All entries

arXiv:2610.17718v1 Announce Type: cross Abstract: Electrocardiogram (ECG) models need supervision that targets evolving weaknesses and remains faithful to waveform evidence. We introduce ECG-RSI, a re

tutorialsarxiv-cs-lg

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Applications

ECHO: Correcting Time Series Foundation Model Forecasts with Historical Analogues and Errors

DGX agent

arXiv:2610.16433v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) provide general-purpose forecasting capabilities, yet their forecasts can remain misaligned with the temporal char

applicationsarxiv-cs-lg

14 Oct 2026

Hardware

Echo: Error-Compensated 4-Bit Tensor-Parallel Communication for LLM Training and Inference

arXiv:2610.14277v1 Announce Type: cross Abstract: Tensor parallelism ends every layer with an all-reduce that sits between two dependent matrix multiplications, so its cost cannot be hidden and grows

hardwarearxiv-cs-lg

ECLIPTICA -- A Framework for Switchable LLM Alignment via CITA - Contrastive Instruction-Tuned Alignment

model-releasesarxiv-cs-lg

arXiv:2601.06157v2 Announce Type: replace Abstract: Alignment in large language models (LLMs) is still largely static: after training, the policy is frozen. DPO, GRPO methods typically imprint one beh

Edge-Native Temporal--Graph Dual-Expert Inference for Dexterous Grasp Classification

arXiv:2610.14926v1 Announce Type: new Abstract: Multi-finger hands contact objects at different times, so a global close command can overload early-contact fingers while others are still moving. Dense

researcharxiv-cs-ro

Effect of Spherical Parameter Space on Number of Non-Forgetting Tasks and Capacity of Continual Learning

model-releasesarxiv-cs-lg

arXiv:2610.17453v1 Announce Type: new Abstract: We propose a continuous learning formulation for T non-forgetting tasks with the spherical parameter space S^{d-1}(sqrt{d}) in asymptotic regime drighta

Efficient Frechet Distributional Decoder Alignment for Latent Generative Models

arXiv:2610.17466v1 Announce Type: new Abstract: Frechet Distributional (FD) objectives provide an effective distribution-level signal for post-training generative models. For latent generative models,

safetyarxiv-cs-cv

Efficient Hybrid Hard--Soft Signal Temporal Logic MPC for Driving under Multimodal Uncertainty

arXiv:2610.15934v1 Announce Type: new Abstract: Motion planning for automated vehicles in interactive traffic has to simultaneously ensure safety, respect traffic rules, and maintain comfortable progr

Agents

Efficient Multi-Agent Reasoning via Confidence-Guided Adaptive Debate

arXiv:2610.16343v1 Announce Type: cross Abstract: Multi-agent debate has shown promise for improving the reasoning of large language models, yet recent theory suggests its benefits are highly regime-d

agentsarxiv-cs-lg

Efficient Preconditioned Optimization Methods with Frequent Directions

arXiv:2610.16792v1 Announce Type: new Abstract: Adaptive optimization methods have received considerable attention in recent years due to their strong empirical performance. However, they generally re

Efficient Resource-Shiftable RL Post-training with Generative Rewards

model-releasesarxiv-cs-lg

arXiv:2610.16866v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training with generative reward models (GenRM) has become a central recipe for improving large language models (LLMs),

Efficient Scene-Text Refinement via Pixel-Space Grouped Image Generation

arXiv:2610.18284v1 Announce Type: new Abstract: Generated images often have coherent layouts and visual content, yet local errors in small text and complex characters still limit their readability and

Applications

Efficient Test-Time Adaptation through Latent Subspace Coefficients Search

arXiv:2510.11068v4 Announce Type: replace Abstract: Real-world deployment often exposes vision models to distribution shifts, making test-time adaptation (TTA) important for reliable prediction. Exist

applicationsarxiv-cs-lg

EgoAlign: Situated 3D Understanding without Situation Descriptions

arXiv:2610.16176v1 Announce Type: new Abstract: Situated 3D Question Answering requires models to interpret a 3D scene relative to an agent's pose. Existing methods commonly convey that pose through a

safetyarxiv-cs-cv

EgoCPT: Learning Dexterous Manipulation from a Single Ego-Centric Video with Contact-Aware Optimization

arXiv:2610.17572v1 Announce Type: new Abstract: While expert demonstrations on real robots are difficult to collect, human ego-centric videos are easy to collect and abundant on the internet. However,

researcharxiv-cs-ro

EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ

arXiv:2610.18872v1 Announce Type: cross Abstract: Learning humanoid loco-manipulation from human demonstrations rests on observing how locomotion, posture, and object interaction evolve together as ta

researcharxiv-cs-cv

EgoMatrix: Physically Faithful Dexterous Robot Demonstrations from Egocentric Human Videos

arXiv:2610.18822v1 Announce Type: new Abstract: Egocentric human videos offer a scalable alternative to robot teleoperation for collecting dexterous manipulation data. However, two challenges limit th

