Question: In Figure 1, what are the two accuracy metrics plotted against training steps? How does the relationship between these metrics change during training, and what does this imply about the learning phases?

Your answer should be a sentence answering the two questions.

The two metrics are in-weights accuracy (IWL, for targets not in context) and in-context accuracy (ICL, for novel classes).

Initially, IWL rises faster than ICL, but later ICL abruptly surges, suggesting a transition from slow feature learning to sudden induction-head formation enabling in-context reasoning.

Question: In Figure 1, what are the two accuracy metrics plotted against training steps? How does the relationship between these metrics change during training, and what does this imply about the learning phases?


Question: In the paper that proposes P-RLHF, where can I find the fine-tuned GPT-J model used as the SFT model?

Your answer should be a Python strings of the website, the website URL starting with "https://", as given in the paper.


Question: A recent paper proposes a novel generative method named TP-EGG for constructing typed entailment graphs (EGs). Unlike prior extractive approaches that rely on large corpora, TP-EGG generates new predicates and entailment relations using pre-trained language models, effectively addressing both predicate and edge sparsity. Please indicate the name of the university affiliated with this research.

Your answer should be the name of the university.


Question: There's a recent paper introduces Human-Aware Vision-and-Language Navigation (HA-VLN), extending traditional VLN by incorporating dynamic human activities and relaxing key assumptions (e.g., egocentric action space, sub-optimal expert supervision). Could you please provide the name of the corresponding author of this paper?

Your answer should be a name of a person.


Question: In ICLR 2024 Spotlight papers, a paper proposes a method named "Heuristic Blending". In this paper, how many theorems are proposed?


Question: Facing the failure of existed methods to address the compound degradations presented in source images, a paper proposes a novel interactive multi-modal image fusion framework based on the text-modulated diffusion model, called Text-DiFuse. In the comparison of the basic version of their Text-DiFuse with current state-of-the-art fusion methods, what is their model's advantage?

Your answer should be a sentence describing the model's advantage in detail.

Ability to correct color casts, restore scene information under low-light conditions, and suppress noise, ability to highlight physiological structure information while maintaining functional distribution.

Question: Facing the failure of existed methods to address the compound degradations presented in source images, a paper proposes a novel interactive multi-modal image fusion framework based on the text-modulated diffusion model, called Text-DiFuse. In the comparison of the basic version of their Text-DiFuse with current state-of-the-art fusion methods, what is their model's advantage?


Question: Which paper published in ICLR 2024 propose a novel method, namely Sorting Krylov Recycling (SKR), to accelerate data generation for neural operators training?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper published in ICLR 2024 propose a novel method, namely Sorting Krylov Recycling (SKR), to accelerate data generation for neural operators training?

Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling


Question: Tell me the core contribution of "Hybrid RSSM".

Question: Tell me the core contribution of "Hybrid RSSM".

"Hybrid RSSM" can reinforce the robustness of state representation within policy learning.


Question: In the paper that characterizes the exact privacy-utility tradeoff for locally private sampling under f-divergences, proposing universally optimal mechanisms for both discrete and continuous spaces, where can I find the code of the paper if I want to reproduce the experiment?

Your answer should be a Python strings of the website, the website URL starting with "https://", as given in the paper.


Question: Give a brief introduction of the innovative points of the essay.

The first self-supervised spike-guided deblurring framework(S-SDM

No need for real clear labels, it ets rid of the dependence on real clear sequences.

The BSN effectively suppresses thermal noise in spiky signals through a blind spot strategy (predicting the center pixel using only the surrounding pixels).

EDSR supersegmentation of spiky signals from low resolution to blurred image size without additional alignment labeling.

The color consistency assumption is proposed to associate single-channel spike signals with RGB blurred images, which solves the problem of lack of color information in spike cameras.

Question: Give a brief introduction of the innovative points of the essay.


Question: Which two main challenges does GeoBFN address when dealing with molecular geometry data? What is a specific manifestation of the first challenge (multimodality)?

Your answer should be two sentences, eaching answer one of the two questions.

The two main challenges are multimodality and noise sensitivity.

Molecular geometry data need to deal with both continuous variables (e.g., atomic coordinates) and discrete variables (e.g., atom types), and the form of the data varies greatly from modality to modality, which makes it more difficult to model directly and uniformly.

Question: Which two main challenges does GeoBFN address when dealing with molecular geometry data? What is a specific manifestation of the first challenge (multimodality)?


Question: In this paper, what is the theoretical basis of "Dynamic Path Feedback"?

Question: In this paper, what is the theoretical basis of "Dynamic Path Feedback"?

The theoretical basis of "Dynamic Path Feedback" is "reward shaping".


Question: How long time can the new model reduce while training a 1B parameter Stage C text conditional diffusion model, compared to the amount SD 2.1 used for training?

Your answer should be a float, rounded to 1 decimal place, evaluating in GPU hours.


Question: There is a paper that introduces a large-scale, multilingual, multi-technique singing corpus designed to address key limitations in existing datasets for singing voice synthesis (SVS) and related tasks. It features 80.59 hours of high-quality recordings from 20 professional singers across 9 languages. Could you please tell me the most frequently occurring segment duration (s) in the proposed dataset?


Question: Is there a paper published in ICLR 2024 that proposes a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Is there a paper published in ICLR 2024 that proposes a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning?

Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo


Question: In order to solve limitation of the application of Causal Temporal Representation Learning (Ctrl) methods in real-world scenarios without prior knowledge of the domain variables, a paper propose CtrlNS. In their model, there are two kinds of variables: domain variables and hidden variables. Which variable remains relatively unchanged in phrase two during the training?

Your answer should be chosen between "domain variables" and "hidden variables".


Question: In ICLR 2024 Poster papers, a paper proposes a framework where an agent first learns a sufficient set of skill primitives to achieve all high-level goals in its environment. Tell me the number of authors of this paper.


Question: A recent paper introduces the first large-scale benchmark specifically designed to evaluate large multimodal models (LMMs) on scientific figure interpretation. It consists of 2,000 curated multiple-choice questions across two tasks, figure-to-caption and caption-to-figure, sourced from arXiv figures using adversarial filtering and human verification. Could you please tell me which subcategory within the general question set has the highest sample proportion?

Your answer should be a name of a subcategory.


Question: Among the oral presentations at NeurIPS 2024 studying causal discovery, how does the proposed method in the paper "Do Finetti: On Causal Effects for Exchangeable Data" perform in estimating causal effects compared to traditional i.i.d.-based methods?

Your answer should be a Python string comparing the performance in terms of estimation error, specifically mentioning the mean squared error (MSE) values.

The proposed method, Do-Finetti, achieves near-zero mean squared error (MSE) in estimating causal effects, outperforming traditional i.i.d.-based methods, which exhibit higher estimation errors.

Question: In the paper "Do Finetti: On Causal Effects for Exchangeable Data," how does the proposed method perform in estimating causal effects compared to traditional i.i.d.-based methods?


Question: Can you recommend me a paper published in ICLR 2024 that studies the required model size when considering average-case and worst-case error scenarios, showing how the model size needs to change based on accuracy, data size and data dimensionality?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Can you recommend me a paper published in ICLR 2024 that studies the required model size when considering average-case and worst-case error scenarios, showing how the model size needs to change based on accuracy, data size and data dimensionality?

Towards Establishing Guaranteed Error for Learned Database Operations


Question: In ICLR 2024 Poster papers, a paper tries to ensemble the reward models to mitigate the over-optimization problem. In Section 5 (Results), how many main findings are reported?


Question: How does ClimODE handle local and global weather effects? List two key points and give me a formula.

Your answer should be a list of 2 strings, the first is a sentence containing the two key points, and the second is a formula in LaTeX format.

A convolutional network (3x3 kernels) captures local effects by aggregating nearby weather information and an attention convolutional network (KQV dot-product) captures global interactions.

Question: How does ClimODE handle local and global weather effects? List two key points.

f_{\theta}(\mathbf{u}(t), \nabla \mathbf{u}(t), \mathbf{v}(t), \psi)=f_{\text {conv }}(\mathbf{u}(t), \nabla \mathbf{u}(t), \mathbf{v}(t), \psi)+\gamma f_{\text {att }}(\mathbf{u}(t), \nabla \mathbf{u}(t), \mathbf{v}(t), \psi) .

Question: How does ClimODE handle local and global weather effects? Give me a formula.


Question: How does the Kantorovich-Rubinstein duality provide a tractable objective for optimizing the Wasserstein dependency measure Iw?

I_W(S;Z) = \sup_{\lVert f \rVert_L \leq 1} \mathbb{E}_{p(s,z)}[f(s,z)] - \mathbb{E}_{p(s)p(z)}[f(s,z)],

Question: How does the Kantorovich-Rubinstein duality provide a tractable objective for optimizing the Wasserstein dependency measure Iw?


Question: In the paper that proposes RETR, what is the use of projimage in formula (2)?

Your answer should be a Python strings describing the use of the projimage function.

It projects the 3D BBox in the camera coordinate system into corresponding 2D image plane normally with a known pinhole camera model.

Question: What is the use of projimage in formula (2)?


Question: In the paper that proposes a temperature-controlled differentiable mapping from free vectors to orthogonal matrices that asymptotically converges to permutation matrices, enabling both gradient-based and stochastic optimization over permutations, what hinders gradient-based optimization for \theta in formula (15) and how to solve it?

Your answer should be a Python strings explaining the obstacle and how to solve it.

There exists a not differentiable mapping \rho(\cdot) which hinders gradient-based optimization for \theta and it can be solved by approximating equation (15) by relaxing the mapping \rho(\cdot) to \psi_{\tau}(\cdot).

Question: In the paper that proposes a temperature-controlled differentiable mapping from free vectors to orthogonal matrices that asymptotically converges to permutation matrices, enabling both gradient-based and stochastic optimization over permutations, what hinders gradient-based optimization for \theta in formula (15) and how to solve it?


Question: What parameterization of f(s,z) is used to simplify the optimization of , and how does it constrain the Lipschitz continuity?

