Search: “evaluation”

MultiEcho: An Experimental Science of Learned Worlds

arXiv:2609.33347v1 Announce Type: new Abstract: World models can be studied as experimental systems with response laws of their own. We introduce MultiEcho, a framework for estimating these laws throu

Muon Under Gradient Noise and the Limits of Orthogonalization Near Optima

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DGX agent

arXiv:2609.32861v1 Announce Type: new Abstract: Muon replaces the momentum buffer of each weight matrix by its orthogonal polar factor. We ask what this orthogonalization does near an optimum, where m

researcharxiv-cs-lg

29 Sept 2026

NanoForecast v0.5: Competitive Time Series Forecasting Through Training Pipeline Optimization

arXiv:2609.31669v1 Announce Type: new Abstract: We present NanoForecast v0.5, a 6.5M-parameter forecaster that competes with models 31x its size (TimesFM, 200M parameters) after training pipeline fixe

Narrow Multimodal Fine-Tuning Can Induce Emergent Misalignment

arXiv:2609.35291v1 Announce Type: cross Abstract: Modern AI models are aligned through post-training to adapt them to downstream tasks. Recent work shows that fine-tuning language models on narrow tas

safetyarxiv-cs-cv

Neural Harmonic Measure Operator

arXiv:2609.35752v1 Announce Type: new Abstract: We introduce Neural Harmonic Measure Operator (NHMO), a neural solver for elliptic PDE problems on variable-shape domains. The harmonic measure of a dom

Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generative Modeling

arXiv:2602.16954v3 Announce Type: replace Abstract: While deep generative models excel at capturing graph data distributions, they struggle to satisfy complex, hard constraints. In unconstrained setti

Agents

NeuronDiscover: Agent-in-Twin for Mechanistic Discovery in Neuronal Microenvironments with World Action Models

arXiv:2609.35338v1 Announce Type: new Abstract: Mechanistic discovery in neuronal microenvironments requires interventions and measurements that separate competing explanations of solute transport and

agentsarxiv-cs-lg

NeuronSifter: Intervention Planning in CNS Microenvironments

arXiv:2609.35445v1 Announce Type: new Abstract: Prioritizing central nervous system (CNS) interventions requires predicting how a dose, route, and schedule act on a partially observed microenvironment

No Free Efficiency: Revisiting the Trade-off Between Training Efficiency and Model Vulnerability

arXiv:2609.33898v1 Announce Type: new Abstract: Training efficiency has become the central driver of recent progress in foundation models. To overcome the massive computational and data requirements o

No Pain, More Gain: Iterative Merging for Effective Multi-Teacher On-Policy Distillation

arXiv:2609.34745v1 Announce Type: new Abstract: Multi-teacher on-policy distillation (MOPD) combines independently developed domain teachers into a single student by distilling their predictions on st

Nudgeability: Reasoning Models Follow Confidence Signals Without Tracking Their Own Competence

arXiv:2609.34572v1 Announce Type: cross Abstract: Reasoning language models that can call tools must decide during inference whether to answer unaided or delegate. Any self-reflection mechanism for th

OmniSmartHome: A Multimodal Reasoning Benchmark for Smart-Home Agents

arXiv:2609.32569v1 Announce Type: new Abstract: Smart-home assistants are expected to handle diverse, realistic requests that arise in daily life. In such interactions, users often rely on the surroun

Local Ai

OmniTide: Co-Designing Algorithms and Systems for Efficient On-Device Omni-LLM Streaming

arXiv:2609.34653v1 Announce Type: cross Abstract: On-device streaming omni-modal inference safeguards user privacy and eliminates prohibitive per-token API costs, but faces a critical bottleneck: the

local-aiarxiv-cs-lg

On-Policy or Off-Policy Learning? A Systematic Study of Distillation Dynamics

arXiv:2609.35259v1 Announce Type: new Abstract: On-policy learning has been argued to reduce catastrophic forgetting, produce sparser parameter updates, and improve generalisation. However, existing c

On-Policy Self-Distillation for Multi-Turn Image Editing

arXiv:2609.35611v1 Announce Type: new Abstract: Instruction-based image editing has achieved strong performance in single-turn settings, yet practical editing is often iterative, with each instruction

