All entries

Ex vivo breach detection using electrical conductivity during robotic pedicle drilling in the spine

arXiv:2610.01310v1 Announce Type: new Abstract: Purpose: Pedicle screw placement is technically demanding in scoliosis treatment. High precision is required due to limited visibility, anatomical varia

researcharxiv-cs-ro

Content type

All Blog X Post Paper YouTube Reddit GitHub

Exact Distinguishability in Non-Markovian Decision Processes

DGX agent

arXiv:2610.01527v1 Announce Type: cross Abstract: Non-Markovian environments are often modeled as Regular Decision Processes (RDPs), where dynamics depend on the interaction history through a finite a

safetyarxiv-cs-ai

2 Oct 2026

Exact information accounting for SGD methods

arXiv:2610.00446v1 Announce Type: new Abstract: As an alternative to the standard geometric analyses, we give an exact, information-theoretic analysis of stochastic gradient descent (SGD) and its vari

Experience-Based Feasibility-Aware Generative Adversarial Imitation from Observation under Embodiment Mismatch

arXiv:2610.01171v1 Announce Type: new Abstract: With the increasing use of robot-free demonstration interfaces that provide state trajectories without action labels, imitation from observation has bec

safetyarxiv-cs-ro

Explainability of Complex AI Models with Correlation Impact Ratio

arXiv:2601.06701v2 Announce Type: replace-cross Abstract: Complex AI systems make better predictions but often lack transparency, limiting trustworthiness, interpretability, and safe deployment. Commo

Explainable Suicide Risk Assessment on Social Media with Multi-Task QLoRA

arXiv:2610.00610v1 Announce Type: cross Abstract: Explainable suicide-risk assessment requires models not only to estimate risk severity, but also to identify supporting language and the risk and prot

Tutorials

Exploring More, Reasoning Better: Stepwise Risk-Sensitive GRPO for Diffusion Language Models

arXiv:2610.00661v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) generate text by denoising a sequence or successive blocks, allowing several tokens to be revealed in parallel

tutorialsarxiv-cs-ai

Exploring Weaknesses of Generative Image Watermarks against Latent Frequency Masking

arXiv:2610.02010v1 Announce Type: cross Abstract: Invisible watermarking has become a central tool for tracing AI-generated images, but its robustness against adaptive removal attacks remains an open

Applications

Exponential quantum advantage in processing massive classical data

arXiv:2604.07639v2 Announce Type: replace-cross Abstract: Broadly applicable quantum advantage, particularly in classical data processing and machine learning, has been a fundamental open problem. In

applicationsarxiv-cs-ai

Applications

Exposing the Cost of Deep Learning Audio Development

arXiv:2610.01619v1 Announce Type: cross Abstract: The environmental impact of deep learning has attracted increasing attention over the past decade. Existing studies mainly focus on the energy and car

applicationsarxiv-cs-ai

External Observers May See More Clearly: Cross-Model Span-Level Hallucination Detection in Large Language Models via Hidden State Probing

arXiv:2610.02066v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly serve as foundational reasoning engines, their tendency to hallucinate remains a critical vulnerability. Wh

External Photoreflective Tactile Sensing Based on Surface Deformation Measurement

arXiv:2511.06311v2 Announce Type: replace Abstract: We present a tactile sensing method enabled by the mechanical compliance of soft robots; an externally attachable photoreflective module reads surfa

researcharxiv-cs-ro

Extreme Length Generalization in a Compact Recurrent Architecture for One-Shot Exploration

model-releasesarxiv-cs-ro

arXiv:2610.01105v1 Announce Type: new Abstract: Autonomous robots on one-shot missions run over horizons far longer than the trajectories seen during training, under a fixed onboard compute budget. We

EyeTAG: Eye Trajectory-Aware Gaze Estimation

arXiv:2610.00922v1 Announce Type: new Abstract: Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict

safetyarxiv-cs-cv

Fable 5.5 is pretty great, did this in a few hours on the chat version on high with minimal prompts 21 worlds, 3D versions, 21 bosses with d…

model-releasesemad-mostaque--x

Fable 5.5 is pretty great, did this in a few hours on the chat version on high with minimal prompts 21 worlds, 3D versions, 21 bosses with different mechanics, leaderboard Try it out! Video made by Cl

FAER: Auditable Utility-Aligned Trajectory Replay for Language Model Post-Training

arXiv:2610.00385v1 Announce Type: cross Abstract: Replay selectors often rank cached trajectories by format feedback, confidence, freshness, or response length, although cache-level correctness and do