I_W(S;Z) \approx \sup_{\lVert \phi \rVert_L \leq 1, \lVert \psi \rVert_L \leq 1} \mathbb{E}_{p(s,z)}[\phi(s)^\top \psi(z)] - \mathbb{E}_{p(s)}[\phi(s)]^\top \mathbb{E}_{p(z)}[\psi(z)],

Question: What parameterization of f(s,z) is used to simplify the optimization of , and how does it constrain the Lipschitz continuity?


Question: In figure 2, describe the global change of angle $\Phi$ and $\Psi$ during the transition.

$\Phi$ increases and $\Psi$ decreases globally.

Question: In figure 2, describe the change of angle $\Phi$ and $\Psi$ during the transition.


Question: A paper studies behavior policy for data-efficient General Value Functions (GVFs) learning to perfect this domain. In their study, which loss function is used to mesure the model's performance?

Your answer should be a string which indicates a name of loss function.


Question: In Figure 1, what are the three types of datasets used to fine-tune GPT-3.5 Turbo in the experiments?  How does the harmfulness score change across the 11 safety categories after fine-tuning?

Your answer should be a sentence answering the two questions.

Explicitly harmful examples, identity-shifting data, and the benign Alpaca dataset.

The harmfulness score increases in all categories after fine-tuning, indicating safety degradation.

Question: In Figure 1, what are the three types of datasets used to fine-tune GPT-3.5 Turbo in the experiments?  How does the harmfulness score change across the 11 safety categories after fine-tuning?


Question: A recent paper presents a token reduction framework for Vision-Language (VL) models that integrates text-informed image token pruning and modality-aware token merging into cross-modal Transformer layers. By progressively removing text-irrelevant visual tokens and merging semantically similar tokens within each modality, the proposed method achieves up to 2x inference speedup and over 50% memory reduction on models such as ViLT and METER. Which university's researchers proposed this work?

Your answer should be a name of a university.


Question: In the paper that proposed Exclusively Penalized Q-learning, to solve unnecessary estimation bias, a new penalty is introduced. What is the formula for this penalty?

Your answer should be a string, the formula in LaTeX format.

\mathcal{P}_\tau := \underbrace{f^{\pi, \hat{\beta}}_\tau(s)}_{\text{penalty adaptation factor}} \cdot \underbrace{\left( \frac{\pi(a \mid s)}{\hat{\beta}(a \mid s)} - 1 \right)}_{\text{penalty term}}

Question: What is the formula for the novel exclusive penalty $\mathcal{P}_\tau$?


Question: By how much does PromptNER outperform the state-of-the-art model on average in the cross-domain few-shot setting?

Your answer should be a Python float between 0 and 100 rounded to 1 decimal place, stating the percentage improvement.


Question: A paper propose a novel training paradigm for GenQA using supervision from automatic QA evaluation models (GAVA). When changing threshold $\theta$, the hyper-parameter, to a lower value, the GAVA-Score of GAVA-SDA models trained on MS-MARCO would likely become lower or higher?

Your answer should be a string chosen between "lower" and "higher"


Question: A paper reports a small number attention heads transport a compact representation of the demonstrated task. They study the AIE per attention head in GPT-J over many tasks, and show it in a figure, which index of head has the highest AIE in the middle layer?

Your answer should be an int between 0 to 15.


Question: A paper introduce BAdam which offers a memory efficient approach to the full parameter finetuning of large language models. Besides its high efficiency, does this model has a better convergence behavior compared with other models?


Question: What is the maximum number of nodes that SDF-Sim can scale to before mesh-based approaches run out of memory?

Your answer should be a Python float value representing the number of nodes in millions, rounded to 1 decimal place.


Question: Among the papers at NeurIPS 2024 researching fair machine learning, how many real-world datasets did the authors of "Unprocessing Seven Years of Algorithmic Fairness" empirically evaluate?

Your answer should be a Python integer representing the number of real-world datasets empirically evaluated.


Question: What two known instabilities are reproduced in the paper?

Attention logit growth: The growth of logits in attention layers and Output logit divergence: Divergence of the output logits from the log probabilities

Question: What two known instabilities are reproduced in the paper?


Question: What is the Bayesian framework equation that decomposes predictive uncertainty (PU) into epistemic (EU) and aleatoric (AU) components?

\text{PU} = \text{EU} + \text{AU} \quad \text{or} \quad H(Y|x) = MI(Y, \Omega|x) + E_{\omega\sim\Omega}[H(Y|\omega, x)]

Question: What is the Bayesian framework equation that decomposes predictive uncertainty (PU) into epistemic (EU) and aleatoric (AU) components?


Question: Among the papers at ACL 2023 researching text style transfer, what is the main innovation of the "Text Style Transfer Back-Translation" method proposed in the paper?

Your answer should be a Python string describing the key innovation of the Text Style Transfer Back-Translation method.

The method introduces a style transfer model to modify the source side of back-translation data, making it more natural to improve the translation of natural inputs.

Question: What is the main innovation of the "Text Style Transfer Back-Translation" method proposed in the paper?


Question: What is the main idea of InfoBatch?

Your answer should be a sentence brefly introducing the method

It's to speed up the training process by dynamic and unbiased data pruning. It randomly prunes some of the less informative samples according to their loss values and scales the gradient of the remaining samples to approximate the gradient expectation of the original dataset, thus realizing lossless training acceleration.

Question: What is the main idea of InfoBatch?


Question: According to the paper that introduces the first adaptive decision-making framework for LLMs that mirrors real-world MDM processes via dynamic collaboration among AI agents based on task complexity, considering the impact of agent number, if we remove one agent from the peak accuracy setting, how much does the accuracy drop?

Your answer should be a float rounded to 1 decimal place.


Question: In ICLR 2024 Poster papers, which paper introduces Uni-RLHF, a comprehensive system implementation tailored for RLHF?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: In ICLR 2024 Poster papers, which paper introduces Uni-RLHF, a comprehensive system implementation tailored for RLHF?

Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedback


Question: In ICLR 2024 Poster papers, which paper tackles the Offline Opponent Modeling problem by harnessing the full potential of the supervised pre-trained Transformers' in-context learning capabilities?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: In ICLR 2024 Poster papers, which papers tackles the Offline Opponent Modeling problem by harnessing the full potential of the supervised pre-trained Transformers' in-context learning capabilities?

Towards Offline Opponent Modeling with In-context Learning


Question: In RobustFill's "Switch-Concept-Order" task, how much more accurate is ExeDec compared to sub-target-free ablation experiments (Ablation)?

Your answer should be a float, rounded to 1 decimal place, evaluating in percentage.


Question: I wonder if there are any datasets and benchmarks that are published as orals in ICLR 2024? Also tell me their respective dataset size (including all data splits).

Your answer should be a Python list of tuples (List[Tuple[str, int]]). For each tuple in the list, the first element is the paper title string and the second element is an integer representing the dataset size.

BooookScore: A systematic exploration of book-length summarization in the era of LLMs

MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

SWE-bench: Can Language Models Resolve Real-world Github Issues?

How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?


Question: A paper propose a SummAttacker, an efficient generator of diverse cases, to bring more variations to the hidden states. When E bar, the average Euclidean distance of paired original and attacked states, decreases, the hidden states in the latent space show smaller or larger diversity?

Your answer should be chosen between "smaller" and "larger".


Question: According to the ablation study in the paper that proposes Variance Reduced Meta-CL, what improvement in final accuracy (Acc) does VR-MCL_2 achieve compared to MCL on the Seq-CIFAR10 benchmark?

Your answer should be a Python float value representing the percentage improvement in accuracy, between 0 and 100 and rounded to 2 decimal places.


Question: When learning framework for State Embedding in SEMANTIC MEMORY EMBEDDING, what is the loss fonction and how can it be expressed with the highest return and a scale factor, noted lambdarocon?

Your answer should be a list of teo formulas

\mathcal{L}(\phi, \psi)=\left(H_{t}-f_{\psi}\left(f_{\phi}\left(s_{t}
ight)
ight)
ight)^{2}\mathcal{L}(\phi, \psi)=\left(H_{t}-f_{\psi}^{H}\left(f_{\phi}\left(s_{t} \mid t
ight) \mid t
ight)
ight)^{2}+\lambda_{	ext {rcon }}\left\|s_{t}-f_{\psi}^{s}\left(f_{\phi}\left(s_{t} \mid t
ight) \mid t
ight)
ight\|_{2}^{2},

Question: When learning framework for State Embedding in SEMANTIC MEMORY EMBEDDING, what is the loss fonction and how can it be expressed with the highest return and a scale factor, noted lambdarocon?


Question: In ICLR 2024 Poster papers, which paper proposes to extract an efficient deterministic inference policy from critic models and pretrained diffusion behavior models, leveraging the latter to directly regularize the policy gradient with the behavior distribution's score function during optimization?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: In ICLR 2024 Poster papers, which paper proposes to extract an efficient deterministic inference policy from critic models and pretrained diffusion behavior models, leveraging the latter to directly regularize the policy gradient with the behavior distribution's score function during optimization?

Score Regularized Policy Optimization through Diffusion Behavior


Question: In ICLR 2024 Poster papers, a paper first constructs a dynamics model from the expert demonstration, enforcing local Lipschitz continuity while skipping the discontinuous regions. How many different aspacts are illustrated in Figure 1?


Question: How does the adversarial accuracy of ATINTER improve compared to the best existing defense on the SST-2 dataset?

Your answer should be a Python string specifying the percentage improvement in adversarial accuracy compared to the best existing defense.

ATINTER improves adversarial accuracy by over 4% compared to the best existing defense on SST-2.


Question: In ICLR 2024 Spotlight papers, a paper attempts to solve how to train a general agent in Reinforcement Learning (RL) that can thoroughly explore the environment and learn new and diverse skills. In Figure 6, how many tasks are considered?


Question: Can you recommend me a paper published in ICLR 2024 that propose a data-driven, physically-informed deep-learning framework for classifying dynamical regimes and characterizing bifurcation boundaries based on the extraction of topologically invariant features?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Can you recommend me a paper published in ICLR 2024 that propose a data-driven, physically-informed deep-learning framework for classifying dynamical regimes and characterizing bifurcation boundaries based on the extraction of topologically invariant features?

