Search: “models”

The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery

arXiv:2610.18933v1 Announce Type: cross Abstract: Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, an

agentsarxiv-cs-cl

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Agents

The AI Scientist v3 is out: The Agentic Scientist. GitHub: https://github.com/SakanaAI/AI-Scientist-v3 Blog: https://pub.sakana.ai/agentic-s…

DGX agent

The AI Scientist v3 is out: The Agentic Scientist. GitHub: https://github.com/SakanaAI/AI-Scientist-v3 Blog: https://pub.sakana.ai/agentic-scientist/ arXiv: https://arxiv.org/abs/2610.18933 The Agenti

agentsdavid-ha--x

15 Oct 2026

The Compiler May Read It, the Agent May Not: Keeping Part of a Research Code Away from a Coding Agent

arXiv:2609.35557v2 Announce Type: replace-cross Abstract: The compiler must read modules a physics-based solver cannot build without; the coding agent must not read that intellectual property. The har

agentsarxiv-cs-ai

The Many Morals of AI: The cautionary bias of North American news narratives in global perspective

arXiv:2610.18573v1 Announce Type: cross Abstract: The stories told about artificial intelligence (AI) play a central role in shaping public understanding, policy debates, and expectations about techno

safetyarxiv-cs-ai

The sudden collapse of the value of CoT (even summarized CoT) in the agentic era changes working with AI. Effectively steering AIs and getti…

The sudden collapse of the value of CoT (even summarized CoT) in the agentic era changes working with AI. Effectively steering AIs and getting any sort of observability now requires you to ask for int

agentsethan-mollick--x

Local Ai

Towards Certifiable State Estimation under Observability Degeneracy

arXiv:2610.20490v1 Announce Type: new Abstract: Certifiable optimization provides global optimality guarantees for non-convex state estimation in robotics. However, under severe physical sensing degen

local-aiarxiv-cs-ro

TRACER: Trace-Regularized Training Against Indirect Prompt Injection

arXiv:2610.19133v1 Announce Type: new Abstract: Indirect prompt injection uses untrusted retrieved content, tool outputs, or agent messages to override an LLM's intended behavior. We show that defense

agentsarxiv-cs-lg

Tutorials

Trainable at Depth, Solvable at Small Size: What the Loss Surface of a Polynomial Activation Dictates for Designing, Initializing and Training Networks

arXiv:2610.20069v1 Announce Type: new Abstract: What makes a deep network train, and what shape is its loss surface? We answer both with one construction at two scales: a polynomial activation f (z) =

tutorialsarxiv-cs-lg

Trustworthy AI Must Account for Interactions

arXiv:2504.07170v3 Announce Type: replace Abstract: Trustworthy AI encompasses many aspirational aspects for aligning AI systems with human values, including fairness, privacy, robustness, explainabil

safetyarxiv-cs-lg

Vision: 'What Don't I Know About User's Preferences?' How Uncertainty Estimation Improves Proactive Assistant Alignment

arXiv:2610.15948v1 Announce Type: new Abstract: Proactive Smart Assistants (PSAs) increasingly use general knowledge, learned patterns from group-level data, patterns from individual user's data, and

safetyarxiv-cs-ai

Weak Segmentation-Guided GAN for~Realistic Color Edition

arXiv:2610.16620v1 Announce Type: new Abstract: Editing the color of images in a realistic way finds many applications such as changing the perception of an image, data augmentation or film post proce

researcharxiv-cs-ai

WeGR: Unified Generative Retrieval for Industrial Search Systems in WeChat

arXiv:2610.16979v1 Announce Type: cross Abstract: Generative retrieval (GR) offers a promising paradigm for industrial search by unifying candidate generation within an autoregressive framework. Howev

safetyarxiv-cs-ai

What Does Disagreement Mean? A Semantic Identifiability Frontier for Pairwise Preference Learning

arXiv:2610.20450v1 Announce Type: cross Abstract: Pairwise comparison data are a standard supervision signal in preference learning, but disagreement in such data can reflect stable latent heterogenei

