You are on slide 3 of chapter 7. Use right arrow to continue. Alternatively, use the open square bracket key and close square bracket key to navigate, and disable left arrow and right arrow key navigation.

Bryan Cranston pictured speaking at a Sag-Aftra strike rally in 2023 in New York. The Breaking Bad actor went to the union with concerns after users of OpenAI's generative video platform Sora 2 were able to generate his likeness without his consent. Bryan Cranston pictured speaking at a Sag-Aftra strike rally in 2023 in New York. The Breaking Bad actor went to the union with concerns after users of OpenAI's generative video platform Sora 2 were able to generate his likeness without his consent. Users of generative AI video app were able to recreate the Breaking Bad actor's likeness without his consent, which OpenAI called'unintentional' Bryan Cranston has said he is "grateful" to OpenAI for cracking down on deepfakes of himself on the company's generative AI video platform Sora 2, after users were able to generate his voice and likeness without his consent.

Ensuring the long-term reliability of AI models in clinical practice requires continuous performance monitoring and corrective actions when degradation occurs. Addressing this need, this manuscript presents ReclAIm, a multi-agent framework capable of autonomously monitoring, evaluating, and fine-tuning medical image classification models. The system, built on a large language model core, operates entirely through natural language interaction, eliminating the need for programming expertise. ReclAIm successfully trains, evaluates, and maintains consistent performance of models across MRI, CT, and X-ray datasets. Once ReclAIm detects significant performance degradation, it autonomously executes state-of-the-art fine-tuning procedures that substantially reduce the performance gap. In cases with performance drops of up to -41.1% (MRI InceptionV3), ReclAIm managed to readjust performance metrics within 1.5% of the initial model results. ReclAIm enables automated, continuous maintenance of medical imaging AI models in a user-friendly and adaptable manner that facilitates broader adoption in both research and clinical environments.

The reliable execution of high-level missions in multi-robot systems with heterogeneous agents, requires robust methods for detecting spurious behaviors. In this paper, we address the challenge of identifying spurious executions of plans specified as a Linear Temporal Logic (LTL) formula, as incorrect task sequences, violations of spatial constraints, timing inconsis- tencies, or deviations from intended mission semantics. To tackle this, we introduce a structured data generation framework based on the Nets-within-Nets (NWN) paradigm, which coordinates robot actions with LTL-derived global mission specifications. We further propose a Transformer-based anomaly detection pipeline that classifies robot trajectories as normal or anomalous. Experi- mental evaluations show that our method achieves high accuracy (91.3%) in identifying execution inefficiencies, and demonstrates robust detection capabilities for core mission violations (88.3%) and constraint-based adaptive anomalies (66.8%). An ablation experiment of the embedding and architecture was carried out, obtaining successful results where our novel proposition performs better than simpler representations.

Scaled post-training now drives many of the largest capability gains in language models (LMs), yet its effect on pretrained knowledge remains poorly understood. Not all forgetting is equal: Forgetting one fact (e.g., a U.S. president or an API call) does not "average out" by recalling another. Hence, we propose a sample-wise paradigm to measure what is forgotten and when backward transfer occurs. Our metric counts 1->0 transitions (correct before post-training, incorrect after) to quantify forgetting and 0->1 transitions to quantify backward transfer. Traditional task averages conflate these effects and obscure large changes. For multiple-choice benchmarks, we add chance-adjusted variants that subtract the expected contribution of random guessing from pre- and post-training accuracies. We apply this framework across post-training stages, model sizes, and data scales. Our large-scale analysis shows that: (1) Domain-continual pretraining induces moderate forgetting with low-to-moderate backward transfer; (2) RL/SFT post-training applied to base models and Instruction tuning yields moderate-to-large backward transfer on math and logic with overall low-to-moderate forgetting; (3) Applying RL/SFT to instruction-tuned models is sensitive on data scale: at small scales, both forgetting and backward transfer are small; at larger scales, effects are mixed and warrant further study with better controls; (4) Model merging does not reliably mitigate forgetting. Overall, our framework offers a practical yardstick for mapping how post-training alters pretrained knowledge at scale -- enabling progress towards generally capable AI systems.

Abstract--Real-world multi-agent systems may require ad hoc teaming, where an agent must coordinate with other previously unseen teammates to solve a task in a zero-shot manner . Prior work often either selects a pretrained policy based on an inferred model of the new teammates or pretrains a single policy that is robust to potential teammates. Instead, we propose to leverage all pretrained policies in a zero-shot transfer setting. We formalize this problem as an ad hoc multi-agent Markov decision process and present a solution that uses two key ideas, generalized policy improvement and difference rewards, for efficient and effective knowledge transfer between different teams. We empirically demonstrate that our algorithm, Generalized Policy improvement for Ad hoc T eaming (GPA T), successfully enables zero-shot transfer to new teams in three simulated environments: cooperative foraging, predator-prey, and Overcooked. We also demonstrate our algorithm in a real-world multi-robot setting. Ad hoc teaming (AHT) is an open challenge for multi-agent systems, in which an autonomous agent must successfully coordinate with other unknown agents [1]. Consider a search-and-rescue mission where robots are deployed from different organizations and expected to cooperate with each other on the fly--these robots may have different biases in how they achieve a given objective (e.g., risky vs. risk-averse search) or have different capabilities (e.g., sensing vs. manipulation). Adapting to such differences would enable agents to effectively and autonomously complete tasks where the team is unknown prior to deployment.

