GenAI4ELab/papercli-papers huggingface dataset:
Efficient Top-m Data Values Identification for Data Selection
Authors: Xiaoqiang Lin; Xinyi Xu; See-Kiong Ng; Bryan Kian Hsiang Low
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Data valuation has found many real-world applications, e.g., data pricing and data selection. However, the most adopted approach -- Shapley value (SV) -- is computationally expensive due to the large number of model trainings required. Fortunately, most applications (e.g., data selection) require only knowing the $m$ data points with the highest data values (i.e., top-$m$ data values), which implies the potential for fewer model trainings as exact data values are not required. Existing work formulates top-$m$ Shapley value identification as top-$m$ arms identification in multi-armed bandits (MAB). However, the proposed approach falls short because it does not utilize data features to predict data values, a method that has been shown empirically to be effective. A recent top-$m$ arms identification work does consider the use of arm features while assuming a linear relationship between arm features and rewards, which is often not satisfied in data valuation. To this end, we propose the GPGapE algorithm that uses the Gaussian process to model the \emph{non-linear} mapping from data features to data values, removing the linear assumption. We theoretically analyze the correctness and stopping iteration of GPGapE in finding an $(\epsilon, \delta)$-approximation to the top-$m$ data values. We further improve the computational efficiency, by calculating data values using small data subsets to reduce the computation cost of model training. We empirically demonstrate that GPGapE outperforms other baselines in top-$m$ data values identification, noisy data detection, and data subset selection on real-world datasets. We also demonstrate the efficiency of our GPGapE in data selection for large language model fine-tuning.
ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
Authors: Ziru Chen; Shijie Chen; Yuting Ning; Qianheng Zhang; Boshi Wang; Botao Yu; Yifei Li; Zeyi Liao; Chen Wei; Zitong Lu; Vishal Dey; Mingyi Xue; Frazier N. Baker; Benjamin Burns; Daniel Adu-Ampratwum; Xuhui Huang; Xia Ning; Song Gao; Yu Su; Huan Sun
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The advancements of language language models (LLMs) have piqued growing interest in developing LLM-based language agents to automate scientific discovery end-to-end, which has sparked both excitement and skepticism about the true capabilities of such agents. In this work, we argue that for an agent to fully automate scientific discovery, it must be able to complete all essential tasks in the workflow. Thus, we call for rigorous assessment of agents on individual tasks in a scientific workflow before making bold claims on end-to-end automation. To this end, we present ScienceAgentBench, a new benchmark for evaluating language agents for data-driven scientific discovery. To ensure the scientific authenticity and real-world relevance of our benchmark, we extract 102 tasks from 44 peer-reviewed publications in four disciplines and engage nine subject matter experts to validate them. We unify the target output for every task to a self-contained Python program file and employ an array of evaluation metrics to examine the generated programs, execution results, and costs. Each task goes through multiple rounds of manual validation by annotators and subject matter experts to ensure its annotation quality and scientific plausibility. We also propose two effective strategies to mitigate data contamination concerns. Using our benchmark, we evaluate five open-weight and proprietary LLMs, each with three frameworks: direct prompting, OpenHands, and self-debug. Given three attempts for each task, the best-performing agent can only solve 32.4% of the tasks independently and 34.3% with expert-provided knowledge. These results underscore the limited capacities of current language agents in generating code for data-driven discovery, let alone end-to-end automation for scientific research.
RazorAttention: Efficient KV Cache Compression Through Retrieval Heads
Authors: Hanlin Tang; Yang Lin; Jing Lin; Qingsen Han; Danning Ke; Shikuan Hong; Yiwu Yao; Gongyi Wang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The memory and computational demands of Key-Value (KV) cache present significant challenges for deploying long-context language models. Previous approaches attempt to mitigate this issue by selectively dropping tokens, which irreversibly erases critical information that might be needed for future queries. In this paper, we propose a novel compression technique for KV cache that preserves all token information. Our investigation reveals that: i) Most attention heads primarily focus on the local context; ii) Only a few heads, denoted as retrieval heads, can essentially pay attention to all input tokens. These key observations motivate us to use separate caching strategy for attention heads.Therefore, we propose RazorAttention, a training-free KV cache compression algorithm, which maintains a full cache for these crucial retrieval heads and discards the remote tokens in non-retrieval heads. Furthermore, we introduce a novel mechanism involving a “compensation token” to further recover the information in the dropped tokens. Extensive evaluations across a diverse set of large language models (LLMs) demonstrate that RazorAttention achieves a reduction in KV cache size by over 70% without noticeable impacts on performance. Additionally, RazorAttention is compatible with FlashAttention, rendering it an efficient and plug-and-play solution that enhances LLM inference efficiency without overhead or retraining of the original model.
Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
Authors: Mufei Li; Siqi Miao; Pan Li
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) addresses these issues by grounding LLM outputs in structured external knowledge from KGs. However, current KG-based RAG frameworks still struggle to optimize the trade-off between retrieval effectiveness and efficiency in identifying a suitable amount of relevant graph information for the LLM to digest. We introduce SubgraphRAG, extending the KG-based RAG framework that retrieves subgraphs and leverages LLMs for reasoning and answer prediction. Our approach innovatively integrates a lightweight multilayer perceptron (MLP) with a parallel triple-scoring mechanism for efficient and flexible subgraph retrieval while encoding directional structural distances to enhance retrieval effectiveness. The size of retrieved subgraphs can be flexibly adjusted to match the query's needs and the downstream LLM's capabilities. This design strikes a balance between model complexity and reasoning power, enabling scalable and generalizable retrieval processes. Notably, based on our retrieved subgraphs, smaller LLMs like Llama3.1-8B-Instruct deliver competitive results with explainable reasoning, while larger models like GPT-4o achieve state-of-the-art accuracy compared with previous baselines—all without fine-tuning. Extensive evaluations on the WebQSP and CWQ benchmarks highlight SubgraphRAG's strengths in efficiency, accuracy, and reliability by reducing hallucinations and improving response grounding.
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
Authors: Julie Kallini; Shikhar Murty; Christopher D Manning; Christopher Potts; Róbert Csordás
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Models that rely on subword tokenization have significant drawbacks, such as sensitivity to character-level noise like spelling errors and inconsistent compression rates across different languages and scripts. While character- or byte-level models like ByT5 attempt to address these concerns, they have not gained widespread adoption—processing raw byte streams without tokenization results in significantly longer sequence lengths, making training and inference inefficient. This work introduces MrT5 (MergeT5), a more efficient variant of ByT5 that integrates a token deletion mechanism in its encoder to dynamically shorten the input sequence length. After processing through a fixed number of encoder layers, a learned delete gate determines which tokens are to be removed and which are to be retained for subsequent layers. MrT5 effectively "merges" critical information from deleted tokens into a more compact sequence, leveraging contextual information from the remaining tokens. In continued pre-training experiments, we find that MrT5 can achieve significant gains in inference runtime with minimal effect on performance, as measured by bits-per-byte. Additionally, with multilingual training, MrT5 adapts to the orthographic characteristics of each language, learning language-specific compression rates. Furthermore, MrT5 shows comparable accuracy to ByT5 on downstream evaluations such as XNLI, TyDi QA, and character-level tasks while reducing sequence lengths by up to 75%. Our approach presents a solution to the practical limitations of existing byte-level models.
VVC-Gym: A Fixed-Wing UAV Reinforcement Learning Environment for Multi-Goal Long-Horizon Problems
Authors: Xudong Gong; Feng Dawei; Kele Xu; weijia wang; Zhangjun Sun; Xing Zhou; Si Zheng; Bo Ding; Huaimin Wang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Multi-goal long-horizon problems are prevalent in real-world applications. The additional goal space introduced by multi-goal problems intensifies the spatial complexity of exploration; meanwhile, the long interaction sequences in long-horizon problems exacerbate the temporal complexity of exploration. Addressing the great exploration challenge posed by multi-goal long-horizon problems depends not only on the design of algorithms but also on the design of environments and the availability of demonstrations to assist in training. To facilitate the above research, we propose a multi-goal long-horizon Reinforcement Learning (RL) environment based on realistic fixed-wing UAV's velocity vector control, named VVC-Gym, and generate multiple demonstration sets of various quality. Through experimentation, we analyze the impact of different environment designs on training, assess the quantity and quality of demonstrations and their influence on training, and assess the effectiveness of various RL algorithms, providing baselines on VVC-Gym and its corresponding demonstrations. The results suggest that VVC-Gym is suitable for studying: (1) the influence of environment designs on addressing multi-goal long-horizon problems with RL. (2) the assistance that demonstrations can provide in overcoming the exploration challenges of multi-goal long-horizon problems. (3) the RL algorithm designs with the least possible impact from environment designs on the efficiency and effectiveness of training.
