Search: “framework”

Grab a Coffee: Future-Aware Guidance for Discrete Diffusion with Compiled Objectives

arXiv:2609.35924v1 Announce Type: new Abstract: Discrete diffusion models generate sequences by iteratively resolving multiple tokens in parallel, offering a flexible alternative to left-to-right gene

researcharxiv-cs-ai

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Research

GRFBrain: Graph-Structured Rectified Flows for EEG Dynamic Modeling

DGX agent

arXiv:2609.37934v1 Announce Type: new Abstract: Forecasting time-varying functional connectivity from electroencephalography (EEG) requires modeling both history-dependent trends and structured variab

researcharxiv-cs-ai

30 Sept 2026

HARMONI: Aligning Human and Scene Priors for Multi-View 4D Reconstruction

arXiv:2603.12789v3 Announce Type: replace Abstract: Recent advances in 3D foundation models have enabled joint reconstruction of humans and their surrounding environments. However, combining independe

HERO: Histology Encoder for Robust Representation in Oncology

model-releasesarxiv-cs-cv

arXiv:2609.35943v1 Announce Type: new Abstract: Foundation models trained on large pathology image corpora now provide strong, transferable representations for computational pathology. Over the past f

Agents

HeurEvo: Agentic Evolution of Hybrid Solver-Augmented Heuristics for Time-Critical Mathematical Optimization

arXiv:2609.36303v1 Announce Type: new Abstract: Recent advances in agentic heuristic design use AI agents and execution feedback to automate algorithm discovery for challenging optimization problems.

agentsarxiv-cs-cl

Hierarchical Compression of Vision-Language Model Benchmarks

arXiv:2609.37515v1 Announce Type: cross Abstract: Thorough evaluation of vision-language models (VLMs) has become prohibitively expensive, as benchmarks span an ever-broader spectrum of capabilities a

HyDI: A hybrid Deep Learning-Inductive Logic Programming ensemble for multi-label classification

arXiv:2609.37740v1 Announce Type: new Abstract: While attaining remarkable results for many applications, Deep Learning models are notoriously difficult to explain. This work introduces HyDI, a hybrid

In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks

arXiv:2609.38173v1 Announce Type: new Abstract: We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations. Despite its

applicationsarxiv-cs-ro

Know Thyself, Teach Thyself: Internal Information Flow for Selective Self-Distillation

arXiv:2609.36695v1 Announce Type: new Abstract: Self-distillation turns knowledge distillation into a closed learning loop and offers a path toward recursive self-improvement. Without an external teac

Industry

Last Week in AI #345 - 5 new models, 9 misalignment incidents, some Dots

Last Week in AI #345 notes that Anthropic and OpenAI are in a close race to launch more capable but cheaper models this month: Anthropic unveiled Claude Opus 5.5 with stricter cybersecurity safeguards

industrylast-week-in-ai

LAURA: Knowledge Distillation for Interpretable Ambiguous Clause Identification in Legal Contracts

arXiv:2609.36707v1 Announce Type: new Abstract: Legal contracts contain ambiguities that expose enterprises to financial and legal risks. Some ambiguities allow flexible interpretation without trigger

applicationsarxiv-cs-cl

Learning Continuous Patient Trajectories from Electronic Health Records

arXiv:2609.36144v1 Announce Type: new Abstract: Electronic health records provide irregular observations of latent patient states that evolve continuously over time. Recent autoregressive models condi

applicationsarxiv-cs-lg

LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction

arXiv:2609.36396v1 Announce Type: cross Abstract: As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical chal

Markovian Nonconvex ADMM for Reinforcement Learning: Bellman-Resolvent Stability Beyond Smooth Blocks

arXiv:2609.36859v1 Announce Type: new Abstract: We identify and study a structural mechanism for Markovian nonconvex ADMM in reinforcement learning. Using finite discounted MDPs as a canonical proving

MotionMaestro: Masked Tokenization for Unified Motion Generation

arXiv:2609.37495v1 Announce Type: new Abstract: Human motion generation plays an important role in applications such as character animation, virtual environments, and embodied interaction. While exist

Normalize-Then-Precondition: A Hierarchical Approach to Marginal Scale and Interaction Geometry for LLM Training

arXiv:2609.36692v1 Announce Type: cross Abstract: Matrix optimizers have emerged as a promising direction, with Muon standing out as a prominent design. Revisiting Muon through its full-Gram represent