EgoOmniWorld: Unifying Egocentric Understanding via Ego-Action-World Interactions

arXiv:2610.18499v1 Announce Type: new Abstract: Egocentric video understanding spans diverse capabilities, modalities, and evaluation formats, yet these are often studied in isolation despite their ti

EgoProbe: Benchmarking Long-Range and Exact Memory in Streaming Egocentric Videos

arXiv:2610.17128v1 Announce Type: new Abstract: Egocentric video provides a continuous first-person record of everyday activities, making it a natural foundation for wearable AI assistants. Unlike off

EgoWholeView: Camera-Only Head-Mounted Capture of Body and Bimanual Hand Motion for Embodied Demonstrations

arXiv:2610.16256v1 Announce Type: new Abstract: Head-mounted body and bimanual motion capture supports self-avatars, teleoperation, and embodied demonstrations. However, a single egocentric camera is

Eight states sue OpenAI, saying ChatGPT isn’t safe for kids

OpenAI is facing a new wave of lawsuits from eight US states that accuse the company of harming children and misleading customers about the safety of ChatGPT, as reported earlier by Reuters. The lawsu

safetythe-verge-ai

ELECT: Settling Identity Before Rendering for Scalable Story Visualization

arXiv:2610.15555v1 Announce Type: new Abstract: Training-free story visualization without reference images must preserve recurring entities across frames while scaling to long narratives. Existing met

Elephant in the Fridge: Constant-Memory Frame Packing for Long Video Understanding

arXiv:2610.16118v1 Announce Type: new Abstract: Hour-long videos, streaming feeds, and movie-length content are pushing vision-language models (VLMs) past their breaking point -- the standard recipe o

Ellipsoids from Tangents

arXiv:2610.15580v1 Announce Type: new Abstract: This paper presents an algorithm for detecting and fitting multiple ellipsoids in 2D, 3D and higher-dimensional data. The representation is based on tan

ELVIS: Enhance Low-Light for Video Instance Segmentation in the Dark

arXiv:2512.01495v3 Announce Type: replace Abstract: Video instance segmentation (VIS) for low-light content remains highly challenging for both humans and machines alike, due to noise, blur and other

researcharxiv-cs-cv

Applications

Embedding Graph Kernel Entropy (EGKE): Fine-grained Hallucination Detection for LLMs from Embeddings

arXiv:2610.14214v1 Announce Type: new Abstract: Hallucination detection is critical for ensuring the reliability of Large Language Models (LLMs) in real-world deployments. While recent works, such as

applicationsarxiv-cs-lg

Embodied Skill Bench: Benchmarking How Well Agents with Embodied Skills Perform on Physical Tasks

model-releasesarxiv-cs-ro

arXiv:2610.16238v1 Announce Type: new Abstract: LLM agents increasingly control robots using VLA policies, perception models, and procedural knowledge. These modules are difficult to reuse because the

Local Ai

Embodied Zero-Shot Task Planning via Hardware-Aware LLM-VLM Integration for Grounded Robotic Manipulation

arXiv:2610.14106v1 Announce Type: new Abstract: Large language models can translate natural-language instructions into plausible manipulation plans, but robotic execution requires physically grounded

local-aiarxiv-cs-ro

EmbodiedEvo: Self-improving Coding Agents for Physical Intelligence

arXiv:2610.18867v1 Announce Type: new Abstract: Bringing general-purpose agents into the physical world calls for an executable representation of physical intelligence. Compared with vision-language-a

Embodiment-Aware Inference-Time Search for Human Reaction Selection in Humanoid Retargeting

model-releasesarxiv-cs-ro

arXiv:2610.15274v1 Announce Type: new Abstract: Training humanoid robots to learn human behaviors requires expensive data collection, and human-robot interaction data can be even more difficult and co

EmbodiSteer: Steering Embodiment-Agnostic Visuomotor Policies with Joint-Space Guidance for Zero-Shot Cross-Embodiment Deployment

arXiv:2606.12965v2 Announce Type: replace Abstract: Scalable robot imitation learning relies on large-scale heterogeneous data from diverse robots or body-free data, making Cartesian end-effector acti

Emergent Loop Geometry in Gauge-Equivariant Graph Neural Networks

arXiv:2610.18742v1 Announce Type: cross Abstract: Gauge-equivariant graph neural networks generate ordered products of gauge links through local message passing, but the geometry of these internal str

