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Today we're announcing Praxis-1, an open-weight world action model that adapts to new robot embodiments with light finetuning. Video is eati…

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Today we're announcing Praxis-1, an open-weight world action model that adapts to new robot embodiments with light finetuning. Video is eating the world Introducing Praxis-1, an open-weight World Acti

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Towards Better Training Signal: Advantage Clipped Policy Optimization

DGX agent

arXiv:2609.36816v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a cornerstone for improving the reasoning capabilities of large language models (LLMs), but the need for on-polic

safetyarxiv-cs-lg

30 Sept 2026

Tracing mechanisms of sycophantic agreement in language models

arXiv:2609.35822v1 Announce Type: new Abstract: Sycophantic agreement in language models refers to the tendency to overly affirm a user's stated beliefs or preferences, often at the expense of factual

safetyarxiv-cs-cl

Track-and-Complete: Learning Humanoid Skills from a Single Failed Human Video

arXiv:2609.36924v1 Announce Type: cross Abstract: Learning humanoid skills from videos typically requires a successful human demonstration, which often demands custom data collection. Although failure

Train Ahead, Distill Back: Bootstrapping On-Policy Self-Distillation for Large Language Models

arXiv:2609.37132v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves large language models by letting a self-teacher with privileged information provide dense token-level superv

Understanding Decision-Making Mechanisms in Neural Routing Solvers

arXiv:2609.36063v1 Announce Type: cross Abstract: Neural Combinatorial Optimization (NCO) has achieved strong empirical success, yet the internal mechanisms driving model decisions remain largely unex

Unlocking the Critic: Reward-Free Policy Optimization for LLM Post-Training

arXiv:2609.37119v1 Announce Type: cross Abstract: Recent approaches to reinforcement learning (RL) post-training for large language models increasingly remove the critic to reduce training instability

V-JEPA Policy: Building Effective World-Action Models on Predictive Visual Latents

arXiv:2609.37250v1 Announce Type: cross Abstract: World-action models (WAMs) couple future visual-state prediction with action generation. By adapting video generators or image-editing models pretrain

VAA-CSEC: Vote-guided Advantage Allocation for Chinese Semantic Error Correction

arXiv:2609.36804v1 Announce Type: cross Abstract: Chinese Semantic Error Correction (CSEC) targets semantic errors in Chinese text, which are typically more subtle and complex than spelling and gramma

VidAct: Learning Manipulation from In-the-Wild Videos with Object-Centric 3D Awareness

arXiv:2609.36870v1 Announce Type: new Abstract: Video demonstrations offer a scalable alternative to costly robot data for learning manipulation, yet existing reconstruction-based approaches often rel

VISTA: Internalizing Collective Visual Experience via On-Policy Distillation for Active Multimodal Agents

arXiv:2609.38086v1 Announce Type: new Abstract: Active multimodal agents use visual tools to acquire task-relevant evidence while reasoning. Although reinforcement learning samples multiple interactio

VStress: Correlation-Aware Auditing and Adaptive Budget Allocation for Repeated Verifiers

arXiv:2609.36958v1 Announce Type: cross Abstract: Repeated verifier calls are useful only when they contribute conditional information. We introduce VStress, an auditable replay contract, and VStress-

WayFinder: Hierarchical Visual-Language-Action for Zero-Shot Waypoint Generation and Low-Level Kinematic Control

arXiv:2609.37922v1 Announce Type: new Abstract: Visual Language Action (VLA) models offer unprecedented generalization for autonomous robots; however, their real-world deployment is frequently bottlen

What if automating AI R&D triggers an intelligence explosion?

arXiv:2609.36054v1 Announce Type: cross Abstract: In contrast to even a year ago, AI systems now write most of the code inside the companies that build them. As more of the AI research and development

When Should Agents Check External State? Budgeting Observations for Stored Intentions

arXiv:2609.37125v1 Announce Type: new Abstract: Prospective memory allows an agent to retain an intention tied to a future condition, but the stored intention does not reveal whether that condition cu

