Search: “evaluation”

When local gains fail to transfer: Frozen Earth-observation embeddings across wildfires

arXiv:2609.34602v1 Announce Type: new Abstract: Frozen Earth-observation embeddings are judged almost entirely by spatially blocked cross-validation inside one study region. We show that this number d

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

When Retrieval Hurts: Measuring and Explaining Retrieval-Induced Hallucination in Chest X-ray Report Generation

DGX agent

arXiv:2609.31733v1 Announce Type: new Abstract: Retrieval-augmented generation is an attractive way to improve chest X-ray reporting, because reports from similar prior studies supply clinical context

researcharxiv-cs-cv

29 Sept 2026

When Should a Satellite Estimate Be Changed? Stress-Testing Neural Corrections for Evapotranspiration

arXiv:2609.35314v1 Announce Type: new Abstract: Neural residuals can improve satellite evapotranspiration (ET) estimates, but selectors must predict when a correction helps and reject unsupported inpu

Applications

When Should the Count Change? Learning State Maintenance for Causal Video Counting

arXiv:2609.35416v1 Announce Type: new Abstract: Continuous video counting requires distinguishing new observations from new objects or completed events. We introduce StaMina (State Maintenance), which

applicationsarxiv-cs-cv

When to Evict, Not What to Keep: Draft-Guided Eviction for Training-Free KV-Cache Compression

arXiv:2609.33334v1 Announce Type: new Abstract: Training-free KV-cache compression methods such as SnapKV, H2O, and PyramidKV evict tokens at the end of prefill, aiming to preserve the attention mass

When World Models Lie: Adaptive Safety Analysis Under Wrong Imaginations

arXiv:2609.34300v1 Announce Type: new Abstract: World models offer a powerful substrate for safety reasoning in high-dimensional robotic systems, but they are also fallible: their predictions can be b

safetyarxiv-cs-ro

Where Does the Watermark Hide? Push-Pull Disentanglement for Invisible Watermark Removal

arXiv:2609.31722v1 Announce Type: new Abstract: Fixed image distortions do not cover an attacker that learns from paired clean and watermarked images. We study this paired-training threat with single-

WhiteCon: Semi-Supervised Domain Adaptation Regression Through Whitening Transform and Dual Consistency

model-releasesarxiv-cs-cv

arXiv:2609.34078v1 Announce Type: new Abstract: Domain adaptation is crucial for addressing distributional shifts that degrade model performance across domains. While most existing research has center

WM-VLM: Probing Internal World Models for Interleaved Visual-Textual Reasoning

arXiv:2609.34826v1 Announce Type: new Abstract: Humans often solve spatial problems by mentally simulating visual transformations. In contrast, conventional vision-language models (VLMs) reason primar

WorldAgent: Verification-Guided Agentic Physical World Construction

arXiv:2609.33208v1 Announce Type: cross Abstract: Constructing complex physical worlds from language requires coordinating extensive 3D environments, detailed structures and objects at different spati

agentsarxiv-cs-cv

WorldPlay2: Extending Real-Time Interactive World Models in Control and Horizon

arXiv:2609.35560v1 Announce Type: new Abstract: Interactive world models require responding in real time to versatile controls and maintaining long-horizon consistency. However, modeling heterogeneous

WSM-Aware HRI: An IoT-Enhanced Framework for Early Detection and Norm-Guided Repair of Failures with LLM Guidance

arXiv:2609.32336v1 Announce Type: new Abstract: Human-robot interaction (HRI) failures remain a major barrier to deploying robots in real-world environments. Prior work often treats failures as isolat

safetyarxiv-cs-ro

X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths

model-releasesarxiv-cs-lg

arXiv:2609.34212v1 Announce Type: new Abstract: Mixture-of-Depths (MoD) enables conditional computation across Transformer depth by routing only a subset of tokens through selected layers, but its ori

Local Ai

XFlow: A Workflow Model for Instruction-Guided Lesion Segmentation in Chest X-rays

arXiv:2609.34513v1 Announce Type: new Abstract: Existing text-guided segmentation models in the medical domain cover only a narrow set of anatomical structures and lesions in chest X-rays (CXRs), and

local-aiarxiv-cs-cv

Local Ai

Zero-Storage Procedural Neural Synthesis via Boundary Dynamics: Formal Verification in Lean 4 and Bare-Metal Gauntlet Validation

arXiv:2609.33066v1 Announce Type: new Abstract: Contemporary neural inference architectures rely on dense floating-point weight matrices stored in high-bandwidth memory (VRAM), incurring severe memory

local-aiarxiv-cs-lg

ZeroBot: Learning from Scratch in Minutes with Generative Real2Sim

arXiv:2609.34010v1 Announce Type: cross Abstract: We present ZeroBot, a real2sim framework for learning a robot manipulation task from scratch in minutes under challenging conditions: zero human demon

safetyarxiv-cs-cv

A Comprehensive Study of Content Representations for Speech Synthesis

arXiv:2609.30975v1 Announce Type: cross Abstract: Speech content representations are central to voice conversion, speech-to-speech translation, and multimodal language models, yet they are rarely comp

