Knowledge catalogue
arXiv:2606.00919v1 Announce Type: new Abstract: Large language models (LLMs) have seen widespread adoption across various domains, yet their reliability is frequently undermined by hallucinations - re
arXiv:2606.01737v1 Announce Type: new Abstract: Traffic accident liability analysis is a critical yet challenging task in intelligent transportation and legal assistance. Existing methods often suffer
arXiv:2606.02350v1 Announce Type: new Abstract: Reconstructing humans and their surrounding environments in a globally consistent 4D space is essential for comprehensive perception. However, prior wor
arXiv:2606.01000v1 Announce Type: cross Abstract: Weak-to-strong generalization studies how to improve a strong student using supervision from a weaker teacher when reliable labels are scarce. We view
arXiv:2606.01249v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent
arXiv:2508.12551v2 Announce Type: replace-cross Abstract: Linux kernel tuning is essential for optimizing operating system (OS) performance, yet remains challenging due to the complex kernel space, sp
arXiv:2606.02120v1 Announce Type: cross Abstract: In this report, we address the problem of determining whether a user performs an action incorrectly from egocentric video data. To this end, we propos
arXiv:2606.00045v1 Announce Type: new Abstract: Classical continuous-space neural networks fundamentally struggle to lock into exact mathematical symmetries, such as modular arithmetic and non-commuta
arXiv:2606.00508v1 Announce Type: cross Abstract: This study introduces an intriguing phenomenon in Video LLMs: rather than merely translating frames into textual embeddings, Video LLMs establish a co
arXiv:2606.00384v1 Announce Type: new Abstract: Fitting quantitative models to data is a central step in scientific workflows, yet it remains one of the least automated. Recent agent-based systems lev
arXiv:2602.04094v2 Announce Type: replace Abstract: Long-form video understanding remains challenging for Vision-Language Models (VLMs) due to the inherent tension between computational constraints an
arXiv:2606.00424v1 Announce Type: new Abstract: As large language models become stronger, weak supervisors may fail to provide reliable labels, preferences, or final judgments for complex outputs, lim
arXiv:2601.21718v2 Announce Type: replace-cross Abstract: Behavior cloning (BC) is a practical offline imitation learning method, but it often fails when expert demonstrations are limited. Recent work
arXiv:2606.00086v1 Announce Type: new Abstract: Inverse kinematics (IK) is a fundamental problem in robotics, requiring the generation of joint configurations that satisfy target end-effector poses. E
arXiv:2606.02482v1 Announce Type: new Abstract: While video streaming understanding has made significant strides, real-world applications, such as live sports broadcasting, autonomous driving, and mul
arXiv:2606.00390v1 Announce Type: cross Abstract: We present Zamba2-VL, a suite of vision-language models built on Zamba2, a hybrid language-model architecture combining Mamba2 state-space layers with
arXiv:2602.08964v2 Announce Type: replace-cross Abstract: Understanding an agent's goals helps explain and predict its behaviour, yet there is no established methodology for reliably attributing goals
This post documents a parallel experiment where Yohei Nakajima built a coding agent using ActiveGraphAI, showcasing the system's architecture through a flattened event log trace that makes all operati
When you build agentic AI solutions, you face unique operational challenges. Agents make unpredictable decisions, costs spiral unexpectedly, and debugging non-deterministic failures seems impossible.
arXiv:2605.30963v1 Announce Type: cross Abstract: We present AMix-2, a protein-text foundation model that establishes protein as a native modality in large language models (LLMs), unifying protein und
arXiv:2605.30599v1 Announce Type: cross Abstract: Medical knowledge is continuously evolving. This creates a need to update or selectively forget information encoded in already-trained medical LLMs. M
An OpenAI model solved the 80-year-old unit distance problem, disproving a major conjecture in discrete geometry and marking a milestone in AI-driven mathematics. The proof came from a new general-pur
arXiv:2605.31444v1 Announce Type: new Abstract: Reinforcement Learning (RL) enables autonomous agents to learn policies from experience, but realistic problems often involve enormous state spaces, mak
arXiv:2605.30912v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) improves vision-language models (VLMs) by optimizing outcome rewards derived from final answers.
