Tackling complex optimization problems often relies on expert-designed heuristics, typically crafted through extensive trial and error. Recent advances demonstrate that large language models (LLMs), when integrated into well-designed evolutionary search frameworks, can autonomously discover high-performing heuristics at a fraction of the traditional cost. However, existing approaches predominantly rely on verbal guidance, i.e., manipulating the prompt generation process, to steer the evolution of heuristics, without adapting the underlying LLM. We propose a hybrid framework that combines verbal and numerical guidance, the latter achieved by fine-tuning the LLM via reinforcement learning based on the quality of generated heuristics. This joint optimization allows the LLM to co-evolve with the search process. Our method outperforms state-of-the-art (SOTA) baselines across various optimization tasks, running locally on a single 24GB GPU using a 7B model with INT4 quantization. It surpasses methods that rely solely on verbal guidance, even when those use significantly more powerful API-based models.

Quantum Phase Estimation (QPE) is a cornerstone algorithm for fault-tolerant quantum computation, especially for electronic structure calculations of chemical systems. To accommodate the diverse characteristics of quantum chemical systems, numerous variants of QPE have been developed, each with distinct qubit and gate cost implications. In this paper, we quantify the impact of three key parameters on the overall quantum resource costs for the QPE algorithm: the choice between trotterization and qubitization, the use of molecular orbitals versus plane-wave basis-sets, and the selection of the fermion-to-qubit encoding scheme. From this, we establish clear performance trade-offs and delineate specific parameter regimes that minimize resource costs for relevant molecular systems. When performing phase estimation on large molecules in the fault-tolerant setting, we found the first-quantized qubitization circuit using the plane-wave basis to be the most efficient, with a gate cost scaling of $\tilde{\mathcal{O}}([N^{4/3}M^{2/3}+N^{8/3}M^{1/3}]/\varepsilon)$ for a system of $N$ electrons and $M$ orbitals, which is the best known scaling to date. On the other hand, when only noisy intermediate-scale or near-term fault-tolerant systems are available, the phase estimation of small molecules can be performed with gate cost of $\mathcal{O}(M^{7}/\varepsilon^{2})$ via trotterization in the MO basis. Furthermore, we provide numerical estimations of qubit and T gate costs required to perform QPE for several real-world molecular systems under these different parameter choices.

A self-correcting quantum memory is a type of quantum error correcting code that can correct errors passively through cooling. A major open question in the field is whether self-correcting quantum memories can exist in 3D. In this work, we propose two candidate constructions for 3D self-correcting quantum memories. The first construction is an extension of Haah's code, which retains translation invariance. The second construction is based on fractals with greater flexibility in its design. Additionally, we review existing 3D quantum codes and suggest that they are not self-correcting.

Gradient-based optimization drives the unprecedented performance of modern deep neural network models across diverse applications. Adaptive algorithms have accelerated neural network training due to their rapid convergence rates; however, they struggle to find ``flat minima" reliably, resulting in suboptimal generalization compared to stochastic gradient descent (SGD). By revisiting various adaptive algorithms' mechanisms, we propose the Frankenstein optimizer, which combines their advantages. The proposed Frankenstein dynamically adjusts first- and second-momentum coefficients according to the optimizer's current state to directly maintain consistent learning dynamics and immediately reflect sudden gradient changes. Extensive experiments across several research domains such as computer vision, natural language processing, few-shot learning, and scientific simulations show that Frankenstein surpasses existing adaptive algorithms and SGD empirically regarding convergence speed and generalization performance. Furthermore, this research deepens our understanding of adaptive algorithms through centered kernel alignment analysis and loss landscape visualization during the learning process. Code is available at this https URL

Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model's accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas.

Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine-tuned models often leads to degraded performance due to overlapping instruction-following components. Task Arithmetic (TA), which combines task vectors derived from fine-tuning, enables multi-task learning and task forgetting but struggles to isolate task-specific knowledge from general instruction-following behavior. To address this, we propose Layer-Aware Task Arithmetic (LATA), a novel approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components. By amplifying task-relevant layers and attenuating instruction-following layers, LATA improves task learning and forgetting performance while preserving overall model utility. Experiments on multiple benchmarks, including WikiText-2, GSM8K, and HumanEval, demonstrate that LATA outperforms existing methods in both multi-task learning and selective task forgetting, achieving higher task accuracy and alignment with minimal degradation in output quality. Our findings highlight the importance of layer-wise analysis in disentangling task-specific and general-purpose knowledge, offering a robust framework for efficient model merging and editing.

Generating realistic and controllable traffic scenes from natural language can greatly enhance the development and evaluation of autonomous driving systems. However, this task poses unique challenges: (1) grounding free-form text into spatially valid and semantically coherent layouts, (2) composing scenarios without predefined locations, and (3) planning multi-agent behaviors and selecting roads that respect agents' configurations. To address these, we propose a modular framework, TTSG, comprising prompt analysis, road retrieval, agent planning, and a novel plan-aware road ranking algorithm to solve these challenges. While large language models (LLMs) are used as general planners, our design integrates them into a tightly controlled pipeline that enforces structure, feasibility, and scene diversity. Notably, our ranking strategy ensures consistency between agent actions and road geometry, enabling scene generation without predefined routes or spawn points. The framework supports both routine and safety-critical scenarios, as well as multi-stage event composition. Experiments on SafeBench demonstrate that our method achieves the lowest average collision rate (3.5\%) across three critical scenarios. Moreover, driving captioning models trained on our generated scenes improve action reasoning by over 30 CIDEr points. These results underscore our proposed framework for flexible, interpretable, and safety-oriented simulation.