What the Machine Invented: Mining New Mathematical Instruments from OpenAI's October 2026 Release of AI-Generated Mathematics

arXiv:2610.17090v1 Announce Type: cross Abstract: On 6 October 2026 OpenAI published an initial 722 AI-written mathematical manuscripts, organized into 372 problem families, in the public repository g

researcharxiv-cs-ai

When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems

arXiv:2610.16039v1 Announce Type: cross Abstract: Multi-agent LLM systems (MALS) introduce a privacy-leakage problem that conventional sensitivity-based defenses miss, since information disclosed to o

agentsarxiv-cs-ai

When Is Enough Not Enough? Illusory Completion in Search Agents

arXiv:2602.07549v3 Announce Type: replace-cross Abstract: In agentic search, an LLM agent searches the web, reads the pages it finds, and decides what to look for next before returning an answer. But

agentsarxiv-cs-cl

Where to Start, How to Move: Analyzing Curricula under GRPO for Long-Horizon LLM Agents

arXiv:2610.19349v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) provides no reward-relative learning signal when all rollouts receive identical rewards. In long-horizon tas

safetyarxiv-cs-cl

3D Gaussian Splatting for Fine-grained Urban Scene Reconstruction via Semantic-guided Density Control and Loss Design

arXiv:2610.16292v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has been becoming a promising 3D reconstruction technique because of its advantage of high-fidelity rendering effect with h

researcharxiv-cs-cv

A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry

arXiv:2610.17705v1 Announce Type: new Abstract: 3D scene graph prediction is commonly supervised with local object and predicate classification losses. Although effective for slot-wise label predictio

researcharxiv-cs-cv

A Geometrical Approach to Constructing Neural Networks for Perfect Training-set Classification

arXiv:2610.17312v1 Announce Type: new Abstract: This paper proposes a method for constructing a neural network that achieves perfect classification of all samples in a given training set, without taki

A Minimum Exact Disjunctive Basis for Two-Robot Solvability Profiles

arXiv:2610.17677v1 Announce Type: cross Abstract: We completely characterize the solvability profiles of tasks for two autonomous mobile robots in the plane under variable disorientation, chirality, a

agentsarxiv-cs-ro

A temporal graph dissimilarity measure capturing epidemic spreading behavior

arXiv:2610.16456v1 Announce Type: cross Abstract: Comparing temporal networks is a challenging problem in network analysis. Commonly adopted methods compare structural properties of temporal graphs an

A unified three-branch deep learning framework for automated detection and counting of wildlife in aerial imagery

arXiv:2610.17067v1 Announce Type: new Abstract: Automating aerial wildlife surveys requires reliable animal detection, consistent delineation of observation strips, and correction for repeated coverag

Active Neural Matter: A Finite-Window Response Framework for Physical Systems and Distributed AI Workflows

arXiv:2610.14549v1 Announce Type: new Abstract: Physical systems and distributed AI workflows can retain past information, receive delayed feedback and combine conflicting signals. These features pose

AERR-Nav: Adaptive Exploration-Recovery-Reminiscing Strategy for Zero-Shot Object Navigation

arXiv:2603.17712v1 Announce Type: cross Abstract: Zero-Shot Object Navigation (ZSON) in unknown multi-floor environments presents a significant challenge. Recent methods, mostly based on semantic valu

Agentic Robot Creation through Generation, Verification, and Revision

arXiv:2610.17881v1 Announce Type: new Abstract: Can an AI agent turn high-level user intent into a functioning robot and teach it how to behave? We study this question from two complementary aspects:

safetyarxiv-cs-ro

AI-Assisted Tumor-Growth Law Discovery from Sparse Data: Refitting and LLM-Based Ranking

arXiv:2610.16891v1 Announce Type: new Abstract: Inferring tumor-growth laws from sparse, noisy observations requires more than accurate curve fitting. We study an AI-assisted workflow involving symbol

All-electron machine-learned orbital-free density functional for periodic CHNO systems

arXiv:2610.17975v1 Announce Type: cross Abstract: We introduce STRUCTURES-P, a machine-learned all-electron orbital-free density functional for periodic CHNO systems. Electrostatics are evaluated anal

All-St3R SLAM: Real-time RGB Dynamic SLAM with Pixel-wise Motion Masking

arXiv:2610.16143v1 Announce Type: new Abstract: SLAM (Simultaneous Localization and Mapping) is a core technology in autonomous systems that enables them to estimate their own position and build a map

agentsarxiv-cs-cv

Amortized Variational Inference of Latent Intensities from Replicated Point Patterns

arXiv:2610.16669v1 Announce Type: cross Abstract: Bayesian inference for latent point process intensities often relies on per-dataset sampling or optimization, making repeated inference computationall

Local Ai

An FPGA-Based Spiking Neural Network for Spatiotemporal Pattern Learning and Classification

arXiv:2610.17463v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are promising for energy-efficient spatiotemporal pattern recognition at the edge, but efficient deployment and on-devi

local-aiarxiv-cs-lg

Anchor-and-Harvest Curriculum: A Fully Unmasked Non-LLM Curriculum for Craftax

arXiv:2610.15328v1 Announce Type: new Abstract: We introduce Anchor-and-Harvest Curriculum (AHC), a domain-informed, rule-based training system for Craftax-Full. AHC combines achievement-oriented task

