Question: In theThe Tile how does Cohen’s correction for chance impact the interpretation of ranking scores, and what geometric effect does it have on the \tile when applied under fixed priors?

Cohen’s correction for chance adjusts a ranking score by accounting for what could be achieved by chance, leading to a new score that is perfectly rank-correlated with the canonical ranking score \(\canonicalRankingScoreBis\). The formula for this correction is:
\[
\frac{\canonicalRankingScore - \canonicalRankingScore\circ\opNoSkill}{1 - \canonicalRankingScore\circ\opNoSkill}.
\]
This adjustment aligns the corrected scores with the fixed priors constraint represented by the \(\tileCurvePriors\).
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Geometrically, Cohen’s correction compresses the entire horizontal line of the \tile into a single point where it intersects with the \(\tileCurvePriors\) curve. This results in a significant loss of diversity in the ranking score representation, as all performances that would have spanned across the horizontal line are now reduced to the same value. While this simplifies the interpretation of scores under fixed priors, it also removes finer distinctions between performances that could otherwise provide more nuanced insights.


Question: How does the SKIM method proposed improve the performance of low-bit quantization for LLMs like LLaMA, and what are the key techniques it introduces?

The SKIM method significantly enhances the performance of low-bit quantization for LLMs, addressing challenges such as performance degradation at lower precision levels and the limited adaptability of standard quantization methods. It introduces two key innovations. SKIM employs a novel greedy algorithm to achieve approximately optimal bit allocation across weight channels. This addresses the observed disparity in data distribution across channels, enabling the model to adapt to mixed precision levels and optimize memory usage while maintaining high performance.
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To overcome the limitations of non-differentiable K-means clustering, SKIM integrates a trainable scaling vector into its iterative optimization strategy. This scaling vector regularizes column-wise data differences and complements the mixed precision quantization method, improving overall quantization quality. These innovations allow SKIM to narrow the performance gap between 3-bit quantized LLaMA models and their full-precision counterparts by 16.3% on average in terms of perplexity, as demonstrated on the WikiText2 dataset. This adaptability to any specified bit level and enhanced performance make SKIM a promising solution for resource-efficient deployment of LLMs.


Question: How does the SKIM method ensure effective bit allocation and optimization objectives for low-bit quantization, and what challenges does it address?

The SKIM method addresses key challenges in low-bit quantization, such as the disparity in channel-wise quantization errors and the need for computationally efficient optimization.
SKIM begins by employing a greedy algorithm to allocate bits across channels based on their quantization errors. This approach adapts to the significant variation in channel-wise errors, as demonstrated in the error histogram for the self-attention layer of LLaMA-7B.
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SKIM carefully selects optimization objectives tailored to different steps in its pipeline. It uses the L-full form for comprehensive error calculation, which balances accuracy and computational feasibility, and the S-diag form for scenarios involving element-wise operations like weighted K-means clustering.
The method incorporates a trainable scaling vector, optimized iteratively while keeping labels fixed. This enables gradient-based optimization to regularize data distribution effectively, compensating for the limitations of non-differentiable K-means clustering.
By addressing these challenges, SKIM ensures that the quantization process is both memory-efficient and effective, making it suitable for large-scale LLMs like LLaMA.


Question: How does MultiTASC++ address the challenges of efficient DNN inference on resource-constrained devices in intelligent environments like smart offices?

MultiTASC++ tackles the challenges of deploying deep neural network (DNN) inference on resource-constrained devices in smart environments through a combination of lightweight model design, optimization techniques, and distributed collaborative inference. These strategies are essential for environments like smart offices where devices like smart cameras and AI speakers lack the computational capacity to support high-accuracy models independently. MultiTASC++ incorporates lightweight models such as MobileNetV2 and EfficientNet-Lite, designed to perform efficiently with minimal computational and memory overhead. Additionally, it leverages techniques like model quantization and pruning to further reduce resource demands without significant loss in accuracy.
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The scheduler supports distributed systems where multiple edge devices collaborate, possibly aided by a central server, to share computational workloads. This approach allows for the efficient handling of complex tasks by distributing inference steps across devices based on their available resources. These strategies collectively enable MultiTASC++ to optimize the scheduling of DNN tasks, ensuring smooth and effective inference in smart office environments while overcoming the limitations of resource-constrained devices.


Question: How does MultiTASC++ manage heterogeneous IoT devices and ensure efficient distributed inference in the context of tasks like object detection?

"MultiTASC++" addresses the challenges of managing heterogeneous IoT devices and enabling efficient distributed inference through its multi-device cascade architecture. Each IoT device hosts a DL model tailored to its computational capabilities, allowing devices with varying resources to participate in the same task, such as object detection. The scheduler ensures high performance by accounting for this heterogeneity in its adaptive scheduling mechanism.
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A confidence-based decision function on each IoT device evaluates the certainty of its predictions. Low-confidence predictions are forwarded to a centralized server for further refinement, optimizing resource usage by minimizing unnecessary data transmission. The server hosts a more accurate and computationally-intensive model to refine predictions flagged by the IoT devices. This collaborative approach enhances overall system accuracy while reducing the computational burden on individual devices. Forwarded samples are organized in a request queue, ensuring efficient data flow to the server. The refined results are then distributed back to the IoT devices, completing the inference cycle with improved accuracy and timeliness. By seamlessly integrating these components, "MultiTASC++" enables efficient, adaptive inference for complex tasks across diverse IoT devices in intelligent environments.


Question: In LaserGuider how does the adversary embed and trigger a backdoor in DNNs using laser-based techniques, and what are the key stages of the attack?

In "LaserGuider," the adversary employs a two-stage process of backdoor embedding and backdoor triggering to execute a laser-based physical backdoor attack on DNNs. In the backdoor embedding stage, the adversary injects the backdoor into the DNN by poisoning the training dataset. This includes designing a digital laser-based trigger δ, selecting a small subset (Tselect) of images from the legitimate training dataset (Ttrain) and modifying these images by adding the trigger δ, assigning a target label (yt) to the modified images to create a poisoned training dataset (Ptrain), and publishing the poisoned dataset online for developers to use so that the backdoor gets embedded into models during their training phase. This dataset poisoning exploits the model's generalizability and enables the backdoor to affect any model trained on the poisoned data.
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During the backdoor triggering stage, during inference, the adversary projects the laser-based trigger onto physical objects from a reasonable distance. The physical trigger activates the embedded backdoor, causing the model to misclassify the input into the specified target label (yt). This method does not require physical modification of objects, making it a practical and stealthy attack strategy. These stages, as depicted in the system overview diagram, highlight the novel use of laser-based triggers in enabling physical backdoor attacks while maintaining stealth and practicality.


Question: In LaserGuide how does the LaserMark dataset facilitate the evaluation of physical backdoor attacks, and what key factors influence the effectiveness of laser-based triggers?

The LaserMark dataset plays a crucial role in evaluating the effectiveness of the laser-based physical backdoor attacks introduced in "LaserGuider". It consists of traffic sign images with and without laser-based triggers, collected and processed under controlled settings to simulate real-world conditions. LaserMark includes 676 poisoned images with red, green, and blue laser-based triggers, and 158 clean images without triggers, covering 32 traffic sign categories. This diversity enables comprehensive testing across various scenarios.
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 Images were collected under varied angles, distances, and lighting conditions to reflect realistic deployment settings. For example, adversaries adjusted the laser pointer focus to ensure the spot's size and brightness were appropriate for the distance and environmental light levels. The study optimizes four key parameters for effective laser-based triggers. The size is controlled by adjusting the laser pointer’s focus based on the projection distance. Transparency is influenced by ambient lighting conditions, with stronger light making triggers less visible. Location refers to the placement of the laser spot on the traffic sign to avoid occluding critical visual elements. Brightness is the center intensity of the laser spot, affecting its detectability by DNNs. These considerations ensure that LaserMark provides a realistic, high-quality dataset to evaluate the success rate and robustness of physical backdoor attacks. It also highlights the adaptability of laser-based triggers under varying environmental constraints.


Question: How does JANUS address the limitations of predefined pattern-based methods in detecting centralization risks, and what is the key innovation in its approach?

"JANUS" overcomes the limitations of traditional predefined pattern-based methods that struggle with detecting unknown patterns or variants and often misclassify secure contracts due to inaccurate pattern definitions. Its key innovation lies in adopting a difference-oriented analysis framework that doesn't solely rely on static predefined patterns. JANUS uses a difference-oriented approach to compare behavioral deviations between expected and observed operations within smart contracts. This enables it to identify centralization risks dynamically, even when they don't conform to known patterns.
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 Unlike static methods like Pied-piper and Tokeer, which rely heavily on predefined Datalog rules and oracles, JANUS adapts to real-world scenarios by focusing on operational discrepancies. This reduces underreporting for unknown risks and minimizes false positives caused by rigid pattern definitions. By leveraging these advancements, JANUS provides a robust framework for identifying financial centralization risks in smart contracts, significantly improving upon the detection limitations of existing tools.


Question: In JANUS how does the analyzer identify privileged account variables in Solidity smart contracts, and why is this identification crucial for detecting centralization risks?

"JANUS" identifies privileged account variables in Solidity smart contracts based on two criteria to ensure precise detection of centralization risks tied to these accounts. The variable must be an address-type state variable, as shown in the example contract’s `owner` variable. Its value must be specified exclusively by the developer or other privileged accounts, ensuring it represents a key control point within the contract.
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 Privileged accounts, such as `owner` in the provided example, often have exclusive rights to execute sensitive functions (e.g., transferring tokens or modifying balances). These rights, if misused or compromised, can lead to financial centralization risks. For instance, the `owner_transfer` function in the example is restricted by the `onlyOwner` modifier, which enforces centralized control by the privileged account. By focusing on these variables, JANUS effectively pinpoints potential vulnerabilities in the logic and structure of smart contracts, facilitating the detection of financial centralization risks that could otherwise go unnoticed.


