Knowledge catalogue
arXiv:2605.07806v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in settings where reliable self-assessment is critical. Assessing model reliability has evolved fro
arXiv:2605.08037v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) aligns language models using pairwise preference comparisons, offering a simple and effective alternative to Rein
arXiv:2509.21637v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) of large language models trains a small task-specific parameter set while keeping the pretrained model frozen
arXiv:2605.06969v1 Announce Type: new Abstract: Infrared-Visible image fusion (IVIF) aims to integrate thermal information and detailed spatial structures into a single fused image to enhance percepti
At Google Cloud Next ’26 we announced Cloud Storage Rapid, a family of object storage capabilities for data-intensive workloads like AI and analytics. Out of the gate, Cloud Storage Rapid consists of
arXiv:2605.07905v1 Announce Type: cross Abstract: Despite the rapid development of AI reviewers, evaluating such systems remains challenging: metrics favor overlap with human reviews over correctness.
arXiv:2604.02753v2 Announce Type: replace Abstract: Open-vocabulary Object Detection (OVOD) enables models to recognize objects beyond predefined categories, but existing approaches remain limited in
arXiv:2605.06785v1 Announce Type: cross Abstract: Inference-time scaling methods rely on Process Reward Models (PRMs), which are often poorly calibrated and overestimate success probabilities. We prop
arXiv:2605.06673v1 Announce Type: cross Abstract: Aggregate metacognitive quality scores mask within-model variation across MMLU benchmark domains. We administered 1,500 MMLU items (250 per domain, un
arXiv:2605.07299v1 Announce Type: cross Abstract: Existing Multimodal Large Language Models (MLLMs) remain primarily reactive, failing to continuously perceive environments or proactively assist users
arXiv:2603.06859v2 Announce Type: replace-cross Abstract: Removing an agent from a cooperative team to measure its contribution seems natural, yet in multi-agent LLM systems this evaluation distorts t
arXiv:2504.11837v2 Announce Type: replace-cross Abstract: Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations. Although large l
arXiv:2605.07962v1 Announce Type: new Abstract: Performance evaluation is essential for assessing the quality of machine learning (ML) models and guiding deployment decisions. In federated learning (F
arXiv:2605.07544v1 Announce Type: new Abstract: When you read a paper about a new Vision-Language Model today, it can be easy to forget how strange this idea would have sounded not so long ago. Teachi
arXiv:2506.04565v2 Announce Type: replace-cross Abstract: Compound AI Systems (CAIS) are an emerging paradigm that integrates large language models (LLMs) with external components, including retriever
arXiv:2512.02991v2 Announce Type: replace Abstract: Despite significant progress in 3D object detection, point clouds remain challenging due to sparse data, incomplete structures, and limited semantic
🚨 In his testimony just now, at the Musk-OpenAI trial, Satya Nadella came off as shrewd, calm, and (mostly) honest, an impressive leader – but also selectively blind. How? He seemed unable to believe
arXiv:2605.07613v1 Announce Type: new Abstract: Conversational news recommendation requires grounding each suggestion in a rapidly evolving article corpus while addressing implicit user intents that l
arXiv:2605.05957v2 Announce Type: replace Abstract: LLMs reliably correct false claims when presented in isolation, yet when the same claims are embedded in task-oriented requests, they often comply r
arXiv:2605.06839v1 Announce Type: cross Abstract: Autonomous experimentation has transformed microscopy and materials discovery by enabling closed-loop optimization including imaging and spectroscopy
arXiv:2605.06894v1 Announce Type: cross Abstract: Machine learning (ML) in real-world systems must contend with concept drift, adversarial actors, and a spectrum of potential features with varying cos
Managing a modern database fleet is both a scale and cognitive problem. As database estates grow, the effort required to monitor, troubleshoot, and optimize them often outpaces teams’ capacity, who fi
arXiv:2605.07877v1 Announce Type: new Abstract: Robot swarms promise scalable assistance in complex and hazardous environments. Task planning lies at the core of human-swarm collaboration, translating
arXiv:2605.06897v1 Announce Type: cross Abstract: The rise of Internet of Things (IoT) devices in the physical world necessitates voice-based interfaces capable of handling complex user experiences. W
arXiv:2601.07247v2 Announce Type: replace-cross Abstract: Learning models that can handle distribution shifts is a key challenge in domain generalization. Invariance learning, an approach that focuses
arXiv:2605.07561v1 Announce Type: new Abstract: Pathological complete response (pCR) is a key prognostic factor in breast cancer patients undergoing neoadjuvant therapy, strongly associated with long-
arXiv:2605.06317v2 Announce Type: replace-cross Abstract: Existing Vision-Language Navigation (VLN) methods typically adopt an egocentric, step-by-step paradigm, which struggles with error accumulatio
arXiv:2603.08256v2 Announce Type: replace Abstract: Word sense plausibility rating requires predicting the human-perceived plausibility of a given word sense on a 1-5 scale in the context of short nar
arXiv:2605.07649v1 Announce Type: cross Abstract: Over the last few years, research on autonomous systems has matured to such a degree that the field is increasingly well-positioned to translate resea
arXiv:2605.07574v1 Announce Type: new Abstract: Mainstream vision-language models (VLMs) fundamentally struggle with severe optical ambiguities, such as reflections and transparent objects, due to the
