Search: “benchmark”

Distribution-Conditioned Task Routing for Class-Incremental Learning

arXiv:2609.34503v1 Announce Type: new Abstract: Parameter-efficient adaptation enables continual learners to acquire task-specific knowledge through compact model updates while maintaining strong with

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Model Releases

DivOPD: Spread Wide, Look Close for Asynchronous On-Policy Distillation of Multi-turn Agents

DGX agent

arXiv:2609.34838v1 Announce Type: new Abstract: On-policy distillation (OPD) trains student agents through teacher supervision on their own interactions with an environment. However, in asynchronous m

model-releasesarxiv-cs-lg

29 Sept 2026

Do We Really Need KL Divergence for On-Policy Distillation of Large Language Models?

arXiv:2609.33791v1 Announce Type: new Abstract: Since the advent of knowledge distillation, KL divergence has been the standard loss in distillation. Recently, on-policy distillation (OPD) has emerged

Does Joint-Embedding Predictive Architecture Pretraining Help Time Series Forecasting?

arXiv:2609.31680v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPA) have emerged as a promising self-supervised pretraining paradigm for time series, learning representatio

Does Transolver really need a Transformer?

arXiv:2609.32525v1 Announce Type: new Abstract: The widely used Transolver family of neural operators is based on physics-attention, which softly assigns the points of an unstructured mesh to a small

Agents

Dr. MAS: Stable Reinforcement Learning for Multi-Agent LLM Systems

arXiv:2602.08847v2 Announce Type: replace Abstract: Multi-agent LLM systems enable advanced reasoning and tool use via role specialization, yet reliable reinforcement learning (RL) post-training for s

agentsarxiv-cs-lg

DRIFT: Disentangled Responsive-Invariant Flow Transport for Single-Cell Perturbation Prediction

arXiv:2609.35106v1 Announce Type: new Abstract: Predicting cellular responses to perturbations is a central problem in cellular biology, with broad applications in systems biology and drug discovery.

Applications

E-CONAN (Entailment, CONtradition And Neutral) Diagnostics Dataset Investigating Linguistic Phenomena in Arabic Natural Language Understanding

arXiv:2609.33530v1 Announce Type: cross Abstract: Natural Language Understanding (NLU) plays a crucial role in various applications, yet its performance suffers from weaknesses in handling the complex

applicationsarxiv-cs-lg

E3J: An Efficient and Open-Source Backend for Euclidean Equivariant Operations on GPU and TPU

arXiv:2609.35099v1 Announce Type: new Abstract: We present e3j, a fast Euclid-equivariance backend for geometric deep learning applications with JAX bindings for GPU and TPU. Leveraging both optimized

Efficient Dynamic Algorithms for Graph Neural Networks with Non-Linear Propagation

arXiv:2609.32929v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are widely used for representation learning on graphs, but most methods assume static topologies, making them inefficient o

Elicitation and Decision Geometry in Single-Index Bandits

arXiv:2609.35622v1 Announce Type: cross Abstract: We study two-arm contextual bandits with arm-specific single indices and a shared unknown monotone link. Monotonicity makes the optimal action depend

Enhanced Video Text Editing with Trajectory-Aligned Glyph Rendering

model-releasesarxiv-cs-cv

arXiv:2609.34178v1 Announce Type: new Abstract: Video text editing aims to replace or add text in a video while keeping the rest of the video unchanged, which requires the edited text to be correct in

Enhancing Foundation Models for Imbalanced SAR Ship Classification via Targeted Oversampling

model-releasesarxiv-cs-cv

arXiv:2609.31657v1 Announce Type: new Abstract: Remote-sensing foundation models offer strong representations for SAR imagery, but their behavior under severe long-tail class imbalance is still not we

Applications

Environment-Conditioned Tail Reweighting for Invariant Learning under Mixed Shifts

arXiv:2601.22944v3 Announce Type: replace Abstract: Out-of-distribution generalization becomes challenging when spurious correlations vary across environments while difficult or underrepresented sampl

applicationsarxiv-cs-lg

Equivariant Neural Primal-Dual Assignment for Maximum Common Edge Subgraphs

arXiv:2609.32661v1 Announce Type: new Abstract: Maximum common edge subgraph (MCES) matching finds a partial vertex correspondence between two labeled graphs that preserves as many labeled edges as po

EvE: An Alternate Optimizer to Adam

arXiv:2609.35614v1 Announce Type: new Abstract: Adam and its variants dominate neural network training, but a single run only reveals whether a configuration works well after most of its budget is spe

EvolvingAvatar: Interactive 3D Head Generation That Adapts as Conversations Unfold

model-releasesarxiv-cs-cv

arXiv:2609.35616v1 Announce Type: new Abstract: Interactive 3D head generation requires coordinated speaking and listening motion that responds to an evolving conversation. Existing generators use inc

