Knowledge catalogue
arXiv:2605.08477v1 Announce Type: new Abstract: Explicit planning is a critical capability for LLM-based agents solving complex data-centric tasks, which require precise tool calling over external dat
arXiv:2605.08441v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) generates hundreds of thousands of tokens per training step, with rollout generation dominating
arXiv:2605.10765v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world deployment often requires continual cap
eBay rejected GameStop's unsolicited 56 billion takeover bid, with eBay's board declaring the offer 'neither credible nor attractive.' The proposal called for purchasing eBay shares at 125 apiece with
arXiv:2605.08177v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has become a practical route for adapting large language models to downstream tasks, with LoRA-style methods be
arXiv:2605.10251v1 Announce Type: new Abstract: We present GraphDepth, a monocular depth estimation architecture that synergistically integrates Graph Neural Networks (GNNs) within a convolutional enc
arXiv:2506.11578v4 Announce Type: replace Abstract: Recently, large language models (LLMs) have demonstrated strong performance, ranging from simple to complex tasks. However, while large models achie
arXiv:2605.09745v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable generative performance, yet their output quality is dependent on the decoding strategy. While sampling
arXiv:2601.21484v2 Announce Type: replace Abstract: Reinforcement Learning (RL) post-training alignment for language models is effective, but also costly and unstable in practice, owing to its complic
arXiv:2510.06371v2 Announce Type: replace-cross Abstract: Large-scale multimodal models achieve strong results on tasks like Visual Question Answering (VQA), but they are often limited when queries re
arXiv:2603.09465v3 Announce Type: replace-cross Abstract: Vision-Language-Action models have shown great promise for autonomous driving, yet they suffer from degraded perception after unfreezing the v
arXiv:2605.08769v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have shown strong potential on complex tasks through agent specialization, tool use, and collaborat
arXiv:2602.05946v3 Announce Type: replace Abstract: Recent work shows that preference alignment objectives can be interpreted as divergence estimators between aligned (preferred) & unaligned (less-pre
arXiv:2605.08925v1 Announce Type: new Abstract: Interactive segmentation allows efficient label generation by leveraging user-provided clicks to progressively refine predictions, which is critical whe
arXiv:2605.10035v1 Announce Type: new Abstract: Conditional molecular optimization aims to edit a molecule to realize a specified property shift. In practice, structurally similar molecule data is sca
arXiv:2605.09027v1 Announce Type: new Abstract: In multi-agent systems (MAS), a single deceptive agent can nullify all gains of an agentic AI collective and evade deployed defenses. However, existing
arXiv:2605.10739v1 Announce Type: cross Abstract: We introduce SMART-HC-VQA, a Sentinel-2-based visual question answering dataset derived from the IARPA SMART Heavy Construction dataset, designed for
GLM models are now live on @tensorix_ai We’re partnering to bring cost-efficient frontier AI models to developers, startups, and enterprises across Europe and beyond — and to back the Sovereign AI eco
arXiv:2605.10386v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly integrated into autonomous driving (AD) systems; however, they remain vulnerable to diverse sa
arXiv:2605.09942v1 Announce Type: new Abstract: Memory retrieval in agentic large language model (LLM) systems is often treated as a static lookup problem, relying on flat vector search or fixed binar
arXiv:2507.15518v5 Announce Type: replace Abstract: Creating an immersive and interactive theatrical experience is a long-term goal in the field of interactive narrative. The emergence of large langua
arXiv:2605.10624v1 Announce Type: new Abstract: Model Predictive Control (MPC) is widely used to operate safety-critical infrastructure by predicting future trajectories and optimizing control actions
arXiv:2404.18923v5 Announce Type: replace Abstract: We introduce Holmes, a new benchmark designed to assess language models (LMs) linguistic competence - their unconscious understanding of linguistic
arXiv:2605.09348v1 Announce Type: cross Abstract: Large Language Models (LLMs) provide flexible natural language processing capabilities, while knowledge graphs (KGs) offer explicit and structured kno
arXiv:2605.09314v1 Announce Type: new Abstract: Language models can be persuaded to abandon factual knowledge. This vulnerability is central to AI safety, but its internal mechanism remains poorly und
arXiv:2605.08533v1 Announce Type: new Abstract: Clinical decision-making in emergency medicine demands rapid, accurate diagnoses under uncertainty. Despite benchmark progress, evidence for LLMs as int
arXiv:2605.10407v1 Announce Type: new Abstract: Modern LLM APIs often reveal only top-K logit scores and censor the remaining vocabulary. We study the per-position distribution-recovery limits of this
arXiv:2010.03496v4 Announce Type: replace-cross Abstract: Knowledge Graphs (KG) are of vital importance for multiple applications on the web, including information retrieval, recommender systems, and
arXiv:2605.10267v1 Announce Type: new Abstract: In industrial procurement, an LLM answer is useful only if it survives a standards check: recommended material must match operating condition, every par
arXiv:2605.08686v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing controllers are typ
