Knowledge catalogue
arXiv:2510.07315v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have catalyzed vibe coding, where users leverage LLMs to generate and iteratively refine code through natural lan
arXiv:2512.20627v2 Announce Type: replace-cross Abstract: Intent-Based Networking (IBN) offers a promising paradigm for intelligent and automated network control in Industrial Internet of Things (IIoT
arXiv:2606.05403v1 Announce Type: cross Abstract: Language models increasingly act as epistemic proxies, synthesizing evidence from multiple sources to inform decisions. Whether they evaluate the qual
arXiv:2606.05531v1 Announce Type: cross Abstract: Despite the rapid progress of Vision-Language Models (VLMs), the field lacks benchmarks that rigorously diagnose their true reasoning abilities and ch
arXiv:2606.05308v1 Announce Type: cross Abstract: With PRECISE, we extended Prediction-Powered Inference to produce bias-corrected estimates of ranking evaluation metrics by combining a small human-la
arXiv:2510.20042v3 Announce Type: replace Abstract: Generative image models produce striking visuals yet often misrepresent culture. Prior work has examined cultural bias mainly in text-to-image (T2I)
This article discusses methods and tools for evaluating routing policies in machine learning models, likely covering techniques to validate that model routing decisions are effective and functioning a
arXiv:2602.16149v2 Announce Type: replace Abstract: Instruction-guided image-to-image (I2I) editors are increasingly used in consumer and professional visual workflows, where trustworthiness depends n
arXiv:2508.08237v4 Announce Type: replace-cross Abstract: The emergence of audio-visual foundation models underscores the importance of reliably assessing their multi-modal understanding. The VGGSound
Brendan Bordelon / Politico: OpenAI diverges from Trump's AI EO in a new policy paper, proposing cyber risk evaluations for advanced AI systems be mandatory and led by CAISI, not the NSA — OpenAI's ne
arXiv:2606.03980v1 Announce Type: cross Abstract: Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) p
arXiv:2606.00027v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed across healthcare, yet existing benchmarks fail to capture model behavior under adversarial or
arXiv:2606.02461v1 Announce Type: new Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future e
arXiv:2403.07008v3 Announce Type: replace-cross Abstract: The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming. AI-labeled synthetic data c
arXiv:2606.01789v1 Announce Type: new Abstract: In graphical causal model, causal discovery aims to construct a causal graph based on numerical data and domain knowledge in plain text. However, the ev
Continual Learning involves engineering whole systems including Efficient Verifiers to make RL/fine-tuning and running evaluations much cheaper at scale! some initial work we’re releasing from LangCha
arXiv:2606.00875v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for tasks involving creative problem solving and idea generation. However, there is a lack of consens
arXiv:2606.02535v1 Announce Type: new Abstract: Large-scale generative models have demonstrated remarkable capabilities across image generation and editing tasks. However, their performance in low-lev
arXiv:2606.00188v1 Announce Type: cross Abstract: While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle. To p
arXiv:2605.30590v1 Announce Type: cross Abstract: Two clinical AI systems can score nearly identically on coverage-based rubrics yet behave radically differently when their patient inputs change: one
arXiv:2605.29698v1 Announce Type: new Abstract: Machine learning for molecular property prediction has focused largely on pure compounds, even though many practical applications depend on mixtures wit
arXiv:2605.29462v1 Announce Type: cross Abstract: The emergence of Large Vision-Language Models (LVLMs) has substantially expanded model capabilities beyond text-only understanding, enabling unified i
arXiv:2605.29256v1 Announce Type: cross Abstract: Role-playing with large language models is fundamentally a session-level task, requiring agents to sustain character identity and interaction quality
arXiv:2509.23074v3 Announce Type: replace-cross Abstract: In the era of increasingly complex AI models for time series forecasting, progress is often measured by marginal improvements on benchmark lea
arXiv:2512.15791v2 Announce Type: replace-cross Abstract: In Artificial Intelligence (AI), language models have gained significant importance due to the widespread adoption of systems capable of simul
arXiv:2512.14340v3 Announce Type: replace Abstract: Interest in utilizing autonomous uncrewed aerial vehicles (UAVs) for under-canopy forest remote sensing has increased in recent years, resulting in
arXiv:2605.28597v1 Announce Type: cross Abstract: This position paper argues that the AI/ML community should stop overclaiming and retire the label 'positive backdoor,' and instead treat trigger-activ
arXiv:2602.16449v2 Announce Type: replace-cross Abstract: Generative model evaluation commonly relies on high-dimensional embedding spaces to compute distances between samples. We show that dataset re
arXiv:2509.26600v2 Announce Type: replace-cross Abstract: As LLMs rapidly saturate existing benchmarks, automated benchmark creation using LLMs (LLM-as-a-benchmark) -- where a model generates test inp
arXiv:2605.25240v1 Announce Type: cross Abstract: Two methodologies dominate current practices of benchmarking: rubric-based scoring evaluates items against predefined criteria, whereas comparative ju
arXiv:2605.25819v1 Announce Type: new Abstract: Membership inference attacks (MIAs) are popular methods for empirically assessing the leakage of sensitive information in the training data through mode
arXiv:2605.25933v1 Announce Type: cross Abstract: Posttraumatic stress disorder (PTSD) is a prevalent and debilitating mental health condition with significant personal and societal impacts. Current c
arXiv:2605.24902v1 Announce Type: cross Abstract: Reasoning-enabled LLMs perform strongly on medical reasoning benchmarks, but it remains unclear whether these gains transfer to structured clinical do
arXiv:2512.24000v3 Announce Type: replace Abstract: Distinguishing fake or untrue news from satire or humor poses a unique challenge due to their overlapping linguistic features and divergent intent.