arXiv:2605.31086v1 Announce Type: new Abstract: In existing memory benchmarks for Large Language Models (LLMs), the evaluated dialogue sessions often lack long-term semantic consistency, and the under
arXiv:2605.30972v1 Announce Type: new Abstract: Accurate 3D medical image segmentation requires both long-range volumetric context and fine boundary preservation. CNN-based methods have limited global
arXiv:2605.31591v1 Announce Type: new Abstract: Models for AI-based skin cancer screening suffer a severe performance drop when shifting from expert dermoscopic (source) images to consumer-grade clini
arXiv:2605.30842v1 Announce Type: new Abstract: Context management enables agentic models to solve long-horizon tasks through iterative summarization of previous interaction histories. However, this p
arXiv:2605.31224v1 Announce Type: cross Abstract: The use of Generative AI Conversational User Interfaces (CUI) as a new way to access and analyze data is growing in all sectors, and the industrial on
Connecting agents directly to Snowflake, Slack, or BigQuery via MCP/CLIs is a multi-token disaster. They default to brute-force exploration, firing 20-30 tool calls just to rediscover context on every
arXiv:2605.31093v1 Announce Type: new Abstract: Breast cancer is a major global health concern, and mammography screening plays a central role in early detection. The large volume of screening examina
arXiv:2605.31336v1 Announce Type: new Abstract: Recent advances in video generative models have promoted rapid progress in controllable world models. However, maintaining fine-grained spatio-temporal
arXiv:2605.30802v1 Announce Type: cross Abstract: Prediction markets aggregate collective intelligence to forecast uncertain events, but their utility depends on reliable outcome resolution. Existing
arXiv:2605.31563v1 Announce Type: new Abstract: Human disagreement is ubiquitous and well-known in labeling. However, variation in explanations, captured through token-level human rationales, remains
arXiv:2601.21686v2 Announce Type: replace Abstract: Key-value (KV) caching enables fast autoregressive decoding but at long contexts becomes a dominant bottleneck in High Bandwidth Memory (HBM) capaci
arXiv:2602.01173v3 Announce Type: replace Abstract: Understanding the multi-dimensional attributes and intensity nuances of image-evoked emotions is pivotal for advancing machine empathy and empowerin
arXiv:2605.30363v1 Announce Type: cross Abstract: Regime shifts in financial markets reorganise the joint dynamics of asset prices and macro variables, breaking any single-regime calibration. They are
arXiv:2605.30654v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conversational partners for companionship, emotional disclosure, and interpersonal advice, but t
arXiv:2605.31349v1 Announce Type: cross Abstract: Hateful meme detection remains a formidable challenge for vision-language models, as existing benchmarks are structurally observational - confounding
arXiv:2605.31423v1 Announce Type: new Abstract: We introduce universal transformers: fixed transformers that can simulate any transformer in a given class via a suitable input embedding. Analogous to
arXiv:2605.31146v1 Announce Type: cross Abstract: This paper investigates how User Experience Research (UXR) methods can be combined with AI-supported analysis to develop clearer design direction for
arXiv:2605.30819v1 Announce Type: new Abstract: Most text-driven 3D indoor scene synthesis methods generate rooms from object-centric prompts, asking what furniture should be placed rather than how th
arXiv:2509.14221v3 Announce Type: replace-cross Abstract: Generative Engine Marketing (GEM) is an emerging ecosystem for monetizing generative engines, such as LLM-based chatbots, by seamlessly integr
arXiv:2408.10441v3 Announce Type: replace Abstract: For many low-resource languages, the only available language models are large multilingual models trained on many languages simultaneously. Despite
arXiv:2404.14928v3 Announce Type: replace-cross Abstract: Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular d
arXiv:2605.30740v1 Announce Type: cross Abstract: Articulated object manipulation is a unique challenge for service robots. Existing methods employ end-to-end policy learning, visionmotion planning, a
arXiv:2605.31056v1 Announce Type: new Abstract: Zero Pronouns (ZPs) are a pervasive linguistic phenomenon in pro-drop languages such as Chinese and have long posed a challenge for natural language pro
Elon Musk posted a brief statement expressing his affection for the United States on X (formerly Twitter). The post consists of a simple declaration of patriotic sentiment without elaboration on speci
Gary Marcus critiques generative AI by applying the 'insanity' aphorism to highlight repetitive patterns in how these systems operate—suggesting they may be fundamentally limited by repeating the same
arXiv:2605.30589v1 Announce Type: cross Abstract: U.S. immigration law spans thousands of pages of official policy, federal regulations, and procedural guidance that change frequently and carry high s
arXiv:2605.30745v1 Announce Type: new Abstract: Large Vision-Language Models have achieved unprecedented success in zero-shot recognition by aligning visual features with broad semantic concepts. Howe
arXiv:2511.21513v2 Announce Type: replace Abstract: Deploying Transformer models on edge devices is limited by latency and energy budgets. While INT8 quantization effectively accelerates the primary m
👏👏 Introducing Qwen3.7-Plus — a multimodal agent model that unifies vision and language into one versatile agent foundation. ✅ Multimodal interactive hybrid agent: unified GUI & CLI operation across v
arXiv:2603.08721v2 Announce Type: replace-cross Abstract: New AI accelerators with novel instruction set architectures (ISAs) often require developers to manually craft low-level kernels, a time-consu
arXiv:2605.30514v1 Announce Type: cross Abstract: Machine unlearning evaluation is structurally skewed: Why-type questions, which probe causal and relational knowledge, comprise less than 0.06% of Cou
arXiv:2605.31199v1 Announce Type: cross Abstract: Capturing dynamic malware behavior in a practical but still semantically precise manner remains a significant challenge in cyber threat intelligence.
arXiv:2509.12440v3 Announce Type: replace-cross Abstract: Deploying Large Language Models (LLMs) in medical applications requires fact-checking capabilities to ensure patient safety and regulatory com
MiniMax M3 launched on June 1, 2026 as the first open-weights model to combine frontier coding, a 1-million-token context window, and native multimodality. The model achieves top-tier performance on c
arXiv:2605.31010v1 Announce Type: new Abstract: Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge
arXiv:2603.05529v2 Announce Type: replace-cross Abstract: Data critical to real-world decision-making is increasingly found within organizations. Such data is heterogeneous, constantly evolving, and o