Search: “evaluation”

ForestQuery: Boundary-Aware and Spatially Anchored Query Learning for Unified Forest Point Cloud Segmentation

arXiv:2610.03403v1 Announce Type: cross Abstract: Forest point cloud segmentation is fundamental for fine-grained 3D forest scene understanding, yet remains challenging due to irregular tree structure

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Agents

From Benchmarks to Production: A Text-to-SQL System for Complex Financial Data

DGX agent

arXiv:2610.03524v1 Announce Type: new Abstract: General-purpose Text-to-SQL systems achieve strong performance on academic benchmarks like Spider and BIRD, where schemas are relatively shallow and col

agentsarxiv-cs-ai

5 Oct 2026

From Language Priors to Field Adaptation: Preference Learning for Traversability Estimation

arXiv:2610.02974v1 Announce Type: cross Abstract: Image-based traversability estimation is inherently dependent on the robot platform, deployment domain, and mission preferences, which limits the appl

researcharxiv-cs-cv

From Migration to Calibration: Preserving Agent Capabilities across Models, Jurisdictions, and Scale

arXiv:2609.35149v2 Announce Type: replace Abstract: Deploying, migrating, or scaling an agent can change its model, harness, infrastructure, application, and intended users. We formulate agent calibra

From Mixing to Tearing: Graph Decomposition in Decentralized Optimization via Message Passing

arXiv:2610.03709v1 Announce Type: cross Abstract: We study the minimization of sums of smooth strongly convex functions over undirected graphs, with each function held by one agent and communication r

agentsarxiv-cs-lg

From Patching to Pruning Visual Computation in Vision Language Models

arXiv:2610.03389v1 Announce Type: new Abstract: Vision language models (VLMs) incur substantial inference cost because every visual token is processed by the attention and MLP projections of every dec

researcharxiv-cs-cv

FrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution

model-releasesarxiv-cs-ai

arXiv:2610.03675v1 Announce Type: cross Abstract: LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as

Gains and Collapse in On-Policy Distillation:A Reinforcement Learning Perspective

arXiv:2610.03185v1 Announce Type: new Abstract: On-policy distillation (OPD) has become an important approach to language model post-training. However, despite its performance gains, OPD can also coll

GAW-PO: Preference Optimization with Gradient-Aligned Token Weights

model-releasesarxiv-cs-cl

arXiv:2610.01511v2 Announce Type: replace Abstract: Most preference optimization methods, such as Direct Preference Optimization (DPO), apply preference supervision at the response level, although aut

GB-LSR: Local Spectral Decoding with a Learned Global Bandwidth for Arbitrary-Scale Super-Resolution

arXiv:2606.19617v2 Announce Type: replace Abstract: We present GB-LSR (Global-Bandwidth Local Spectral Representation), a fixed-grid local spectral representation for continuous image decoding. The im

Generalizable single-cell perturbation response prediction using energy-guided flow matching

arXiv:2610.02232v1 Announce Type: cross Abstract: Predicting phenotypic and transcriptional responses to perturbations at single-cell resolution provides a powerful tool for probing biological systems

researcharxiv-cs-lg

Geometry-Aware Time Reparameterization for Flow-Map Distillation

arXiv:2610.02427v1 Announce Type: cross Abstract: Flow-map distillation enables one- and few-step generation by learning finite-time transitions of a pretrained generative ODE. We investigate whether

GTDD: Generative Test-Driven Development for AI Coding Agents with Adversarial Testing

arXiv:2610.02952v1 Announce Type: cross Abstract: Test-driven development gives AI coding agents executable requirements for implementing software. Because these agents can adapt their implementations

safetyarxiv-cs-lg

HASTE: Evolving Agent Harnesses Against Emerging Attacks Using Sparse Evidence

arXiv:2610.02920v1 Announce Type: new Abstract: Agent harnesses play a critical role in defenses by enforcing safety constraints to prevent unsafe actions. However, rapidly emerging attacks outpace ma

How Causality Bridges the Semantic Gap

arXiv:2610.02594v1 Announce Type: cross Abstract: Numerical measurements capture how a system behaves, but often leave the meanings of its variables unspecified. Some variables are measured but never

How to Find and Reuse Policies for Continuous Adaptation in Lifelong Reinforcement Learning

arXiv:2610.03119v1 Announce Type: cross Abstract: In lifelong reinforcement learning, retaining previously learned policies is not sufficient for effective transfer to a new task. Useful knowledge may