Where Predictive Supervision Goes Shapes What VLA Policies Learn

arXiv:2609.36645v1 Announce Type: cross Abstract: Future prediction is increasingly used to improve vision-language-action (VLA) policies, based on the premise that anticipating scene evolution encour

Where the Model Changes Its Mind: Hindsight-Divergence Localization for Efficient Reinforcement Learning with Verifiable Rewards

arXiv:2609.36864v1 Announce Type: new Abstract: Group-relative methods for reinforcement learning with verifiable rewards (RLVR) learn from differences in rollout outcomes. Independently sampling comp

Which Attention Heads are like the Human Head? Not the Ones that Compute

arXiv:2609.37991v1 Announce Type: new Abstract: Brain-AI alignment is often interpreted as a sign that model and brain perform similar computations. Whether the aligned units are causally involved in

Why Adaptive Optimizers Underestimate Rare Tokens

arXiv:2609.37535v1 Announce Type: new Abstract: In the softmax output layer, a rare token receives a small positive logit gradient on most steps and a much larger negative gradient on the few steps wh

WorldLine: Action-Driven Visual Simulation for Robotic Manipulation

arXiv:2609.38059v1 Announce Type: cross Abstract: Real-world robot learning is constrained by the cost of collecting experience and evaluating candidate behaviors. Video generation models offer a scal

Wrench-ACT: Enhancing Robot Policies for Contact Rich Behavior Using Direct Wrench Control

arXiv:2609.37552v1 Announce Type: new Abstract: While contact-rich manipulation requires deliberate regulation of interaction forces, recent approaches to robot manipulation learning predominantly rep

You Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight LLM Inference

arXiv:2609.37902v1 Announce Type: new Abstract: Existing LLM routers choose among models using static per-model costs. We show that open-weight inference markets introduce a second, largely ignored de

3-D Emissions Mapping and Social Cost Estimation for US Domestic Aviation at West Coast Hubs

arXiv:2609.31686v1 Announce Type: new Abstract: Existing aviation emissions inventories lack accurate trajectory data for high-resolution social cost and health impact assessment. This paper develops

A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models

arXiv:2607.14522v2 Announce Type: replace Abstract: We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control

A Journey to the Edge of Stability

arXiv:2609.32290v1 Announce Type: new Abstract: It has recently been found that deep learning often occurs at the 'edge of stability (EoS),' where the maximum Hessian eigenvalue of the model is stabil

ABC-Align: Prediction-Powered Alignment with Adaptive Bias Control

arXiv:2609.34374v1 Announce Type: new Abstract: Language model post-training is often bottlenecked by the need for human-collected preference data, which is expensive and difficult to scale. Reinforce

ABFR-KAN: Kolmogorov-Arnold Networks for Functional Brain Analysis

arXiv:2601.00416v2 Announce Type: replace Abstract: Functional connectivity (FC) analysis, a valuable tool for computer-aided brain disorder diagnosis, traditionally relies on atlas-based parcellation

Achieve What You Imagined: Learning to Align Actions with Visual Plans

arXiv:2609.33832v1 Announce Type: cross Abstract: World-action models can jointly predict future visual observations and robot actions. However, discrepancies may exist between their visual prediction

Action-Driven Processes for Continuous-Time Control

arXiv:2510.26672v2 Announce Type: replace-cross Abstract: At the heart of reinforcement learning are actions -- decisions made in response to observations of the environment. Actions are equally funda

Action Shaping: Policies Absorb What They Can Express

arXiv:2609.32752v1 Announce Type: cross Abstract: Reward shaping has a theorem: a potential-based term can be removed without changing the optimal policy. The same practice on the action channel, an o

ActionGround: Training-Free Runtime Refinement of Frozen VLA Policies

arXiv:2609.33256v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models map visual observations and language instructions directly to robot actions, but they do not explicitly represent th

Active Data Acquisition with Side Information via Discrete Diffusion Priors

arXiv:2609.32252v1 Announce Type: cross Abstract: Acquiring data is costly: higher measurement fidelity costs power and storage and risks collecting irrelevant content, while aggressive cost reduction

