Search: “framework”

Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool

arXiv:2609.05364v1 Announce Type: cross Abstract: Machine-learning performance modeling is a uniquely hostile terrain for long-lived software: the assumptions baked into today's abstractions are inval

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Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation

DGX agent

arXiv:2609.05363v1 Announce Type: new Abstract: Trade-up recommendation identifies higher-quality alternatives that preserve a customer's purchase intent while offering upgraded benefits. Large langua

model-releasesarxiv-cs-lg

7 Sept 2026

Enhancing Affine Maximizer Auctions with Correlation-Aware Payment

arXiv:2602.09455v2 Announce Type: replace-cross Abstract: Affine Maximizer Auctions (AMAs), a generalized mechanism family from VCG, are widely used in automated mechanism design due to their inherent

researcharxiv-cs-lg

Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning

arXiv:2609.04272v1 Announce Type: cross Abstract: This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a ze

researcharxiv-cs-cl

EvoCUA-1.5: Online Reinforcement Learning for Multi-turn Computer-Use Agents

arXiv:2607.09773v2 Announce Type: replace Abstract: Computer-use agents must solve long-horizon tasks through repeated interaction with partially observable, multimodal desktop environments. Although

EyeMakeYou: Identity-, Task-, and Subjective-State-Conditioned Diffusion for High-Frequency Gaze Synthesis

arXiv:2609.04501v1 Announce Type: cross Abstract: Eye movement biometrics (EMB) is an emerging behavioral modality for user authentication, particularly in virtual- and augmented-reality systems, wher

FailSAE: Towards Interpretable Failure Prediction for Vision-Language Models via Sparse Autoencoders

arXiv:2609.04276v1 Announce Type: new Abstract: Vision-language models (VLMs), such as CLIP, have achieved strong performance across multimodal tasks by aligning visual and textual representations in

FailureSpot: Label-Efficient Timestamp-Level Failure Detection for Vision-Language-Action Models

arXiv:2609.04277v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies have shown strong potential for general-purpose robotic manipulation, but they can still fail unpredictably duri

Fractal and Chaotic Activation Functions in Echo State Networks: Preprocessing Topology Governs the Echo State Property

model-releasesarxiv-cs-lg

arXiv:2512.14675v2 Announce Type: replace Abstract: Contemporary reservoir computing relies heavily on globally Lipschitz, well-behaved activation functions, limiting applications in defense, disaster

From Interaction Traces to Persistent Skills: Online Evolution for Computer-Use Agents

arXiv:2609.04869v1 Announce Type: new Abstract: Computer-use agents can execute increasingly complex tasks in graphical interfaces, but their interaction experience is typically transient: procedural

FSPGD: Rethinking Black-box Attacks on Semantic Segmentation

arXiv:2502.01262v3 Announce Type: replace Abstract: Black-box adversarial attacks on semantic segmentation remain a challenging problem, particularly in the black-box transfer attack setting where per

Hakken: Predicting future discoveries to fill the gaps in today's knowledge

arXiv:2609.04494v1 Announce Type: cross Abstract: We present Hakken, a domain-agnostic prediction and explanation system performing knowledge prediction, i.e., growing scientific knowledge by establis

HiSfM: Disambiguating Structure-from-Motion via Scaffold-Anchored Hierarchical Reconstruction

arXiv:2609.04718v1 Announce Type: new Abstract: Structure-from-Motion (SfM) is a fundamental tool for sparse 3D reconstruction with broad impact in robotics and vision, supporting mapping, localizatio

Human-Human & Human-Robot Interaction Transformer (H2INT) for Robot Navigation in Dense and Uncertain Crowds

arXiv:2609.05300v1 Announce Type: new Abstract: Safe robot navigation in dense crowds requires reasoning about pedestrian motion and how it may change in response to a robot. However, many learning-ba

safetyarxiv-cs-ro

Influence Score and Transformers interpretability: Measure of the Effective Impact of Attention Heads at inference time

arXiv:2609.05074v1 Announce Type: new Abstract: We propose an influence score to quantify the contribution of attention heads to classification decisions in Transformer-based models designed for promp

researcharxiv-cs-cl

Intrinsic Temporal Adaptation of CLIP for Partially Relevant Video Retrieval

model-releasesarxiv-cs-cv

arXiv:2609.04800v1 Announce Type: new Abstract: Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos that contain moments relevant to a text query. Since the target moment occup

