Knowledge catalogue
arXiv:2606.30190v1 Announce Type: cross Abstract: Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem
arXiv:2606.30473v1 Announce Type: cross Abstract: We study retrieval over catalogs of structured metadata, where each record is a small schema whose fields answer different kinds of query. Embedding a
arXiv:2606.28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text gen
arXiv:2606.29414v1 Announce Type: new Abstract: Periodic Implicit Neural Representations (INRs) such as SIREN and FINER assign every neuron, the same global frequency, spending the representational bu
arXiv:2603.16016v2 Announce Type: replace-cross Abstract: A single egocentric image typically captures only a small portion of the floor, yet a complete metric traversability map of the surroundings w
arXiv:2606.28962v1 Announce Type: cross Abstract: Model quantization is essential for the efficient deployment of Large Language Models (LLMs), but introduces a critical vulnerability: Quantization-Co
arXiv:2606.29059v1 Announce Type: cross Abstract: World modeling requires forecasting uncertain futures while preserving information useful for downstream perception. Existing visual world models ofte
arXiv:2606.30566v1 Announce Type: cross Abstract: We discover a behavioral invariant in LLM agents under persistent memory poisoning: in architectures where routing information is retrieved through ob
arXiv:2505.16903v2 Announce Type: replace Abstract: Prompt tuning has become a key mechanism for adapting pre-trained Graph Neural Networks (GNNs) to new downstream tasks. However, existing approaches
arXiv:2606.30220v1 Announce Type: new Abstract: High benchmark accuracy does not guarantee genuine use of visual evidence. We study this problem in traffic accident Video Question Answering (VideoQA),
arXiv:2606.30191v1 Announce Type: new Abstract: How does an agent that can tell self from world come to be durably shaped by that distinction? Recent work shows that a predictive system can detect its
arXiv:2606.30059v1 Announce Type: new Abstract: Industry-scale video and live-streaming moderation imposes requirements that are difficult to satisfy with generic pretrained public models or external
arXiv:2606.29093v1 Announce Type: new Abstract: A deep defogging pipeline pretrained on controlled laboratory fog and fine-tuned with domain-randomized synthetic fog applied to clear outdoor scenes ge
arXiv:2606.28353v1 Announce Type: cross Abstract: Linking FDA-approved medical devices to their underlying United States Patent and Trademark Office (USPTO) patents enables critical applications such
arXiv:2606.29073v1 Announce Type: cross Abstract: Model Context Protocol (MCP)-style ecosystems give language-model applications a practical connection layer for tools, resources, prompts, and transpo
Fully solving document parsing includes covering every point on the Pareto curve of accuracy, cost, and latency: 1️⃣ High-accuracy parsing - requires 99%+ accuracy, price insensitive. Especially relev
arXiv:2606.29256v1 Announce Type: cross Abstract: In recent years, models based on the Transformer architecture have seen widespread applications and have become one of the core tools in the field of
arXiv:2606.30003v1 Announce Type: new Abstract: Precisely manipulating objects in a single photograph (translation, rotation, scaling) while obeying 3D physical constraints remains unsolved for diffus
arXiv:2606.28572v1 Announce Type: cross Abstract: The axiom of choice has divided the foundations of mathematics for over a century, but the distinction between classical and constructive proofs has r
arXiv:2510.01878v2 Announce Type: replace Abstract: Low-rank gradient optimization for large language models is currently divided into two categories: structured methods that rigorously identify subsp
GLM-5.2 just tied for #2 on SportEval’s World Cup Exact Score Hit Rate leaderboard. ⚽ 13 exact-score hits out of 76 matches 📊 17.1% accuracy 🥈 Second only to GPT-5.5 That puts @Zai_org ahead of Claude
arXiv:2606.30599v1 Announce Type: new Abstract: Existing instruction-based video editing datasets commonly focus on single-task appearance editing, failing to meet the complex creative demands of real
The Keyword: Google launches Nano Banana 2 Lite, a low cost text-to-image model that delivers outputs in four seconds, and rolls out Gemini Omni Flash to developers — We're making it easier to experim
Google LLC is enhancing its generative artificial intelligence capabilities for creators with the debut of a pair of new media-focused models in the Gemini Enterprise Agent Platform. The new additions
arXiv:2606.22528v2 Announce Type: replace Abstract: Modern LLM agents increasingly rely on context compaction, summarization, or eviction to keep long-running sessions within a token budget. We show t
arXiv:2606.28514v1 Announce Type: new Abstract: Multimodal models are increasingly deployed to solve tasks collaboratively with humans or other artificial agents. Existing benchmarks show that these m
arXiv:2501.17559v2 Announce Type: replace Abstract: After the achievement of solving two-player zero-sum games, more AI researchers focus on solving multiplayer games. Urban Network Security Games (ex
arXiv:2603.22876v2 Announce Type: replace-cross Abstract: Learning a generalist control policy for robotic manipulation typically relies on large-scale datasets. Given the high cost of real-world data
arXiv:2511.05879v5 Announce Type: replace-cross Abstract: Hydrogen crossover is a critical safety and efficiency constraint in high-pressure polymer electrolyte membrane water electrolysis (PEMWE), bu
shot-scraper video is a new command introduced in today's shot-scraper 1.10 release which accepts a storyboard.yml file defining a routine to run against a web application and uses Playwright to recor
