Knowledge catalogue
arXiv:2605.08270v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) offer notable advantages in biological plausibility and energy efficiency, making them promising candidates for buildin
arXiv:2602.00953v2 Announce Type: replace Abstract: Engineered image-based biomarkers offer a clinically interpretable alternative to black-box AI in computational pathology, yet their discovery remai
arXiv:2510.20129v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) remain vulnerable to jailbreak attacks, where adversarially crafted prompts induce policy-violating responses des
arXiv:2605.08334v1 Announce Type: new Abstract: We present SalesSim, a framework and testbed for evaluating the ability of Multimodal Large Language Models (MLLMs) to simulate realistic, persona-drive
OpenAI CEO Sam Altman says Elon Musk did 'huge damage' to the culture of the AI startup. During testimony as part of Musk's lawsuit against OpenAI, Altman said Musk required OpenAI president Greg Broc
Sam Altman made statements under oath in May 2023 regarding AI safety and OpenAI's practices, but Gary Marcus critiqued these statements as incomplete or misleading, suggesting Altman failed to fully
OpenAI CEO Sam Altman has begun his testimony against Elon Musk in a high-profile jury trial in a California federal courtroom. Altman, alongside OpenAI president Greg Brockman, is a primary defendant
After two weeks of hearing from assorted witnesses that he was a lying snake, the jury finally heard from the lying snake himself: Sam Altman. At the end of the testimony, his lawyer William Savitt as
arXiv:2605.09417v1 Announce Type: new Abstract: Multi-object tracking (MOT) is a fundamental task in computer vision that requires continuously tracking multiple targets while maintaining consistent i
arXiv:2605.10289v1 Announce Type: new Abstract: Offline-to-online learning aims to improve online decision-making by leveraging offline logged data. A central challenge in this setting is the distribu
Reuters: Samsung and its South Korean labor union fail to reach a pay deal; the union has said workers will strike for 18 days from May 21 if its demands are not met — Samsung Electronics (005930.KS)
arXiv:2605.08346v1 Announce Type: cross Abstract: Hallucination detection methods for large language models increasingly operate on chain-of-thought reasoning traces, yet it remains unclear whether th
SAP SE today introduced at Sapphire 2026, the company’s annual conference, what it calls Autonomous Enterprise, a suite of artificial intelligence tools and agents designed to enhance how humans and A
In today's hyper-connected market, an enterprise's most valuable asset — mission-critical data — often remains trapped in legacy silos. For years, leadership teams have navigated a data pipeline dilem
arXiv:2602.04712v2 Announce Type: replace-cross Abstract: We present a visual-context image-retrieval-augmented generation (ImageRAG)- assisted AI agent for automatic target recognition (ATR) of synth
arXiv:2603.27977v2 Announce Type: replace Abstract: Reinforcement learning is critical to improving large reasoning models, but its success relies heavily on verifiable rewards (RLVR), making it hard
arXiv:2602.00327v2 Announce Type: replace Abstract: We explore the use of large language models (LLMs) for next-utterance anticipation in human dialogue. Despite recent advances in LLMs demonstrating
arXiv:2311.03600v3 Announce Type: replace Abstract: Robots capable of learning from demonstration (LfD) must exhibit stability while executing learned motion skills. To be effective in the real world,
arXiv:2605.10285v1 Announce Type: cross Abstract: We present a theoretically grounded Gaussian process framework that leverages neural feature maps to construct expressive kernels. We show that the le
arXiv:2603.16593v2 Announce Type: replace Abstract: Inspection planning is concerned with computing the shortest robot path to inspect a given set of points of interest (POIs) using the robot's sensor
arXiv:2605.10681v1 Announce Type: cross Abstract: Forward error correction is essential for reliable communication over noisy channels. Attention-based model-free neural decoders have shown strong per
arXiv:2605.10327v1 Announce Type: cross Abstract: In this paper, we present SCALAR (Symbolic Conjecture and LLM-Assisted Reasoning), a neurosymbolic framework for automated conjecture generation in qu
arXiv:2605.08505v1 Announce Type: cross Abstract: We study the long-context limit of softmax self-attention with a fixed query and a random context of n i.i.d. keys on the sphere, viewing the inverse
arXiv:2605.08124v1 Announce Type: cross Abstract: Mobile agent systems are emerging as a key paradigm for enabling intelligent applications on edge devices and in AIoT ecosystems. However, their scala
arXiv:2605.10119v1 Announce Type: new Abstract: Recent evidence suggests that Adam performs robustly when its momentum parameters are tied, eta_1=eta_2, reducing the optimizer to a single remaining pa
arXiv:2605.10142v1 Announce Type: cross Abstract: Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of po
arXiv:2509.02372v3 Announce Type: replace-cross Abstract: Large Language Models have become critical to modern software development, but their reliance on uncurated web-scale datasets for training int
arXiv:2605.08528v1 Announce Type: cross Abstract: Autonomous-driving simulators typically trade physical fidelity for scalable parallelism. Physics-based platforms such as CARLA and MetaDrive provide
arXiv:2601.22638v2 Announce Type: replace-cross Abstract: The exponential growth of machine learning submissions has strained the traditional peer review process, resulting in slow feedback loops for
arXiv:2605.10246v1 Announce Type: new Abstract: AI scientist systems are increasingly deployed for autonomous research, yet their academic integrity has never been systematically evaluated. We introdu