Agents

La Agente Optima: Towards Agentic Self-Driving Laboratories

arXiv:2609.04564v1 Announce Type: new Abstract: Self-driving laboratories (SDLs) combine automated experimentation with adaptive decision-making to accelerate scientific discovery. Their operation nev

agentsarxiv-cs-ai

Last Week in AI #343 - GPT-6, OpenAI’s agents chatted on a wiki, Fable 5.1

model-releaseslast-week-in-ai

OpenAI released GPT‑6 “Astra” — a model benchmarked for superior computer navigation, coding and complex math that can book DMV appointments and hunt apartments faster than an average user. The rollou

Learning 3D Editing without Paired Supervision via Generative Prior Distillation

arXiv:2609.04942v1 Announce Type: new Abstract: Instruction-guided 3D editing is essential for interactive content creation, yet it faces a significant bottleneck: the severe scarcity of high-quality

Learning Spatial-Spectral Refinement and Calibrating Complementary Observations for Hyperspectral Image Super-Resolution

model-releasesarxiv-cs-cv

arXiv:2609.05303v1 Announce Type: new Abstract: Hyperspectral and multispectral image fusion (HMIF) aims to reconstruct a high-resolution hyperspectral image (HR-HSI) by combining the fine spatial det

LentEx: Generalizable Latent Entity Extraction via Synthetic Data and Instruction-Tuned LLMs

model-releasesarxiv-cs-cl

arXiv:2609.04511v1 Announce Type: new Abstract: Latent entity extraction (LEE) tackles the challenge of identifying implicit, contextually inferred entities within free text-an area where traditional

Local Ai

Lightweight Vision Transformer Compression for On-Device Plant Disease Detection in Resource-Constrained Agricultural Field Conditions

arXiv:2609.05334v1 Announce Type: cross Abstract: Chilli (Capsicum annuum) is one of India's most economically significant crops, yet its productivity is persistently threatened by diseases that are d

local-aiarxiv-cs-ai

LLM-Driven Algorithm Design for Quantum Circuit Synthesis based on Binary Decision Diagrams

arXiv:2609.05327v1 Announce Type: new Abstract: Quantum circuits are central to implementing quantum algorithms on quantum devices, where quantum gates must be reversible. Many quantum algorithms rely

Applications

Long Horizon Transformer Quantile Fault Prediction for Multi Site Industrial Predictive Maintenance

arXiv:2609.04840v1 Announce Type: new Abstract: Long-horizon predictive maintenance requires models to distinguish slowly evolving degradation from normal operating-regime variation over planning wind

applicationsarxiv-cs-ai

Mapping Dark-Matter Clusters via Physics-Guided Diffusion Models

model-releasesarxiv-cs-cv

arXiv:2603.14503v2 Announce Type: replace Abstract: Galaxy clusters are powerful probes of astrophysics and cosmology through gravitational lensing: the clusters' mass, dominated by 85% dark matter, d

Measured Sliders: Learning Continuous Controls from Differentiable Image Measurements

arXiv:2609.05234v1 Announce Type: new Abstract: Continuous sliders are useful only when coefficient changes produce predictable image changes. Yet most diffusion sliders derive their axes from text or

MedFlow: Class-Aware Multi-Scale Generation for Medical Time-Series Synthesis

arXiv:2609.04804v1 Announce Type: new Abstract: Synthetic medical time-series generation can alleviate data scarcity and support the development of reliable clinical prediction models. However, existi

researcharxiv-cs-ai

MedProb: Probing Internal Representations of Vision-Language Models for Medical Question Answering

arXiv:2609.04336v1 Announce Type: new Abstract: Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or complex multi-agent pipelines. We revisit

safetyarxiv-cs-cl

Local Ai

MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution

arXiv:2603.18718v2 Announce Type: replace Abstract: Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction, yet most existing systems treat construction, retrie

local-aiarxiv-cs-ai

MePo++: Unifying Representation Refinement and Reconciliation for General Continual Learning

arXiv:2609.05075v1 Announce Type: new Abstract: General continual learning (GCL) aims to learn from evolving data streams without task identities, explicit boundaries, or repeated access to previous d

safetyarxiv-cs-ai

Morphology and actuation as inductive biases in robotic hand manipulation

arXiv:2609.05206v1 Announce Type: new Abstract: Robotic hands vary widely in anatomical fidelity and mechanical complexity, and these structural choices influence the coordination of joint motions and

researcharxiv-cs-ro

Motion-Omni: End-to-End Joint Speech and Full-Body Motion for Spoken Dialogue

arXiv:2609.04250v1 Announce Type: cross Abstract: An avatar that holds a conversation should decide what to say and to move while saying it, yet these abilities live in separate model families: spoken

