Knowledge catalogue
arXiv:2605.16479v1 Announce Type: cross Abstract: Job seekers often initiate search with short, underspecified queries. At LinkedIn, over 80% of job-related queries contain three or fewer keywords, ma
arXiv:2605.16588v1 Announce Type: new Abstract: Safety-critical autonomy in unstructured environments poses significant challenges for online safety certification under evolving constraints. We propos
arXiv:2605.16828v1 Announce Type: cross Abstract: We consider the following two-player game: using observational data, the leader chooses a prediction function for a response variable Y from given cov
arXiv:2605.17618v1 Announce Type: new Abstract: Autism Spectrum Disorder (ASD) is characterized by challenges with social interaction and communication and by restricted or repetitive patterns of thou
arXiv:2605.16747v1 Announce Type: new Abstract: We develop a quantitative statistical theory of transformers in the large-context regime by adopting the abstraction of contextual flow maps (CFMs): dyn
arXiv:2602.21426v2 Announce Type: replace Abstract: We consider the problem of sampling from a posterior distribution arising in Bayesian inverse problems in science, engineering, and imaging. Our met
arXiv:2605.16793v1 Announce Type: new Abstract: Time series forecasting under non-stationarity faces a fundamental tension between capturing stable representations and adapting to distribution shifts.
arXiv:2605.18130v1 Announce Type: new Abstract: Medical image segmentation is more clinically valuable when it supports diagnosis rather than merely producing lesion masks. However, diagnostically rel
arXiv:2509.13270v2 Announce Type: replace-cross Abstract: We introduce RadGame, an AI-powered gamified platform for radiology education that targets two core skills: localizing findings and generating
arXiv:2605.17503v1 Announce Type: new Abstract: The decoding of linguistic information from electroencephalography (EEG) signals remains an extremely challenging problem in brain-computer interface (B
arXiv:2605.17477v1 Announce Type: new Abstract: Flexible robotic manipulators (FRMs) offer advantages in lightweight design and large workspace, but their structural flexibility induces vibrations, ac
arXiv:2605.16535v1 Announce Type: cross Abstract: Industrial multi-label document understanding pipelines score candidate labels and threshold or rank them to form a label set per document. This early
Read more on what we've learned while developing long-horizon agent evals: Curious finding while creating evals and benchmarks for long-horizon (100+ turn) agents While it’s generally thought that a d
arXiv:2605.18563v1 Announce Type: new Abstract: A key question in psycholinguistics is how inferences about the meaning of linguistic input unfold incrementally a comprehender's mind. In this work, we
arXiv:2602.12633v2 Announce Type: replace Abstract: Reconstructing physically valid 3D scenes from single-view observations is a prerequisite for bridging the gap between visual perception and robotic
arXiv:2601.08454v2 Announce Type: replace Abstract: Constructing physically accurate simulation environments (Real2Sim) traditionally relies on manual system identification or rigid, exhaustive explor
arXiv:2605.17967v1 Announce Type: new Abstract: This paper explores a scientific question in supervised fine-tuning (SFT): why SFT is broadly effective for small-scale deep neural networks, yet can pr
arXiv:2605.17548v1 Announce Type: cross Abstract: Code review has evolved for decades, from informal peer checking to today's pull request (PR) workflows, yet it remains a largely manual, uneven, and
arXiv:2605.18684v1 Announce Type: cross Abstract: Legacy systems concentrate business rules, architectural decisions, and operational exceptions that often remain implicit in code, data, configuration
arXiv:2605.17014v1 Announce Type: new Abstract: Reconstructing people, objects, and their interactions in 3D is a long-standing goal for intelligent systems. Often the input is RGB video from a moving
arXiv:2605.18729v1 Announce Type: cross Abstract: The ability to navigate and interact with complex environments is central to real-world embodied agents, yet navigation in unseen environments remains
arXiv:2509.17666v2 Announce Type: replace Abstract: Object insertion tasks are prone to failure under pose uncertainty and environmental variation, often requiring manual fine-tuning or controller ret
arXiv:2605.18338v1 Announce Type: cross Abstract: Champion recommendation in multiplayer online battle arena games is usually framed informally as a problem of metagame strength, personal comfort, or
arXiv:2605.18181v1 Announce Type: new Abstract: Generalizable agents should adapt to diverse tasks and unseen environments beyond their training distribution. This position paper argues that such gene
arXiv:2605.16620v1 Announce Type: new Abstract: Learning causal relationships between variables from data is a fundamental research area with many applications across disciplines. Most existing causal
arXiv:2605.18018v1 Announce Type: cross Abstract: We present SWIM (See What I Mean), a novel training strategy that aligns vision and language representations to enable fine-grained object understandi
arXiv:2605.18557v1 Announce Type: new Abstract: The brain learns abstract representations of high-dimensional sensory input, but the plasticity rules that enable such learning are unknown. We study bi
arXiv:2605.16440v1 Announce Type: cross Abstract: Deep neural networks are vulnerable to adversarial perturbations, limiting deployment in safety-critical applications such as synthetic aperture radar
arXiv:2605.18156v1 Announce Type: new Abstract: Lens flare removal is challenging due to the large spatial extent of flare artifacts and their entanglement with scene structures, while existing method
arXiv:2605.17999v1 Announce Type: new Abstract: This paper proposes a Shared Backbone Proximal Policy Optimization (Shared Backbone PPO) algorithm. By sharing the base module between the Actor and Cri