On the Approximation and Convergence of Distributional Policy Gradient Algorithms for Risk-Sensitive Reinforcement Learning

arXiv:2405.14749v3 Announce Type: replace-cross Abstract: Risk-sensitive reinforcement learning (RL) is crucial for maintaining reliable performance in high-stakes applications. While traditional RL m

safetyarxiv-cs-ai

Pixel-wise Exposure for Highly Robust In-Vehicle Remote-PPG

arXiv:2609.36607v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) offers a promising non-contact solution for heart rate monitoring, yet its real-world robustness is fundamentally lim

applicationsarxiv-cs-cv

Planning Oriented 3D Scene Completion via Coupled TUDF Occupancy Representation Learning from Partial Observations

arXiv:2609.36543v1 Announce Type: new Abstract: Partial observability remains a fundamental challenge in robotic navigation, where limited sensor coverage and occlusions leave large portions of the en

researcharxiv-cs-ro

PowerSim: Differentiable Physics Simulation and Rendering with Power Diagrams

arXiv:2609.38153v1 Announce Type: new Abstract: We introduce PowerSim, a method to bring physically grounded, differentiable dynamics to PowerFoam's power diagram based 3D representation. PowerSim dir

safetyarxiv-cs-cv

PrivacySkills: How Privacy Guidance Shapes Source Selection in LLM Agents

arXiv:2609.35937v1 Announce Type: cross Abstract: While prior work has documented privacy failures in LLM agents, it remains unclear how the presentation of privacy guidance influences their choice of

researcharxiv-cs-ai

Probability Contracts: Accuracy, Coherence, and Decisions Across LLM Interfaces

model-releasesarxiv-cs-lg

arXiv:2609.37470v1 Announce Type: new Abstract: A probability used for a decision should refer to the same event across equivalent requests. We introduce probability contracts, a benchmark connecting

RACE: Relation-Level Counterfactual Explanations for Heterogeneous Graph Neural Networks

arXiv:2609.37650v1 Announce Type: new Abstract: Counterfactual explanations of graph neural networks identify edge deletions that flip a prediction. On heterogeneous graphs, however, existing methods

Local Ai

Receptive-field-constrained stimulus optimization for human early and intermediate visual cortex

arXiv:2609.36391v1 Announce Type: cross Abstract: An ongoing challenge in sensory neuroscience is to characterize the feature dimensions encoded by cortical populations. Recent approaches probe featur

local-aiarxiv-cs-lg

Agents

Reconstructing Implicit Scientific Knowledge: Evaluating LLM Agents through End-to-End Reproduction of Astronomy

arXiv:2609.35900v1 Announce Type: cross Abstract: The integration of large language models (LLMs) into scientific workflows is accelerating, yet their ability to reconstruct the reasoning underlying p

agentsarxiv-cs-ai

Tutorials

RED: Reconstruction Evolution Dynamics for Generalizable AI-Generated Image Detection

arXiv:2609.36822v1 Announce Type: new Abstract: The rapid evolution of image generators calls for forensic cues that generalize beyond known generation mechanisms. Existing detectors often rely on sta

tutorialsarxiv-cs-cv

Reliability Testing of Medical Model Performance under Distributed Deployment

arXiv:2609.36525v1 Announce Type: cross Abstract: Distributed inference has become an indispensable part of deploying medical models under practical latency, memory, and throughput constraints. Althou

Risk-Aware Semantic Grounding for Trustworthy LLM-Based Robot Planning

arXiv:2609.37554v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as high-level planners in robot navigation, but their outputs may become unreliable when instructio

RNA Design via Conditioned Flow Matching and Finite-Policy Reinforcement Learning

arXiv:2609.36885v1 Announce Type: cross Abstract: RNA design aims to identify sequences that fold into specified secondary structures. Existing methods formulate the task as target-specific search or

Safe-by-Design Learning via Energy-based Neural Networks

arXiv:2609.36942v1 Announce Type: cross Abstract: Learning neural-network models of dynamical systems with safety guarantees is a fundamental requirement for their deployment in safety-critical settin

safetyarxiv-cs-ai

Scene-Consistent Illumination Transfer for Inserted Advertising Graphics

arXiv:2609.37951v1 Announce Type: new Abstract: Replacing a visible advertisement in a broadcast frame is geometrically straightforward but photometrically delicate. A pasted graphic can have the corr

