Forecasting large-scale IoT sensor data over long time horizons is critical for maintenance and scheduling, but current time-series foundation models rely on static learned patterns without accessing relevant historical examples at inference time. CrossRAG solves this with retrieval-augmented forecasting: shape-aware memory retrieval robust to magnitude differences, contrastive learning that filters out misleadingly similar-but-divergent historical references, and cross-attention fusion of retrieved data into predictions. Tested on seven benchmarks, it outperforms both standard and existing retrieval-based forecasting methods. This is useful for industrial IoT monitoring, energy grid management, and any long-horizon forecasting task involving heterogeneous sensor networks.

Authors: Yu Sun, Yuan Chang, Xiaohou Shi, Yan Sun

Paper: https://arxiv.org/abs/2607.29459v1

Self-play lets AI agents generate their own training problems, but without persistent memory, past failures don\'t shape future practice in a lasting way. SESA introduces an evolving skill memory system where a \"challenger\" poses problems, a solver retrieves relevant skills, and failures get distilled into reusable skills written back to memory --- creating a feedback loop where task difficulty and skill knowledge co-evolve. Tested across seven QA benchmarks, it improves accuracy over strong baselines while supporting both memory-based and memory-free deployment. This benefits agentic AI systems needing continual improvement in search, tool use, and multi-hop reasoning.

Authors: Zenghuang Fu, Zhaoyang Li, Qiuyuan Ai, Haoyu Wu, Minghui Wu,

Paper: https://arxiv.org/abs/2607.29468v1

Reinforcement-learning-based quantum architecture search is expensive because it repeatedly runs costly quantum simulations (VQE) after every circuit change, even though circuit construction itself is fully deterministic. DreamQAS improves efficiency by only learning to predict the expensive post-simulation feedback, using an ensemble model for uncertainty-aware planning and selective real verification. It achieves the lowest energy error on most molecular tasks while needing far fewer real quantum evaluations --- up to 10x fewer in some cases. This is valuable for quantum computing research, particularly in designing efficient quantum circuits for chemistry and materials simulation under limited computational budgets.

Authors: Jiayang Niu, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad

Paper: https://arxiv.org/abs/2607.29491v1

AI coding agents now produce more code than human reviewers can realistically evaluate, and existing AI review tools focus on trivial style issues rather than correctness or security. ARCTIC reframes this with three capabilities: predicting developer intent from conversation logs, detecting drift between intent and generated code, and spotlighting the diff regions most needing human attention. Grounded in analysis of 18,000 real code reviews, it achieves strong accuracy and reduces token cost significantly. This is directly applicable to software engineering teams managing AI-generated code at scale, improving review efficiency and catching misalignment early.

Authors: Chandra Maddila, Mashrur Rashik, Euna Mehnaz Khan, Smriti Jha,

Paper: https://arxiv.org/abs/2607.29516v1

Modeling Earth and human society together is hard because physical data is continuous and border-agnostic, while societal data is reported by country --- a geometric mismatch existing models handle poorly. TerraNova solves this with dedicated encoders for location, country, time, and task, fusing them via cross-modal transformers and a hypernetwork-generated decoder that outputs uncertainty-aware predictions. It matches specialized geospatial models while adding time, ocean, and uncertainty coverage, and can adapt to new variables quickly on ordinary hardware. Applications include climate modeling, economic forecasting, and policy analysis that require reasoning jointly across physical and administrative data.

Authors: Carlos Rodriguez-Pardo, Massimo Tavoni

Paper: https://arxiv.org/abs/2607.29527v1

AI-text detectors are typically evaluated on human-versus-LLM text, but this may not reflect real-world cases where humans revise their own writing using LLMs. ARB tests this directly, creating matched text variants --- human-written, LLM-generated, human-text rewritten by LLMs, and LLM-text rewritten by LLMs --- across multiple source datasets and generator models. Results show detectors that catch direct LLM text well fail dramatically when detecting LLM-rewritten human writing. This is critical for academic integrity systems, content moderation platforms, and journalism tools relying on AI-text detection, revealing a major blind spot in current detection methods.

Authors: Gaetano Perrone, Simon Pietro Romano

Paper: https://arxiv.org/abs/2607.29539v1

LLMs are good at solving math problems but poor at reliably verifying their own answers, since natural-language self-reflection lacks precision and code-generation approaches couple reasoning too tightly to implementation. AMTFV resolves this with a Mathematical Tool Flow interface that separates verification logic from execution: a verification agent builds a workflow and sends structured requests to a toolbox agent for exact computation. Tested across five datasets and multiple model families, it improves accuracy notably on complex problems. This has applications in automated tutoring, scientific computing assistants, and any system needing trustworthy LLM-based mathematical verification.

Authors: Rui Zou, Yutao Zhu, Mengqi Wei, Ji-Rong Wen

Paper: https://arxiv.org/abs/2607.29549v1

Organizations track vast operational metrics, but connecting these structured metric catalogues to formal ontologies is typically manual and labor-intensive, leaving valuable domain knowledge disconnected. COntExt automates this by using metric definitions and their context to suggest how new concepts should be integrated into existing ontologies, breaking the task into parent-class prediction, relation-type prediction, and data-property assignment. Tested across four cybersecurity ontologies, it outperforms context-free baselines. This is useful for organizations maintaining knowledge graphs or ontologies in domains like cybersecurity, compliance, or systems monitoring, significantly reducing the cost of ontology upkeep.

Authors: Hussain Hussain, Stefan Schöberl, Angelika Schneider, Verena

Paper: https://arxiv.org/abs/2607.29553v1

Real-world decision-making often involves competing goals --- like performance versus efficiency --- where defining a single reward function is difficult or impossible. LEMUR addresses this by combining multi-objective reinforcement learning with preference-based feedback, letting an agent learn from multiple humans\' preferences rather than requiring predefined reward functions. It jointly learns policies and objective-specific reward models, enabling agents to balance tradeoffs adaptively. This approach is valuable for real-world RL applications like robotics, resource allocation, or personalized systems, where human feedback naturally encodes competing priorities that are hard to hand-engineer.

Authors: Manith Adikari, Bei Peng, Samuele Vinanzi, Angelo Cangelosi

Paper: https://arxiv.org/abs/2607.29559v1

Discovering scientific equations from data is central to modeling physical systems, but existing LLM-based symbolic regression methods often ignore variable relationships and optimize only for fitting accuracy, causing premature convergence. MOT-SR solves this with a multi-objective framework balancing accuracy, complexity, and generalization, using collaborative LLM modules that select analytical tools and generate candidate equations along a Pareto front. Validated on 40 tasks and applied to gravitational-wave orbital modeling, it produces interpretable corrections with the lowest long-term trajectory errors. This has strong applications in physics, astronomy, and any scientific field requiring interpretable, generalizable equation discovery.

Authors: Boxiao Wang, Runxiang Wang, Kai Li, Chongming Li, Zhiwei Chen,

Paper: https://arxiv.org/abs/2607.29561v1