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JEFFERIES: “.. the reason credit risk has become more of an issue is that it can no longer be assumed .. that large language models will eve…

JEFFERIES: “.. the reason credit risk has become more of an issue is that it can no longer be assumed .. that large language models will ever be profitable .. .. in this respect, there is a 2008 real

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JoyInside-VLM-Agent: Product-Conditioned Multimodal Policy Learning for Embodied Agents

DGX agent

arXiv:2610.12957v1 Announce Type: cross Abstract: Embodied agents must translate multimodal observations and user requests into executable actions while adapting to product constraints, changing goals

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12 Oct 2026

KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting

arXiv:2610.12738v1 Announce Type: cross Abstract: Forecasting rare, high-cost events in multivariate time-series systems is hard because their precursors are seldom observed, so a purely data-driven m

L_0 Feature Selection via Monte Carlo Tree Search in High-Dimensional Financial Regression

arXiv:2610.13574v1 Announce Type: new Abstract: Feature selection is a central problem in high-dimensional financial regression, where large numbers of engineered variables and strong correlations amo

LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos

arXiv:2610.13732v1 Announce Type: cross Abstract: Scaling generalist robotic policies is limited by the high cost of collecting embodied interaction data. Large-scale egocentric human videos offer a r

Learning Schwarzschild Geodesic Motion with Lagrangian Neural Networks

arXiv:2610.13234v1 Announce Type: new Abstract: Lagrangian Neural Networks (LNNs) learn dynamics from trajectory data by parameterizing a scalar Lagrangian and deriving accelerations through the Euler

Learning to accelerate Krasnosel'skii-Mann fixed-point iterations with guarantees

arXiv:2601.07665v2 Announce Type: replace-cross Abstract: We introduce a principled learning to optimize (L2O) framework for solving fixed-point problems involving general nonexpansive mappings. Our i

Learning to Report Unsafe Tasks in a Multi-Agent Game

arXiv:2610.09002v2 Announce Type: replace Abstract: When a team of agents shares a reward for completed tasks, reporting and stopping unsafe work reduces the team's reward. Random audits that penalize

LeWAM: End-to-End World and Action Modeling with JEPAs

arXiv:2610.12818v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) learn compact latent world models without pixel reconstruction. Existing JEPA pipelines typically tra

Matched-Context Group Policy Optimization for LLM Agent Training

arXiv:2610.13755v1 Announce Type: cross Abstract: Training long-horizon language model agents requires assigning credit to intermediate decisions from sparse terminal feedback. Within a finite group o

Mind the Temporal Gap: Evaluating Video Models for Temporal Understanding in Driving

arXiv:2610.12575v1 Announce Type: cross Abstract: Modern video-language models are increasingly treated as general-purpose video reasoners, yet it remains unclear whether they robustly capture tempora

Mitigating Capability Degradation in LLM Self-Improvement via Representation Geometry Regularization

arXiv:2610.12855v1 Announce Type: new Abstract: On-Policy Self-distillation is a self-improvement paradigm that enables large language models (LLMs) to improve their capabilities by leveraging the sam

MORAD: Reinforcement Learning with Multi-Objective Rewards for RNA Inverse Design

arXiv:2610.13305v1 Announce Type: new Abstract: We introduce MORAD, an online reinforcement learning framework for backbone-conditioned RNA diffusion models that trains a single shared policy across d

MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing

arXiv:2610.12963v1 Announce Type: cross Abstract: Video editing models achieve strong performance in modifying appearance, backgrounds, styles, and motions, empowered by large-scale training data. Bey

No Generative AI. Just one gorgeous building, beautiful light, and an Phone 16 Pro.