AnyRank: Reliable Anytime Policy Ranking via Sequential Testing

arXiv:2610.14603v1 Announce Type: new Abstract: Due to the high cost of hardware experiments, robot policies are predominantly evaluated by comparing their empirical performance on a limited number of

safetyarxiv-cs-ro

Applications

Articulated 3D Scene Graphs for Open-World Mobile Manipulation

arXiv:2602.16356v2 Announce Type: replace-cross Abstract: Semantic scene understanding has enabled robots to reason and perform affordance-driven object interaction. Despite this, real-world manipulat

applicationsarxiv-cs-cv

Applications

Bayesian Optimization of CVaR with Unknown Hyperparameters

arXiv:2610.18306v1 Announce Type: new Abstract: We study Bayesian optimization of the Conditional Value-at-Risk (CVaR) of an expensive black-box function that depends on a decision and an environmenta

applicationsarxiv-cs-lg

Before Attention Sinks Form: Hidden-State Geometry Reveals and Modulates Their Formation

arXiv:2610.16745v1 Announce Type: new Abstract: Attention sinks are usually studied after they have formed. We ask whether a measurable hidden-state change appears beforehand and whether changing that

Beyond Safe Execution: Safe Online Reinforcement Learning for Autonomous Driving with Value Calibration and Adaptive Guidance

arXiv:2610.17799v1 Announce Type: new Abstract: Online reinforcement learning (RL) enables driving policies to adapt to changing deployment conditions, but requires safe exploration throughout trainin

safetyarxiv-cs-ro

Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations

arXiv:2610.17802v1 Announce Type: cross Abstract: Robot manipulation has traditionally relied on end-effector-centric interactions. In contrast, humans routinely exploit whole-arm contacts to improve

Bottlenecked semantic compression by representation reconstruction

arXiv:2610.17412v1 Announce Type: new Abstract: Vision Transformers incur high computational costs when processing long visual token sequences. Existing reduction methods typically rank tokens indepen

researcharxiv-cs-cv

C2Flight: A Closed-Loop Co-Simulation Framework for Early-Stage Evaluation of Heterogeneous UAV Computing Systems

arXiv:2610.14715v1 Announce Type: new Abstract: Early-stage design of heterogeneous UAV computing systems requires evaluating how algorithm, hardware, communication, and system topology affect closed-

researcharxiv-cs-ro

C^2LUE: Clinically Aligned Patient-Level Ultrasound Diagnosis via Evidence-Aware Multi-Instance Learning

arXiv:2610.14143v1 Announce Type: cross Abstract: Patient-level ultrasound diagnosis demands not only high predictive accuracy but also clinically aligned mitigation of asymmetric diagnostic risks. Wh

C{AE}NL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control

arXiv:2610.17509v1 Announce Type: cross Abstract: Active learning changes the representations used to acquire subsequent labels. We study this interaction through Collapse-Based Active Neural Learning

Applications

Can We Trust This Evaluation? Instance-Level Reliability of LLM-as-a-Judge

arXiv:2610.14029v1 Announce Type: new Abstract: LLM-as-a-judge evaluation commonly aggregates multiple judges' decisions into a majority vote, yet this panel decision process does not guarantee that t

applicationsarxiv-cs-lg

Tutorials

CartoVec: General-Purpose Location Embeddings from Open Map Data

arXiv:2610.17190v1 Announce Type: new Abstract: Geographic coordinates are common inputs in applied machine learning, but latitude and longitude describe position rather than the surrounding environme

tutorialsarxiv-cs-lg

Local Ai

Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach

arXiv:2610.18075v1 Announce Type: new Abstract: Selection bias is a difficult yet widespread problem in causal discovery, occurring whenever non-random selection processes lead to data that is not rep

local-aiarxiv-cs-lg

ChampionKV: Head-Wise Adaptive Budget Allocation for KV Cache Compression

arXiv:2610.18026v1 Announce Type: new Abstract: Some observation-window-based KV cache compressors use prompt attention to identify important tokens but allocate uniform or predefined budgets across h

Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI

arXiv:2506.15468v2 Announce Type: replace-cross Abstract: We propose co-creative learning as a novel paradigm where humans and AI, i.e., biological and artificial agents, mutually integrate their part

safetyarxiv-cs-lg

CoCA: Criterion-Aware Credit Assignment for Rubric-Based Reinforcement Learning

arXiv:2610.17733v1 Announce Type: new Abstract: Rubric-based reinforcement learning (RL) evaluates responses against explicit criteria to construct rewards, offering a viable way to train large langua