How To Train Your World Model: Fine-tuning vs RAG for LM-based World Modeling

arXiv:2610.02542v1 Announce Type: new Abstract: World models (WMs) simulate the transition dynamics of environments, enabling agents to plan over the consequences of their actions. In text-based envir

Local Ai

HXAI: Hierarchical Privacy-Preserving Explainable AI in Distributed Energy Systems

arXiv:2610.02504v1 Announce Type: new Abstract: Balancing electricity demand and supply is increasingly difficult due to the inherent intermittency of renewable power generation and the stochastic pow

local-aiarxiv-cs-ai

Hypothesis-guided discovery of cognitive algorithms via program refinement

arXiv:2610.02523v1 Announce Type: new Abstract: Developing cognitive models of algorithmic reasoning from behavioral data is a central problem in cognitive science that challenges current methods. Tra

In-Distribution Forcing for Long Video Generation at Test Time

arXiv:2610.03120v1 Announce Type: new Abstract: Modern autoregressive (AR) video diffusion models excel at short-horizon video generation, yet generating long videos remains challenging due to driftin

IntentCoding: Amplifying User Intent in Code Generation

model-releasesarxiv-cs-ai

arXiv:2602.00066v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong capabilities in code generation, but their adherence to fine-grained user intent with multiple constrai

Interpretable Deepfake Detection in Videos via Explicit Forensic Features and Temporal Modeling

arXiv:2610.03380v1 Announce Type: new Abstract: Deepfake detection in videos remains challenging, as manipulated content may appear visually consistent at the frame level while exhibiting subtle tempo

Introducing Google Cloud Modernize, transforming for (and with) AI

model-releasesgoogle-cloud-ai

Today, we’re announcing Google Cloud Modernize, an end-to-end transformation portfolio to help enterprises collapse multi-year roadmaps with the power of AI. Google Cloud Modernize brings together Goo

Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation

arXiv:2610.02300v1 Announce Type: new Abstract: Training-free safeguards for text-to-image generation often rely on a reusable safety signal, such as an unsafe direction or global toxic subspace, appl

Kinematics-Induced Multimodal 3D Human Pose Estimation with Subject-Level Privacy

arXiv:2610.02943v1 Announce Type: new Abstract: Multimodal 3D Human Pose Estimation (3D HPE) combines complementary information from RGB, LiDAR, and mmWave radar, but models trained on correlated obse

Knowledge or Calculator? Decomposing the Skill Premium in Verifiable Financial Agent Workflows

arXiv:2610.03564v1 Announce Type: new Abstract: Financial AI agents must do more than retrieve facts: investment workflows require correct quantitative execution, reliable use of procedural resources,

agentsarxiv-cs-ai

Large Language Continuous Diffusion Models

arXiv:2610.02665v1 Announce Type: cross Abstract: Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajecto

LatticeSMC: Where to Spend Inference-Time Compute in Chunked Sequence Generators

arXiv:2610.02774v1 Announce Type: new Abstract: Long-form generators for music, motion and video produce sequences chunk by chunk, with each chunk generated by iterative denoising while rewards are de

safetyarxiv-cs-lg

Local Ai

Law And Order: Tax Law Autoformalization

arXiv:2610.02792v1 Announce Type: new Abstract: Legal systems are increasingly implemented through software, yet scalable methods for translating legal texts into accurate symbolic representations rem

local-aiarxiv-cs-ai

Learn Feasibility Once, Optimize All Objectives: Derivative-Free Diffusion Models for Chance-Constrained Programming

arXiv:2610.03071v1 Announce Type: new Abstract: Chance-constrained programs (CCPs) optimize decisions under uncertainty by limiting the probability of constraint violation. Despite advances in traditi

tutorialsarxiv-cs-lg

Learning from Evolving Errors: Adaptive Iterative Repair for On-Policy Distillation

arXiv:2610.02700v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) supplies dense token-level feedback on trajectories sampled from the student's own policy, a richer training signal

safetyarxiv-cs-cl

Local Ai

Learning from Repaired Reasoning: Root-Cause-Guided On-Policy Distillation

arXiv:2610.03515v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) uses reference solutions as privileged hindsight to supervise student-generated reasoning trajectories. However, refe

local-aiarxiv-cs-cl

Learning Jazz Pianist Style with Cross-Attention Conditioning

arXiv:2610.02918v1 Announce Type: cross Abstract: Jazz pianists develop distinctive traits that experienced listeners can often identify within seconds, yet the features underlying this recognition re

researcharxiv-cs-lg

Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion

arXiv:2209.14887v3 Announce Type: replace-cross Abstract: Robotic locomotion is often approached with the goal of maximizing robustness and reactivity by increasing motion control frequency. We challe

