Search: “benchmark”

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

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Hamiltonian JEPA: Action-Conditioned World Models with an Inherited Control State

DGX agent

arXiv:2609.33497v1 Announce Type: new Abstract: Planning from pixels needs more than a latent space that is stable and predictable. The state the planner scores must also be organized by how actions m

model-releasesarxiv-cs-lg

29 Sept 2026

Hard Vision, Easy Vision: What GPT-6 Astra Reveals Across Computer Vision

arXiv:2609.35718v1 Announce Type: new Abstract: Frontier general-purpose systems are rapidly expanding beyond visual understanding into capabilities traditionally handled by dedicated computer-vision

HeroFrame-Bench: Reference-Anchored Evaluation via Rubric--Ranking Co-Evolution for Movie Hero Frame Selection

arXiv:2609.32280v1 Announce Type: new Abstract: Hero frames are in-film stills used as source imagery for theatrical posters, streaming cover art, film database listings, and other promotional placeme

safetyarxiv-cs-cv

Local Ai

HoTS: Homophily-Aware Temperature Scaling for Graph Neural Network Calibration

arXiv:2609.32426v1 Announce Type: new Abstract: For graph node classification, calibrated class probabilities are needed when confidence scores, usually the maximum predicted class probability, are us

local-aiarxiv-cs-lg

How Much Imprecision is Enough Imprecision in my Classifier? A Practical Elicitation Procedure

arXiv:2609.33352v1 Announce Type: new Abstract: Set-valued classifiers, whether derived from precise probabilities and an adapted cost function, from convex sets with a robust inference mechanism, or

How Synthetic Labels Improve Conformal Prediction: A Perspective on Conditional Coverage

arXiv:2609.33482v1 Announce Type: new Abstract: Conformal prediction provides distribution-free finite-sample marginal coverage, but post-hoc calibration data may be too scarce to learn how uncertaint

Humanoid Loco-Manipulation With Discrete VLA Model

model-releasesarxiv-cs-ro

arXiv:2609.35709v1 Announce Type: new Abstract: Vision-language-action (VLA) models using discrete action tokens have proven effective for controling robotic arms on manipulation tasks. For a humanoid

Hyperbolic Manifold Constrained Tabular Neural Network

arXiv:2607.09710v2 Announce Type: replace Abstract: Tabular prediction is central to a wide range of real-world applications. Tabular data typically contain heterogeneous features as well as rich and

HyperLabel: Multi-Label Classification via Hypergraph-Based Label Correlation Modeling

arXiv:2609.32276v1 Announce Type: new Abstract: Multi-label classification (MLC) requires predicting multiple relevant labels for each instance, where a central challenge is modeling complex label dep

I created a personality test for models, need more TESTS!!

model-releasesr-localllama

First 3 disposition results. Hi there! My name is Jerry and I recently built Enclosure, e deterministic environment for testing LLMs. It's a simulated office enfironment https://github.com/openconstru

Agents

ICMAPE: In-Context Multiagent Pure Exploration

arXiv:2609.33986v1 Announce Type: new Abstract: In some multi-agent systems, the quantity to be optimized is not an externally specified reward but the information acquired about unknown properties of

agentsarxiv-cs-lg

Identifiable Convex-Concave Regression via Sub-gradient Regularised Least Squares

arXiv:2506.18078v2 Announce Type: replace-cross Abstract: We propose a novel nonparametric regression method that models complex input-output relationships as the sum of convex and concave components.

ILP-BO: Integer Linear Programming-Based Black-Box Optimization

arXiv:2609.33131v1 Announce Type: new Abstract: Black-box Optimization (BO) is a powerful framework for optimizing expensive objective functions or unknown functions with a limited number of evaluatio

Improving Test-Time Scaling with Adaptive Looped Transformers

arXiv:2609.35748v1 Announce Type: cross Abstract: Looped transformers have demonstrated promising parameter efficiency by reusing layers for latent computation. Prior studies compare looped and non-lo

Inductive Feedback for Mixed-Policy Distillation

arXiv:2609.35390v1 Announce Type: new Abstract: Verbal feedback can identify errors and prescribe corrections, providing rich supervision for language-model post-training even when reliable programmat