Fast-Slow Thinking RM: Efficient Integration of Scalar and Generative Reward Models

arXiv:2603.20212v2 Announce Type: replace-cross Abstract: Reward models (RMs) are critical for aligning Large Language Models via Reinforcement Learning from Human Feedback (RLHF). While Generative Re

Faster Block-Diffusion Serving with Distribution-Free Risk Guarantees

arXiv:2609.33887v1 Announce Type: cross Abstract: Block-diffusion language models are served at hand-picked operating points, such as acceptance thresholds, buffer depth, schedule, checkpoint and prec

Tutorials

Feasible Flow Matching for Graph Reconstruction via Within-Sampling Primal-Dual Guidance

arXiv:2609.32980v1 Announce Type: new Abstract: Graph reconstruction from partial observations often comes with structural side information, such as degree bounds, triangle counts, or an edge-density

tutorialsarxiv-cs-lg

FedHV: Low-Overhead Hypervolume Weighting for Federated Multi-Objective Optimization

arXiv:2609.32790v1 Announce Type: new Abstract: Task-wise federated multi-objective optimization (FedMOO) trains a shared model for competing prediction objectives under heterogeneous data, partial pa

Fewer Tokens, More Self-Teaching: On-Policy Self-Distillation for Extreme Visual Token Reduction

arXiv:2609.32353v1 Announce Type: new Abstract: Visual token reduction is an effective way to accelerate multimodal large language models (MLLMs), but performance deteriorates rapidly under extremely

safetyarxiv-cs-cv

Hardware

Fill2SR: Repurposing Inpainting Diffusion Transformers for Real-World Super-Resolution

arXiv:2609.33582v1 Announce Type: new Abstract: Recent real-world image super-resolution (SR) methods often adapt text-to-image (T2I) backbones with ControlNet-style branches or spatial conditioning t

hardwarearxiv-cs-cv

Filtered Conformal Ellipsoids for Graph-Native Time Series

arXiv:2606.17014v2 Announce Type: replace Abstract: Joint prediction sets for multivariate time series should control a single event while adapting to cross-coordinate dependence. We study filtered co

Finite Probes Suffice: Identifiability and Universality for Weight-Space Learning

arXiv:2609.33901v1 Announce Type: new Abstract: Learning properties of neural networks has recently attracted growing interest, with existing approaches operating either directly on network parameters

FlexiWorld: Learning and Planning via Flexible Action Chunks Across Multiple Time Scales

arXiv:2609.35138v1 Announce Type: new Abstract: Latent world models predict future states for goal-directed planning using action chunks spanning multiple primitive steps. Existing methods typically u

FoLD: Force-Informed Learning for Dexterous Articulated Object Manipulation

model-releasesarxiv-cs-ro

arXiv:2609.33551v1 Announce Type: new Abstract: Transferring human demonstrations to dexterous robots remains challenging because differences in hand morphology and contact dynamics often cause retarg

Forecast-Necessary Causal Discovery for Nonlinear Political Panel Data: Feedback, Functional Form, and the Dynamics of Democratization

arXiv:2609.34115v1 Announce Type: cross Abstract: A non-significant coefficient in a dynamic panel model need not imply the absence of a relationship. It may instead reflect heterogeneous effects aver

Forecasting Bacterial Antimicrobial Resistance Trends Using Machine Learning on WHO GLASS Surveillance Data: A Retrieval-Augmented Generation Approach for Policy Decision Support

arXiv:2602.22673v3 Announce Type: replace Abstract: Background: Antimicrobial resistance (AMR) is a global health threat. While the WHO Global Antimicrobial Resistance and Use Surveillance System (GLA

ForeFly: A Dual-Horizon World Action Model for Aerial Vision-Language Navigation

arXiv:2609.33581v1 Announce Type: new Abstract: Aerial Vision-Language Navigation (AVLN) requires UAVs to maintain reliable instruction following over long trajectories in complex 3D environments. How

researcharxiv-cs-cv

Forest-Guided Semantic Transport for Label-Supervised Manifold Alignment

arXiv:2602.00974v2 Announce Type: replace Abstract: Label-supervised manifold alignment bridges the gap between unsupervised and correspondence-based paradigms by leveraging shared label information t

FORGE: Form-Optimal Routing of Grounded Evidence for Frozen LLM Agents

arXiv:2609.34358v1 Announce Type: new Abstract: In agentic AI systems, frozen foundation models are increasingly deployed as closed-weight API endpoints, making downstream adaptation possible only thr