Question: How does ELEMENT address the challenge of vanishing novelty in lifelong exploration, and what is the role of episodic and lifelong intrinsic rewards in this approach?

"ELEMENT" tackles the challenge of vanishing novelty in lifelong exploration by introducing a dual intrinsic reward framework that combines episodic and lifelong rewards to ensure balanced exploration while maintaining novelty over time. ELEMENT computes the lifelong intrinsic reward of a state s as the distance to its k-nearest neighbors in a representation space derived from a fixed neural encoder. This reward reflects the state’s rarity across all previous interactions, encouraging exploration of less-visited states.
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 The episodic reward is calculated as the average state entropy within the episode, emphasizing novelty within the current exploration episode. This episodic mechanism helps counteract the diminishing novelty problem by resetting the exploration perspective periodically. By integrating these two rewards, ELEMENT ensures that agents are motivated to explore both globally novel states (lifelong) and locally novel states within each episode (episodic). A separate DRL module optimizes the policy to maximize the combined reward, facilitating efficient and continuous exploration. This dual approach enables ELEMENT to overcome the limitations of existing methods, such as vanishing novelty, and achieves effective exploration across episodic and lifelong horizons.


Question: How does ELEMENT balance episodic and lifelong exploration to promote comprehensive state coverage, and what role does the fixed neural encoder play in achieving this?

"ELEMENT" achieves a balance between episodic and lifelong exploration by combining rewards from both perspectives to encourage diverse state coverage while avoiding redundant revisitation. Episodic intrinsic rewards maximize entropy within each episode, motivating the agent to capture as many distinct states as possible during a single episode. The objective is decomposed into a sum of state-action rewards across the episode, allowing the policy to focus on trajectory-wise feedback effectively.
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 Lifelong rewards discourage revisiting states across episodes, promoting continuous exploration in new directions. This counteracts the tendency of episodic rewards to focus on a single direction, ensuring a more holistic exploration strategy. The fixed neural encoder maps observations to a consistent representation space, avoiding meaningless exploration caused by changing state representations. ELEMENT employs a randomly initialized encoder for Mujoco environments and a pre-trained encoder using Spatiotemporal Deep Infomax for Mario, ensuring robust entropy estimation across diverse environments. By combining episodic entropy maximization with lifelong intrinsic motivation and leveraging a fixed neural encoder, ELEMENT fosters comprehensive exploration without being confined to a single direction or revisiting known states unnecessarily.


Question: How does TinyLlama v1.1 perform continual pre-training for specific domains?

TinyLlama v1.1 employs a three-stage continual pre-training process for specific domains, incorporating three distinct corpora: the general SlimPajama dataset, Math and Code data combining Starcoder (Python and Jupyter subsets) and Proof Pile 2, and the Skypile dataset for Chinese language understanding. This diverse corpus selection enables the creation of three specialized model variants: the general-purpose TinyLlama v1.1, TinyLlama v1.1 - Math&Code for mathematical reasoning and programming tasks, and TinyLlama v1.1 - Chinese for processing Chinese text. This tailored approach enables TinyLlama v1.1 to gain domain-specific expertise while retaining its general applicability.<PIC>


Question: How did the adjustment of increasing the batch size instead of adjusting the learning rate in TinyLlama v1.1 affect the loss curves of the different variants?

During the cooldown phase, TinyLlama v1.1 opted to increase the batch size instead of adjusting the learning rate because the cosine learning rate schedule had already reduced the learning rate to a very low value by the later stages of training. Increasing the batch size (from 1.8 million tokens to 7.2 million tokens) helps to improve training efficiency and aids in better model convergence. This adjustment facilitates smoother optimization in the final stages of training, avoiding poor training outcomes caused by excessively low learning rates. The loss curves for different variants show a continued decline in training loss, with the Math&Code and Chinese variants showing a steadier convergence, indicating that this adjustment had a positive effect on the model's final performance.<PIC>


Question: How does MINILLM's performance compare to SeqKD as the teacher model size increases?

MINILLM consistently outperforms SeqKD as the teacher model size increases. Specifically, on the UnNI dataset, when the teacher model size grows from GPT-2-125M to GPT-2-1.5B, MINILLM's Rouge-L score improves from 35.5 to 37.7, while SeqKD's score only rises from 32.5 to 32.9. This demonstrates MINILLM's potential for compressing large-scale models effectively.<PIC>


Question: Why does MINILLM achieve significantly higher Rouge-L scores than other methods when response lengths are longer?

This is because MINILLM optimizes the objective by minimizing the reverse KL divergence (reverse KLD), enabling the student model to better capture the teacher model's primary generation patterns rather than simply covering all possible patterns. This approach is particularly effective for generating long responses, as longer texts typically contain more diverse information, which is challenging for the student model's limited capacity.Therefore, MINILLM shows superior accuracy and coherence when generating longer texts.<PIC>


Question: How does targeted structured pruning produce compact and dense models?

Targeted structured pruning generates a compact and dense model by learning pruning masks to remove unnecessary parameters from the model. Specifically, the algorithm first sets a target architecture, then learns pruning masks at different granularities (such as layers, hidden dimensions, attention heads, and intermediate dimensions). Each mask variable controls the retention or removal of the corresponding substructure. By optimizing these masks, the algorithm reduces the number of parameters to the shape specified by the target architecture while maintaining model performance, thereby achieving model compression and acceleration.<PIC>


Question: What is the purpose of dynamic batch loading in the experiments?

The purpose of dynamic batch loading is to balance the rate of loss reduction across different domains, ensuring that the losses reach the reference value at roughly the same time. This results in more efficient data use by reducing the loss more evenly across domains.<PIC>


Question: Why is Layer Redundancy feasible?

Layer redundancy is feasible because studies reveal significant functional redundancy between layers in large language models (LLMs). Some layers contribute minimally to the overall performance, so their removal has little impact on the model's inference capabilities. By introducing the Block Influence (BI) metric, the importance of each layer can be quantified, allowing for targeted removal of less critical layers. Research shows that removing about 25% of the layers can retain approximately 90% of the model's performance. Additionally, reducing layer redundancy can be combined with techniques like quantization to further optimize deployment efficiency.<PIC>


Question: Why is the BI metric useful?

The Block Influence (BI) metric is useful because it effectively measures the degree to which each layer transforms the hidden states, allowing for the quantification of its importance. BI calculates the similarity between the input and output of each layer to evaluate how much the layer modifies the hidden states. A low BI score indicates minimal transformation, suggesting that the layer is less critical.As showed in figure,<PIC> the relationship between the BI score and the perplexity (PPL) change after removing a layer is illustrated. For layers with low BI scores, removing these layers causes minimal changes in PPL, indicating they have little impact on overall model performance.For layers with high BI scores, removing these layers significantly increases PPL, showing their crucial role in maintaining model performance.This demonstrates that the BI metric effectively identifies redundant layers. By targeting low-BI layers for removal, significant parameter reductions can be achieved with minimal performance loss, highlighting its utility for model compression in large language models (LLMs). Experimental results validate this approach's practicality and potential.


Question: How are priors formulated in RLFP?

In the RLFP (Reinforcement Learning from Foundation Priors) framework, the agent leverages three types of foundational priors: policy, value, and success-reward priors, to solve tasks. Taking the "press the button" task in the figure as an example, the agent's goal is to press the button and trigger the success state.As shown in figure<PIC>,Policy Prior Knowledge answers the question "What should I do?" by providing goal-oriented behavioral commonsense, such as guiding the agent to first move closer to the button and then press it.Value Prior Knowledge answers the question "Am I closer to the goal?" by evaluating the value of the current state and guiding the agent to take actions that more effectively approach the goal (e.g., moving from position A to position B to make the button easier to press).Success-Reward Prior Knowledge answers the question "Did I succeed?" by using a binary success-reward function to determine whether the task has been completed, such as triggering a success state when the button is pressed (e.g., the "Ring" notification).By combining these prior knowledge sources, the RLFP framework avoids the reliance on handcrafted reward functions and random trial-and-error explorations typical in traditional RL, significantly improving sample efficiency and the automation of task resolution.


Question: How are the Actor and Critic optimized in FAC?

In the FAC Actor-Critic algorithm, the Actor is optimized using policy gradients, with the objective of minimizing the discrepancy between the Critic's Q-value predictions and the actual actions. Additionally, the Actor improves its policy by imitating successful trajectories and applying policy regularization. The Critic, on the other hand, is optimized by minimizing the error between its Q-value predictions and the actual target, using temporal difference (TD) learning to update the value function. Through this dual optimization process, FAC effectively learns both the policy and the value estimation, while leveraging prior knowledge (such as success rewards, policy regularization, and reward shaping) to enhance learning efficiency.<PIC>


Question: How does the framework enable knowledge transfer from the teacher model to the student model while protecting data and label privacy?

The framework achieves knowledge transfer and privacy protection through the following steps: First, the teacher model is trained on private data.Then, The teacher model is used as a discriminator to train a generator that creates synthetic data, without using the original data. The generated synthetic data is used to train the student model. A selective random response mechanism is used to protect label privacy, ensuring the labels are differentially private.Last, The student model is trained using the differentially private synthetic data and labels, completing the knowledge transfer process.Through these steps, the framework enables effective knowledge transfer from the teacher model to the student model without exposing private data or labels.<PIC>


Question: Why does the impact of the number of stages become more significant as the classification difficulty of the dataset increases?

