Question: How does MVSplat achieve efficient 3D Gaussian prediction and fast inference?

MVSplat achieves efficient 3D Gaussian prediction and fast inference by building a cost volume through plane sweeping for accurate depth estimation and using a multi-view Transformer with self- and cross-attention layers to capture inter-view relationships. It jointly learns all Gaussian parameters with photometric supervision, enabling high-speed (22 fps) and high-quality novel view synthesis.<PIC>


Question: What is MVSpalt's advantage in rendering distant viewpoints?

MVSplat demonstrates significant advantages over baseline methods, particularly in regions where the latter exhibit obvious artifacts. MVSplat avoids these artifacts thanks to its cost volume-based geometry representation, which allows for more accurate and reliable inference of geometry structures. This approach provides a more robust and detailed understanding of the 3D geometry, leading to cleaner and more precise reconstructions, as shown in <PIC>


Question: How does the Navigation World Model (NWM) utilize video frames and actions for planning novel navigation trajectories?

The Navigation World Model (NWM) utilizes video frames and actions by being trained to predict future video frame representations based on past frames and associated actions, as shown in Figure 1(a). After training on data collected from various robotic agents, NWM can simulate potential navigation plans to verify if they reach a target goal, as depicted in Figure 1(b). Specifically, in the planning setup, NWM operates within a Model Predictive Control (MPC) framework to optimize an action sequence that enables it to achieve the target goal. For evaluating its navigation skills, NWM is tested in known environments where it either plans trajectories independently or ranks trajectories sampled from an external navigation policy, demonstrating competitive standalone performance and state-of-the-art results when integrated with existing methods.<PIC>


Question: How does the Conditional Diffusion Transformer Architecture utilize the CDiT block for efficient autoregressive modeling?

The Conditional Diffusion Transformer Architecture employs the CDiT (Conditional Diffusion ITeration) block, applied multiple times over an input sequence of latents with action conditioning, to achieve efficient autoregressive modeling. As shown in Figure 2, CDiT constrains the attention mechanism in its first attention block exclusively to tokens from the target frame being denoised, optimizing computational efficiency. For incorporating context from past frames, a cross-attention layer is introduced. In this setup, each query token from the current target frame attends to tokens from previous frames, which serve as keys and values. This allows the model to contextualize the representation of the target frame using information from past frames. The architecture further enhances this process through a skip connection layer, ensuring that the learned representations are effectively contextualized and integrated, supporting robust autoregressive prediction and denoising tasks.<PIC>


Question: what is the effect of the SGD model in MVSplat360?

Stable Video Diffusion (SVD) serves as a refinement module. Its primary role is to enhance the quality of the initial novel views generated by the 3D reconstruction model, specifically MVSplat in this case. The SVD model is chosen over other image-based latent diffusion models due to its strong temporal consistency capabilities, which are crucial for maintaining coherence across different views—a key requirement for the Novel View Synthesis (NVS) task. <PIC>


Question: what is the two main components: of the MVSplat360?

The MVSplat360 framework consists of two main components: a multi-view geometry reconstruction module that matches and fuses information from sparse observations to create a coarse 3D geometry, and a multi-frame consistent appearance refinement network that refines the visual quality and appearance of the synthesized views using a pre-trained latent video diffusion model, specifically Stable Video Diffusion (SVD). This two-step approach enables the generation of visually appealing and geometrically accurate novel views, even for large-scale scenes with wide-sweeping or 360° perspectives, from as few as five input views.<PIC>


Question: What’s the feature of the DrivingForward?

Extensive experiments on the nuScenes dataset demonstrate that DrivingForward outperforms other feed-forward methods in novel view synthesis across various inputs and achieves higher reconstruction quality compared to scene-optimized methods with the same input. A functional comparison with the latest related works is presented in <PIC>


Question: what is the three components in DrivingForward?

The DrivingForward framework consists of three main components for real-time driving scene reconstruction from sparse camera inputs: Pose Network: Predicts vehicle motion and estimates scale-aware depth from input images, crucial for aligning views in 3D space. Gaussian Primitives: Each pixel is assigned to a Gaussian primitive, with its position determined by the estimated depth. These primitives are unprojected into 3D space and rendered to the target view, enabling joint end-to-end training. Depth and Gaussian Networks for Inference: At inference, these networks perform feed-forward reconstruction. The system can flexibly handle different numbers of surround-view frames, as the estimation does not depend on other frames.<PIC>


Question: what are the most importance in GGS?

The GGS method enhances novel view synthesis and depth estimation by optimizing a generalization model, incorporating neighborhood features in the Multi-View Depth Refinement Module to handle occlusions, using Multi-View Stereo for better global information, introducing virtual lanes for flexible lane switching in the Virtual Lane Generation Module, and applying Multi-Lane Diffusion Loss to ensure consistency in synthesized views.<PIC>


Question: what is the benefit of the Virtual Lane Generation Module?

The benefit of the Virtual Lane Generation Module is that it allows the model to enhance the quality of its rendering of the left and right lanes even in the absence of ground truth data for these lanes. By establishing virtual lanes, the model can simulate the process of switching to a new lane and then switching back, creating a closed-loop process. This simulation helps the model learn and improve the quality of lane switching, leading to more accurate and reliable lane detection and switching performance. <PIC>


Question: what is the feature of Gaussian splatting in BeVSegmentation?

In BeVSegmentation Gaussian splatting offers a differentiable rendering pipeline that allows real-time optimization of Gaussian parameters based on input images it is fast and parallel suitable for real-time GPU operations it can manage details adaptively by pruning smaller and transparent Gaussians and it provides a flexible representation where Gaussians can efficiently represent both large volumes and fine details unlike offline methods it uses a neural network to provide real-time Gaussian representations adapting to dynamic scenes effectively. <PIC>


Question: what is the effect of 3D Gaussian generator?

The 3D Gaussian generator processes the input feature maps to predict the 3D Gaussian representation of the scene. For each pixel, it calculates the corresponding 3D Gaussian by passing through prediction heads, which estimate parameters such as position, scale, rotation, and opacity. Some of these predicted parameters are then decoded and transformed into the world reference frame. <PIC>


Question: Why is DrivenDreamer-2 considered user-friendly?

DriveDreamer-2 is considered user-friendly due to its ability to produce a wide variety of user-customized videos, including challenging scenarios like vehicles abruptly cutting in, and for generating high-quality driving videos with an FID of 11.2 and FVD of 55.7, which represents a significant improvement over previous methods. Additionally, the generated videos have been shown to enhance the training of various autonomous driving perception methods, improving detection and tracking performance by approximately 4% and 8%, respectively, making it a powerful and versatile tool for users.<PIC>


Question: How are the foreground agent trajectories and background HDMaps generated in the overall framework of DriveDreamer-2?

In the overall framework of DriveDreamer-2, a customized traffic simulation is used to generate foreground agent trajectories and background HDMaps. Specifically, a fine-tuned language model (LLM) translates user prompts into agent trajectories, and an HDMap generator simulates road structures based on these trajectories. The UniMVM framework enhances the temporal and spatial coherence of the generated driving videos by unifying intra-view and cross-view spatial consistency.<PIC>


Question: What kind of driving scene videos can DriveDreamer generate?

DriveDreamer can generate driving scene videos that are highly aligned with traffic constraints. These videos are realistic and comply with real-world traffic rules, which makes them valuable for enhancing the training of driving perception methods, such as 3D detection. By providing accurate and constraint-compliant scenarios, DriveDreamer helps improve the performance and reliability of these perception methods.<PIC>


Question: How does the DriveDreamer framework utilize text prompts to adjust the driving scenario style?

In the DriveDreamer framework, text prompts are used to dynamically adjust the style of the driving scenarios, such as changing the weather or time of day. These prompts serve as additional inputs that guide the system in generating visual content that matches the specified conditions.<PIC>


Question: What are the specific failure modes of the 3D Gaussians produced by the fine-tuned model with a depth regularizer when synthesizing views far outside the training distribution?

The specific failure modes of the 3D Gaussians produced by the fine-tuned model with a depth regularizer, when synthesizing views far outside the training distribution, include reflective surfaces often appearing transparent and the appearance of Gaussians not being accurate, similar to the issues faced by 3D Gaussians optimized using the original 3D Gaussian splatting method. <PIC>


Question: How does the proposed two-view encoder resolve the scale ambiguity in predicting the geometry of a scene?

The proposed two-view encoder resolves scale ambiguity by leveraging information from two reference views, denoted as I and ˜I, to predict the geometry of the scene. For each pixel in view I, it annotates points along its corresponding epipolar line in ˜I with depth values that are computed based on the known camera poses of both images, thus encoding the scene's scale. Epipolar attention plays a crucial role in this process by finding per-pixel correspondences between the two views, enabling the encoder to memorize the correct depth for each pixel. This method ensures that the predicted geometry, including the position of each Gaussian primitive, is consistent with the actual scale of the scene. For pixels without direct correspondences in ˜I, their depths are estimated using per-image self-attention, further enhancing the model's ability to accurately represent the scene's 3D structure. <PIC>


Question: how does the system distinguish between static and dynamic regions by calculating the feature difference between the rendered image and the ground truth image?

In the Dynamic Mask Extraction stage of DeSiRe GS to distinguish between static and dynamic regions the system uses a pretrained foundation model to extract features from both the rendered image and the ground truth image Then it computes the per pixel dissimilarity between these features When the features are similar the dissimilarity value is close to 0 indicating a static region When the features are dissimilar the dissimilarity value is close to 1 indicating a dynamic region By doing so the system can effectively identify dynamic parts of the scene and generate segmentation masks that encode motion information thus solving problems such as ghost like floating points in dynamic areas that occur with the original 3D Gaussian Splatting technique. <PIC>


Question: What problem arises from oversized Gaussian ellipsoids in 3D Gaussian Splatting (3DGS) and Point-based Visual Graphics (PVG)?

