Knowledge catalogue
arXiv:2601.04778v2 Announce Type: replace-cross Abstract: Video-language models (VLMs) achieve strong multimodal understanding but remain prone to hallucinations, especially when reasoning about actio
arXiv:2608.26757v1 Announce Type: new Abstract: Faithful chart generation in real-world data-science workflows requires grounding visualizations in scattered evidence, computing chart-ready quantities
arXiv:2510.12979v2 Announce Type: replace Abstract: Large language models (LLMs) augmented with multi-step reasoning and action generation abilities have shown promise in leveraging external tools to
arXiv:2608.26735v1 Announce Type: new Abstract: Specializing large language models to vertical domains improves domain-specific behavior but often degrades general capabilities such as reasoning, codi
arXiv:2608.26587v1 Announce Type: new Abstract: Biomedical knowledge graphs (KGs) offer structured medical knowledge that can ground large language model (LLM) reasoning in clinical diagnosis applicat
arXiv:2411.15129v3 Announce Type: replace-cross Abstract: What can we learn about language from studying how it is used by ChatGPT and other large language model (LLM)-based chatbots? In this paper, w
arXiv:2608.25559v1 Announce Type: new Abstract: Video deep research answers complex questions by jointly understanding video content and retrieving external knowledge from the open Web. However, diver
arXiv:2608.25948v1 Announce Type: new Abstract: Medical image foundation models can predict clinical phenotypes from computed tomography (CT), but strong performance leaves open whether they read dise
arXiv:2608.25152v1 Announce Type: new Abstract: Multi-agent LLM systems increasingly debate answers, coordinate research, simulate users, and mediate information flows, making agent-to-agent persuasio
arXiv:2608.24934v1 Announce Type: new Abstract: Accurate field plant disease diagnosis requires reliable fusion of uncertain and conflicting perceptual evidence. We present the Hybrid Hierarchical Mul
arXiv:2409.18073v2 Announce Type: replace-cross Abstract: For AI agents to be helpful to humans, they should be able to follow natural language instructions to complete everyday cooperative tasks in h
arXiv:2608.25435v1 Announce Type: new Abstract: Fine-grained segmentation of communication-tower components in UAV imagery is essential for automated inspection, yet task-specific models are hard to d
arXiv:2608.25935v1 Announce Type: new Abstract: Traffic Anomaly Understanding (TAU) requires models and systems to detect, reason about, and explain anomalous events in transportation videos. To addre
arXiv:2607.18098v2 Announce Type: replace Abstract: Large language models are increasingly used in practical systems, making efficient model selection important for reducing deployment cost. LLM routi
arXiv:2608.25356v1 Announce Type: new Abstract: Vision-language models (VLMs) have made substantial progress in long-video understanding, with standard backbone models typically answering questions fr
arXiv:2504.18346v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been transformative across many domains. However, hallucination, i.e., confidently outputting incorrect info
arXiv:2608.24105v1 Announce Type: new Abstract: Purpose: Automatic radiology report generation (RRG) has been widely explored to improve reporting accuracy and reduce radiologists' workload. Most exis
arXiv:2608.23644v1 Announce Type: new Abstract: Large language models (LLMs) are becoming routine instruments of scientific research, assisting with literature synthesis, hypothesis development, codin
arXiv:2608.24570v1 Announce Type: new Abstract: Clinical diagnosis is an active evidence-seeking process in which clinicians acquire evidence, update competing hypotheses, and decide when the availabl
arXiv:2608.24020v1 Announce Type: cross Abstract: Generating executable parametric CAD code from dimension-annotated orthographic drawings is a challenging task requiring geometric understanding, proc
arXiv:2608.24571v1 Announce Type: new Abstract: Tool-augmented language models are bounded by the APIs humans bothered to write; existing tool-creation systems patch this by prompting a frozen LLM at
arXiv:2608.24087v1 Announce Type: cross Abstract: Current LLM agent systems decide delegation before reasoning begins (a router picks a model) or after a response is complete (a verifier scores it and
arXiv:2608.24115v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) can integrate long visual histories, reason under partial observability, and infer behavior from a few exampl
arXiv:2608.22232v1 Announce Type: new Abstract: Real-world situation appearances can deviate from their underlying physical states, challenging the reliability of multimodal large language models (MLL
arXiv:2608.21864v1 Announce Type: cross Abstract: The current progress of Clinical Vision Large Language Models (C-VLLMs) has substantially improved digital diagnostics, still these frameworks often e
arXiv:2512.01946v4 Announce Type: replace-cross Abstract: Robust robotic manipulation requires reliable failure detection and recovery. Although recent Vision-Language Models (VLMs) show promise in ro
arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external tool
arXiv:2608.22236v1 Announce Type: cross Abstract: Large audio-language models (LALMs) have shown promising progress in understanding speech, music, and general sound events, yet their ability to reaso
arXiv:2608.21727v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is deployed to make models better at reasoning tasks, but its side effect on what models will di
arXiv:2608.22354v1 Announce Type: cross Abstract: Delta-Rule recurrent models maintain a fixed-size state, enabling O(1) inference memory but potentially becoming unstable under extreme-context extrap
arXiv:2608.22174v1 Announce Type: new Abstract: Unified multimodal models (UMMs) can perform both understanding and generation, raising a central question: can visual generation improve understanding?