Let's do the time-warp-attend: Learning topological invariants of dynamical systems


Question: In the paper that introduces a new family of non-Gaussian distributions for deep neural networks, derived via Hermite polynomials, providing significantly more accurate scaling laws than classical Gaussian approximations, what value is the para-Gaussian correction term if \phi(x) = \sqrt{2}(x)_+ and the input z is a standard N(0, 1) Gaussian?

Your answer should be an integer of the value.


Question: In ICLR 2024 Spotlight papers, a paper explores the scalability problem of large-scale Inverse Reinforcement Learning (IRL) in practical applications. In this paper, how author frames Inverse reinforcement learning algorithms as the two-player zero-sum game?

Your answer should be the formula in LaTeX format.

Question: In ICLR 2024 Spotlight papers, a paper explores the scalability problem of large-scale Inverse Reinforcement Learning (IRL) in practical applications. In this paper, how author frames Inverse reinforcement learning algorithms as the two-player zero-sum game?

\underset{\pi\in\Pi}{\min}\underset{\theta\in\Theta}{\max} J(\pi_E, r_{\theta}) - J(\pi, r_{\theta}) = \underset{\pi\in\Pi}{\min}\underset{\theta\in\Theta}{\max} f(\pi_E, \pi, r_{\theta})


Question: What is the size of the Large Reconstruction Model, and how quickly can it reconstruct a 3D object from a single image?

Your answer should be a sentence describing the model size and reconstruction time.

The LRM model has 500 million parameters and can reconstruct a 3D object from a single image in just 5 seconds.

Question: What is the size of the LRM model, and how quickly can it reconstruct a 3D object from a single image?


Question: A paper propose a learning paradigm that directly establishes causation between events in the course of time. According to its diabetes simulator, will the variation of the dose of SI1 impact GS1, Glu. sto. 1?


Question: Which benchmark is used in the main experiment of the paper? In this benchmark, how is the 'probe' defined?

Your answer should be a single python list like this: ["benchmark_name", "probe_definition"]. Note that for the benchmark name, the abbreviation is required. For the probe definition, you should give a short string to describe the definition.

Specifying a feature's stage, location and type fully determines its role in the dataflow. A tuple (stage, loc, type, values) is referred to as a probe.

Question: What's the definition of a probe in the CLRS benchmark?


Question: Among the poster presentations at NeurIPS 2024 on reinforcement learning, what is the sample complexity bound for weakly communicating average-reward MDPs established by the paper "Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs"?

Your answer should be a Python string containing the mathematical expression for the sample complexity bound in LaTeX-like format.

Question: Among the poster presentations at NeurIPS 2024 on reinforcement learning, what is the sample complexity bound for weakly communicating average-reward MDPs established by the paper "Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs"?


Question: In NeurIPS 2024 Poster papers, which paper builds a Python program based on the interaction with the environment to represent agent's understanding of the world?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: In NeurIPS 2024 Poster papers, which paper builds a Python program based on the interaction with the environment to represent agent's understanding of the world?

WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment


Question: Why using cascaded pipeline?

However, limited input resolution of the diffusion model is a major hindrance. We address this with a cascaded pipeline, starting with a low-resolution model, followed by a super-resolution model that successively upsamples and incorporates finer details to the matching field.

To alleviate resolution constraint, we further propose a flow upsampling diffusion model that finetunes the pretrained denoising model, thereby injecting fine details into the matching field with minimal optimization.

Question: Why using cascaded pipeline?


Question: In the joint context transfer module of the paper, which attention mechanism is used? Please give me the pdf url to the paper that proposed this attention mechanism.

Your answer should be a single link like this: https://arxiv.org/abs/xxxx.xxxxx.


Question: Given Figure 1's timeline illustration, suppose a new clip v_3' is inserted into the video stream V at timestamp t_3, while the text stream T remains unchanged. What type of multi-granularity noisy correspondence (MNC) does this introduce?

The new clip introduces coarse-grained asynchronous misalignment, the temporal inconsistency between actions and descriptions.

Question: Given Figure 1's timeline illustration, suppose a new clip v_3' is inserted into the video stream V at timestamp t_3, while the text stream T remains unchanged. What type of multi-granularity noisy correspondence (MNC) does this introduce?


Question: Which paper published in ICLR 2024 proposes a synchronized multiview diffusion model that models the joint probability distribution of multiview images, enabling the generation of multiview-consistent images in a single reverse process?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper published in ICLR 2024 proposes a synchronized multiview diffusion model that models the joint probability distribution of multiview images, enabling the generation of multiview-consistent images in a single reverse process?

SyncDreamer: Generating Multiview-consistent Images from a Single-view Image


Question: A paper  introduce a framework called GRaded Generative Retrieval to address challenges of normal generative retrieval. To simulate the low-resource retrieval setting, the author randomly samples different fixed limited numbers of queries from the training set. How is GR methods' performance comparing with BM25, under the zero-resource setting?

Your answer should be chosen between "better" and "worse".


Question: Among the papers at ACL 2023 that introduced new dialogue datasets, what is the scale of the LiveChat dataset presented in the paper "LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming"?

Your answer should be a Python dictionary with keys 'total_dialogues', 'personas', and 'sessions_per_persona', containing the numerical values for each statistic.


Question: In ICLR 2024 Spotlight papers, a paper tries to solve the performance issues of the DICE (DIstribution Correction Estimation) method in offline reinforcement learning (RL) and imitation learning (IL). How many pages are there in this paper's appendix?

Your answer should be an integer representing the number of pages in the appendix of the paper.


Question: Which paper introduced the DevBench dataset? This dataset is a multimodal developmental benchmark consisting of seven tasks across lexical, syntactic, and semantic domains, and it incorporates behavioral data from both children and adults.

Question: Which paper introduced the DevBench dataset? This dataset is a multimodal developmental benchmark consisting of seven tasks across lexical, syntactic, and semantic domains, and it incorporates behavioral data from both children and adults.

DevBench: A multimodal developmental benchmark for language learning


Question: A paper introduces Sequoia, a scalable, robust, and hardware-aware algorithm for speculative decoding. In the comparison of the wall-clock time speedup of Sequoia trees of various sizes, under which size, the tree with L40 GPUs maximize sppedup value?


Question: In the paper at ICLR 2024 that employs submodular mechanism for model interpretability, what performance improvements does the proposed method achieve over HSIC-Attribution on the CUB-200-2011 dataset for incorrectly predicted samples?

Your answer should be a Python dictionary with two keys: 'confidence_gain' and 'insertion_score_gain', with values as float percentages between 0 and 100 rounded to 1 decimal place.


Question: How does MC-SMoE identify the dominant experts in each expert group?

Your answer should be the context that describes the method used by the paper.

MC-SMoE treats the most commonly active experts as dominant experts, where the expert utilization is calculated by inputting and routing a randomly picked subset of training data.

Question: How does MC-SMoE identify the dominant experts in each expert group?


Question: There is a theoretical paper that establishes the tight parallel complexity of boosting algorithms within the weak-to-strong learning framework. It closes a longstanding gap between the upper and lower bounds on the trade-off between the number of parallel rounds and the total work per round. Could you please tell me which university the authors of this paper are affiliated with?

Your answer should be a name of a university.

Aarhus University
The Aarhus University
Aarhus


Question: What novel metric does the paper "Improving Environment Novelty Quantification for Effective Unsupervised Environment Design" introduce to enhance Unsupervised Environment Design (UED)?

Your answer should be a Python string containing the full name of the metric introduced in the paper.

The paper introduces the Coverage-based Evaluation of Novelty In Environment (CENIE) metric.

Question: What novel metric does the paper "Improving Environment Novelty Quantification for Effective Unsupervised Environment Design" introduce to enhance Unsupervised Environment Design (UED)?


Question: Which Tiny Paper published in ICLR 2024 builds an efficient pipeline for automated evaluation of priming attacks against open-source LLMs?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Which Tiny Paper published in ICLR 2024 builds an efficient pipeline for automated evaluation of priming attacks against open-source LLMs?

Bypassing the Safety Training of Open-Source LLMs with Priming Attacks


Question: In the paper that developes a model of an agent that navigates using noisy egocentric visual and self-motion signals. In the figure 3F, which model consistently achieves a smaller DKL between its place field orientation distribution and the animal data?

Your answer should be a Python strings indicating the name of the model.


Question: In ICLR 2024 Poster papers, a paper attempts to propose a meta-reinforcement learning algorithm that is improved in multiple aspects, especially in terms of sample efficiency, generalization ability, and handling of high-dimensional task distributions, by combining the latest model-based RL techniques and meta-RL techniques. Tell me the number of authors.


Question: What innovative technologies are included in the benchmark specifically designed for practical TTA settings used in the paper "Persistent Test-time Adaptation in Recurring Testing Scenarios"?

Question: What innovative technologies are included in the benchmark specifically designed for practical TTA settings used in the paper "Persistent Test-time Adaptation in Recurring Testing Scenarios"?

They present a robust batch normalization scheme to estimate the normalization statistics.

A memory bank is utilized to sample category-balanced data with consideration of timeliness and uncertainty

They develop a time-aware reweighting strategy with a teacher-student model.


Question: What are the three key architectural improvements of the state-space models (SSMs) introduced in R2I's world model over traditional RNNs? Among these improvements, how does the "Parallel Scan" mode of SSMs address the efficiency of training long sequences?

Your answer should be a list of two strings (sentences), each answering one of the two questions.

1. Structured state matrix initialization (DPLR matrix of the HiPPO framework) enables the model to capture very long-range dependencies.
2. Dual computational paradigm of convolutional and parallel scanning modes.
3. Linear time-invariant system properties allow recursive representation after discretization.

Question: What are the three key architectural improvements of the state-space models (SSMs) introduced in R2I's world model over traditional RNNs?

