Knowledge catalogue
arXiv:2604.17282v1 Announce Type: new Abstract: Process-Level Reward Models (PRMs) are essential for guiding complex reasoning in large language models, yet existing PRM benchmarks cover only general
arXiv:2604.18418v1 Announce Type: new Abstract: Recent advances in deep research systems enable large language models to retrieve, synthesize, and reason over large-scale external knowledge. In medici
arXiv:2601.09853v2 Announce Type: replace Abstract: Real-world health questions from patients often unintentionally embed false assumptions or premises. In such cases, safe medical communication typic
arXiv:2601.05488v3 Announce Type: replace Abstract: Maintaining consistency in long-term dialogues remains a fundamental challenge for LLMs, as standard retrieval mechanisms often fail to capture the
arXiv:2604.17054v1 Announce Type: new Abstract: Scalable Vector Graphics (SVGs) function both as visual images and as structured code that encode rich geometric and layout information, yet most method
arXiv:2602.11092v2 Announce Type: replace Abstract: Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmi
arXiv:2510.07739v2 Announce Type: replace Abstract: Recursive transformers reuse parameters and iterate over hidden states multiple times, decoupling compute depth from parameter depth. However, under
arXiv:2507.11687v4 Announce Type: replace-cross Abstract: Large language models excel at code generation but struggle with code linting, particularly in generalizing to unseen or evolving best practic
arXiv:2604.16425v1 Announce Type: cross Abstract: This paper presents a method for the automated collection and aggregation of unstructured data from diverse web sources, utilizing Large Language Mode
arXiv:2603.02618v3 Announce Type: replace Abstract: Out-of-distribution (OOD) detection seeks to identify samples from unknown classes, a critical capability for deploying machine learning models in o
arXiv:2602.16144v3 Announce Type: replace Abstract: As multimodal systems increasingly process sensitive personal data, the ability to selectively revoke specific data modalities has become a critical
arXiv:2512.21510v2 Announce Type: replace-cross Abstract: Real-world multi-view data often exhibit highly inconsistent missing patterns, posing significant challenges for incomplete multi-view cluster
arXiv:2604.18164v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have been increasingly used as automatic evaluators-a paradigm known as MLLM-as-a-Judge. However, their reliabi
arXiv:2601.03331v2 Announce Type: replace Abstract: Recent advances in Vision-Language Models (VLMs) have improved performance in multi-modal learning, raising the question of whether these models tru
arXiv:2604.16943v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown impressive capabilities, yet they often struggle to effectively capture the fine-grained textual inf
arXiv:2604.17007v1 Announce Type: new Abstract: Mobile deployment of facial age estimation requires models that balance predictive accuracy with low latency and compact size. In this work, we present
arXiv:2604.18226v1 Announce Type: new Abstract: Automated analysis of customer feedback on social media is hindered by three challenges: the high cost of annotated training data, the scarcity of evalu
arXiv:2604.17713v1 Announce Type: new Abstract: Resting-state functional magnetic resonance imaging (fMRI) has emerged as a cornerstone for psychiatric diagnosis, yet most approaches rely on pairwise
arXiv:2604.17174v1 Announce Type: new Abstract: Modeling human cognitive states is essential for advanced artificial intelligence. Existing Large Language Models (LLMs) mainly address isolated tasks s
arXiv:2604.17972v1 Announce Type: new Abstract: Emotional Support Conversation (ESC) aims to assist individuals experiencing distress by generating empathetic and supportive dialogue. While prior work
arXiv:2604.16461v1 Announce Type: cross Abstract: Physics-guided sampling with diffusion priors has recently shown strong performance in solving complex systems of partial differential equations (PDEs
arXiv:2604.17354v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at photorealistic generation, yet often struggle to represent abstract meaning such as idiomatic interpretations of
arXiv:2603.21298v2 Announce Type: replace Abstract: Combating hate speech on social media is critical for securing cyberspace, yet relies heavily on the efficacy of automated detection systems. As con
arXiv:2604.16503v1 Announce Type: new Abstract: Training strong video generation models usually requires massive datasets, large parameter counts, and substantial compute. In this work, we ask whether
arXiv:2510.12831v3 Announce Type: replace Abstract: Multi-turn Text-to-SQL aims to translate a user's conversational utterances into executable SQL while preserving dialogue coherence and grounding to
arXiv:2604.17567v1 Announce Type: new Abstract: Multi-camera systems are widely employed in sports to capture the 3D motion of athletes and equipment, yet calibrating their extrinsic parameters remain
arXiv:2604.16577v1 Announce Type: new Abstract: Human activity recognition (HAR) refers to the process of identifying human actions and activities using data collected from sensors. Neural networks, s
arXiv:2604.16311v1 Announce Type: new Abstract: Automated Fact-Checking (AFC) relies on claim extraction as a first step, yet existing methods largely overlook the multimodal nature of today's misinfo
arXiv:2601.05707v2 Announce Type: replace Abstract: Automatic speech recognition (ASR) still covers only a small fraction of the world's languages, mainly due to supervised data scarcity. In-context l
arXiv:2604.18203v1 Announce Type: new Abstract: Multimodal LLMs can accurately perceive numerical content across modalities yet fail to perform exact multi-digit multiplication when the identical unde