A New Non-archimedean Metric on Persistent Homology

arXiv:2012.02655v4 Announce Type: cross Abstract: In this article, we define a new non-archimedean metric structure, called cophenetic metric, on persistent homology classes of all degrees. We then sh

A Safety-Bounded SDC-to-MCP Gateway for Medical AI Agents

arXiv:2609.31358v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) provides a common interface through which AI applications discover and use external resources and tools. It allows la

safetyarxiv-cs-ai

AC Power Flow Contingency Analysis Using a Single Deep Neural Network

arXiv:2609.30859v1 Announce Type: cross Abstract: Contingency analysis using the AC power flow (AC-PF) model is a critical tool for accurate grid security assessment, but its computational burden incr

Acoustic-to-Text KV Compression for Full-Duplex Speech Models

arXiv:2609.31224v1 Announce Type: cross Abstract: Full-duplex speech language models continuously accumulate acoustic key-value (KV) states, making long-running interactions memory-intensive. During l

Actively Resolving Contextual Uncertainty for Underspecified Tasks in Natural Language

arXiv:2609.30428v1 Announce Type: cross Abstract: Foundation models provide robots with the ability to interpret natural language and reason about environmental context, yet most language-conditioned

safetyarxiv-cs-ai

Adapting Personalized Speech Enhancement for Low-Latency Audio-Visual Target-Speaker Extraction

arXiv:2609.30631v1 Announce Type: cross Abstract: Online audio-visual target-speaker extraction aims to remove competing voices while preserving speech quality and bounding lookahead. Existing extract

Adaptive Multi-Value Control in LLMs via Causal Activation Steering

arXiv:2609.30405v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings where responses must reflect multiple, potentially interacting social norms and human

safetyarxiv-cs-lg

Adaptive Pilot Selection for Unified Semantic Communication and Semantic Sensing in ISAC

arXiv:2609.30891v1 Announce Type: cross Abstract: Semantic communication (SemCom) and integrated sensing and communication (ISAC) are promising technologies for future 6G wireless networks. Existing s

AlphaEarth distinguishes cities but compresses urban variation

arXiv:2609.30356v1 Announce Type: new Abstract: Cities differ in built form, land cover and development history, complicating comparison across places and time. Satellite foundation models map Earth's

An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making

arXiv:2609.30396v1 Announce Type: cross Abstract: This paper presents an end-to-end causal machine learning (ML) pipeline designed for real-world applications with continuous treatments. The proposed

safetyarxiv-cs-lg

Analyzing and Mitigating Cost-Inefficient Behaviors in Coding Agents

model-releasesarxiv-cs-ai

arXiv:2609.30725v1 Announce Type: new Abstract: Although effective, coding agents often incur substantial monetary costs. Their recurring cost-inefficient behaviors remain underexplored. We conduct th

Atlases Are Already Inside: Recovering Population Templates from Pretrained Diffusion Models

model-releasesarxiv-cs-cv

arXiv:2609.30566v1 Announce Type: new Abstract: We present a new inference-time sampler for diffusion models that gives a pretrained model a capability it was never trained for: constructing the atlas

Audio emotion recognition for atypical hearing

arXiv:2609.31168v1 Announce Type: new Abstract: My doctoral work aims to explore Audio Emotion Recognition (AER) in the context of atypical listening. This research focuses on auditory hypersensitivit

Auditing Latent-Space Monitors for Autonomous Driving

arXiv:2609.30557v1 Announce Type: cross Abstract: Runtime failure monitors can use a model's internal representations to anticipate failures. We audit this monitoring strategy across two autonomous-dr

agentsarxiv-cs-ai

AuthGuard-R: Safety-Compliant Mission Hijacking and Dual-Gate Defense for LLM-Controlled Robots

model-releasesarxiv-cs-ro

arXiv:2609.31110v1 Announce Type: new Abstract: Large language models are increasingly used as high-level planners for mobile robots, robot manipulators, and autonomous vehicles. Recent studies show t