MURAL: Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning

arXiv:2609.04574v1 Announce Type: cross Abstract: Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data with content features. Yet current arch

safetyarxiv-cs-lg

MZ-Rain: Moisture-Budget-Guided Zero-Inflated Model for Station-Level Precipitation Nowcasting

arXiv:2609.04864v1 Announce Type: new Abstract: Accurate station-level precipitation nowcasting is critical for agriculture, water resource management, and disaster prevention, which typically is form

researcharxiv-cs-ai

NavArena: Automated Construction of Goal-Oriented Navigation Benchmarks from 3D Gaussian Splatting Reconstructions

model-releasesarxiv-cs-ro

arXiv:2609.04602v1 Announce Type: new Abstract: Fixed 3D Gaussian Splatting (3DGS) reconstructions provide realistic novel views but lack the traversability constraints, valid goals, and closed-loop p

NEAT-POCKET: Pocket-Conditioned Autoregressive 3D Molecular Generation with a Neighborhood-Guided Set Transformer

arXiv:2609.05097v1 Announce Type: cross Abstract: AI-driven de novo molecular design offers a promising route to accelerate early-stage drug discovery by generating novel ligands directly within targe

researcharxiv-cs-ai

Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

arXiv:2609.05385v1 Announce Type: new Abstract: LLM decision components that can operate within agent workflows often produce action-relevant recommendations or judgements together with explanations.

On Epistemic Diversity in Large Language Models

arXiv:2609.04835v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used not only to retrieve information, but to answer questions, explain, teach, and support inquiry. In su

safetyarxiv-cs-cl

One Diffusion Model, Two Roles: Guided Trajectory Planning and Safety-Critical Scenario Generation in Closed-Loop Simulation

arXiv:2609.04921v1 Announce Type: cross Abstract: Diffusion probabilistic models can capture the multi-modal, interaction-rich distribution of joint future trajectories in driving scenes. We show that

Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography

arXiv:2602.06761v2 Announce Type: replace-cross Abstract: Fetal echocardiography is essential for detecting congenital heart disease (CHD), facilitating pregnancy management, optimized delivery planni

Out-of-Distribution Semantic Occupancy Prediction

arXiv:2506.21185v3 Announce Type: replace Abstract: 3D semantic occupancy prediction is crucial for autonomous driving, providing a dense, semantically rich environmental representation. However, exis

PAPT++: Risk-Aware Adversarial Tuning and Generation for Single Domain Generalization

arXiv:2609.04837v1 Announce Type: new Abstract: Single domain generalization (SDG) aims to learn a model from one labeled source domain that generalizes to unseen target domains. A common strategy is

Physics-Aware Random Walk Fingerprints for Scalable Power Grid Graph Classification

model-releasesarxiv-cs-lg

arXiv:2609.04943v1 Announce Type: new Abstract: Recent benchmarks such as PowerGraph provide large collections of power-grid graphs for cascading-failure classification. Graph neural networks (GNNs) a

PLUME: Parameter-Efficient Personalization of Large Language Models via Low-Rank User Modulation in Shared Subspaces

arXiv:2609.04715v1 Announce Type: new Abstract: Personalizing large language models (LLMs) is essential for delivering AI assistance that aligns with individual users' styles, intents, and preferences

ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding

arXiv:2609.05094v1 Announce Type: new Abstract: Electroencephalogram (EEG) visual decoding aims to recover visual semantics from non-invasive neural time-series signals, for which robust alignment bet

safetyarxiv-cs-ai

ProtLingo: Efficient Protein Language Modeling via Conditional Memory and Expert Routing

arXiv:2609.04793v1 Announce Type: new Abstract: Proteins perform diverse cellular functions, and even single amino-acid substitutions can alter stability, activity, or molecular interactions. Protein

ProToMEx: Rapid, Interpretable Explanations via Structured Representations

arXiv:2609.04265v1 Announce Type: new Abstract: Existing post-hoc explainers for machine learning classifiers primarily focus on feature attribution, assigning importance scores to individual features

researcharxiv-cs-lg

QoNext: Towards Next-generation QoE for Foundation Models

arXiv:2509.21889v3 Announce Type: replace Abstract: Existing evaluations of foundation models predominantly focus on output correctness, treating interaction as a static exchange of information. Howev

researcharxiv-cs-cl