Knowledge catalogue
arXiv:2607.27463v1 Announce Type: new Abstract: Dimensionality Reduction (DR) is a fundamental tool for high-dimensional data exploration, reducing the complexity of latent spaces of machine learning
arXiv:2607.27511v1 Announce Type: new Abstract: Imitation learning has shown increasing promise for autonomous robotic surgery, yet safe deployment remains challenging due to the safety-critical natur
arXiv:2607.28319v1 Announce Type: new Abstract: This work presents Fairness Pruning, a lightweight structural intervention method designed for the management and future mitigation of demographic bias
arXiv:2607.28225v1 Announce Type: new Abstract: Agentic vision-language models (VLMs), which interleave textual reasoning with explicit tool calls such as cropping and code-based image manipulation, h
arXiv:2607.16387v2 Announce Type: replace-cross Abstract: An agent system's execution traces record how it fails, and procedures that improve such a system without changing model weights (trajectory s
arXiv:2607.26723v1 Announce Type: cross Abstract: Inversion-based watermarking is a promising approach to authenticate diffusion-generated images, yet practical use is bottlenecked by inversion that i
arXiv:2607.28416v1 Announce Type: new Abstract: Curved tactile fingertips for dexterous manipulation must resolve fine contact geometry, distinguish normal and tangential loads, and capture transient
arXiv:2607.27800v1 Announce Type: new Abstract: Existing invisible watermark removal methods often struggle to accurately capture the watermark-bearing features, leading to an unfavorable trade-off be
arXiv:2607.27842v1 Announce Type: new Abstract: Diffusion models are widely used to generate high-quality images and videos, but their iterative denoising process remains computationally intensive. A
arXiv:2607.27665v1 Announce Type: new Abstract: Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information. As such risks evolve, clien
arXiv:2607.26611v1 Announce Type: new Abstract: AI-assisted coding increasingly translates informal user intent into executable software, yet coding requests often contain ambiguities that recur in us
arXiv:2607.28196v1 Announce Type: new Abstract: Practitioners accept a compressed language model once it clears a stack of data-cheap quality guards: perplexity within a small factor of the original,
Financial Times: Filing: Amazon has completed its 50B investment in OpenAI; source: OpenAI received the final tranche this week, taking Amazon's position to roughly 5% — Ecommerce giant has roughly 5%
arXiv:2607.28293v1 Announce Type: new Abstract: While depth sensors have the potential to complement RGB data for affordance segmentation in wearable robots, their usage seems to remain underexplored.
arXiv:2607.27853v1 Announce Type: new Abstract: Powered by advances in LLMs and autonomous agents, deep research has become one of the most widely adopted agentic products. However, most deep research
arXiv:2607.26076v1 Announce Type: cross Abstract: Retrieval-augmented generation services over mutable enterprise documents repeatedly execute semantically equivalent analysis requests. Answer reuse c
arXiv:2607.28571v1 Announce Type: new Abstract: Operational Earth observation increasingly calls for answering queries such as ``find the image pairs where a new building appeared.'' This means search
Fine-tune your own embedding model for the price of a coffee. A great reranker can't surface a doc that was never retrieved. A RAG pipeline cannot cite a case it failed to retrieve. See how contrastiv
arXiv:2607.28127v1 Announce Type: new Abstract: Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs). H
arXiv:2607.27959v1 Announce Type: new Abstract: Due to their strong generalizable multimodal processing and reasoning capabilities, Multimodal Large Language Models (MLLMs) have demonstrated significa
arXiv:2607.27632v1 Announce Type: new Abstract: With the rapid advancement of the Internet of Things (IoT), massive amounts of data are generated across distributed edge networks. Training models on f
arXiv:2607.27275v1 Announce Type: new Abstract: Post-training quantization to 4-bit weights is widely reported to be nearly lossless. We test this claim for multi-turn, tool-calling agents, where it n
arXiv:2607.17563v2 Announce Type: replace Abstract: Many fine-grained recognition tasks contain hierarchical labels such as order, family and species. Although this supervision should be beneficial, j
arXiv:2607.28022v1 Announce Type: new Abstract: Large language model training in open-ended domains lacks verifiable rewards, making task preferences difficult to formalize as effective supervision. C
arXiv:2607.27969v1 Announce Type: new Abstract: Despite substantial progress in remote sensing multi-temporal change detection (MTCD), most existing MTCD methods still represent the dynamic process at
arXiv:2607.27217v1 Announce Type: cross Abstract: Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring rema
arXiv:2508.02092v3 Announce Type: replace-cross Abstract: Large language models represent significant investments in computation, data, and engineering expertise, making them extraordinarily valuable
arXiv:2607.28097v1 Announce Type: new Abstract: Mathematically equivalent expert-reduction orders can produce observably different sparse-MoE executions. We isolate this effect in native DeepSeek-V4-F
arXiv:2607.27654v1 Announce Type: new Abstract: Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of do
arXiv:2607.28568v1 Announce Type: new Abstract: Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a
arXiv:2607.28311v1 Announce Type: cross Abstract: Query optimization of Basic Graph Patterns (BGP) SPARQL queries over Knowledge Graphs (KG) requires accurate cardinality estimation. Recently publishe
arXiv:2606.13097v2 Announce Type: replace-cross Abstract: Code-writing large language models (CodeLLMs) generate executable code policies for embodied agents by translating natural language goals and
arXiv:2607.27389v1 Announce Type: new Abstract: Learning-to-optimize (L2O) methods accelerate repeated optimization by training models to predict solutions, warm starts, branching decisions, or other
arXiv:2607.27995v1 Announce Type: cross Abstract: Adversarial training has emerged as a powerful approach for protecting models against adversarial attacks in a broad range of real-world applications.
