Search: “framework”

E-S2Feat:Semantic-Guided Spiking Local Feature Detection and Description for Event Cameras

arXiv:2608.14027v1 Announce Type: new Abstract: Benefiting from high temporal resolution and dynamic range, event-based local feature methods have attracted increasing attention. However, event sparsi

local-aiarxiv-cs-cv

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EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models

DGX agent

arXiv:2608.13676v1 Announce Type: new Abstract: Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores

researcharxiv-cs-lg

17 Aug 2026

Emergent Models: Intelligence from Tiny Substrates

arXiv:2608.14019v1 Announce Type: cross Abstract: Emergent Models (EMs) are a machine learning paradigm based on simple yet open-ended substrates, such as cellular automata, in which modeling is treat

researcharxiv-cs-lg

Engineering Reliable Coding Agents: Evaluating and Operating the System Around the Model

arXiv:2608.13867v1 Announce Type: cross Abstract: AI coding agents are commonly evaluated as models but deployed as systems. Their reliability depends not only on model capability, but on the harness,

Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes

arXiv:2608.14481v1 Announce Type: cross Abstract: As heterogeneous robotic systems deploy across diverse urban zones, maintaining safety amid complex human-robot interactions remains a critical challe

Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT

arXiv:2608.13681v1 Announce Type: cross Abstract: Translating C code into safe, idiomatic Rust is a longstanding software-engineering goal because it can eliminate entire classes of memory-safety vuln

High-dimensional nonparametric changepoint detection via low-rank degree-two density projection

model-releasesarxiv-cs-lg

arXiv:2608.13922v1 Announce Type: new Abstract: Detecting distributional changes in high dimension is difficult when neither the pre-change nor post-change density is parametrically specified. We intr

How Compliant is Sepsis Treatment? An Expert-Guided Neuro-symbolic Pipeline for Generating Clinical Compliance Insights

arXiv:2608.13617v1 Announce Type: new Abstract: Verifying whether clinical care follows evidence-based protocols is a natural neuro-symbolic problem, yet the safety-critical setting defeats either par

Applications

How Much Do Legal RAG Systems Still Hallucinate?

arXiv:2608.14210v1 Announce Type: cross Abstract: Hallucination is a major challenge for retrieval-augmented generation (RAG) systems in the legal domain, where ungrounded answers can lead to serious

applicationsarxiv-cs-ai

IRGNN: Efficient Invariant Radar Graph Neural Network for Radar Point Cloud Object Detection

arXiv:2608.14394v1 Announce Type: new Abstract: Perception is a fundamental component of autonomous driving systems. While LiDAR-based methods have achieved remarkable progress in object detection, th

local-aiarxiv-cs-cv

Knowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations

arXiv:2608.14425v1 Announce Type: new Abstract: LLM evaluations often use fixed sampling budgets, testing every item the same number of times even after estimates are precise. We introduce optstop, a

Label-Free Deep-Tissue Peripheral Nerve Detection with a Handheld Multimodal OCT Probe and NerveDetNet

arXiv:2608.13807v1 Announce Type: cross Abstract: Peripheral nerves buried beneath intact tissue are difficult to visualize during surgery and remain inaccessible to white light wide-field imaging and

researcharxiv-cs-cv

LLM-Advisor: An LLM Advisor for Cost-efficient Path Planning across Multiple Terrains

arXiv:2503.01236v3 Announce Type: replace-cross Abstract: This paper addresses fixed-graph terrain-aware path refinement, in which a global planner is restricted to a predefined route space and may re

Tutorials

MathForm: Scaling Mathematical Autoformalization with Knowledge Retrieval and Verification-Guided Refinement

arXiv:2608.14221v1 Announce Type: new Abstract: Autoformalization is commonly framed as translating natural-language mathematical statements into machine-verifiable formal languages such as Lean 4. Ho

tutorialsarxiv-cs-ai

Measuring Fairness in Large Audio Language Models via Semantic-Aware Bias Estimation

arXiv:2608.13624v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) have seen increasing use for audio understanding tasks such as speech recognition and audio question answering, ra

Meteorology-driven Causal Nowcasting of Fugitive Landfill Emissions Enables Proactive Public Health Response

arXiv:2608.14254v1 Announce Type: cross Abstract: Fugitive emissions from waste sites increasingly expose communities to toxic and odorous gases, yet public-health responses remain largely retrospecti

Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processes

arXiv:2608.14367v1 Announce Type: cross Abstract: The early detection of delayed cases in business processes is a critical capability for organizations. Predictive process monitoring (PPM) supports th

Modular Cognitive Architecture Emerges in Large Language Models

arXiv:2608.13567v1 Announce Type: new Abstract: The human brain exhibits a striking degree of functional specialization, with distinct networks supporting language, formal reasoning, reasoning about o

Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model

model-releasesarxiv-cs-lg

arXiv:2602.15572v3 Announce Type: replace Abstract: Agent-based modelling (ABM) is a widespread approach to simulate complex systems. Advancements in computational processing and storage have facilita

OctoSplat: Hybrid OctoMap-Gaussian Splatting for Active Semantic Mapping and Phenotyping with Horticultural Robots

arXiv:2601.12122v2 Announce Type: replace Abstract: Semantic reconstruction of agricultural scenes plays a vital role in tasks such as phenotyping and yield estimation. However, traditional approaches

researcharxiv-cs-ro

Optimal Power Allocation and AI Receiver Design for Superimposed DMRS and Data Transmission

arXiv:2608.13809v1 Announce Type: cross Abstract: In this paper, we consider transmissions with superimposed (SI) demodulation-reference-symbol (DMRS) and data in orthogonal frequency-division multipl

Ordinal-Aware Calibration for Ordinal Classification

model-releasesarxiv-cs-cv

arXiv:2410.15658v4 Announce Type: replace-cross Abstract: Deep neural networks frequently produce overconfident, miscalibrated predictions. In ordinal classification, predictions must also adhere to a

PILOT: Privileged Imitation Learning for End-to-End Motion Planning of Autonomous UAVs under Partial Observability

arXiv:2608.14082v1 Announce Type: new Abstract: Autonomous navigation in cluttered environments is hampered by partial observability and dynamic constraints. This paper presents PILOT, a constraint-aw

safetyarxiv-cs-ro

PRM-as-a-Judge 1.5: A Toolkit for Robot Process Assessment

model-releasesarxiv-cs-cv

arXiv:2608.14284v1 Announce Type: cross Abstract: Fine-grained robotic evaluation matters for understanding embodied models, going beyond binary success rates and rule-based process scores. We present

Proxy-Validated LLM UX Micro-Simulations: An Artifact-First Protocol for Early-Stage Decision Support

arXiv:2608.13563v1 Announce Type: cross Abstract: Early-stage teams often lack users, time, and budget to run repeated UX studies, yet still need decision-oriented signals to iterate safely. We study

Applications

Reading Between The Lines: Modeling and Evaluating Behavioral Realism in Legal Simulation

arXiv:2608.13712v1 Announce Type: cross Abstract: Deposition training requires attorneys to manage dynamic witness behavior, yet legal-AI evaluations largely focus on factual accuracy, reasoning, or r

applicationsarxiv-cs-ai

Regime-Conditional Verification: Correctness Estimation for Adapting and Monitoring Safety Classifiers

arXiv:2608.14089v1 Announce Type: new Abstract: Safety classifiers deployed with large language models often fail for two reasons: their decisions reflect the policy learned during training rather tha

Reinforcement Learning-Based Production Scheduling in an Industry-Based Coating Scenario Using the Digital Model Playground

arXiv:2608.14122v1 Announce Type: new Abstract: Production scheduling in complex manufacturing environments is challenging when sequence-dependent setup times, stochastic disturbances, and due-date co

Removing Temporal Note Redundancy Improves Multimodal Reinforcement Learning for Medicine

arXiv:2608.14157v1 Announce Type: new Abstract: Mechanical ventilation is a critical life-support intervention, requiring dynamic adjustments to ventilator settings as a patient's condition evolves. W

Separation capacity of linear reservoirs with random connectivity matrix

arXiv:2404.17429v4 Announce Type: replace-cross Abstract: A natural hypothesis for the success of reservoir computing in generic tasks is the ability of the untrained reservoir to map distinct input t

researcharxiv-cs-lg

Shift Aware Transfer Learning with Adaptive Dual-Encoder Fusion for PM Forecasting in Data-Limited Environments

arXiv:2608.14456v1 Announce Type: new Abstract: Short-horizon forecasting of fine particulate matter (PM2.5) remains difficult when observations from the target domain are limited and the statistical

SimpleOPD: Simple Tokenizer-Agnostic On-Policy Distillation for Long-Context Reasoning

arXiv:2608.14277v1 Announce Type: cross Abstract: On-policy distillation (OPD) offers a promising way to transfer reasoning capabilities from stronger teacher models, but applying it to long-context r