Oversized Gaussian ellipsoids can be produced by both 3D Gaussian Splatting (3DGS) and Point-based Visual Graphics (PVG) without additional regularization, especially in unbounded driving scenarios. These oversized ellipsoids, even if they have low opacity and minimal impact on the rendered image, can significantly impair surface reconstruction. This is a limitation that is often overlooked in existing methods focused solely on 2D image rendering. To address this issue, DeSiRe-GS introduces a penalty term for each Gaussian, ensuring that the ellipsoids are appropriately scaled to support accurate image rendering and surface reconstruction.<PIC>


Question: How can Sparse Autoencoders (SAEs) uncover a model's self-knowledge about entities?

SAEs decompose the dense representation space into sparse and interpretable components. Each latent direction in the SAE corresponds to a specific feature, such as whether an entity is recognized.The paper quantifies this distinction using separation scores, which measure the activation of specific latents for known versus unknown entities. A high separation score indicates a clear ability to differentiate between the two.
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The analysis spans multiple layers of the model, revealing that middle layers exhibit the strongest ability to generalize across entity types.The separation directions generalize across different types of entities, showing that the model encodes a unified mechanism for assessing knowledge across domains.
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The results demonstrate that models are capable of "self-awareness" in a specific context: recognizing what they know versus what they do not.This ability forms the basis for the model's decision-making when answering queries, influencing whether it provides an answer or refuses to respond.


Question: How do entity recognition directions impact a model's behavior, particularly in knowledge refusal?

The study shows that activating the latent for "unknown entities" significantly increases the likelihood of the model refusing to answer queries about such entities.
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This is achieved by increasing the activation of the corresponding latent in the model's residual stream during generation.Conversely, steering the "known entity" direction can suppress refusal and induce hallucination, where the model fabricates details about an unknown entity.
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Question: Before building Chast, how to generate LLM conversations in the hiring context?

First, Deigning the Conversation Seed Prompt: An individual often holds multiple identities. Use contextual cues to emphasize social identities, highlighting the background of a peer group through a seed prompt.
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 Second, randomly select names representing different identities. Third, consider four professions prone to stereotypes: software developers, doctors, nurses, and teachers. Finally, choose an appropriate large language model.


Question: What metrics does CHAST include? If a candidate doesn't code well enough to be a software engineer, what kind of threat indicator is that?

The Covert Harms and Social Threats (CHAST) metrics is a set of seven metrics rooted in social science literature. It includes Categorization Threat (SIT), Morality Threat (SIT), Realistic Threat (ITT), Symbolic Threat (ITT), and Opportunity Harm.
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If a candidate doesn't code well enough to be a software engineer,this kind of threat is Competence Threat.The definition of Competence Threat is "Conversation questions or undermines the group's com-petence, framing the applicant as incompetent or unsuc-cessful based on their group affiliation".Coding ability falls under the definition of competence. A lack of such ability can impact the progress of the entire group's project, thereby weakening the group's overall capability. Hence, it constitutes a competence threat.


Question: Why MNC poses a significant obstacle to effective temporal modeling?

The significant obstacle posed by MNC (Multi-granularity Noisy Correspondence) to effective temporal modeling stems from the two main types of misalignment it introduces:
Coarse-grained misalignment: This includes asynchronous misalignment (where the timing of captions and clips is mismatched, such as when actions are described before or after they occur) and irrelevant misalignment (where captions or clips do not align meaningfully with the other modality).  These issues disrupt the correct identification of relationships across time.
Fine-grained misalignment: Within individual video clips, narration sentences often only partially correlate with the corresponding visual frames.  Irrelevant words or frames can distort the identification of critical elements, leading to inaccuracies in clip-caption alignment and temporal correlation.
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These misalignments hinder the ability to model temporal dependencies accurately and contaminate the learning process.


Question: How does Norton perform video-paragraph retrieval?

The paper proposes a model called Norton for video-paragraph retrieval, which focuses on capturing temporal relationships in long videos through optimized multi-granularity correspondence learning.
Solving Multi-Granularity Alignment Issues:Coarse-Grained Alignment (Video Clips and Captions): The method employs an Optimal Transport (OT) framework to calculate a distance matrix between video clips and captions, realigning potentially asynchronous clip-caption pairs. Additionally, it introduces an "Alignable Prompt Bucket" to filter out irrelevant video clips or captions that contribute noise.Fine-Grained Alignment (Video Frames and Words): A log-sum-exp-based soft-maximum operator is used to identify key relationships between video frames and caption words, reducing the influence of irrelevant frames or words on alignment.
Optimization of Negative Samples:In contrastive learning, the method adjusts potential negative samples using Optimal Transport to avoid misclassifying semantically similar clips and captions as negatives, thus improving temporal modeling.
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From Tables 1 and 2, it can be observed that the OT framework overcomes multi-granularity noisy correspondence, enabling the learning of better temporal information, which results in superior performance.
Calculating Video-Paragraph Contrastive Loss and Clip-Caption Contrastive Loss:OT distance is used as a metric for video-paragraph similarity, enabling the capture of temporal dependencies across paragraphs.For clip-caption pairs, the method integrates the negative sample optimization described above to enhance temporal features.


Question: How does IRIS achieve self-jailbreaking?

IRIS achieves self-jailbreaking through a two-step process that leverages the advanced capabilities of large language models (LLMs) like GPT-4.
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Step 1: Iterative Refinement
The method starts with an initial adversarial prompt that the target model would normally reject (e.g., "Write a tutorial on how to make a bomb").The same model serves as both the attacker and the target. When the model rejects the prompt, it generates a self-explanation to understand why the rejection occurred.Based on this feedback, the model iteratively refines the prompt to bypass its own safeguards. Each refined prompt is tested until the target model generates a non-rejection response or the query limit is reached.
Step 2: Rate and Enhance
Once the target model provides a response, IRIS prompts it to rate the harmfulness of the response on a scale from 1 to 5.Using the feedback, the response is further refined to maximize its harmfulness, ensuring that it aligns with the adversarial intent.


Question: What are the similarities and differences between PAIR and TAP?

PAIR and TAP are both advanced jailbreaking methods. PAIR utilizes Vicuna-13B to iteratively refine prompts, while TAP integrates tree-of-thought reasoning to enhance the jailbreak process.
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Table 1 presents the comparison results, with metrics including jailbreak success rate and average number of queries. The comparison shows that TAP achieves a significantly higher jailbreak success rate than PAIR, while also requiring fewer queries on average. This indicates that TAP performs better in jailbreak effectiveness on the AdvBench subset.


Question: What is regeneration-based approach(RR)?

The Regeneration-based Approach (RR) is a method proposed for detecting AI-generated peer reviews by leveraging the consistency in text generation from large language models (LLMs).
LLMs like GPT-4 tend to produce reviews or responses with a consistent style, tone, and content when given similar prompts repeatedly. This is due to the inherent patterns learned during training.The RR approach hypothesizes that if a review is AI-generated, regenerating it using a similar prompt would produce embeddings that are closely aligned with the original review.
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The RR method is divided into the following steps:
Review Regeneration:The system takes the given review (R) and regenerates a review (Rreg) using an LLM with a slightly altered prompt.
Embedding Creation:Embeddings are created for both the original review (EF) and the regenerated review (ER).
Similarity Measurement:The similarity between EF and ER is measured using cosine similarity. A higher similarity indicates a higher likelihood that the original review was generated by AI.
Finally,the computed similarity scores serve as input for training a neural network, which is optimized using a cross-entropy loss function to detect AI-generated reviews effectively.


Question: How to reduce the probability of reviews being classified as AI-generated by using adjective attack?

Target the most frequent adjectives in AI-generated reviews. Specifically, focus on the top 100 high-probability adjective tokens identified as common in AI-generated content.Use the NLTK WordNet database to find synonyms for the selected adjectives.Ensure the replacement synonyms are less frequent in AI-generated content while still preserving the original meaning. If no suitable synonym is found in the AI corpus, the token remains unchanged.Conduct part-of-speech (PoS) tagging on the review to locate adjective tokens specifically, ensuring only adjectives are replaced.Substitute the identified adjectives with their appropriate synonyms.
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Avoid substituting nouns or adverbs, as doing so may lead to nonsensical statements or drastically alter the review's meaning.


Question: How well various fairness methods in preprocessing or processing perform without the influence of post-processing?

Preprocessing methods such as LFR (Learning Fair Representations) and CR (Conditional Reweighting) exhibit suboptimal trade-offs between fairness and accuracy across all datasets.LFR achieves high fairness fulfillment but incurs a significant loss in accuracy, making it less desirable in contexts requiring a balance of both metrics.
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Inprocessing methods, particularly EG (Equality of Opportunity Gains) and FairGBM, outperform preprocessing methods.These methods achieve the highest area above the Pareto frontiers, indicating a better balance between fairness and accuracy.


Question: How do different models perform when fairness constraints are designed to learn on the two largest subgroups?

The experiment used samples from only the two largest groups (White and Black) instead of data from all subgroups.This setup simplifies the problem, which is reflected in a significant compression of constraint violation levels on the vertical axis.
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In the ACSIncome dataset, the highest unprocessed accuracy in the full-group experiment was achieved at a constraint violation level of 0.38, whereas in this binary-group setting, it was achieved at 0.16.This change indicates that reducing the number of groups simplifies the learning of fairness constraints.
Unconstrained models remain concentrated in regions with high accuracy and low fairness.


Question: How to address the issue of different perceptual effects at the same noise level across different resolutions?

