Knowledge catalogue
arXiv:2505.10760v2 Announce Type: replace Abstract: Learning from humans is challenging because people are imperfect teachers. When everyday humans show the robot a new task they want it to perform, h
arXiv:2608.18524v1 Announce Type: cross Abstract: Equipping Large Language Models (LLMs) with multi-turn tool-calling capabilities is essential for building autonomous agents. However, progress is fun
arXiv:2608.18294v1 Announce Type: cross Abstract: An increasing number of scholars use AI to measure variables they subsequently include in downstream analyses. Although AI-measured variables are ofte
arXiv:2608.18746v1 Announce Type: cross Abstract: JEPA-style latent world models can use Euclidean distance to a goal latent as the cost for model-predictive control (MPC). Strong decoding of task var
arXiv:2608.18787v1 Announce Type: new Abstract: Learning robotic policies requires dense rewards that remain informative when behavior departs from successful demonstrations. Progress-based rewards es
arXiv:2608.18673v1 Announce Type: new Abstract: Active geo-localization enables low-altitude UAVs to search for specified targets from limited local aerial observations, supporting time-sensitive appl
arXiv:2608.18711v1 Announce Type: new Abstract: Egocentric vision systems capture human behavior from visible cues, but overlook physiological indicators of autonomic states such as stress, engagement
arXiv:2608.18690v1 Announce Type: cross Abstract: The European Union has committed to reducing greenhouse gas emissions 55% below 1990 levels by 2030, but whether current trends are compatible with th
arXiv:2608.18580v1 Announce Type: new Abstract: Training terminal agents requires scalable executable supervision, yet synthesizing high-quality terminal tasks remains challenging. Each task couples a
arXiv:2510.15483v3 Announce Type: replace-cross Abstract: We study the problem of stochastic contextual bandits in the agnostic setting, where the goal is to compete with the best policy in a given cl
arXiv:2608.18590v1 Announce Type: new Abstract: Compact object detectors are suitable for resource-constrained visual perception, but their limited representation capacity creates an accuracy gap rela
arXiv:2608.18148v1 Announce Type: cross Abstract: We study data-driven prediction of coarse-grained dynamics in multiscale PDE systems. Adopting a closure-free operator-learning viewpoint, we apply a
arXiv:2608.18098v1 Announce Type: cross Abstract: Key-value (KV) caching is essential for efficient autoregressive inference in transformer based dialog systems, yet existing strategies treat all cach
arXiv:2608.18136v1 Announce Type: new Abstract: Conversational agents now act for end users through tools while holding access to customer databases and internal policy documents that a caller can rea
arXiv:2608.18339v1 Announce Type: cross Abstract: Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during infere
arXiv:2608.18581v1 Announce Type: cross Abstract: Although Large Language Models (LLMs) encode rich factual knowledge in their parameters, reliably recalling and verifying such knowledge remains a key
arXiv:2509.19893v3 Announce Type: replace Abstract: Reinforcement learning (RL) has emerged as a key driver of post-training for complex reasoning in large language models (LLMs), yet online RL introd
arXiv:2608.18254v1 Announce Type: new Abstract: Trajectory planning for autonomous driving requires both high-level reasoning and precise low-level control. Large Language Models (LLMs) offer semantic
arXiv:2608.19055v1 Announce Type: new Abstract: Music-driven character animation enables and enhances transformative applications in entertainment and interactive education. However, synthesizing real
arXiv:2608.18238v1 Announce Type: new Abstract: Genomic analysis on viruses such as SARS-CoV-2 variants: Beta, Gamma, Delta, and Omicron is heavily dominated by classical bioinformatics methods, inclu
arXiv:2608.18749v1 Announce Type: new Abstract: Geometric Data Perturbation (GDP) enables one-shot, privacy-preserving collaborative learning: each participant applies a distance-preserving transforma
arXiv:2608.18234v1 Announce Type: cross Abstract: Whole-body motion tracking policies turn a humanoid into a robust control interface: the teleoperator---or an upstream model---only supplies a coarse
arXiv:2608.18268v1 Announce Type: cross Abstract: Today most media content is consumed based on algorithmic recommendations. Evidence suggests that this can lead to politically biased media consumptio
arXiv:2608.18996v1 Announce Type: cross Abstract: Visual grounding in Unmanned Aerial Vehicle (UAV) imagery aims to localize a target object in complex bird's-eye-view scenes according to a natural la
arXiv:2608.18446v1 Announce Type: new Abstract: End-to-end imitation learning avoids hand-made robot motion for approaching and grasping, but the policy must still decide which fruit to pick and where
arXiv:2608.18265v1 Announce Type: cross Abstract: We introduce a general, easy-to-implement AI-based method for studying the structure and complexity of human behavior. We assign a large language mode
arXiv:2608.18107v1 Announce Type: cross Abstract: We investigate whether large language models (LLMs) systematically discriminate in candidate evaluations based on applicant name ethnicity and/or inst
Karp got so much shit and was actually right. I know the feeling. 🙄 🚨OPENAI AND ANTHROPIC JUST FOLDED ON THEIR DATA RETENTION POLICY AFTER PALANTIR CEO EXPOSED THEIR REAL BUSINESS MODEL Alex Karp: >'s
arXiv:2512.00020v3 Announce Type: replace-cross Abstract: Code generation has emerged as a critical research area at the intersection of Software Engineering (SE) and Artificial Intelligence (AI), att
arXiv:2608.19168v1 Announce Type: new Abstract: A single training example's contribution to a finished model is normally estimated rather than measured, because measuring it takes two expensive full p