Commit0: Library Generation from Scratch
Authors: Wenting Zhao; Nan Jiang; Celine Lee; Justin T Chiu; Claire Cardie; Matthias Gallé; Alexander M Rush
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: With the goal of benchmarking generative systems beyond expert software development ability, we introduce Commit0, a benchmark that challenges AI agents to write libraries from scratch. Agents are provided with a specification document outlining the library’s API as well as a suite of interactive unit tests, with the goal of producing an implementation of this API accordingly. The implementation is validated through running these unit tests. As a benchmark, Commit0 is designed to move beyond static one-shot code generation towards agents that must process long-form natural language specifications, adapt to multi-stage feedback, and generate code with complex dependencies. Commit0 also offers an interactive environment where models receive static analysis and execution feedback on the code they generate. Our experiments demonstrate that while current agents can pass some unit tests, none can yet fully reproduce full libraries. Results also show that interactive feedback is quite useful for models to generate code that passes more unit tests, validating the benchmarks that facilitate its use. We publicly release the benchmark, the interactive environment, and the leaderboard.
Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control
Authors: Aleksandar Makelov; Georg Lange; Neel Nanda
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Disentangling model activations into human-interpretable features is a central
problem in interpretability. Sparse autoencoders (SAEs) have recently attracted
much attention as a scalable unsupervised approach to this problem. However, our
imprecise understanding of ground-truth features in realistic scenarios makes it
difficult to measure the success of SAEs. To address this challenge, we propose
to evaluate SAEs on specific tasks by comparing them to supervised
feature dictionaries computed with knowledge of the concepts relevant to the
task. 

Specifically, we suggest that it is possible to (1) compute supervised sparse
feature dictionaries that disentangle model computations for a specific task;
(2) use them to evaluate and contextualize the degree of disentanglement and
control offered by SAE latents on this task. Importantly, we can do this in a
way that is agnostic to whether the SAEs have learned the exact ground-truth
features or a different but similarly useful representation.

As a case study, we apply this framework to the indirect object identification
(IOI) task using GPT-2 Small, with SAEs trained on either the IOI or OpenWebText
datasets. We find that SAEs capture interpretable features for the IOI task, and
that more recent SAE variants such as Gated SAEs and Top-K SAEs are competitive
with supervised features in terms of disentanglement and control over the model.
We also exhibit, through this setup and toy models, some qualitative phenomena
in SAE training illustrating feature splitting and the role of feature
magnitudes in solutions preferred by SAEs.
CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic Screening
Authors: Gen Zhou; Sugitha Janarthanan; Yutong Lu; Pingzhao Hu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Due to the rise in antimicrobial resistance, identifying novel compounds with antibiotic potential is crucial for combatting this global health issue. However, traditional drug development methods are costly and inefficient. Recognizing the pressing need for more effective solutions, researchers have turned to machine learning techniques to streamline the prediction and development of novel antibiotic compounds. While foundation models have shown promise in antibiotic discovery, current mainstream efforts still fall short of fully leveraging the potential of multimodal molecular data. Recent studies suggest that contrastive learning frameworks utilizing multimodal data exhibit excellent performance in representation learning across various domains. Building upon this, we introduce CL-MFAP, an unsupervised contrastive learning (CL)-based multimodal foundation (MF) model specifically tailored for discovering small molecules with potential antibiotic properties (AP) using three types of molecular data. This model employs 1.6 million bioactive molecules with drug-like properties from the ChEMBL dataset to jointly pretrain three encoders: (1) a transformer-based encoder with rotary position embedding for processing SMILES strings; (2) another transformer-based encoder, incorporating a novel bi-level routing attention mechanism to handle molecular graph representations; and (3) a Morgan fingerprint encoder using a multilayer perceptron, to achieve the contrastive learning purpose. The CL-MFAP outperforms baseline models in antibiotic property prediction by effectively utilizing different molecular modalities and demonstrates superior domain-specific performance when fine-tuned for antibiotic-related property prediction tasks.
SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators
Authors: Rasoul Shafipour; David Harrison; Maxwell Horton; JEFFREY MARKER; Houman Bedayat; Sachin Mehta; Mohammad Rastegari; Mahyar Najibi; Saman Naderiparizi
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large Language Models (LLMs) have transformed natural language processing, but face significant challenges in widespread deployment due to their high runtime cost. In this paper, we introduce SeedLM, a novel post-training compression method that uses seeds of a pseudo-random generator to encode and compress model weights. Specifically, for each block of weights, we find a seed that is fed into a Linear Feedback Shift Register (LFSR) during inference to efficiently generate a random matrix. This matrix is then linearly combined with compressed coefficients to reconstruct the weight block. SeedLM reduces memory access and leverages idle compute cycles during inference, effectively speeding up memory-bound tasks by trading compute for fewer memory accesses. Unlike state-of-the-art methods that rely on calibration data, our approach is data-free and generalizes well across diverse tasks. Our experiments with Llama3 70B, which is particularly challenging, show zero-shot accuracy retention at 4- and 3-bit compression to be on par with or better than state-of-the-art methods, while maintaining performance comparable to FP16 baselines. Additionally, FPGA-based tests demonstrate that 4-bit SeedLM, as model size increases, approaches a 4x speed-up over an FP16 Llama 2/3 baseline.