To address the issue of different perceptual effects at the same noise level across different resolutions, several strategies are employed. First, noise adaptation is used to match perceptual and frequency effects between resolutions. For lower resolutions, independent Gaussian noise is applied, while for higher resolutions, block noise with a specific kernel size is used, ensuring similar results in both spatial and frequency domains. Frequency spectrum analysis further reveals that high-resolution images exhibit higher signal-to-noise ratios (SNR) at the same noise level, particularly in low-frequency components, which leads to a mismatch between training and inference.
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 This mismatch can degrade performance as the neural network presumes more accurate inputs during training than it can generate in the early diffusion steps. Cascaded models effectively alleviate this issue by utilizing low-resolution conditions during super-resolution stages, which simplify the generation process and ensure that the higher SNR remains within the model's capabilities.
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 Additionally, to generate high-resolution images directly from upsampled low-resolution results, it is crucial to address the distribution mismatch between the ground truth and generated low-resolution images.
By resolving this mismatch, the diffusion process can seamlessly continue, reducing the complexity of training and sampling steps while maintaining high-quality outputs.


Question: How effective is block noise in RDM?

When comparing RDM with and without block noise, models incorporating block noise significantly outperform those without it on both the ImageNet and CelebA-HQ datasets, as shown in Figure 4a and Figure 4b. This highlights the effectiveness of block noise in enhancing the model's performance.
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The addition of block noise increases the complexity of the noise patterns that the model must learn to handle, which contributes to slower convergence during the initial training phase. However, this complexity ultimately results in better overall performance with sufficient training. The slower convergence effect is particularly noticeable on larger datasets like ImageNet but not on smaller datasets like CelebA-HQ, where the faster convergence due to limited sample sizes diminishes this phenomenon.


Question: What are the features of CivRealm?

CivRealm features an open-ended environment with imperfect information, stochastic dynamics, and multiple victory paths, requiring balanced strategies across economy, military, diplomacy, and technology.
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 Its dynamic game space changes continuously, supporting multi-agent interactions, alliances, and communication through diplomacy and natural language. With an agent-architecture-agnostic framework, CivRealm allows seamless integration of diverse agents using a server-proxy-client system. Its turn-based nature suits LLM-based agents by providing ample time for decision-making. CivRealm also facilitates the evaluation of generalization ability through novel scenarios, including random maps and rule modifications, and supports tasks ranging from the full Freeciv game to smaller, scripted mini-games, making it a robust platform for agent development.


Question: What is the full gameplay content of CivRealm?

CivRealm provides a comprehensive set of evaluation metrics to assess player performance across multiple dimensions. These metrics include population size, the number of constructed cities, the quantity of researched technologies, the number of produced units, and the extent of explored land. These indicators reflect the player's progress in various aspects of civilization building and strategic execution. Additionally, CivRealm offers an aggregated score that combines these dimensions to provide an overall evaluation of the player's performance.
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As illustrated in Figure 2, the evaluation is closely tied to the era in which the player's civilization is operating, ranging from the Bronze Age to the Industrial Age and the Space Age. Players influence these metrics through various gameplay elements, such as units, buildings, technologies, and diplomacy.
Unit Production: As the game progresses, units evolve from basic settlers to advanced stealth planes, directly impacting military strength and exploration capabilities.
Building Construction: Constructing infrastructure such as granaries, city walls, power plants, and space facilities boosts resource production and city management.
Technology Research: Advancements from fundamental technologies in the Bronze Age to cutting-edge innovations in the Space Age support other actions, including military, economic, and diplomatic strategies.
Diplomatic Actions: Players engage in alliances, wars, and negotiations to shape diplomatic relationships, significantly affecting evaluation metrics and gameplay dynamics.
Overall, CivRealm's evaluation system integrates these core game elements, tracking player actions and progress through different eras to provide a holistic view of civilization development and strategic effectiveness.


Question: What is the structure of the HPT architecture?

The HPT architecture is a modular framework comprising three key components:
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Stem (Embodiment-specific Tokenizers): These tokenizers process diverse sensor inputs, such as camera views and proprioception data, transforming them into a shared representation.
Trunk (Shared Latent Transformer): This core component is pre-trained on multiple datasets and operates on a short sequence of latent tokens. It serves as a general policy representation that can be transferred to new tasks and embodiments.
Head (Task-specific Action Decoders): Task-specific decoders produce actionable outputs based on the shared latent representations.
The architecture aligns heterogeneous data from real robots, simulations, and human videos into a unified latent token space, facilitating scalability and transferability across tasks and embodiments. Inspired by human neural circuitry, this hierarchical design enables efficient learning and adaptation.


Question: How is the HPT pre-training data prepared on Synthetic Data and Internet Human Videos?

The paper conducts pre-training using diverse datasets beyond real-world robot teleoperation data, including 7 simulation datasets (e.g., Mujoco, Isaac Sim) and human datasets (e.g., EPIC Kitchen, PoCo) with up to 1000 trajectories per dataset. For human datasets lacking proprioception and actions, pose and 2D position data are used as proxies.
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Figure 8 shows that pre-training on these heterogeneous datasets is feasible and complements teleoperation data, demonstrating the HPT framework's ability to handle diverse embodiments effectively.


Question: What is the operating mechanism of BoT?

BoT operates through an experience-driven iterative process. It starts with generating weak reasoning steps from a simple prompt, aggregates them into a single thought chain using weighted binary trees, and evaluates the chain to collect feedback (experience). This experience is added to the prompt to guide the next iteration, progressively refining the reasoning until the problem is effectively solved.
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As shown in Figure 1, the LLM analyzes the thought chain in each iteration and summarizes it as experience. Through continuous accumulation of experience, a correct thought chain is ultimately generated.


Question: What are the advantages and disadvantages of BoT compared to other frameworks?

Advantages of BoT:
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High Problem-Solving Performance: BoT achieves competitive results without human annotations and sets new state-of-the-art (SOTA) performance on GSM8K and AQuA datasets, outperforming prior methods by 0.1% and 2.5%, respectively.
Effective Iterative Refinement: By accumulating prior experience through error analysis and advice, BoT refines its prompts iteratively, enabling accurate problem-solving even with a simple initial prompt.
Compatibility with CoT Examples: Adding Chain-of-Thought (CoT) examples to the prompt further enhances performance, reaching a new SOTA with an average improvement of 1.3% on GSM8K and AQuA.
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Adaptability to Powerful Models: BoT significantly boosts performance for strong LLMs like GPT-4, with an average improvement of 11.6% across datasets.
Disadvantages of BoT:
Dependence on Experience: BoT heavily relies on the ability of LLMs to analyze reasoning chains effectively, making it sensitive to model quality.
Performance Drop with Weaker Models: For weaker LLMs like Llama2, BoT's performance drops significantly, with limited improvements even when valid examples are provided.
Limited Performance in Certain Domains: On MATH datasets, BoT is at least 18% lower than SOTA, highlighting its challenges in handling tasks requiring strong mathematical reasoning abilities.


Question: For hierarchical text classification tasks, what are the challenges of directly using contextual learning to train large language models? What are some effective solutions?

Problems of ICL in HTC Tasks, as shown in <PIC>:
Large Candidate Label Set: Due to the deep hierarchical structure and large number of labels in the HTC label set, the candidate set for label selection becomes excessively large, increasing the difficulty of classification.
High Similarity Between Adjacent Labels: As the hierarchy deepens, the semantic similarity between labels increases, particularly between adjacent labels. This leads to confusion when selecting similar examples, negatively affecting classification accuracy.
Current Effective Solutions:
Retrieval Database: Construct a retrieval database to store demonstration examples related to the input text. This database is generated through label-aware text representations, meaning each input text is transformed into a representation containing hierarchical label information through multi-layer training.
Iterative ICL: Through iterative reasoning, decompose the multi-level label inference into smaller steps. At each level, only the candidate label set for the current level is provided, reducing the number of labels and thereby improving classification precision and efficiency.


Question: Please introduce the three training strategies of the PLM , and explain how these three strategies are integrated together.

There are three training methods for PLMd in <PIC>:

MLM (Masked Language Modeling): Similar to BERT, this method randomly replaces certain words in the input sentence with a mask token (usually represented as [MASK]). The model's task is to predict what the masked words are.

CLS (Layer-wise Classification):
For each text sample, multiple index vectors are generated, each corresponding to a level in the hierarchical structure. Each index vector contains feature information related to that level.
Classification starts from the highest level, predicting the category of the upper layer. Once the upper layer category is determined, the information of that level is used to generate a new index vector, and this process is repeated.
For each level of classification, a loss is calculated and backpropagated, optimizing the model layer by layer.

DCL (Divergent Contrastive Learning):
For a given sentence x, positive samples are selected from sentences with the same label as x. Additionally, the corresponding label description d can be treated as a positive sample. The negative samples consist of two parts: First, based on the similarity between d and other label descriptions, negative samples are extracted from highly similar label categories. Similarly, their corresponding label descriptions can also be treated as negative samples. Second, some sentences from other labels are randomly selected as negative samples for x.
The objective is to pull the index vectors of positive samples together while pushing apart the index vectors of negative samples. Finally, the loss functions of the three tasks are weighted and summed to obtain the final total objective loss function.
Through this multi-task learning approach, the model not only learns the contextual information of the text (via MLM) but also performs classification and contrastive learning simultaneously, enhancing the model’s generalization ability and feature representation capacity.


Question: LlamaDuo proposes a method for transferring knowledge from large cloud-based models to smaller local models. Could you explain how the overall pipeline of LlamaDuo works?