arXiv:2605.09487v1 Announce Type: new Abstract: Modern embodied agents achieve impressive performance, but their task knowledge is often stored in neural weights, latent state, or prompt-bound memory,
arXiv:2601.11258v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) face the 'knowledge cutoff' challenge, where their frozen parametric memory prevents direct internalization of ne
arXiv:2605.08605v1 Announce Type: cross Abstract: We introduce the Lattice Deduction Transformer (LDT), a recurrent transformer that approximates logically sound deduction by projecting its latent sta
arXiv:2605.09295v1 Announce Type: new Abstract: Text-to-SQL translates natural language questions into executable SQL queries, enabling intuitive database access for non-experts. While large language
arXiv:2605.09665v1 Announce Type: cross Abstract: Data selection is a key component of efficient instruction tuning for large language models, as recent work has shown that data quality often matters
arXiv:2605.10456v1 Announce Type: new Abstract: Point clouds are a fundamental representation for robotic perception tasks such as localization, mapping, and object pose estimation. However, LiDAR-acq
arXiv:2605.09963v1 Announce Type: new Abstract: Existing self-supervised learning (SSL) methods primarily learn object-invariant representations but often neglect the spatial structure and relationshi
arXiv:2605.10186v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into legal drafting and research workflows, where incorrect citations or fabricated precedent
arXiv:2512.23025v2 Announce Type: replace-cross Abstract: Multimodal health sensing offers rich behavioral signals for assessing mental health, yet translating these numerical time-series measurements
arXiv:2508.16070v3 Announce Type: replace Abstract: Approximately 283 million people worldwide live with visual impairments, motivating increasing research into leveraging Visual Language Models (VLMs
arXiv:2605.09542v1 Announce Type: new Abstract: Extracting multi-step explanations from knowledge graphs poses a combinatorial challenge requiring both heuristic guidance (as candidates proliferate wi
arXiv:2605.10574v1 Announce Type: new Abstract: As artificial intelligence advances, models are not improving uniformly. Instead, progress unfolds in a jagged fashion, with capabilities growing uneven
arXiv:2605.10807v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor i
arXiv:2509.20909v2 Announce Type: replace Abstract: Large language models (LLMs) are commonly evaluated on challenging benchmarks such as AIME and Math500, where benchmark contamination can make memor
love seeing my discord stay in sync with Hermes Kanban..everything here was done in plain English. I just asked my coordinator to check if we have an update and the system understood the intent, route
arXiv:2511.01008v2 Announce Type: replace Abstract: Large Language Models (LLMs) often struggle with the precise logic and schema alignment required for complex Text-to-SQL tasks. While current method
arXiv:2507.23511v3 Announce Type: replace-cross Abstract: While large audio-language models have advanced open-ended audio understanding, they still fall short of nuanced human-level comprehension. Th
arXiv:2603.13452v2 Announce Type: replace Abstract: Fairness in machine learning is predominantly evaluated through outcome-oriented metrics, such as Demographic parity, which measure whether predicti
arXiv:2605.08816v1 Announce Type: new Abstract: In the animal kingdom, mirror self-recognition is a canonical probe of higher-order cognition, emerging only in some species. We ask whether an analogou
arXiv:2502.20213v2 Announce Type: replace Abstract: Depression is a mental disorder and can cause a variety of symptoms, including psychological, physical, and social. Speech has been proved an object
arXiv:2605.08678v1 Announce Type: new Abstract: Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes. As large language models demonst
arXiv:2605.10833v1 Announce Type: cross Abstract: Industrial anomaly detection is critical for manufacturing quality control, yet existing datasets mainly focus on static images or sparse views, which
arXiv:2605.09918v1 Announce Type: cross Abstract: Reconciling platform revenue with user experience in LLM advertising motivates a data-centric foundation. We introduce NaiAD, the first comprehensive
arXiv:2605.09939v1 Announce Type: new Abstract: Autonomous and safe navigation of tractor-trailer systems requires accurate, real-time collision avoidance and dynamically feasible control, particularl
arXiv:2506.01250v2 Announce Type: replace Abstract: In this paper, we address the contextual dueling bandit problem by proposing variance-aware algorithms that leverage neural networks to approximate
arXiv:2605.10804v1 Announce Type: new Abstract: Campus well-being underpins academic success, yet many universities lack effective methods for monitoring satisfaction and detecting mental health risks
arXiv:2605.10379v1 Announce Type: new Abstract: Large language models (LLMs) have become capable mathematical problem-solvers, often producing correct proofs for challenging problems. However, correct
arXiv:2510.03895v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models represent a pivotal advance in embodied intelligence, yet they confront critical barriers to real-world de
arXiv:2605.08762v1 Announce Type: cross Abstract: Current omni-modal benchmarks mainly evaluate models under settings where multiple modalities are provided simultaneously, while the ability to start
arXiv:2605.09184v1 Announce Type: new Abstract: We present Open Ontologies, an open-source ontology engineering system implemented in Rust that integrates LLM-driven construction with formal OWL reaso