My hunch for now is that this was an ill-considered test which they didn't anticipate would be instantly spotted and cause (justified) uproar - here's hoping they decide that the 'test' isn't a good i
Anthropic PBC’s Claude Mythos model has emerged as the most widely discussed artificial intelligence solution without being fully released. Information about the model, which reportedly has the abilit
arXiv:2604.18012v1 Announce Type: new Abstract: We prove error bounds for operator surrogates of solution operators for partial differential and boundary integral equations on families of domains whic
arXiv:2604.17340v1 Announce Type: new Abstract: Clinical guidelines, typically developed by independent specialty societies, inherently exhibit substantial fragmentation, redundancy, and logical contr
Jonathan Stempel / Reuters: New York Attorney General Letitia James sues Coinbase and Gemini, claiming their prediction markets violate state laws against illegal gambling — New York's attorney genera
arXiv:2604.18105v1 Announce Type: cross Abstract: Integrating large language models (LLMs) into automatic speech recognition (ASR) has become a mainstream paradigm in recent years. Although existing L
arXiv:2604.16493v1 Announce Type: cross Abstract: Natural Language to SQL (NL2SQL) technology empowers non-expert users to query relational databases without requiring SQL expertise. While large langu
arXiv:2604.17070v1 Announce Type: new Abstract: This report presents the NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge, which targets automatic rip current understanding in i
arXiv:2604.18326v1 Announce Type: new Abstract: Recent advancements in audio-video joint generation models have demonstrated impressive capabilities in content creation. However, generating high-fidel
arXiv:2506.06024v2 Announce Type: replace-cross Abstract: Signal restoration and inverse problems are key elements in most real-world data science applications. In the past decades, with the emergence
arXiv:2604.17857v1 Announce Type: new Abstract: Can syntactic processing emerge spontaneously from purely local interaction? We present a concrete instance on a minimal system: an 18,658-parameter two
arXiv:2604.17402v1 Announce Type: new Abstract: Symbolic regression (SR) with genetic programming (GP) aims to discover interpretable mathematical expressions directly from data. Despite its strong em
arXiv:2604.18311v1 Announce Type: new Abstract: Explainable AI (XAI) aims to make the behaviour of machine learning models interpretable, yet many explanation methods remain difficult to understand. T
arXiv:2506.24106v2 Announce Type: replace Abstract: We show that a language model's ability to predict text is tightly linked to the breadth of its embedding space: models that spread their contextual
one of my favorite quality of life features Claude Code in the terminal will now show recaps when you switch focus away from the session and then come back. This should help you stay more in flow whil
Only the fraud was stopped by DOGE and, even then, only some of the fraud On Friday, @StateDeptGHSD released its first wave of PEPFAR data, covering July-Sept 2025, after State took over USAID’s lifes
arXiv:2604.16957v1 Announce Type: new Abstract: We present Open-TQ-Metal, the first implementation of fused compressed-domain attention on Apple Silicon, enabling 128K-context inference for Llama 3.1
OpenAI Group PBC today launched ChatGPT Images 2.0, an upgraded version of the image generator built into its popular chatbot. The company also debuted a new technical training service called Codex La
arXiv:2506.05606v5 Announce Type: replace Abstract: Can large language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generat
arXiv:2604.17135v1 Announce Type: new Abstract: Offline vectorized maps constitute critical infrastructure for high-precision autonomous driving and mapping services. Existing approaches rely predomin
arXiv:2510.02798v2 Announce Type: replace Abstract: Black-box optimization (BBO) underpins advances in domains such as AutoML and Materials Informatics, yet implementations of algorithms and benchmark
arXiv:2604.18319v1 Announce Type: cross Abstract: Selection bias arises when the probability that an observation enters a dataset depends on variables related to the quantities of interest, leading to
arXiv:2604.17219v1 Announce Type: cross Abstract: We derive explicit non-asymptotic PAC-Bayes generalization bounds for Gibbs posteriors, that is, data-dependent distributions over model parameters ob
arXiv:2510.07745v4 Announce Type: replace Abstract: Parallel test-time scaling (TTS) is a pivotal approach for enhancing large language models (LLMs), typically by sampling multiple token-based chains
ParseBench is the first benchmark to include VLM chart understanding 📊📈📉 over enterprise documents. 🟠 Existing benchmarks (ChartQA, ChartXiv) test over charts specifically and not the chart's inclusio
arXiv:2604.17570v1 Announce Type: new Abstract: Peripheral Blood Smear (PBS) is a critical microscopic examination in hematopathology that yields whole-slide imaging (WSI). Unlike solid tissue patholo
arXiv:2604.17819v1 Announce Type: new Abstract: Large language models (LLMs) perform substantially below human level on existing theory-of-mind (ToM) benchmarks, even when augmented with chain-of-thou
arXiv:2604.16515v1 Announce Type: new Abstract: The rapid proliferation of Multimodal Large Language Models (MLLMs) has enabled mobile agents to execute high-stakes financial transactions, but their a
people compared GPT-5.4's solution of erdos #1196 to alphago's move 37 but i think a tighter analogy is to alphago's unusual (at the time) preferences for the 3-3 opening and early 3-3 invasion agains
arXiv:2604.16813v1 Announce Type: cross Abstract: Agentic AI systems are rapidly advancing toward real-world applications, yet their readiness in complex and personalized environments remains insuffic