Learning Reflexive Behavior for Contact-Rich Manipulation

arXiv:2610.02811v1 Announce Type: new Abstract: During contact-rich manipulation, interactions between a robot and its environment carry information about local geometry: a surface prevents penetratio

safetyarxiv-cs-ro

Applications

Learning the Latent Structure: A Feature-Centric Approach to Graph Data Augmentation

arXiv:2610.02517v1 Announce Type: new Abstract: Graph-structured data plays a pivotal role in modeling complex relationships. However, real-world graphs are often incomplete due to data collection and

applicationsarxiv-cs-lg

LLA-MPC on Embedded Hardware: Rapid Adaptive Control with Thousands of Parallel Models

arXiv:2610.03616v1 Announce Type: new Abstract: We present a generalized implementation of Look-Back and Look-Ahead Adaptive Model Predictive Control (LLA-MPC), a learning-free framework for real-time

agentsarxiv-cs-ro

Localized Conformal Safety Monitoring with Vision-Language Models for Autonomous Driving

model-releasesarxiv-cs-lg

arXiv:2610.02765v1 Announce Type: cross Abstract: Monitoring planned driving trajectories requires accurately estimating the collision likelihood with actors whose motion is itself impacted by the ego

Applications

LOCUS: Landmark-Oriented Container Discrimination Using Spatial Graphs

arXiv:2610.02803v1 Announce Type: new Abstract: As robots are increasingly deployed in unstructured, real-world environments, the ability to reason about complex spatial and semantic relationships amo

applicationsarxiv-cs-ro

LoGo: Local-Global Rewards for Consistent Long-Horizon Video Generation

model-releasesarxiv-cs-ai

arXiv:2610.03636v1 Announce Type: cross Abstract: Camera-controlled video models are rapidly advancing toward long generation horizons and complex camera control. A key failure mode is 3D inconsistenc

LS-AR: Future-Predictive Latent Steering in Autoregressive LLMs

model-releasesarxiv-cs-lg

arXiv:2610.03093v1 Announce Type: new Abstract: Standard autoregressive (AR) models process high-level task instructions, state history, and transient tokens within a single shared sequence of tokens.

LUMOS: Tracing Parametric Knowledge from Training Data to Behavioral Outputs in LLMs

arXiv:2610.02902v1 Announce Type: new Abstract: Current analyses of LLMs' parametric knowledge are largely output-centric, drawing conclusions about what a model knows without verifying what it was ac

MACTS-EM: Multi-Agent Collaborative Time Series Forecasting with Emergent Memory

arXiv:2610.02255v1 Announce Type: new Abstract: Time series forecasting remains a critical challenge across numerous domains. Despite significant advancements, existing approaches struggle with comple

agentsarxiv-cs-lg

ManifoldSplat: Language-Guided Semantic Shape Editing of 3D Gaussian Head Avatars

arXiv:2610.03599v1 Announce Type: new Abstract: High-fidelity 3D head avatars have reached near-photorealistic quality. While recent methods enable text-driven manipulation, they struggle to provide f

safetyarxiv-cs-cv

ManiPhysicsBench: Physics-Based Assessment of Object Preservation in VLA Manipulation

arXiv:2610.02802v1 Announce Type: new Abstract: Vision-language-action (VLA) models aim to perform diverse manipulation tasks, but task success in existing rigid-body benchmarks does not indicate whet

researcharxiv-cs-ro

Mitigating Private Data Leakage in LLMs with Whiteout

arXiv:2610.02418v1 Announce Type: cross Abstract: Modern large language models (LLMs) are trained on massive, largely unfiltered datasets, including content scraped from nearly every accessible websit

MobiAgent: Dual-Loop Recursive Policy Self-Improvement for Long-Horizon Mobile Manipulation

arXiv:2610.03476v1 Announce Type: cross Abstract: Long-horizon mobile manipulation presents significant challenges due to compounding execution errors and capacity interference between locomotion and

Moving Forward with Video Saliency: A New Dataset and Benchmark where Motion Matters

arXiv:2610.03276v1 Announce Type: new Abstract: Video saliency prediction is inherently harder to model than static image saliency due to the additional temporal dimension. Video saliency benchmarks r