Search: “benchmark”

Bidirectional Information Flow (BIF) - A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimization

arXiv:2505.11294v3 Announce Type: replace Abstract: Hierarchical Gaussian Process (H-GP) models divide problems into different subtasks, allowing different components to address each part, making them

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Bridging Generative and Discriminative Noisy-Label Learning via Direction-Agnostic EM Formulation

DGX agent

arXiv:2308.01184v4 Announce Type: replace Abstract: Although noisy-label learning is often approached with discriminative methods for simplicity and speed, generative modeling offers a principled alte

safetyarxiv-cs-cv

29 Sept 2026

Industry

Bring near-Astra intelligence to everyday work with GPT-6.1 Sol on Amazon Bedrock

GPT‑6.1 Sol is now generally available on Amazon Bedrock, providing enhanced reasoning for coding, computer use, and professional workflows. It runs on a high‑performance inference engine optimized fo

industryaws-ml-blog

CAESAR: Clustering via Autonomous Embedding-Space Agglomerative Reorganization

arXiv:2609.32570v1 Announce Type: new Abstract: Clustering algorithms that operate on nearest-neighbor graphs, such as FINCH (First Integer Neighbor Clustering Hierarchy), depend heavily on the qualit

CalibHyper: Chance-Corrected Relational Hypergraphs for Few-Shot Molecular Property Prediction

arXiv:2609.33342v1 Announce Type: new Abstract: Molecular property prediction is central to drug development and materials discovery, but experiments are costly and labeled data are scarce. Context-aw

Can Circuit Alignment Predict OOD Generalization?

arXiv:2609.31996v1 Announce Type: new Abstract: Can out-of-distribution (OOD) generalization be predicted from a trained model's weights alone, without any target-domain data? Existing representationa

safetyarxiv-cs-lg

Can Tabular Foundation Models Amortize Statistical Inference?

arXiv:2609.33114v1 Announce Type: cross Abstract: For decades, statistical inference has largely been developed one problem at a time. Given a scientific target, such as a treatment effect or a regres

Can't Find Waldo: Evaluating VLMs' Sensitivity to Image Resolution and Detail Level

arXiv:2609.31706v1 Announce Type: new Abstract: Visual Language Models (VLMs) have achieved remarkable success across diverse tasks, yet they struggle with high-resolution inputs where critical inform

researcharxiv-cs-cv

CaptchaArena: A Large-Scale, Fine-Grained Dataset for Training Computer-Use Agents on Interactive CAPTCHAs

arXiv:2609.31957v1 Announce Type: new Abstract: Interactive CAPTCHAs remain challenging for computer-use agents, while existing datasets face trade-offs among type coverage, interaction fidelity, and

safetyarxiv-cs-cv

CarveMix-RC: Addressing Rare-Class Imbalance Through Lesion-Aware Synthetic Augmentation for Brain Metastasis Segmentation

arXiv:2609.35195v1 Announce Type: new Abstract: Accurate segmentation of post-treatment brain metastases is essential for treatment planning, longitudinal disease monitoring, and quantitative assessme

Tutorials

CasEm: A Cascade Architecture for Long-Horizon Neural Emulation

arXiv:2609.34246v1 Announce Type: new Abstract: Autoregressive neural emulators can drift or diverge over long rollouts despite accurate short-term predictions. We introduce Cascaded Emulation (CasEm)

tutorialsarxiv-cs-lg

CFLoRA: Federated Fine-tuning of LLMs with Complementary Factors for Error-free Aggregation

arXiv:2609.33147v1 Announce Type: new Abstract: Federated low-rank adaptation (LoRA) enables collaborative fine-tuning of large language models without centralizing private client data. Its factorized

Claude 5.5 Opus responding to “AI is a normal technology” is still the best AI-created video I have ever seen

model-releasesemad-mostaque--x

On September 29, 2026, Twitter user Severin Field (@sevdeawesome) posted that an AI‑generated video from Claude 5.5 Opus responding to the phrase “AI is a normal technology” was the best AI‑created vi

CoCurve: Cross-Module Co-Pruning Curvature for Structured LLM Pruning

arXiv:2607.17568v2 Announce Type: replace Abstract: Resource-constrained deployment requires sustaining large language model (LLM) capabilities as models scale under fixed memory and computation budge