On the Interaction of Compressibility and Adversarial Robustness

arXiv:2507.17725v3 Announce Type: replace-cross Abstract: As demands for resource efficiency and safety in modern neural networks intensify, substantial research effort has gone into model compression

safetyarxiv-cs-cv

Tutorials

On the Relation Between Interval Regret and Dynamic Regret

arXiv:2609.34423v1 Announce Type: new Abstract: Non-stationary online learning has attracted much attention in recent years, as static regret is insufficient to guide algorithm design in changing envi

tutorialsarxiv-cs-lg

One Attack to Fool Them All: Highly Transferable Black-Box Adversarial Attacks on Frontier MLLMs

arXiv:2609.33833v1 Announce Type: new Abstract: Adversarial attacks have long posed a fundamental threat to machine learning systems. As multimodal large language models (MLLMs) rapidly evolve and bec

One Proposal for Every Margin: Zero-Shot Amortized Sequential Importance Sampling for Binary Matrices

arXiv:2609.35514v1 Announce Type: new Abstract: In ecology, psychometrics, and the analysis of social and financial networks, binary matrices are often analyzed conditional on their observed row and c

One-Step Is Optimal: Unconditional Rectified Flows are Noise2Noise Denoisers, and Multi-Step Integration Provably Hurts---A Benchmark and Task-Based Detectability Study on Low-Dose CT

arXiv:2609.31670v1 Announce Type: new Abstract: Iterative and generative denoisers are increasingly used under the assumption that multi-step refinement outperforms a single regression pass. We show t

Optimizing H-Graph Hybridization for Diffusion-Guided RRT

arXiv:2609.32897v1 Announce Type: new Abstract: Sampling-based motion planners guided by diffusion models produce high-quality trajectories in a single run, yet the stochastic diversity available at i

Optimizing the Phi-2 Small Language Model for Real-time Chatbot Applications Using Parameter-Efficient Fine-Tuning (PEFT) with QLoRA Quantization

arXiv:2609.33927v1 Announce Type: new Abstract: This study explores the optimization of the Phi-2 Small Language Models (SLMs) for real-time chatbot applications through Parameter-Efficient Fine-Tunin

ORAV: Benchmarking Audio-Video Generation from Multimodal Contexts

arXiv:2609.34843v1 Announce Type: new Abstract: Audio-video generation using heterogeneous multimodal references has emerged as a new challenge, requiring both compositional control over generation an

ORPG: Reconciling Multiple Reward Objectives through Objective-wise Policy Gradients

arXiv:2609.34985v1 Announce Type: new Abstract: Multi-reward policy optimization requires a joint update that reflects both the learning signals and the intended relationships among objectives. We int

Parameter-Efficient 3D Segmentation of Liver and Liver tumors: Depthwise factorization Scales Better Than Dense Convolution with Spatial Dimensionality

arXiv:2609.33077v1 Announce Type: new Abstract: Three-dimensional dense convolutional networks are the strongest performers on volumetric medical image segmentation, but their parameter counts scale p

Agents

Path Planning with Motion Primitives in Dynamic Environments: SIPP on Lattices

arXiv:2609.31803v1 Announce Type: new Abstract: Autonomous navigation in dynamic environments is a critical challenge, particularly when spaces are shared with other mobile agents whose future traject

agentsarxiv-cs-ro

Local Ai

Perturb-and-Solve: Efficient Learned-Operator Conditioning for Latent Diffusion Inverse Problems

arXiv:2609.33171v1 Announce Type: cross Abstract: Latent diffusion models serve as powerful priors for solving inverse problems in image restoration, such as deblurring, inpainting, and super-resoluti

local-aiarxiv-cs-cv

Phenomenon-Graph JEPA: Label-Efficient Representation Learning for Contactless Cardiorespiratory Sensing

arXiv:2609.32928v1 Announce Type: new Abstract: Millimeter-wave (mmWave) radar and RGB-D cameras can record cardiac and respiratory waveforms continuously and without contact, but labeled recordings r

Physics-Attested Federated Learning: Securing Collaborative Anomaly Detection in Critical Water Infrastructure

arXiv:2609.34804v1 Announce Type: cross Abstract: Federated learning enables industrial operators to train shared intrusion detection models without disclosing proprietary operational telemetry. Howev

PI-NOMT: Physics-Informed Neural Optimal Mass Transport for Brain Fluid Dynamics

arXiv:2609.33857v1 Announce Type: new Abstract: Recovering hidden transport mechanisms from sparse spatiotemporal observations is a fundamental inverse problem in scientific machine learning. In brain

PIVOT: Pivot-Aware On Policy Self Distillation for Multi-Turn VLM Agents

arXiv:2609.35303v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) via Group-Relative Policy Optimization (GRPO) is widely used for multi-turn VLM agent training, ye