Scene Retargeting: Learning Object Placement with Analogical Transfer

arXiv:2609.36801v1 Announce Type: new Abstract: Interactive simulations of embodied AI or spatial computing applications build on realistic 3D scenes that support daily activities. However, sparse, ir

safetyarxiv-cs-cv

Tracing the Evolution of Oracle Bone Characters Across Three Millennia

arXiv:2609.35674v2 Announce Type: replace Abstract: Of the approximately 4,500 Oracle Bone Inscription (OBI) characters discovered from the Shang dynasty, only about 1,600 have been deciphered. Many c

researcharxiv-cs-cl

Update: Strix Halo + R9700 with llama-halo-hybrid - now beats DGX Spark

model-releasesr-localllama

Hi folks, I've spent the last couple of months experimenting with Strix Halo and previously I released a proof of concept I called llama-halo-hybrid. I've continued updating it and it now performs ver

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

safetyarxiv-cs-ai

When do data mixtures improve scaling laws? Insights from high-dimensional regression

model-releasesarxiv-cs-lg

arXiv:2609.38011v1 Announce Type: new Abstract: Modern machine learning systems are trained on mixtures of data from different domains, and choosing the right mixture can substantially improve downstr

When Semantics Matter: Reliability-Aware Semantic-Rhythm Control for Co-Speech Gesture Generation

model-releasesarxiv-cs-cv

arXiv:2609.36685v1 Announce Type: new Abstract: Co-speech gesture generation aims to synthesize natural gestures that are both temporally synchronized with speech and semantically consistent with the

XU-RS: Explaining Credal Width in Random-Set Language Models

arXiv:2609.37594v1 Announce Type: new Abstract: Uncertainty estimates tell us how unsure a model is, but not why. Without knowing which parts of an input influences a model's uncertainty, we cannot te

Accelerating agentic RL and evaluation research velocity with 45x faster GKE Agent Sandbox

model-releasesgoogle-cloud-ai

When scaling up agentic reinforcement learning (RL) and evaluation across massive parallel rollouts, frontier AI labs inevitably hit a bottleneck: Expensive GPU clusters sit idle, waiting minutes for

Aerial GRIPPER: A Gradient-based Real-time Inverse-game Predictor and Planner

arXiv:2609.34594v1 Announce Type: new Abstract: Accurate capture of non-cooperative targets is critical. In an attempt to tackle this intractable challenge, an aerial gripper system integrated with a

applicationsarxiv-cs-ro

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

Agents

AgentWare: Automating the Lifecycle of Agentic Applications across the Edge-to-Cloud Continuum

arXiv:2609.34586v1 Announce Type: cross Abstract: Deploying LLM-enabled agentic applications across the Edge-to-Cloud continuum remains challenging due to hardware heterogeneity, deployment complexity

agentsarxiv-cs-lg

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

safetyarxiv-cs-ro

ANaLOG: Anisotropic Native-Latent Operator Guidance for Solving Inverse Problems

arXiv:2609.31933v1 Announce Type: cross Abstract: Native-latent guidance is a recent paradigm for solving inverse problems with latent diffusion models. It replaces repeated evaluations of the image-s

AnyStep-WAM: Budget-Aligned Distillation and Adaptive Inference for World Action Models

arXiv:2609.33748v1 Announce Type: new Abstract: World-action models (WAMs) couple predictive visual modeling with action generation, typically relying on iterative denoising with a fixed denoising ste

applicationsarxiv-cs-ro

AquaBEV-Nav: Learned BEV Occupancy for Underwater Navigation and Exploration

arXiv:2609.32156v1 Announce Type: cross Abstract: Safe underwater exploration requires a robot to understand where surrounding structures are located and which regions are available for motion. Existi

Beyond Inpainting: Unleash 3D Understanding for Stable Camera-Controlled Video Generation

arXiv:2601.10214v3 Announce Type: replace Abstract: Camera control has been extensively studied in conditioned video generation; however, performing precisely altering the camera trajectories while fa

Beyond the Graph: An Adaptive Meta-Learner Fuses Explainability, Weather, and Dynamics for Robust Bus ETA Prediction

arXiv:2609.31667v1 Announce Type: new Abstract: Accurate bus Estimated Time of Arrival (ETA) prediction is vital for urban mobility, passenger satisfaction, and transit efficiency, yet existing models