According to the experimental results shown in Figure 3, we observe that as the number of stages increases, the accuracy of the student model gradually improves on the MNIST, FMNIST, and CIFAR10 datasets. This effect becomes more pronounced as the classification difficulty of the dataset increases. The reason behind this phenomenon can be attributed to the model's need to learn more complex features.
Firstly, as the classification difficulty of the dataset increases, the features that the model needs to learn become more complex. A single round of model training may not be sufficient to capture these intricate patterns and relationships. By increasing the number of training stages, the model has more opportunities to iteratively improve its predictive ability. This is especially true when the initial predictions of the student model are rough, and more stages allow for gradual refinement of these predictions.
Secondly, the framework uses the predictions of the student model as prior knowledge, meaning that the output of the student model at each stage influences the learning process in the subsequent stages. As the training progresses, the predictions of the student model become more accurate, which increases the probability of outputting the correct label. Each stage not only allows the student model to improve its understanding of the data, but also enables it to receive more fine-grained guidance from the teacher model. This process helps the student model better adapt to more complex datasets, especially challenging ones like CIFAR10.<PIC>


Question: What are the advantages of performing alignment after LLM decoding using KD-Regularization?

The advantage of performing alignment after LLM decoding using Knowledge Distillation (KD) regularization lies in reducing information loss, optimizing alignment quality, and enhancing the synergy between speech and text modalities. By using KL-divergence to measure the differences between the predicted distributions, KD regularization flexibly adjusts the alignment between the decoded text and speech modalities. This approach avoids the modality bias common in traditional methods. Furthermore, alignment after decoding is more adaptable to varying input data, improving the model's generalization ability and robustness in multimodal tasks.As shown in figure<PIC>, figure (a) shows the effect of alignment before LLM decoding, while Figure (b) shows the effect of alignment after LLM decoding.These charts indicate that using Knowledge Distillation (KD) for alignment after LLM decoding can significantly improve the alignment performance between speech and text.


Question: How is the KD (Knowledge Distillation) performed in the last step of KD-Regularization?

In the final step of Knowledge Distillation (KD), the core goal is to optimize the alignment between the student model and the teacher model’s distributions to improve the speech-to-text generation quality. Specifically, the prediction distribution generated by the teacher model based on text input is used as the "teacher distribution," while the prediction distribution generated by the student model based on speech input is considered the "student distribution." The difference between these two distributions is minimized by reducing the KL divergence. KL divergence measures the difference between two probability distributions, and the smaller the divergence, the closer the student model’s output is to the teacher model’s output.During training, the student model takes speech input and generates corresponding emotional text descriptions, guided by the text prompts (e.g., emotional cues and context) provided by the teacher model. To achieve this alignment, the teacher model’s parameters remain frozen, and only the student model is fine-tuned. This enables the student model to learn how to generate outputs based on speech input that resemble the outputs the teacher model generates from text input, thereby bridging the distribution gap between speech and text inputs.To enhance training efficiency and effectiveness, the LoRA (Low-Rank Adaptation) fine-tuning method is used, which adjusts only a subset of the student model’s parameters. This avoids retraining the entire model, reducing computational resource consumption. As a result, the student model is able to generate emotional descriptions based on speech input that align with the text input, improving the alignment between speech and text and enhancing the model's cross-modal generation capabilities.<PIC>


Question: How does the Tone2Vec framework improve the representation of tonal variations?

The Tone2Vec framework improves tonal representation by mapping tone transcriptions into a feature space based on pitch similarity, constructing smooth pitch variation curves. This method not only effectively captures tonal variations but also provides more accurate tonal representations across different dialect regions. Unlike traditional methods that treat each tone as an isolated category, Tone2Vec analyzes the continuous variation of tones, enabling better clustering of dialect regions and reflecting the inherent relationships between tones, thus improving the representation of tonal variations.<PIC>


Question: How are the differences between tone transcriptions quantified in the Tone2Vec method?

In the Tone2Vec method, the differences between tone transcriptions are quantified by mapping them to smooth pitch variation curves. For example, a linear curve is used to represent pitch variation for transcriptions with two units, while a quadratic curve is employed for those with three units to smoothly interpolate the points. The difference between two tone transcriptions is quantified by calculating the area between their pitch variation curves. This measure captures the subtle differences in pitch variations.<PIC>


Question: Why are existing self-correction methods difficult to implement in small language models?

Existing self-correction methods are difficult to implement in small language models for several reasons.First, these methods rely on large-scale parameters and complex prompt engineering. In large language models, the self-correction process typically involves complex multi-step prompts and zero-shot prompt designs. This multi-step feedback and correction process requires the model to have strong reasoning and self-awareness abilities, which are typically achievable in large models. For smaller models, especially those with only a few billion parameters, they often struggle to process these complex prompts and feedback, unable to understand and accurately execute these steps.Second, small language models usually lack sufficient self-verification abilities. In large models, self-verification can be used to check whether the generated answer is correct and make necessary corrections. However, small models have limited self-awareness and are unable to effectively assess the quality of their own generated content, nor can they determine when corrections are needed. Due to this lack of self-awareness, these models tend to be overly confident in their generated results and do not recognize potential errors.Finally, existing self-correction methods depend on relatively complex multi-step pipelines and additional instructions to guide the model through the self-correction process, which is too complicated for small models. The understanding and reasoning capabilities of smaller models are limited, making it difficult for them to successfully execute these complex correction steps, resulting in poor performance on self-correction tasks.<PIC>


Question: In the self-correction data construction process, how is an incorrect answer corrected and a correct answer generated?

When the generated answer is incorrect, the correction process involves several steps to ensure the model can self-correct and generate the correct answer. First, the model performs self-verification to check whether the generated answer matches the ground truth. If the self-verification result is negative (i.e., the answer is incorrect), the model will correct the answer based on the feedback.During the correction process, the ground truth is used as a reference for correction, and a COT analysis process is generated to provide step-by-step reasoning for arriving at the correct answer. Specifically, a COT process is generated by gpt-3.5-turbo, which helps the model understand how to correct the answer from incorrect to correct.The data format for the corrected answer includes several parts: the incorrect answer and its corresponding COT analysis , followed by negative verification, indicating that the answer is incorrect, and then the corrected answer along with its COT analysis . This method ensures that the model not only generates correct answers but can effectively self-correct when an error is made, improving its self-correction ability.<PIC>


Question: Can you summarize how the AIDBench framework uses LLMs for authorship identification?

The AIDBench framework guides large language models (LLMs) to perform authorship identification tasks using carefully designed prompts. The process involves randomly selecting multiple texts from an author, designating one as the target text and the others as candidate texts. These texts are embedded into a prompt presented to the LLM, which determines whether the target text and candidate texts are authored by the same individual. The model generates outputs, such as similarity judgments or rankings, which are averaged over repeated runs to calculate performance metrics. Finally, metrics like precision and recall are used to evaluate the model's capabilities. This framework leverages prompt engineering to systematically assess LLMs' performance in authorship identification.<PIC>


Question: How does the RAG-based method ensure its effectiveness?

The RAG-based method ensures its effectiveness by using pre-trained embedding models to compute the semantic similarity between the target text and candidate texts, filtering out the most relevant candidates. This reduces the length of the input context, ensuring that all candidate texts can be processed within the LLM's context window. By only passing the top k most relevant candidates, the RAG method avoids redundant information, improving the LLM's processing efficiency and decision accuracy. At the same time, it effectively handles the challenge of processing long texts when dealing with a large number of candidates. This approach enhances both efficiency and accuracy in completing the task.<PIC>


Question: How is the performance of medical models compared to general-domain models in zero-shot and few-shot settings?

The performance of medical models generally does not show consistent improvement over their general-domain counterparts in both zero-shot and few-shot (3-shot) settings. <PIC>


Question: In the zero-shot setting, what effect does optimizing the prompt only for the medical model and comparing based on absolute accuracy have on the win rate of the medical model?

In the zero-shot setting, optimizing the prompt only for the medical model and using absolute accuracy as the basis for comparison significantly increases the win rate of the medical model. Specifically, as shown in the figure<PIC>, the win rate for medical LLMs (large language models) rises from 9.4% to 70.5%, and for medical VLMs (vision-language models), it increases from 6.3% to 62.5%.


Question: How does the multi-layer analysis framework help in understanding information processing and security constraints within LLM systems?

By defining key components, such as objects (e.g., the LLM model and plugins), and categorizing actions and interactions, this framework captures both internal processing within objects and information transmission between them. <PIC>


Question: About security concerns in the real world, how can an LLM system does not inadvertently display inappropriate or unethical images?

Implementing strict security controls at both the LLM output stage and during transmission to the frontend can prevent the display of unethical images. Specific measures include restricting the LLM from generating external image links and thoroughly reviewing and filtering link content before it reaches the frontend. <PIC>


Question: What are the essential components of the CogMir framework that enable the study of cognitive biases in LLM agents?

The essential components of the CogMir framework include Mirror Environmental Settings, Framework Structures, Cognitive Bias Subsets, and Sample Use Cases. These elements allow for various experimental setups, including required object sets, communication modes, and multi-human-LLM agent (Multi-H-A) interaction combinations to study cognitive biases.<PIC>


Question:
How do LLM Agents respond differently to social status
differences, such as the Authority Effect and the Herd Effect in MCQ scenarios?

LLM Agents exhibit a higher obedience rate to the Authority Effect than to the Herd Effect in both certain and uncertain scenarios, suggesting a stronger sensitivity to authoritative commands over peer influence. <PIC>


Question: How does NEFTune impact the performance of LLaMA-2-7B on AlpacaEval, and what does this indicate about its effectiveness in instruction fine-tuning?

