Knowledge catalogue
arXiv:2511.18931v2 Announce Type: replace-cross Abstract: Modern large language models increasingly integrate internal web-based retrieval to provide real-time answers, yet it remains unclear how effe
arXiv:2410.19245v3 Announce Type: replace-cross Abstract: With the rapid advancement of Large Language Models (LLMs), LLM-based approaches have demonstrated strong problem-solving capabilities across
arXiv:2510.13387v3 Announce Type: replace Abstract: Large language models (LLMs) still struggle with strategic persuasion, largely because existing approaches either neglect information asymmetry or r
arXiv:2608.30114v1 Announce Type: new Abstract: Brazilian Portuguese remains under-served by open language models, and the few that exist are difficult to reproduce and are often compared without meas
arXiv:2608.29677v1 Announce Type: cross Abstract: Despite rapid advances in MIS, fair and reproducible comparisons of segmentation models remain challenging due to heterogeneous datasets, inconsistent
arXiv:2603.03677v2 Announce Type: replace-cross Abstract: Psychiatric consultation requires agents to elicit discriminative evidence, map uncertain narratives to diagnostic criteria, and decide when e
arXiv:2406.00434v4 Announce Type: replace Abstract: In this paper, we propose MoDGS, a new pipeline to render novel views of dy namic scenes from a casually captured monocular video. Previous monocula
arXiv:2608.29753v1 Announce Type: new Abstract: Multi-hop question answering in retrieval-augmented gener?ation (RAG) often benefits from retrieving beyond the few candidates that will finally be read
arXiv:2608.28596v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly embedded in scientific workflows for literature analysis, drafting, and review. Existing systems adva
arXiv:2608.29996v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) achieve strong performance across many multimodal tasks; however, they often exploit spurious object-background c
arXiv:2603.10009v2 Announce Type: replace-cross Abstract: Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences
arXiv:2608.24921v2 Announce Type: replace Abstract: Graph RAG connects facts no single passage states, but implementations pay three times: in infrastructure, keeping vector store, graph database and
arXiv:2608.21719v1 Announce Type: cross Abstract: AI inference clusters are increasingly constrained by instantaneous power, not just energy: grid operators condition new capacity on demand response,
arXiv:2608.29301v1 Announce Type: new Abstract: Predicting future organ dysfunction in Intensive Care Unit (ICU) patients is critical for early clinical intervention, yet existing machine learning app
Pure scaling of LLMs did actually hit a wall; neurosymbolic AI rescued it. AI didn’t hit a plateau; pure LLMs did — until people used harnesses and tools to complement their weaknesses. Which was alwa
arXiv:2603.12165v3 Announce Type: replace Abstract: Synthetic data has become essential for training code generation models, yet it introduces significant noise and hallucinations that are difficult t
arXiv:2608.30820v1 Announce Type: new Abstract: Occlusion boundaries (OBs) are pixel-level image boundaries corresponding to surface visibility discontinuities caused by occlusion. Through precise bou
arXiv:2607.16325v2 Announce Type: replace Abstract: Foundation models provide powerful representations for brain MRI analysis, but their predictions remain difficult to interpret in anatomically meani
arXiv:2608.29899v1 Announce Type: cross Abstract: Dense retrieval over long documents is expensive. Token-level encoders scale quadratically in sequence length, and most long-context embedding models
arXiv:2608.30912v1 Announce Type: new Abstract: Artificial intelligence (AI) and natural language processing (NLP) are increasingly used to extract, integrate, and interpret biomedical knowledge relev
arXiv:2608.29748v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive language model inference by having a lightweight draft model propose multiple candidate tokens, which ar
arXiv:2608.30910v1 Announce Type: cross Abstract: Spectroscopic structure elucidation is central to molecular analysis, but recent Large Language Model (LLM)-based methods mostly formulate it as direc
arXiv:2608.30889v1 Announce Type: cross Abstract: Deploying a new control policy for voltage control in active distribution grids requires evidence that physical limits will be satisfied before the po
arXiv:2608.28647v1 Announce Type: new Abstract: Target-only post-training can improve performance in a specialized domain while degrading behaviors that a general-purpose base model acquired before ad
arXiv:2512.15052v4 Announce Type: replace-cross Abstract: Disclaimer: Samples in this paper may be harmful and cause discomfort. Multimodal large language models (MLLMs) enable multimodal understandin
arXiv:2608.29481v1 Announce Type: new Abstract: Pediatric serious illness communication (SIC) is critically important, yet scalable communication training for clinicians remains limited. Compared with
arXiv:2608.29841v1 Announce Type: new Abstract: Generating formally verified programs from natural language remains challenging: existing approaches either produce code in a single pass without recour
arXiv:2608.29607v1 Announce Type: new Abstract: Mobile AI acts as a visual oracle, empowering users to snap a picture of something and ask for information. Snap-and-ask retrieval is now one of the mos
arXiv:2608.28593v1 Announce Type: new Abstract: With the increasing development of AI regulatory frameworks, ensuring that artificial intelligence systems, particularly generative models, operate in a
arXiv:2608.31108v1 Announce Type: new Abstract: Efficient evaluation changes the protocol used to support claims about model behavior, yet it is rarely tested whether those claims remain stable after