On October 12, 2026, Gary Marcus (@GaryMarcus) posted on Twitter saying, “No Generative AI. Just one gorgeous building, beautiful light, and an Phone 16 Pro.” The tweet, timestamped 7:01 AM UTC, recei

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Noise Space Learning for VLA Adaptation

arXiv:2610.13943v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) models encode broad manipulation priors, yet adapting them to specific tasks requires balancing limited robot in

Non-Prehensile Multi-Object Placement with Mobile Robots via Diffusion-Based Trajectory Planning

arXiv:2610.12684v1 Announce Type: new Abstract: Non-prehensile manipulation with mobile robots has primarily targeted placement in coarse goal regions. Precision placement has been studied with manipu

Opening the Black Box of Classifier-Free Guidance via Information Bottleneck

arXiv:2610.13369v1 Announce Type: new Abstract: Classifier-free guidance (CFG) is a standard technique for improving conditional generation in diffusion models, yet its activation schedule is usually

OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation

arXiv:2610.13826v1 Announce Type: cross Abstract: Recent advances in automatic pronunciation assessment (APA) have driven a shift from scripted to free-form assessment. However, existing methods often

PanTact: Teaching Whole-Body Dexterity with a Wearable Contact Interface

arXiv:2610.12558v1 Announce Type: new Abstract: Humanoid robots have many degrees of freedom and contact surfaces, yet most loco-manipulation research restricts manipulation to the hands and avoids ot

Personalized Federated Direct Preference Optimization via LoRA Adapters

arXiv:2610.12885v1 Announce Type: new Abstract: Federated Direct Preference Optimization (FedDPO) enables privacy-preserving alignment of language models across distributed agents without centralizing

Playing the network backward: A Game Theoretic Attribution Framework

arXiv:2605.06212v2 Announce Type: replace Abstract: Attribution methods explain which input features drive a model's prediction, making them central to model debugging and mechanistic interpretability

Proactive Agentic Orchestration

arXiv:2610.13485v1 Announce Type: new Abstract: Agentic orchestration delegates tasks to sub-agents with independent contexts, providing a key path to extending the effective execution horizon of larg

Randomized Momentum Transport with Cheap Predictors

arXiv:2610.13789v1 Announce Type: new Abstract: Momentum methods such as STORM and second-order momentum track the gradient by adding a transport correction to momentum, at the cost of an extra oracle

RARA: Robust Policy Fine-Tuning through Retrieval-Anchored Representation Alignment for Robotic Manipulation

arXiv:2610.13578v1 Announce Type: cross Abstract: Pre-trained robot policies, such as Vision-Language-Action (VLA) models, provide a strong foundation for generalist robotic manipulation, but adapting

ReAT: Residual Action Tokenization for Autoregressive Robot Policies

arXiv:2610.13093v1 Announce Type: new Abstract: Autoregressive robot policies rely on action tokenizers to convert continuous action chunks into compact sequences of discrete prediction targets. Exist

Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures

arXiv:2606.18328v2 Announce Type: replace Abstract: Intelligent robots should not only recover from failures, but also acquire the abstract knowledge needed to avoid them in the future. While reinforc

Reflect-VLA: Internalizing Reflective Guidance via Dual-Path On-Policy Self-Distillation for Autonomous Driving

arXiv:2610.13453v1 Announce Type: new Abstract: Reinforcement Learning (RL) substantially improves planning performance in autonomous-driving Vision-Language-Action (VLA) models. However, existing RL

Reflex: Visually Grounded Reactive Humanoid Control

arXiv:2610.12772v1 Announce Type: new Abstract: Humans effortlessly react to the world; they adeptly return a tennis shot, catch a falling cup, step aside from a passing cart, or pass a box tossed dur

Reinforcement Learning via Fixed-point Flow Matching

arXiv:2610.12848v1 Announce Type: new Abstract: In maximum entropy and behavior-constrained reinforcement learning (RL), the optimal policy is given by a Boltzmann distribution. Generating samples fro

REMM: Revisable Embodied Memory for Manipulation

arXiv:2610.12710v1 Announce Type: new Abstract: Long-horizon manipulation requires robots to retain task-relevant history while revising conclusions no longer supported by physical evidence. Retaining