arXiv:2608.18681v1 Announce Type: new Abstract: We introduce a failure-aware adversarial retrieval-augmented framework for improving robustness in natural language understanding. Rather than selecting
arXiv:2607.16841v2 Announce Type: replace Abstract: Large vision-language models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding. However, they remain prone to hallucinat
arXiv:2608.19059v1 Announce Type: cross Abstract: Long-term robot operation in evolving environments requires object-level understanding that persists across repeated revisits. Existing systems either
Most “AI influencers” here probably don’t even understand this important (and very basic) point. okay so everyone is talking about this but you guys realize it's not an LLM/foundation model or even a
arXiv:2608.18579v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential for image quality assessment (IQA) by improving consistency between quality ratin
okay so everyone is talking about this but you guys realize it's not an LLM/foundation model or even a neural network, right???? it's probably just like some XGBoost or logistic regression algorithm o
arXiv:2608.18474v1 Announce Type: new Abstract: Cross-lingual sequence alignment is fundamental for building and exploiting parallel corpora, spanning mappings from documents and sentences down to wor
🦔OpenAI's CFO held an all-hands meeting today to tell employees the company will go public by 2027 and not to worry about Anthropic beating them to it. This came days after the CRO, COO, and product c
arXiv:2506.03079v3 Announce Type: replace Abstract: Recent embodied intelligence suffers from data scarcity, while conventional simulators lack visual realism. Controllable video generation is emergin
arXiv:2608.18627v1 Announce Type: new Abstract: No-reference point cloud quality assessment (PCQA) has been an active topic in recent years and is used to measure and optimize the visual experience of
arXiv:2602.08326v2 Announce Type: replace Abstract: Driving without considering the preferred separation distance from surrounding vehicles may cause discomfort for users. To address this limitation,
arXiv:2608.19121v1 Announce Type: cross Abstract: Improving molecular properties, such as drug-likeness or binding affinity, is a recurring task in early-stage drug discovery. However, molecules optim
arXiv:2608.18631v1 Announce Type: new Abstract: As large language models evolve into decision-making agents, the ability to reason over preferences becomes fundamental to alignment, coordination, and
arXiv:2608.19115v1 Announce Type: new Abstract: Modern pretrained encoders make representations from heterogeneous views increasingly reusable, but the procedure that determines view utility and combi
arXiv:2608.18669v1 Announce Type: new Abstract: Humans solve complex problems by constructing plans and mentally simulating their outcomes with an internal model of the world. Machine learning has pro
arXiv:2608.18271v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) trains a student on its own responses using token-level supervision from the same model conditioned on privileged ref
arXiv:2608.18593v1 Announce Type: new Abstract: Single-image rephotography aims to synthesize new shots of a scene from a single reference image with specified viewpoints, focal lengths, and photograp
arXiv:2608.18682v1 Announce Type: new Abstract: Training multi-turn agentic workflows with reinforcement learning (RL) enables large language models to perform complex reasoning, use external tools, a
arXiv:2608.18108v1 Announce Type: cross Abstract: Large language models are being incorporated into sensitive and important decision-making processes across nearly all fields. While prior work studies
arXiv:2608.19134v1 Announce Type: new Abstract: Accurate visual decoding can reveal how the brain represents visual information and recover perceived content from neural signals such as electroencepha
arXiv:2608.18755v1 Announce Type: new Abstract: Synthetic aperture radar (SAR) object detection is an important part of remote sensing interpretation. However, because of variations in frequency band,
arXiv:2608.18091v1 Announce Type: cross Abstract: As LLM-as-a-judge systems become increasingly widespread, self-preference in LLMs -- the tendency to favor one's own outputs -- raises growing concern
arXiv:2608.18263v1 Announce Type: new Abstract: Traffic signal control is a complex sequential decision-making problem requiring real-time adaptation and trade-offs among throughput, delay fairness, s
arXiv:2608.18915v1 Announce Type: new Abstract: Hardware shifts, color variations, and changing patient characteristics between development and deployment routinely break trained medical image classif
arXiv:2608.18852v1 Announce Type: new Abstract: Agent frameworks increasingly package procedural knowledge as skills: instruction files an agent reads on demand, while public libraries now hold thousa
arXiv:2608.19080v1 Announce Type: new Abstract: Object detectors often produce over-confident predictions for objects outside their training categories, leading to so-called out-of-distribution (OoD)
arXiv:2608.18087v1 Announce Type: cross Abstract: Existing subword tokenizers optimize statistical compression but ignore morphological structure, particularly the relationship between roots and affix
arXiv:2608.18415v1 Announce Type: new Abstract: The edge of stability refers to a phenomenon in deep learning with gradient-based optimizers where the Hessian eigenvalues of the loss remain stable abo
this has always been OpenAI’s game plan. (I first warned of it in 2024, saying that surveillance might be their only viable business plane.) 🦔ChatGPT can now read your entire iMessage history and send
arXiv:2608.18770v1 Announce Type: cross Abstract: Learning a reward model from human feedback and optimizing a policy against it is one approach to aligning AI systems with individual users. From a fa