SPARGen: Unifying Spatial Perception and Reasoning through Native Multimodal Generation

arXiv:2608.14138v1 Announce Type: cross Abstract: Spatial perception and reasoning from visual observations require recovering geometric structure, establishing correspondences, and understanding spat

Stable Miscalibration in Large Language Models: A Practical View of High-Confidence Errors

arXiv:2608.13591v1 Announce Type: new Abstract: High-confidence errors in large language models are often treated as evidence of fragile internal inference. We study a different possibility: stable mi

Structure-Guided Spatiotemporal Attention Graph Neural Network for Traffic Flow Prediction

arXiv:2608.14177v1 Announce Type: cross Abstract: Deep spatiotemporal models integrating graph convolutions and attention mechanisms have demonstrated excellent performance in network-level traffic fl

Towards Efficient Multimodal and Multilingual Opinion Extraction for STI: A QLoRA-Based Fine-Tuning Approach

arXiv:2608.14152v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have reshaped semantic analysis. Opinion Extraction (OE) for Science and Technology Intelligence (STI) r

TRIAGE: Risk-Controlled Pseudo-Label Admission for Annotation-Efficient Semi-Supervised Retinal OCT Classification

arXiv:2608.14321v1 Announce Type: new Abstract: The advanced retinal disease diagnosing imaging modality, optical coherence tomography (OCT), encounters a lack of automation because of the high expens

researcharxiv-cs-cv

Applications

TRUE-Colon: Exposing a Consistent Transfer Asymmetry in Real-Time Polyp Detection

arXiv:2608.13711v1 Announce Type: cross Abstract: Computer-aided detection (CADe) systems for colonoscopy promise to reduce clinical miss rates, yet reliable real-world deployment remains elusive. Thi

applicationsarxiv-cs-cv

Agent Behavioral Contracts II: Certifying Compositional Reliability Without Assuming Independence

arXiv:2608.12895v1 Announce Type: new Abstract: Compositional reliability bounds for multi-agent systems multiply component reliabilities, a step licensed by a conditional-independence assumption that

AI and Consumer Rights in India Working Paper

arXiv:2608.12863v1 Announce Type: new Abstract: As AI systems proliferate in consumer facing applications, questions about liability for AI related harms remain unresolved. This working paper examines

AirForesight: Current-to-Future Spatial Map Imagination with Cross-Space Planning Consistency for UAV-VLN

arXiv:2608.12835v1 Announce Type: new Abstract: Unmanned Aerial Vehicle Vision-Language Navigation (UAV-VLN) requires agents to follow language instructions, infer spatial structure from sparse multi-

researcharxiv-cs-ro

Are you Talking Logic to Me? Assessing Language Models Syllogistic Reasoning Capabilities

arXiv:2608.12374v1 Announce Type: cross Abstract: Language models (LMs) struggle with logical tasks like reasoning on syllogisms. It has been shown that Knowledge Representation (KR) plays a crucial r

Auditable agentic AI for evidence-grounded thyroid ultrasound diagnosis and reporting

arXiv:2608.12590v1 Announce Type: new Abstract: Thyroid ultrasound diagnosis requires coordinated lesion localization, measurement, risk stratification and reporting, yet most AI systems address these

AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design

arXiv:2608.13560v1 Announce Type: cross Abstract: Transforming multimodal sources into condensed and structured media outputs can be fundamentally conceptualized as a long-horizon agentic process cent

Behavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds

arXiv:2608.13069v1 Announce Type: new Abstract: Large language models (LLMs) are predominantly aligned to function as passive, sycophantic assistants. We challenge this default paradigm by empirically

Branch and Bound for Relational Verification of Neural Networks

arXiv:2608.13118v1 Announce Type: new Abstract: Verification of neural networks against relational specifications, such as global robustness, is crucial for safety-critical applications of cyber-physi

safetyarxiv-cs-lg

Capability Sheaves for Compositional Agent-Harness Repair: Controlled Quotients and a Real-Repository Stress Test

arXiv:2608.13228v1 Announce Type: new Abstract: Agent harnesses combine retrieval, routing, state, provenance, and verification, but locally successful components may disagree on shared state. We mode

Concept Drift Detection and Adaptive Retraining of Malware Classification Models

arXiv:2608.13465v1 Announce Type: cross Abstract: Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Mach