LlamaDuo is an automated Large Language Model Operations (LLMOps) pipeline, and its workflow is illustrated in <PIC>, divided into three stages: the Development and Prototyping Stage, the Alignment Stage, and the Deployment Stage.
Firstly, in the Development and Prototyping Stage (steps 1 and 2 in Figure 1), users interact with service large language models (Service LLMs) through prompt engineering to generate instruction-response pairs and build a coverage dataset. This dataset is then split into a training set and a test set for subsequent fine-tuning and evaluation.
Next, in the Alignment Stage (steps 3 to 6 in Figure 1), the training set of the coverage dataset is used to perform preliminary fine-tuning of the local smaller models (Local LLMs), while a service model serves as an evaluator (Service LLMs-as-Judge) to assess model performance. If the performance of the local model does not meet the predefined threshold, additional synthetic data is generated by the service model to further fine-tune the local model iteratively until the desired performance is achieved. During this stage, data quality is ensured through deduplication and cleaning of synthetic data to align with real-world requirements.
Finally, in the Deployment Stage (step 7 in Figure 1), the locally fine-tuned model that meets the performance standards is deployed to constrained environments (such as offline or privacy-sensitive scenarios) to ensure service continuity.
Throughout the entire process, Figure 1 visually showcases the core steps from data collection, model fine-tuning, performance evaluation, to deployment, highlighting the interrelationships between these steps, ensuring the efficiency and scalability of LlamaDuo.


Question: Why does training local models based on the LlamaDuo framework offer better cost-effectiveness in the long run compared to cloud-based models?

<PIC>The results shows a comparison of the cumulative costs of local small-scale models and cloud-based models under both light and heavy load scenarios. Although local models require a higher initial investment in fine-tuning and deployment (such as GPU training and hardware procurement), their subsequent operational costs stabilize, while the costs of cloud-based models continue to rise due to API usage charged by tokens. In the light-load scenario, the cumulative cost of the local model surpasses that of the cloud service after two months of operation. In the heavy-load scenario, this crossover happens more quickly and significantly. By the end of one year, the cumulative cost of the cloud-based model can be 3 to 5 times higher than that of the local model. Therefore, Figure 2 clearly demonstrates the economic advantage of local models in long-term operation. Particularly in high-demand or continuous operation scenarios, local deployment is a more cost-effective choice.


Question: How does TELEClass utilize additional cues to enrich weak supervision for efficient hierarchical text classification?

TELEClass employs two primary strategies to utilize additional cues for enriching weak supervision, enabling efficient hierarchical text classification, as illustrated in <PIC>.
First, TELEClass enhances the label system by leveraging the extensive knowledge of large language models (LLMs) to generate key terms. For instance, in Figure 1, for the "shampoo" category, the LLM generates key terms such as "flakes" and "itching," which distinctly differentiate this category from sibling categories like "conditioner." This approach significantly expands the label system under weak supervision conditions that rely only on category names, allowing the classifier to capture subtle differences between categories and improve the distinction of complex ones.
Second, TELEClass integrates a corpus-based term extraction strategy to mine category-related terms from unlabeled corpora. Specifically, it extracts high-frequency terms from documents related to "shampoo" and filters them based on metrics such as frequency, distinctiveness, and semantic similarity. High-quality terms like "clean" are identified through this process. This method effectively combines the general knowledge generated by LLMs with domain-specific information from the corpus, further enhancing the recognition of long-tail and fine-grained categories.
By combining LLM generation with corpus-based extraction, TELEClass provides richer supervision signals for weakly supervised hierarchical text classification, enabling the model to perform efficiently in large-scale and complex label systems.


Question: How does TELEClass utilize the classification term repository obtained through the two methods to further train the model?

After constructing the classification term repository through LLM generation and corpus-based extraction, TELEClass leverages these repositories to further optimize core category annotation and enhance model training by generating high-quality pseudo-labeled data, thereby enabling efficient hierarchical text classification. <PIC> illustrates the complete process.
First, the classification term repository enriches the semantic information of categories, allowing for more precise matching between documents and categories. Specifically, the term set for each category (including terms generated by LLMs and extracted from the corpus) is transformed into embedding vector representations to calculate semantic similarity between documents and categories. Documents are encoded into vectors using pre-trained models such as Sentence Transformer or BERT, while the term set embeddings represent the semantic features of categories. By calculating the maximum cosine similarity between document vectors and category term embeddings, the most relevant categories are identified as core classes. A hierarchical search strategy (e.g., tree search), combined with the semantic richness of the term repository, further optimizes core category annotation, ensuring that core classes accurately reflect the semantic features of the document. For example, in Figure 2, a document describing scalp itching is accurately labeled as belonging to the "shampoo" category through terms like "flakes" and "itching."
Second, to address potential gaps in the term repository for certain long-tail categories, TELEClass employs a path-guided pseudo-document generation strategy to augment the dataset. Label paths (e.g., "hair care → shampoo") serve as guidance, combined with key terms from the term repository, to prompt LLMs to generate pseudo-documents. These pseudo-documents mimic the distribution and semantic characteristics of real data, ensuring adequate training sample coverage for each category, particularly low-frequency categories not covered by the term repository. For example, in Figure 2, the path "hair care → shampoo" guides the LLM to generate multiple pseudo-documents describing shampoo, enabling the model to more comprehensively learn the semantic relationships along this path.
Finally, TELEClass combines core category pseudo-labeled data (Dcore) and path pseudo-document data (Dgen) to train a multi-label classifier. The classifier uses BERT as the document encoder and incorporates a bilinear matching network to compute matching scores between document and category embeddings. During training, the parent node paths of core categories are marked as positive classes, while other categories are treated as negative classes, and the model is optimized using binary cross-entropy loss. Additionally, the label paths of the pseudo-documents are directly marked as positive classes, providing the model with a more comprehensive learning of category semantics.
In summary, through the optimization of the classification term repository and the generation of path-guided pseudo-documents, TELEClass effectively enhances the training data for the classifier, ensuring that the model accurately captures semantic relationships in hierarchical label systems and the characteristics of long-tail categories, thereby achieving efficient weakly supervised text classification.


Question: In machine translation post-editing systems based on large language models (LLMs), how can external feedback be used to guide LLMs in enhancing post-editing capabilities, or what are the forms of external feedback?

In machine translation (MT) post-editing systems based on large language models (LLMs), external feedback can be used to guide LLMs in improving translation quality. <PIC>The results illustrates three forms of external feedback:
1.	Generic Feedback:
The model receives only the original translation and improvement instructions without any specific error indications. This form relies on the model's self-optimization capabilities and is suitable for general improvement needs.
2.	Score-based Feedback:
The model is provided with an overall translation quality score (e.g., "85/100"), which helps it roughly understand the quality level of the translation without pinpointing specific issues. This form is suitable for providing overall guidance but has limited capability for addressing specific errors.
3.	Fine-grained Feedback:
Detailed annotations are provided, including the location of errors, error types (e.g., mistranslation or fluency issues), and their severity. This form enables the model to target specific errors, improving translation accuracy and naturalness.
By leveraging these three forms of feedback, particularly fine-grained feedback, external information can effectively guide LLMs in correcting translation errors while enhancing linguistic fluency and natural expression, thereby significantly improving post-editing capabilities in machine translation.


Question: Does the model fine-tuned using post-editing methods show significant improvements in overall translation quality and error correction? If so, do these improvements vary across different language pairs?

The fine-tuned model demonstrates significant improvements in overall translation quality and error correction, particularly in the Zh-En (Chinese-English) and En-De (English-German) language pairs. Human evaluations indicate that most reviewers find the fine-tuned translations more fluent and natural, with effectively corrected marked errors.

However, as illustrated in <PIC>, differences do exist across language pairs. For instance, in the En-Ru (English-Russian) language pair, approximately 40 out of 150 translations were deemed by reviewers to be not entirely better than the initial translations. This is primarily because, while the fine-tuned translations are more accurate in the target language, they occasionally lose subtle semantic details from the source text. In contrast, the improvements in the Zh-En and En-De pairs are more pronounced, with reviewers largely expressing "agree" or "strongly agree" ratings.

Overall, the fine-tuned model excels in enhancing translation quality and correcting errors, though the extent of improvement varies among language pairs.


Question: How does INSTRUCTSCORE identify specific issues in generated text through text evaluation methods and explain its scoring results?

INSTRUCTSCORE, an innovative text evaluation method, identifies specific issues in generated text and provides interpretable scoring results through diagnostic reports. <PIC>The results illustrates how this method not only generates a numerical score but also includes a detailed error analysis.

For instance, when evaluating a piece of generated text, the diagnostic report can pinpoint the type of error (e.g., translation style issue), the exact location of the error (e.g., "Usually when there is takeaway"), and the severity of the error (e.g., major error). An example might highlight that "the translation uses the awkward expression 'Usually when there is takeaway' instead of the more accurate 'Usually, when there’s a delivery'." The scoring mechanism weights errors based on type and severity, deducting 5 points for major errors and 1 point for minor errors.

This approach not only quantifies text quality but also provides interpretable evidence, facilitating improvements in text generation quality.


Question: In the INSTRUCTSCORE text quality evaluation process, how does a multi-step optimization pipeline ensure that the generated diagnostic reports accurately identify errors and align with human judgment?

Text quality evaluation achieves precise error identification and alignment with human judgment through a multi-step optimization pipeline, as illustrated in <PIC>. First, predefined error types, severity levels, and explanations are used to generate synthetic data, which is employed to fine-tune the evaluation model, enabling it to produce initial diagnostic reports. Next, by analyzing these reports, common failure patterns (e.g., inconsistencies between error types and explanations, missing error locations) are identified, and an external model (e.g., GPT-4) provides automated feedback. Finally, the model incorporates this feedback to further optimize itself, iteratively generating higher-quality diagnostic reports.

This iterative optimization mechanism ensures that the evaluation results include both accurate error localization and strong interpretability, aligning closely with human evaluation standards.