arXiv:2605.07154v1 Announce Type: new Abstract: Referring Audio-Visual Segmentation (Ref-AVS) seeks to localize and segment target objects in video frames based on visual, auditory, and textual referr
arXiv:2605.07422v1 Announce Type: cross Abstract: Qualitative analysis plays a pivotal role in understanding the human and social aspects of software engineering. However, it remains a demanding proce
arXiv:2605.07477v1 Announce Type: new Abstract: Recent text-guided image editing (TIE) models have achieved remarkable progress, however, many edited results still suffer from artifacts, unintended mo
arXiv:2605.06483v2 Announce Type: replace Abstract: Signal Temporal Logic (STL) is an expressive formal language for specifying spatio-temporal requirements over real-valued, real-time signals. It has
arXiv:2605.08043v1 Announce Type: cross Abstract: While text-to-image models have made strong progress in visual fidelity, faithfully realizing complex visual intents remains challenging because many
arXiv:2605.07711v1 Announce Type: new Abstract: On-policy distillation (OPD) is a standard tool for transferring teacher behavior to a smaller student, but it implicitly assumes that teacher and stude
arXiv:2605.07001v1 Announce Type: cross Abstract: Architectural code smells erode software maintainability and are costly to repair manually, yet unlike localized bugs, they require cross-module reaso
arXiv:2605.06763v1 Announce Type: new Abstract: Sparse attention improves LLM inference efficiency by selecting a subset of key-value entries, but at the cost of potential accuracy degradation. In par
arXiv:2605.08003v1 Announce Type: new Abstract: Video anomaly detection (VAD) aims to automatically identify events that deviate from normal patterns in untrimmed surveillance videos. Existing methods
arXiv:2605.07390v1 Announce Type: new Abstract: Generative models have achieved success in producing apparently coherent 2D videos, but remain challenging in the physical world due to lack of 4D spati
Starlink is shutting down a GPS-style positioning feature that customers could previously access through its local API, with the company having restricted access to this capability that some users had
arXiv:2605.07908v1 Announce Type: cross Abstract: Belief functions are a powerful and popular framework for the mathematical characterisation of uncertainty, in particular in situations in which lack
arXiv:2605.07112v1 Announce Type: new Abstract: Agentic AI systems that invoke external tools are powerful but costly, leading developers to default to large models and overspend inference budgets. Mo
arXiv:2605.07639v1 Announce Type: new Abstract: Tacit knowledge plays a central role in human expertise, yet it remains difficult to capture, formalize, and reuse in machine-interpretable form. This c
arXiv:2605.06988v1 Announce Type: cross Abstract: Distributed agents in real-world settings frequently must coordinate under uncertainty with only partial observations. Coordination is necessary to sh
arXiv:2605.07186v1 Announce Type: cross Abstract: Existing Large Language Model (LLM) benchmarks primarily focus on syntactically correct inputs, leaving a significant gap in evaluation on imperfect t
arXiv:2605.07158v1 Announce Type: cross Abstract: Vector search and retrieval-augmented generation (RAG) rest on the assumption that cosine similarity between text embeddings reflects conceptual relat
arXiv:2605.08054v1 Announce Type: new Abstract: Generating human motion that satisfies customized zero-shot goal functions, enabling applications such as controllable character animation and behavior
arXiv:2605.06812v1 Announce Type: new Abstract: LLM-based agentic systems are rapidly evolving to perform complex autonomous tasks through dynamic tool invocation, stateful memory management, and mult
arXiv:2605.06966v1 Announce Type: new Abstract: Autonomous vehicles (AVs) require extensive testing in simulation, but test case generation for driving scenarios is laborious. The desired scenarios ar
arXiv:2605.07148v1 Announce Type: new Abstract: Decades of cognitive science establish that humans navigate environments by forming cognitive maps, defined as allocentric and topology-preserving repre
arXiv:2605.07481v1 Announce Type: cross Abstract: In this paper, we investigate the recent state-of-the-art schemes for watermarking large language models (LLMs) outputs. These techniques are claimed
arXiv:2602.10693v3 Announce Type: replace-cross Abstract: Off-policy updates are inevitable in reinforcement learning (RL) for large language models (LLMs) due to rollout staleness from asynchronous t
arXiv:2506.21582v5 Announce Type: replace-cross Abstract: Text analytics has traditionally required specialized knowledge in Natural Language Processing (NLP) or text analysis, which presents a barrie
arXiv:2605.07552v1 Announce Type: new Abstract: The rapid advances in deep learning have significantly enhanced the accuracy of multimodal 3D human pose estimation (HPE). However, the state-of-the-art
Everyone knows about the 300 Spartans at Thermopylae. Almost nothing they know is the full story. Start with the number. There weren't 300 Greeks at that pass. There were around 7,000. Spartans, Thesp
(INTENTIONALLY) LOST IN TRANSLATION: Democrats: We'd like cops to stop killing minorities. Republicans: Dems hate police. Democrats: Women should have the right to choose. Republicans: Dems want to ki
François Chollet argues that intuition serves as the foundational precursor to the development of significant ideas and innovations. This perspective suggests that before formal analysis, rigorous met
ERNIE 5.1 is here 🚀 ERNIE 5.1 significantly reduces pretraining cost while compressing total parameters to ~1/3 and activated parameters to ~1/2 — using only ~6% of the pretraining cost compared to mo
Sam Altman solicited feedback from the public on X (formerly Twitter) regarding desired improvements for OpenAI's next model release. The post likely gathered community input on priorities such as rea