It converts the sequence computation into parallel prefix sum operations by Blelloch algorithm, which reduces the time complexity from O(L) to O(log L), maintaining the same O(1) time complexity as traditional RNNs, avoiding the storage of the complete computational graph by hiding the state compression (x_t's pass), as shown in Table 1, keeping the total elapsed time O(1) in the imagining phase (H-step), utilizing the parallel computing units of modern GPUs, and reducing the graphic memory footprint by 75% compared to Transformer's attention mechanism

Question: Among these improvements, how does the "Parallel Scan" mode of SSMs address the efficiency of training long sequences?


Question: A paper use the variational framework to study models' ability of planning and show that planning corresponds exactly to a different set of weights. In the experiment where they use 6 different domains from IPPC2011, each with 10 instances (factored MDPs) of increasing difficulty. For the game of life domain instance index, which model performs the worst?

Your answer should be a string, a name of model.


Question: In this paper, what is the number of the authors?


Question: In NeurIPS 2024 Poster papers, a paper proposes a new problem in offline MBRL called "The Edge-of-Reach Problem". In this paper, how many papers are cited?


Question: In ICLR 2024 Poster papers, which paper proposes AlignDiff, a novel framework that leverages RLHF to quantify human preferences, covering abstractness, and utilizes them to guide diffusion planning for zero-shot behavior customizing, covering mutability?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: In ICLR 2024 Poster papers, which paper proposes AlignDiff, a novel framework that leverages RLHF to quantify human preferences, covering abstractness, and utilizes them to guide diffusion planning for zero-shot behavior customizing, covering mutability?

AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model


Question: In the paper introducing Sequence Parallelism, a system-level approach that facilitates Transformer training on arbitrarily long sequences by distributing input sequences across multiple GPUs, the authors present Ring Self-Attention (RSA) as a mechanism for computing attention across devices without requiring the full sequence to be stored on any single GPU. My question is: How many times longer sequence length does Sequence Parallelism achieve compared to the state-of-the-art method of tensor parallelism, under the given experimental conditions?


Question: In ICLR 2024 Poster papers, a paper attempts to address the hidden confounding problem in Offline Reinforcement Learning (RL). How many authors are there in this paper?


Question: How to represent the convolution operation of an image in the paper that first provides competitive performanceon LRA with Transformers and diagonal linear RNNs

Your answer should be a string, the formula in LaTeX formula.

(I * K)(x, y) = \sum_{i=-\infty}^{\infty} \sum_{j=-\infty}^{\infty} I(x-i, y-j) \cdot K(i, j)

Question: How to represent the convolution operation of an image with a formula in the paper Never Train from Scratch: FAIR COMPARISON OF LONGSEQUENCE MODELS REQUIRES DATA-DRIVEN PRIORS?


Question: In ICLR 2024 Poster papers, a paper proposes a reward smoothing method called "DreamSmooth". What is the formula of "DreamSmooth"?

Your answer should be the formula in LaTeX format.

Question: In ICLR 2024 Poster papers, a paper proposes a reward smoothing method called "DreamSmooth".

\tilde{r}_t \leftarrow f(r_{t - L:t + L}) = \sum_{i = -L}^{L} f_i \cdot r_{	ext{clip}(t + i, 0, T)} \quad s.t. \quad \sum_{i = -L}^{L} f_i = 1


Question: There is a paper that introduces NDCR, the first end-to-end framework for image retrieval from linguistically complex text by integrating analogical and logical reasoning. The research institutions involved in this study are all from the same country. Please provide the name of this country.

Your answer should be a name of a country.


Question: Why MINDER consumes relatively more memory than other models?

Your answer should be a phrase which concludes the reason.

Since MINDER considers multiview identifiers, it also consumes more memory to store these identifiers.

Question: Why MINDER consumes relatively more memory than other models?


Question: There is a recent paper conducting a comprehensive empirical evaluation of eight model selection methods for unsupervised domain adaptation (UDA), revealing their vulnerability to worst-case selections across diverse scenarios. The authors utilized a pool of 28 models and examined the performance of different selection methods as additional models were incrementally included. Which algorithm among SND, Ensemble, and EnsV demonstrates the best performance when 15 models are added?

Your answer should be one of the following: ['SND', 'Ensemble', 'EnsV'].


Question: There is a paper that introduces the first large-scale multimodal dataset specifically tailored for autonomous trucking, addressing challenges unique to heavy-duty vehicles such as dynamic trailer occlusion, sensor placement, and terminal environments. It features 747 diverse 20-second scenes annotated with high-quality 3D bounding boxes across 27 object classes. I would like to know which subcategory within the area category has the highest proportion in the distribution of scene tags for all 747 dataset scenes.

Your answer must be one of the following: ['city', 'highway', 'terminal', 'rural']


Question: Among the spotlight papers at ICLR 2024 focusing on neural architecture search, how many pretrained models and hyperparameter configurations did Quick-Tune evaluate to generate its large-scale meta-dataset?

It evaluated 24 pretrained image classification models across over 20,000 hyperparameter configurations on 87 datasets.

Question: How many pretrained models and hyperparameter configurations did Quick-Tune evaluate to generate its large-scale meta-dataset?


Question: Is there a paper published in ICLR 2024 which studies the estimation of a planted signal hidden in a recently introduced nested matrix-tensor model, which is an extension of the classical spiked rank-one tensor model, motivated by multi-view clustering?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Is there a paper published in ICLR 2024 which studies the estimation of a planted signal hidden in a recently introduced nested matrix-tensor model, which is an extension of the classical spiked rank-one tensor model, motivated by multi-view clustering?

Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model


Question: According to the paper at ICLR 2024 that proposed SPT, on which supervised task did incorporating data-driven pretraining improve the best reported results of state space models by 20 absolute points?

Your answer should be a Python string specifying the name of the supervised task that saw a 20-point improvement.


Question: In the ray resolution ablation, how does the Rotation Accuracy augment during the increase of number of camera rays from 2*2 to 16*16?

Your answer should be a float, rounded to 1 decimal place.


Question: Which paper proposes the new task open-world video instance segmentation and captioning?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper proposes the new task open-world video instance segmentation and captioning?

OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning


Question: In the paper that demonstrates that the dispersion of self-attention scores underlies Transformers' working memory limits in N-back tasks, mirroring human attention-based memory constraints, how many independent models the author uses for each N in N-backed tasks? How many epochs does the author train each model?

Your answer should be a Python list of two integers, the first is the number of independent models and the second is the number of epochs.


Question: What is the maximum accuracy improvement achieved by DapperFL over other federated learning frameworks?

Your answer should be a Python float value representing the percentage improvement in accuracy, between 0 and 100, rounded to 2 decimal places.


Question: Is there a paper published in ICLR 2024 which provides the first instantiation of the white-box design paradigm that can be applied to large-scale unsupervised representation learning?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Is there a paper published in ICLR 2024 which provides the first instantiation of the white-box design paradigm that can be applied to large-scale unsupervised representation learning?

Masked Completion via Structured Diffusion with White-Box Transformers


Question: Which paper published in ICLR 2024 propose the first LMaaS-compatible approach for leveraging LLMs to enhance representation learning on text-attributed graphs?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper published in ICLR 2024 propose the first LMaaS-compatible approach for leveraging LLMs to enhance representation learning on text-attributed graphs?

Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning


Question: A paper shows that language models' learning novel factual knowledge effectively from finetuning on limited textual demonstrations is due to its bias to learn word co-occurrence statistics instead of true factual associations. In the study of effect of layer wise ablation on the models' performance on simple QA and multiple choice tasks, which part of layer show a high responsibility to the models' performance?

Your answer should be a string, which indicates the part of layer. e.g. "upper 1/6 layers".


Question: How well does the DoRA, pretrained on a single Walking Tours video, perform compared to DINO, pretrained on ImageNet-1k in terms of semantic segmentation?

Your answer should be a sentence specifying the performance difference between DoRA and DINO in terms of semantic segmentation metrics, including the percentage improvement in mIoU.

DoRA pretrained on a single Walking Tours video surpasses DINO pretrained on ImageNet-1k by 1.5% in mean Intersection over Union (mIoU) for semantic segmentation tasks.

Question: How does the DoRA method's performance, when pretrained on a single Walking Tours video, compare to DINO pretrained on ImageNet-1k in terms of semantic segmentation?


Question: Is there a paper that introduces an image dataset consisting of over 20,000 images specifically designed for the task of emotion recognition? The dataset should feature complex scenes depicting multiple individuals in various naturalistic social settings.

Your answer should be the name of the paper.

Question: Is there a paper that introduces an image dataset consisting of over 20,000 images specifically designed for the task of emotion recognition? The dataset should feature complex scenes depicting multiple individuals in various naturalistic social settings.

FindingEmo: An Image Dataset for Emotion Recognition in the Wild


Question: In the paper that proposed Soft MoE, among the models trained for hundreds or thousands of TPU days, the performance on ImageNet of B/16 model with proposed method is close to that of another model with ViT. What's the difference in train TPU days of that two models?

Your answer should be a float, rounded to 1 decimal place.


Question: In ICLR 2024 Poster papers, which papers attempts to mitigate the error accumulation issue in model-based estimation resulting from the classical training of conventional diffusion models?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: In ICLR 2024 Poster papers, which papers attempts to mitigate the error accumulation issue in model-based estimation resulting from the classical training of conventional diffusion models?

DMBP: Diffusion model-based predictor for robust offline reinforcement learning against state observation perturbations


Question: A paper propose ConvGQR to reduce the re-training cost. In its experiment, after normalizing the generated answer by the length of its corresponding relevant passage, how the PCC value of co-occurence performs?

Your answer should be a phrase which concludes the overall tendance of the mentionned data.

Question: After normalizing the generated answer by the length of its corresponding relevant passage, how the PCC value of co-occurence performs?


Question: In the paper proposing Self Pretraining (SPT), experiments demonstrate that self-pretraining on downstream task data using standard denoising objectives significantly enhances the performance of long-sequence models. This finding indicates that vanilla Transformers can achieve or exceed the performance of state-of-the-art models, such as S4, on the Long Range Arena without requiring architectural modifications. Please answer the following question: Which method, Masked SPT or Causal SPT, demonstrates higher average performance on the Long Range Arena in the paper?