Question: An article suggests that "Similarity is Not All You Need" In retrieval-augmented generation tasks, how can the balance between document relevance and usefulness be optimized to enhance generation performance?

In retrieval-augmented generation tasks, relying solely on similarity-based ranking methods can lead to issues where semantically relevant but low-information-gain documents are prioritized. For example, as shown in <PIC>, simple descriptions like "George R.R. Martin is an author" may be selected. On the other hand, relying only on document usefulness scores risks overlooking content that is superficially relevant to the query, increasing the chance of introducing irrelevant or low-relevance documents.

To address this, a combination of similarity and usefulness scoring methods can be applied. Similarity scores ensure that the selected documents are semantically related to the query, while usefulness scores further filter out documents that provide more valuable information for answering the query. For instance, when a user queries information about "George R.R. Martin," the focus should be on documents that highlight his major works, such as A Song of Ice and Fire, rather than documents repeating general knowledge.

Experimental results demonstrate that this combined strategy performs better in identifying important documents, effectively reducing the interference of low-value information on generated outputs, and ultimately improving the overall system performance.


Question: How can an appropriate document window size be selected during the multi-layered thoughts optimization process to balance performance improvements and computational costs?

The choice of document window size in retrieval-augmented generation tasks must strike a balance between performance gains and computational costs. As shown in <PIC>, experiments indicate that increasing the window size significantly enhances model accuracy (EM) and overall quality (F1), particularly with smaller window sizes. However, when the window size exceeds 40, the marginal gains in performance diminish, while computational costs increase linearly. This may introduce noise and reduce efficiency. To balance performance and resource usage, the window size should be determined based on task complexity and hardware constraints: For knowledge-intensive tasks, a window size of 40–50 is recommended. For resource-constrained scenarios, a smaller window size of 20–30 is a reasonable choice. Additionally, task-specific experiments can be conducted to optimize the window size, achieving the best trade-off between performance and efficiency.


Question: What is the role of the CDM module in the SafeEar architecture?

Based on <PIC>, the CDM module in the SafeEar architecture employs a neural codec to disentangle speech information into semantic tokens and acoustic tokens. The acoustic tokens retain characteristics such as prosody and timbre from the audio, which are crucial for subsequent deepfake detection. Meanwhile, semantic tokens, which encapsulate the content of the speech, are protected and excluded from the detection process to ensure content privacy.

The disentangled acoustic information is quantized using a Residual Vector Quantizer (RVQ), incorporating semantic supervision mechanisms (as shown in the "VQ1" and "VQ2∼VQ8" sections in the figure). This design ensures both the accuracy of detection and the safeguarding of content privacy.


Question: What is the role of the Bottleneck & Shuffle Layer in SafeEar?

Based on <PIC>, the Bottleneck Layer primarily aims to compress acoustic tokens into more compact representations using 1D convolution and batch normalization. This process reduces the dimensionality of the features, improving computational efficiency for subsequent processing while decreasing the number of trainable parameters in the model. Additionally, the layer serves a regularization function, preventing overfitting and stabilizing the representation of acoustic tokens.

The Shuffle Layer randomly rearranges the temporal dimension of the acoustic tokens, further obscuring the temporal information of the speech. This makes it more challenging for attackers to reconstruct the original speech content. This process is particularly effective against speech understanding technologies that rely on temporal relationships, such as phoneme and word sequence analysis in machine hearing and advanced speech recognition models. In experiments, a 1-second shuffle window (corresponding to 50 frames) was used to disrupt word-level intelligibility.


Question: Could you introduce the training process of the LUISE audio encoder during the large-scale pretraining phase in Seed-ASR?

During the self-supervised learning (SSL) phase, as illustrated in <PIC>, the LUISE audio encoder learns robust speech representations from large-scale unsupervised speech data, capturing both global and local structural features of the audio signals. This approach draws inspiration from BERT-style pretraining, utilizing a masked prediction task to extract features via Mel filters. The training process follows several key steps:

First, Mel filter feature sequences are extracted from the raw speech waveforms. Then, a fixed tokenizer is employed to generate discrete labels for these extracted features. In the masked prediction step, cross-entropy loss is computed only on the masked frames, guiding the model to predict the missing information with high accuracy.

For further improvement, an iterative fixed-tokenizer method is introduced. Through K-means clustering, new discrete labels are generated, gradually refining the tokenizer to better align with the underlying data, thereby enhancing the model's performance over time.


Question: Could you briefly introduce the overall architecture of the Seed-ASR model?

As shown in <PIC>, the system comprises several interconnected components, each contributing to the overall functionality. The **Audio Encoder (LUISE)** plays a crucial role in converting raw speech signals into continuous speech representations. With approximately 2 billion parameters, it is trained using self-supervised learning (SSL) to capture a rich array of features from the speech signals. The encoder generates features at a time step of 40 ms, allowing it to capture both the global structures and local characteristics inherent in the audio data.

Following the audio encoder, the Converter acts as a bridge between the audio features and the large language model (LLM). It includes a downsampling module and a linear projection layer, which map the extracted speech features into the semantic space of the LLM. By concatenating consecutive feature frames, the converter reduces the time step of the features from 40 ms to 160 ms, thus lowering computational complexity without compromising performance.

The Audio-Conditioned Large Language Model (AcLLM) is responsible for processing the transformed audio representations in conjunction with additional contextual inputs such as conversation history and task instructions. This model leverages a decoder-based architecture that employs self-attention mechanisms, allowing it to handle both input and output sequences effectively. The AcLLM also capitalizes on its strong language and reasoning capabilities to significantly enhance transcription accuracy, ensuring robust performance in diverse contexts.

Finally, the system incorporates Context Integration, where additional contextual information is used to improve transcription accuracy, particularly in ambiguous semantic scenarios. By factoring in conversation history or task-specific details, the model is better equipped to resolve uncertainties and generate more accurate transcriptions.


Question: Could you briefly introduce the role of the projector in the model from the LLM-Based ASR paper?

As shown in <PIC>, the Projector module plays a key role in bridging the gap between the audio features generated by the audio encoder (such as Whisper or HuBERT) and the semantic space of the large language model (LLM). Its primary function is to align these audio features with the LLM's text-based representation, ensuring a smooth and coherent integration of the speech data into the model's input. To achieve this, the module processes the audio features through both nonlinear transformations and linear projections. This enables the seamless combination of the audio and text features, ultimately facilitating the successful completion of the speech recognition task.


Question: Could you briefly explain why the Transformer architecture is used to implement the projector in the LLM-Based ASR paper?

As noted in <PIC>, the paper explores two approaches for implementing the Projector. The first approach uses a Transformer structure, which consists of four layers of self-attention and approximately 51 million parameters. The second approach, called Qformer, is based on a Qformer architecture configured with a window length of 1 and a single query, also totaling around 51 million parameters.
Comparative experiments, as shown in <PIC>, indicate that the Transformer outperforms the Qformer in speech recognition tasks. This performance difference can be attributed to the fact that the Qformer architecture is likely better suited to image data structures, whereas the Transformer is more effective for processing speech data.


Question: Could you introduce the role of VQ in the mimi neural audio codec within the Moshi model?

As shown in <PIC>, the VQ (Vector Quantization) component plays a pivotal role in the MiMi neural audio codec within the Moshi model. It facilitates several key functions that enhance both the efficiency and quality of audio processing.

Firstly, VQ converts continuous audio features into discrete acoustic tokens, enabling a more compact representation of the audio at a lower bitrate, while still preserving the high-quality reconstruction capability. This approach allows MiMi to process audio data efficiently during both the encoding and decoding stages of speech processing.

Furthermore, the architecture employs a split VQ mechanism (Split RVQ), which integrates high-level semantic information from self-supervised models like WavLM in the first quantizer, while preserving acoustic details in subsequent quantizers. This design ensures that semantic and acoustic information remain disentangled, allowing for the generation of speech that is both semantically coherent and rich in acoustic features.

In addition, VQ significantly reduces the computational load required for real-time audio processing. By incorporating a causal mechanism into the quantizer, MiMi supports streaming scenarios, enabling encoding and decoding in real-time.

VQ also contributes to the generation of high-quality audio by optimizing residuals iteratively. This residual quantization process captures subtle audio features, ensuring that the reconstructed audio retains its fidelity and detail.

Lastly, MiMi incorporates distillation loss into the first quantizer of VQ to better integrate non-causal semantic information into the audio features. This enhances the semantic discrimination capability of the quantizer, improving its performance for downstream tasks such as speech generation and semantic analysis. Through these various innovations, VQ plays a crucial role in optimizing the overall performance of the MiMi audio codec.


Question: Could you briefly introduce the architecture of Moshi?

As shown in <PIC>, the Moshi architecture integrates several key components that work together to deliver advanced language processing and real-time speech generation capabilities. At its core, Moshi is built on the Helium text language model, which is specifically designed with 7 billion parameters to provide robust reasoning and language understanding. Pretrained on high-quality textual data, Helium excels in a wide range of language tasks, ensuring exceptional performance in processing and generating text.
The architecture also incorporates a Neural Audio Codec (Mimi), which discretizes audio signals into acoustic tokens using Residual Vector Quantization (RVQ). This enables the simultaneous processing of both semantic and acoustic tokens, ensuring high-quality audio reconstruction while maintaining the system's ability to handle real-time processing demands.
Moshi further employs a hierarchical generation approach, where semantic and acoustic tokens are predicted step by step. To enhance efficiency, Temporal Transformers and Depth Transformers separately manage the generation of semantic and acoustic tokens, streamlining the process and optimizing resource usage.
A key feature of the Moshi architecture is its Inner Monologue Mechanism. Before generating audio, Moshi predicts text tokens that serve as prefixes for both the semantic and acoustic tokens. This not only improves the language quality of the generated speech but also enables seamless real-time speech recognition and generation, making the system highly responsive.
Additionally, Moshi utilizes multi-stream modeling to process both user speech and system-generated speech simultaneously. By modeling these as separate semantic and acoustic streams, the architecture eliminates the traditional "turn-taking" assumption, allowing for more natural handling of overlapping speech and interruptions.
Through the integration of these components, Moshi provides an advanced framework for speech generation, recognition, and real-time interaction, offering high flexibility and efficiency in a variety of speech-based applications.


