arXiv AI
Jun 19
Speeding up the annotation process in semantic segmentation industrial applications
arXiv:2606. 19934v1 Announce Type: cross Abstract: Current machine learning models commonly require large and well-annotated datasets.
Benchmarks and evaluation
Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv AI
Jun 19
Evaluation of EEG Foundation Models for Event-Based Burst-Suppression Detection in ICU
arXiv:2606. 20074v1 Announce Type: cross Abstract: Burst suppression (BS) is a clinically relevant electroencephalographic (EEG) pattern used to monitor sedation depth and brain activity in critically ill patients, particularly during induced coma in Intensive Care Units (ICUs).
arXiv Machine Learning
Jun 19
Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL
arXiv:2605. 05481v2 Announce Type: replace Abstract: We revisit a classic "chicken-and-egg" problem in reinforcement learning: to safely improve a policy, the value function must be accurate on the state-visitation distribution of the updated policy.
arXiv AI
Jun 19
BIM-Edit: Benchmarking Large Language Models for IFC-Based Building Information Modeling
arXiv:2606. 20146v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to computer-aided design (CAD) to generate design artifacts from textual instructions.
arXiv AI
Jun 19
Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning
arXiv:2605. 22748v2 Announce Type: replace-cross Abstract: Autonomous systems have achieved superhuman performance in isolation or simulation, yet they remain brittle in shared, dynamic real-world spaces.
arXiv AI
Jun 19
CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generation
arXiv:2605. 10873v2 Announce Type: replace-cross Abstract: Recovering editable CAD programs from images or 3D observations is central to AI-assisted design, but progress is difficult to measure because existing evaluations are fragmented across datasets, modalities, and metrics.
arXiv AI
Jun 19
The Autonomy Tax: Defense Training Breaks LLM Agents
arXiv:2603. 19423v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents increasingly rely on external tools (file operations, API calls, database transactions) to autonomously complete complex multi-step tasks.
arXiv AI
Jun 19
Reinforcement-aware Knowledge Distillation for LLM Reasoning
arXiv:2602. 22495v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) post-training has recently driven major gains in long chain-of-thought reasoning large language models (LLMs), but the high inference cost of such models motivates distillation into smaller students.
arXiv AI
Jun 19
Beyond Accuracy: Measuring Logical Compliance of Predictive Models
arXiv:2606. 20208v1 Announce Type: new Abstract: Machine learning models are predominantly evaluated through predictive performance metrics such as ranking quality, prediction error, or classification accuracy.
arXiv AI
Jun 19
StaminaBench: Stress-Testing Coding Agents over 100 Interaction Turns
arXiv:2606. 19613v1 Announce Type: cross Abstract: We introduce StaminaBench, a benchmark that measures the stamina of coding agents: how many consecutive interaction turns (change requests) they can handle before failing.
arXiv Machine Learning
Jun 19
Comparative Study on Agility, Efficiency, and Impact Absorption of Bipedal Robots with Active Toes
arXiv:2606. 19699v1 Announce Type: cross Abstract: Human legs exhibit high efficiency, agility, and impact absorption, with toes playing a crucial role in these capabilities.
arXiv AI
Jun 19
FAPO: Fully Autonomous Prompt Optimization of Multi-Step LLM Pipelines
arXiv:2606. 19605v1 Announce Type: cross Abstract: Multi-step LLM pipelines fail through interactions among retrieval, reasoning, and formatting steps, so prompt-only optimization can miss bottlenecks in the chain.
arXiv Machine Learning
Jun 19
A Solver-Free Training Method for Predict-then-Optimize
arXiv:2606. 19587v1 Announce Type: cross Abstract: We propose a scalable method for training prediction (machine learning) models in the predict-then-optimize paradigm, where model outputs serve as coefficients for a subsequent linear optimization task.
arXiv AI
Jun 19
Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software
arXiv:2606. 20502v1 Announce Type: cross Abstract: Whether LLMs scoring well on vulnerability benchmarks genuinely reason about security or merely pattern-match on contaminated data remains unresolved.
arXiv AI
Jun 19
AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models
arXiv:2509. 25148v2 Announce Type: replace Abstract: Post-training alignment of large language models often combines supervised fine-tuning (SFT) on expert demonstrations with reinforcement learning (RL) from preference or verifiable feedback.
arXiv AI
Jun 19
CSWinUNETR: Segmentation of Thin Anatomical Structures in Medical Images
arXiv:2606. 19824v1 Announce Type: cross Abstract: Accurate segmentation of thin, tortuous anatomical structures, such as retinal vessels, cerebral vasculature, and facial wrinkles, remains challenging due to low contrast, frequent discontinuities, and severe class imbalance.
arXiv Machine Learning
Jun 19
Prompt, Plan, Extract: Zero-Shot Agentic LLMs Workflows for Lung Pathology Extraction from Clinical Narratives
arXiv:2606. 19852v1 Announce Type: cross Abstract: Information extraction from pathology reports is essential for cancer staging, tumor registry population.
arXiv AI
Jun 19
Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models
arXiv:2510. 21978v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning and has become a standard post-training paradigm for contemporary language and vision-language models.
arXiv AI
Jun 19
Neural Additive and Basis Models with Feature Selection and Interactions
arXiv:2606. 19850v1 Announce Type: cross Abstract: Deep neural networks (DNNs) exhibit attractive performance in various fields but often suffer from low interpretability.
arXiv AI
Jun 19
ScholarQuest: A Taxonomy-Guided Benchmark for Agentic Academic Paper Search in Open Literature Environments
arXiv:2606. 20235v1 Announce Type: cross Abstract: Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for iterative, intent-driven literature exploration.