Search: “evaluation”

Construction-Driven Injection: Linguistically-Grounded Edit-Based Code-Mixing Fingerprints for Large Language Models

arXiv:2607.25633v1 Announce Type: cross Abstract: Large language models (LLMs) are costly intellectual assets that remain exposed to unauthorized redistribution and commercial misuse. Injected fingerp

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Contextual Deconvolution for Variance-Stable Demand Sensing: Kernel-Modulated Operators in Promotional Retail

DGX agent

arXiv:2607.25664v1 Announce Type: new Abstract: Machine learning demand forecasts optimize statistical accuracy yet leave excess operational volatility that inflates safety stock and amplifies the Bul

safetyarxiv-cs-lg

29 Jul 2026

ContractHIL-HLS: Contract-Aligned Multi-Agent Workflow with Hardware-in-the-Loop Feedback for HLS Design

arXiv:2607.25283v1 Announce Type: new Abstract: This paper presents ContractHIL-HLS, a contract-aligned multi-agent workflow for practical high-level synthesis (HLS) engineering. The workflow makes th

COVENANT: Natural-Language Workflow Compilation for Aligned Agent Execution

arXiv:2607.25400v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly entrusted with natural-language workflow instructions (e.g., retail-payment policies) that specify no

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization

arXiv:2607.25480v1 Announce Type: new Abstract: Constrained online convex optimization requires minimizing regret against adversarial convex costs while satisfying a convex constraint at every round,

Dataset Poisoning Attacks on Behavioral Cloning Policies

arXiv:2511.20992v2 Announce Type: replace Abstract: Behavior Cloning (BC) is a popular framework for training sequential decision policies from expert demonstrations via supervised learning. As these

Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks

arXiv:2607.25227v1 Announce Type: cross Abstract: Large Language Models (LLMs) have been widely applied in high-stakes decision-making scenarios such as corporate strategy, and users are increasingly

DensFiLM: Density-Conditioned Video Saliency for Crowd Scenes

arXiv:2607.25465v1 Announce Type: new Abstract: Video saliency models typically apply a single fixation strategy across crowd scenes, despite systematic changes in attention with crowd density. Sparse

safetyarxiv-cs-cv

Desktop-Delta Bench: Do Computer-Use Models Understand Desktop GUI Transitions?

arXiv:2607.26041v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly act through desktop GUIs to complete long-horizon tasks. Current benchmarks primarily measure end-task success o

Diff2DGS: Reliable Reconstruction of Occluded Surgical Scenes via 2D Gaussian Splatting

arXiv:2602.18314v2 Announce Type: replace Abstract: Real-time reconstruction of deformable surgical scenes is vital for advancing robotic surgery, improving intraoperative guidance, and enabling autom

DisasterTD: Disaster Toponym Disambiguation Using Multimodal LLMs and Cross-View Geolocalization

arXiv:2607.24856v1 Announce Type: cross Abstract: Social media imagery (SMI) provides timely and fine-grained ground perspectives that are valuable for situational awareness and emergency response. Un

DocAnnot -- Accelerating the Creation of Key Information Extraction Datasets with GenAI-Powered Auto-annotation

arXiv:2607.24745v1 Announce Type: cross Abstract: Key Information Extraction (KIE) is vital for many document applications, but creating training datasets is traditionally a time-consuming manual proc

Does Runtime Topology Context Improve LLM-Generated Kubernetes Security Patches?

arXiv:2607.25995v1 Announce Type: cross Abstract: Kubernetes is central to the cloud-native ecosystem, orchestrating containerised workloads. Recent work suggests that large language models (LLMs) can

DRIFT: Direct-Recursive Intervention-Conditioned Forecasting of ICU Physiological Trajectories

arXiv:2607.25864v1 Announce Type: new Abstract: Many time-series forecasts depend not only on prior observations but also on actions specified during the forecast period. In intensive care units (ICUs

researcharxiv-cs-lg

DSCD-Nav: Dual-Stance Cooperative Debate for Object Navigation

arXiv:2601.21409v3 Announce Type: replace Abstract: Adaptive navigation in unfamiliar indoor environments is crucial for household service robots. Despite advances in zero-shot perception and reasonin

safetyarxiv-cs-ro

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks

arXiv:2607.24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science. While graph neural networks (GNNs) such as CGCNN