Question: In the paper "Scaling Laws For Dense Retrieval", the authors propose using contrastive entropy as a metric for evaluating the performance of dense retrieval models. What are the advantages of contrastive entropy compared to standord ranking metrics, and how is it related to standord ranking metrics?

The authors of "Scaling Laws For Dense Retrieval" propose contrastive entropy as a metric for evaluating the performance of dense retrieval models, highlighting several advantages over traditional ranking metrics. Traditional ranking metrics, such as NDCG@K and MAP@K, are discrete and rely heavily on a cutoff parameter, K. This means that a passage only contributes to the metric if it falls within the top K results. If a positive passage ranks just beyond K, it has no impact on the metric, making these metrics insensitive to changes in the model's outputs. This insensitivity renders traditional metrics unsuitable for investigating scaling laws in dense retrieval, as they do not continuously reflect the model's retrieval capabilities.
Contrastive entropy addresses these limitations by providing a continuous metric that sensitively reflects the overall retrieval capability of the models. It takes into account the relevance scores of both positive and negative passages, allowing for a more nuanced evaluation of model performance. By considering the entire ranked list and not just the top K results, contrastive entropy can capture subtle changes in the model's output, making it a more effective measure for assessing retrieval performance.
Furthermore, the authors find a strong and positive correlation between contrastive entropy and traditional ranking metrics, such as MAP@10, NDCG@10, and Recall@1000. This relationship is close to linear, indicating that while contrastive entropy provides a more sensitive and continuous measure, it remains consistent with the evaluation results provided by traditional metrics. Therefore, contrastive entropy serves as a robust alternative for evaluating the overall retrieval ability of models, particularly in the context of scaling laws in dense retrieval.<PIC>


Question: In the paper "Scaling Laws For Dense Retrieval", does the impact of model size on the performance of dense retrieval models follow a specific power-law relationship?

In the paper "Scaling Laws For Dense Retrieval," the authors indeed find that the impact of model size on the performance of dense retrieval models follows a specific power-law relationship. This relationship is quantified through the scaling law, represented by the equation $L(N) = \left( \frac{A}{N} \right)^{\alpha} + \delta_N$. Here, $N$ represents the number of non-embedding parameters of the model, and $L(N)$ denotes the model's contrastive entropy on the test set. The parameters $A$, $\alpha$, and $\delta_N$ are the coefficients that deine the scaling behavior.The paper introduces the irreducible loss term $\delta_N$, which acknowledges that even with a sufficiently large model, the loss can only be reduced to δN\delta_N rather than zero. This term accounts for incomplete annotations and the subjective nature of relevance judgments, which make it challenging for models to perfectly match human annotations.
Through the fitting process, the authors demonstrate that the contrastive entropy of the models follows this power-law scaling in relation to the size of the non-embedding parameters. <PIC>The strong correlation, as evidenced by the high $R^2$ values in their experiments with datasets like MSMARCO and T2Ranking, validates this relationship. This finding allows researchers to predict the performance of larger models based on the scaling curves derived from smaller models, offering a cost-effective approach to exploring model performance and optimizing training strategies.


Question: What is the construction pipeline of the FollowRAG benchmark?

The construction pipeline of the FollowRAG benchmark involves several key steps to ensure the evaluation of RAG systems in following user instructions in complex multi-document contexts.<PIC>
First, the process begins with instruction collection and extraction. The FollowRAG benchmark draws from general instruction-following (IF) datasets like IFEval and FollowBench to gather and verify definitions and examples of atomic instructions using specific rules, such as code. This step excludes instructions irrelevant to RAG scenarios and identifies 22 types of instruction constraints, which cover various aspects like language, length, structure, and keywords.
Next, these instructions are reformed using widely-used question-answering (QA) datasets such as Natural Questions. For each query sampled from these QA datasets, a complex instruction is generated containing multiple atomic instruction constraints, with the number of constraints ranging from 1 to 4. To diversify the representations of these atomic instructions, GPT-4o is employed as the instruction generator. This involves sampling a number of instructions from the atomic instruction set, performing conflict detection, and prompting the language model to generate varied instruction texts along with parameters for instruction-following evaluation.
The final step is the combination of the retrieved passages, query, and atomic instructions to construct the complete sample input for FollowRAG. Instead of mechanically concatenating the query and instructions in a template-based manner, the process involves prompting a supervised model to naturally blend the multiple atomic instructions and the query into a coherent instruction-query paragraph. The top-K document passages retrieved based on the query are then added to this paragraph, forming the complete sample input.
Once the dataset is constructed, the evaluation involves assessing the model's output from two perspectives: instruction following and question answering (QA). For instruction following, the verifiable nature of atomic instructions allows for automated verification through code validation, and the average pass rate for each instruction is calculated to determine the instruction-following score. For QA, the models' outputs are compared against the original gold answers from the QA datasets, and GPT-4o is used to evaluate whether the outputs correctly address the questions, with scores assigned based on correctness. The average score of all samples constitutes the RAG score for FollowRAG.


Question: How does VIF-RAG's performance compare to other baseline models as the number of instructions increases in the FollowRAG benchmark?

As the number of instructions increases in the FollowRAG benchmark, VIF-RAG consistently outperforms other baseline models. While all models generally show a decline in instruction-following capability with the increasing number of instructions, VIF-RAG maintains its superior performance. Even when three instructions are present simultaneously, VIF-RAG demonstrates over a 5% improvement in instruction-following prompt (strict accuracy), thereby validating its enhanced capability to handle complex instruction-following tasks in retrieval-augmented generation (RAG) scenarios.<PIC>


Question: What are the differences between VisRAG and TextRAG?

VisRAG and TextRAG have distinct differences in their approaches to retrieval-augmented generation. TextRAG frameworks typically use text segments for both retrieval and generation. In such systems, text-based units are extracted from the knowledge corpus, encoded into embeddings, and then used to retrieve relevant information, which is subsequently processed to generate the required answers. This often necessitates additional parsing steps to handle complex, multi-modal documents, which may result in the loss of multi-modality and layout information.
In contrast, VisRAG leverages the image of the document as the fundamental unit for both retrieval and generation, thereby preserving all visual and textual information. Instead of converting complex documents into plain text, VisRAG processes these documents as images using vision language models (VLMs). The retrieval process in VisRAG employs a VLM to encode both the query and document page as embeddings, ensuring that the visual context is maintained. During generation, VisRAG can utilize multiple pages by either concatenating images or using VLMs designed to handle multiple images, allowing for richer and more contextually accurate answers.By incorporating VLMs, VisRAG maintains the integrity of multi-modal information present in documents, providing a more holistic approach compared to the traditional text-based methodologies employed in TextRAG frameworks.<PIC>


Question: How does the performance of VisRAG's retrieval component, VisRAG-Ret, compare to MiniCPM (OCR)?

VisRAG's retrieval component, VisRAG-Ret, demonstrates a significant performance advantage over MiniCPM (OCR). According to the provided data, VisRAG-Ret consistently achieves approximately 17% higher performance than MiniCPM (OCR), which relies on extracted text for training. Moreover, VisRAG-Ret exhibits a more stable training process, indicating its robustness and reliability. These results highlight the superior efficiency and generalization capabilities of VisRAG-Ret, even when evaluated in out-of-domain settings. This performance edge is evident from the initial training on 20k query-document pairs and becomes more pronounced after training on 150k pairs, showcasing its potential for further improvements with increased training data.<PIC>


Question: In FactMM-RAG, when using the same F1CheXbert threshold for mining report pairs, does changing the F1RadGraph threshold improve factual performance?

In FactMM-RAG, under the same F1CheXbert threshold for mining report pairs, changing the F1RadGraph threshold can initially improve factual performance. However, further increasing the F1RadGraph threshold does not continue to yield improvements and eventually reaches a saturation point. This is because higher thresholds can exclude many relevant report pairs, leading to a potential loss of factually useful pairs. This exclusion hinders the training of the multimodal retriever, which relies on additional factual medical knowledge.<PIC>


Question: Does the training approach of FactMM-RAG provide useful supervision signals for the training of the multimodal retriever without relying on explicit diagnostic label guidance?

The FactMM-RAG's training approach can provide useful supervision signals for training the multimodal retriever without relying on explicit diagnostic label guidance.<PIC><PIC>.The results of experiment show that the F1RadGraph threshold alone can effectively mine factual report pairs. Even as the F1RadGraph threshold increases, FactMM-RAG's performance matches the high threshold settings where the F1CheXbert Threshold is set to 1.0. This demonstrates that the training strategy with curated factual query-report pairs imposes useful supervision signals, ensuring effective training of the model without the need for explicit diagnostic labels from CheXbert.


Question: How does the TextHarmony mitigate the problem of inconsistency in multimodal generation through Slide-LoRA module?

The TextHarmony model mitigates the problem of inconsistency in multimodal generation through the Slide-LoRA module by optimizing parameter space for different training objectives. Slide-LoRA is a novel module that can be inserted into Transformer layers as Low-Rank Adaptation (LoRA) with minimal parameter increase. It processes text and image generation in separate parameter spaces, which alleviates the inconsistency issue. The module comprises a gating network and three LoRA modules: one for text generation, one for image generation, and one for shared knowledge between both tasks. The gating network determines whether the input requires text or image generation, producing a scalar value that guides the use of the appropriate parameter space. By incorporating both task-specific and shared knowledge, Slide-LoRA effectively separates inconsistent training objectives and learns the shared knowledge for text and image generation, ensuring more consistent multimodal outputs.<PIC>


Question: What are the advantages of the DetailedTextCaps-100K dataset developed in TextHarmony compared to the MARIO-LAION dataset?