FairSSL: Fair Multimodal Self-Supervised Learning

arXiv:2508.16748v2 Announce Type: replace-cross Abstract: Prevalent multimodal self-supervised learning (SSL) methods rely on the redundancy assumption: that different views share substantial task-rel

Applications

Faithful Chart Generation for Multimodal Deep Research: Frame-Evidence Co-Adaptation

arXiv:2610.00374v1 Announce Type: cross Abstract: Analytical charts in multimodal deep research encode quantitative claims, requiring every visualized value to be faithfully grounded in supporting evi

applicationsarxiv-cs-ai

False Floors: LLM Safety Routing Evaluations Break Under Distribution Shift

arXiv:2610.01535v1 Announce Type: cross Abstract: Safety routers send each request to one of several models and are judged against the best single model. A major routing benchmark picks that comparato

FAME: Force-Adaptive RL for Expanding the Manipulation Envelope of a Full-Scale Humanoid

arXiv:2603.08961v2 Announce Type: replace Abstract: Maintaining balance under external hand forces is critical for humanoid bimanual manipulation, where interaction forces propagate through the kinema

safetyarxiv-cs-ro

Hardware

Fast Polynomial Transcendentals for LLMs

arXiv:2610.00049v1 Announce Type: new Abstract: Graphics processing unit (GPU) generations scale matrix, special-function, and memory pipelines at different rates, so kernel bottlenecks move as hardwa

hardwarearxiv-cs-lg

Hardware

FastCI: Efficient GPU-Intensive CI for LLM Training Frameworks

arXiv:2610.01967v1 Announce Type: new Abstract: As large language models (LLMs) keep growing in size and complexity, their training frameworks evolve at a rapid pace as well. Therefore, continuous int

hardwarearxiv-cs-lg

Fault-Tolerant Budget Conservation in Distributed Multi-Agent Delegation

arXiv:2610.00349v1 Announce Type: new Abstract: Resource limits are becoming an authorization boundary for AI agents that delegate work across concurrent and failure-prone workers. Parent-child alloca

Faynt: Scaling and Optimizing Policies for Competitive Melee

model-releasesarxiv-cs-lg

arXiv:2610.02144v1 Announce Type: new Abstract: We introduce Faynt, a family of 10M- and 75M-parameter Transformer policies for Super Smash Bros. Melee, each controlling all 26 characters with a singl

Feature Selective Model Collapse in Diffusion Models: Total Replacement versus Fixed-Budget Training

arXiv:2610.01318v1 Announce Type: cross Abstract: Model collapse arises when generative models are trained on synthetic data produced by earlier models. The phenomenon has attracted considerable atten

FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains

model-releasesarxiv-cs-cv

arXiv:2610.01510v1 Announce Type: new Abstract: Robust perception in intelligent vehicles demands 3D object detectors that remain dependable under domain shifts, such as changes in time of day, locati

Federated Agent Optimization

arXiv:2610.01195v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate in private environments and accumulate valuable experience from task execution, tool use, feedbac

local-aiarxiv-cs-ai

Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport

arXiv:2610.01304v1 Announce Type: cross Abstract: Federated LLM fine-tuning enables large models to be adapted using private and geographically distributed data at the network edge, creating recurring

FedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantization

arXiv:2610.01537v1 Announce Type: new Abstract: Federated Learning (FL) enables privacy-preserving fine-tuning of Large Language Models (LLMs), yet the massive communication overhead remains a critica

FedLore: Communication and Memory Efficient Federated Learning via Shared Gradient Low-Rank Projection

arXiv:2610.01620v1 Announce Type: new Abstract: Federated training of foundation models is constrained by client memory and communication costs. LoRA-based methods reduce these costs through low-rank

FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classification

arXiv:2610.00693v1 Announce Type: new Abstract: Federated learning (FL) has recently attracted increasing attention in remote sensing (RS) since it enables collaborative model training across decentra

safetyarxiv-cs-cv

FedMIX-P: Mixing Local and Global Preconditioners for Federated Vision and Language Model Training

arXiv:2610.01515v1 Announce Type: cross Abstract: Adaptive preconditioners accelerate model training, but heterogeneous client geometries can bias federated updates even when gradients are evaluated a

local-aiarxiv-cs-ai

FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices

arXiv:2610.01638v1 Announce Type: new Abstract: Federated learning (FL) on heterogeneous edge devices must jointly accommodate unequal resource budgets and domain-shifted local data. Existing resource

local-aiarxiv-cs-lg

Feedback Without the Wait: Piloting a Generative AI Practice Platform in a Large Maths Class

arXiv:2610.01262v1 Announce Type: new Abstract: Timely and specific feedback is one of the strongest influences on student learning, yet it is difficult to sustain in large electrical engineering clas

FERPO: Forward Entropy-Regularized Policy Optimization

arXiv:2610.02198v1 Announce Type: cross Abstract: Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic.

