Search: “persistence”

arXiv:2610.16177v1 Announce Type: new Abstract: Open-vocabulary 3D Gaussian segmentation typically binds semantic features to the per-Gaussian state via per-scene gradient training (1-4 h) or the more

hardwarearxiv-cs-cv

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Do Artificial Neural Networks Need Dendritic Nanotubes? Two Reasons for Shrinking Importance Estimates in Continual Learning

DGX agent

arXiv:2610.14258v1 Announce Type: new Abstract: Regularization-based continual learning protects each parameter in proportion to an estimate of its importance, yet these estimates come from finite sam

model-releasesarxiv-cs-lg

14 Oct 2026

DyJR: Preserving Diversity in Reinforcement Learning with Verifiable Rewards via Dynamic Jensen-Shannon Replay

arXiv:2603.16157v2 Announce Type: replace Abstract: While Reinforcement Learning (RL) enhances Large Language Model reasoning, on-policy algorithms like GRPO are sample-inefficient as they discard pas

local-aiarxiv-cs-lg

Early Detection of Pension-Sponsor Distress from Public Filings: A Sponsor Vulnerability Index, Stress-Augmented VaR, and Dynamic Self-Investment Caps

arXiv:2610.14120v1 Announce Type: cross Abstract: A defined-benefit pension is only as safe as its sponsor, yet sponsor distress is under-instrumented relative to plan-side modelling. We present a tra

EAU: Environment-Agent-User Co-evolution for Training User-Centric Agents

arXiv:2610.16523v1 Announce Type: cross Abstract: Training LLM agents to interact with users over multiple turns is challenging because the learning signal depends not only on the agent, but also on t

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

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

safetyarxiv-cs-ro

Tutorials

Feature Normalization under Finite Training Budgets

arXiv:2610.16845v1 Announce Type: cross Abstract: Standardizing a rare feature can help stochastic gradient descent learn its effect within a finite budget, but it also changes the noise cost of learn

tutorialsarxiv-cs-lg

Foundation Models for Event Prediction Across Heterogeneous Event Logs

arXiv:2610.17503v1 Announce Type: new Abstract: Event logs are ubiquitous across real-world domains, including business processes, manufacturing workflows, healthcare, and many other operational syste

G^2WAM: World Action Models for Efficient and Geometry Grounded Image-Goal Navigation

arXiv:2610.16558v1 Announce Type: cross Abstract: World models capture environment dynamics and support counterfactual reasoning for planning. In image-goal navigation, where the target often lies bey

local-aiarxiv-cs-ro

Galaxy S21 + Gemma 4 E2B Q3_K_M + Qwen3.5-0.8B Q4_K_M: same Amazon task, two different valid carts via structured browser perception

model-releasesr-localllama

This continues the small-model browser experiments from the previous posts. Two standard, off-the-shelf models ran locally on the same Samsung Galaxy S21: Gemma 4 E2B, Q3_K_M as the reasoner Qwen3.5-0

GUARD: Guided Unlearning and Retention via Data Attribution for Large Language Models

arXiv:2506.10946v3 Announce Type: replace Abstract: Unlearning large language models (LLMs) is important due to copyright and privacy concerns. A key challenge is unintended forgetting, where removing

Applications

Harnessing SPAD Noise: Test-Time Gated Attention for Indoor Depth Estimation

arXiv:2610.15431v1 Announce Type: new Abstract: Monocular depth estimation (MDE) has emerged as a cost-effective alternative to hardware-dependent 3D sensing modalities, yet predicting precise metric

applicationsarxiv-cs-cv

History Replays Itself: Unleashing Long-Context Capability in Linear Attention

arXiv:2610.15715v1 Announce Type: cross Abstract: Linear attention promises the efficiency needed for long-context language modelling, yet its fixed-size recurrent state must simultaneously support on

HodgePhaseNet: Discrete Hodge Decomposition of Semantic Flows in Transformer Attention

arXiv:2610.14971v1 Announce Type: new Abstract: WavePhaseNet introduced frequency-resolved semantic representations, QuantumPhaseNet introduced gauge-covariant phase connections, and ModularPhaseNet m

