveys combine household characteristics, variable-length person rosters, vehicle rosters, and trip chains, existing synthesis

MaskSense: Confronting the Visual Exploration Trap in Masked Image Generation

arXiv:2610.17009v1 Announce Type: new Abstract: Reinforcement learning (RL) has shown strong potential for aligning generative models with human intent, but adapting RL to masked generative models (MG

Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models

arXiv:2610.18001v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable flexible parallel generation by resolving tokens according to the model's evolving predictability rather than a

mathbf{S^2LM}: Towards Semantic Steganography via Large Language Models

arXiv:2511.05319v3 Announce Type: replace Abstract: Despite remarkable progress in steganography, embedding sentence-level information into cover media remains challenging. In this work, we present th

MDN-VJEPA: Multimodal Probabilistic Joint-Embedding World Models

arXiv:2610.13956v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting future representations rather than reconstructing observations. Variat

tutorialsarxiv-cs-lg

Mean Accuracy Is the Wrong Estimand for a Release Decision

arXiv:2610.16825v1 Announce Type: new Abstract: A release decision about a fine-tuned model is an operational commitment about the next rerun of its training recipe. Yet standard benchmark reporting g

Meaningful Moments: A Corpus of VLM-Derived Temporal Importance Labels for Video Action Recognition

arXiv:2610.16009v1 Announce Type: new Abstract: Not every moment in a video is equally useful for recognizing an action. A few decisive moments often carry the evidence: the instant an object moves, t

Measure Neural Mappings

arXiv:2610.17384v1 Announce Type: new Abstract: We introduce and define measure neural mappings (MNMs), which combine measure-dependent neural transport with ensemble consistency for learning operator

Measuring all the noises of LLM Evals

arXiv:2512.21326v3 Announce Type: replace Abstract: Applying well-established statistical methods effectively to LLM evals requires consideration of model correlations and prediction noises. Leading e

Measuring Learned Equivariance: An Information Theoretic Approach

arXiv:2610.17269v1 Announce Type: new Abstract: Learning symmetries through data augmentation offers an alternative to architectural equivariance, but does not guarantee that a network respects those

Measuring Variable Importance Using Proportional Inclusive Marginal Effects

arXiv:2610.17053v1 Announce Type: cross Abstract: Shapley-based measures are widely used for measuring variable importance in statistics and machine learning. However, these measures have a serious is

MedEvo: Medical Reasoning Evolution through Knowledge Navigation

arXiv:2610.17690v1 Announce Type: cross Abstract: Medical multimodal reasoning models are typically trained on teacher-generated rationales, discarding the valid reasoning in students' own failed traj

Agents

MemAct: Streaming Spatial Understanding with Memory and Active Exploration

arXiv:2610.17640v1 Announce Type: new Abstract: Spatial understanding is essential for embodied agents navigating and interacting with the physical world. As observations arrive incrementally, agents

agentsarxiv-cs-cv

Memorization in image classification: test set behavior and scaling laws

arXiv:2610.16090v1 Announce Type: new Abstract: Deep neural networks are capable of both learning generalizing solutions on natural tasks and memorizing arbitrary mappings. We argue that understanding

tutorialsarxiv-cs-lg

Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies

arXiv:2610.14406v1 Announce Type: cross Abstract: It is widely appreciated that robotic policies require long-term memory to perform complex tasks. However, directly conditioning policies on all past