Knowledge catalogue
Stocks should be ripping today given the move in oil. But they're not thanks to NVDA and it's unbelievable 250 Billion roundtrip with OpenAI. There's no hiding anymore that the whole AI bubble is real
arXiv:2607.21970v1 Announce Type: new Abstract: Automating radiology report generation is important for improving reporting consistency and clinical workflows . While Contrastive Language--Image Pretr
arXiv:2603.05789v5 Announce Type: replace-cross Abstract: Repeated multi-agent interactions require evaluation metrics that capture not only payoff distributions but also their temporal organization.
arXiv:2607.22465v1 Announce Type: cross Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI. Ex
arXiv:2607.22531v1 Announce Type: new Abstract: Unified multimodal models seek a shared visual token space that supports both multimodal understanding and image generation. Discrete methods unify the
Very cool paper from Microsoft. The idea is to train agents on replayed teacher trajectories instead of live environment rollouts. On-policy distillation for agentic tasks is expensive because every u
arXiv:2607.22530v1 Announce Type: new Abstract: Contact-rich robot manipulation requires physical interaction cues that are often invisible to cameras, making tactile sensing essential for robust cont
arXiv:2604.00024v2 Announce Type: replace Abstract: Large language models are increasingly used for medical guidance, but women's health remains under-evaluated in benchmark design. We present the Wom
arXiv:2505.19212v2 Announce Type: replace Abstract: Recent advances in LLMs have enabled their use in complex agentic roles, involving decision-making with humans or other agents, making ethical align
arXiv:2607.22218v1 Announce Type: new Abstract: Despite the growing use of large language models (LLMs) as creativity evaluators, evidence of their alignment with human evaluations remains mixed, rais
important thread; don’t read just the first tweet (which has a caveat in the second). In case you think the problem of science slop is hypothetical: Here's the president of OpenAI retweeting wrong sci
training to benchmarks ≠ getting to AGI This suggests that Opus 5 's gain on ARC-AGI-3 was the result of specific training to improve on that eval, and not a generalized increase in abstract reasoning
All anyone has to do is go back 15 years and look at all the ridiculous predictions Elmo has made about Mars, self driving cars, his ridiculous Boring Company. No one should take anything he says seri
OpenAI and Anthropic’s language models keep their internal reasoning encrypted; what users see in the UI is only a filtered summary of that reasoning. This practice was highlighted in a tweet by Sarah
The two giant generative AI startups were treated like gods for a couple years. Both are now facing massive pushback. Anthropic employees go nuclear on the popularity of open source AI. It is getting
When a normal company does a bad thing, they take responsibility + apologize + outline how it won't happen again. OpenAI instead is like 'we are entering a new era. this will happen again. no one amon
arXiv:2607.20700v1 Announce Type: cross Abstract: The operation of blockchain is governed by consensus algorithms (CA). Several consensus mechanisms require significant computational power, while othe
arXiv:2607.18597v2 Announce Type: replace Abstract: Counterfactual credit assignment has proven effective in multi-agent reinforcement learning (MARL) for discrete action spaces, yet its extension to
arXiv:2607.21279v1 Announce Type: new Abstract: Large language models (LLMs) have developed rapidly and become valuable tools in everyday life. However, how to align LLMs to a particular set of human
arXiv:2607.21291v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong generation and reasoning performance, but the Transformer architecture incurs high inference cost. Existing
arXiv:2607.18042v2 Announce Type: replace-cross Abstract: End-to-end vision-language navigation (VLN) with causal vision-language models maps instructions and egocentric observations directly to actio
arXiv:2607.21121v1 Announce Type: cross Abstract: In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed. QSP is a challenging task, since th
arXiv:2606.21262v2 Announce Type: replace Abstract: Reinforcement learning for multi-step LLM agents often relies on scalar rewards that indicate success but cannot explain why a trajectory is good or
arXiv:2607.21370v1 Announce Type: new Abstract: Quantitative drug-induced sleep endoscopy (DISE) requires reliable airway boundaries at specific anatomical levels. Pixel-level DISE annotations are sca
arXiv:2607.21106v1 Announce Type: new Abstract: Effective memory is crucial for LLM agents, yet constructing it effectively remains challenging. A memory-construction policy decides what information t
arXiv:2607.20660v1 Announce Type: new Abstract: Recent 3D generative models produce high-quality geometry from a single image using large-scale priors and diffusion architectures. However, they assume
arXiv:2607.20441v1 Announce Type: new Abstract: Every information ecosystem produces beliefs that shape strategic decisions. Both human analysts and AI systems inherit the blind spots of their informa
arXiv:2607.21558v1 Announce Type: new Abstract: Building socially calibrated large language models, which can learn from others without simply yielding to them, requires more than reducing sycophancy
arXiv:2607.21444v1 Announce Type: cross Abstract: Reliable electric vehicle (EV) charging infrastructure is a cornerstone of sustainable, low-carbon cities, yet urban climate stress such as extreme he
arXiv:2607.20444v1 Announce Type: cross Abstract: Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal.