Hardware

AutoResearch at Production Scale: Failure Modes and a Multi-Agent Framework

arXiv:2609.30541v1 Announce Type: new Abstract: Optimizing embedding systems for production recommendation pipelines demands systematic exploration that consumes disproportionate engineering effort at

hardwarearxiv-cs-lg

AUWave: A Data-Driven Model for Reconstructing Significant Wave Heights Using Sparse Observations

arXiv:2509.19384v2 Announce Type: replace-cross Abstract: Reconstructing high-resolution regional significant wave height (SWH) fields from sparse buoy observations is a critical challenge for ocean m

AxonSynth: Domain-Randomized Synthetic Data for Zero-Shot 3D Axon Segmentation in Light-Sheet Microscopy

arXiv:2609.31431v1 Announce Type: new Abstract: Accurate segmentation of axons in 3D microscopy data is important for analyzing white-matter organization, but dense ground truth labels are expensive t

safetyarxiv-cs-cv

AYLA: Amplifying Gradient Sensitivity via Loss Transformation in Non-Convex Optimization

arXiv:2504.01875v3 Announce Type: replace Abstract: Stochastic Gradient Descent (SGD) and its variants, such as ADAM, are foundational to deep learning optimization, adjusting model parameters through

BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment

model-releasesarxiv-cs-lg

arXiv:2609.31546v1 Announce Type: new Abstract: Electrocardiogram (ECG) foundation models typically tokenize the signal into fixed-length patches that ignore cardiac structure, so a patch may split a

Benchmarking Attention for Tabular Foundation Models

model-releasesarxiv-cs-lg

arXiv:2609.31306v1 Announce Type: new Abstract: Tabular in-context learners such as TabPFN, Mitra, or ConTextTab rely on alternating row and column attention over 2D sequences of latent embeddings. Th

Benchy: towards a universal language for task-oriented AI benchmarks

model-releasesarxiv-cs-ai

arXiv:2609.30550v1 Announce Type: new Abstract: Benchy is a semantic language and execution engine for benchmarking AI programs. A benchmark is completely specified by a program, a scoring function, a

Beyond Approved Actions: Runtime Validation of Persistent Outcomes in Agent Workflows

arXiv:2609.31301v1 Announce Type: cross Abstract: Large language model agents increasingly act on software systems, no longer merely generating text but also changing databases and online services. Ho

agentsarxiv-cs-ai

Beyond Bag-of-Words: Diagnosing Compositional Binding Failures in Vision-Language Models

arXiv:2602.02043v2 Announce Type: replace-cross Abstract: Modern vision-language models struggle with basic compositional reasoning, failing to bind attributes to objects or relations to their referen

Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Framework for Environmentally Accountable AI

arXiv:2609.30747v1 Announce Type: cross Abstract: As artificial intelligence (AI) becomes embedded in everyday life, its environmental footprint, particularly water consumption remains largely invisib

Block Sparse Attention with Log-Linear Complexity

arXiv:2609.31093v1 Announce Type: new Abstract: Scaling language models to long contexts is limited by the quadratic cost of self-attention. Block sparse attention offers an efficient alternative, but

Bootstrapping Conversational Recommendation Agents At Spotify: Synthetic Data Generation and Self-Improvement Loops

arXiv:2609.30297v1 Announce Type: cross Abstract: Conversational recommendation agents are a new paradigm for content discovery, enabling users to express complex intents through natural language (e.g

agentsarxiv-cs-ai

Breaking Homogeneity: Diversifying Persona Sets for Creative LLM Outputs

arXiv:2609.30492v1 Announce Type: cross Abstract: Language models often produce homogeneous responses to open-ended tasks; such homogeneity can spawn groupthink-the convergence of ideas toward a singu

Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning

arXiv:2609.31363v1 Announce Type: new Abstract: Distributionally robust optimization (DRO) provides a principled framework for learning under distribution shift, but its practical use is hindered by t

Bundled Contact Gradients: Stabilizing Differentiable Simulation for Deployable Dynamic Tasks

arXiv:2609.30951v1 Announce Type: new Abstract: Differentiable simulation provides analytic gradients of robot dynamics, enabling fast and sample-efficient first-order policy optimization. However, ob

safetyarxiv-cs-ro

CacheReforge: Bounded Recovery for Stale KV Caches under Evolving Adapters

arXiv:2609.30884v1 Announce Type: new Abstract: Large language models rely on KV caching to reduce repeated prefill computation in long context and interactive applications. As lightweight adapters ev