tutorials

Physical AI’s bottleneck shifts from what robots can do to whether factories trust them

AI is moving beyond the data centers where models are trained and onto factory floors where machines must handle constant change. But traditional industrial robots work behind a cage, repeat a single

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PointWAM: 3D World Action Modeling for Dexterous Robotic Manipulation

DGX agent

arXiv:2610.02840v1 Announce Type: cross Abstract: World action models jointly learn to forecast world dynamics and predict robot actions, such that the learned internal world dynamics guide accurate a

tutorialsarxiv-cs-cv

5 Oct 2026

Post-Training Frontier Text-to-Image Models by Composing Preference and Rubric Rewards

arXiv:2610.02967v1 Announce Type: cross Abstract: Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because n

Reliable Self-Evolution with Imperfect Proxy Rewards

arXiv:2610.02975v1 Announce Type: new Abstract: Large language model (LLM)-based self-evolving search is a promising approach to scientific discovery. However, high-fidelity evaluation of every candid

SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs

arXiv:2610.03313v1 Announce Type: cross Abstract: Existing machine learning weather forecasting models typically generate forecasts through autoregressive rollouts at a fixed temporal resolution. Whil

The AI Risk Observatory: What Can We Learn from AI Disclosures in Annual Reports About Societal Resilience?

arXiv:2610.02281v1 Announce Type: new Abstract: Societal resilience research relies on access to useful and actionable data, which motivates our main research question: Can annual reports, processed a

The Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMs

arXiv:2610.03124v1 Announce Type: cross Abstract: Open-weight language models can be downloaded, modified, and deployed beyond their developers' control, limiting the effectiveness of centrally enforc

Toward Controlling Biology with Language:Offline Learning of Prompt-Conditioned Interventions for Cells, Organoids, and Biobots

arXiv:2610.02247v1 Announce Type: cross Abstract: Artificial intelligence increasingly serves as a natural-language interface to complex technical systems, letting people accomplish sophisticated task

Toward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach

arXiv:2610.02881v1 Announce Type: new Abstract: Multimodal graph foundation models (MGFMs) seek to learn generalizable representations from large-scale graphs with heterogeneous node modalities. Howev

Validated Data Onboarding for AI Demand Forecasting on U.S. Building Meter Data: Design, Controlled Evaluation, and a Corrected Negative Result

arXiv:2610.02397v1 Announce Type: new Abstract: Electric utilities and grid operators increasingly rely on machine-learning models to forecast next-day demand, and those models learn from meter data t

When History Fails to Become Experience: Action Calibration in Language Agents

arXiv:2610.02769v1 Announce Type: cross Abstract: Language agents should draw on prior attempts and environmental feedback to improve subsequent decisions within the same task. However, providing addi

Where to Look Is Not How to Fix: Pre-Denoising Diagnostics and Modality-Dependent Control in Diffusion Composition

arXiv:2610.03068v1 Announce Type: new Abstract: Understanding compositional failures in text-to-image diffusion requires identifying both where stress is detectable and how intervention changes the ou

World Action Learning via Interaction-Centric Spectral Latent Guidance

arXiv:2610.03607v1 Announce Type: new Abstract: Learning general-purpose robot policies requires large-scale real-world interaction data, yet collecting robot demonstrations remains expensive and diff

tutorialsarxiv-cs-ro

XGenAct: Geometry-Enhanced World Action Models through Cross-Task Generation

arXiv:2610.03516v1 Announce Type: cross Abstract: World action models (WAMs) have advanced robot control by predicting how observations and actions evolve over time. Despite this progress, RGB and act

PSA: one of the most common rhetorical techniques in tech is to start with something very plausible (increase in capex to $4t) and go to som…

PSA: one of the most common rhetorical techniques in tech is to start with something very plausible (increase in capex to 4t) and go to something that has almost zero chance (doubling GDP in less than

tutorialsgary-marcus--x

4 Oct 2026

AI is absolutely not going to double humanity’s GDP by the early 2030s. Why do we platform these crazy claims that never come true?

