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NKI-Agent: Domain-Specific Fine-Tuning and Agentic Tool Use for Neuron Kernel Generation

arXiv:2607.04395v1 Announce Type: new Abstract: Recent agentic approaches to LLM-based kernel generation have achieved impressive results on CUDA. For emerging AI accelerators such as AWS Trainium and

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Model Releases

No Reliable Evidence of Self-Reported Sentience in Small Large Language Models

DGX agent

arXiv:2601.15334v2 Announce Type: replace-cross Abstract: Whether language models possess sentience has no empirical answer. But whether they believe themselves to be sentient can, in principle, be te

model-releasesarxiv-cs-ai

7 Jul 2026

No Time Like the Present: Agentic Test-Time Training for LLM Agents

arXiv:2607.03441v1 Announce Type: cross Abstract: LLM agents often degrade over long episodes: as trajectories grow, they revisit explored states, repeat failed actions, and lose strategies that previ

Noisy-Channel Minimum Bayes Risk Decoding

arXiv:2607.05198v1 Announce Type: cross Abstract: Minimum Bayes Risk (MBR) decoding yields more robust and higher-quality text generation than maximum a posteriori (MAP) decoding by selecting hypothes

Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models

arXiv:2607.04775v1 Announce Type: cross Abstract: Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications. While the statistica

Non-Asymptotic Error Bounds for SMC with Biased Proposals: Application to Conditional Diffusion Sampling

arXiv:2607.04780v1 Announce Type: cross Abstract: Sequential Monte Carlo (SMC) methods are a natural tool for post-hoc conditioning of pretrained generative models, but in many applications the mutati

safetyarxiv-cs-lg

Non-Convex Sparse Reinforcement Learning via Non-Monotone Inclusions

arXiv:2607.04990v1 Announce Type: new Abstract: This work delivers two key contributions: one to efficient feature selection in reinforcement learning (RL), the other to the theory of non-monotone inc

Nonparametric Control Koopman Operators

arXiv:2405.07312v5 Announce Type: replace-cross Abstract: This paper presents a novel Koopman composition operator representation framework for control systems in reproducing kernel Hilbert spaces (RK

NormWorlds-CF: Solver-Verified Counterfactual Normative Reasoning with Metamorphic-Relation GRPO

arXiv:2607.03957v1 Announce Type: cross Abstract: Language models can reach the right normative verdict for the wrong reason. We introduce NormWorlds-CF, a solver-verified environment for counterfactu

Not All Refusals Are Equal: How Safety Alignment Fails Cybersecurity at Scale

arXiv:2607.02714v1 Announce Type: cross Abstract: There is no doubt that safety alignment is an essential step in LLM training. However, conceptually it does not distinguish between various domains an

Local Ai

Not Every Sync Is Safe: Calibrated DiLoCo Scheduling for Shared AI Infrastructure

arXiv:2607.02544v1 Announce Type: cross Abstract: DiLoCo-style training reduces communication by letting learner islands train locally before occasional outer synchronization, making it attractive for

local-aiarxiv-cs-ai

NouveauVoice: Generating Novel Pseudo Speakers for Voice Anonymization

arXiv:2607.03985v1 Announce Type: cross Abstract: Advanced neural technologies in speech synthesis and voice conversion (VC) have introduced severe risks to personal privacy, necessitating robust Spea

NRT-Bench: Benchmarking Multi-Turn Red-Teaming of LLM Operator Agents in Safety-Critical Control Rooms

arXiv:2606.20408v3 Announce Type: replace-cross Abstract: Large language model (LLM) agents are increasingly proposed as supervisory components for safety-critical systems, yet their robustness under

Obey, Diverge, Collapse: Blind Obedience to Incorrect Instructions Drives Code LLMs to Irrecoverable Code Semantic Collapse

arXiv:2607.04537v1 Announce Type: cross Abstract: Code language models are now trusted collaborators in production workflows for debugging, refactoring, and iterative repair, and every benchmark that