NEFTune significantly boosts the performance of LLaMA-2-7B on AlpacaEval, increasing the win rate from 29.8% to 64.7%, which is an improvement of approximately 35 percentage points. This improvement demonstrates NEFTune's effectiveness in enhancing conversational quality without additional computational or data overhead. <PIC>


Question: How does NEFTune affect the performance of various models and datasets on AlpacaEval, and what does this indicate about its general effectiveness?

NEFTune improves the AlpacaEval win rate across all tested models (OPT-6.7B, LLaMA-1-7B, LLaMA-2-7B) and datasets (Alpaca, Evol-Instruct, ShareGPT, and OpenPlatypus), with an average increase of 15.1% on the 7B scale models. This consistent improvement suggests that NEFTune significantly enhances conversational ability and answer quality. <PIC>


Question: What is an "evil twin" prompt?

An "evil twin" prompt refers to an optimized prompt that is functionally similar to the original prompt but appears garbled and unintelligible to humans. <PIC>


Question: What is the most effective method for generating evil twins for given prompts?

Given two prompts to compare, we
compute the KL divergence for both prompts with
respect to the ground truth, and the method with lower
KL wins. And the most effective method is GCG with warm starts. <PIC>


Question: What are "Known Knows" and "Unknown Knows" in the context of LLMs?

"Known Knows" are areas where the model clearly understands and can articulate the information, while "Unknown Knows" represent areas where the model has knowledge but is unable to articulate or access it effectively. <PIC>


Question: In terms of self-knowledge, how does the performance of GPT-4 compare to the human benchmark?

GPT-4 achieves an F1 score of 75.47%, which is the highest among the tested models. However, it still falls short of the human benchmark of 84.93%, indicating a gap in the self-knowledge capabilities of LLMs compared to human performance. <PIC>


Question: Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones?”

Large language models cannot always solve easy problems even if they can solve harder ones. Research has found that while these models are capable of solving complex problems, they sometimes fail on relatively simple ones, which is termed as "hard-to-easy inconsistency." <PIC>


Question: How is the hard data collection process carried out in ConsisEval?

ConsisEval uses a semi-automatic process combining GPT-4 generation with human annotation, which significantly reduces the workload of human annotators. <PIC>


Question: How does QuIST address challenges in cross-lingual transfer for automatic question generation?

QuIST enables small mPLMs to learn interrogative structures without relying on target language data during training. <PIC>


Question: How does the proposed model determine the type of question to generate in cross-lingual question generation?

The model uses QTC to classify the type of question to generate, focusing on Wh-questions. The classification is based on both the context and the type of answer, as the same answer can correspond to different types of questions depending on the context. <PIC>


Question: What is Code Translation Error (CTE), and why does it occur in large language models during mathematical reasoning tasks?

Code Translation Error (CTE) refers to reasoning or comprehension mistakes made by large language models (LLMs). CTE occurs because training data for natural language far exceeds that for code, with LLMs trained on trillions of natural language tokens versus a smaller fraction of code tokens. Natural language is better suited for semantic analysis, planning, and abstract reasoning, making PoT less effective in some scenarios. <PIC>


Question: How does the Human-Think Language (HTL) framework improve code generation in large language models?

The HTL framework enhances code generation by integrating natural language reasoning (CoT) with programmatic reasoning (PoT). It uses a Focus Attention mechanism to bias code generation towards faithful adherence to CoT reasoning. This ensures that the reasoning process aligns with code outputs, avoiding repetitive or verbose steps through reinforcement learning with PPO-based error assessment. <PIC>


Question: What is structured entity extraction?

Structured entity extraction is the task of identifying named entities from unstructured text and associating them with properties and relationships. It combines named-entity recognition, entity-property extraction, relationship extraction, and coreference resolution. <PIC>


Question: How does the MuSEE model handle structured entity extraction across its three stages?

The MuSEE model uses an encoder-decoder architecture and processes structured entity extraction in three stages: Entity Identification, Type and Property Key Prediction and Property Value Prediction. <PIC>


Question: How does the Imagine2Servo model address the limitations of traditional visual servoing methods, such as the requirement for a goal image and the lack of flexibility in real-world robotic tasks?

The Imagine2Servo model significantly enhances the traditional visual servoing framework by introducing a pipeline that generates intermediate goal images using diffusion-based image editing techniques. This approach overcomes the traditional reliance on pre-defined goal images, enabling robots to "imagine" goal configurations dynamically based on the task context.
For instance, in a scenario where a drone is tasked with crossing a door, the Imagine2Servo pipeline can generate a visual subgoal approximating the position of the door. The robot uses this imagined goal to navigate toward the desired position and then applies a hardcoded skill to complete the task. This pattern—imagining a goal, servoing to it, and executing a predefined action—makes the model versatile for both navigation and manipulation tasks, such as unplugging a charger where precise reaching is required.
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This process leverages a single eye-in-hand camera combined with language instructions to perform tasks efficiently, demonstrating its application in long-range navigation and precise manipulation, thus addressing the limitations of traditional methods that require a predefined map or significant image overlap between the start and target states.


Question: How does the Imagine2Servo framework integrate diffusion-based foresight and flow-based visual servoing to enable long-horizon robotic tasks, and why is subgoal generation critical for its success?

The Imagine2Servo framework addresses the challenges of long-horizon robotic tasks by combining diffusion-based foresight for subgoal generation with a flow-based Image-Based Visual Servoing (IBVS) controller. This integration enables the robot to handle tasks that require navigating through complex environments or interacting with objects using limited initial data.
At each step, the robot's monocular eye-in-hand camera provides the current image \(I_t\) and a textual prompt \(P\), describing the task. The foresight model, driven by diffusion-based techniques, generates intermediate subgoal images \(I_g\) conditioned on \(I_t\) and the task context. These subgoal images provide a step-by-step guidance system, translating high-level task objectives into achievable intermediate visual goals.
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The IBVS controller then uses these subgoal images to calculate 6-degree-of-freedom (DOF) actions \([v_t, \omega_t]\) to incrementally guide the robot toward the ultimate target configuration. This iterative loop of subgoal generation and servoing bridges the gap between long-horizon goals and real-time execution, solving limitations of traditional methods that rely on predefined final images or are constrained by limited overlap between initial and target views.
Subgoal generation is critical as it divides complex tasks into manageable visual steps, ensuring precise navigation and interaction even in dynamically changing environments. This makes the framework adaptable to various scenarios, such as navigating through doorways or reaching for objects, while maintaining efficiency and reliability.


Question: How does the use of thin HMPE composite vacuum windows in BICEP3 enhance the optical and mechanical performance of millimeter-wave telescopes compared to traditional bulk plastic windows?

The integration of thin high modulus polyethylene (HMPE) composite vacuum windows in BICEP3 significantly improves both the optical and mechanical performance of millimeter-wave telescopes. Traditional bulk plastic windows, such as high-density polyethylene (HDPE), require substantial thickness to withstand atmospheric pressure, leading to high optical loading and reduced transmission efficiency. In contrast, the HMPE composite material offers high strength at reduced thickness, enabling a much thinner window—around 1 mm—while maintaining structural integrity.
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This reduction in thickness directly decreases optical loading, especially at higher frequency bands above 40 GHz, as shown by the modeled improvements in white noise and mapping speed. Additionally, the laminated structure of HMPE with low-density polyethylene (LDPE) ensures durability and optical homogeneity. The LDPE acts as a binder that maintains the HMPE fibers' structural integrity without compromising mechanical strength.
Mechanically, these thin windows can withstand significant pressure deflection (up to 75 mm in the BICEP3 cryostat), as shown in the cutaway model, while optical properties like polarization and anti-reflection remain intact. Thus, the HMPE composite windows not only reduce survey time by improving signal-to-noise ratio but also set a new standard for lightweight, high-throughput components in modern millimeter-wave telescopes.


Question: How does the hydrostatic testing process validate the structural reliability and safety of the thin HMPE composite vacuum windows for the BICEP Array cryostat?

The hydrostatic testing process for the thin HMPE composite vacuum windows demonstrated their structural reliability and safety through controlled pressure evaluations. By utilizing water as the testing medium, minimal energy was stored in the system, ensuring safety during failure tests. The test setup included a unique clamping configuration involving a knurled pattern frame and clamps specifically designed for BICEP Array cryostats. This configuration maximized the concentration of force across the window's surface.
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During the test, the window sustained a pressure of up to 85 psi (5.7 atm), exceeding the predicted failure threshold for a safety factor of 3, as visible in the hydrostatic test image with deflection and water leakage. The window experienced a maximum deflection of approximately 75 mm, corresponding to a radial strain of 3%, well below the failure strain of 10%. These results confirmed a safety factor of at least 5.7, validating the window’s capability to withstand operational and accidental loads in real-world applications.


Question: In the context of the SDSS-V Local Volume Mapper (LVM): Data Analysis Pipeline, how are emission line parameters (e.g., flux, velocity, and velocity dispersion) and stellar population properties analyzed and separated using the DAP methodology? How does the provided analysis flow diagram aid in understanding this process?