AI is absolutely not going to double humanity’s GDP by the early 2030s. Why do we platform these crazy claims that never come true? Anthropic's Sholto Douglas says AI capex could rise from 1 trillion

tutorialsgary-marcus--x

one of the lessons aviation learned was that regulation is useful for gaining public trust. the AI industry refuses to learn that lesson - a…

one of the lessons aviation learned was that regulation is useful for gaining public trust. the AI industry refuses to learn that lesson - and then wonders why everyone hates them. I disagree with the

tutorialsgary-marcus--x

That video is 2 years old. Two years later, we are still far from human-level AI. Sure, AI has superhuman performance in a number of tasks (…

That video is 2 years old. Two years later, we are still far from human-level AI. Sure, AI has superhuman performance in a number of tasks (particularly in mathematics and coding and answering questio

tutorialsyann-lecun--x

Yann LeCun's (@ylecun ) latest talk at ETH Zürich Scaling LLMs to reach AGI is 'impossible' A large language model is trained on about 30 tr…

Yann LeCun's (@ylecun ) latest talk at ETH Zürich Scaling LLMs to reach AGI is 'impossible' A large language model is trained on about 30 trillion tokens, which is roughly 10^14 bytes of text and woul

tutorialsyann-lecun--x

A Large Scale Investigation of Scaling Limits in Chemical Language Models

arXiv:2508.13408v3 Announce Type: replace Abstract: Chemical Language Models (CLMs) are increasingly used in de novo drug design, driven by recent growth in model scale, compute, and dataset size. How

ABDA-NL: A Natural-Language Scenario Explorer for Argument-Based Reasoning

arXiv:2610.00947v1 Announce Type: new Abstract: ABDA-NL adds a natural-language interface to ABDA, a system for argument-based discussion using ASPIC- knowledge bases under grounded semantics. Users s

AI is rewriting the developer career ladder. Here’s how to stand out.

Learn three ways to get noticed and grow your career as AI reshapes how developers build software. The post AI is rewriting the developer career ladder. Here’s how to stand out. appeared first on The

tutorialsgithub-ai-blog

Backdoor Containment via Expert Quarantine and Shutdown in LLMs

arXiv:2610.00663v1 Announce Type: new Abstract: Backdoored large language models (LLMs) can behave normally on benign inputs while producing attacker-specified outputs under hidden triggers. Existing

Distillation of Tabular Foundation Models into Efficient Predictors

arXiv:2610.01435v1 Announce Type: new Abstract: Tabular foundation models (TFMs) achieve strong predictive performance through in-context learning, yet repeatedly conditioning on labeled data makes in

Efficient Generative Modeling beyond Memoryless Diffusion via Adjoint Schrodinger Bridge Matching

arXiv:2602.15396v2 Announce Type: replace Abstract: Diffusion models often yield highly curved trajectories and noisy score targets due to an uninformative, memoryless forward process that induces ind

EndoLive: Real-Time Style Transfer for Endoscopic Endonasal Skull Base Surgical Video

arXiv:2610.01956v1 Announce Type: new Abstract: Complex surgical procedures around critical anatomy, such as the endoscopic endonasal skull base surgery, requires significant practice and training on

EvoGen-Harness: Learning Where and How to Evolve Image-Generation Harnesses

arXiv:2610.00383v1 Announce Type: new Abstract: Modern text-to-image (T2I) systems can be improved without modifying generator parameters by adapting the external system around frozen generators. Howe

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

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

Four Ways to Grow a Classifier and Why One of Them Cannot Learn

arXiv:2610.00180v1 Announce Type: cross Abstract: Constructive classifiers add structure while they train: a level to a tree, a unit to a hidden layer, a split at a leaf. This paper asks what each of

From Redundancy to Minimality: Fixed-Point-Guided Hierarchical Reduction of Learned Piecewise-Linear Dynamics

arXiv:2610.01369v1 Announce Type: new Abstract: Understanding a nonlinear dynamical system from time series requires not only reproducing its trajectories, but also identifying a simple representation

Group-Invariant Statistics Determine Embedding Geometry: Harmonic Analysis of Representations from Bach to the Night Sky

arXiv:2610.00647v1 Announce Type: new Abstract: The representations that language models learn for concepts such as months, weekdays, and places display consistent geometric structure: circles and sad