Your answer should be one of [Masked SPT, Causal SPT]


Question: What specific graph structures can r-loopy Weisfeiler-Leman (r-ℓWL) test count that the classical 1-WL cannot?

Your answer should be a Python string specifying the type of graph structures that r-ℓWL can count but 1-WL cannot.

The r-ℓWL test can count homomorphisms of cactus graphs, which the classical 1-WL cannot.

Question: What specific graph structures can r-loopy Weisfeiler-Leman (r-ℓWL) test count that the classical 1-WL cannot?


Question: According to this paper, what is the rate at which the asymptotic covariance of the optimization iterate errors decreases with respect to the self-repellence parameter $\alpha$?

Your answer should be a Python string containing the rate of decrease in big O notation, including the exact mathematical expression.

Question: According to this paper, what is the rate at which the asymptotic covariance of the optimization iterate errors decreases with respect to the self-repellence parameter $\alpha$?


Question: In the paper that proposes RSTC model, for the heavy imbalanced dataset GoogleNews-T, with which value of the proper hyper-parameter $\epsilon_2$ the accurancy reaches its peak?

Your answer should be a float rounded to 3 decimal places.


Question: In ICLR 2024 Spotlight papers, a paper proposes a method called "SimNorm" for normalizing the latent representation. What is the core advantage of "SimNorm"?

Question: In ICLR 2024 Spotlight papers, a paper proposes a method called "SimNorm" for normalizing the latent representation. What is the core advantage of "SimNorm"?

The core advantage of "SimNorm" is that it can enforce the latent representation to be sparse, making it easier for the model to learn.


Question: Which model is used as the language component in PaLI? Besides the sizes used in PaLI, how many sizes of this model are available in the source paper?

Your answer should be a single python list like this: ["model_name", integer].Note that the model name should use the abbreviation and not inuclude the size information. For the integer, you should give the number of sizes not used in the paper of PaLI, not the total number of sizes.


Question: In "Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations", which paper inspired the authors to calculate the expert-level token contribution?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: In "Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations", which paper inspired the authors to calculate the expert-level token contribution?

ShortGPT: Layers in Large Language Models are More Redundant Than You Expect


Question: Among the datasets and benchmarks at NeurIPS 2024 evaluating large language models' capabilities, what is the name of the benchmark introduced to evaluate AI models' understanding of humorous contradictions in comics?

Your answer should be a Python string containing the name of the benchmark.


Question: A paper proposes Bayesian red teaming (BRT) to reduce the model's potentiel harmful response. In the experiment, does the author maintain all the offensive test cases during the generation?


Question: In the paper that establishes one of the first low-degree polynomial lower bound for tree broadcasting below the Kesten-Stigum threshold in a non-product-measure setting, what is the critical condition of the proof of formula (7)?

Your answer should be a Python strings indicating the critical condition of the formula.

It is to show that the inequality holds when X_{\leq w_1} and  X_{\leq w_2} are conditionally independent given X_w by the Markov Property, especially when some entries of M can be 0.

Question: In the paper that establishes one of the first low-degree polynomial lower bound for tree broadcasting below the Kesten-Stigum threshold in a non-product-measure setting, what is the critical condition of the proof of formula (7)?


Question: Among the long papers at ACL 2023 focusing on emotion recognition in conversations, which modeling strategies (e.g., graph-based, representation learning, or fusion networks) are most commonly employed for improving fine-grained emotion recognition, and which approaches show the most notable performance gains on benchmarks like IEMOCAP or MELD?

Your answer should be a Python string describing the most common modeling strategies and which specific approaches (with their names) showed the best performance gains on IEMOCAP or MELD benchmarks.

Graph-based models and cross-modal fusion networks were the most effective strategies for fine-grained emotion recognition in conversations at ACL 2023, with DualGATs and CMCF-SRNet showing the strongest performance gains on IEMOCAP and MELD.

Question: What modeling strategies (e.g., graph-based, representation learning, or fusion networks) are most commonly employed in ACL 2023 for improving fine-grained emotion recognition in conversations, and which approaches show the most notable performance gains on benchmarks like IEMOCAP or MELD?


Question: According to the DP-OPT paper, while varing privacy parameters, which base model trade-off the less?

Your answer should be a string, the name of the base model along with its size, as originally presented in the paper.


Question: A recent paper introduces a universal framework for dataset characterization that integrates 23 types of model-driven meta-information, encompassing static measures, training dynamics, model uncertainty, and pre-trained knowledge, into a unified multidimensional feature space. The paper tests 10 selected samples using different selection methods on the QNLI dataset, employing their log determinant as a proxy measure for set informativeness. Which selection method corresponds to the lowest log determinant?

Your answer should be one of: ['Ambig', 'Hard', 'CoreSet', 'InfoVerse (DPP)']


Question: In the paper that proposed the R2I method, which dataset covered in the experiment is the newest? In that dataset, how many environments are there for each tag?

Your answer should be a Python list of 2 elements, the first is a string, the name of the dataset, the second is a Python dict, the key is the tag and the value is the number of environments.


Question: Among the spotlight papers at NeurIPS 2024 researching contrastive learning, what is the key mechanism identified in the paper "Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering" that enhances the performance of Graph Contrastive Learning (GCL) methods?

Your answer should be a Python string describing the key mechanism and how it enhances performance of Graph Contrastive Learning methods.

The key mechanism identified is "representation scattering," which involves distributing node representations away from a central point to enhance diversity and uniformity in the embedding space.

Question: What is the key mechanism identified in the paper "Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering" that enhances the performance of Graph Contrastive Learning (GCL) methods?


Question: Which paper published in ICLR 2024 proposes LLM-grounded Video Diffusion (LVD), the first training-free pipeline that leverages LLM-generated dynamic scene layouts for enhanced ability to generate videos from intricate text prompts?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper published in ICLR 2024 proposes LLM-grounded Video Diffusion (LVD), the first training-free pipeline that leverages LLM-generated dynamic scene layouts for enhanced ability to generate videos from intricate text prompts?


Question: What is the main motivation of eliminating the homogeneous distractors in image or video?

Question: What is the main motivation of eliminating the homogeneous distractors in image or video?

The main motivation is that, compared with the heterogeneous distractors, the homogeneous distractors are more difficult to be eliminated, and implies a high degree of relevance to the task.


Question: Look at Figure 1 in the paper. What two main components does the diagram show as inputs to the Language Model block? Based on the figure, what is the output of the model, and how is it evaluated?

Your answer should be a sentence answering the two questions.

The diagram shows the Issue text and the Codebase as inputs.

The output is a Generated PR (pull request), which is evaluated by applying the patch to the codebase and running tests.

Question: Look at Figure 1 in the paper. What two main components does the diagram show as inputs to the Language Model block? Based on the figure, what is the output of the model, and how is it evaluated?


Question: A recent paper introduces the first standardized benchmark for video-language continual learning, designed to evaluate models on three novel query-incremental tasks: Moment Query (MQ), Natural Language Query (NLQ), and Visual Query (VQ). Could you please specify which egocentric video-language dataset the proposed data collection is derived from?

Your answer should be a name of a dataset.


Question: In the experiment section of "InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews", where does the character data come from, and how many characters are there in each character source dataset?

Your answer should be a Python dictionary of one or more key-value pairs, where each key is the name of the source character dataset and each value is an integer indicating the number of characters in that dataset. e.g. {"dataset1": 3, "dataset2": 5}


Question: In the paper where a novel approach named SLAN is proposed, what is the role of \delta_{i}^{k} and P_k in formula (3)?

Your answer should be a Python strings indicating the role of \delta_{i}^{k} and P_k.

\delta_{i}^{k} is a indicator variable, where \delta_{i}^{k} = 1 if lk is not associated with xi; otherwise \delta_{i}^{k} = 0. P_k is a $(q - 1) \times q$ projection matrix, which removes the k-th row of the identity matrix

Question: In the paper where a novel approach named SLAN is proposed, what is the role of \delta_{i}^{k} and P_k in formula (3)?


Question: How many vision encoders did Cambrian-1 evaluate to study different visual representation choices?

Your answer should be an integer indicating the number of vision encoders evaluated.


Question: In ICLR 2024 Spotlight papers, a paper proposes a new Adversarial Imitation Learning (AIL) algorithm, aiming to address the sample efficiency and scalability issues of existing AIL methods when dealing with off-policy data. How many different tasks are considered in Figure 2.


Question: According to the spotlight paper at NeurIPS 2024 that applies user-level differentially private algorithms in federated learning, what range of privacy loss $\varepsilon$ did the one-shot empirical estimation method report in the scenario where only the final trained model is released?

Your answer should be a Python list of two float values: [lower_bound, upper_bound],  representing the range of privacy loss $\varepsilon$ values, both rounded to 4 decimal places.


Question: What is the main difference between formula (5) and (6) in the paper "Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale"?

Question: What is the main difference between formula (5) and (6) in the paper proposing Voicebox?

Formula (5) is a function computing the loss on all frames, including those that are not masked and would not be required during inference. Formula (6) is a masked version of L_{audio-CFM}, which leads to better results.


Question: There is a paper that introduces a curriculum learning framework that leverages prior knowledge about sample difficulty, measured through annotation entropy and loss, to discover effective, often non-monotonic curricula tailored to NLP models and datasets. Which university's researchers proposed this work?

Your answer should be a name of a university.

University of Massachusetts Lowell
Massachusetts Lowell


Question: How much more accurate is InfoBatch compared to the static pruning method EL2N-2 on CIFAR-100 with 50% prune ratio?

Your answer should be a nomber, indicating the precentage of the difference of accuracy, rounded to 1 decimal place.


Question: For the second-best method shown in Figure 2, where can I find their GitHub repository to reproduce the results?

Please provide the GitHub repository URL for this dataset in the format: 'https://github.com/xxx'.


Question: According to the first comprehensive study for LLM attribution at ACL 2023, what is the accuracy of the best attribution method in tracing fine-tuned models back to their pre-trained base models?

