Graphical models have been widely used as parsimonious encoders of assumptions of the underlying causal system and provide a basis for causal inferences. Models encoding stronger constraints tend to require higher expressive power, which are also harder, and sometimes impossible to empirically falsify. In this paper, we introduce two new collections of distributions that include counterfactual quantities which are experimentally accessible under counterfactual randomizations. Correspondingly, we define two new classes of graphical models for encoding empirically testable constraints in these distributions. We further present a sound and complete calculus, based on counterfactual calculus, which licenses inferences in these two new models with rules that are within the empirically falsifiable boundary. Finally, we formulate a hierarchy over several graphical models based on the constraints they encode and study the fundamental trade-off between the expressive power and empirical falsifiability of different models across the hierarchy.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Artemis crew says they wanted to'connect with humanity,' show what can be done when they put their mind to it Scientists revive ancient 24,000-year-old'zombie worm' from Arctic ice -- then it reproduced'Gigantic' ancient octopus used jaws to crush prey and hunted alongside the dinosaurs 100M years ago: study Scientists uncover identity of mysterious'golden orb' discovered miles underwater in 2023 Artemis astronauts enter eerie 40-minute communication blackout on Moon's far side NASA chief Jared Isaacman says Artemis II would not be possible'if it wasn't for President Trump' Researchers pinpoint source of black hole's 3,000-light-year-long jet stream using enhanced telescope network Is Spielberg's new UFO film more fact than fiction? Auburn University's bald eagle tradition celebrates its 25th anniversary American public'can handle' truth about UAPs, whistleblower says Google's AI unleashes new powerful scam-busting features for Android The CyberGuy explains steps you can take to protect yourself from scams. Scam texts are annoying for everyone.

Generative models trained on natural sequences are increasingly used to predict the effects of genetic variation, enabling progress in therapeutic design, disease risk prediction, and synthetic biology. In the zero-shot setting, variant impact is estimated by comparing the likelihoods of sequences, under the assumption that likelihood serves as a proxy for fitness. However, this assumption often breaks down in practice: sequence likelihood reflects not only evolutionary fitness constraints, but also phylogenetic structure and sampling biases, especially as model capacity increases. We introduce Likelihood-Fitness Bridging (LFB), a simple and general strategy that improves variant effect prediction by averaging model scores across sequences subject to similar selective pressures. Assuming an Ornstein-Uhlenbeck model of evolution, LFB can be viewed as a way to marginalize the effects of genetic drift, although its benefits appear to extend more broadly. LFB applies to existing protein and genomic language models without requiring retraining, and incurs only modest computational overhead. Evaluated on largescale deep mutational scans and clinical benchmarks, LFB consistently improves predictive performance across model families and sizes. Notably, it reverses the performance plateau observed in larger protein language models, making the largest models the most accurate when combined with LFB. These results suggest that accounting for phylogenetic and sampling biases is essential to realizing the full potential of large sequence models in variant effect prediction.

Text-guided image editing involves modifying a source image based on a language instruction and, typically, requires changes to only small local regions. However, existing approaches generate the entire target image rather than selectively regenerate only the intended editing areas. This results in (1) unnecessary computational costs and (2) a bias toward reconstructing non-editing regions, which compromises the quality of the intended edits. To resolve these limitations, we propose to formulate image editing as Next Editing-token Prediction (NEP) based on autoregressive image generation, where only regions that need to be edited are regenerated, thus avoiding unintended modification to the non-editing areas. To enable any-region editing, we propose to pre-train an any-order autoregressive text-to-image (T2I) model. Once trained, it is capable of zero-shot image editing and can be easily adapted to NEP for image editing, which achieves a new state-of-the-art on widely used image editing benchmarks. Moreover, our model naturally supports test-time scaling (TTS) through iteratively refining its generation in a zero-shot manner.

