Large Neighborhood Search meets Iterative Neural Constraint Heuristics
arXiv:2603.20801v1 Announce Type: new Abstract: Neural networks are being increasingly used as heuristics for constraint satisfaction. These neural methods are often recurrent, learning to iteratively refine candidate assignments. In this work, we make explicit the connection between such iterative neural...
Achieving $\widetilde{O}(1/\epsilon)$ Sample Complexity for Bilinear Systems Identification under Bounded Noises
arXiv:2603.20819v1 Announce Type: new Abstract: This paper studies finite-sample set-membership identification for discrete-time bilinear systems under bounded symmetric log-concave disturbances. Compared with existing finite-sample results for linear systems and related analyses under stronger noise assumptions, we consider the more challenging...
Court reverses ruling on qualified immunity, denies review of death-row case and First Amendment challenge by citizen journalist
In a list of orders released on Monday morning, the Supreme Court reversed a ruling by a federal appeals court, holding that a Vermont police officer is entitled to qualified […]The postCourt reverses ruling on qualified immunity, denies review of...
Birthright citizenship: reading the text and sidestepping the parent trap
“The text is the law, and it is the text that must be observed,” Justice Antonin Scalia famously insisted at page 22 of a notable book on legal interpretation. “Only […]The postBirthright citizenship: reading the text and sidestepping the parent...
Court appears ready to overturn state law allowing for late-arriving mail-in ballots
The Supreme Court on Monday appeared ready to overturn a Mississippi law that allows mail-in ballots to be counted as long as they are postmarked by, and then received within […]The postCourt appears ready to overturn state law allowing for...
Announcement of opinions for Wednesday, March 25
On Wednesday, March 25, will be live blogging as the court potentially releases opinions in one or more argued cases from the current term. Click here for a list of […]The postAnnouncement of opinions for Wednesday, March 25appeared first onSCOTUSblog.
The bottom line
Nuts and Bolts is a recurring series by Stephen Wermiel providing insights into the mechanics of how the Supreme Court works. Supreme Court watchers are accustomed to poring over the […]The postThe bottom lineappeared first onSCOTUSblog.
SCOTUStoday for Monday, March 23
Good morning, and welcome to the March argument session, which includes the argument on birthright citizenship on Wednesday, April 1. This Thursday, March 26, SCOTUSblog is teaming up with Briefly […]The postSCOTUStoday for Monday, March 23appeared first onSCOTUSblog.
Air Street becomes one of the largest solo VCs in Europe with $232M fund
London’s Air Street Capital has raised a large Fund III with eyes locked on backing early-stage European and North American AI companies.
Sam Altman-backed fusion startup Helion in talks to sell power to OpenAI
OpenAI CEO Sam Altman is stepping down as board chair of Helion. His departure comes as reports that the two companies are negotiating a deal that would see Helion sell 12.5% of its power output to OpenAI.
GeoChallenge: A Multi-Answer Multiple-Choice Benchmark for Geometric Reasoning with Diagrams
arXiv:2603.19252v1 Announce Type: cross Abstract: Evaluating the symbolic reasoning of large language models (LLMs) calls for geometry benchmarks that require multi-step proofs grounded in both text and diagrams. However, existing benchmarks are often limited in scale and rarely provide visually...
MAPLE: Metadata Augmented Private Language Evolution
arXiv:2603.19258v1 Announce Type: cross Abstract: While differentially private (DP) fine-tuning of large language models (LLMs) is a powerful tool, it is often computationally prohibitive or infeasible when state-of-the-art models are only accessible via proprietary APIs. In such settings, generating DP...
When Prompt Optimization Becomes Jailbreaking: Adaptive Red-Teaming of Large Language Models
arXiv:2603.19247v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into high-stakes applications, making robust safety guarantees a central practical and commercial concern. Existing safety evaluations predominantly rely on fixed collections of harmful prompts, implicitly assuming non-adaptive adversaries...
On the Ability of Transformers to Verify Plans
arXiv:2603.19954v1 Announce Type: new Abstract: Transformers have shown inconsistent success in AI planning tasks, and theoretical understanding of when generalization should be expected has been limited. We take important steps towards addressing this gap by analyzing the ability of decoder-only...
A comprehensive study of LLM-based argument classification: from Llama through DeepSeek to GPT-5.2
arXiv:2603.19253v1 Announce Type: cross Abstract: Argument mining (AM) is an interdisciplinary research field focused on the automatic identification and classification of argumentative components, such as claims and premises, and the relationships between them. Recent advances in large language models (LLMs)...
