All entries

arXiv:2510.27527v2 Announce Type: replace Abstract: Large Language Models (LLMs) training is prohibitively expensive, driving interest in low-precision fully-quantized training (FQT). While novel 4-bi

arXiv:2504.20605v2 Announce Type: replace Abstract: Moral stories are a time-tested vehicle for transmitting values, yet modern NLP lacks a large, structured corpus that couples coherent narratives wi

Thank you @UN Secretary General @antonioguterres for an excellent discussion on the current state of AI capabilities, and the urgent need for robust global governance. International collaboration is c

arXiv:2605.02672v1 Announce Type: cross Abstract: The 2026 ACII Dyadic Conversations (ACII-DaiKon) Workshop & Challenge introduces a benchmark for modeling interpersonal affect and social dynamics in

Digital native companies often face significant challenges in scaling AI systems beyond initial proof-of-concept stages, despite their technical advantages and cloud infrastructure. The article examin

arXiv:2605.01637v1 Announce Type: new Abstract: We introduce the Banach-Butterfly Invariant (BBT), an influence-adaptive Banach geometry on the Walsh-Hadamard butterfly factorization. For a Boolean fu

The brain does not predict every upcoming word during language comprehension with maximum precision, contradicting the assumption that next-word prediction serves as the central computational objectiv

arXiv:2605.01611v1 Announce Type: cross Abstract: Due to ambiguity in the wording of the EU AI Act, we examine the question of to what extent frontier biological foundation models such as ESM3 are sub

arXiv:2605.02177v1 Announce Type: cross Abstract: Pearl's causal hierarchy shows that observational, interventional, and counterfactual queries are qualitatively distinct. We ask a quantitative versio

the cofounder 2 launch by @intelligenceco hits hard for me especially because this was the prompt i tried 3 years ago that turned into babyagi @jennyai I’m about 30 prompts/iterations in from this sta

FLOPs (floating-point operations) serve as a practical metric for measuring AI computational capacity because they have a precise technical definition, can be directly priced and measured, and offer a

arXiv:2603.04299v3 Announce Type: replace Abstract: Large Language Models (LLMs) often exhibit highly agreeable and reinforcing conversational styles, also known as AI-sycophancy. Although this behavi

arXiv:2605.01771v1 Announce Type: new Abstract: An auditor instructs an AI assistant: 'open each file individually using the Read tool -- no scripts, no agents.' The AI replies 'Yes' -- then issues a

arXiv:2605.02398v1 Announce Type: cross Abstract: As frontier AI models are deployed in high-stakes decision pipelines, their ability to maintain metacognitive stability -- knowing what they do not kn

The crux of the Musk-OpenAI trial so far is that Brockman keeps acting as if selling chatbots and API access for profit (which is what they do now) is exactly the same as the original mission (which w

arXiv:2605.01844v1 Announce Type: new Abstract: Steering is a widely used technique for controlling large language models, yet its effects are often unstable and hard to predict. Existing theoretical

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Week one of the Musk v. Altman trial: what it was like in the

arXiv:2603.03235v2 Announce Type: replace-cross Abstract: Selecting the number of clusters remains a fundamental challenge in unsupervised learning. Existing approaches typically focus on identifying

arXiv:2605.00197v1 Announce Type: cross Abstract: Studies attempting to simulate human behavior with extit{Silicon Societies} grow in numbers while LLM-only social networks have started appearing outs

arXiv:2603.05228v3 Announce Type: replace Abstract: Mechanistic interpretability typically relies on post-hoc analysis of trained networks. We instead adopt an interventional approach: testing hypothe

arXiv:2601.19597v3 Announce Type: replace Abstract: While InfoNCE underlies modern contrastive learning, its geometric mechanisms remain under-characterized beyond the canonical alignment--uniformity

arXiv:2510.01020v2 Announce Type: replace Abstract: We study sequential testing for a binary disease outcome when risk follows an unknown logistic model. At each round, the decision maker may either p

arXiv:2605.01537v1 Announce Type: new Abstract: Isolated word meanings are inherently uncertain. This uncertainty reduces when they are combined and anchored in context. We propose that grammar compre