Fewer Tokens, Better Action: GPT-6 Astra Robot Agents with 14% Higher Success Rate but 65% Fewer Tokens

arXiv:2610.01939v1 Announce Type: new Abstract: Vision language model (VLM) agents can control robots through visual feedback and action primitives, but repeated model invocations and redundant observ

local-aiarxiv-cs-cv

FFBL-Coop: Association-Decoupled Cooperative 3D Multi-Object Tracking

model-releasesarxiv-cs-cv

arXiv:2610.01750v1 Announce Type: new Abstract: Cooperative 3D tracking must integrate complementary observations across agents and time while maintaining consistent identities. When evidence integrat

Finding the Right Fit: Model-Harness Interactions across Agent Tasks

arXiv:2610.00917v1 Announce Type: new Abstract: Choosing an agent system means choosing both a language model and the harness through which it acts. We ask whether a strong model, harness, or pairing

Agents

Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI

Fine-tuning teaches a small search agent your tools and environment, giving it the reliability of a frontier model at lower latency and cost. In this post, we fine-tune an LLM-powered search agent wit

agentsaws-ml-blog

Finetuning with Sampling: SFT Learns Better Than You Think

arXiv:2610.02140v1 Announce Type: cross Abstract: Introducing new capabilities to frontier models has long been the goal of posttraining, which predominantly employs supervised finetuning (SFT) and re

Finite-Data Safety Informativity Under Dynamic Asymmetric Actuation

arXiv:2610.01820v1 Announce Type: cross Abstract: When the system model is not fully known, measurement error and limited excitation can leave several models consistent with the same finite data. A co

local-aiarxiv-cs-ro

FiVOS: A Fish Segmentation Algorithm Based on Interactive Video Object Segmentation and Filter Enhancement

arXiv:2610.01480v1 Announce Type: new Abstract: With the continuous expansion of aquaculture, precise and efficient monitoring of fish behavior has become increasingly critical for improving farming e

researcharxiv-cs-cv

Fixed-point neural samplers on discrete spaces

arXiv:2610.01739v1 Announce Type: new Abstract: Sampling from discrete, unnormalized distributions without access to data is a challenging problem. Neural samplers offer a promising approach by traini

Fixing a Model That Learned Worse Cancer Means Lower Risk: Monotonic Constraints in Bladder Cancer Recurrence Prediction

arXiv:2610.00858v1 Announce Type: new Abstract: Background and Objective: Clinicians expect recurrence risk to climb with cancer severity. In a UK multicentre trial, an unconstrained XGBoost model lea

Tutorials

FlashBack: Knowing When to Remember in Streaming Vision-Language Models

arXiv:2610.01192v1 Announce Type: new Abstract: Streaming vision-language models must process continuously growing video streams under a bounded compute budget, creating a persistent tension between r

tutorialsarxiv-cs-cv

FlashDexRetarget: Accelerating Dexterous Manipulation Data Generation through Multi-Motion Retargeting

model-releasesarxiv-cs-ro

arXiv:2610.01849v1 Announce Type: new Abstract: Human hand-object demonstrations offer a reusable source of dexterous robot manipulation data, but transferring them across embodiments requires physica

FlashNav: Training Deployable Robot Navigation Policies in Seconds

model-releasesarxiv-cs-ro

arXiv:2606.15846v2 Announce Type: replace Abstract: Training Deep Reinforcement Learning (DRL) navigation policies for different robot configurations remains time-consuming. We present FlashNav, a GPU

Flow Matching Reinforcement for 3D Mesh Generation via Dynamic Homing Optimization

arXiv:2610.01233v1 Announce Type: new Abstract: Flow matching is central to 3D generation, yet in practice its reinforcement learning (RL) methods are largely adapted from 2D visual generation. Repres

researcharxiv-cs-cv