LARFT: Closing the Cognition-Action Gap for Length Instruction Following in Large Language Models
arXiv:2603.19255v1 Announce Type: cross Abstract: Despite the strong performance of Large Language Models (LLMs) on complex instruction-following tasks, precise control of output length remains a persistent challenge. Existing methods primarily attempt to enforce length constraints by externally imposing length signals...
When both Grounding and not Grounding are Bad -- A Partially Grounded Encoding of Planning into SAT (Extended Version)
arXiv:2603.19429v1 Announce Type: new Abstract: Classical planning problems are typically defined using lifted first-order representations, which offer compactness and generality. While most planners ground these representations to simplify reasoning, this can cause an exponential blowup in size. Recent approaches instead...
A Subgoal-driven Framework for Improving Long-Horizon LLM Agents
arXiv:2603.19685v1 Announce Type: new Abstract: Large language model (LLM)-based agents have emerged as powerful autonomous controllers for digital environments, including mobile interfaces, operating systems, and web browsers. Web navigation, for example, requires handling dynamic content and long sequences of actions,...
Grounded Multimodal Retrieval-Augmented Drafting of Radiology Impressions Using Case-Based Similarity Search
arXiv:2603.17765v1 Announce Type: cross Abstract: Automated radiology report generation has gained increasing attention with the rise of deep learning and large language models. However, fully generative approaches often suffer from hallucinations and lack clinical grounding, limiting their reliability in real-world...
Hyperagents
arXiv:2603.19461v1 Announce Type: new Abstract: Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such...
HATL: Hierarchical Adaptive-Transfer Learning Framework for Sign Language Machine Translation
arXiv:2603.19260v1 Announce Type: cross Abstract: Sign Language Machine Translation (SLMT) aims to bridge communication between Deaf and hearing individuals. However, its progress is constrained by scarce datasets, limited signer diversity, and large domain gaps between sign motion patterns and pretrained...
HyEvo: Self-Evolving Hybrid Agentic Workflows for Efficient Reasoning
arXiv:2603.19639v1 Announce Type: new Abstract: Although agentic workflows have demonstrated strong potential for solving complex tasks, existing automated generation methods remain inefficient and underperform, as they rely on predefined operator libraries and homogeneous LLM-only workflows in which all task-level computation...
DuCCAE: A Hybrid Engine for Immersive Conversation via Collaboration, Augmentation, and Evolution
arXiv:2603.19248v1 Announce Type: cross Abstract: Immersive conversational systems in production face a persistent trade-off between responsiveness and long-horizon task capability. Real-time interaction is achievable for lightweight turns, but requests involving planning and tool invocation (e.g., search and media generation) produce...
Teaching an Agent to Sketch One Part at a Time
arXiv:2603.19500v1 Announce Type: new Abstract: We develop a method for producing vector sketches one part at a time. To do this, we train a multi-modal language model-based agent using a novel multi-turn process-reward reinforcement learning following supervised fine-tuning. Our approach...
Pitfalls in Evaluating Interpretability Agents
arXiv:2603.20101v1 Announce Type: new Abstract: Automated interpretability systems aim to reduce the need for human labor and scale analysis to increasingly large models and diverse tasks. Recent efforts toward this goal leverage large language models (LLMs) at increasing levels of...
PA2D-MORL: Pareto Ascent Directional Decomposition based Multi-Objective Reinforcement Learning
arXiv:2603.19579v1 Announce Type: new Abstract: Multi-objective reinforcement learning (MORL) provides an effective solution for decision-making problems involving conflicting objectives. However, achieving high-quality approximations to the Pareto policy set remains challenging, especially in complex tasks with continuous or high-dimensional state-action space....
Learning Dynamic Belief Graphs for Theory-of-mind Reasoning
arXiv:2603.20170v1 Announce Type: new Abstract: Theory of Mind (ToM) reasoning with Large Language Models (LLMs) requires inferring how people's implicit, evolving beliefs shape what they seek and how they act under uncertainty -- especially in high-stakes settings such as disaster...
Experience is the Best Teacher: Motivating Effective Exploration in Reinforcement Learning for LLMs
arXiv:2603.20046v1 Announce Type: new Abstract: Reinforcement Learning (RL) with rubric-based rewards has recently shown remarkable progress in enhancing general reasoning capabilities of Large Language Models (LLMs), yet still suffers from ineffective exploration confined to curent policy distribution. In fact, RL...
Stepwise: Neuro-Symbolic Proof Search for Automated Systems Verification
arXiv:2603.19715v1 Announce Type: new Abstract: Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet constructing large proof scripts remains highly manual and limits scalability. Advances in large language models (LLMs), especially in...