The SDSS-V Local Volume Mapper (LVM) Data Analysis Pipeline (DAP) employs a comprehensive methodology to separate and analyze stellar population properties and emission line features from the observed spectra. The stellar spectrum analysis involves deriving non-linear parameters such as velocity (\(v_*\)), velocity dispersion (\(\sigma_*\)), and dust attenuation (\(A_{\rm V,*}\)). The stellar spectrum is also synthesized by decomposing it into a set of predefined stellar population templates (RSP templates), which represent physical properties like effective temperature (\(T_{\rm eff}\)), surface gravity (\(\log g\)), and metallicity (\([{\rm Fe/H}]\)).
For the emission line fitting, strong emission lines are initially fitted using parametric models like Gaussian functions to extract integrated flux (\(F_{\rm EL}\)), velocity (\(v_{\rm EL}\)), and velocity dispersion (\(\sigma_{\rm EL}\)). The residual spectrum, obtained after subtracting both the stellar and parametric emission line components, is used for further analysis of faint emission lines using a non-parametric method.
Both stellar and emission line analyses involve Monte Carlo loops to account for uncertainties and to generate robust estimates of all derived parameters. The outcomes include probability density functions (PDFs) of stellar population properties and equivalent widths (\(EW_{\rm EL}\)) of emission lines.
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Question: In the article "The SDSS-V Local Volume Mapper (LVM): Data Analysis Pipeline," how does the clustering of stellar population templates (RSPs) impact the analysis of physical parameters like \(T_{\mathrm{eff}}\), \(\log(g)\), \([\mathrm{Fe/H}]\), and \([\alpha/\mathrm{Fe}]\)? How do the Probability Distribution Functions (PDFs) help address degeneracies in parameter estimation?

The clustering of RSP templates in the LVM Data Analysis Pipeline significantly improves the efficiency of analyzing physical parameters while preserving their intrinsic characteristics. The RSP library consists of 108 templates generated by grouping spectra based on similar shapes, ensuring computational efficiency without sacrificing accuracy. However, this clustering does not imply identical physical parameters within a cluster, as evidenced by the Probability Distribution Functions (PDFs).
The PDFs are vital for understanding the distribution of physical properties (\(T_{\mathrm{eff}}\), \(\log(g)\), \([\mathrm{Fe/H}]\), \([\alpha/\mathrm{Fe}]\)) across clusters. They reveal both the typical values and the uncertainties or degeneracies within each parameter pair. For instance, \(T_{\mathrm{eff}}\) and \(\log(g)\) show minimal degeneracy, while \([\mathrm{Fe/H}]\) and \([\alpha/\mathrm{Fe}]\) exhibit a notable anticorrelation.
These PDFs can also guide the choice of the number of clusters (\(n_{cl}\)) to balance between resolution and computational efficiency. Increasing \(n_{cl}\) reduces degeneracies but only up to a point (e.g., beyond 108 clusters), beyond which the degeneracies translate into fitting ambiguities rather than improving the analysis.
By exploring the contours of the PDFs (e.g., Fig. \ref{fig:MaStar_PDF}), the degeneracies and multi-valuated distributions are visualized. This aids in refining parameter estimation and highlights the importance of external constraints like Gaia distances to break degeneracies, although such integrations are beyond the current manuscript's scope.
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Question: How does MolParser address the challenges posed by traditional SMILES formats when parsing complex molecular structures, such as Markush representations, from unstructured chemical documents like patents?

MolParser is designed to overcome the limitations of traditional SMILES formats in parsing complex molecular structures, particularly Markush representations commonly found in patents. Traditional SMILES struggles with representing flexible molecular constructs like Markush, abstract rings, and polymers, which are critical for patent-level protection and cheminformatics applications. These limitations arise from the linear and rigid nature of SMILES, which lacks the hierarchical and flexible structure required for advanced chemical representation.
To address these challenges, MolParser introduces an extended SMILES format that enhances the capability of representing complex molecular structures. This extension enables it to handle variability in molecular features such as connection points and duplicated groups, making it well-suited for parsing Markush structures and other chemically intricate entities.
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By leveraging an end-to-end transformer architecture, MolParser directly extracts chemical structures from document images, converting them into machine-readable formats. This not only improves the representation of molecular structures but also bridges the gap between unstructured chemical data in documents and advanced cheminformatics tools, unlocking new opportunities in drug discovery and patent analysis.


Question: In the paper "MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild," how does the extended SMILES representation improve the ability to encode complex molecular structures, such as Markush and polymers, compared to traditional and FG-SMILES formats?

The extended SMILES representation introduced in "MolParser" addresses the limitations of traditional SMILES and FG-SMILES by enabling the encoding of complex molecular structures, such as Markush, abstract rings, ring attachments, and polymers, which are often encountered in patents. Traditional SMILES cannot express these structures due to its linear format, and FG-SMILES provides limited enhancements for functional group labeling but lacks flexibility for abstract or dynamic compositions.
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MolParser’s extended SMILES enhances this capability with XML-like tokens, such as `<a>` for R-groups and `<r>` for ring attachments, encapsulating the additional descriptive information needed for Markush structures and other complex cases. These tokens allow hierarchical and flexible representations, making the extended SMILES both RDKit-compatible and suitable for LLMs.


Question: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection," how does the TSINR framework leverage the spectral bias of INR and the transformer encoder to improve the detection of anomalies in time series data?

The TSINR framework leverages the spectral bias property of INR and transformer-based architecture to enhance time series anomaly detection by prioritizing low-frequency components while amplifying anomaly-specific fluctuations. INR parameterizes the signal as a continuous function that learns temporal continuity, making it highly sensitive to disruptions caused by anomalies. This approach mitigates the negative impact of unlabeled anomalous data during training and improves robustness in reconstruction.
The framework includes a transformer encoder, which predicts INR tokens serving as the parameters of the INR continuous function. This function decomposes the time series data into trend, seasonal, and residual components, allowing the model to capture complex temporal dynamics effectively.
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Additionally, a pre-trained large language model (LLM) encodes the original data into a feature space, amplifying anomaly-related fluctuations across both time and channel dimensions. This amplified representation enhances the sensitivity of INR to anomalies, enabling better differentiation between normal and abnormal points. Extensive experiments validate TSINR's effectiveness across multiple benchmark datasets, demonstrating its capability to outperform state-of-the-art methods.


Question: How does the group-based architecture and the pre-trained LLM encoder enhance TSINR's ability to detect anomalies in multivariate time series data?

The group-based architecture in TSINR enhances anomaly detection by dividing variables into smaller groups, allowing each group to be modeled by independent fully connected layers. This approach improves representational capacity by reducing the complexity of modeling inter- and intra-channel relationships within multivariate data. Global layers further capture inter-channel information, while group layers selectively focus on specific channels, ensuring no knowledge is lost.
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The pre-trained LLM encoder amplifies the fluctuations of anomalies in both the time and channel dimensions. By mapping the original data into a feature domain, the encoder makes anomalies more pronounced, particularly in multivariate datasets. This amplified representation aligns with INR's spectral bias, improving sensitivity to discontinuities caused by anomalies. Visualization of anomaly scores and reconstructed data validates that TSINR effectively detects anomalies across various types and time intervals.


Question: How does the ACING pipeline leverage actor-critic principles to optimize prompt learning for black-box large language models (LLMs)?

The ACING pipeline applies actor-critic reinforcement learning principles to optimize prompt generation for black-box LLMs. The actor network, parameterized by neural networks, proposes actions (soft prompts) as part of a continuous action space, while the critic network evaluates these actions by estimating their quality based on the rewards received.
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In this pipeline, a soft prompt and task examples are provided to a white-box LLM to generate an instruction. This instruction is then used to query the black-box LLM, whose output is scored and returned as a reward. Both the actor and critic networks update iteratively based on this reward, ensuring the optimization of prompts for effective task performance under a constrained evaluation budget.


Question: How does ACING use entropy-based exploration and actor-critic dynamics to optimize soft prompts for instruction learning?

The ACING framework employs entropy-based exploration by dynamically adjusting the entropy coefficient \(\alpha\) to maintain a target entropy level \(H_{\text{target}}\). This ensures sufficient exploration while optimizing the policy. The actor-critic mechanism integrates a policy network (actor) to propose actions (soft prompts) and a Q-network (critic) to evaluate these actions based on the received rewards.
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Using a soft prompt, the white-box LLM generates instructions that are tested on a black-box LLM. The black-box LLM's outputs are scored against true labels from the validation set, providing feedback as rewards. These rewards help refine the actor's policy and the critic's evaluations, iteratively optimizing the soft prompts for effective instruction learning.


Question: How does the Leadsee-Precip model ensure accurate precipitation predictions while addressing the challenges of class imbalance in extreme precipitation events?

The Leadsee-Precip model combines advanced architectural design and a novel Information Balance (IB) scheme to enhance prediction accuracy. Its encoder-decoder structure integrates feature extraction, hidden translator, and precipitation upsampling components. The feature extraction module uses 3D ConvNets for upper-air variables and 2D ConvNets for surface variables, with zonal circular padding to handle boundary conditions effectively. A shortcut connection improves accuracy by incorporating original variables during upsampling.
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To address the challenge of long-tail precipitation data distribution, the IB scheme assigns weights based on the information content of precipitation samples using a logit form \(-\log P(y_i)\). This ensures that rare extreme precipitation events contribute proportionally to the loss function, mitigating biases inherent in training deep learning models with RMSE. The combination of these elements enables Leadsee-Precip to produce balanced and accurate precipitation forecasts.


Question: How does the Leadsee-Precip model perform in diagnosing 6-hour accumulated precipitation globally, and what are its strengths and limitations based on NOAA CMORPH data?

The Leadsee-Precip model demonstrates strong diagnostic capabilities for 6-hour accumulated precipitation on a global scale, accurately predicting the intensity and location of large rainfall events. For instance, it successfully identifies areas in the eastern and western Pacific with precipitation exceeding 50 mm/6h, showing good agreement with NOAA CMORPH ground truth.
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However, the model tends to overestimate light precipitation, such as below 1 mm/6h, and produces smoothed finer details. Evaluation metrics TS and FSS across different thresholds indicate robust performance, with FSS above 0.5 for heavy precipitation at 25 mm/6h. These results highlight the model's capability to handle extreme rainfall while addressing challenges like overestimation of smaller rainfalls.