arXiv:2603.06638v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning. Yet, existing be
arXiv:2606.29948v1 Announce Type: new Abstract: Tactile sensors are inherently heterogeneous: a model trained on one sensor cannot be directly used on another, which limits learning contact-rich manip
arXiv:2606.29315v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to take actions in the real world and support human decision-making, yet most agents rely on parametr
arXiv:2606.29593v1 Announce Type: cross Abstract: In 1937, Stefan Kaczmarz proposed a simple algorithm for solving systems of linear equations. This algorithm turned out to be the earliest known examp
ChatGPT adoption has grown significantly since its launch, expanding across diverse user demographics, industries, and use cases globally. OpenAI's analysis likely covers metrics such as user growth,
arXiv:2606.28358v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) aims to enhance the trustworthiness of Large Language Models (LLMs) by grounding their outputs in external docume
arXiv:2606.29043v1 Announce Type: new Abstract: Sharpness and complexity are two central factors in the generalization analysis of deep neural networks. Existing quantitative evaluations of generaliza
arXiv:2606.29809v1 Announce Type: cross Abstract: Hallucination detection has become a pressing requirement for trustworthy AI deployment at scale. The most accurate detection methods depend on GPU-in
arXiv:2606.29733v1 Announce Type: new Abstract: Organizations that cannot send data to a cloud API increasingly ask: how good is Text-to-SQL if the model must run on-premises on open weights, and whic
arXiv:2606.29672v1 Announce Type: new Abstract: Evaluating the originality of visual images poses enduring challenges for creativity assessment. Automated scoring using AI models has proven effective
arXiv:2606.29605v1 Announce Type: new Abstract: Clinical machine learning increasingly relies on training corpora generated by large language models (LLMs) rather than annotated by clinicians, and suc
Computational chemistry researchers have traditionally faced a frustrating trade-off when simulating molecular interactions: use fast classical force fields that sacrifice precision or rely on accurat
arXiv:2602.15257v3 Announce Type: replace-cross Abstract: We present the first comprehensive, large-scale study of training long-context vision language models up to 344K context, targeting long-docum
arXiv:2606.28336v1 Announce Type: cross Abstract: Methodology Inspiration Retrieval (MIR) asks a system to retrieve prior papers whose methods can inspire a new research proposal. Unlike general scien
arXiv:2606.29675v1 Announce Type: new Abstract: Bogomolny, Bohigas and Schmit (BBS) found that the spectrum of the pairwise distance matrix on N points sampled from a smooth d-dimensional manifold enc
arXiv:2606.30024v1 Announce Type: cross Abstract: Recent advances in deep learning have notably improved steganographic message hiding. However, designing a generalizable steganographic approach for 3
arXiv:2606.28556v1 Announce Type: new Abstract: Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical app
arXiv:2606.29951v1 Announce Type: new Abstract: Interpretable Mesomorphic Neural Networks (IMNs) offer a promising framework that combines the predictive power of deep neural networks with the interpr
arXiv:2606.28553v1 Announce Type: new Abstract: In many real-world applications, such as retail sales, energy usage, and supply chain planning, forecasting is performed across hierarchical structures.
Etched Inc., a developer of artificial intelligence inference chips, launched today with 800 million in funding. The startup raised the capital over multiple rounds. The most recent investment, which
Genebench-Pro appears to be a case study or tool from OpenAI focused on benchmarking or evaluating genetic/genomic analysis capabilities, likely demonstrating how OpenAI's models or tools can be appli
arXiv:2606.28333v1 Announce Type: cross Abstract: egin{quote} The biases in Large Language Models' (LLMs) outputs remain inadequately theorised, particularly from the perspective of the Global South.
arXiv:2405.01906v3 Announce Type: replace Abstract: In modern intelligent transportation systems (ITS), particularly in freight transportation and logistics, real-time route planning is crucial. It pr
arXiv:2603.13082v2 Announce Type: replace Abstract: Text-guided 3D motion editing has seen success in single-person scenarios, but its extension to multi-person settings is less explored due to limite
arXiv:2606.30262v1 Announce Type: new Abstract: Text-to-image (T2I) diffusion models often fail to faithfully render explicit textual descriptions, instead defaulting to strongly learned visual priors
arXiv:2606.30449v1 Announce Type: new Abstract: Probes on model internals could help monitor agentic systems if they identify harmful text or tool actions before those actions are generated. We ask wh
arXiv:2604.02371v2 Announce Type: replace-cross Abstract: Visual long-document understanding is critical for enterprise, legal, and scientific applications, yet the best performing open recipes have n
Today, we’re excited to announce the availability of Anthropic’s most advanced Sonnet model, Claude Sonnet 5, on Amazon Bedrock and Claude Platform on AWS. Claude Sonnet 5 is the first Sonnet model of
GeneBench-Pro is a comprehensive benchmarking tool or dataset introduced by OpenAI designed to evaluate AI model performance on genomic and gene-related tasks. It likely provides standardized metrics
Introducing SWE-Together: a multi-turn benchmark built from real user–agent coding sessions. Coding agents are often benchmarked like exam-takers: given the full spec up front, then graded on the fina