Knowledge catalogue
arXiv:2605.30448v1 Announce Type: cross Abstract: Black-box LLM distillation is usually evaluated as an output-matching problem: a student is considered successful when its responses are semantically
arXiv:2602.08885v5 Announce Type: replace-cross Abstract: Symbolic regression (SR) aims to discover interpretable analytical expressions that accurately describe observed data. Amortized SR promises t
OpenAI announced plans to build significant AI infrastructure in Michigan to support the growing computational demands of advanced AI systems. The project, referred to as Stargate, represents investme
arXiv:2605.30447v1 Announce Type: cross Abstract: Calibration, the alignment of predicted probabilities with true outcome frequencies, is essential for reliable decision-making. While extensively stud
arXiv:2605.30459v1 Announce Type: new Abstract: Large language models (LLMs) remain limited on tasks requiring indirect reasoning, cultural knowledge, and coordinated hypothesis testing. We investigat
arXiv:2605.30470v1 Announce Type: new Abstract: Graph Machine Learning as a Service (GMLaaS) platforms increasingly implement explainability interfaces to meet regulatory transparency requirements. Ho
arXiv:2605.30497v1 Announce Type: new Abstract: RAG-based legal assistants have been growing in popularity, but LLM hallucinations remain a key issue and potentially undermines justice. While benchmar
arXiv:2605.30583v1 Announce Type: new Abstract: We present Caspar, a library that makes the power of modern GPUs more accessible in robotics and provides a state-of-the-art nonlinear GPU solver that c
arXiv:2503.08679v5 Announce Type: replace Abstract: Recent studies indicate that when faced with explicit biases in prompts, models often omit mentioning these biases in their Chain-of-Thought (CoT) o
Juro Osawa / The Information: Chinese AI developer MiniMax launches M3, a new coding model that it says rivals Opus 4.7, costing 0.12 per 1M input tokens, compared with 5 for Opus 4.7 — Chinese AI dev
arXiv:2605.30394v1 Announce Type: cross Abstract: This paper introduces Code Bench, a benchmark capable of evaluating Large Language Models (LLMs) concise code generation abilities in 60 programming l
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
Omkar Godbole / CoinDesk: Coinbase launches direct Indian rupee deposit and withdrawal rails via the Immediate Payment Service, aiming to remove its reliance on P2P and intermediaries — Coinbase annou
arXiv:2512.11779v2 Announce Type: replace-cross Abstract: Evaluating conditional coverage remains one of the most persistent challenges in assessing the reliability of predictive systems. Although con
arXiv:2605.30689v1 Announce Type: cross Abstract: Zero-shot Temporal Action Localization (ZS-TAL) aims to detect and locate previously unseen actions in untrimmed videos. However, existing approaches
arXiv:2605.30648v1 Announce Type: new Abstract: Recent work has analyzed the convergence of first-order methods under non-uniform smoothness assumptions that better model the loss landscape in machine
arXiv:2605.31172v1 Announce Type: new Abstract: This work studies the convergence of two-timescale stochastic approximations (SA), a class of iterative algorithms that update two sets of parameters in
arXiv:2605.30846v1 Announce Type: new Abstract: Object counting remains fragmented across domain-specific datasets and task formulations, despite rapid progress in generalist vision models. Existing c
arXiv:2605.11946v2 Announce Type: replace Abstract: Large Language Model agents are increasingly augmented with agent skills. Current evaluation methods for skills remain limited. Most deployed benchm
arXiv:2605.30611v1 Announce Type: cross Abstract: Scientific figures are among the most effective means of communicating complex research ideas, yet producing publication-quality illustrations remains
arXiv:2605.30640v1 Announce Type: cross Abstract: Low-rank adaptation has become a standard method for parameter-efficient fine-tuning of large language models, but even small amounts of unsafe or adv
arXiv:2602.10809v2 Announce Type: replace Abstract: Existing multimodal retrieval systems excel at semantic matching but implicitly assume that query-image relevance can be measured in isolation. This
arXiv:2605.31286v1 Announce Type: cross Abstract: Real-world household robots require Vision-Language-Action (VLA) foundation models that can acquire reusable manipulation skills across diverse object
arXiv:2605.30686v1 Announce Type: cross Abstract: ReAct agents that interleave chain-of-thought reasoning with tool calls are increasingly deployed for real tasks such as scheduling, file retrieval, a
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:2605.30444v1 Announce Type: new Abstract: Recent advances in 4D Human-Object Interaction (HOI) generation have enabled increasingly realistic motion synthesis, particularly for single-object man
arXiv:2511.19923v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have recently shown significant advancements in video understanding, especially in feature alignment, event reas
arXiv:2602.01267v2 Announce Type: replace Abstract: Kronecker adapters have emerged as a promising approach for fine-tuning large-scale models, enabling high-rank updates through tunable component str
arXiv:2603.29972v2 Announce Type: replace-cross Abstract: Scientists often want to explain why an outcome is different in two groups. For instance, differences in patient mortality rates across two ho