arXiv:2605.16311v1 Announce Type: new Abstract: Distributed training of large neural networks is bottlenecked by full-precision gradient communication and by coordinatewise optimizers that ignore the
arXiv:2511.00392v2 Announce Type: replace-cross Abstract: Accurate 3D reconstruction in visually-degraded underwater environments remains a formidable challenge. Single-modality approaches are insuffi
arXiv:2510.23641v2 Announce Type: replace-cross Abstract: Transformers are very effective in capturing both global and local correlations within high-energy particle collisions, but they present deplo
arXiv:2512.05335v3 Announce Type: replace Abstract: We study visual domain transfer for end-to-end imitation learning in a realistic and challenging setting where target-domain data are strictly off-p
arXiv:2605.16746v1 Announce Type: new Abstract: LLM agents increasingly rely on persistent state, including transcripts, summaries, retrieved context, and memory buffers, to support long-horizon inter
arXiv:2605.18618v1 Announce Type: cross Abstract: Constrained machine learning enables fairness-aware training, physics-informed neural networks, and integration of symbolic domain knowledge into stat
arXiv:2605.17019v1 Announce Type: new Abstract: Streaming video effect generation is highly desirable for live human-centric applications such as e-commerce streaming, entertainment, and vlogging, yet
arXiv:2605.16398v1 Announce Type: cross Abstract: Contact-rich robot dynamics are hybrid: a single observation can match several latent states and contact regimes (free, impact, stick--slip). A standa
arXiv:2505.17674v2 Announce Type: replace Abstract: Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. However, existing SNNs still exhibit a signif
arXiv:2602.16473v2 Announce Type: replace Abstract: C-RASP is a simple programming language that was recently shown to capture concepts expressible by transformers. In this paper, we develop new algor
arXiv:2605.16895v1 Announce Type: cross Abstract: End-to-end LLM trading agents have moved quickly from research curiosity to a small ecosystem of named systems, including FinCon, FinMem, TradingAgent
arXiv:2605.16436v1 Announce Type: cross Abstract: For decades, the security of digital interaction has rested on an unacknowledged economic constraint. Attackers faced a tradeoff between the fidelity
arXiv:2605.17042v1 Announce Type: new Abstract: While RGB-Thermal crowd counting has shown promise, the paradigm faces critical limitations: RGB data raises privacy concerns in public surveillance, an
This is true of all agents, not just coding agents. Probably the biggest challenge that most companies run into in their agent strategy is getting agents the right constrained context to work with for
arXiv:2605.17581v1 Announce Type: cross Abstract: Flow in porous media is difficult to address using standard analytical or numerical methods due to its complexity. However, since synthetic representa
arXiv:2605.17217v1 Announce Type: cross Abstract: Marine oil spills require rapid detection to mitigate severe ecological and economic damage. While satellite-based Synthetic Aperture Radar (SAR) prov
arXiv:2605.16524v1 Announce Type: cross Abstract: Probabilistic search algorithms, such as Monte Carlo Tree Search (MCTS), have proven very effective in solving sequential decision-making tasks under
arXiv:2511.02610v2 Announce Type: replace Abstract: The development of smart systems (i.e., systems enhanced with AI components) has thrived thanks to the rapid advancements in neural networks (NNs).
arXiv:2605.17994v1 Announce Type: cross Abstract: New item growth is critical for maintaining a healthy ecosystem in large-scale e-commerce platforms. However, existing systems tend to prioritize pres
arXiv:2605.18385v1 Announce Type: cross Abstract: We present UbiSLAM, an innovative solution for real-time mapping and localization in dynamic indoor environments. By deploying a network of fixed RGB-
arXiv:2605.16269v1 Announce Type: cross Abstract: Modern military operations expose soldiers to sustained psychological stress, leading to acute reactions, post-traumatic stress symptoms, and other me
arXiv:2605.17660v1 Announce Type: cross Abstract: Transformers have become the dominant architecture in modern machine learning, yet the theoretical understanding of their training dynamics remains li
arXiv:2510.06388v2 Announce Type: replace Abstract: Calibrated predictions are useful because their numerical values can be interpreted as probabilities. Calibration errors are therefore widely used t
arXiv:2605.16443v1 Announce Type: cross Abstract: Despite the success of deep neural networks in vision, medical diagnosis, and IoT scenarios, their deployment on resource-limited platforms poses seri
arXiv:2502.17007v2 Announce Type: replace-cross Abstract: In this paper we argue that, to its detriment, transparency research overlooks many foundational concepts of artificial intelligence. As an il
arXiv:2506.10959v3 Announce Type: replace-cross Abstract: While in-context learning (ICL) has achieved remarkable success in natural language and vision domains, its theoretical understanding-particul
arXiv:2605.16815v1 Announce Type: cross Abstract: Graph neural networks (GNNs) have achieved remarkable success in relational learning. However, their vulnerability to graph backdoor attacks (GBAs) po
arXiv:2605.17976v1 Announce Type: new Abstract: Scientific discovery is increasingly constrained by costly experiments and limited resources, underscoring the need for efficient optimization in AI for
arXiv:2605.17748v1 Announce Type: new Abstract: In the field of Blind Image Quality Assessment (BIQA), accurately predicting the perceptual quality of authentically distorted images remains highly cha
arXiv:2605.18490v1 Announce Type: new Abstract: We preregistered a comparison of two ways to help an LLM answer questions over a small research corpus: a single-round Vector RAG system and an LLM-comp