PMOPD: Task Ordering, Cycling, and Parameter-Update Subspace Protection in Multi-Teacher On-Policy Distillation

arXiv:2609.34605v1 Announce Type: new Abstract: Multi-teacher on-policy distillation (MOPD) has emerged as a popular post-training paradigm for integrating specialized capabilities in frontier languag

PORTER: Edge-Cloud Residency for Persistent 3D Scene Graph Memory

arXiv:2609.33258v1 Announce Type: cross Abstract: Recent task-driven and just-in-time 3D Scene Graph (3DSG) methods reduce per-task representations by constructing or activating only task-relevant inf

Agents

PReCache: Efficient KV Cache Sharing for Multi-LoRA Agents via Low-Rank Precomputation and Neutral Reconstruction

arXiv:2609.34054v1 Announce Type: new Abstract: Multi-LoRA agent systems enable efficient role specialization by sharing a common backbone model. However, each agent repeatedly processes the growing s

agentsarxiv-cs-lg

Precise Editing and Flexible Referencing for Interactable Worlds

arXiv:2609.34470v1 Announce Type: new Abstract: We present EditWorld, a video world model for precise editing and flexible referencing in interactable worlds. Existing video world models primarily foc

Predicting Block-Coordinate Performance via Cross-Curvature

arXiv:2609.33489v1 Announce Type: new Abstract: Simultaneous and sequential block updates are two basic optimization strategies used across machine learning, such as neural-network training, federated

Predicting the Next State Is Not Enough: JEPA Representations for Lean Theorem Proving

arXiv:2609.32908v1 Announce Type: new Abstract: Neural theorem provers must both propose tactics and decide which valid successor states to explore. We study whether one-step Lean transitions provide

Predictive Semantic Safety: From Visual Physical Reasoning to Safety-Critical Control

arXiv:2609.34356v1 Announce Type: cross Abstract: Physical interactions can create future hazards that are not apparent from the robot's current geometric surroundings. We present a framework termed P

Hardware

PrefLUT: Reusable and Refinable Personalized Color Editing from Pairwise Preferences

arXiv:2609.34133v1 Announce Type: new Abstract: Photographic color editing is inherently personal: the same image can appear too warm, too muted, or already satisfactory to different users. Most looku

hardwarearxiv-cs-cv

Principal Steering Subspaces for Online Adaptation of Frozen Generative Robot Policies

model-releasesarxiv-cs-ro

arXiv:2609.33765v1 Announce Type: new Abstract: Generative robot policies provide expressive behavior priors, but updating a large diffusion or flow-matching model through online interaction is costly

PROACT-Agent: Progressive Runtime Oversight and Active Circuit-breaking for Real-Time Safety

arXiv:2609.34415v1 Announce Type: new Abstract: The transition from Large Language Models (LLMs) to agents shifts safety stakes from toxic text to irreversible environmental harm. While current defens

Program-Verified Self-Evolution for Vision-Language Models

arXiv:2609.33855v1 Announce Type: new Abstract: Self-evolving vision-language models train on questions they generate from unlabeled images. Since these questions have no gold answers, prior methods l

Progressive Risk Estimation for Accident Anticipation

arXiv:2609.32811v1 Announce Type: new Abstract: Accident anticipation aims to recognize anomalous driving cues before a crash while avoiding false alarms during normal driving. Existing approaches typ

Progressive-View On-Policy Distillation for Regional-to-Global Transfer in Multimodal LLMs

arXiv:2609.32333v1 Announce Type: new Abstract: Regional-to-global distillation uses crop-conditioned guidance to improve full-image understanding. The challenge is to effectively transfer the teacher

safetyarxiv-cs-cv

Prompt-Anchored Residual Adaptation for Biomedical Vision-Language Models

arXiv:2609.33701v1 Announce Type: new Abstract: Pretrained biomedical vision-language models achieve strong zero-shot performance in biomedical image classification. However, downstream biomedical cla

Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks

arXiv:2609.31862v1 Announce Type: cross Abstract: Reconstruction and simulation at a collider experiment are long chains of specialised algorithms, each tuned to a single step. We explore how one mode

ProtoSeam: Lifting Classifier Training with Latent Gaussian Mixture Models

arXiv:2609.35174v1 Announce Type: new Abstract: We propose a lifted reformulation of supervised classification that improves the final accuracy of standard classifiers without changing the architectur

PSM: Dataset Distillation Based on Precise Statistical Matching by Difficulty

arXiv:2609.34299v1 Announce Type: new Abstract: Dataset distillation (DD) condenses a large original dataset into a small distilled dataset with high training utility. Decoupled statistical matching m