Knowledge catalogue
Agent evals are repeatable tests that score whether AI agents completed a task correctly. Learn how to design rubrics, test suites, and trace-based evals that catch failures and prevent reward hacking
arXiv:2606.30850v1 Announce Type: new Abstract: Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic unce
arXiv:2606.31207v1 Announce Type: new Abstract: The rapid advance of smart cities increasingly depends on trajectory data mining, yet underrepresented demographic groups, particularly the elderly, are
arXiv:2606.28684v1 Announce Type: cross Abstract: Causally linking disease-related factors to image-derived biomarkers provides a powerful pathway to understanding disease mechanisms. Despite growing
arXiv:2606.28593v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) have achieved significant improvements on static visual-to-code tasks such as generating code for webpages,
arXiv:2510.03310v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate human behavior in business, economics, and the social sciences, offering a low-
arXiv:2606.28465v1 Announce Type: cross Abstract: This work examines perturbation generalization in spatial foundation-model embeddings derived from fluorescence microscopy images. Although these mode
arXiv:2606.29957v1 Announce Type: cross Abstract: Most coding-agent benchmarks are static: an agent receives a complete task description up front and is judged only by its final code. Real coding assi
arXiv:2606.28615v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in high-stakes domains, where free-text explanations such as chain-of-thought and post-hoc rati
arXiv:2606.28332v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for medical and health-related questions, yet their safety in high-risk medical scenarios remains p
Explore how the GitHub Copilot agentic harness delivers strong results across multiple benchmarks and leading token efficiency, while maintaining flexibility to choose among more than 20 models. The p
arXiv:2606.19416v2 Announce Type: replace Abstract: Loan origination is the process by which a lender creates a new loan, from application and underwriting through approval and funding. This process s
arXiv:2606.22274v1 Announce Type: cross Abstract: Low-resource African languages lack text corpora needed for language model training. We investigate whether ASR pipelines can extend text resources fo
arXiv:2606.23669v1 Announce Type: new Abstract: Text-to-image models can generate visually plausible city streets, but whether their outputs correspond to a requested road segment rather than a generi
arXiv:2601.11541v2 Announce Type: replace-cross Abstract: To address the scalability of feedback in computer science while mitigating the privacy and cost limitations of commercial Large Language Mode
arXiv:2604.01039v2 Announce Type: replace-cross Abstract: System Instructions in Large Language Models (LLMs) are commonly used to enforce safety policies, define agent behavior, and protect sensitive
arXiv:2606.07548v1 Announce Type: cross Abstract: The MedHopQA challenge presents a critical test for Large Language Models (LLMs): complex, multi-hop reasoning in the high-stakes biomedical domain. T
arXiv:2606.08013v1 Announce Type: new Abstract: Catastrophic forgetting, the abrupt loss of previously acquired knowledge upon learning new information, remains the central challenge in Continual Lear
arXiv:2510.20182v2 Announce Type: replace Abstract: Pedestrian simulation traditionally relies on expert-tuned, hand-crafted models that limit scalability and generalization. Meanwhile, large-scale vi
arXiv:2606.08091v1 Announce Type: new Abstract: Recent agent frameworks such as Claude Code, Codex, and OpenClaw are strong at tool use and orchestration, but whether they can handle long video genera
arXiv:2606.07300v1 Announce Type: new Abstract: Language is a vehicle for thought, intricately tied to sounds, symbols, and meaning. However, most large language model (LLM) research focuses on meanin
arXiv:2606.07103v1 Announce Type: new Abstract: Style classifiers can use content cues that correlate with style labels in naturally collected data, yet we lack a systematic way to measure this relian
arXiv:2606.07237v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in healthcare for tasks such as clinical question answering, diagnosis support, and report summariz
arXiv:2606.05548v1 Announce Type: cross Abstract: The rapid proliferation of Agent Development Kits (ADKs), SDK-level frameworks for building LLM-powered autonomous agents, has outpaced any empirical
arXiv:2606.05622v1 Announce Type: new Abstract: Planning for real-world problems by language models often involves both world and user constraints, which may not be fully specified upfront and are pro
arXiv:2606.05180v1 Announce Type: new Abstract: Automated scoring models are increasingly used to assign rubric-based quality ratings to complex language performances, including classroom transcripts,
arXiv:2606.06266v1 Announce Type: new Abstract: Hate speech detection is inherently subjective: people from different demographic groups perceive the same content very differently. Collecting enough a
arXiv:2510.22768v2 Announce Type: replace Abstract: As autonomous agents increasingly interact, they inevitably attempt to influence one another. While prior work in text-only settings has explored th
arXiv:2606.04072v1 Announce Type: cross Abstract: Deep learning models are increasingly central to autonomous vehicle (AV) pipelines, yet their integration has traditionally followed a monolithic desi
arXiv:2602.23312v3 Announce Type: replace-cross Abstract: Leader-follower interaction is an important paradigm in human-robot interaction (HRI). Yet, assigning roles in real time remains challenging f