Protein language models (PLMs) are often assumed to capture evolutionary information by training on large protein sequence datasets. Yet it remains unclear whether PLMs can reason about evolution--that is, infer evolutionary relationships between sequences. We test this capability by evaluating whether standard PLM usage, frozen or fine-tuned embeddings with distance-based comparison, supports evolutionary reasoning. Existing PLMs consistently fail to recover phylogenetic structure, despite strong performance on sequence-level tasks such as masked-token and contact prediction. We present PHYLA, a hybrid state-space and transformer model that jointly processes multiple sequences and is trained using a tree-based objective across 3,000 phylogenies spanning diverse protein families.

We use prompts to LLMs to act as language tools for two types of tasks in our work. The first being to798 read through and retrieve the relevant information from news articles to caption our image sequences,799 figures 6 and 7 The second being utilizing our captions to generate event specific question-answer800 pairs, figures 8 and 9.801 We conducted human validation on 144 events sampled across 15 disaster types to assess caption803 quality. Human evaluators were asked to classify each event as: (1) clear alignment between images,804 captions, and sources, (2) mismatch, or (3) inconclusive where imagery was insufficient to verify805 caption details. Overall results showed 65.3% clear alignment between images, captions, and sources,806 18.8% had mismatches, and 16.0% were inconclusive where imagery was insufficient to verify807 caption details. Excluding inconclusive cases, 77.7% of determinable events showed alignment,808 demonstrating reasonable caption quality for LLM-generated annotations.809

B: 2022-06-01Natural disasters cause devastating damage to communities and infrastructure everyyearareas.

Plus: SK Hynix has overtaken Samsung as South Korea's most valuable company. I'm under the iconic fjords of Norway to visit what will soon become the world's longest and deepest subsea road tunnel--an exceptional engineering feat that will carry drivers deep beneath the North Sea. I'm here to understand how you make a 16.6-mile highway that sits 1,280 feet below the sea at its deepest point. And also--at a time when it can feel hard to get anything done--to reassure myself that ambitious engineering is still possible. That we can still make things. Step inside Norway's Rogfast tunnel and see how engineers are making it happen .

Large vision-language models (LVLMs), which integrate a vision encoder (VE) with a large language model, have achieved remarkable success across various tasks. However, there are still crucial challenges in LVLMs such as object hallucination, generating descriptions of objects that are not in the input image. Here, we argue that uncertain visual tokens within the VE is a key factor that contributes to object hallucination. Our statistical analysis found that there are positive correlations between visual tokens with high epistemic uncertainty and the occurrence of hallucinations. Furthermore, we show theoretically and empirically that visual tokens in early VE layers that exhibit large representation deviations under small adversarial perturbations indicate high epistemic uncertainty. Based on these findings, we propose a simple yet effective strategy to mitigate object hallucination by modifying the VE only. Our method comprises a proxy method with adversarial perturbations for identifying uncertain visual tokens efficiently and a method to mask these uncertain visual tokens during the self-attention process in the middle layers of the VE, suppressing their influence on visual encoding and thus alleviating hallucinations. Extensive experiments show that our method significantly reduces object hallucinations in LVLMs and can synergistically work with other prior arts.

We study online decision making problems under resource constraints, where both reward and cost functions are drawn from distributions that may change adversarially over time. We focus on two canonical settings: (i) online resource allocation where rewards and costs are observed before action selection, and (ii)online learning with resource constraints where they are observed after action selection, under full feedback or bandit feedback. It is well known that achieving sublinear regret in these settings is impossible when reward and cost distributions may change arbitrarily over time. To address this challenge, we analyze a framework in which the learner is guided by a spending plan--a sequence prescribing expected resource usage across rounds. We design general (primal-)dual methods that achieve sublinear regret with respect to baselines that follow the spending plan. Crucially, the performance of our algorithms improves when the spending plan ensures a well-balanced distribution of the budget across rounds. We additionally provide a robust variant of our methods to handle worst-case scenarios where the spending plan is highly imbalanced. To conclude, we study the regret of our algorithms when competing against benchmarks that deviate from the prescribed spending plan.