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BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting

arXiv:2609.33303v1 Announce Type: new Abstract: Despite recent progress in irregular time series forecasting, the field still lacks a unified benchmark for fair and comprehensive evaluation. Existing

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Safety

Black-Box Auditing of Epistemic Reliability in Multi-Agent Debate Distillation

DGX agent

arXiv:2609.32361v1 Announce Type: new Abstract: Debate distillation adapts weaker verifiers using multi-agent debate transcripts to improve their judgement in subsequent debates, but gains on monitore

safetyarxiv-cs-lg

29 Sept 2026

BMND: Direct Poisson Denoising by N-Dimensional Block Matching and Collaborative Filtering

arXiv:2609.34622v1 Announce Type: new Abstract: Poisson denoising of scientific data requires methods that account for signal-dependent noise while accommodating different data dimensionalities and pr

BOCCHI: A More Realistic and Challenging Benchmark for Local Motion Blur Detection with MSDCT-UNet

arXiv:2607.10427v2 Announce Type: replace Abstract: Local motion blur detection requires pixel-level localization of blurred regions. Existing benchmarks let models rely on gradient shortcuts that fai

BOReFT: Manifold Steering of Language Models for Black-box Optimization

arXiv:2609.33722v1 Announce Type: new Abstract: Language models are increasingly used as proposal models for black-box search, from program optimization to molecular design. Existing approaches typica

Local Ai

Bounding Retraining Equivalence and the Deletion Floor in Materials Machine Unlearning

arXiv:2609.35635v1 Announce Type: new Abstract: In materials machine learning, closely related retained structures can sustain accurate property predictions even after removing a specific record, rend

local-aiarxiv-cs-lg

BR-MPPI: Barrier-Rate Guided MPPI for Enforcing Multiple Inequality Constraints with Learned Signed Distance Fields

model-releasesarxiv-cs-ro

arXiv:2506.07325v2 Announce Type: replace Abstract: Model Predictive Path Integral (MPPI) control provides a sampling-based framework for optimal control, while Control Barrier Functions (CBFs) provid

Brain-Conditioned Action Policies for Neural Motor Decoding

arXiv:2609.34561v1 Announce Type: new Abstract: Motor brain-computer interfaces (BCIs) aim to decode motor intention, enabling people with paralysis to control external devices. Neural motor decoding

Applications

BREAKING: Elon Musk was sitting next to President Trump at the White House Super Intelligence luncheon. Here is President Trump’s full addre…

BREAKING: Elon Musk was sitting next to President Trump at the White House Super Intelligence luncheon. Here is President Trump’s full address: • Praised the technology leaders as some of the world’s

applicationselon-musk--x

BREAKING: Grok is helping power America .gov, the U.S. government’s new AI website. 🇺🇸 U.S. Chief Design Officer Joe Gebbia says Grok & Ge…

model-releaseselon-musk--x

BREAKING: Grok is helping power America .gov, the U.S. government’s new AI website. 🇺🇸 U.S. Chief Design Officer Joe Gebbia says Grok & Gemini power the site. Americans can ask questions and get answe

🚨BREAKING: “Months before OpenAI's artificial intelligence went rogue, two employees raised an alarm with top executives. They were ignored…

🚨BREAKING: “Months before OpenAI's artificial intelligence went rogue, two employees raised an alarm with top executives. They were ignored….OpenAI executives told the employees that the tests needed

safetygary-marcus--x

Agents

BREAKING: OpenAI just APOLOGIZED and ADMITTED their AI agent breached not one, but FOUR Australian government departments 'We are sorry and …

BREAKING: OpenAI just APOLOGIZED and ADMITTED their AI agent breached not one, but FOUR Australian government departments 'We are sorry and working to do better in the future.' The breached agencies:

agentsgary-marcus--x

BREAKING: OpenAI postpones 6.1 for safety reasons. If we can take OpenAI at face value, this is quite a decision—and also a sign that no mag…

BREAKING: OpenAI postpones 6.1 for safety reasons. If we can take OpenAI at face value, this is quite a decision—and also a sign that no magical solution is in immediate view. Per @sheeraf at NYT “GPT

safetygary-marcus--x

Bregman geometry-aware split Gibbs sampling for Bayesian Poisson inverse problems

arXiv:2511.12257v2 Announce Type: replace-cross Abstract: This paper proposes a novel Bayesian framework for solving Poisson inverse problems by devising a Monte Carlo sampling algorithm which account

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

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

Bridging Stochastic Flow Maps and Boltzmann Generators with Normalizing Flows

arXiv:2609.31978v1 Announce Type: cross Abstract: Generating independent, equilibrium samples of molecular systems at scale remains a central obstacle in computational statistical mechanics. Boltzmann

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

Broken Symmetry in BF16 Attention: Why FlashAttention Gradients Blow Up Late in Training

arXiv:2609.34272v1 Announce Type: new Abstract: BF16 is now standard in large-scale pretraining, including in fused attention kernels such as FlashAttention, and these kernels are widely trusted. When

BudgetVerify: Budget-Tiered Verification for Financial QA

arXiv:2609.33052v1 Announce Type: cross Abstract: Financial question answering often requires precise numerical extraction, unit handling, and arithmetic over tables and text, but applying expensive v

Industry

Building an AI-powered contract intelligence platform with Amazon Quick and Amazon Bedrock AgentCore