Local Ai

Object-Centric Environment Modeling for Agentic Tasks

arXiv:2607.02846v1 Announce Type: new Abstract: Large language model (LLM) agents can improve through accumulated experience, but free-form textual memories become difficult to maintain, validate, and

local-aiarxiv-cs-ai

ObjRetarget: An Object-Aware Motion Retargeting Framework with Anthropomorphic Arm Constraints and Polyhedral Hand Modeling

arXiv:2607.03828v1 Announce Type: new Abstract: Learning robot dexterous manipulation from human manipulation videos requires reliably retargeting human intent to executable robot actions while mainta

researcharxiv-cs-ro

Observable- and Positional-Encoding-Dependent Symmetry Readout from Neural Network Weights

arXiv:2607.03108v1 Announce Type: cross Abstract: Post-hoc analysis of trained neural network weights often seeks to recover geometric structure directly from the parameters. We show that, for positio

researcharxiv-cs-cv

Applications

Occluding the Solution Space: Planner-Agnostic Adversarial Attacks on Tolerance-Aware Manipulation

arXiv:2607.03758v1 Announce Type: new Abstract: Adversarial attacks on motion planning are crucial for evaluating and quantifying the intrinsic robustness of robotic manipulation. However, existing ap

applicationsarxiv-cs-ro

Octax: Accelerated CHIP-8 Arcade Environments for Reinforcement Learning in JAX

arXiv:2510.01764v3 Announce Type: replace Abstract: Reinforcement learning (RL) research requires diverse, challenging environments that are both tractable and scalable. While modern video games may o

Hardware

OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling

arXiv:2509.23722v2 Announce Type: replace-cross Abstract: Pipeline parallelism is widely used to train large language models (LLMs). However, increasing heterogeneity in model architectures exacerbate

hardwarearxiv-cs-ai

Omni-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion

arXiv:2603.06577v2 Announce Type: replace Abstract: While recent multimodal large language models (MLLMs) have made impressive strides, they predominantly employ a conventional autoregressive architec

researcharxiv-cs-cv

OmniDS: Dual-Stream Context Fusion for Omnidirectional Depth from Fisheye Cameras

arXiv:2607.03038v1 Announce Type: new Abstract: Omnidirectional depth estimation from multi-fisheye camera rigs is complicated by visibility conflicts: wide baselines cause different cameras to observ

safetyarxiv-cs-cv

OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models

arXiv:2607.03050v1 Announce Type: cross Abstract: Omni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate larg

OmniLayout: A Schematic-Coupled Multimodal Benchmark for Constraint-Aware Geometric Reasoning in PCB Layout

model-releasesarxiv-cs-cv

arXiv:2607.03261v1 Announce Type: new Abstract: Recent large language models (LLMs) have demonstrated remarkable progress in 3D spatial reasoning, spatial grounding, and fine-grained geometric underst

OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers

arXiv:2607.04033v1 Announce Type: cross Abstract: Optimizer selection for large-scale model training has become a system-level design decision constrained jointly by compute, memory, tuning budget, an

OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies

arXiv:2607.03723v1 Announce Type: cross Abstract: Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipu

On a Geometry of Interbrain Networks

arXiv:2509.10650v4 Announce Type: replace-cross Abstract: Effective analysis in neuroscience benefits significantly from robust conceptual frameworks. Traditional metrics of interbrain synchrony in so

On Pairwise Quantile Regression -- Statistical Guarantees and Applications

arXiv:2607.04431v1 Announce Type: cross Abstract: Quantile regression provides a powerful tool for summarizing the conditional distribution of a real valued random variable (r.v.) of interest Y as a f

On Preserving Geometrical Invariance for Superpixel Image Classification using Graph Transformer

arXiv:2607.04262v1 Announce Type: new Abstract: Convolutional Neural Network (CNN) and Vision Transformer (ViT) for image classification exploit a dense grid of pixels containing redundant information

On Regularization via Early Stopping for Least Squares Regression

arXiv:2406.04425v2 Announce Type: replace Abstract: A fundamental problem in machine learning is understanding the effect of early stopping on the parameters obtained and the generalization capabiliti

Tutorials

On the Ability of Transformers to Verify Plans

arXiv:2603.19954v2 Announce Type: replace Abstract: Transformers have shown inconsistent success in AI planning tasks, and theoretical understanding of when generalization should be expected has been

tutorialsarxiv-cs-ai

On the Convergence of Adam, Revisited

arXiv:2607.03519v1 Announce Type: new Abstract: We show that projected Adam for online optimization with arbitrary moment decay parameters eta_1,eta_2in[0,1) can have average regret bounded away from