HAWK: Rethinking Multimodal Drafting for Speculative Decoding

arXiv:2610.00623v1 Announce Type: new Abstract: Speculative decoding has achieved substantial lossless speedups for LLMs, but remains less effective for large vision-language models (LVLMs), where lig

How many labelers do you have? A closer look at gold-standard labels

arXiv:2206.12041v3 Announce Type: replace-cross Abstract: The construction of most supervised learning datasets revolves around collecting multiple labels for each instance, then aggregating the label

Information propagation dynamics in Deep Graph Networks

arXiv:2410.10464v3 Announce Type: replace Abstract: Graphs are a highly expressive abstraction for modeling entities and their relations, such as molecular structures, social networks, and traffic net

Introducing the official Pinecone plugin for @OpenAI Codex and ChatGPT! Pinecone now has an official plugin for Codex/ChatGPT Desktop users …

Introducing the official Pinecone plugin for @OpenAI Codex and ChatGPT! Pinecone now has an official plugin for Codex/ChatGPT Desktop users via the OpenAI Plugin Directory. Use Pinecone with Codex to

tutorialspinecone--x

IQS-BO: In-Context Query Selection for Bayesian Optimisation

arXiv:2610.01269v1 Announce Type: new Abstract: Bayesian Optimisation (BO) is a powerful framework for the optimisation of expensive black-box functions, but typically requires refitting a surrogate a

Learn the Directions, Normalize the Gains: Post-Training Normalization for LoRA

arXiv:2610.02067v1 Announce Type: new Abstract: While Low-Rank Adaptation (LoRA) enables efficient task specialization, its learned updates can compromise capabilities beyond the target task. We ident

Learning to Ask: Information Acquisition for SLM-LLM Collaboration, under a budget

arXiv:2610.01236v1 Announce Type: new Abstract: Collaboration between a small language model (SLM) and a large language model (LLM) offers an opportunity to combine the efficiency of smaller models wi

Learning Transferable Skills using Goal-Conditioned Bisimulation

arXiv:2610.00676v1 Announce Type: cross Abstract: Unsupervised skill discovery has emerged as a promising approach for leveraging reward-free datasets to pretrain general-purpose policies. However, cu

MotionPersona: Real-Time Locomotion Control across Personas, Bodies, and Styles

arXiv:2506.00173v2 Announce Type: replace-cross Abstract: We present MotionPersona, a generative framework for character-aware locomotion control, in which the motion for a command depends on the capt

tutorialsarxiv-cs-ro

On the Relationship between Model Quantization and Model Inversion Attacks

arXiv:2610.00382v1 Announce Type: cross Abstract: Model quantization reduces the numerical precision of neural network weights and activations to lower storage and computational costs. Model inversion

PI-AMFM: Permutation-Invariant Learning for Variable-Cardinality AM-FM Mode Decomposition in Biomedical Signal Analysis

arXiv:2610.00819v1 Announce Type: cross Abstract: Physiological recordings often contain nonstationary oscillatory components whose number and dynamics vary across signals. Amplitude- and frequency-mo

Prediction-powered Neural Architecture Search

arXiv:2610.01317v1 Announce Type: new Abstract: Evaluating candidate architectures in neural architecture search (NAS) faces an inherent trade-off: on the one hand, reliable performance labels are lim

Program Learning with Verifiable Rewards: Symbolic Backpropagation for Post-Training LLMs

arXiv:2608.28421v2 Announce Type: replace Abstract: Post training a language model to reason means updating its weights. Supervised finetuning and reinforcement learning both place the acquired capabi

Questionnaire-Guided Disaggregation of Energy Appliance Use for Domestic Smart Meter Data

arXiv:2610.01297v1 Announce Type: new Abstract: Ireland's smart metering programme records electricity use at 30-minute resolution, with smart meters installed in over 80% of households as of late 202

Reason in Style: Discovering and Controlling Style in Language Models

arXiv:2610.00724v1 Announce Type: cross Abstract: Language models learn content and style jointly, making stylistic variation in their outputs difficult to identify and control. We study whether recur

RelICL: Training-free Relational Learning with Tabular Foundation Models

arXiv:2610.01725v1 Announce Type: new Abstract: Tabular foundation models achieve state-of-the-art performance on single-table tasks without any training. Recent work suggests that they are also well-