arXiv:2608.22347v1 Announce Type: new Abstract: A cognitive architecture is more than the module that reasons: it must also decide how long to think and what deserves the effort. We built a minimal bu
arXiv:2608.20519v1 Announce Type: cross Abstract: Background: Magnetic resonance imaging-guided linear accelerators (MR-Linacs) allow diffusion-weighted imaging (DWI) to be acquired at every treatment
arXiv:2608.21187v1 Announce Type: cross Abstract: Pairwise reciprocal matrices are fundamental to the Analytic Hierarchy Process (AHP), a decision-making model. While the Direct Least Squares (DLS) me
arXiv:2608.20390v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) are increasingly used to answer religious questions, but for Islamic content they carry two serious risks
arXiv:2608.20406v1 Announce Type: new Abstract: Public health forecasts must respond to abrupt changes in surveillance data without over-extrapolating noise, reporting artifacts, or temporary trends.
arXiv:2608.21305v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has demonstrated significant gains in image captioning, yet it is still limited in encouraging Large Vision-Language Model
Thanks to Unsloth Dynamic 3.0 quants coming in slightly leaner and better preserved, I settled on Qwen 3.8 27B (`UD_Q4_K_XL`) at 100K context as my daily driver for Hermes Agent and OpenCode. On an RT
arXiv:2608.20157v1 Announce Type: new Abstract: Egocentric action understanding is often addressed using large video models pretrained on extensive exocentric datasets. However, many first-person acti
arXiv:2608.20202v1 Announce Type: new Abstract: Memory has become a key component of large language models, enabling them to retain information and learn from long-term interactions. However, existing
AI agents can take actions that do not match your organization's policies. Policy in Amazon Bedrock AgentCore lets teams enforce controls across agents, now including time-based constraints. This post
arXiv:2608.18543v1 Announce Type: new Abstract: Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities for proactive c
arXiv:2608.18084v1 Announce Type: new Abstract: Theorem proving in real-world Lean 4 projects is challenging because proofs often depend on project-specific context. While iterative refinement can use
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:2608.18627v1 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:2608.19197v1 Announce Type: cross Abstract: Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environ
arXiv:2608.18222v1 Announce Type: cross Abstract: Recurrent-depth reasoners aim to solve harder problems by iterating their update longer at test time, but additional iterations can improve, preserve,
arXiv:2608.18166v1 Announce Type: cross Abstract: Vision language models have transformed 2D medical imaging, yet extending them to 3D white matter tractography remains challenging due to the complex
Hello, any params I missing or mis-tuning for my coding model ? FROM ./models/unsloth/Qwen3.8-27B-Q5_K_M.gguf # Generation parameters optimized for logical reasoning and coding tasks PARAMETER tempera
arXiv:2608.19072v1 Announce Type: new Abstract: Large language model (LLM) agents can now post-train an LLM end-to-end. They can write code, launch training, evaluate checkpoints, and improve downstre
arXiv:2608.17095v1 Announce Type: new Abstract: Vision-language-action (VLA) driving models couple a reasoning stage with a diffusion-based trajectory decoder, but do not give a direct way to redirect
arXiv:2608.17330v1 Announce Type: new Abstract: Large language models for medical consultation are often evaluated after a clinical problem has already been made clear, although real consultations may
arXiv:2604.03237v2 Announce Type: replace-cross Abstract: As AI systems increasingly support human decision making, a central challenge is determining what information helps people recognize when to r
arXiv:2608.17044v1 Announce Type: cross Abstract: The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and physical A
arXiv:2604.02118v2 Announce Type: replace Abstract: Natural language explanations of time series data are increasingly produced by foundation models in high stakes domains, making factual correctness
arXiv:2608.15634v1 Announce Type: new Abstract: How can common ground between societies in conflict be identified when citizens' acceptability of peace agreements is shaped by contested narratives? Su
arXiv:2608.15254v1 Announce Type: new Abstract: Clinical-AI guidance increasingly recommends prompting language models to reason with attention to diversity, equity, and inclusion (DEI). We measure a
arXiv:2608.15065v1 Announce Type: new Abstract: Large Reasoning Models produce diverse, sometimes inconsistent answers across repeated queries on the same problem, so multi-sample inference is a prere
arXiv:2608.14615v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well on established code-generation and mathematical-reasoning benchmarks, but their capabilities in mechanics and
arXiv:2608.15336v1 Announce Type: new Abstract: Current surgical scene graph generation methods depend on dense multi-modal supervision and specialized hardware (synchronized RGB-D sensors, calibratio