Affordance understanding, the task of identifying actionable regions on 3D objects, plays a vital role in allowing robotic systems to engage with and operate within the physical world. Although Visual Language Models (VLMs) have excelled in high-level reasoning and long-horizon planning for robotic manipulation, they still fall short in grasping the nuanced physical properties required for effective human-robot interaction. In this paper, we introduce PAVLM (Point cloud Affordance Vision-Language Model), an innovative framework that utilizes the extensive multimodal knowledge embedded in pre-trained language models to enhance 3D affordance understanding of point cloud. PAVLM integrates a geometric-guided propagation module with hidden embeddings from large language models (LLMs) to enrich visual semantics. On the language side, we prompt Llama-3.1 models to generate refined context-aware text, augmenting the instructional input with deeper semantic cues. Experimental results on the 3D-AffordanceNet benchmark demonstrate that PAVLM outperforms baseline methods for both full and partial point clouds, particularly excelling in its generalization to novel open-world affordance tasks of 3D objects. For more information, visit our project site: this http URL.

We investigate microwave interference from a spin ensemble and its mirror image in a one-dimensional waveguide. Away from the mirror, the resonance frequencies of the Kittel mode (KM) inside a ferrimagnetic spin ensemble have sinusoidal shifts as the normalized distance between the spin ensemble and the mirror increases compared to the setup without the mirror. These shifts are a consequence of the KM's interaction with its own image. Furthermore, the variation of the magnon radiative decay into the waveguide shows a cosine squared oscillation and is enhanced twofold when the KM sits at the magnetic antinode of the corresponding eigenmode. We can finely tune the KM to achieve the maximum adsorption of the input photons at the critical coupling point. Moreover, by placing the KM in proximity to the node of the resonance field, its lifetime is extended to more than eight times compared to its positioning near the antinode.

As Large Language Models (LLMs) are increasingly deployed as black-box components in real-world applications, evaluating their safety-especially under adversarial prompting-has become critical. Arguably, effective safety evaluations should be adaptive, evolving with LLM capabilities, and also cover a broad spectrum of harmful topics and real-world scenarios to fully expose potential vulnerabilities. Existing manual safety benchmarks, built on handcrafted adversarial prompts, are limited by their static nature and the intensive labor required to update them, making it difficult to keep pace with rapidly advancing LLMs. In contrast, automated adversarial prompt generation offers a promising path toward adaptive evaluation. However, current methods often suffer from insufficient adversarial topic coverage (topic-level diversity) and weak alignment with real-world contexts. These shortcomings stem from the exploration-exploitation dilemma in black-box optimization and a lack of real-world contextualization, resulting in adversarial prompts that are both topically narrow and scenario-repetitive. To address these issues, we propose Reality-Oriented Safety Evaluation (ROSE), a novel framework that uses multi-objective reinforcement learning to fine-tune an adversarial LLM for generating topically diverse and contextually rich adversarial prompts. Experiments show that ROSE outperforms existing methods in uncovering safety vulnerabilities in state-of-the-art LLMs, with notable improvements in integrated evaluation metrics. We hope ROSE represents a step toward more practical and reality-oriented safety evaluation of LLMs. WARNING: This paper contains examples of potentially harmful text.

Red teaming has proven to be an effective method for identifying and mitigating vulnerabilities in Large Language Models (LLMs). Reinforcement Fine-Tuning (RFT) has emerged as a promising strategy among existing red teaming techniques. However, a lack of a unified benchmark hinders current RFT-based red teaming methods. Implementation details, especially in Proximal Policy Optimization (PPO)-based RFT, significantly affect outcome stability and reproducibility. To address this issue, we introduce RedRFT, a lightweight benchmark designed to simplify and standardize the implementation and evaluation of RFT-based red teaming. RedRFT combines the design strengths of both single-file CleanRL and highly modularized Tianshou, offering high-quality single-file red teaming implementations and modular PPO core components, such as the General Advantage Estimator. It supports a variety of token and sentence diversity metrics, featuring modularized intrinsic reward computation that facilitates plug-and-play experimentation. To clarify their influence on RFT performance, we conducted an extensive ablation study on key components, including Low-Rank Adaptation (LoRA), Kullback-Leibler (KL) divergence, and Lagrange Multiplier. We hope this work contributes to 1) gaining a comprehensive understanding of the implementation nuances of RFT-based red teaming algorithms, and 2) enabling rapid prototyping of innovative features for RFT-based red teaming. Code for the benchmark can be accessed at this https URL

The increasing adoption of large language models (LLMs) for code-related tasks has raised concerns about the security of their training datasets. One critical threat is dead code poisoning, where syntactically valid but functionally redundant code is injected into training data to manipulate model behavior. Such attacks can degrade the performance of neural code search systems, leading to biased or insecure code suggestions. Existing detection methods, such as token-level perplexity analysis, fail to effectively identify dead code due to the structural and contextual characteristics of programming languages. In this paper, we propose DePA (Dead Code Perplexity Analysis), a novel line-level detection and cleansing method tailored to the structural properties of code. DePA computes line-level perplexity by leveraging the contextual relationships between code lines and identifies anomalous lines by comparing their perplexity to the overall distribution within the file. Our experiments on benchmark datasets demonstrate that DePA significantly outperforms existing methods, achieving 0.14-0.19 improvement in detection F1-score and a 44-65% increase in poisoned segment localization precision. Furthermore, DePA enhances detection speed by 0.62-23x, making it practical for large-scale dataset cleansing. Overall, by addressing the unique challenges of dead code poisoning, DePA provides a robust and efficient solution for safeguarding the integrity of code generation model training datasets.