Question: How does the ULTra framework utilize hierarchical clustering to enhance interpretability in Vision Transformers (ViTs)?

The ULTra framework introduces a hierarchical clustering approach to interpret the latent token representations in Vision Transformers (ViTs). By organizing token relevance maps into a clustering tree, ULTra enables the visualization of semantic patterns at multiple levels of granularity. Lower clustering thresholds (\(\zeta\)) reveal finer details, while higher thresholds highlight broader semantic groupings, demonstrating the inherent ability of ViTs to identify meaningful patterns within their latent space.
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This hierarchical clustering not only enhances interpretability but also allows for unsupervised semantic segmentation. Unlike existing methods that require additional training, ULTra leverages pre-trained ViTs to achieve zero-shot segmentation, showcasing the semantic understanding embedded within their token representations. This process facilitates tasks like object selection and model interpretation, pushing the boundaries of understanding in transformer-based architectures.


Question: How does the ULTra framework enable unsupervised semantic segmentation using Vision Transformers?

The ULTra framework leverages relevance maps derived from latent token representations to perform unsupervised semantic segmentation. By clustering these relevance maps using hierarchical clustering techniques, ULTra identifies distinct semantic clusters within the image. The clustering threshold \(\zeta\) adjusts the granularity of the segmentation, enabling flexibility to capture either broad categories or finer details, such as specific object features.
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Question: How does the UMGAD framework utilize original-view and augmented-view graph reconstruction to detect anomalies in multiplex heterogeneous graphs?

The UMGAD framework leverages both original-view and augmented-view graph reconstruction to capture anomalies in multiplex heterogeneous graphs. In the original-view graph reconstruction, it masks node attributes and edges to encode missing information using GCN-Masked Encoders. These encoded representations are decoded to reconstruct attributes and structure, with reconstruction errors highlighting anomalies.
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The augmented-view graph reconstruction introduces attribute and subgraph-level augmentations to enhance sensitivity to complex anomalies. By combining the outputs from both views, UMGAD effectively detects anomalies using multi-layer reconstruction losses while balancing structural and attribute irregularities, providing robust anomaly scores. This dual-view design ensures comprehensive anomaly detection across different graph relationships and modalities.


Question: How does UMGAD perform compared to state-of-the-art methods in ranking anomaly scores across datasets with real anomalies?

UMGAD achieves superior performance in ranking anomaly scores across datasets with real anomalies. In the Amazon dataset, UMGAD significantly outperforms ADA-GAD, TAM, GADAM, and AnomMAN by consistently identifying anomalous nodes with lower anomaly scores at a higher ranking. This indicates UMGAD’s efficiency in detecting anomalies with higher precision compared to its competitors.
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In the YelpChi dataset, UMGAD continues to exhibit outstanding performance, maintaining lower anomaly scores while achieving accurate ranking for nodes with anomalies. The results validate UMGAD's capability to handle diverse datasets, effectively distinguishing anomalous nodes from normal ones across various graph types and anomaly densities.
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Question: How does the libcll toolkit address the challenges of reproducibility and standardization in Complementary Label Learning (CLL) research?

The libcll toolkit provides a standardized and reproducible framework for evaluating Complementary Label Learning (CLL) algorithms, addressing key challenges in the field. By incorporating a diverse set of 15 datasets, including synthetic and real-world scenarios, as well as 14 CLL algorithms and 5 widely used models, libcll ensures consistent evaluation and facilitates meaningful comparisons across studies.
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Furthermre, the toolkit supports customization, enabling researchers to experiment with various CLL assumptions and distributions, such as uniform, biased, and noisy complementary labels. Built with PyTorch-Lightning, libcll simplifies implementation and benchmarking, making it easier to develop and refine CLL algorithms. This comprehensive approach positions libcll as a vital resource for advancing CLL research and promoting collaboration within the community.


Question: How does the libcll toolkit integrate key developments in Complementary Label Learning (CLL) to provide a comprehensive research platform?

The libcll toolkit incorporates major advancements in Complementary Label Learning (CLL) by implementing three core categories of methods: URE (Unbiased Risk Estimator), CPE (Complementary Probability Estimation), and MCL (Multiple Complementary Label) frameworks. These approaches address critical challenges in learning from complementary labels, such as unbiased risk estimation, transition matrix decoding, and handling multiple complementary labels per instance.
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Additionally, the toolkit supports synthetic and real-world datasets with diverse distributions (uniform, biased, and noisy) to facilitate fair and reproducible evaluations. By integrating these methods into a unified platform, libcll not only benchmarks algorithm performance but also accelerates innovation and collaboration in weakly-supervised learning.


Question: In what ways do the choice of projects and the criteria for identifying repository deprecation on GitHub affect the accuracy of models predicting the lifespan of open-source software projects?

In predicting the lifespan of open-source projects, the accuracy of the model significantly depends on the choice of data and the standards for defining repository deprecation. According to the study's methodology illustrated in the research framework <PIC>, a comprehensive dataset was constructed by leveraging GitHub's vast repository of projects. In this dataset, deprecation is defined using GitHub's "archived" status, which signals that a project is no longer active or has been officially deprecated.
During data collection, GitHub's GraphQL and Search APIs were used to retrieve repository data. As shown on the left side of the research framework <PIC>, the study collected and labeled data for 51,677 projects, distinguishing between active and deprecated repositories through a combination of manual and machine learning-based labeling. This rigorous dataset labeling ensures high accuracy in identifying repository statuses.
Additionally, survival analysis techniques, such as the Accelerated Failure Time (AFT) model and Dynamic Relative Survival Analysis (DRSA), enable the model to detect reliable lifespan patterns from the curated sample. By setting strict definitions and filtering standards, the study enhances the model’s precision in identifying the factors that influence the lifespan of open-source projects.


Question: How effective is the HITS weight compared to other centrality metrics in forecasting deprecation trends in open-source software, and what evidence supports its predictive value?

The HITS weight has shown itself to be a more robust predictor of repository deprecation compared to traditional metrics such as stars, issues, and pull requests. The Brackets project serves as a representative case study. Repository features over time of Adobe/Bracket <PIC> illustrates various activity statistics for the Brackets repository, including stars, issues, PRs, commits, comments, and tags. From this figure, we observe that while the number of stars remained relatively high and stable, the HITS weight steadily declined, signaling a trend towards deprecation even as other metrics fluctuated. This steady decline in HITS weight highlights its ability to filter out noise and better reflect the project's actual trajectory towards deprecation.

Further supporting this, ∆HITS over time of HomeWork <PIC> shows that Project 0age, which has not been deprecated, maintained a stable or positive ∆HITS throughout the observation period, indicating active maintenance. Conversely, ∆HITS over time of Discord-Themes <PIC> and of shattered-pixel-dungeon-gdx <PIC> reveal negative peaks in ∆HITS in the months leading up to deprecation, marking a sharp decline that serves as a reliable indicator of impending deprecation. This consistent pattern across different projects illustrates the sensitivity of the HITS metric in forecasting deprecation trends.


Question: What challenges do diffusion models like SDXL and AuraFlow face as they scale up in parameters, and how might low-precision inference offer a solution in terms of computational efficiency?

As diffusion models scale up to billions of parameters, their computational requirements rise sharply, presenting significant challenges in terms of memory and processing power. For example, Stable Diffusion (SD) 1.4 has 800M parameters, whereas SDXL and AuraFlow v0.1 push this boundary to 2.6B and 6B parameters, respectively.
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 This rapid increase in computational demand poses a barrier to deploying these models in real-world applications that require low latency.

To address this, hardware vendors are exploring low-precision inference, such as NVIDIA’s new 4-bit FP4 precision, which significantly enhances performance by reducing memory usage and latency. This approach not only compresses model size but also boosts processing speed, making it a promising solution for deploying large-scale diffusion models in latency-sensitive applications.


Question: How does SVDQuant minimize quantization errors in 4-bit diffusion models by leveraging low-rank approximations, and what effect does this have on the distribution of singular values?

SVDQuant reduces quantization errors in 4-bit diffusion models by applying a low-rank approximation to absorb outliers. According to the quantization error bound, the quantization error \( \|\mR - Q(\mR)\|_F \) is controlled by minimizing the residual magnitude \( \|\mR\|_F \). This is achieved by decomposing the weight matrix \( \hat{\mW} \) using Singular Value Decomposition (SVD), allowing only the top singular values to be retained while discarding lower values that contribute to outliers.
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By concentrating the largest singular values in a low-rank form, the residual matrix \( \mR \) is compressed, effectively reducing outlier influence on the quantized model.

This approach reshapes the singular value distribution. The original weight matrix \( \mW \) shows a highly imbalanced distribution of singular values, while the low-rank approximation sharpens this distribution, reducing the magnitude of \( \mR \) and thereby the overall quantization error. Through iterative decomposition, SVDQuant further minimizes errors, enhancing model efficiency while maintaining accuracy in a 4-bit format.


Question: How does the structural composition of visual tokens in transformer models, such as DALL-E and Chameleon, parallel the structure of natural language, and what are the design implications for visual tokenization?

Transformer-based models like DALL-E and Chameleon use "visual sentences" composed of discrete visual tokens, a structure that parallels natural languages by linearizing images into sequential representations. This approach enables models to perform multimodal tasks by integrating image and text data into a shared token space.
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However, visual tokens differ from natural languages in their statistical patterns. While visual tokens exhibit Zipfian distributions, they do so with higher per-token entropy and lower compression ratios, suggesting that vision models may require more complex architectures with additional attention heads, larger embeddings, and extended training times.