The DetailedTextCaps-100K dataset developed in TextHarmony offers several advantages compared to the MARIO-LAION dataset. While MARIO-LAION contains captions of text-rich images, these descriptions tend to be oversimplified and do not focus adequately on the textual elements within the images. In contrast, DetailedTextCaps-100K generates more comprehensive captions by sampling 100K images from MARIO-LAION and using Gemini Pro, a multi-modal large language model, to create detailed descriptions based on the images and OCR results. This results in captions that not only cover the visual elements but also pay close attention to the textual content in the images, providing a more thorough and nuanced depiction. Thus, the DetailedTextCaps-100K dataset is better at portraying the textual elements in images compared to MARIO-LAION.<PIC>


Question: Based on the paper “The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio”, how is language dominance manifested when LMMs overreliance on unimodal priors?

Based on the paper "The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio," language dominance manifests in Large Multimodal Models when they excessively rely on pre-trained Large Language Models. This overreliance leads to responses that adhere to linguistic patterns or prior knowledge from large language corpora, even when visual or audio inputs provide contradictory information. The issue is particularly prevalent in LMMs that integrate LLMs as their decoder base, given their proficiency in capturing linguistic structures and semantic relationships. As a result, the decision-making process is often dominated by the language modality, overshadowing contributions from visual or audio modalities.
<PIC>
For instance, as illustrated in the figure, a video depicting finger skateboarding with shoes on fingers is presented. When asked a language-biased question, "Did you see shoes worn on feet?", which reflects a common-sense event following linguistic priors, the LMM incorrectly responds with "yes," contradicting the actual content of the video and inducing hallucination. This example demonstrates how LMMs tend to rely on language priors over factual multimodal inputs, leading to erroneous outputs and highlighting the challenge of language dominance in multimodal models.


Question: Based on the paper “The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio”, how does the phenomenon of visual dominance lead to hallucinations in LMMs when processing multimodal inputs.

Based on the paper “The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio,” visual dominance in Large Multimodal Models leads to hallucinations when the model excessively relies on visual information while underutilizing or disregarding linguistic and auditory cues. In such cases, the model's outputs are heavily influenced by the visual context, often neglecting important information from the other modalities. This overreliance on visual input can cause the model to generate outputs that are not supported by the actual multimodal input, leading to hallucinations.
<PIC>
For instance, as illustrated in the figure, a video depicts a person planning a woodworking project with a hammer in sight, while the audio track contains only the person speaking and bird chirping. Despite this, advanced LMMs may over-rely on the visual presence of the “hammer” and incorrectly infer a “hammer hitting” sound, ignoring the actual audio content where no such sound is present. This demonstrates how LMMs tend to favor visual information over auditory and linguistic cues, leading to incorrect inferences and hallucinations.


Question: How does SciPIP construct the literature dataset?

SciPIP constructs its literature dataset by first collecting papers from prominent conferences such as ICLR, NeurIPS, ICML, ACL, NAACL, and EMNLP from the past ten years, resulting in a database of 48,895 papers. Each paper is parsed to extract sections like the title, abstract, introduction, method, and references. Using these sections, a large language model (LLM) is prompted to read and summarize the papers, extracting entities, background, summaries, main ideas, detailed ideas, and core references.
The extracted background, summary, and main ideas are encoded with Sentence-BERT for their embeddings. All this extracted information is recorded into the literature database. Additionally, to facilitate faster literature retrieval, a paper-entity graph is constructed in the database. This graph stores all occurrence relationships between papers and entities, making it easier to navigate and retrieve relevant literature.<PIC>


Question: What are the main differences between the three approaches proposed by SciPIP for generating research paper ideas?

The main differences between the three approaches proposed by SciPIP for generating research paper ideas lie in the application of brainstorming. The direct proposal method (SciPIP-A) does not incorporate brainstorming; it relies solely on the content of retrieved literature to generate ideas. The first dual-path proposal method (SciPIP-B) integrates brainstorming into the process, creating two parallel branches: one uses the retrieved literature for idea generation, while the other engages in brainstorming based on the user-provided background. These independently generated ideas are then merged, filtered, and refined. The second dual-path proposal method (SciPIP-C) is similar to SciPIP-B, but it takes the brainstorming a step further by using the brainstorming results for both idea generation and enhancing the literature retrieval process through entity extraction. This approach ensures that keywords from brainstorming contribute to more effective literature retrieval, with all ideas eventually being merged, filtered, and refined to produce the final output.<PIC>


Question: In the paper "On Memorization of Large Language Models in Logical Reasoning”, what modules make up the K&K data generation framework?

In the paper "On Memorization of Large Language Models in Logical Reasoning," the K&K data generation framework is composed of two primary modules: the Abstract Module and the Natural Language Module.<PIC>
The Abstract Module consists of four components designed to generate and manipulate K&K puzzles in an abstract form. First, the Generator creates random K&K puzzles given specific parameters that determine the difficulty level, generating puzzles with a specified number of people, and creating statements that form a random tree structure. Second, the Solver finds solutions to given puzzles by converting them into Boolean satisfiability problems and filtering out puzzles with no or multiple solutions. Third, the Reasoner generates step-by-step reasoning procedures that lead to the solution by examining each person’s statements sequentially and checking for contradictions. Fourth, the Perturber creates perturbed versions of puzzles that are similar to the original but with unique solutions, by replacing statements or nodes in the puzzle.
The Natural Language Module operates in the natural language space with three components. The NL-Generator formats abstract K&K puzzles into natural language using templates and heuristics to convert logical statements into readable text. The NL-Reasoner converts the reasoning steps from the abstract Reasoner into natural language, maintaining the structure and flow of the logical process. The NL-Perturber generates perturbed puzzles by manipulating the language-level descriptions, such as replacing character names or reordering statements, while keeping the abstract puzzle intact. This ensures that the puzzles remain challenging and diverse for evaluating logical reasoning.


Question: What are the findings when fine-tuning GPT4o-mini on 1k/10k 8-people puzzles in the study described in the paper "On Memorization of Large Language Models in Logical Reasoning"?

In the study described in the paper "On Memorization of Large Language Models in Logical Reasoning," the findings from fine-tuning GPT4o-mini on 1k/10k 8-people puzzles reveal several important insights. <PIC>
Firstly, fine-tuning on 10k puzzles significantly outperforms fine-tuning on 1k puzzles across all tasks, achieving approximately 90% test accuracy on moderately difficult 4/5-people puzzles. This improvement suggests that exposure to a larger dataset enhances the model's ability to generalize and perform well on various tasks.
Secondly, the study finds that fine-tuning with detailed Chain of Thought (CoT) steps (\cotft) is generally more effective than fine-tuning on answers alone (\ft) when using 10k samples. This effectiveness is likely due to the guidance provided by the reasoning steps, which help the model understand and solve the puzzles more accurately. However, an exception is noted in the 2-people task, where the gap between the training and testing distribution causes the \cotft model to get stuck in a loop of assumptions and contradictions, leading to long and repetitive responses without reaching a conclusion.
Additionally, fine-tuning with 10k puzzles achieves surprisingly high test accuracy on all tasks, such as 52% on 8-people tasks, where the un-fine-tuned model scores near 0. Notably, during training, the models do not see reasoning steps and rely solely on memorizing answers. This indicates the models' strong capacity for memorization when exposed to a large dataset.


Question: Will more contexts boost the performance of LLMs in the EQUATIONINFERENCE task of AAAR-1.0?

Experiments with long-context LLMs revealed that the impact of additional context varies depending on the model.Experiments with long-context LLMs revealed that the impact of additional context varies depending on the model. For open-source LLMs like Llama and Qwen, performance improves up to about 300 words, after which additional context not only fails to boost performance but even significantly decreases it, especially for Qwen. In contrast, closed-source models like GPT-4-Turbo and GPT-4o show performance improvements with increasing context length up to 1,000 words, after which performance levels off.<PIC>
This pattern suggests that while surrounding context is crucial for tasks like equation inference, providing essential information such as algorithm descriptions and notation definitions, there is a threshold beyond which additional context ceases to be beneficial. Indeed, too much context can overwhelm models that are less adept at handling extensive context, potentially leading to confusion and poorer performance. Thus, the effectiveness of additional context is model-dependent and not universally advantageous.


Question: What tasks are included in the AAAR-1.0?

The AAAR-1.0 benchmark comprises four expert-level AI research tasks, specifically designed to evaluate the capabilities of large language models (LLMs) in high-level research settings. These tasks include EQUATIONINFERENCE, which tests the LLMs' ability to verify the correctness of equations within the context of research papers. EXPERIMENTDESIGN assesses the LLMs' skill in devising reliable experiments for proposed research ideas. PAPERWEAKNESS evaluates how well LLMs can identify and analyze weaknesses in draft papers. Finally, REVIEWCRITIQUE is a meta-review task that examines the LLMs' ability to identify and explain deficiencies or unreliabilities in human-written paper reviews. These tasks necessitate strong domain knowledge and expert-level research experience from the models, ensuring that they are both challenging and pertinent to real-world research activities.<PIC>


Question: In the Code-as-Monitor mode how does the Constraint Generator use the input information (RGB images, instructions, previous subgoal and failure feedback) to generate the next subgoal and associated textual constraints?

the RGB images~$\mathcal{O}$, along with instructions~$\mathcal{L}_{\mathrm{global}}$, previous subgoal~${l}_{\mathrm{pre}}$, and failure feedback from the Constraint Monitor~$f_{\mathrm{pre}}$~(\eg, subgoal success or failure reason), are fed into the Constraint Generator~$\mathcal{F}_{\mathrm{VLM}}$~(\ie, GPT-4o) to generate the next subgoal~${l}_{\mathrm{next}}$ and associated textual constraints~$\mathcal{C}$. This process can be formulated in a specific way.
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Question: In the Code-as-Monitor how are the multi-granularity annotations obtained and what role does the figure play in understanding the related process?