arXiv:2608.30252v1 Announce Type: new Abstract: Long-context LLM applications such as document summarization and multi-turn agents require generation from prefixes spanning tens of thousands of tokens
arXiv:2508.02601v2 Announce Type: replace-cross Abstract: Tabular data derives its value from inter-feature dependencies, yet preserving them during synthesis is fragile when samples are scarce. Exist
arXiv:2608.29759v1 Announce Type: cross Abstract: We present SynCrash, a multi-stage pipeline for zero-shot accident detection, spatial localization, and collision-type classification in fixed-view CC
arXiv:2608.30068v1 Announce Type: new Abstract: Films communicate through deliberate creative choices, including lighting, color, composition, editing, dialogue, music, and sound. Humans naturally int
arXiv:2608.28641v1 Announce Type: cross Abstract: Most evaluations for coding agents are conducted exclusively in English, which does not reflect real-world multilingual deployment. We present Termina
arXiv:2608.29843v1 Announce Type: cross Abstract: Posted prices for AI inference have fallen steadily since 2024, yet the measured speed of that fall depends almost entirely on the method of measureme
This video captures the crux of the debate: Cybersecurity experts are pissed off by the METR/Redwood Research report because AI alignment has sucked the oxygen out of AI security, even though the Open
This was a great article and I learned a lot from it. But while there's a lot of writing on robotics timelines, I've seen less on what happens once competent robots do inevitably arrive. US agricultur
arXiv:2608.29459v1 Announce Type: new Abstract: Skills, as a useful abstraction for the procedural capabilities of large language models (LLMs), capture how models perform structured, multi-step reaso
arXiv:2602.01390v3 Announce Type: replace-cross Abstract: Digital video is central to communication, education, and entertainment, but without audio description (AD), blind and low-vision users are ex
arXiv:2604.18364v2 Announce Type: replace Abstract: Generating programmatic animation using libraries such as Manim presents unique challenges for Large Language Models (LLMs), requiring spatial reaso
arXiv:2608.29577v1 Announce Type: new Abstract: Traditional video highlight detection relies on a narrow, event-centric definition of saliency, which often fails to generalize to unconstrained persona
arXiv:2602.09888v2 Announce Type: replace Abstract: Mobile manipulators broaden the operational envelope for robot manipulation. However, the whole-body teleoperation of such robots remains a problem:
arXiv:2608.30704v1 Announce Type: new Abstract: AI-generated video detection, which aims to distinguish AI-generated videos from real ones, has recently received increasing research attention. To perf
arXiv:2601.18552v2 Announce Type: replace Abstract: LLMs expand accessibility and provide wide-reaching access to information. Yet these interactions also create opportunities to embed subtle, goal-or
arXiv:2608.30508v1 Announce Type: new Abstract: Long-term memory is increasingly important for conversational agents, yet existing benchmarks primarily measure memory through pointwise factual recall:
arXiv:2608.28981v1 Announce Type: new Abstract: As air traffic volumes in the National Airspace System continue to expand, in particular in the low altitude airspaces, the need for scalable decision s
arXiv:2608.30135v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference by using a draft model to generate candidate tokens, which are verified by the target mo
arXiv:2608.30964v1 Announce Type: new Abstract: Pretrained vision embeddings are increasingly used as general-purpose representations for modelling how people appraise urban scenes, and are validated
arXiv:2608.30705v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) struggle with fine-grained Visual Search, the task of locating small or rare objects in high-resolution images.
arXiv:2608.28062v2 Announce Type: replace Abstract: Multimodal search agents extend parametric knowledge with newly emerging and long-tail evidence from the open web. Yet many existing agentic search
arXiv:2606.13218v2 Announce Type: replace Abstract: Arabic and Hebrew, as closely related Semitic languages, share many words with similar surface forms, including true cognates, false friends, and mo
arXiv:2608.29446v1 Announce Type: new Abstract: Judgments about psychological distress are socially situated: what counts as concerning hinges on community norms around emotional expression, vulnerabi
arXiv:2607.18084v2 Announce Type: replace Abstract: Predicting a football match before kickoff requires more than knowing past results: a model must use changing information and make a clear predictio
arXiv:2608.29948v1 Announce Type: new Abstract: Evaluating data-text alignment remains challenging: existing metrics often provide limited explanations for the scores, while prompt-based LLM-as-Judge
arXiv:2502.09245v3 Announce Type: replace-cross Abstract: In contrast to RNNs, which compress their history into a single hidden state, Transformers can attend to all past tokens directly. However, st
arXiv:2608.27997v1 Announce Type: new Abstract: Air-ground cross-view referring person detection is a necessary component in the language-to-perception-to-control chain of collective embodied intellig
'AI agents formed secret civilizations inside OpenAI and hacked Hugging Face.' No. Read it again without the sci-fi. OpenAI ran thousands of agents in parallel, safeguards OFF, and the whole goal of t
arXiv:2608.28432v1 Announce Type: new Abstract: Recent advances in in-context learning (ICL) text-to-SQL have substantially improved execution accuracy on public benchmarks by assembling increasingly
arXiv:2608.28182v1 Announce Type: cross Abstract: Populations of large language model agents are increasingly used as experimental societies. Three doubts shadow every such result: whether the agents