Representation Alignment from a Few Paired Data via Distilled Optimal Transport

arXiv:2610.12781v1 Announce Type: new Abstract: Aligning two independently pretrained unimodal encoders typically demands millions of paired examples. In this paper, we study the regime where only N s

RESCUE: Is This Alarm Worth Acting On? Counterfactual Evaluation of VLA Runtime Monitors

arXiv:2610.13516v1 Announce Type: cross Abstract: Runtime failure monitors for robot policies are typically evaluated by how accurately they distinguish failed from successful executions. Yet an accur

Rethinking Relative Policy Differences Transfer of On-Policy Distillation

arXiv:2610.13205v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a target policy on its own rollouts using token-level supervision from a stronger teacher, but typically relies on t

Reward Hacking as a Transmissible Strategy in Multi-Agent Coding Systems

arXiv:2610.12822v1 Announce Type: new Abstract: Multi-agent systems are increasingly used to solve coding tasks by coordinating agents that share code and plans. These agents are often optimized with

Rollout-Time Intervention: Unlocking Agentic Reinforcement Learning Beyond Policy Limits

arXiv:2610.13438v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a key paradigm for improving large language model (LLM) agents, enabling stronger reasoning, planning, and to

RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance

arXiv:2602.00183v2 Announce Type: replace-cross Abstract: Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the perva

RSI-Anything: Unlocking Recursive Self-Improvement with World Models

arXiv:2610.13912v1 Announce Type: new Abstract: Recursive self-improvement (RSI) aims to transform interaction experience into persistent improvements, yet external feedback is costly and world-model

SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning

arXiv:2610.13737v1 Announce Type: cross Abstract: Cooperative multi-agent tasks often impose constraints on a shared objective or collective safety. A team level violation cost function can measure th

SAP-CoPE: Social-Aware Planning using Cooperative Pose Estimation with Infrastructure Sensor Nodes

arXiv:2504.05727v3 Announce Type: replace Abstract: Autonomous mobile robots must operate smoothly in human-populated indoor environments, where onboard-only sensing suffers from limited perception ra

SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection

arXiv:2610.13207v1 Announce Type: new Abstract: Fake news detection under low resource conditions is challenged by the scarcity of labeled data and the difficulty of capturing subtle deceptive pattern

SOAR: Source-Aware Reinforcement Learning with Precise Credit Assignment for Search Agents

arXiv:2610.13157v1 Announce Type: new Abstract: Reinforcement learning (RL) for search agents often suffers from evidence fabrication, a failure mode where agents bypass external information (E_{ext})

Sources: the US restores the UK AISI's access to the latest US models for safety testing, after ordering OpenAI and Anthropic to stop sharing models last month (Tony Diver/Telegraph)

Tony Diver / Telegraph: Sources: the US restores the UK AISI's access to the latest US models for safety testing, after ordering OpenAI and Anthropic to stop sharing models last month — US had blocked

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Splat2Act: From Captured Scenes to Personalized VLA Manipulation

arXiv:2610.13945v1 Announce Type: new Abstract: A user who brings a generalist manipulation policy into their home or office wants it to pick up their objects from their shelves, counters, and tables.

Staggered Integral Online Conformal Prediction for Safe Dynamics Adaptation with Multi-Step Coverage Guarantees

arXiv:2604.06058v2 Announce Type: replace-cross Abstract: Safety-critical control of uncertain, adaptive systems often relies on conservative, worst-case uncertainty bounds that limit closed-loop perf

State-OPD: State-Grounded On-Policy Distillation for Multi-Turn Agents

arXiv:2610.13238v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has emerged as a powerful paradigm for transferring reasoning capabilities from frontier models to smaller models, while ex

StereoEngine: Scaling Stereo Bimanual Robot Data without Stereo Cameras

arXiv:2610.12734v1 Announce Type: cross Abstract: Stereo vision is increasingly used for robotic manipulation, with a growing body of work incorporating it into Vision-Language-Action (VLA) models. Ho