Dissecting Adversarial Robustness of Multimodal LM Agents
Authors: Chen Henry Wu; Rishi Rajesh Shah; Jing Yu Koh; Russ Salakhutdinov; Daniel Fried; Aditi Raghunathan
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: As language models (LMs) are used to build autonomous agents in real environments, ensuring their adversarial robustness becomes a critical challenge. Unlike chatbots, agents are compound systems with multiple components taking actions, which existing LMs safety evaluations do not adequately address. To bridge this gap, we manually create 200 targeted adversarial tasks and evaluation scripts in a realistic threat model on top of VisualWebArena, a real environment for web agents. To systematically examine the robustness of agents, we propose the Agent Robustness Evaluation (ARE) framework. ARE views the agent as a graph showing the flow of intermediate outputs between components and decomposes robustness as the flow of adversarial information on the graph. We find that we can successfully break latest agents that use black-box frontier LMs, including those that perform reflection and tree search. With imperceptible perturbations to a single image (less than 5% of total web page pixels), an attacker can hijack these agents to execute targeted adversarial goals with success rates up to 67%. We also use ARE to rigorously evaluate how the robustness changes as new components are added. We find that inference-time compute that typically improves benign performance can open up new vulnerabilities and harm robustness. An attacker can compromise the evaluator used by the reflexion agent and the value function of the tree search agent, which increases the attack success relatively by 15% and 20%. Our data and code for attacks, defenses, and evaluation are at https://github.com/ChenWu98/agent-attack
ACC-Collab: An Actor-Critic Approach to Multi-Agent LLM Collaboration
Authors: Andrew Estornell; Jean-Francois Ton; Yuanshun Yao; Yang Liu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large language models (LLMs) have demonstrated a remarkable ability to serve as general-purpose tools for various language-based tasks. 
  Recent works have demonstrated that the efficacy of such models can be improved through iterative dialog between multiple models.
  While these 
  paradigms show promise 
  in
  improving model efficacy, most works in this area treat collaboration as an emergent behavior, rather than a learned behavior. 
  In doing so, current multi-agent frameworks rely on collaborative behaviors to have been sufficiently trained into off-the-shelf models. 
  To address this limitation, we propose ACC-Collab, an **A**ctor-**C**riti**c** based learning framework to produce a two-agent team (an actor-agent and a critic-agent) specialized in collaboration.
  We demonstrate that ACC-Collab outperforms SotA multi-agent techniques on a wide array of benchmarks.
Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention
Authors: Tongzhou Liao; Barnabas Poczos
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Graph Neural Networks (GNNs) have become important tools for machine learning on graph-structured data. In this paper, we explore the synergistic combination of graph encoding, graph rewiring, and graph attention, by introducing Graph Attention with Stochastic Structures (GRASS), a novel GNN architecture. GRASS utilizes relative random walk probabilities (RRWP) encoding and a novel decomposed variant (D-RRWP) to efficiently capture structural information. It rewires the input graph by superimposing a random regular graph to enhance long-range information propagation. It also employs a novel additive attention mechanism tailored for graph-structured data. Our empirical evaluations demonstrate that GRASS achieves state-of-the-art performance on multiple benchmark datasets, including a 20.3% reduction in mean absolute error on the ZINC dataset.
NeSyC: A Neuro-symbolic Continual Learner For Complex Embodied Tasks In Open Domains
Authors: Wonje Choi; Jinwoo Park; Sanghyun Ahn; Daehee Lee; Honguk Woo
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We explore neuro-symbolic approaches to generalize actionable knowledge, enabling embodied agents to tackle complex tasks more effectively in open-domain environments. A key challenge for embodied agents is the generalization of knowledge across diverse environments and situations, as limited experiences often confine them to their prior knowledge. To address this issue, we introduce a novel framework, NeSyC, a neuro-symbolic continual learner that emulates the hypothetico-deductive model by continually formulating and validating knowledge from limited experiences through the combined use of Large Language Models (LLMs) and symbolic tools. Specifically, we devise a contrastive generality improvement scheme within NeSyC, which iteratively generates hypotheses using LLMs and conducts contrastive validation via symbolic tools. This scheme reinforces the justification for admissible actions while minimizing the inference of inadmissible ones. Additionally, we incorporate a memory-based monitoring scheme that efficiently detects action errors and triggers the knowledge refinement process across domains. Experiments conducted on diverse embodied task benchmarks—including ALFWorld, VirtualHome, Minecraft, RLBench, and a real-world robotic scenario—demonstrate that NeSyC is highly effective in solving complex embodied tasks across a range of open-domain environments.