How Shared Representations Become Specialized: Formation, Maintenance, and Latent Susceptibility

arXiv:2610.15830v1 Announce Type: new Abstract: When several predictions use the same internal representation, information about some targets can remain easier to recover than information about others

InfraWorld: Motion-Aware World Modeling for Roadside 4D Occupancy Forecasting

arXiv:2610.14544v1 Announce Type: new Abstract: Roadside 4D occupancy forecasting predicts future semantic occupancy in a fixed infrastructure coordinate system. Roadside scenes are globally structure

Interactive Navigation Beyond Reachability: Benchmark and Agentic Framework

model-releasesarxiv-cs-ro

arXiv:2610.17089v1 Announce Type: new Abstract: Existing embodied navigation tasks commonly assume that traversability and object states remain fixed during an episode. Yet reaching a goal may require

Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents

arXiv:2610.18684v1 Announce Type: cross Abstract: Personal-agent memory has largely focused on learning what users know, prefer, and want. However, knowing the user does not necessarily mean knowing w

Language2Drive: Do Vision-Language-Action Models Really Follow the Instructions in Autonomous Driving?

arXiv:2610.16634v1 Announce Type: cross Abstract: Recent Vision-Language-Action (VLA) models increasingly use natural-language instructions to condition autonomous driving behavior. Yet existing close

Applications

Learning Deployable Causal Action Geometry under Temporal Non-Stationarity

arXiv:2610.16644v1 Announce Type: new Abstract: Learning a response law for continuous decisions from observational time series is central to many real-world decision systems, yet temporal non-station

applicationsarxiv-cs-lg

Learning Transferable World-Action Models from Task-Paired Human Videos

arXiv:2610.14351v1 Announce Type: new Abstract: World-action models inherit broad visual and physical priors from video pre-training. However, standard within-video next-frame prediction allows them t

safetyarxiv-cs-cv

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

Mixed Precision Scaling Laws for Quantized Pretraining

arXiv:2610.17637v1 Announce Type: new Abstract: Since scaling laws were introduced for modern LLMs (Kaplan et al., 2020), several works have extended them to account for quantization, tracking the eme

NeuroVLA: A Brain-inspired Embodied Intelligence for Fluid and Fast Reflexive Robotics Control

arXiv:2601.14628v2 Announce Type: replace Abstract: Vision-language-action (VLA) models inherit powerful semantic representations from large language models, but carrying over their design priorities

local-aiarxiv-cs-ro

Agents

Observation-Referenced Tissue Deformation Forecasting with Tool-Driven and Residual Dynamics

arXiv:2610.14191v1 Announce Type: new Abstract: Forecasting tissue deformation from future instrument motion could support predictive planning and increasingly autonomous surgical systems. We study th

agentsarxiv-cs-ro

On the Theoretical Limits of Learning Unconstrained Interatomic Potentials

arXiv:2610.15078v1 Announce Type: new Abstract: Machine-learned interatomic potentials (MLIPs) accelerate molecular dynamics (MD) simulations by learning the energies and forces that govern atomic mot

PID-Gated Continual Prompt Adaptation Under Noisy Feedback

arXiv:2610.16129v1 Announce Type: new Abstract: Deploying LLM agents in non-stationary environments requires continually revising their instructions as the task drifts, yet rewriting after every repor

PILOT: Guiding Long-Horizon World-Model Planning for Robot Manipulation with Visual Subgoals and Policy Priors

arXiv:2610.14335v1 Announce Type: new Abstract: Latent world-model planning has shown promising results in goal-conditioned robotic manipulation, but remains challenging for long-horizon planning due

local-aiarxiv-cs-ro

Poisoned into Action: Context Attacks on Typed Decision Workflows

arXiv:2610.15537v1 Announce Type: cross Abstract: Typed decision interfaces restrict models to predefined actions, yet schema-valid choices can still trigger policy-violating state changes. Evaluating

Agents

Predict Before You Modify: Learning Improvement Dynamics for Self-Evolving Agents

arXiv:2610.13991v1 Announce Type: new Abstract: Self-evolving agents improve through repeated cycles of modification and evaluation. However, existing methods receive feedback on modifications only af

agentsarxiv-cs-lg

PreSO: Privileged Representation Isolation for On-Policy Self-Distillation

arXiv:2610.14001v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) uses a self-teacher conditioned on privileged information to turn the sparse outcome signal of reinforcement learning

PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models

arXiv:2610.14635v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress across a wide range of multimodal tasks. However, object hallucination remains

Recovering from Pose-Selection Failures in Repeated Visual Localization

arXiv:2610.16409v1 Announce Type: new Abstract: Repetitive structures can give an incorrect pose candidate more inlier support than an accurate one, causing visual localization to select it. History-G

researcharxiv-cs-cv

Revisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models

arXiv:2610.17590v1 Announce Type: new Abstract: Diffusion language models (DLMs) have attracted wide attention for their fast, parallel decoding, but they are believed to be far less efficient to trai

Same Goal, Different Words: Spatial Instruction Sensitivity in World-Action Models

arXiv:2610.16242v1 Announce Type: new Abstract: World-action models should preserve the intended manipulation across equivalent descriptions of a spatial goal. We evaluate this consistency in 864 roll

researcharxiv-cs-ro

Tutorials

Scaling Unified Multimodal Models to Long-Horizon Interleaved Generation

arXiv:2610.17080v1 Announce Type: new Abstract: Unified multimodal models (UMMs) provide an interface for interleaved text-image generation, where generated visual states can directly inform subsequen

tutorialsarxiv-cs-cv

Social Priors: Descriptions of AI Users Shape How Language Models Trust and Cooperate

arXiv:2610.16946v1 Announce Type: new Abstract: Descriptions of how people use AI are becoming training data for the next generation of models. We ask whether what a model reads about its users change

Teacher-Guided Tool Learning for Small Language Models via Bidirectional KL Constraints

arXiv:2610.18459v1 Announce Type: new Abstract: External tools can extend the capabilities of small language models (SLMs), enabling their deployment in resource constrained environments. However, the

The Geometry of the Set of Equivalent Linear Neural Networks

arXiv:2404.14855v2 Announce Type: replace Abstract: We characterize the geometry of the set of all weight vectors for which a fully-connected linear neural network computes the same linear transformat

The Gradient That Top-k Distillation Never Computes

arXiv:2610.15589v1 Announce Type: new Abstract: Many deployed distillation pipelines show the student a truncated view of the teacher: keep the top-k probabilities, renormalize, and minimize a diverge

Totem: Object-Centric 3D Memory for World-Consistent Video Generation

arXiv:2610.15807v1 Announce Type: new Abstract: Generating coherent videos grounded in a unified underlying world remains a major challenge, particularly under large viewpoint changes, where existing

researcharxiv-cs-cv

Triage-PiEvo: Evidence Admission for Principle-Evolving Scientific Agents

arXiv:2610.14683v1 Announce Type: new Abstract: Large language model (LLM)-based AI Scientists advance scientific discovery, yet they suffer from inefficiencies due to noisy observations. Existing pri

Two Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards

arXiv:2610.18853v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) relies on costly model-generated responses, motivating larger rollout batches and square-root lear

local-aiarxiv-cs-lg

VIEWCRAFT: Evolving What Multimodal Agents See and How They Read It

arXiv:2610.15915v1 Announce Type: new Abstract: Reusable skills improve multimodal agents without updating model weights, but their effectiveness depends on both the evidence presented and the procedu

When Dynamic Scaling Fails: A Scale-State Feedback Governs Recurrent-State Quantization in Linear-Attention and SSM LLMs

arXiv:2610.15139v1 Announce Type: new Abstract: Linear-attention and state-space LLMs carry long context in a fixed-size recurrent state, and quantizing that state is the natural way to cut its cost d

Hardware

When Log Parsing Removes What Matters: Detecting Failures in Distributed GPU Workloads

arXiv:2610.14009v1 Announce Type: new Abstract: Large AI models increasingly rely on distributed GPU execution, yet it remains unclear whether established failure analysis methods transfer effectively

hardwarearxiv-cs-lg

Agents

When Trackers Fail: VLM-Guided Verification and Recovery for Robust Video Object Segmentation

arXiv:2610.15929v1 Announce Type: new Abstract: Recent trackers built on foundation models such as the Segment Anything Model (SAM) achieve strong performance by propagating object masks through memor

agentsarxiv-cs-cv