Hardware

On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization

arXiv:2607.02834v1 Announce Type: new Abstract: Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures. At test time, howeve

hardwarearxiv-cs-lg

On the effectiveness of reward functions in reinforcement learning for confidence calibration of large language models

arXiv:2607.04332v1 Announce Type: new Abstract: In this paper, we consider the setting where large language models (LLMs) are trained using reinforcement learning (RL) to simultaneously improve reason

Applications

One Framework for All: Cross-Modal Membership Inference for Generative Models

arXiv:2607.04339v1 Announce Type: cross Abstract: Large generative models across text-to-text, text-to-image, and image-to-text modalities have been shown to pose significant privacy risks. One fundam

applicationsarxiv-cs-ai

One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization

arXiv:2601.09448v3 Announce Type: replace-cross Abstract: Conventional audio equalization is a static process that requires manual and cumbersome adjustments to adapt to changing listening contexts (e

Online Linear Programming for Multi-Objective Routing in LLM Serving

arXiv:2607.03948v1 Announce Type: new Abstract: We study the online routing problem in large language model serving, where requests arrive sequentially and must be dispatched to parallel decode worker

Open-Attribute Person Retrieval: Finding People Through Distinctive and Novel Attributes

arXiv:2508.01389v3 Announce Type: replace Abstract: Person retrieval in surveillance videos often depends on attributes described by witnesses or operators. However, the most useful cues in practice a

safetyarxiv-cs-cv

Open Problem: Is Interaction Necessary for Order-Optimal 1-bit Mean Estimation?

arXiv:2607.02896v1 Announce Type: cross Abstract: We ask whether interaction is necessary for order-optimal 1-bit mean estimation over nonparametric finite-moment classes. Adaptive threshold-query pro

Open Problems in AI Incident Governance

arXiv:2607.05163v1 Announce Type: cross Abstract: AI systems may produce failures after deployment that pre-deployment safety assessments do not anticipate. Managing these failures requires what we re

Open-Set Source Tracing as Compositional Factors via Structured Prototypes

arXiv:2607.03134v1 Announce Type: cross Abstract: Recent research expands beyond binary anti-spoofing with the emergence of Source Tracing, the task of identifying the specific generative origins of s

Open-Weather Robust 3D Detection via Dual-Critic Diffusion Alignment

arXiv:2607.01983v1 Announce Type: cross Abstract: Robust 3D object detection under adverse weather remains a critical hurdle for autonomous driving. Despite progress with LiDAR-4D radar fusion, most m

Local Ai

OpenGlass: A Sensing-Computing Split Architecture for Local MLLM-Driven Real-Time Visual Assistance

arXiv:2607.03213v1 Announce Type: cross Abstract: We present OpenGlass, an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance, with a primary focus on blind

local-aiarxiv-cs-ai

OpenSIR: Open-Ended Self-Improving Reasoner

arXiv:2511.00602v4 Announce Type: replace Abstract: Recent advances in large language model (LLM) reasoning through reinforcement learning rely on annotated datasets for verifiable rewards, which may

researcharxiv-cs-cl

OpenTinker: Separating Concerns in Agentic Reinforcement Learning

arXiv:2601.07376v2 Announce Type: replace Abstract: We introduce extsc{OpenTinker}, an open infrastructure for training large language model (LLM) agents with many LoRA-backed policies over shared exe

Operator-on-F complements value-equivalence: a planning-time diagnostic for latent world models

arXiv:2607.04464v1 Announce Type: cross Abstract: World-model evaluation for model-based reinforcement learning typically asks whether the learned model predicts reward and value well, which can leave

Applications

OpFlow: Learning Opportunity-Conditioned Choice Potentials for Robust OD Flow Prediction

arXiv:2607.03200v1 Announce Type: new Abstract: Origin-destination (OD) flow prediction is central to urban analytics, yet deep models trained on raw counts remain vulnerable to distribution shift. Th

applicationsarxiv-cs-lg

Agents

OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement

arXiv:2607.05346v1 Announce Type: new Abstract: We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-r

agentsarxiv-cs-ai