arXiv:2604.03687v2 Announce Type: replace Abstract: Long-tailed recognition has benefited from foundation models and fine-tuning paradigms, yet existing studies and benchmarks are mainly confined to n
arXiv:2605.10187v1 Announce Type: new Abstract: Scientific reasoning is a key aspect of human intelligence, requiring the integration of multimodal inputs, domain expertise, and multi-step inference a
arXiv:2604.07383v2 Announce Type: replace Abstract: Cross-city transfer improves prediction in label-scarce cities by leveraging labeled data from other cities, but it becomes challenging when cities
arXiv:2605.09630v1 Announce Type: new Abstract: Tokenizer-free language models eliminate the tokenizer step of the language modeling pipeline by operating directly on bytes; patch-based variants furth
arXiv:2605.05736v2 Announce Type: replace Abstract: Vector quantization (VQ) with autoregressive (AR) token modeling is a widely adopted and highly competitive paradigm for time-series generation. How
arXiv:2605.08322v1 Announce Type: cross Abstract: Sparse MoE models achieve a good balance between capacity and compute by routing each token to a small subset of experts. However, in most MoE archite
arXiv:2603.14889v2 Announce Type: replace-cross Abstract: The rapid evolution of end-to-end spoken dialogue systems demands transcending mere textual semantics to incorporate paralinguistic nuances an
arXiv:2605.09956v1 Announce Type: cross Abstract: High-quality, real-time talking head synthesis remains a fundamental challenge in computer vision. Existing reconstruction- and rendering-based method
arXiv:2602.04811v2 Announce Type: replace-cross Abstract: True self-evolution requires agents to act as lifelong learners that internalize novel experiences to solve future problems. However, rigorous
arXiv:2605.09038v1 Announce Type: new Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries. This is especia
arXiv:2605.09039v1 Announce Type: new Abstract: We introduce SeasonScapes framework and a the SeasonScapes dataset: Swiss Sparse-view Mountain Scenes with Seasonal Changes that covers over 50 km x 60
arXiv:2605.08519v1 Announce Type: new Abstract: Learning from scarce labeled data with a larger pool of unlabeled samples, known as semi-supervised few-shot learning (SS-FSL), remains critical for app
arXiv:2605.08382v1 Announce Type: cross Abstract: LLM coding agents now generate code at an unprecedented scale, yet LLM-generated code introduces cybersecurity vulnerabilities into codebases without
arXiv:2605.09721v1 Announce Type: cross Abstract: Tool-enabled AI agents are increasingly deployed in cloud-hosted environments and offered as services, where they perform side-effecting operations th
arXiv:2605.08313v1 Announce Type: cross Abstract: Large language models (LLMs) rely on deterministic pseudorandom number generators (PRNGs) for autoregressive sampling, creating a critical supply-chai
arXiv:2605.09266v1 Announce Type: new Abstract: We introduce SeePhys Pro, a fine-grained modality transfer benchmark that studies whether models preserve the same reasoning capability when critical in
arXiv:2605.10603v1 Announce Type: new Abstract: Despite strong zero-shot performance, SAM is unreliable under domain shift due to Mask-level Confidence Confusion (MCC), where a single IoU-based mask s
arXiv:2407.11906v3 Announce Type: replace Abstract: Surgical data science has seen rapid advancement with the excellent performance of end-to-end deep neural networks (DNNs). Despite their successes,
arXiv:2602.04054v2 Announce Type: replace-cross Abstract: Understanding how neural representations respond to geometric transformations is essential for evaluating whether learned features preserve me
arXiv:2605.09209v1 Announce Type: cross Abstract: We study optimistic bilevel optimization when the lower-level problem has a non-isolated manifold of minimizers. In this setting, the hyper-objective
arXiv:2605.09945v1 Announce Type: new Abstract: Many high-stakes decisions in health care, public policy, and clinical development require committing to a single policy that will be applied uniformly
arXiv:2605.09345v1 Announce Type: new Abstract: We identify a Selection Plateau phenomenon in one-shot neural network pruning: all rank-monotone weight scorers converge to identical accuracy at fixed
arXiv:2603.26089v2 Announce Type: replace-cross Abstract: The ability to represent oneself and others as agents with knowledge, intentions, and belief states that guide their behavior - Theory of Mind
arXiv:2512.19219v2 Announce Type: replace-cross Abstract: Low-rank adaptation (LoRA) is widely used for parameter-efficient fine-tuning, but its standard all-token, all-head design ignores the heterog
arXiv:2605.10466v1 Announce Type: new Abstract: Large language models (LLMs) exhibit two striking and ostensibly unrelated behaviours: in-context learning (ICL) and repetitive generation. In both, the
arXiv:2605.08145v1 Announce Type: cross Abstract: Current vision language models face hallucination and robustness issues against ambiguous or corrupted modalities. We hypothesize that these issues ca
arXiv:2605.08936v1 Announce Type: new Abstract: Large Reasoning Models possess remarkable capabilities for self-correction in general domain; however, they frequently struggle to recover from unsafe r
arXiv:2605.08874v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation requires adapting image-level vision-language models such as CLIP to dense pixel-level prediction, which is challe
arXiv:2605.08680v1 Announce Type: cross Abstract: LLM code-generation pipelines often sample multiple candidates and select one final answer without access to a complete oracle. Existing pipelines mix
arXiv:2605.09485v1 Announce Type: new Abstract: Latent representations learned by neural networks often exhibit semantic structure, where concept similarity is reflected by geometric proximity in embe