arXiv:2605.23093v1 Announce Type: new Abstract: Topic modeling in applied psychology increasingly spans two methodological traditions: probabilistic bag-of-words models and newer embedding-based appro
arXiv:2602.05472v2 Announce Type: replace Abstract: The quest for expert-level reasoning in Large Language Models (LLMs) has been hampered by a persistent extit{reward bottleneck}: traditional reinfor
arXiv:2605.23595v1 Announce Type: cross Abstract: The rapid growth of machine learning has produced an ever-expanding ecosystem of models, making it increasingly challenging to verify the reliability
arXiv:2604.00003v2 Announce Type: replace-cross Abstract: Extracting structured information from academic PDF documents is non trivial: a single page typically combines free text metadata with tabular
arXiv:2605.22608v1 Announce Type: new Abstract: Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious
arXiv:2510.08759v2 Announce Type: replace Abstract: Understanding the capability bottlenecks of embodied multimodal large language models (MLLMs) is crucial for improving embodied agents. However, exi
arXiv:2510.09724v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly capable of generating complete applications from natural language instructions, creating new opp
arXiv:2605.21800v1 Announce Type: cross Abstract: World models are central to building agents that can reason, plan, and generalize beyond their training data. However, research on world models is cur
Self-Evolving in the Wild：Over the course of ~35 hours of continuous autonomous execution, the model performed 432 kernel evaluations across 1,158 tool calls. It wrote, compiled, profiled, and iterati
arXiv:2605.20763v1 Announce Type: new Abstract: Rapid progress in aerodynamic shape optimization (ASO) has outpaced currently-available standardized evaluation frameworks. Fair comparison requires a u
arXiv:2605.19986v1 Announce Type: cross Abstract: Fine-grained manipulation marks a regime where global scene context no longer suffices, and success hinges on the tight coupling of local attribute gr
arXiv:2605.20165v1 Announce Type: new Abstract: Vision-Language Models (VLMs) achieve strong performance on spatial question answering benchmarks, yet it remains unclear whether such gains reflect gen
arXiv:2605.19233v1 Announce Type: cross Abstract: Unmanned aerial vehicles (UAVs) are cyber-physical systems whose attack surface spans networked avionics and on-board sensor fusion: a compromised GPS
arXiv:2605.18700v1 Announce Type: new Abstract: Prior work on fine-grained image recognition (FGIR) has established the importance of the backbone selection, but has neglected the accuracy-vs-cost tra
arXiv:2605.17775v1 Announce Type: cross Abstract: Large language models (LLMs) can generate or synthesize clinical text for a wide range of applications, from improving clinical documentation to augme
arXiv:2605.15246v1 Announce Type: new Abstract: Trajectory data is fundamental to modern urban intelligence, yet its sensitivity raises significant privacy concerns. Generative models such as Generati
arXiv:2602.20571v2 Announce Type: replace Abstract: Many benchmarks for automated causal inference evaluate a system's performance based on a single numerical output, such as an Average Treatment Effe
arXiv:2605.15108v1 Announce Type: cross Abstract: Off-policy evaluation (OPE) estimates the value of a target treatment policy (e.g., a recommender system) using data collected by a different logging
arXiv:2507.07776v3 Announce Type: replace Abstract: Unrestricted adversarial attacks aim to fool computer vision models without being constrained by ell_p-norm bounds to remain imperceptible to humans
We’re releasing a 30B-A3B reasoning model that reaches gold-medal level across both physics and math Olympiad evaluations: IPhO directly, and IMO/USAMO with test-time self-verification and refinement.
arXiv:2505.15616v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved significant advances in integrating visual and linguistic information, yet their ability to r
arXiv:2605.11504v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have enabled agentic systems for complex, multi-step tasks; cybersecurity is emerging as a prominent app
Our evaluations show that frontier AI's cyber capabilities are advancing quickly. The length of cyber tasks frontier models can complete has been doubling every few months, and this rate has become fa
arXiv:2605.10787v1 Announce Type: new Abstract: Current LLM agents are proficient at calling isolated APIs but struggle with the 'last mile' of commercial software automation. In real-world scenarios,
arXiv:2605.10593v1 Announce Type: new Abstract: We demonstrate LLARS (LLM Assisted Research System), an open-source platform that bridges the gap between domain experts and developers for building LLM
arXiv:2605.10458v1 Announce Type: new Abstract: Atomic properties such as partial charges or multipoles encode chemically meaningful information that can inform downstream molecular property predictio