From HL to H+L-1 Parameters: A Hankel-Toeplitz Forecaster for Long-Term Time Series Forecasting

arXiv:2609.33984v1 Announce Type: new Abstract: Linear forecasters have shown competitive accuracy against Transformer-based models in long-term time series forecasting. We study how classical station

from our blog post on how we made Claude faster: https://claude.dev/blog/how-we-made-claude-ai-faster/

Claude.ai achieved roughly a three‑fold speed increase in just two weeks by implementing targeted engineering optimisations. The blog post on claude.dev explains the key changes—such as streamlined co

model-releasesthariq--x

From Phase Transition to Systemic Failure: A Decoupled Analytics Framework for GNN Robustness

arXiv:2609.31656v1 Announce Type: new Abstract: Data quality is a major bottleneck for the reliable deployment of graph neural networks (GNNs) in real-world graph mining tasks. Among various sources o

Functional Autoencoders for Amplitude-Phase Representation Learning

arXiv:2609.34207v1 Announce Type: cross Abstract: Functional data are intrinsically infinite-dimensional, and often exhibit phase variation, where corresponding events occur at different times across

Applications

Functional Hand Type Prior for 3D Hand Pose Estimation and Action Recognition from Egocentric View Monocular Videos

arXiv:2609.34149v1 Announce Type: new Abstract: Current methods for egocentric view action recognition often face challenges in perceiving dynamic hand movements relying solely on geometrical or physi

applicationsarxiv-cs-cv

Tutorials

FUND: Density Flow for Sampling Unnormalised Distributions

arXiv:2609.32296v1 Announce Type: new Abstract: Efficient sampling from Boltzmann distributions is central to modelling complex physical systems. Markov Chain Monte Carlo (MCMC) methods suffer from cr

tutorialsarxiv-cs-lg

FuseAlign: Forced Alignment in the Wild

arXiv:2609.33650v1 Announce Type: new Abstract: Word-level forced alignment estimates when each transcript word occurs in an audio recording. It underpins text-based media editing, subtitling, speech-

FUSION: a skill-based research agent for publicly obtainable nuclear-physics codes

arXiv:2609.04742v1 Announce Type: cross Abstract: Running an unfamiliar nuclear-physics code is rarely difficult because of the physics alone. One must find and build the program, learn its input conv

Future Information-Directed Sampling for Bayesian Nonstationary Bandits

arXiv:2609.33981v1 Announce Type: new Abstract: Exploration--exploitation is a central trade-off in bandit learning. While classical algorithms such as upper confidence bound methods and Thompson Samp

Agents

Game-Theoretic Control with Constrained Potential Surgery

arXiv:2609.31901v1 Announce Type: new Abstract: Constrained general-sum dynamic games are a popular formulation for highly interactive multi-agent planning problems. In recent years, Generalized Nash

agentsarxiv-cs-ro

Generative Classifier for Domain Generalization

arXiv:2504.02272v2 Announce Type: replace Abstract: Domain generalization (DG) aims to improve the generalizability of computer vision models toward distribution shifts. The mainstream DG methods pred

researcharxiv-cs-cv

GeoCFM: Positive-Only Conditional Flow Matching for Mineral Occurrence Sampling

arXiv:2609.34398v1 Announce Type: new Abstract: Critical mineral discovery is a positive-only problem: deposits are observed as sparse locations, while unlabeled regions are not reliable negatives, an

Geometry-Preserving Blind Watermarking for Raw 3D Point Clouds

arXiv:2609.32222v1 Announce Type: new Abstract: Raw 3D point clouds are a core geometric representation. Establishing their ownership is challenging because point sets are irregular, unstructured, and

safetyarxiv-cs-cv

Gravity Falls: A Comparative Analysis of Domain-Generation Algorithm (DGA) Detection Methods for Mobile Device Spearphishing

arXiv:2603.03270v2 Announce Type: replace-cross Abstract: Mobile devices are frequent targets of eCrime threat actors through SMS spearphishing (smishing) links that leverage Domain Generation Algorit

Hardware

Greenpixie's AI Token Methodology: Assessing the Energy, Water and CO_2ext{-eq} Impact of AI Tokens for Open and Closed Weight Models

arXiv:2609.33965v1 Announce Type: cross Abstract: We describe a methodology for estimating the per-token energy cost of cloud-hosted large language model (LLM) inference, separating between input (pre

hardwarearxiv-cs-lg

Agents

GUIDE-FBO: Guidance via Uncertainty Intervention and Distributional Exchange for Federated Bayesian Optimization

arXiv:2609.35038v1 Announce Type: cross Abstract: Federated Bayesian Optimization (FBO) enables distributed agents to collaboratively optimize expensive black-box objectives without sharing raw local

agentsarxiv-cs-lg