Knowledge catalogue
arXiv:2608.28681v1 Announce Type: new Abstract: Probability calibration aligns model confidence with predictive accuracy, enabling clinicians to identify unreliable segmentation regions. This alignmen
arXiv:2604.01113v2 Announce Type: replace Abstract: Large language model (LLM) systems are increasingly used to support high-stakes decision-making, but they typically perform worse when the available
arXiv:2608.30922v1 Announce Type: new Abstract: Masked diffusion language models predict tokens from a partially observed response canvas, enabling bidirectional conditioning and parallel token refine
arXiv:2608.30147v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in long-horizon, interactive, and stateful environments. In these settings, a single wrong a
arXiv:2608.30295v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in handling long-context tasks, but processing such long contexts remains challengi
arXiv:2608.28924v1 Announce Type: new Abstract: Linguistic theory has long recognized cross-linguistic syntactic regularities, leading to claims that these similar structures are processed by similar
Information technology services company CBTS Technology Solutions LLC today launched Forge Agents, a platform that turns a plain-language description of a job into a working artificial intelligence ag
arXiv:2608.28628v1 Announce Type: new Abstract: Compound drought-to-extreme-precipitation (CDEP) events are recognized in climate science as a growing driver of extreme impact, but whether this recogn
arXiv:2608.28599v1 Announce Type: new Abstract: Clinical diagnosis is a step-by-step, cost-aware process: a physician orders examinations one at a time, observes the results, and updates the diagnosis
arXiv:2608.30149v1 Announce Type: new Abstract: Individual tree measurements derived from Light Detection and Ranging (LiDAR) mounted on Unmanned Aerial Vehicles (UAV) provide valuable information for
arXiv:2608.30427v1 Announce Type: new Abstract: Speculative decoding speeds up generation with an efficient draft model (drafter) that proposes tokens for a target model to verify in one pass, preserv
arXiv:2608.30263v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) incur substantial inference costs due to their long and highly redundant visual-token sequences. Diversity-based
arXiv:2608.30201v1 Announce Type: new Abstract: Directly robustifying small-signal stability in AC optimal power flow is challenging since the stability boundary in the original uncertainty space is i
arXiv:2608.29114v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) requires agents to reason over accumulated observations while continuously exploring unseen regions. However, exi
arXiv:2608.29464v1 Announce Type: cross Abstract: Chain-of-thought (CoT) monitoring assumes that reasoning traces faithfully record the information that shapes a model's answer. Existing faithfulness
arXiv:2608.30466v1 Announce Type: new Abstract: Generative Engine Optimization (GEO) is increasingly used to improve content visibility in LLM-based retrieval systems, yet its population-level effects
arXiv:2608.29137v1 Announce Type: new Abstract: Recent work on image content manipulation based on vision-language pre-training models has been effectively extended to text-driven 3D scene editing. Ho
arXiv:2608.29887v1 Announce Type: new Abstract: Multiple-choice question answering (MCQA) benchmarks in NLP use number-right scoring (accuracy), but in educational testing, the scoring scheme, the com
arXiv:2608.30762v1 Announce Type: new Abstract: Chess board recognition is the task of mapping the image of a chess board to the information of which piece is on which square. So far this task has two
arXiv:2608.30758v1 Announce Type: new Abstract: Recent radiology multi-modal language models have made substantial progress in chest X-ray report generation, visual question answering, and temporal re
arXiv:2608.30673v1 Announce Type: new Abstract: Source term estimation (STE), which aims to estimate key properties of the gas source, is essential for identifying hazardous gas releases. Information-
arXiv:2608.29621v1 Announce Type: cross Abstract: Long-horizon story-driven video generation requires a production agent to coordinate narrative decomposition, state tracking, shot design, prompt cons
arXiv:2412.11061v2 Announce Type: replace Abstract: Previous studies showed that image datasets lacking geographic diversity can lead to biased performance in models trained on them. While earlier wor
Ben Patterson / PCWorld: Claude Fable 5.1 and Mythos 5.1 are Anthropic's first models to watermark text outputs; a detection API is available to eligible groups as required under EU law — Aside from b
Today is Claude Fable (and Mythos) 5.1 day. Anthropic say that Fable 5.1 'sets a new standard for coding, knowledge work, and long-running problem-solving tasks'. Their announcement spends a notable a
Claude Fable 5.1 tops the Artificial Analysis Intelligence Index but costs 20% more per task than Fable 5 despite a 75% cache read price cut We supported @AnthropicAI with pre-release evaluation of Cl
arXiv:2608.28784v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have recently made strong progress in visual--linguistic understanding. However, their performance on text-cent
arXiv:2608.30754v1 Announce Type: new Abstract: Evaluating creativity in large language model (LLM) outputs remains challenging because creativity is multidimensional and human-centered. We examine ho
arXiv:2502.18176v3 Announce Type: replace-cross Abstract: In this paper, we aim to build an adversarially robust zero-shot image classifier. We ground our work on CLIP, a vision-language pre-trained e
arXiv:2608.29284v1 Announce Type: new Abstract: Deploying large language models for legal question answering raises challenges that general-purpose leaderboards do not capture, particularly for low-re