Agents

Inertia-Corrected Newton Method For Generalized Nash Equilibria in Dynamic Games with Optimality Verification

arXiv:2609.31929v1 Announce Type: new Abstract: Newton methods efficiently find Generalized Nash Equilibria (GNE) in dynamic games by solving for the KKT necessary conditions. These methods are fast a

agentsarxiv-cs-ro

Inference Engines will become a series of one-offs

model-releasesr-localllama

ninfer, dwarfstar, Splash, llamAmpere, gufo, etc. We've all seen them popping up, great tok/s, people loving them. Forks of llama.cpp or another engine, or made from scratch. For better or worse, the

InfiMed2: A Generalist Medical Multimodal Foundation Model from Contextual Evidence and Stability-Aware Supervision

arXiv:2609.34798v1 Announce Type: cross Abstract: Recent medical multimodal models have benefited from larger corpora, broader modality coverage, and stronger reasoning-oriented training, yet effectiv

InfiniHand: Streaming World-Space Hand Motion Estimation from Egocentric Video

arXiv:2609.35743v1 Announce Type: new Abstract: World-space hand motion estimation from egocentric video requires recovering 3D articulated hand geometry while tracking camera egomotion. Existing appr

InterTab: Interleaved Visual-Structure Alignment for Multi-Modal Table Reasoning

model-releasesarxiv-cs-cv

arXiv:2609.32660v1 Announce Type: new Abstract: Table images preserve structural information that are often lost in text serialization, and reasoning over them requires locating relevant rows, columns

Introducing dots, powered by GPT-6 Astra. Remarkably capable, always-on agents built to handle everything.

OpenAI announced the launch of **dots**, an always‑on agent system powered by the GPT‑6 Astra model. According to OpenAI, dots are “remarkably capable” agents designed to handle a wide range of tasks

model-releasesopenai--x

JIVE: Jacobian-Informed Volume Expansion for Diverse Generative Sampling

arXiv:2609.33906v1 Announce Type: cross Abstract: Generative models often suffer from mode collapse and limited sample diversity. While prior works attempt to mitigate this by jointly generating a bat

Just-In-Time Agent Memory with Runtime Agentic Research

arXiv:2609.34385v1 Announce Type: cross Abstract: Memory is critical for AI agents. Many existing agent-memory systems follow an Ahead-of-Time (AOT) design, constructing memory before a specific reque

Just MLPs: Efficient Visual State Reconstruction for Multimodal Language Models

arXiv:2609.34972v1 Announce Type: new Abstract: Long visual token sequences often account for a substantial fraction of the computational overhead in multimodal large language models~(MLLMs). Existing

Tutorials

Kernel-Based Steering of CLIP with Vision-Language Model Preferences

arXiv:2609.32203v1 Announce Type: new Abstract: Large vision-language models (VLMs) can judge visual similarity, but their judgments are not directly available as compact image embeddings for efficien

tutorialsarxiv-cs-cv

KeyRec: Bounded Visual Memory for Streaming and Long-Video Understanding

arXiv:2609.32182v1 Announce Type: new Abstract: Vision-language models are increasingly used to understand long videos and continuous streams. However, dense visual tokens accumulate with video durati

lambda-JEPA Spectral Anti-Collapse Regularization for Self-Supervised Learning

arXiv:2609.35288v1 Announce Type: cross Abstract: Joint-embedding self-supervised learning typically combines an invariance objective across augmented views with additional mechanisms to prevent repre

lapanda: A Matrix-Free Differentiable Solver for Nonconvex Constrained Optimization Layers

arXiv:2609.33630v1 Announce Type: cross Abstract: Differentiable optimization brings the structural guarantees of mathematical optimization to network pipelines, allowing them to be trained end-to-end

Applications

Learning New Tasks via Reusable Skills: Skill-Compositional Experts for Embodied Continual Learning

arXiv:2606.15685v2 Announce Type: replace-cross Abstract: Embodied Continual Learning (ECL) aims to enable robots to continually acquire new manipulation tasks while retaining previously learned behav

applicationsarxiv-cs-cv

Learning Propagation Geometry from Message-Passing Feedback

arXiv:2609.34711v1 Announce Type: new Abstract: Learning local geometry enables graph neural networks (GNNs) to adapt how they compare and integrate neighborhood information. However, estimating geome

Learning the Graph and the Embedding Together: Classifier-Independent Rewiring for Heterophilic Node Classification

arXiv:2609.32613v1 Announce Type: new Abstract: Graph neural networks lose much of their advantage on heterophilic graphs, where connected nodes often carry different labels. Graph rewiring is a popul