Your answer should be a Python string in the format 'X out of Y models', where X is the number of correctly attributed models and Y is the total number of models tested.


Question: In ICLR 2024 Poster papers, a paper first constructs a dynamics model from the expert demonstration, enforcing local Lipschitz continuity while skipping the discontinuous regions. What is the number of the pages of this paper?


Question: In the paper that reveals that retrieved information helps retrieval-augmented language models' (RALMs) performance when it is relevant, for Llama-2-13B few-shot prompted on five QA tasks, how many kinds of benchmarks benefit from a strong retrieval?


Question: In ICLR 2024 Poster papers, which paper introduces a generalized attack framework that has the flexibility to model to what extent the adversary is able to control the agent, and allows the attacker to regulate the state distribution shift and produce stealthier adversarial policies?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: In ICLR 2024 Poster papers, which paper introduces a generalized attack framework that has the flexibility to model to what extent the adversary is able to control the agent, and allows the attacker to regulate the state distribution shift and produce stealthier adversarial policies?

Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL


Question: In the paper that proposed CutSSL, how much does the CutSSL method outperform the state-of-the-art on Cifar-10 dataset when only a single sample from each class is provided and when in the large label-rate regime respectively?

Your answer should be a python list of two strings representing the outperformance in the form of percentage, rounded to 1 decimal place, e.g. "10.0%". Recall that order matters.


Question: A paper introduce Induced Model Matching (IMM) to train a full-featured (often larger) model with help of  a very accurate (often small) predictive model using restricted features. In the comparaison of MDP trained without and with IMM incorporating POMDP, which model shows a high stability during the training, with IMM or no IMM?

Your answer should be chosen between "with IMM" and "no IMM".


Question: A paper introduces FlexLoRA, a simple yet effective aggregation scheme for Large Language Models' fine-tuning. In their experiment of FlexLoRA's performance in a controlled environment using homogeneous LoRA ranks, does FlexLoRA's aggregation negatively impact model performance?


Question: Which method aims to recover camera poses and scene geometry from a large set of unordered or ordered images, in particular, by optimizing photometric errors?

Your answer should be a string, the name of the method

Question: What aims to recover camera poses and scene geometry from a large set of unordered or ordered images, in particular, by optimizing photometric errors?


Question: There is a recent paper introducing a large-scale, high-resolution video-text dataset annotated with detailed, script-like captions averaging 145 words per clip, over 10 times longer than existing datasets. It uniquely captures not only scene content but also camera operations (e.g., shot types and movements). Could you please tell me which category within this dataset has a video count that exceeds 10% of the total?


Question: A paper extend the notion of a risk controlling prediction set (RCPS) to the sequential setting, in their evaluation of our methods on the Imagenet dataset, among four methods, one method got the highest average rate of safety violations, what is it?

Your answer should be a string, a name of method which corresponds to the label in the figure.


Question: Figure 1 illustrates the cosine value density plots for different noise levels and different number of canary repetitions. How these density plots illustrate the increased privacy preserving effect of canary as the noise increases?

As the noise increases, the observed distribution of canary cosine values (blue curve) gradually approaches the distribution of canaries that did not participate in training (black curve, i.e., N(0,1/d)), suggesting that the noise masks the effect of canaries, making it more difficult for an attacker to differentiate which canaries participated in the training, thus enhancing the privacy protection effect.

Question: Figure 1 illustrates the cosine value density plots for different noise levels and different number of canary repetitions. How these density plots illustrate the increased privacy preserving effect of canary as the noise increases?


Question: What is the affiliation of the first author of the paper?

School of Artificial Intelligence, Nanjing University, China


Question: What is the challenge of estimating camera poses now?

Your answer should be a string, describing the condition

Estimating camera poses is a fundamental task for 3D reconstruction and remains challenging given sparsely sampled views (<10)

Question: What is the challenge of estimating camera poses now?


Question: A recent paper introduces GEEP (GEnder Equality Prompt), a novel debiasing method that mitigates gender bias in large pre-trained language models such as RoBERTa without degrading their performance on downstream tasks. Could you please tell me who the first author of this paper is?

Your answer should be a name of a person.


Question: In the question that proposes a practical rephrasing-based method to estimate uncertainty in closed-source LLMs, combining simple memorizable rules with a theoretical framework for calibrated confidence scores, which model, dataset, and rephrasing method are used in Figure 3c to validate the logistic distribution assumption?

Your answer should be a Python list of three strings, the first is the model name, the second is the dataset name and the third is the rephrasing method.


Question: In Figure 2, what three filtering stages are depicted for constructing SWE-bench tasks? What specific criteria must a PR meet to pass the Execution Filter stage?

Your answer should be a sentence answering the two questions.

The stages are: (1) Scrape PRs, (2) Attribute Filter,  (3) Execution Filter.

The PR must install successfully and pass all tests (fail-to-pass tests).

Question: In Figure 2, what three filtering stages are depicted for constructing SWE-bench tasks? What specific criteria must a PR meet to pass the Execution Filter stage?


Question: A recent paper presents the first comprehensive benchmark for disentangling aleatoric and epistemic uncertainty in deep learning. It evaluates 19 uncertainty quantification methods across 13 tasks on ImageNet and CIFAR-10, revealing that existing decomposition formulas fail to produce truly disentangled estimators. Could you please provide the email address of the first author of this paper?


Question: A paper propose a SummAttacker to generate adversarial samples based on language models efficiently. Performance of different models on the Gigaword test set vary when attacked by SummAttacker with different candidate number K. Generally, a larger K gives model positive or negative impact?

Your answer should be chosen between "positive" and "negative".


Question: I remember there is a paper that develops a language, perhaps MathDL? How does it measure whether target model generates more concise solutions or not?

Your answer should be a math formula in LaTeX format WITHOUT ANY EXPLANATION.

C(s_A, s_B \mid s_A, s_B \text{ both solve } e) = \frac{f(s_B) - f(s_A)}{f(s_B)}

Question: I remember there is a paper that develops a language, perhaps MathDL? How does it measure whether target model generates more concise solutions or not?


Question: Among the papers at NeurIPS 2024 researching multi-agent systems, what is the maximum accuracy improvement achieved by MDAgents when incorporating moderator review and external medical knowledge in group collaboration?

Your answer should be a Python float value representing the percentage improvement in accuracy, between 0 and 100, rounded to 1 decimal place.


Question: Which paper at ICLR 2024 proposed a framework for efficient fine-tuning of bidirectional interleaved visual languages for reference image segmentation, which achieved an average score of 66.5 on three RefCOCO-related benchmarks? In this paper, how many images are there in the group of images related to kid runningninng?

Your answer should be a list of two strings, with the first being the paper title and second being a number.

Question: Which paper at ICLR 2024 proposed a framework for efficient fine-tuning of bidirectional interleaved visual languages for reference image segmentation, which achieved an average score of 66.5 on three RefCOCO-related benchmarks?

BarLeRIa: An Efficient Tuning Framework for Referring Image Segmentation


Question: In ICLR 2024 Poster papers, which paper proposes GRAD, a game-theoretic approach that treats the temporally-coupled robust RL problem as a partially-observable two-player zero-sum game?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: In ICLR 2024 Poster papers, which paper proposes GRAD, a game-theoretic approach that treats the temporally-coupled robust RL problem as a partially-observable two-player zero-sum game?

Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations


Question: In ICLR 2024 Poster papers, a paper mainly studies how to more effectively utilize data augmentation techniques to improve sample efficiency and generalization ability in image-based deep reinforcement learning (DRL). Tell me the number of authors in this paper.


Question: Can you recommend me a paper published in ICLR 2024 that proposes a framework that synergistically integrates compiled neural networks (CoNNs) into the standard transformer architecture?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Can you recommend me a paper published in ICLR 2024 that proposes a framework that synergistically integrates compiled neural networks (CoNNs) into the standard transformer architecture?

Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks


Question: Among the long papers at ACL 2023 researching text classification, how many benchmark datasets were used in the experiments to validate the effectiveness of GetMTL?

Your answer should be a Python integer representing the number of benchmark datasets used in the experiments.


Question: What percentage of experts can be pruned from the NLLB-200 model without further finetuning and with negligible loss in translation quality?

Your answer should be a Python float value representing the percentage of experts that can be pruned, between 0 and 100, rounded to 1 decimal place.


Question: A recent paper introduces the first document-level relation extraction (RE) dataset in the historical domain, which includes bilingual annotations in both Korean and Hanja. Constructed from the Yeonhaengnok travel records of the Joseon dynasty, the proposed dataset provides annotated entities, relations, and supporting evidence across variable-length textual units. How many documents does this dataset contain?


Question: In the paper proposing a unified framework to model semantic segmentation and semantic image synthesis as a pair of reverse problems, why can the ODE model model these two problems simultaneously?

Question: In the paper proposing a unified framework to model semantic segmentation and semantic image synthesis as a pair of reverse problems, why can the ODE model model these two problems simultaneously?

ODE framework aims to learn the mapping between two distributions through straight trajectories. We aim to learn the velocity field using neural networks v_\theta (z_t, t) and solve it with optimization methods: L = \int_0^1 \mathbb{E}_{(z_0, z_1) \sim \gamma} \left\| v_\theta(z_t, t) - \frac{\partial \varphi_t(z_0, z_1)}{\partial t} \right\|^2 dt  = \int_0^1 \mathbb{E}_{(z_0, z_1) \sim \gamma} \left\| v_\theta(z_t, t) - (z_1 - z_0) \right\|^2 dt. And the formula as a time-symmetric form, which results in an equivalent problem by exchanging z0 and z1 and flipping the sign of v_\theta. Interestingly, the transportation problem from \pi_1 to \pi_0 indicates the semantic image synthesis task. This means that semantic segmentation and semantic image synthesis essentially become a pair of mutually reverse problems that share the same ODE and have solutions with opposite signs.


Question: What performance improvements does LoftQ achieve over QLoRA in 2-bit quantization settings on the LLaMA-2-13B model for the GSM8K dataset?