arXiv:2607.27975v1 Announce Type: new Abstract: We derive finite-sample generalization bounds for Transformers trained with dynamic programming recursions. Building on the doubly lifted, measure-value
arXiv:2607.28505v1 Announce Type: new Abstract: The rise of generative artificial intelligence (GenAI) in academic writing and publishing (AWP) raises questions about linguistic inclusivity and the le
arXiv:2607.28082v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities in structured query generation, making them a natural choice for Text-to-SPARQL, whic
arXiv:2607.28397v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) over knowledge graphs requires retrievers that can effectively capture both graph structure and semantic informat
arXiv:2603.02291v2 Announce Type: replace Abstract: We investigate an integrated sensing and communication (ISAC)-enabled BS for the unmanned aerial vehicle (UAV) obstacle avoidance task, and propose
arXiv:2607.26181v1 Announce Type: new Abstract: Functional verification dominates integrated circuit (IC) front-end engineering effort, and a single missed bug that escapes to silicon can trigger a co
arXiv:2607.27422v1 Announce Type: new Abstract: Good action rankings do not make a contrastive critic safe to maximize. These critics increasingly act as value-like objectives for best-of-K selection,
Abner Li / 9to5Google: Google starts rolling out access to Gemini Spark for Google AI Pro subscribers to over 160 countries and adds a Chrome auto browse integration on desktop — Gemini Spark is getti
arXiv:2607.27766v1 Announce Type: new Abstract: On-device in-context learning (ICL) relies on pre-inference retrieval to select demonstrations for useful context before downstream model inference. Thi
arXiv:2602.14649v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit strong reasoning abilities, but their high computational costs limit their practical deployment. Recent studies
arXiv:2607.26656v1 Announce Type: cross Abstract: Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real C
arXiv:2607.28456v1 Announce Type: cross Abstract: Solving large, sparse linear systems is a core task in scientific computing, and efficient iterative solvers rely critically on effective and robust p
arXiv:2607.28537v1 Announce Type: cross Abstract: Metallic magnets exhibit complex spin dynamics governed by electronically generated interactions. Predictive simulations of such dynamics typically re
arXiv:2607.28076v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is effective for training large language model agents. However, terminal rewards provide only co
arXiv:2607.26634v1 Announce Type: cross Abstract: Organizational digitalization expands cybersecurity risks, making cybersecurity an increasingly important research area in Information Systems (IS). A
arXiv:2607.26071v1 Announce Type: cross Abstract: In this work, we propose GuidedRAG, a novel extension to traditional Retrieval-Augmented Generation (RAG) that introduces a dedicated selection stage
arXiv:2607.26160v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather t
arXiv:2607.28073v1 Announce Type: cross Abstract: In demanding professional environments and meeting review scenarios, lengthy text often imposes a high cognitive load. To facilitate efficient informa
arXiv:2607.27694v1 Announce Type: cross Abstract: Low-bit quantization is essential for efficient LLM inference, and both rotation and fine-grained group quantization have shown individual promise. Ho
arXiv:2502.10497v2 Announce Type: replace Abstract: Recent advancements in Generative AI have significantly improved the efficiency and adaptability of natural language processing (NLP) systems, parti
arXiv:2607.27823v1 Announce Type: new Abstract: Large vision-language models (LVLMs) often hallucinate objects that are absent from an image. Despite recent progress, existing mitigation methods still
arXiv:2607.27636v1 Announce Type: cross Abstract: Recent agentic AI systems may return a heterogeneous response containing notices, requests, handoffs, and actions. Conditions can change before extern
arXiv:2607.28394v1 Announce Type: new Abstract: Hand-object interaction (HOI) modeling remains challenging because it requires joint reasoning about hand articulation, object geometry, contact, semant
arXiv:2607.28301v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) can equip large language models (LLMs) with domain knowledge for high-performance computing (HPC) tasks such as data race d
arXiv:2607.27652v1 Announce Type: new Abstract: Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions u
arXiv:2607.27928v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) is fundamentally influenced by the distributional composition of multi-domain pre-training data. While m