Adam or Gauss-Newton? A Comparative Study In Terms of Basis Alignment and SGD Noise

arXiv:2510.13680v2 Announce Type: replace Abstract: Preconditioned methods are central to deep learning optimization. Predominant approaches include computationally light diagonal preconditioners such

Adapting Nonstationary Multi-output Gaussian Processes to Bayesian Optimization

arXiv:2609.32464v1 Announce Type: new Abstract: Multi-objective Bayesian optimization (MOBO) commonly relies on independent Gaussian processes (GPs) with stationary kernels, limiting its ability to re

Adjoint Guidance Flow: Amortized Critic Guidance for VLA Policies

arXiv:2609.34944v1 Announce Type: cross Abstract: Flow-based Vision-Language-Action (VLA) policies are typically trained by behavior cloning and thus do not explicitly optimize long-term task return.

Adversarial Reward Auditing for Active Detection and Mitigation of Reward Hacking

arXiv:2602.01750v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) remains vulnerable to reward hacking, where models exploit spurious correlations in learned

Agent Priors-guided Policy Learning

arXiv:2609.35690v1 Announce Type: new Abstract: Robots that learn from a few demonstrations often require two forms of generalization. Compositional generalization recombines skills to solve new tasks

Agentic Multi-Turn Reasoning: A Fairness Approach

arXiv:2609.33323v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have enabled agentic systems capable of solving complex tasks through multi-turn planning, tool use, v

AGRO-SUVIDE: Agentic Robotics for Surgical Viscoelastic Debridement

arXiv:2609.34823v1 Announce Type: new Abstract: Augmented dexterity has the potential to reduce the fatigue experienced by surgeons during repetitive surgical tasks. In this paper, we propose the firs

AlignBeat: A Latent Variable Model for Multi-Class Beat Tracking from Partially Labeled Data

arXiv:2510.14391v3 Announce Type: replace-cross Abstract: Recent neural beat trackers predict beats and downbeats with two independent frame-wise binary classification heads, a convention adopted beca

An analysis of Mirror-Descent Soft Actor-Critic

arXiv:2609.35466v1 Announce Type: new Abstract: Soft Actor-Critic (SAC) is widely used for entropy-regularised reinforcement learning with continuous action spaces, and practical implementations perfo

An Empirical Study on What Matters for Viewpoint-Generalizable Policies in Visual Imitation Learning

arXiv:2609.32762v1 Announce Type: cross Abstract: Visual imitation learning is a promising approach to training robot manipulation policies capable of completing a wide variety of tasks. However, poli

An Imperfect Verifier is Good Enough: Learning with Noisy Rewards

arXiv:2604.07666v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is widely used for post-training Large Language Models, but practical verifiers can make error

An RL View of OPD: Least Square Policy Distillation for Sample-Efficient LLM Reasoning

arXiv:2609.35505v1 Announce Type: new Abstract: We study on-policy distillation (OPD) through the lens of reinforcement learning, establishing a connection between the reverse-KL objective in OPD and

Attribution Without a Second Pass: Inline Per-Sample Gradient Provenance at ~1% Overhead

arXiv:2609.32380v1 Announce Type: new Abstract: Data attribution methods used in practice (TRAK, LoGRA, EK-FAC) are post-hoc: after training they make a second pass over the training set to recompute

Audit-First VAPO: Risk-Certified Selective Updates under Imperfect Verification

arXiv:2609.33662v1 Announce Type: cross Abstract: Imperfect verifiers can assign a harmful update direction even when clipping and regularization bound its magnitude. We introduce Audit-First VAPO, wh

Benign Overfitting for General Norms and Distributions

arXiv:2609.33675v1 Announce Type: new Abstract: Understanding why predictors can generalize despite interpolating noisy training data is a central puzzle in machine learning. Most work on such 'benign

Beyond Conservatism: Recoverability-Conditioned Exploration for Model-Based Imitation Learning

arXiv:2609.33336v1 Announce Type: new Abstract: Model-based imitation learning (MBIL) improves real-environment interaction efficiency by optimizing policies on imagined rollouts from a learned world