Timely and reliable decision-making is vital for flood emergency response, yet it remains severely hindered by limited and imprecise situational awareness due to various budget and data accessibility constraints. Traditional flood management systems often rely on in-situ sensors to calibrate remote sensing-based large-scale flood depth forecasting models, and further take flood depth estimates to optimize flood response decisions. However, these approaches often take fixed, decision task-agnostic strategies to decide where to put in-situ sensors (e.g., maximize overall information gain) and train flood forecasting models (e.g., minimize average forecasting errors), but overlook that systems with the same sensing gain and average forecasting errors may lead to distinct decisions. To address this, we introduce a novel decision-focused framework that strategically selects locations for in-situ sensor placement and optimize spatio-temporal flood forecasting models to optimize downstream flood response decision regrets. Our end-to-end pipeline integrates four components: a contextual scoring network, a differentiable sensor selection module under hard budget constraints, a spatio-temporal flood reconstruction and forecasting model, and a differentiable decision layer tailored to task-specific objectives. Central to our approach is the incorporation of Implicit Maximum Likelihood Estimation (I-MLE) to enable gradient-based learning over discrete sensor configurations, and probabilistic decision heads to enable differentiable approximation to various constrained disaster response tasks.

Parameter-efficient fine-tuning (PEFT) has become the standard approach for adapting large language models under limited compute and memory budgets. Although previous methods improve efficiency through low-rank updates, quantization, or heuristic budget reallocation, they often decouple the allocation of capacity from the way updates evolve during training. In this work, we introduce CTR-LoRA, a framework guided by curvature trust region that integrates rank scheduling with stability-aware optimization. CTR-LoRA allocates parameters based on marginal utility derived from lightweight second-order proxies and constrains updates using a Fisher/Hessian-metric trust region. Experiments on multiple open-source backbones (7B-13B), evaluated on both in-distribution and out-of-distribution benchmarks, show consistent improvements over strong PEFT baselines. In addition to increased accuracy, CTR-LoRA enhances training stability, reduces memory requirements, and achieves higher throughput, positioning it on the Pareto frontier of performance and efficiency. These results highlight a principled path toward more robust and deployable PEFT.

Multimodal learning systems often struggle in non-stationary environments due to concept drift, where changing data distributions can degrade performance. Modality-specific drifts and the lack of mechanisms for continuous, stable adaptation compound this challenge. This paper introduces LS-OGD, a novel adaptive control framework for robust multimodal learning in the presence of concept drift. LS-OGD uses an online controller that dynamically adjusts the model's learning rate and the fusion weights between different data modalities in response to detected drift and evolving prediction errors. We prove that under bounded drift conditions, the LS-OGD system's prediction error is uniformly ultimately bounded and converges to zero if the drift ceases. Additionally, we demonstrate that the adaptive fusion strategy effectively isolates and mitigates the impact of severe modality-specific drift, thereby ensuring system resilience and fault tolerance. These theoretical guarantees establish a principled foundation for developing reliable and continuously adapting multimodal learning systems.

We present Lean Finder, a semantic search engine for Lean and mathlib that understands and aligns with the intents of mathematicians. We further align Lean Finder with mathematicians' preferences using In addition, Lean Finder is compatible with LLM-based theorem provers, bridging retrieval with formal reasoning. Advances in Lean and mathlib (De Moura et al., 2015; Moura & Ullrich, 2021) are turning mathematical discovery into a collaborative and verifiable research workflow. Despite these advances, state-of-the-art LLMs still cannot solve math research problems. Lean's syn tax, gram mar, and tac tics in cur a steep learn ing curve. All experiments and data processing were conducted outside Meta. Figure 1: In the evaluation with user queries, real users preferred Lean Finder in 81.6% of cases, compared with Consider the two queries below. Lean search engines handle (Gao et al., 2024a;b; Ju & Dong, 2025; Asher, 2025): Denote L/K a field extension, x, y in L are algebraic elements over K with the same minimal polynomial. I'm working with algebraic elements over a field extension and I have two elements, say x and y in L. I know x is algebraic over K, and I've shown that y is a root of the minimal polynomial of x. Does this imply that the minimal polynomials of x and y are actually equal? T arget Statement 2: 1 theorem eq_of_root {x y: L} (hx: IsAlgebraic K x) (h_ev: Polynomial.aeval y (minpoly K x) = 0): minpoly K y = minpoly K x):= -- proof omitted for brevity This user latent (motivation, perspective, abstraction) cannot be inferred or encoded by a purely syntactic informalization. Addressing this challenge calls for Lean search engines that can understand a mathematician's intent, not merely We defer a more rigorous analysis in Section 2.2, and ask our core question: Our approach analyzes and clusters public discussions, then synthesizes queries that simulate user intents (Section 3.1).