Your answer should be a sentence, stating the accuracy achieved by LoftQ and the comparison with QLoRA.

LoftQ achieves a 25.4% accuracy on GSM8K with 2-bit quantization, outperforming QLoRA, which fails to converge under the same settings.

Question: What performance improvements does LoftQ achieve over QLoRA in 2-bit quantization settings on the LLaMA-2-13B model for the GSM8K dataset?


Question: In the paper that reveals that LLMs develop a two-phase abstraction process during training, and give initial evidence that their brain-like encoding ability stems from compositional learning rather than next-word prediction, why is the choose of k which controls the neighborhood size duing the nonlinear ID estimation necesary?

Your answer should be a Python string indicating the reason why we shoud have a scale analysis on k.

if k is too small, the I_d likely describes local noise, and if k is too large, the curvature of the manifold will produce a faulty estimate.

Question: In the paper that reveals that LLMs develop a two-phase abstraction process during training, and give initial evidence that their brain-like encoding ability stems from compositional learning rather than next-word prediction, why is the choose of k which controls the neighborhood size duing the nonlinear ID estimation necesary?


Question: What is the main purpose of Figure 1 and how does it demonstrate the role specialization in MetaGPT?

Your answer should be a sentence answering the two questions.

It illustrates the Standardized Operating Procedures (SOPs) in software development, comparing the workflow between MetaGPT's multi-agent framework and real-world human teams. It highlights how tasks are decomposed and assigned to specific roles (e.g., Product Manager, Architect, Engineer) in both settings.

The figure visually maps each role (e.g., Product Manager creating PRDs, Architect designing system components) to sequential workflow stages, showing how specialized outputs (like PRDs or interface designs) are handed off between roles, mirroring human team collaboration.

Question: What is the main purpose of Figure 1 and how does it demonstrate the role specialization in MetaGPT?


Question: A paper introduce ROBUSTAL PACAEVAL, a new benchmark, to address the sensitivity of large language models (LLMs) to the phrasing of prompts. In their examination of the model-agnostic attributes of the worst prompts, Llama family or Gemma family get the higher overlap rate of the worst-k prompts?

Your answer should be chosen between "Llama" and "Gemma".


Question: In the paper of offline Q-learning, which state-of-the-art return-conditioned supervised method was mentionned? Which conference was this method published in?

Your answer should be a single python list like ["paper_title", "conference_name_and_year"], the paper title should be the full title of state-of-the-art return-conditioned supervised method, and conference_name_and_year should be the abbreviation of the conference and the year like "ACL2021".Note that arxiv should not be included as the conference name.


Question: The paper titled "Automatic Camera Pose Estimation by Key-Point Matching of Reference Objects" was conducted by researchers from which country?

Your answer should be a name of a country


Question: In the paper that introduces POLICY-LEARN, a new approach that learns how to select subgraphs in an iterative manner, what is formula 3 used for?

Your answer should be a brief summary of the formula's purpose and it should begin with 'To ...'.

To reduce the channel dimension to one and to obtain the following un-normalized node probabilities from which we sample the next root node.

Question: In the paper that introduces POLICY-LEARN, a new approach that learns how to select subgraphs in an iterative manner, what is formula 3 used for?


Question: A paper introduce a novel training algorithm, Learn-To-be-Efficient (LTE), to achieve a better trade-off between sparsity and performance. In the the 5-shot MMLU accuracy comparison, which model performs the worst across all sparsity levels?

Your answer should be chosen among "Deja Vu", "R-Llama" and "LTE".


Question: Which is one of the common error appears across the models?

Your answer should be a string which mentions a type of error.

Valid but incorrect translations
In-context but irrelevant words

Question: Which is one of the common error appears across the models?


Question: How does Code4Struct perform in zero-resource event types when utilizing 10-shot training data from a sibling event type?

Your answer should be an integer between 0 and 100 specifying the absolute F1 improvement over the zero-shot baseline.


Question: In the paper that proposes a graph rewiring framework that establishes express connections between distant nodes for non-local message passing, overcoming the over-smoothing problem without requiring deep architectures, list the name of nine graph datasets where the experiments are carried out.

Your answer should be a Python list of strings indicating the name of nine datasets.

Cora PubMed Citeseer Texas Wisconsin Chameleon Squirrel Roman-empire Amazon-ratings


Question: What overall accuracy does GPT-4V achieve on the MathVista benchmark?

Your answer should be a Python float value representing the percentage accuracy, between 0 and 100, rounded to 1 decimal place.


Question: According to this paper, among Open-weight LLMs, which model has the best memory sub-score?

Your answer should be a string, a name of model


Question: A recent paper introduces the first large-scale, open-source, high-fidelity 3D CFD dataset based on 355 geometrical variations of the Windsor car body. Each case is simulated using GPU-native Wall-Modeled Large-Eddy Simulations (WMLES) with over 280 million cells, capturing detailed aerodynamic flow features relevant to real road vehicles. Under what license is this dataset available as open source?

Your answer should be a license name and must adhere precisely to the format presented in the paper without version information.


Question: What is the main contribution of the proposed method which called Averaged-DQN in this paper?

Question: What is the main contribution of the proposed method which called Averaged-DQN in this paper?

Averaged-DQN can alleviate the overestimation problem of DQN by ensembling the historical Q-values.


Question: What is the training objective (loss function) for the reverse diffusion process in the paper?

L(\theta) := E_{t\sim U(0,T),x0\sim q0(x0),\epsilon\sim N(0,I)} \left[ \|\epsilon_\theta (\alpha_t x0 + \sigma_t \epsilon, t) - \epsilon\|^2_2 \right].

Question: What is the training objective (loss function) for the reverse diffusion process in the paper?


Question: What is the mathematical expression for the first loss function proposed to measure approximate Nash equilibria?

Question: What is the mathematical expression for the first loss function proposed to measure approximate Nash equilibria?


Question: A paper propose a tractable surrogate model of choice (CRCS), which show a better basis for preference learning, could this model works on choice sets of variable size?


Question: A paper propose a two-stage Differentially Private (DP) generation method, in the second step, it generates utterances based on the parses. Does the distribution function p_priv(x) discribes private user utterances with a high accurancy comparing with selecting them by active learning?


Question: In the paper that proposes SGRLD, the author compare their SGRLD method to other MCMC methods. Which optimizer does the second method extends to the SCLD setting? And in the paper that proposes this optimizer, which two methods eventually converge considerably faster in Convolutional neural networks training cost?

Your answer should be a python list of two elements, the first is a single word, the name of the optimizer, and the second is a python list of two words, the name of the two methods as given in the paper.


Question: What is the simplification to S4 called Diagonal Linear RNN according to the paper that  improves the best reported results of SSMs on the PathX-256 task by 20 absolute points

Your answer should be a list of formulas

\vec{x}_n &= \Lambda \vec{x}_{n-1} + I u_n &\Lambda &\in \operatorname{diag}(\mathbb{C}^{N\times N})

\ y_n &= C \vec{x}_n &C &\in \mathbb{C}^{1\times N}

Question: What is the simplification to S4 called Diagonal Linear RNN (DLR) proposed by Gupta et al?


Question: In the paper that proposes MHCD_IFF, list three models in figures 3 which have the best performance.

Your answer should be a python list of three strings, each string being the name of a model.


Question: In the test of Qwen2-72B-Instruct, Qwen2.5-Turbo, Qwen2-0.5B-Instruct, Qwen2-57B-A14B-Instruct based on the context length of a given document and the ability of document depth retrieval, what is the difference in the retrieved Context Length?

Your answer should be a python lidt of four strings, explaining the retrieval content length of the four models respectively. eg " model name: range from 0 to roughly 20k tokens".

Qwen2-72B-Instruct: range from 0 to roughly 128k tokens

Qwen2.5-Turbo: range from 0 to 1000k tokens

Qwen2-0.5B-Instruct: range from 0 to 32k tokens

Qwen2-57B-A14B-Instruct: range from 0 to 64k tokens

Question: In the test of Qwen2-72B-Instruct, Qwen2.5-Turbo, Qwen2-0.5B-Instruct, Qwen2-57B-A14B-Instruct based on the context length of a given document and the ability of document depth retrieval, what is the difference in the retrieved Context Length?


Question: In ICLR 2024 Poster papers, a paper attempts to address the challenges faced when learning from pixel-level inputs in multi-object manipulation tasks. Tell me the affiliation of the first author of this paper.

Department of Electrical and Computer Engineering, Technion- Israel Institute of Technology


Question: A recent paper introduces a scientist-curated benchmark for evaluating language models on real-world scientific coding problems across 16 natural science subfields. Comprising 80 main problems decomposed into 338 subproblems, each annotated and validated by domain experts, the benchmark assesses models' abilities in knowledge recall, reasoning, and code synthesis. Please retrieve the paper and provide me with the link for data, code, and the leaderboard corresponding to this work.

Your answer should be a link only without any additional prefixes or suffixes.


Question: What is the sample complexity bound for achieving an $\varepsilon$-optimal estimator in the non-parametric distributional TD learning (NTD) method?

Your answer should be a Python string containing the mathematical expression for the sample complexity bound in LaTeX-like format.

Question: What is the sample complexity bound for achieving an $\varepsilon$-optimal estimator in the proposed non-parametric distributional TD learning (NTD) method?


Question: There is a paper that introduces a novel constrained decoding algorithm called Prefix-Suffix Guided Decoding (PSGD) for the Translation Suggestion (TS) task in interactive machine translation. Unlike prior methods that require retraining or generate the full sequence, PSGD decodes only the selected incorrect span while maximizing the probability of the entire sentence, conditioned on given prefix and suffix constraints. Question: What is the average BLEU score of PSGD in the experiments conducted on the WMT22-TS test sets?

Your answer should be a Python float rounded to 2 decimal places.


Question: In ICLR 2024 Poster papers, which paper addresses the curse of dimensionality by learning the inherent structure of action-wise similar MDP to appropriately balance the performance degradation versus sample/computational complexity?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: In ICLR 2024 Poster papers, which paper addresses the curse of dimensionality by learning the inherent structure of action-wise similar MDP to appropriately balance the performance degradation versus sample/computational complexity?

Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping


Question: What're the 4 main findings in the spotlight paper at NeurIPS 2024 that investigate the relationship between self-recognition and self-preference for LLMs?

Your answer should be a Python string describing the 4 main findings of the paper.

Frontier LLMs exhibit self-preference in selfevaluation.

LLMs have non-trivial self-recognition capability out of the box.

Fine-tuning leads to near-perfect self-recognition.

Self-preference strength is linearly correlated with self-recognition.

Question: What're the 4 main findings in the spotlight paper at NeurIPS 2024 that investigate the relationship between self-recognition and self-preference for LLMs?


Question: Recent advancements use knowledge transfer techniques like Score Distillation Sampling (SDS) to overcome the limited availability of comprehensive annotated training data. A paper study this method deeply. In the figure which shows the comparison of using normal-SDS jointly with RGB-SDS, which two methods generate a panda with armor on its body, give one of them.

Your answer should be a string, which gives the name of method.

Fantasia3D+SDXL+PGC
Ours
Fantasia3D+SDXL+PGC w/o normal-SDS
Ours w/o nrm

Question: In the figure which shows the comparison of using normal-SDS jointly with RGB-SDS, which two methods generate a panda with armor on its body, give one of them.


Question: In ICLR 2024 Poster papers, a paper attempts to solve the problem of how to enhance the arithmetic reasoning capabilities of large language models (LLMs) through zero-sample hint optimization. Tell me the codebase url of the paper.


Question: In the paper that introduces a novel method that employs dynamic and directed weight adjustment techniques to modulate the synthesis process during dataset distillation, how much percent does the DWA method outperform the SRe2L method in accuracy compared to the baseline on Tiny-lmageNet, lmageNet-1K and CIFAR100 dataset respectively?

Your answer should be a Python list of three float numbers, all rounded to 1 decimal place.


Question: In ICLR 2024 Poster papers, a paper attempts to propose a meta-reinforcement learning algorithm that is improved in multiple aspects, especially in terms of sample efficiency, generalization ability, and handling of high-dimensional task distributions, by combining the latest model-based RL techniques and meta-RL techniques. What is the formula of "General Regret Bounds"?

Your answer should be the formula in LaTeX format.

Question: In ICLR 2024 Poster papers, a paper attempts to propose a meta-reinforcement learning algorithm that is improved in multiple aspects, especially in terms of sample efficiency, generalization ability, and handling of high-dimensional task distributions, by combining the latest model-based RL techniques and meta-RL techniques. What is the formula of "General Regret Bounds"?

\mathbb{E}_{\theta\sim f}\left[\mathbb{E}_{\pi_{BO}, M = g(\theta)}\left[\sum_{t = 1}^{H} r_t\right] - \mathbb{E}_{\pi, M = g(\theta)}\left[\sum_{t = 1}^{H} r_t\right]\right]


Question: Among the Model-based Reinforcement Learning papers in ICLR 2024, which one proposes the model called "Skipper". Tell me what $\pi$ means in figure 2.

Your answer should be a python string about the meaning of the math expreesion in the paper. You"d better use the names as they are referred to in the paper.


Question: In ICLR 2024 Poster papers, a paper proposes a novel framework named PARL (Policy Alignment in Reinforcement Learning), aiming to address the policy alignment problem in Reinforcement Learning (RL). What is the formula of the standard finite horizon policy optimization problem in this paper?

Question: In ICLR 2024 Poster papers, a paper proposes a novel framework named PARL (Policy Alignment in Reinforcement Learning), aiming to address the policy alignment problem in Reinforcement Learning (RL). What is the formula of the standard finite horizon policy optimization problem in this paper?

\max_{\theta} V_s(\theta) := \mathbb{E} \left[ \sum_{h = 0}^{H - 1} \gamma^h r(s_h, a_h) \mid a_h \sim \pi_{\theta}(a_h \mid s_h), s_0 = s \right]


Question: Compare to the baseline called "DreamerV3", how much improvement does "Hybrid RSSM" achieve in "Lift Cube" in average?

Your answer should be a Python float number rounded to 1 decimal place. e.g. 20.3


Question: Among the papers in ICLR 2024, which paper proposes the conception called "Policy Rehearsing"? Explain the conception of "Policy Rehearsing" in the paper.

Question: Among the papers in ICLR 2024, which paper proposes the conception called "Policy Rehearsing"? Explain the conception of "Policy Rehearsing" in the paper.

"Policy Rehearsing" is a method that trains a generalizable policy for reinforcement learning by replaying past experiences.


Question: In the paper that proposed MDAgents, for image+text queries, how much higher is the accuracy of the Adaptive setting, compared to the High setting?

Your answer should be a float rounded to 1 decimal place.


Question: What visual elements in Figure 1 distinguish the three fine-tuning scenarios (a, b, c)?  How does the Initial harmfulness score differ between subfigures (a), (b), and (c)

Your answer should be a sentence answering the two questions.

Each scenario is labeled with a subfigure title (a/b/c), uses distinct color-coded harmfulness score bars, and includes example input-output pairs for clarity.

It  vary slightly due to different system prompts used for each dataset.

Question: What visual elements in Figure 1 distinguish the three fine-tuning scenarios (a, b, c)?  How does the Initial harmfulness score differ between subfigures (a), (b), and (c)?


Question: How does ClimODE simulate weather and climate physics? Give me the formula of its equation.

Your answer should be a python list containing a sentence and a formula, eaching answer one of the two questions.

The core idea of ClimODE is to simulate weather and climate physics through continuous-time processes (Neural ODEs), specifically implementing the advection principle from statistical mechanics where weather changes result from spatial movement of quantities over time.

Question: How does ClimODE simulate weather and climate physics? Give me the formula of its equation.

\frac{\partial u_k}{\partial t} = -v_k \cdot \nabla u_k - u_k \nabla \cdot v_k

Question: Give me the formula of the equation of ClimODE.


Question: A paper use a mean-based decomposition method to extend the context window and achieve length extrapolation of transformer-based large language models (LLMs). In their study of relationship between positional vectors and length extrapolation ability, besides TL-Window-RoPE, which model maintains stable PPL across longer texts?

Your answer should be a string which indicates a name of model


Question: What two key components of MetaGPT are depicted in Figure 2 and how does the right panel emphasize the improvement in code quality?

Your answer should be a sentence answering the two questions.

Figure 2 shows (left) the shared message pool and subscription mechanism for agent communication, and (right) the iterative programming process with executable feedback, where the Engineer debugs code based on test results.

It demonstrates the feedback loop: the Engineer runs generated code, checks for errors, and iteratively refines it by referencing past messages (e.g., PRDs, designs). This ensures runtime correctness and reduces hallucinations, as noted in the caption.

Question: What two key components of MetaGPT are depicted in Figure 2 and how does the right panel emphasize the improvement in code quality?


Question: In ICLR 2024 Oral papers, a paper attempts to solve the problem of how to accelerate the learning process and avoid getting trapped in local optimal solutions in Cooperative Multi-Agent Reinforcement Learning (MARL). Tell me the number of authors of this paper.


Question: In the paper that proves gradient-based algorithms achieve polynomial smoothed complexity for solving zero-sum games, eliminating exponential dependence on condition numbers through a novel perturbation-stability analysis, what is the main conclution of formula (3) and definition 1.3 on \kappa?

Your answer should be a Python strings indicating the main conclusion.

The modulus \kappa is likely to be polynomial in the smoothed complexity model.

Question: In the paper that proves gradient-based algorithms achieve polynomial smoothed complexity for solving zero-sum games, eliminating exponential dependence on condition numbers through a novel perturbation-stability analysis, what is the main conclution of formula (3) and definition 1.3 on \kappa?


Question: In the paper at ACL 2023 that first analyzed instance-level pretraining data to interpret in-context learning (ICL), what is the maximum improvement in ICL ability achieved by continued pretraining on a supportive subset of data?

Your answer should be an integer between 0 and 100, stating the maximum percentage improvement in ICL ability achieved by continued pretraining.


Question: In the paper that proposes Normalize-and-Project (NaP), we can find a paper in the references in which the C4 dataset is released. In which journal was this paper published?

Your answer should only be a Python strings of the name of the journal.

The Journal of Machine Learning Research

Question: In the paper that proposes Normalize-and-Project (NaP), we can find a paper in the references in which the C4 dataset is released. In which journal was this paper published?


Question: In ICLR 2024 Poster papers, which paper introduces the Hierarchical Diffuser, a simple, fast, yet effective planning method combining the advantages of hierarchical and diffusion-based planning?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: In ICLR 2024 Poster papers, which paper introduces the Hierarchical Diffuser, a simple, fast, yet effective planning method combining the advantages of hierarchical and diffusion-based planning?

Simple Hierarchical Planning with Diffusion


Question: In ICLR 2024 Spotlight papers, a paper attempts to alleviate the over-optimization problem that occurs when LLMs are optimized by reward models through human feedback. What is the formula of the results in mixed advantages which are a convex combination of the task and constraint advantages?

Your answer should be the formula in LaTeX format.

Question: In ICLR 2024 Spotlight papers, a paper attempts to alleviate the over-optimization problem that occurs when LLMs are optimized by reward models through human feedback. What is the formula of the results in mixed advantages which are a convex combination of the task and constraint advantages?


Question: How is mutual information (MI) used to represent epistemic uncertainty (EU) in the Bayesian framework?

Question: How is mutual information (MI) used to represent epistemic uncertainty (EU) in the Bayesian framework?


Question: In ICLR 2024 Spotlight papers, a paper introduces a new conception named "Effective Horizon". How many figures in this paper?


Question: In the experiment where they isolate the influence of the LLM in an LLM-based forecaster and propose three ablations, does a simple ablations of LLM-based methods cause worse performance?


Question: Investigating the correlation of sharpness, curvatures, and validation loss on MNIST, Fashion-MNIST, and CIFAR-10, which has a strong positive correlation with validation loss?