CodeActionBench: Evaluating Agentic Code-as-Policy for Embodied Manipulation

model-releasesarxiv-cs-ro

arXiv:2609.33807v1 Announce Type: new Abstract: How well can general-purpose multimodal models turn visual understanding and reasoning into embodied manipulation via executable code? We introduce Code

CoDrive: Cross-Vehicle World-Consistent Video Generation with Precise Trajectory Control for Cooperative Driving

arXiv:2609.34749v1 Announce Type: new Abstract: Real-world driving is inherently multi-agent, yet most existing driving world models generate observations from a single ego vehicle. Independently exte

Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Silos

arXiv:2609.33754v1 Announce Type: new Abstract: Organizations seek analytical value from AI, yet relevant data are often fragmented across organizations and constrained by privacy. This is acute in fi

Communication-Aware Heterogeneous Graph Learning for Decentralized Multi-Human Multi-Robot Task Allocation

model-releasesarxiv-cs-ro

arXiv:2609.32935v1 Announce Type: new Abstract: Multi-human multi-robot (MH-MR) teams combine robotic autonomy with human expertise, but effective task allocation requires coordinating scarce, dynamic

Commutator Memory: Sparse, Path-Local Reading and Steering in Language Models

arXiv:2609.34348v1 Announce Type: new Abstract: Gradient updates on different data generally do not commute: training a language model on two data sources in opposite orders gives different weights, e

Compositional Objectives: Learning Structure in Structure

arXiv:2609.32566v1 Announce Type: new Abstract: Intelligence is defined in many ways. One of these definitions defines intelligence as the pursuit of learnable novelty. However, learnable novelty can

Compute Time Scaling with Recursive Models for Combinatorial Optimization

arXiv:2609.34585v1 Announce Type: new Abstract: We propose Tiny Recursive Models for Combinatorial Optimization (ours{}), a general neural method for combinatorial optimization that scales both depth

Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness

arXiv:2603.08309v3 Announce Type: replace Abstract: Vision Transformers (ViTs) often degrade under distribution shifts because they rely on spurious correlations, such as background cues, rather than

safetyarxiv-cs-cv

Conditioned Direct Feedback Alignment via Activity and Error Geometry

arXiv:2607.18574v2 Announce Type: replace Abstract: Direct feedback alignment (DFA) trains hidden layers with fixed random projections of the output error. Even when this feedback provides useful cred

safetyarxiv-cs-lg

Constella Preview: Swap Query Models Without Re-Embedding

As query traffic grows, so does the compute bill for embedding it. On a low-power device, a large model may not fit in memory. A smaller query model could reduce that cost, but switching usually means

model-releasesqdrant

Controlling Speaking Rate in Autoregressive TTS via Activation Steering

arXiv:2609.33810v1 Announce Type: cross Abstract: Autoregressive text-to-speech (TTS) systems synthesize natural speech but, once trained, offer little control over speaking rate. We show that speakin

ControlTrace: Recovering Control Fields for Hidden-Content Recognition

arXiv:2609.34807v1 Announce Type: new Abstract: Spatially conditioned diffusion models can embed words and contours in natural-looking images, but vision-language models (VLMs) may fail to recognize t

Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning

arXiv:2502.02332v3 Announce Type: replace-cross Abstract: We study task selection to enhance sample efficiency in model-agnostic meta-reinforcement learning (MAML-RL). Traditional meta-RL typically as

Correct then Forecast: Observer State-Space Models for Time Series Forecasting

arXiv:2609.33566v1 Announce Type: new Abstract: Time series forecasting requires extrapolating the dynamics of an observed process beyond the last available measurement. Yet recurrent forecasting mode

Counterfactual Attention Policy Distillation for Temporal Video Grounding

arXiv:2609.34581v1 Announce Type: new Abstract: Temporal video grounding is a key capability of advanced Multimodal Large Language Models (MLLMs) for the thorough understanding of video events, which

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Local Ai

Cross-Rollout Bellman Closure for Long-Horizon Agentic Reinforcement Learning

arXiv:2609.35082v1 Announce Type: new Abstract: Group-based reinforcement learning such as GRPO trains LLM agents by comparing rollouts sampled for each task, without a learned critic. In long-horizon

local-aiarxiv-cs-lg

CT-OPD: Counterfactual Trace On-Policy Distillation for Diffusion Vision-Language Models

arXiv:2609.32781v1 Announce Type: new Abstract: Diffusion vision-language models generate answers by gradually resolving masked tokens, making accurate conditional prediction in partially resolved sta