arXiv:2401.07386v5 Announce Type: cross Abstract: This study expands on previous work that introduced the AIcon2abs method (AI from Concrete to Abstract: Demystifying Artificial Intelligence to the ge
arXiv:2606.02791v1 Announce Type: new Abstract: Watershed networks exhibit convergent topologies in which multiple tributaries merge into downstream channels,integrating diverse upstream hydrological
arXiv:2606.03858v1 Announce Type: new Abstract: Despite the pivotal role of numerical reasoning as the cornerstone of mathematical capabilities in large language models (LLMs) across applications, few
arXiv:2606.02835v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assu
arXiv:2606.02965v1 Announce Type: new Abstract: Benchmarks for autonomous agents measure whether agents complete tasks, yet this framing is systematically blind to whether an agent should have proceed
arXiv:2606.03238v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) makes large-scale post-training possible by replacing an underspecified human objective with learned
arXiv:2602.09492v2 Announce Type: replace-cross Abstract: Low-rank adaptation (LoRA) is a standard approach for fine-tuning large language models, yet its many variants report conflicting empirical ga
arXiv:2606.02016v1 Announce Type: new Abstract: Algorithm Selection (AS) aims to automatically identify the most suitable optimization algorithm for a given problem instance by leveraging measurable p
arXiv:2606.00750v1 Announce Type: new Abstract: Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding. However, existing
arXiv:2606.00622v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) demonstrate remarkable visual understanding, yet their reliability in interactive settings is severely undermin
arXiv:2606.00832v1 Announce Type: new Abstract: Recent advances in agentic AI have enabled agents to complete complex tasks through tool use, reasoning, and multi-step planning. Yet existing benchmark
arXiv:2606.02010v1 Announce Type: cross Abstract: PlanarBench tests whether LLMs can draw planar graphs as ASCII art given only an edge list -- a spatial reasoning task that resists memorization becau
arXiv:2605.31506v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is the current industry standard for grounding AI in real-world facts. Traditional retrieval methods rely on keyw
arXiv:2605.30646v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in clinical applications. However, their behavior remains highly sensitive to subtle linguistic var
arXiv:2605.30200v1 Announce Type: new Abstract: The double-edged sword of integrating Large Language Models (LLMs) requires an effective triadic collaboration mechanism among LLMs, teachers and studen
arXiv:2605.27618v1 Announce Type: new Abstract: Despite the wide use of explainability techniques to attempt to understand the behavior of Artificial Intelligence (AI), the generated explanations may
arXiv:2605.27950v1 Announce Type: new Abstract: Accurately assessing dietary behavior change receptivity is essential for designing effective just-in-time adaptive interventions (JITAIs) that promote
arXiv:2605.28598v1 Announce Type: cross Abstract: LLM-powered social agents are increasingly used to simulate online social behavior, yet their realism remains difficult to validate. Existing work has
arXiv:2501.06708v5 Announce Type: replace-cross Abstract: Large-scale web-crawled datasets contain noise, bias, and irrelevant information, necessitating data selection techniques. Existing methods de
arXiv:2605.26955v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed to users around the world, they are integrated into everyday tasks across diverse cultural c
arXiv:2601.07737v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in mainstream visual understanding tasks, but their ability
arXiv:2605.25696v1 Announce Type: new Abstract: The process of decision-making in football is characterized by a complex interplay between spatial positioning, opponent pressure, and player intent. Th
arXiv:2605.24286v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning is useful for monitoring language models only when the reasoning trace faithfully reflects the computation that produ
arXiv:2605.24351v1 Announce Type: new Abstract: Large language models (LLMs) can support scientific literature synthesis, but remain prone to hallucinated references, uneven coverage, and weakly groun
arXiv:2605.24417v1 Announce Type: new Abstract: Supervised classification for tabular data remains a core machine learning task, yet its reliance on large labeled datasets limits applicability in data
arXiv:2601.03260v2 Announce Type: replace-cross Abstract: AI agents have seen widespread adoption in information retrieval for scientific research, giving rise to tools such as Deep Research. However,
arXiv:2605.20200v1 Announce Type: cross Abstract: This article presents a multimodal emotion recognition module integrated into a proactive Socially Interactive Agent (SIA) powered by generative artif
arXiv:2605.20920v1 Announce Type: new Abstract: Recent advances in machine learning and the availability of articulatory datasets allow vocal tract synthesis to be conditioned on phonetic sequences, a
arXiv:2510.08482v3 Announce Type: replace-cross Abstract: Iconicity, the resemblance between linguistic form and meaning, is pervasive in signed languages, offering a natural testbed for visual ground
arXiv:2605.19529v1 Announce Type: new Abstract: When the same LLM generates assessment items, simulates student responses, and scores them, the validation loop is self-referential. We introduce Genera