Manually extracting data from hundreds of vendor contracts doesn't scale, and RAG chat tools fall short on portfolio-wide questions. This post shares a contract intelligence platform on AWS that uses

industryaws-ml-blog

Agents

Building Intelligent Agents with Neuro-Symbolic Concepts

arXiv:2505.06191v2 Announce Type: replace-cross Abstract: This article presents a concept-centric paradigm for building agents that can learn continually and reason flexibly. The concept-centric agent

agentsarxiv-cs-cv

Building Transformation Layers for Riemannian Neural Networks

arXiv:2609.35436v1 Announce Type: cross Abstract: Recently, deep neural networks on manifold-valued representations have garnered significant attention across various machine learning applications. On

Byzantine-Robust Federated RAG via Aligned Calibration and Fixed-Membership Conformal Prediction

arXiv:2609.33037v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) lets language models answer questions more accurately by consulting relevant documents. Many valuable collections

Cache-Aware Conv3D Lowering Across Embedded World-Model Decoders

arXiv:2609.31938v1 Announce Type: new Abstract: Generative world models can provide visual rollouts for embodied planning, yet their feasibility on edge devices depends not only on the learned model b

CacheRepair: Learning to Repair Cross-Chunk Context in RAG for KV Cache Fusion

arXiv:2609.35139v1 Announce Type: new Abstract: Multi-document retrieval-augmented generation (RAG) requires a language model to process multiple retrieved text chunks before answering a question. Pre

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

Applications

CAFE: Counterfactual Prediction via Fast Posterior Estimation

arXiv:2609.32167v1 Announce Type: new Abstract: Counterfactual prediction estimates an individual's outcome under an alternative intervention given their factual observations. Such outcomes are genera

applicationsarxiv-cs-lg

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

Calibrated Derivative-Process Sensitivity for Gaussian-Process Variable Selection

arXiv:2609.33549v1 Announce Type: cross Abstract: Automatic relevance determination (ARD), the default tool for variable selection in Gaussian-process (GP) regression, ranks inputs by inverse lengthsc

Applications

Calibration-Free Surface Normals Estimation in Vision-Based Tactile Sensing using Universal Photometric Stereo

arXiv:2609.31754v1 Announce Type: new Abstract: Vision-based tactile sensors are a popular solution for capturing rich contact surface geometry. However, to obtain high-detail contact surface normals

applicationsarxiv-cs-cv

CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecasting

arXiv:2609.33143v1 Announce Type: cross Abstract: Quarter-ahead revenue forecasting requires company-scale numerical accuracy, strict temporal validity, and company-specific interpretation of narrativ

Can Attack Difficulty Be Characterized Before Optimization? A Study of Pre-optimization Difficulty in Person-Vanishing Attacks

arXiv:2609.34501v1 Announce Type: new Abstract: Adversarial attacks against object detectors are traditionally studied from an optimization perspective, where attack difficulty is regarded as an outco

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

Can Motion-Language Models Ground Structure? STRIDE for Evaluating the Evaluators

arXiv:2609.32462v1 Announce Type: new Abstract: Motion-language models are typically scored by motion-language evaluators, but how well these evaluators ground language structure remains unclear. Here

Can Protein-Derived Knowledge Improve Pathology Foundation Models?

arXiv:2609.33178v1 Announce Type: new Abstract: Molecularly guided pathology foundation models (PFMs) exploit transcriptomic or proteomic information to enrich whole-slide image (WSI) representations,

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

CAPEX: Efficiently Distilling Foundation Model Behavior into Deployable Robot Policies through Experience-Adaptive Reasoning

arXiv:2609.33007v1 Announce Type: cross Abstract: Robot learning has largely relied on human-teleoperated demonstrations to acquire effective learnable behaviors. However, human-operated data collecti

CapField-OPD: Learning Continuous Capability Fields via Joint-Anchored Multi-Teacher On-Policy Distillation for Flow Models

arXiv:2609.34658v1 Announce Type: new Abstract: Reward-specialized post-training produces strong experts for flow-based generative models, while multi-teacher on-policy distillation (OPD) consolidates

safetyarxiv-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

CAR-VLA: Complexity-Aware and Risk-Adaptive Reasoning for Autonomous Driving

arXiv:2609.34387v1 Announce Type: new Abstract: Existing adaptive reasoning methods for driving Vision-Language-Action (VLA) models primarily focus on whether to reason, overlooking how reasoning shou

Cardinality-Stratified Interaction Decomposition for Interpretable Pairwise and Higher-Order Structure in Transactional Basket Data

arXiv:2609.34191v1 Announce Type: new Abstract: Transactional basket data can reveal associations among items, but observed co-occurrence conflates item-specific relations with basket-size structure a

Cartridges++: KV Cache Compression without Off-Context Derailment

arXiv:2609.35621v1 Announce Type: new Abstract: Serving long documents to a Large Language Model (LLM) repeatedly is expensive: computations grow with context length, and the memory footprint of the k

CARVE: Breaking Data Barriers in Chip Placement by Harnessing Reusable Expertise

arXiv:2609.33106v1 Announce Type: new Abstract: Pretrained macro-placement policies can reduce repeated optimization across circuits, but deployment often exposes them to unfamiliar designs when the o

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

CAST: Reconstruction-Coupled Acceleration of Interactive World Models

arXiv:2609.34144v1 Announce Type: new Abstract: Interactive world models must respond quickly to controls while preserving scene consistency. Existing acceleration methods can miss heterogeneous contr

Tutorials

CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?

arXiv:2512.11352v2 Announce Type: replace Abstract: Trust prediction provides valuable support for decision-making, risk mitigation, and system security enhancement. Recently, Graph Neural Networks (G

tutorialsarxiv-cs-lg