The analysis further reveals that visual tokens operate at an intermediate granularity level, often representing parts of objects rather than whole objects or finer details. This granularity difference underscores why visual tokens align differently with natural languages in embedding spaces, motivating a modality-specific approach to enhance visual language processing.


Question: To what extent do Context-Free Grammars (C-PCFGs) effectively capture the structure of visual languages in models discussed in 'Analyzing The Language of Visual Tokens,' and what are the observed limitations?

C-PCFGs have been used to approximate the structure of visual languages, aiming to capture the grammar of visual tokens similarly to natural language. However, visual languages are less compressible using context-free grammars compared to natural languages. This is evident in the final parse tree perplexity (PPL) and reduction in perplexity (PPL-R) observed across datasets, where visual grammars exhibit higher PPL values than textual grammars. The higher initial PPL for visual grammars is also influenced by the generally longer "visual sentence" length, which consists of 32 tokens per image.
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The non-terminal node frequencies in visual grammars further reveal structural differences; although both modalities show similar tree height ratios (FR) and branching behaviors (MBF), visual languages demonstrate a more balanced branching. These findings imply that while C-PCFGs capture some structural aspects of visual languages, they may not fully approximate them as they do for natural languages. This suggests that visual languages might be better represented by alternative grammatical formalisms, like mildly context-sensitive grammars, which can handle dependencies across token spans more effectively.


Question: What are the stages in the question generation pipeline for the HourVideo benchmark, and how do they ensure the quality of the video-language understanding tasks?

The HourVideo benchmark employs a multi-stage question generation pipeline designed to ensure high-quality video-language understanding tasks. Beginning with video curation, the pipeline selects relevant videos from the Ego4D dataset, chosen for its egocentric perspective and detailed narrations, which are well-suited for generating diverse questions. Next, candidate multiple-choice questions (MCQs) are generated by extracting key information from 20-minute video segments, which includes summaries and lists of objects, locations, and other contextual details.
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Human feedback is then utilized to refine initial MCQs (labeled as $\QAW_{2}$), addressing issues like inconsistent terminology in narrations by verifying question validity, answer accuracy, and distinctiveness of incorrect options.

Subsequent stages apply blind filtering, where questions answerable through prior knowledge alone are removed by testing them on language models without video input. Finally, an expert refinement phase enhances remaining MCQs (now $\QAW_{4}$) by making questions more precise and contextually accurate, culminating in a high-quality set of questions ($\QAW_{5}$). This structured, iterative process, supported by extensive human review, helps to create a robust dataset for video-language understanding.


Question: What types of scenarios and question categories are covered in the HourVideo dataset, and how does this diversity contribute to the depth of video-language understanding?

The HourVideo dataset features a broad range of 77 daily life scenarios, including activities such as cooking, cleaning, and watching TV, making it highly representative of common, real-world contexts. Each video is accompanied by an average of 26 multiple-choice questions across a diverse set of categories, including perception, summarization, spatial relationships, and temporal sequencing.
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This diversity in both scenarios and question types enhances the dataset's ability to evaluate various aspects of video-language understanding, challenging models to interpret spatial layouts, predict outcomes, and recall sequences. The comprehensive coverage enables a more nuanced assessment of models' capabilities in understanding and reasoning over long-form, egocentric video content.


Question: How does SG-I2V achieve zero-shot trajectory control in image-to-video generation, and what challenges does it address in feature alignment compared to traditional video diffusion models?

SG-I2V introduces a novel approach to zero-shot trajectory control by leveraging semantic feature alignment in a pre-trained video diffusion model, enabling precise control over object motion and camera dynamics for arbitrary input images. Traditional video diffusion models face challenges in feature alignment across frames, as feature maps from upsampling blocks often lack consistent alignment, making spatial manipulation difficult.

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To address this, SG-I2V uses self-attention layers for better alignment by replacing key and value tokens in each frame with those from the first frame. This approach allows the model to maintain semantic consistency across frames, which is essential for effective trajectory control. By optimizing the latent space based on these aligned features, SG-I2V ensures that the generated video follows the specified trajectories, overcoming limitations in feature misalignment present in standard video diffusion methods.


Question: How does SG-I2V use feature alignment and frequency-based post-processing to control object trajectories in image-to-video generation, and what benefits do these steps provide?

SG-I2V employs a unique approach to trajectory control by first aligning feature maps across frames and then applying frequency-based post-processing to refine the output. In the feature alignment step, SG-I2V modifies the spatial self-attention mechanism of Stable Video Diffusion (SVD) by using the key and value tokens from the first frame across all frames, which strengthens cross-frame semantic alignment. This alignment enables consistent control over object trajectories throughout the video frames.

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Following feature alignment, SG-I2V applies frequency-based post-processing, which preserves high-frequency noise in the latent space to prevent overfitting during optimization. This post-processing step helps maintain the realism and quality of the generated video, ensuring that the optimized latent remains within the distribution expected by the diffusion model. Together, these techniques allow SG-I2V to generate controlled, high-quality image-to-video sequences with precise trajectory management.


Question: How does the Few-Shot Task Learning through Inverse Generative Modeling (FSTL-IGM) approach in SG-I2V utilize generative models for learning new task concepts, and what domains demonstrate its effectiveness?

The Few-Shot Task Learning through Inverse Generative Modeling (FSTL-IGM) approach in SG-I2V leverages generative models to learn new task concepts from limited demonstrations by optimizing a latent representation, referred to as a "concept," to maximize the likelihood of reproducing observed behavior. During pretraining, a generative model \( \mathcal{G}_{\theta} \) is trained on a large set of paired behaviors and task concepts, which provides strong priors for generating trajectories based on these task representations.
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This approach is evaluated across various domains, including object rearrangement, goal-oriented navigation, motion capture (MoCap) for human actions, autonomous driving, and real-world table-top manipulation.
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By employing FSTL-IGM, SG-I2V demonstrates the ability to generate diverse trajectories that embody learned concepts, showcasing compositional and interpolation capabilities essential for generalizing behavior across new scenarios within these domains.


Question: How does Few-Shot Task Learning through Inverse Generative Modeling (FSTL-IGM) perform in learning new concepts in object rearrangement tasks, and what limitations were observed?

Few-Shot Task Learning through Inverse Generative Modeling (FSTL-IGM) demonstrates strong capabilities in learning new object arrangement concepts through a few demonstrations. In the object rearrangement domain, the model is trained to understand pairwise relations such as “right of” and “above,” which it then composes into more complex spatial arrangements like “A right of B and B above C.” FSTL-IGM successfully generalizes these learned relations to form new concepts such as “diagonal” or “circle,” enabling the model to create novel arrangements that were not seen during training.

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However, some limitations were noted. For example, the accuracy for the “circle” concept is lower compared to other tasks, potentially due to the concept’s distance from the training distribution. Additionally, complex compositions such as “square right of circle and triangle above circle” have lower accuracy, which may arise from challenges in the concept-weight optimization process, where weights lack explicit regularization and can diverge, affecting performance. These findings suggest that while FSTL-IGM is effective in many cases, certain novel or complex compositions require further refinement for optimal generalization.


Question: How does BitNet a4.8 utilize hybrid quantization and sparsification to manage outlier activations in 1-bit LLMs, and what benefits does this approach provide?

BitNet a4.8 combines hybrid quantization and sparsification to address the challenges of outlier activations in 1-bit LLMs, where weights are quantized to 1.58 bits. This approach strategically applies 4-bit quantization to activations at critical points, such as inputs to attention and feed-forward networks (FFN), while using 8-bit sparsification for intermediate activations. By doing so, BitNet a4.8 effectively reduces the computational burden posed by large outliers, minimizing quantization errors that typically degrade performance in low-bit models.
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This hybrid strategy enhances inference efficiency by retaining only 55% of active parameters and utilizing a 3-bit key-value (KV) cache, further optimizing memory and computational costs without sacrificing model accuracy. This design allows BitNet a4.8 to perform comparably to higher-bit models with substantially reduced resource demands, making it ideal for deploying efficient LLMs.


Question: How does BitNet a4.8 handle quantization for the down projection of the FFN layer, and what effect does different quantization and activation choices have on training performance?

In BitNet a4.8, the quantization strategy for the down projection of the feed-forward network (FFN) layer is crucial for balancing model efficiency and performance. By default, activations are maintained at 8 bits, with the down projection of FFN utilizing either INT8 quantization through an absmax quantizer or FP4 quantization via a MinMax quantizer. When FP4 quantization was applied to the inputs of the down projection, a notable drop in performance was observed, while INT4 activations combined with STE led to divergence, indicating instability in training.
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Among the activation functions tested, squared ReLU provided slightly better training perplexity compared to Swish, with the added benefit of promoting higher sparsity. These findings underscore the importance of choosing appropriate quantization and activation functions, as they directly impact both training stability and efficiency in low-bit configurations.


Question: How does the Steepest Perturbed Gradient Descent (SPGD) algorithm compare to other optimization methods in finding the global minimum on the Peaks function, and what performance benefits does it offer?

The Steepest Perturbed Gradient Descent (SPGD) algorithm demonstrates superior efficiency on the Peaks function, a challenging optimization landscape characterized by multiple local minima, flat regions, and a complex surface. Unlike traditional Gradient Descent (GD) and Perturbed Gradient Descent (PGD), which tend to get stuck in local minima, SPGD effectively navigates the landscape to reach the global minimum with lower computational overhead.
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In terms of convergence, as shown in the convergence history plot, SPGD quickly reaches the global minimum compared to other methods, including Simulated Annealing (SA) and MATLAB's \(fmincon\), both of which achieve the global minimum but with significantly higher CPU time. SPGD achieves the desired solution with fewer function evaluations and a notably faster execution time, approximately 20 times quicker than \(fmincon\), despite the latter requiring fewer evaluations.
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Overall, SPGD provides an efficient optimization solution for complex, non-convex functions like the Peaks function, achieving high accuracy with minimal computational resources and proving robust against challenging landscapes.
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Question: How does the Steepest Perturbed Gradient Descent (SPGD) method achieve superior optimization outcomes in Scenario 1 involving four identical cubes compared to traditional Gradient Descent (GD), and what factors contribute to this difference?