Learning to Reason with Persistent Object States for Video Instance Segmentation

arXiv:2609.35539v1 Announce Type: new Abstract: Video segmentation models maintain object identities by carrying instance information across frames. Under prolonged occlusion, reappearance, or interac

Learning When to Recur: Token-Adaptive Recursion for Imbalanced Ophthalmic Domain Incremental Learning

model-releasesarxiv-cs-cv

arXiv:2609.32785v1 Announce Type: cross Abstract: Domain incremental learning is essential for adapting ophthalmic deep learning models to sequential clinical domains while preserving diagnostic exper

Tutorials

Learning Where and What to Restore for Composite Image Restoration

arXiv:2503.17915v2 Announce Type: replace-cross Abstract: Real-world degraded images often contain multiple co-occurring degradation types, making composite image restoration fundamentally different f

tutorialsarxiv-cs-cv

Local Ai

LEGAU: Learning Semantic Gaussian Priors for Scalable Category-level Pose Estimation

arXiv:2609.35046v1 Announce Type: new Abstract: Category-level 6D pose estimation from a single RGB-D observation is inherently under-constrained, since partial visible geometry must be interpreted to

local-aiarxiv-cs-cv

Length-Independent State Tracking Under a Parallel Scan

arXiv:2609.32447v1 Announce Type: new Abstract: Learning robust and scalable finite-state tracking is fundamental to sequence processing. While linear recurrent neural networks (RNNs), linear attentio

Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning

arXiv:2601.06641v3 Announce Type: replace Abstract: Membership inference attacks (MIAs) pose a serious privacy threat in federated learning (FL). While MIAs have been extensively studied in standard F

LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models

arXiv:2603.01563v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has achieved remarkable success in improving autoregressive models, especially in domains requ

LINC: Decoupling Local Consequence Scoring from Hidden Matching in Constructive Neural Routing

arXiv:2605.06332v2 Announce Type: replace Abstract: Constructive neural routing solvers usually score the next action by matching a decoder context to candidate embeddings, leaving deterministic one-s

Livin' on a Prior: Likelihood Score Approximation for Inverse Problems

arXiv:2609.34446v1 Announce Type: new Abstract: Generative models have found great success as data-driven methods of solving inverse problems. Two popular approaches work either by combining a pretrai

LLM4Trust: Exploring the Capabilities of Large Language Models for Trust Evaluation

arXiv:2609.33521v1 Announce Type: new Abstract: Trust evaluation plays a critical role in cybersecurity by supporting risk mitigation and decision-making. A variety of trust evaluation methods have be

LLMs as Adaptive Meta-Solvers: Strategy-Diverse RL for Industrial-Scale Optimization

arXiv:2609.34427v1 Announce Type: new Abstract: Scaling LLM-based optimization from textbook-scale instances to real-world, industrial tasks remains a critical open challenge. Existing approaches are

Local Ai

LocalProp: Neuro-Localized Memory-Efficient Backpropagation

arXiv:2609.32517v1 Announce Type: cross Abstract: The current deep learning training paradigm employs end-to-end backpropagation, regardless of the training stage, i.e. pre-training or fine-tuning. Ho

local-aiarxiv-cs-cv

LogicEnvGen: Task-Logic Driven Generation of Diverse Simulated Environments for Embodied AI

model-releasesarxiv-cs-ro

arXiv:2601.13556v2 Announce Type: replace Abstract: Simulated environments play an essential role in embodied AI, functionally analogous to test cases in software engineering. However, existing enviro

Long-Horizon Scaling: How Model Capabilities Shape the Returns to Computation

arXiv:2609.35236v1 Announce Type: new Abstract: Long-horizon agents improve solutions through sustained interaction, execution, and task feedback. Scaling studies relate performance to resources and c

LoopLUT: 3D Lookup Tables with Progressive Region Refinement for Real-Time 4K Image Enhancement

arXiv:2609.33593v1 Announce Type: new Abstract: Color enhancement of 4K images must meet a quality target under a tight compute budget. Three-dimensional lookup tables (3D LUTs) dominate real-time enh

Tutorials

LVMT: Video Mask Transformer for Long-term Video Segmentation

arXiv:2609.34895v1 Announce Type: new Abstract: Existing online video segmentation methods struggle to track objects in long, complex videos with long-term occlusions. We hypothesize that this limitat

tutorialsarxiv-cs-cv