Emergent Latent-State Computation under Stochastic Volatility

arXiv:2607.25459v1 Announce Type: cross Abstract: Mechanistic interpretability has largely focused on language models and deterministic toy tasks. Much less is known about how sequence models internal

Endpoint Replay: Compressing the Recency Buffer in Deep Reinforcement Learning

arXiv:2607.25123v1 Announce Type: new Abstract: Experience replay remains one of the most practical and useful algorithmic tools in the deep reinforcement learning (DRL) toolbox. Aside from the limite

Every Time I Hire a Linguist, Inference Costs Go Down: On Linguistic Rules as Effective Prompt Compressors

arXiv:2607.25335v1 Announce Type: cross Abstract: Prompt compression shortens LLM input to reduce inference cost, yet existing methods score token importance through LM forward passes. It remains ques

Everyone is unique: Towards Behaviorally Heterogeneous Negotiation Dialogue Systems for Debt Collection

arXiv:2607.25218v1 Announce Type: new Abstract: Debt collection is a critical negotiation task in the financial industry, with strong practical relevance and exceptional academic value as a behavioral

Face De-Identification: A Domain-Centric Survey from Capture to Processing

arXiv:2607.25926v1 Announce Type: cross Abstract: Face de-identification (De-ID) aims to remove or conceal personally identifiable facial features in images or videos to prevent identity recognition w

Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms

arXiv:2607.24960v1 Announce Type: cross Abstract: We present a neural network surrogate model that emulates the NRSur7dq4 gravitational waveform model for precessing binary black hole mergers. The sur

FIDAC: An Easy-to-use Pipeline to Extract and Interpret Interpersonal Distance From Video

arXiv:2607.25146v1 Announce Type: new Abstract: The distance between persons reveals significant information about their perception of each other. However, such information is not easily extractable a

FORGE: Frame Orthogonality in Relevance Geometry for Long-Form Video Understanding

arXiv:2607.25266v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have enabled long-form video understanding at a scale that was not previously possible. However, the density of

Tutorials

From Idea to Classroom in Days: Using 'Vibe Coding' to Create a Programming Process Visualizer from IDE Activity Logs

arXiv:2607.24757v1 Announce Type: cross Abstract: This paper reports on the rapid development and classroom deployment of a Thonny log visualizer built using AI-assisted ``vibe coding'' to make studen

tutorialsarxiv-cs-ai

FunnelAL: Retrieve-then-Rank Active Learning for Single-Class Discovery

arXiv:2607.25276v1 Announce Type: new Abstract: We present FunnelAL, a retrieve-then-rank active learning system for single-class discovery, which adapts the multi-stage funnel architecture of industr

Generalization from Low- to Moderate-Resolution Spectra with Neural Networks for Stellar Parameter Estimation: A Case Study with DESI

arXiv:2602.15021v2 Announce Type: replace-cross Abstract: Cross-survey generalization is a critical challenge in stellar spectral analysis, particularly in cases such as transferring from low- to mode

GLIDE: Guided Layerwise Hybrid Attention for Efficient LLM Inference

arXiv:2607.24788v1 Announce Type: new Abstract: As Large Language Models scale to increasingly long contexts, the memory I/O and computational overhead of the Key-Value (KV) cache during decoding emer

GraphRareBench: An Auditable Graph-Evidence Benchmark for Phenotype-Driven Rare-Disease Diagnosis

arXiv:2607.24878v1 Announce Type: cross Abstract: Phenotype-driven diagnostic benchmarks usually report the rank of the reference disease, but they rarely reveal which plausible alternatives are ranke

Grounded in Consensus, In Step With Emerging Science: A Consensus-Anchored Multi-Corpus Clinical Chatbot for Long COVID

arXiv:2607.25038v1 Announce Type: cross Abstract: Long COVID (LC) poses a challenge for clinical decision support because relevant evidence is distributed across sources with different update cycles,

Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM

arXiv:2508.05775v3 Announce Type: replace Abstract: Large Language Models (LLMs) have revolutionized content creation across digital platforms, offering unprecedented capabilities in natural language

safetyarxiv-cs-cl

Local Ai

HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone

arXiv:2607.25895v1 Announce Type: cross Abstract: Learning deployable manipulation policies is bottlenecked by the scarcity of data that is both high-fidelity and scalable. Real-robot teleoperation is

local-aiarxiv-cs-cv

HOLODECK 2.0: Vision-Language-Guided 3D World Generation with Editing

arXiv:2508.05899v3 Announce Type: replace Abstract: 3D scene generation plays a crucial role in gaming, artistic creation, virtual reality, and many other domains. However, current 3D scene design sti

HOME: Robust Hough-space Matching Method for Structured and Textureless Videos

arXiv:2607.25389v1 Announce Type: new Abstract: Visual front-ends for robotic localization typically rely on point-based features such as Oriented FAST and Rotated BRIEF (ORB), which frequently fail i

Tutorials

HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document

arXiv:2607.24861v1 Announce Type: cross Abstract: Question answering (QA) over complex documents requires models to retrieve and integrate evidence distributed across distant document regions and moda

tutorialsarxiv-cs-ai

Hybrid Analysis for Secure MCP Tool Use in LLM Agents

arXiv:2607.25297v1 Announce Type: cross Abstract: The rapid development of large language model (LLM) agents has enabled their broad adoption across diverse real-world tasks. To standardize interactio

Applications

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data

arXiv:2607.25710v1 Announce Type: new Abstract: Fall represents a significant risk of accidental death among individuals aged over 65, presenting a global health concern. A fall is defined as any even

applicationsarxiv-cs-cv

IMPRINT: Image-Conditioned Query Enrichment for Long-Tail Object Goal Navigation

model-releasesarxiv-cs-cv

arXiv:2607.25106v1 Announce Type: new Abstract: Embodied AI increasingly relies on queryable semantic maps built from pre-trained vision-language models to enable zero-shot Object Goal Navigation (Obj

Influence of Prompt Engineering on Small Language Models for Guarded Query Routing

model-releasesarxiv-cs-cl

arXiv:2607.24801v1 Announce Type: cross Abstract: We study the problem of guarded query routing, where we assume that a user query first meets a router that either determines the ideal endpoint for in

Interpretable GOHR Agents via Sparse Autoencoders

arXiv:2607.25132v1 Announce Type: new Abstract: A central challenge in interpreting learned decision-making systems is to determine whether their internal representations contain concepts that help ex

JobMatchAI-An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI

arXiv:2603.14558v3 Announce Type: replace Abstract: Recruiters and job seekers rely on search systems to navigate labor markets, making candidate matching engines critical for hiring outcomes. Most sy

LaP-Forensics: Latent-Pixel Consistency Guided Multimodal Reasoning for Deepfake Detection

model-releasesarxiv-cs-cv

arXiv:2607.25962v1 Announce Type: new Abstract: Recent generative models can produce images with few obvious visual artifacts, weakening detectors and explanations that rely only on surface appearance

Leak-Free Cross-Validated Stacking with Per-Architecture Calibration for Sand-Boil Segmentation in Earthen Levees

arXiv:2607.25367v1 Announce Type: new Abstract: Sand boils, points where water seeping beneath an earthen levee re-emerges at the surface, are early warnings of internal erosion, and deep segmentation

Learning from the Unseen: Offline Reinforcement Learning with Hidden Actions

arXiv:2607.25241v1 Announce Type: cross Abstract: Standard offline reinforcement learning (RL) algorithms typically assume that the actions in the dataset are observed without error. However, in many

Leveraging Semantic Maps for City-Scale Cross-View Localization

arXiv:2607.25215v1 Announce Type: cross Abstract: We want robots to localize in previously untraversed environments against commonly available prior data. Rich semantic data available from OpenStreetM

LGFNet: A CTC-Guided Local-Global Fusion Framework for Single-Channel Sleep Staging

arXiv:2607.25197v1 Announce Type: new Abstract: Sleep staging remains challenging due to long-range temporal dependencies, ambiguous stage transitions-particularly in N1-and substantial distribution s

safetyarxiv-cs-cv

Local Ai

Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models

arXiv:2602.10282v2 Announce Type: replace Abstract: Large language models (LLMs) have shown potential in identifying qualitative causal relations, but their ability to perform quantitative causal reas

local-aiarxiv-cs-lg

LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models

arXiv:2607.24892v1 Announce Type: cross Abstract: Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account