EmoGenie: Benchmarking and Evaluating Expressed and Evoked Emotion in T2I Generation with MLLMs

arXiv:2610.18455v1 Announce Type: new Abstract: Rapid advances in artificial intelligence-generated content (AIGC) have significantly promoted using text-to-image (T2I) models to generate images with

EmoTrace-Bench: Diagnosing Emotional Expression in Text-to-Video Generation through Controlled Cues and Intensity

arXiv:2610.15452v1 Announce Type: new Abstract: Text-to-video (T2V) models are increasingly used to generate videos with targeted emotional expressions, yet existing evaluation methods mainly rely on

Enabling Token-wise Importance Sampling in Off-Policy RLVR with Proximal KL Regularization

arXiv:2610.18738v1 Announce Type: new Abstract: Off-policy data in reinforcement learning with verifiable rewards (RLVR) can arise from asynchronous generation, rollout-training mismatch, and response

Agents

EnactOpt: Diagnosis-Guided Harness Evolution for Embodied Agents

arXiv:2610.14977v1 Announce Type: new Abstract: Embodied agents built on foundation models rely on an execution harness to organize observations, guide actions, and incorporate execution feedback into

agentsarxiv-cs-ro

Encoding Context into LLM Weights: What Makes It Context?

arXiv:2610.17686v1 Announce Type: cross Abstract: Longer contexts allow large language models (LLMs) to condition their predictions on more information, but inference costs grow prohibitively with con

End-to-End Spoken Grammatical Error Correction

arXiv:2506.18532v2 Announce Type: replace-cross Abstract: Grammatical Error Correction (GEC) and feedback play a vital role in supporting second language (L2) learners, educators, and examiners. While

Hardware

Energy-Efficient Deep Learning Without Backpropagation: A Rigorous Hardware-Validated Benchmarking Study of Forward-Only Algorithms

arXiv:2511.01061v2 Announce Type: replace Abstract: Backpropagation (BP) powers modern deep learning, but it must hold every activation in memory until a global backward sweep consumes it, and that sw

hardwarearxiv-cs-lg

Tutorials

Energy-Guided Learning for Heat Equation with Noisy Source: Optimal Estimation and Generalization Bounds

arXiv:2610.15111v1 Announce Type: cross Abstract: Data and the governing partial differential equations (PDEs) of continuous-time dynamical systems provide rich structure for statistical learning. A c

tutorialsarxiv-cs-lg

Engram Neuron Inspired Adaptive Memorization for Lifelong Learning

arXiv:2610.14940v1 Announce Type: new Abstract: Lifelong learning enables artificial systems to adapt to changing environments. To achieve this, an effective artificial learner should dynamically reor

Agents

Enhanced Trading Systems with Intelligent Market Analysis

arXiv:2610.13998v1 Announce Type: new Abstract: This paper proposes a hybrid reinforcement learning trading framework integrating intraday market data with transformer-based financial sentiment signal

agentsarxiv-cs-lg

Applications

Enhancing Zero-Shot Domain Transfer in Low-Resource Sentiment Analysis: A Case Study in Marathi

arXiv:2610.15757v1 Announce Type: cross Abstract: Sentiment analysis is a key task in Natural Language Processing that determines the polarity of opinions in text. For low-resource languages like Mara

applicationsarxiv-cs-lg

Ensemble-MIX: Enhancing Sample-Efficiency in Multi-Agent RL via Selective Ensemble-Based Exploration

arXiv:2506.02841v5 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms

Entropy-Regularized Mean-Field Games with Average-Reward Criterion: Span-Seminorm Contractions and Model-Free Learning

arXiv:2610.15871v1 Announce Type: cross Abstract: We study stationary discrete-time mean-field games with finite state and action spaces under the average-reward criterion, with an entropy-regularized

EqC: Equilibrium Energy Control for Introspective Lifelong Robot Learning

arXiv:2610.18790v1 Announce Type: new Abstract: Long-term robot autonomy depends on more than effective action generation: a policy must recognize when its behavior is no longer sufficiently supported

Equilibrium contrastive learning for imbalanced image classification

arXiv:2602.09506v4 Announce Type: replace Abstract: Contrastive learning (CL) is a predominant technique in image classification, but they showed limited performance with an imbalanced dataset. Recent

safetyarxiv-cs-cv

Equilibrium Learning in Unbounded Stochastic Games with Gaussian Transitions

arXiv:2610.16149v1 Announce Type: cross Abstract: Learning equilibria from data is difficult when several decision makers interact indefinitely and the system state has no prescribed bounds. We examin