arXiv:2607.20862v1 Announce Type: new Abstract: At present, reliable evaluation of non-verifiable tasks remains challenging. Existing approaches often fail to adequately capture the diverse evaluative
arXiv:2607.21371v1 Announce Type: cross Abstract: Open-vocabulary semantic segmentation (OVSS) leverages textual semantics to segment objects beyond predefined categories. While the self-supervised mo
arXiv:2607.20984v1 Announce Type: new Abstract: This paper proposes the Distribution-Alignment Bridge (DAB), a framework that reconceptualizes text-to-video retrieval as a distribution alignment task
arXiv:2607.21138v1 Announce Type: new Abstract: Accurate forest inventory and large-scale mapping are essential for ecosystem monitoring and sustainable forest management. Multiple low-cost edge platf
arXiv:2508.21797v2 Announce Type: replace-cross Abstract: Industry 4.0's highly networked Machine Tool Controllers (MTCs) are prime targets for replay attacks that use outdated sensor data to manipula
arXiv:2607.21013v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved impressive performance in multimodal emotion recognition (MER) tasks and lifted MER to a new leve
arXiv:2602.11658v2 Announce Type: replace Abstract: Immersive affective content generation aims to create visually compelling VR imagery with controllable emotional nuance, yet existing methods typica
arXiv:2607.20814v1 Announce Type: new Abstract: The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained by limited i
arXiv:2512.04966v2 Announce Type: replace-cross Abstract: Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation
arXiv:2604.25077v2 Announce Type: replace Abstract: Weak-to-strong alignment offers a promising route to scalable supervision, but it can fail when a strong model becomes confidently wrong on examples
arXiv:2607.20489v1 Announce Type: new Abstract: Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generation, including
arXiv:2607.21302v1 Announce Type: new Abstract: Behavior prior reinforcement learning (BPRL) has emerged as a promising paradigm to improve sample efficiency in online reinforcement learning (RL) by l
arXiv:2607.21209v1 Announce Type: cross Abstract: In the field of Artificial Intelligence, an agent is a system which is able to autonomously make decisions in order to reach a desired goal. As these
arXiv:2607.20819v1 Announce Type: new Abstract: Stress is a dynamic process characterized by significant individual variability in facial expression. Traditional architectures, such as Recurrent Neura
arXiv:2607.20683v1 Announce Type: new Abstract: The sense of touch is central to manipulation, especially when vision is occluded or ambiguous. Although combining vision and touch improves manipulatio
arXiv:2607.21227v1 Announce Type: new Abstract: Force-conditioned reinforcement learning (RL) enables tight-clearance assembly under a commanded force ceiling, but practical deployment requires determ
arXiv:2607.20668v1 Announce Type: cross Abstract: TextGrad improves language-model systems by revising text from feedback. Its core thesis is that natural-language feedback can act as a gradient for o
arXiv:2607.21327v1 Announce Type: cross Abstract: Bibliometric indicators - citation counts, h-indexes, co-authorship networks - have long anchored science, technology, and innovation (STI) analytics,
arXiv:2411.00631v2 Announce Type: replace-cross Abstract: This scoping review examines the relationship between Generative AI (GenAI) and agency in education, analyzing the literature available throug
arXiv:2511.03354v2 Announce Type: replace-cross Abstract: Generative artificial intelligence (GenAI) is transforming bioinformatics by advancing genomics, proteomics, transcriptomics, structural biolo
arXiv:2607.21416v1 Announce Type: cross Abstract: Existing Gaussian-splatting-based monocular Simultaneous Localization and Mapping (SLAM) systems are either tailored to short sequences, are not real-
arXiv:2607.21027v1 Announce Type: new Abstract: Current identity customized video generation methodologies are predominantly limited to single-identity scenarios, as the lack of explicit identity sepa
arXiv:2607.21049v1 Announce Type: new Abstract: End-to-end visuomotor policies provide little opportunity for humans to understand or correct the policy's visual attention. We propose GuidedAttention,
arXiv:2607.21591v1 Announce Type: new Abstract: Diffusion and flow-matching models dominate conditional image generation, yet inference-time scaling for these models is far less developed than for aut
arXiv:2607.20556v1 Announce Type: new Abstract: In large-scale text analysis tasks, pre-trained language models are often used to embed text corpora for downstream analysis. However, such models may s
Let’s build seven trillion dollars worth of data centers for sports betting! Statistics of week: “More Americans pay for sports betting apps (5%) than pay for AI… 37% of consumers say none of AI's use
arXiv:2508.05502v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) perform strongly in high-resource languages, yet often produce fluent but culturally 'thin' descripti
arXiv:2508.11780v3 Announce Type: replace-cross Abstract: Recent developments in computer vision have made segmented images widely available across many domains, such as medicine, where segmented radi
arXiv:2607.20641v1 Announce Type: new Abstract: Federated learning (FL) enables multiple clinical institutions to collaboratively train a shared disease classifier without centralizing patient data. I
arXiv:2607.20729v1 Announce Type: cross Abstract: A record system declares when two records refer to the same entity, occurrence, scope, or rule. Its disclosed implementation mechanisms induce a corre