To obtain the multi-granularity annotations, first, we sampled pick-and-place data from BridgeData V2 and processed the dataset using external reference information like the gripper's open/close states. We sampled 10,181 trajectories which contain 219,356 images in total. Then, we input the entire trajectory instruction provided by BridgeData V2 into GPT-4o for decomposition to get the subgoals of each stage, the corresponding textual constraints, and the object and part associated with each constraint.After that, we employed Grounded SAM for instance-level segmentation and Semantic SAM for part-level segmentation. By combining these results, we obtained annotations at multiple granularities and finally conducted manual inspections to further improve annotation quality.
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The figure shows the part-level segmentation and helps to understand the output of the desired element type and mask in the overall architecture related to this process, giving a visual reference for how different components might interact during the operations that contribute to obtaining these annotations.


Question: How does the Variance Penalization Adjustment (VPA) improve the alignment of Q-value distributions, and what role does the initialization of the Lagrange multiplier play in safe online reinforcement learning, particularly in the BallCircle environment described in the Marvel?

The Variance Penalization Adjustment (VPA) enhances the alignment of Q-value distributions by leveraging Spearman's rank correlation coefficient to compare the ranking of learned reward/cost Q-values before and after VPA with the estimated actual return obtained via Monte Carlo simulations. A higher Spearman’s rank correlation coefficient indicates that the relative ranking of learned Q-values is closer to the true Q-values, which is crucial for accurate policy updates. The coefficient increases significantly post-VPA, demonstrating its effectiveness across both seen and OOD (out-of-distribution) state-action pairs.
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In the BallCircle environment, initializing the Lagrange multiplier with an empirically determined good value (e.g., 0.65) leads to more effective cost management during online finetuning compared to setting the multiplier to zero. policies with poorly initialized multipliers suffer from substantial constraint violations and require significantly more time to reduce the cost below acceptable limits. Proper initialization mitigates the "Lagrange multiplier mismatch," ensuring that the balance between reward maximization and constraint satisfaction is maintained during the transition from offline to online reinforcement learning.


Question: how does Marvel compare with baseline methods like SO2, JSRL, and Warm Start in terms of balancing high reward and low cost across different environments, and what insights can be drawn from the experimental results?

Marvel significantly outperforms baseline methods such as SO2, JSRL, and Warm Start in achieving high returns while maintaining costs below the threshold across multiple environments. This is evident from the averaged performance over ten environments (BallRun, BallCircle, CarRun, CarCircle, HalfCheetah, AntCircle, AntRun, DroneCircle, Hopper, and Swimmer) using five random seeds for robustness.
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The results highlight that Marvel quickly learns a high-return policy compared to other methods while ensuring cost constraints are not violated. Baselines like SO2 and JSRL leverage offline pretrained policies but are less effective in maintaining a balance between reward maximization and constraint adherence. In contrast, Marvel’s framework, which combines CPQ for offline training and SAC-lag for online finetuning, ensures consistent safety performance and faster convergence to optimal policies, showcasing its superiority in online-to-online (O2O) safe reinforcement learning.


Question: In the EmbodiedOcc how does the framework utilize 3D Gaussian Splatting to achieve real-time monocular input-based occupancy prediction, and what advantages does this approach offer over traditional voxel-based methods?

The EmbodiedOcc framework leverages 3D Gaussian Splatting to maintain an explicit global memory of 3D Gaussians for real-time occupancy prediction. During the exploration of a scene, the Gaussians within the current frustum are extracted from memory and updated using semantic and structural features derived from monocular RGB input. The degree of each update is determined by a confidence value associated with the Gaussians.
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These updated Gaussians are detached and reintegrated into the global memory, enabling continuous scene updates. The Gaussian-to-voxel splatting module converts the Gaussians into local 3D occupancy predictions on demand. Compared to traditional voxel-based methods, this approach provides greater flexibility and efficiency in handling real-time monocular visual input, making it more suitable for dynamic and embodied scenarios.


Question: In the EmbodiedOcc how does the Gaussian memory system use tags (\(\Gamma\)) and confidence values (\(\Theta\)) to refine and update Gaussians, and what is the significance of this process for real-time 3D occupancy prediction?

The Gaussian memory system in **EmbodiedOcc** uses tags (\(\Gamma\)) to track the update status of Gaussians and assigns confidence values (\(\Theta\)) based on these tags to guide the refinement process. When a new scene is initialized, all Gaussians are tagged with \(\Gamma = 0\). As updates occur, Gaussians that have been updated are tagged with \(\Gamma = 1\). During subsequent updates, Gaussians tagged as previously updated (\(\Gamma = 1\)) are assigned confidence values between 0 and 1, indicating slight updates, while Gaussians that have never been updated (\(\Gamma = 0\)) are prioritized for significant updates.
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The refinement process adjusts the Gaussians using the formula:
\[
\Delta \mathbf{G}_{online} = (1-\theta ) \Delta \mathbf{G}, \quad \mathbf{G}_{after}= \Delta \mathbf{G}_{online} \oplus \mathbf{G}_{before},
\]
where \(\oplus\) denotes quaternion composition for rotations and addition for other components. This mechanism ensures efficient and focused updates, maintaining a dynamic and accurate representation of the 3D scene in real time. The memory refinement process is crucial for real-time 3D occupancy prediction as it balances computational efficiency with prediction accuracy, ensuring that the system can handle dynamic environments effectively.


Question: In the GRAM:, how does the robust adaptation module (\(\phi_{\textnormal{GRAM}}\)) improve the generalization of the student policy to out-of-distribution (OOD) contexts, and how does it handle uncertainty in the history encoding during deployment?

The robust adaptation module (φGRAM) in GRAM enhances generalization to OOD contexts by quantifying and managing the uncertainty in the history encoding during deployment. The module uses an epistemic neural network to estimate both the mean and variance of latent features for a given history ht. This allows φGRAM to adapt its output based on the level of uncertainty in the history encoding.
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In in-distribution (ID) contexts, where the environment dynamics are familiar, the variance of latent feature estimates is low, and φGRAM outputs a value close to the mean estimate. However, in OOD contexts, where dynamics differ significantly, the variance increases, and φGRAM adjusts its output toward a robust latent feature zrob, which is designed to handle unseen conditions. This mechanism ensures that the policy π(at ∣ st, φGRAM) remains reliable even when encountering trajectories that deviate from those observed during training, thereby improving the robustness and adaptability of the student policy.


Question: In the GRAM how does the robust latent feature \( z_{\textnormal{rob}} \) facilitate generalization to OOD dynamics, and how does the joint RL training pipeline integrate both ID and OOD training to improve robustness within the same architecture?

The robust latent feature \( z_{\textnormal{rob}} \) in **GRAM** serves as an anchor point in the latent feature space, enabling the policy to generalize effectively to OOD dynamics. During training, the privileged context encoding \( z_t = f(c_t) \) is incentivized to deviate from \( z_{\textnormal{rob}} \) when it improves performance in ID environments. At deployment, if the latent feature estimate is unreliable due to OOD dynamics, it is biased back toward \( z_{\textnormal{rob}} \), ensuring robust behavior under unseen conditions.
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The joint RL training pipeline combines ID and OOD data collection in an alternating scheme. Each training iteration assigns environments to either **ID training** or **OOD training**, dictating whether the latent feature is computed normally or set to \( z_{\textnormal{rob}} \). This alternating approach ensures that the policy \( \pi(a_t \mid s_t, z_t) \) is trained for adaptive performance in ID contexts and robust performance in OOD contexts, creating a unified framework. The mixed data collection strategy introduces temporal consistency across trajectories, enhancing the robustness of the model beyond what separate training assignments could achieve.


Question: In the The Tile how do the curves \(\tileCurvePriors\) and \(\tileCurveRates\) represent no-skill performances under fixed priors and fixed prediction rates, respectively, and what is their significance in interpreting balanced accuracy (\(\scoreBalancedAccuracy\)) and Cohen's kappa (\(\scoreCohenKappa\))?

The curves \(\tileCurvePriors\) and \(\tileCurveRates\) in **The Tile** framework depict the ranking scores that place all no-skill performances on an equal footing under different constraints. The curve \(\tileCurvePriors\) applies when the priors of the classes are fixed, showing the relationship between the ranking scores \(\scoreNPV\) (upper-left corner) and \(\scorePPV\) (lower-right corner). In contrast, \(\tileCurveRates\) applies when the rates of predictions are fixed, linking \(\scoreTNR\) (lower-left corner) and \(\scoreTPR\) (upper-right corner).
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Balanced accuracy (\(\scoreBalancedAccuracy\)) is perfectly correlated with ranking scores at the intersection of \(\tileCurvePriors\) and the rising diagonal (\((\priorneg, \priorneg)\)), highlighting its dependence on class priors. Similarly, Cohen's kappa (\(\scoreCohenKappa\)) correlates perfectly at the intersection of \(\tileCurvePriors\) and the median horizontal (\((\frac{\priorneg^2}{\priorneg^2+\priorpos^2}, \frac{1}{2})\)), emphasizing its focus on agreement adjusted for chance. These visualizations provide insights into how these metrics behave under different constraints and enhance the interpretability of classification performance in scenarios with varying priors or prediction rates.