Gary Marcus highlights a concerning trend where non-technical teams are directly deploying production code, which raises significant risks around quality control, security, and system reliability in f

arXiv:2605.01756v1 Announce Type: cross Abstract: Existing auto-bidding algorithms in digital advertising often treat the value of an ad opportunity as the revenue obtained when an ad is shown and/or

arXiv:2509.05865v2 Announce Type: replace Abstract: Motivated by privacy regulations and the need to mitigate the effects of harmful data, machine unlearning seeks to modify trained models so that the

arXiv:2605.02427v1 Announce Type: cross Abstract: A recurring pattern in 'reasoning without training' is that base LLMs already assign non-trivial probability mass to correct multi-step solutions; the

arXiv:2512.22331v2 Announce Type: replace Abstract: Non-invasive inference of molecular tumor characteristics from medical imaging is a central goal of radiogenomics, particularly in glioblastoma (GBM

Sam Altman, CEO of OpenAI, recommended trying ChatGPT's new instant model, suggesting it performs significantly better than thinking-model-only approaches. The post encourages users who have relied ex

arXiv:2603.13331v2 Announce Type: replace-cross Abstract: Grokking -- the sudden generalisation that appears long after a model has perfectly memorised its training data -- has been widely observed bu

arXiv:2605.01311v1 Announce Type: new Abstract: Offline evaluation of language models from usage logs is biased when model choice is confounded: the same user-side factors that influence which model i

arXiv:2411.04696v5 Announce Type: replace Abstract: Learning correlations from data forms the foundation of today's machine learning (ML) and artificial intelligence research. While contemporary metho

arXiv:2605.01407v1 Announce Type: cross Abstract: Masked Language Modeling (MLM) pre-training is one of the primary ways to initialize Neural Information Retrieval (IR) models prior to retrieval fine-

arXiv:2602.13595v2 Announce Type: replace Abstract: Neural scaling laws provide a predictable recipe for AI advancement: reducing numerical precision should linearly improve computational efficiency a

arXiv:2605.01704v1 Announce Type: new Abstract: When copies of the same language model are prompted to debate, they produce diverse phrasings of one perspective rather than diverse perspectives. Multi

arXiv:2604.04465v2 Announce Type: replace Abstract: This paper identifies a structural limitation in current multimodal AI architectures that is topological rather than parametric. Contrastive alignme

The unreasonable effectiveness of LLMs is what makes them so weird. The labs don’t need to decide what kind of AI to build, because better LLMs do better at most things. Finance? Pig disease identific

There’s a bunch of conflicting stances I don’t fully understand in the debate of Proprietary RL’d vs Open Harness, Model intelligence, and Agent Labs building harnesses for bespoke tasks Not all of th

arXiv:2602.12361v2 Announce Type: replace Abstract: Human-machine interfaces in industrial automation need sensing modules that monitor operator actions and physiological state. This is important in f

arXiv:2504.15077v5 Announce Type: replace Abstract: Large Language Models (LLMs) can translate natural language into SQL, but small models struggle with multi-table and complex queries in Zero-Shot Le

arXiv:2605.02496v1 Announce Type: cross Abstract: Tibetan text-to-speech (TTS) has long been challenged by scarce speech resources, significant dialectal variation, and the complex mapping between wri

arXiv:2605.02041v1 Announce Type: new Abstract: The extraction of entities and relationships from threat intelligence reports into structured formats, such as cybersecurity knowledge graphs, is essent

arXiv:2507.15774v2 Announce Type: replace Abstract: While deep learning is facing an homogenization across modalities led by Transformers, they are still challenged by shallow linear models in the tim

arXiv:2601.05300v2 Announce Type: replace-cross Abstract: Reasoning-oriented language models typically expose explicit reasoning as a long, front-loaded chain of 'thinking' tokens before the main outp

arXiv:2603.07119v2 Announce Type: replace Abstract: Recent text-to-image models have improved global realism, but text rendering remains a persistent failure mode: images may look convincing overall,

arXiv:2605.00737v1 Announce Type: new Abstract: Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities. However, tool use is not always beneficial; some calls may be