In Scenario 1, where four identical cubes are initially arranged in a non-optimal configuration, the Steepest Perturbed Gradient Descent (SPGD) method outperforms traditional Gradient Descent (GD) by successfully aligning the cubes into a compact global optimum. This configuration is challenging due to collision constraints, which can trap standard gradient-based methods in suboptimal local minima.
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Unlike GD, which struggles to navigate these constraints and often converges prematurely, SPGD introduces controlled perturbations that allow the optimization process to escape local minima, thereby achieving a more optimal configuration. The final alignment achieved by SPGD effectively fulfills the collision constraints while minimizing the objective function, demonstrating its robustness in overcoming complex landscape challenges.
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As seen in the comparative images, SPGD not only reaches a solution that respects spatial constraints but also achieves a convergence closer to the theoretical global optimum, highlighting the method’s advantage in navigating intricate constraint scenarios.


Question: In the context of open-set object detection using OW-DETR, how does the OW-DETR$^{++}$ model utilize DINOv2 and clustering methods to improve unknown object pseudo-labeling?

OW-DETR$^{++}$ enhances pseudo-labeling for unknown objects by integrating DINOv2’s activation maps and applying clustering techniques to filter and segment the image effectively. First, each input image is processed through the DINOv2 backbone to obtain a feature map, with DBSCAN used to distinguish between foreground and background by identifying and filtering out the largest cluster, which typically represents the background. To further refine object regions, agglomerative clustering is applied on the feature space, creating semantically coherent clusters.

Each cluster undergoes additional processing: DINOv2-derived attention maps are normalized and averaged, with clusters maintaining high average activation values identified as containing potential objects. These clusters are then filtered, with spatially isolated instances surrounded by bounding boxes. Afterward, Non-Maximum Suppression (NMS) removes overlapping regions, ensuring a clear selection of pseudo-labeled unknown objects.
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Question: How does the OW-DETR$^{++}$ model compare to OpenDet in terms of handling unknown object detection and localization, particularly in the OpenImagesRoad benchmark?

The OW-DETR$^{++}$ model excels in object localization and high-granularity classification, outperforming OpenDet in $AP_{all}$ and $AP_{sc}$ metrics. This advantage suggests that OW-DETR$^{++}$, with its pseudo-labeling approach, effectively identifies object locations with detailed classifications. However, OpenDet, based on contrastive learning, surpasses OW-DETR$^{++}$ in $AP_{u}$, indicating a stronger ability to detect and classify unknown objects distinctly. This contrast is visible in cases where OpenDet frequently avoids double detections, identifying unknown objects without mistaking them for known classes.
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OW-DETR$^{++}$’s reliance on pseudo-labeling enables it to handle localization but struggles with nuanced differentiation between unknown and known objects, an area where OpenDet shows proficiency. Depending on the focus—precise unknown object detection or robust localization—the choice between OpenDet and OW-DETR$^{++}$ can be made accordingly.


Question: How does ClusterGraph handle non-Euclidean data structure visualization compared to standard dimensionality reduction techniques like UMAP, t-SNE, and PHATE?

ClusterGraph visualizes global data structure effectively by preserving inter-cluster distances without the distortions common in Euclidean embeddings. For instance, in a dataset with four clusters—0, 1, 2, and 3—where each cluster has specific pairwise distances, ClusterGraph encodes these distances as edge labels, retaining the exact spatial relationships. This approach contrasts with UMAP, t-SNE, and PHATE, where the projection into Euclidean space alters these distances.

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UMAP fails to capture the overall layout accurately, while t-SNE and PHATE introduce further distortions, making ClusterGraph a more reliable tool for preserving and visualizing non-Euclidean structures in high-dimensional data. This characteristic makes ClusterGraph especially valuable for datasets that do not conform to low-dimensional Euclidean spaces.


Question: How does ClusterGraph use the k-nearest neighbor (k-NN) graph to estimate intrinsic distances in high-dimensional data, and what challenges might arise from disconnected k-NN graphs?

ClusterGraph utilizes the k-nearest neighbor (k-NN) graph to approximate intrinsic distances in high-dimensional datasets by connecting each point to its k-nearest neighbors. This approach helps estimate geodesic distances, which are often challenging to calculate directly. However, a k-NN graph may sometimes be disconnected, especially if the parameter \( k \) is set too low or the data originates from distinct substructures. In cases where the data is sampled from two or more separate distributions, even with a high \( k \), the k-NN graph may still contain isolated components, representing disconnected regions within the data.

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To address this, ClusterGraph handles each connected component separately, producing a distinct ClusterGraph for each component. This segmentation allows the method to preserve data structure more accurately without being limited by disconnected regions in the initial k-NN graph.


Question: In the paper "Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion", what is the second stage of the method proposed? What kind of functions does this stage mainly accomplish?

The second stage of the method is the graph construction and normalization module. <PIC> The main function of this stage is to transform the function code into a code semantic graph to capture the rich control-flow and data-flow semantics in the code.


Question: The paper, "Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion", proposes a new network structure to fuse global and local features and output interpretable feature weights. What mechanisms are included in this network structure?

The attentive multi-encoder network proposed in the paper includes a self-attention mechanism that captures the internal relationships and importance weights within each feature type, and a cross-attention mechanism that enables dynamic interaction between global graph features and local expert patterns, allowing the model to effectively combine different information sources and generate interpretable feature weights for vulnerability detection. <PIC>


Question: What are the key components of iAudit's overall architecture and what are their responsibilities?

The architecture of iAudit consists of four key components, each with specific responsibilities.  <PIC> 1) Detector serves as the core component, using fine-tuned LLM to detect vulnerabilities in code, functioning similarly to human hacker intuition; 2) Reasoner, after Detector's initial judgment, is responsible for in-depth analysis of potential vulnerability causes, forming a two-stage fine-tuning mechanism with Detector; 3) Ranker acts as an evaluation component, responsible for assessing various possible vulnerability causes and selecting the most appropriate explanation; 4) Critic serves as the final judge, responsible for reviewing Ranker's judgment and deciding whether to accept, rerank, or merge multiple explanations. These four components work collaboratively to form a complete smart contract auditing system.


Question: What strategy does iAudit use to improve detection performance and how effective is it?

iAudit improves detection performance through a dual strategy approach: multiple prompts and majority voting. <PIC> The multiple prompts strategy serves two key purposes: expanding the training dataset volume and reducing single prompt bias. Combined with majority voting, this approach significantly enhances result reliability. The effectiveness is demonstrated by iAudit achieving superior performance metrics, with the highest scores among all methods: an F1 score of 0.9121, Recall of 0.8934, and Accuracy of 0.9111.


Question: What is the overall architecture of GraphGPT?

GraphGPT's architecture consists of three main components. <PIC> 1.A text-graph grounding paradigm that aligns graph structures with natural language space. 2.A dual-stage graph instruction tuning paradigm that includes  self-supervised instruction tuning and task-specific instruction tuning. 3. A Chain-of-Thought (COT) distillation component that enhances step-by-step reasoning abilities


Question: How is text-graph structure alignment achieved in GraphGPT?

GraphGPT processes graph structure and text information through graph encoder and text encoder respectively, then uses contrastive learning to achieve alignment between the two modalities. <PIC>


Question: What strategy does Clear use to collect training data?

Clear collects training data by using a sampling strategy that creates pairs of contracts based on two key relationships: vulnerable-vulnerable (V-V) and vulnerable-non-vulnerable (V-N). <PIC> Specifically, it first extracts all vulnerable contracts into a POS set and pairs each contract in the original dataset with a randomly selected contract from this POS set. The strategy deliberately avoids N-N relationships, and the resulting contract pairs are assigned correlation labels to guide the CL model's training process.


Question: How does Clear's contrastive learning affect model performance?

The CL module enhances performance by facilitating the convergence of dispersed vulnerability samples in the feature space, which increases the proximity between similar vulnerability samples. <PIC> Through a unique sampling strategy, it strengthens the correlation among samples within the same vulnerability category and promotes their clustering behavior. This improved clustering enables the model to more effectively identify and discover potential smart contract vulnerabilities, resulting in significantly enhanced detection performance.


Question: How does BAClassifier achieve Bitcoin address behavior classification?

BAClassifier achieves Bitcoin address behavior classification through three modules. <PIC> first, constructing a transaction graph for each address; then, performing graph representation learning using a graph neural network; finally, conducting address-level behavior classification. Specifically, it first builds a chronological transaction graph to reflect each address's behavior, then employs a graph neural network to learn graph representations and generate embeddings, and finally aggregates these embeddings to produce address-level representations for classification predictions.


Question: What is the purpose and process of Single-Transaction Address Compression?

the single-transaction address compression technique mainly consists of two key steps: First, it identifies and merges address nodes that are connected to only one transaction, compressing them into an entity called a "single-transaction hyper node". Second, to preserve the transaction information of these merged addresses, the system uses Statistical Feature Extraction (SFE) method to calculate and retain the transfer features of these addresses, which ultimately become the attributes of the single-transaction hyper node in the graph. <PIC> Through this approach, the complexity of the graph can be significantly reduced while retaining critical transaction information.