arXiv:2605.31271v1 Announce Type: new Abstract: Driving Vision-Language-Action Models (Driving VLAs) aim to use language to improve end-to-end planning, but the language-action gap limits this promise
arXiv:2602.13812v3 Announce Type: replace-cross Abstract: Document-to-table (Doc2Table) extraction derives structured tables from unstructured documents under a target schema, enabling reliable and ve
arXiv:2605.30431v1 Announce Type: new Abstract: Recent progress in video diffusion models has enabled remarkable generative fidelity, yet leveraging these priors for restoration remains limited by the
arXiv:2605.31377v1 Announce Type: cross Abstract: Agentic Retrieval-Augmented Generation improves retrieval by integrating planning, tool use, and iterative reasoning, but existing agentic RAG methods
arXiv:2602.09276v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning and its variants have substantially improved the performance of language models on complex reasoning tasks, y
arXiv:2605.31557v1 Announce Type: new Abstract: Continuous episodic memory is a core capability for autonomous agents operating in dynamic, real-world environments, yet current streaming video benchma
arXiv:2605.30637v1 Announce Type: new Abstract: Clinical decision-making (CDM) is central to real-world clinical workflows, where clinicians infer diagnoses, select treatments, or anticipate future he
arXiv:2605.30690v1 Announce Type: new Abstract: Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. Howeve
arXiv:2605.30924v1 Announce Type: new Abstract: MLLM-powered embodied agents deployed in real-world environments encounter physical hazards. However, existing approaches lack explicit mechanisms for i
arXiv:2605.31170v1 Announce Type: cross Abstract: Monitoring autonomous language model agents currently relies mostly on surface behavior. But what happens when agent populations invent new languages
arXiv:2602.05649v2 Announce Type: replace Abstract: The long-standing dominance of gradient-boosted decision trees for tabular data has recently been challenged by in-context learning tabular foundati
arXiv:2605.31251v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential as embodied agents, yet embodied geo-localization remains underexplored due to th
arXiv:2601.21778v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) can achieve competitive performance by converting already existing well-trained Artificial Neural Networks (ANN
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:2605.31506v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is the current industry standard for grounding AI in real-world facts. Traditional retrieval methods rely on keyw
arXiv:2605.31140v1 Announce Type: cross Abstract: Large Language Models (LLMs) remain highly vulnerable to diverse attacks, particularly in black-box settings where the internals of target models are
arXiv:2510.04500v2 Announce Type: replace Abstract: This work demonstrates how increasing the number of neurons in a network without increasing its total number of non-zero parameters improves perform
arXiv:2510.16138v2 Announce Type: replace Abstract: Existing expert merging strategies for Sparse Mixture of Experts (SMoE) typically rely on input-dependent or input-independent averaging of expert p
arXiv:2605.30712v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scr
arXiv:2605.30407v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without hig
arXiv:2605.30947v1 Announce Type: new Abstract: LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and quantit
arXiv:2605.30771v1 Announce Type: new Abstract: AI agents that persist across sessions need memory they can retrieve, audit, update, and erase. Existing memory systems often collapse source evidence,
arXiv:2605.31410v1 Announce Type: new Abstract: Food-as-Medicine requires models to reason beyond what a dish is or what nutrition it contains: they must decide whether a concrete food choice is appro
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:2512.20732v2 Announce Type: replace-cross Abstract: As LLMs advance their reasoning capabilities about the physical world, the absence of rigorous benchmarks for evaluating their ability to gene
Device intelligence company FingerprintJS Inc. today launched a preview of two products built to identify traffic from artificial intelligence assistants, addressing a detection gap that has opened as
arXiv:2601.22787v2 Announce Type: replace Abstract: Post-training compression is currently divided into two contrasting regimes. On the one hand, fast, data-free, and model-agnostic methods (e.g., NF4
arXiv:2605.31145v1 Announce Type: cross Abstract: In-context localization (ICL) seeks to localize a target object specified by a small set of support examples in a query image, operating on the fly wi
arXiv:2605.30858v1 Announce Type: new Abstract: Agentic forecasting is important for decision-making in dynamic environments, but it remains challenging because agents must reason from incomplete, tim
arXiv:2605.31042v1 Announce Type: cross Abstract: LLM agents are evolving from conversational chatbots to operational tools in real-world workspaces. In local agentic harnesses, an LLM can read and wr
arXiv:2603.18382v2 Announce Type: replace Abstract: Anonymization is often assumed to protect privacy once explicit identifiers are removed, because re-identification has historically required special