Agents

CueKFS: Agentic Cue-Driven Keyframe Selection for Long Video Understanding

arXiv:2609.31873v1 Announce Type: new Abstract: Keyframe selection (KFS) has long produced compact video summaries for browsing and retrieval, and representative frames for thumbnails. More recently,

agentsarxiv-cs-cv

D-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Frequency and Pixel Spaces

arXiv:2511.11286v3 Announce Type: replace Abstract: Out-of-domain (OOD) robustness is challenging to achieve in real-world computer vision, especially in unsupervised domain adaptation scenarios, wher

D-JEPA: Design-Recoverable JEPA Representation with Swappable Physics Decoders

arXiv:2609.33110v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) provide a framework for learning compact representations without directly reconstructing high-dimension

d-OPD: Future-Aware On-Policy Distillation for Block Diffusion Language Models

arXiv:2609.35362v1 Announce Type: new Abstract: Large language models (LLMs) typically generate text autoregressively (AR), predicting one token at a time. Block diffusion language models (dLLMs) inst

D^2-VLA: Dual-Memory Dual-Frequency Vision-Language-Action Model For Long Dynamic Manipulation

arXiv:2609.34792v1 Announce Type: new Abstract: Long-horizon manipulation requires robots to remember cues that are no longer in view while responding to moving objects. Yet vision-language-action (VL

DBCF: Dual-Branch Complementary Fusion of Foundation Models for Generalized Deepfake Detection

arXiv:2609.34720v1 Announce Type: new Abstract: As image generation and editing technologies have progressed substantially, facial forgeries pose significant challenges to privacy and public safety. D

DeltaSeek: Toward Active Perception in Evolving Construction Environments

arXiv:2609.33836v1 Announce Type: cross Abstract: Construction environments evolve continuously, causing large geometric changes that degrade static mapping and registration performance. This necessit

Depth Any Seen: Which Surfaces and How Far?

arXiv:2609.32027v1 Announce Type: new Abstract: When several surfaces are visible along a ray, recovering visible 3D structure from one image requires jointly estimating their presence and metric dept

DepthBench: Measuring How Residual Connections Enable More Computational Depth

arXiv:2609.32534v1 Announce Type: new Abstract: Depth is a natural way to increase the computational capacity in Transformers, yet the contribution of deeper layers can diminish as depth grows larger.

Detection of Adversarial Attacks on Super-Resolvers Using Spectral Features

arXiv:2609.35022v1 Announce Type: new Abstract: The integration of deep learning models into image preprocessing pipelines such as super-resolution introduces a largely unexplored attack vector for ad

DiffPTS: Rethinking Diffusion ELBO for Probabilistic Time Series Forecasting

arXiv:2609.32363v1 Announce Type: new Abstract: Probabilistic time series forecasting requires modeling and predicting complex and time-varying distributions. Recently, Denoising Diffusion Probabilist

Diffusion Reward Models

arXiv:2609.33803v1 Announce Type: new Abstract: Reward models underpin the alignment of large language models, yet the dominant designs reduce each prompt--response pair to a point estimate or to a di

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Applications

Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection

arXiv:2510.17611v3 Announce Type: replace Abstract: Unsupervised anomaly detection (UAD) has evolved from specialized single-class models to unified multi-class models, yet existing multi-class models

applicationsarxiv-cs-cv

Agents

Discovering What You Can Control: Interventional Boundary Discovery for Reinforcement Learning

arXiv:2603.18257v3 Announce Type: replace Abstract: When an RL agent's observations contain distractors driven by the same confounders as its true state, observational data alone cannot identify which

agentsarxiv-cs-lg

Tutorials

DisKO: Deep Koopman Learning in Distribution Space from Unpaired Snapshots

arXiv:2609.34629v1 Announce Type: new Abstract: Many complex systems are observed only through temporally unpaired distribution snapshots, making trajectory-based dynamical learning difficult without

tutorialsarxiv-cs-lg

Distance-KV: Exploiting Relative Distance for Efficient Long-Context Inference

arXiv:2609.32663v1 Announce Type: new Abstract: The memory usage and decoding latency of LLM inference grow rapidly with context length. To reduce these costs, key-value (KV) cache compression methods

Tutorials

Distilling Visual Reasoning into Text Space

arXiv:2609.34408v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have shown strong promise for multimodal reasoning, yet often struggle with tasks requiring concepts beyond what is

tutorialsarxiv-cs-cv