arXiv:2512.05844v3 Announce Type: replace-cross Abstract: Transformer-based autoregressive models offer an efficient alternative to diffusion- and flow-matching-based approaches for generating 3D molecules. One challenge remains: standard transformer architectures require a sequential ordering of tokens, which is not inherently defined for the atoms in a molecule. Prior works have addressed this by using canonical atom orderings. However, […]
iWorld-Bench: A Benchmark for Interactive World Models with a Unified Action Generation Framework
arXiv:2605.03941v2 Announce Type: replace-cross Abstract: Achieving Artificial General Intelligence (AGI) requires agents that learn and interact adaptively, with interactive world models providing scalable environments for perception, reasoning, and action. Yet current research still lacks large-scale datasets and unified benchmarks to evaluate their physical interaction capabilities. To address this, we propose iWorld-Bench, a comprehensive benchmark for […]
Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout
arXiv:2605.05092v1 Announce Type: cross Abstract: Safe L2/L3 driving automation requires anticipating human-in-the-loop reactions during shared-control transitions. While most driving world models forecast the external environment, in-cabin intelligence remains strictly recognition-oriented and lacks multi-step rollout capabilities for driver dynamics. We introduce Driver-WM, a driver-centric latent world model that rolls out in-cabin dynamics causally conditioned on out-cabin […]
Almost-Orthogonality in Lp Spaces: A Case Study with Grok
arXiv:2605.05192v1 Announce Type: cross Abstract: Carbery proposed the following sharpened form of triangle inequality for many functions: for any $pge 2$ and any finite sequence $(f_j)_jsubset L^p$ we have [ Big|sum_j f_jBig|_p le left(sup_j sum_k alpha_jk^,cright)^1/p’ Big(sum_j |f_j|_p^pBig)^1/p, ] where $c=2$, $1/p+1/p’=1$, and $alpha_jk=sqrtfrac\$. In the first part of this paper we construct a counterexample […]
Defining Operational Conditions for Safety-Critical AI-Based Systems from Data
arXiv:2601.22118v2 Announce Type: replace Abstract: Artificial Intelligence (AI) has been on the rise in many domains, including numerous safety-critical applications. However, for complex systems in the real world, defining the underlying environmental conditions in which the AI-based system must operate — the Operational Design Domain (ODD) — is extremely challenging. This often results in an […]
In-Context Prompting Obsoletes Agent Orchestration for Procedural Tasks
arXiv:2604.27891v2 Announce Type: replace Abstract: Agent orchestration frameworks — LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others — place an external orchestrator above the LLM, tracking state and injecting routing instructions at every turn. We present a controlled comparison showing that for procedural tasks, this architecture is dominated by a simpler alternative: putting the […]
Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap
arXiv:2505.19625v3 Announce Type: replace-cross Abstract: Search-based software engineering (SBSE), which integrates metaheuristic search techniques with software engineering, has been an active area of research for about 25 years. It has been applied to solve numerous problems across the entire software engineering lifecycle and has demonstrated its versatility in multiple domains. With recent advances in Artificial […]
Quantum-inspired Reinforcement Learning for Synthesizable Drug Design
arXiv:2409.09183v2 Announce Type: replace-cross Abstract: Synthesizable molecular design (also known as synthesizable molecular optimization) is a fundamental problem in drug discovery, and involves designing novel molecular structures to improve their properties according to drug-relevant oracle functions (i.e., objective) while ensuring synthetic feasibility. However, existing methods are mostly based on random search. To address this issue, […]
ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor
arXiv:2605.04193v1 Announce Type: new Abstract: Inductive Logic Programming (ILP) aims to learn interpretable first-order rules from data, but existing symbolic and neuro-symbolic approaches struggle to scale to noisy and probabilistic settings. Classical ILP relies on discrete combinatorial rule search and is brittle under uncertainty, while differentiable ILP methods typically depend on predefined rule templates or […]
Discovering New Theorems via LLMs with In-Context Proof Learning in Lean
arXiv:2509.14274v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated significant promise in formal theorem proving. In this study, we investigate the ability of LLMs to discover novel theorems and produce verified proofs. We propose a pipeline called textitConjecturing-Proving Loop (CPL), which iteratively generates mathematical conjectures and attempts to prove them in Lean 4. […]
A Neuro-Symbolic Framework for Accountability in Public-Sector AI
arXiv:2512.12109v4 Announce Type: replace-cross Abstract: Automated eligibility systems increasingly determine access to essential public benefits, but the explanations they generate often fail to reflect the legal rules that authorize those decisions. This thesis develops a legally grounded explainability framework that links system-generated decision justifications to the statutory constraints of CalFresh, California’s Supplemental Nutrition Assistance Program. […]
torch-sla: Differentiable Sparse Linear Algebra with Adjoint Solvers and Sparse Tensor Parallelism for PyTorch
arXiv:2601.13994v2 Announce Type: replace-cross Abstract: Differentiable sparse linear algebra is foundational for scientific machine learning, yet PyTorch lacks a unified library for it: texttttorch.sparse provides only low-level kernels and a non-differentiable, CPU-only textttspsolve, and texttttorch.linalg is dense-only. We present torchsla, an open-source library that fills this gap. It exposes a single autograd-aware API for direct, […]