arXiv:2608.29102v1 Announce Type: new Abstract: This paper develops a unified theoretical framework showing that a broad family of clustering methods, including k-means, fuzzy c-means, kernel k-means,
arXiv:2608.30498v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable success in STEM domains, where progress is often driven by vertical, step-by-step deducti
I got the CMP170 cards and unlocked them. I wanted to share my set up for CUDA since maybe it would be useful to others. First off, I hate e-waste and we are in a special time for RAM. I wanted to hav
arXiv:2608.30352v1 Announce Type: new Abstract: Clinical AI often optimizes predictive performance without engaging how clinicians decide where to look and what to write. We present Co-Annotator, whic
arXiv:2608.29467v1 Announce Type: new Abstract: Zero-shot anomaly detection (ZSAD) has gained significant attention for its practical value in industrial inspection. Recently, CLIP-based approaches ha
arXiv:2608.30397v1 Announce Type: new Abstract: Natural-language critiques provide supervision beyond scalar rewards for non-verifiable generation, which lacks deterministic verifiers. In critique-gui
arXiv:2608.30337v1 Announce Type: new Abstract: Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than
arXiv:2608.28909v1 Announce Type: new Abstract: Large-scale, publicly available datasets have driven advances in deep learning, but privacy and legal restrictions often limit data sharing in medical i
arXiv:2608.29492v1 Announce Type: new Abstract: Large Language Models (LLMs) often favor Western-associated entities across cultural contexts. Conventional debiasing methods aim for uniform neutrality
I was poking around in my ~/.cache/ folder using OmniDiskSweeper when I spotted something interesting. The OpenAI Codex desktop app (since rebranded to just ChatGPT) has 1.7GB of stuff in there in a f
arXiv:2608.29000v1 Announce Type: new Abstract: Autonomous vehicles stream high-resolution surround-camera frames into memory before perception runs. This sensor-to-memory path consumes energy when ce
arXiv:2511.22707v2 Announce Type: replace-cross Abstract: In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific items
arXiv:2608.30968v1 Announce Type: cross Abstract: We present CogEvol, a family of models trained specifically for Learning Environment Generation: turning a course brief into a finished learning artif
arXiv:2608.28677v1 Announce Type: new Abstract: This paper presents l{CogRun}, a framework that enables safety-critical ground robots to perform cognitively-grounded runtime learning entirely on edge-
arXiv:2608.30109v1 Announce Type: new Abstract: Large Language Models (LLMs) trained on extensive scientific research are increasingly integrated as assistants for scientific discovery. However, most
arXiv:2608.30974v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architecture (JEPA) has shown strong performance in learning rich representations through self-supervised prediction in lat
arXiv:2608.30234v1 Announce Type: new Abstract: Automatic medical coding assigns ICD codes to clinical notes, but it remains challenging due to long documents, imbalanced label distributions, and dive
arXiv:2608.30568v1 Announce Type: cross Abstract: Background: Population level assessments of predictive artificial intelligence (AI) can conceal performance disparities across subgroups. Fairness eva
arXiv:2608.30289v1 Announce Type: new Abstract: Vision-language-action (VLA) models remain brittle in manipulation tasks that require physical commonsense. Current physical VQA data is typically disem
CommerceAgentBench starts with real commercial demand, and Qwen3.8-Max delivers the strongest overall performance among open-weight models. Let's test Qwen on your real-world workflows! 🔥 Most AI benc
arXiv:2608.30374v1 Announce Type: cross Abstract: We study null-space estimation from a noisy matrix. For a simple left null space, we first derive an exact compact expression for the error of the sma
arXiv:2608.29230v1 Announce Type: new Abstract: Snapshot Spectral Imaging (SSI) provides high-dimensional temporal-spatial-spectral observation to uncover intrinsic physical characteristics. However,
arXiv:2608.30674v1 Announce Type: cross Abstract: Accurate molecular property prediction requires both statistical reliability and chemical reasoning. Graph neural networks can be calibrated directly
arXiv:2502.12527v2 Announce Type: replace Abstract: Text-to-image diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the
arXiv:2608.29934v1 Announce Type: new Abstract: KV-cache compression reduces LLM inference memory by evicting context tokens, but when the evicted tokens contain answer-bearing evidence, the model may
arXiv:2608.29223v1 Announce Type: new Abstract: Accurate through-thickness measurement of subsurface delamination depth in Carbon Fiber Reinforced Polymer (CFRP) is important for structural assessment
arXiv:2608.30183v1 Announce Type: cross Abstract: Lightweight channel attention mechanisms are widely used in image classification, yet their effectiveness in fine-grained visual recognition (FGVR) re
arXiv:2603.26509v2 Announce Type: replace Abstract: Computed tomography (CT) provides rich 3D anatomical detail but is often constrained by high radiation exposure, substantial costs, and limited avai
arXiv:2608.29644v1 Announce Type: cross Abstract: Attributing an artwork to an artist has traditionally relied on detailed visual observations and descriptions, known as stylistic analysis in art hist
arXiv:2608.29970v1 Announce Type: new Abstract: Artistic Text Recognition (ATR) remains challenging because word images often combine decorative fonts, curved layouts, object-like characters, clutter,