GenAI4ELab/papercli-papers huggingface dataset:
Facilitating Multi-turn Function Calling for LLMs via Compositional Instruction Tuning
Authors: Mingyang Chen; sunhaoze; Tianpeng Li; Fan Yang; Hao Liang; KeerLu; Bin CUI; Wentao Zhang; Zenan Zhou; weipeng chen
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large Language Models (LLMs) have exhibited significant potential in performing diverse tasks, including the ability to call functions or use external tools to enhance their performance. While current research on function calling by LLMs primarily focuses on single-turn interactions, this paper addresses the overlooked necessity for LLMs to engage in multi-turn function calling—critical for handling compositional, real-world queries that require planning with functions but not only use functions. To facilitate this, we introduce an approach, BUTTON, which generates synthetic compositional instruction tuning data via bottom-up instruction construction and top-down trajectory generation. In the bottom-up phase, we generate simple atomic tasks based on real-world scenarios and build compositional tasks using heuristic strategies based on atomic tasks. Corresponding function definitions are then synthesized for these compositional tasks. The top-down phase features a multi-agent environment where interactions among simulated humans, assistants, and tools are utilized to gather multi-turn function calling trajectories. This approach ensures task compositionality and allows for effective function and trajectory generation by examining atomic tasks within compositional tasks. We produce a dataset BUTTONInstruct comprising 8k data points and demonstrate its effectiveness through extensive experiments across various LLMs.
SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations
Authors: Zhaorun Chen; Francesco Pinto; Minzhou Pan; Bo Li
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: With the rise of generative AI and rapid growth of high-quality video generation, video guardrails have become more crucial than ever to ensure safety and security across platforms. Current video guardrails, however, are either overly simplistic, relying on pure classification models trained on simple policies with limited unsafe categories, which lack detailed explanations, or prompting multimodal large language models (MLLMs) with long safety guidelines, which are inefficient and impractical for guardrailing real-world content. To bridge this gap, we propose SafeWatch, an efficient MLLM-based video guardrail model designed to follow customized safety policies and provide multi-label video guardrail outputs with content-specific explanations in a zero-shot manner. In particular, unlike traditional MLLM-based guardrails that encode all safety policies autoregressively, causing inefficiency and bias, SafeWatch uniquely encodes each policy chunk in parallel and eliminates their position bias such that all policies are attended simultaneously with equal importance. In addition, to improve efficiency and accuracy, SafeWatch incorporates a policy-aware visual token pruning algorithm that adaptively selects the most relevant video tokens for each policy, discarding noisy or irrelevant information. This allows for more focused, policy-compliant guardrail with significantly reduced computational overhead. Considering the limitations of existing video guardrail benchmarks, we propose SafeWatch-Bench, a large-scale video guardrail benchmark comprising over 2M videos spanning six safety categories which covers over 30 tasks to ensure a comprehensive coverage of all potential safety scenarios. We have conducted extensive experiments, showing that SafeWatch outperforms all SOTA video guardrails on SafeWatch-Bench by 28.2%, and achieves a 13.6% improvement on existing benchmarks, all while reducing inference costs by an average of 10%. SafeWatch also demonstrates strong policy-following abilities and outperforms previous SOTAs by 5.6% and 15.6% in zero-shot generalizability to new policies and new prompting tasks. Additionally, both LLM-as-a-judge and human evaluators confirm the high quality of the explanations provided by SafeWatch. Our project is open-sourced at https://safewatch-aiguard.github.io.
TRENDy: Temporal Regression of Effective Nonlinear Dynamics
Authors: Matt Ricci; Guy Pelc; Zoe Piran; Noa Moriel; Mor Nitzan
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Spatiotemporal dynamics pervade the natural sciences, from the morphogen dynamics underlying patterning in animal pigmentation to the protein waves controlling cell division. A central challenge lies in understanding how controllable parameters induce qualitative changes in system behavior called bifurcations. This endeavor is particularly difficult in realistic settings where governing partial differential equations (PDEs) are unknown and data is limited and noisy. To address this challenge, we propose TRENDy (Temporal Regression of Effective Nonlinear Dynamics), an equation-free approach to learning low-dimensional, predictive models of spatiotemporal dynamics. TRENDy first maps input data to a low-dimensional space of effective dynamics through a cascade of multiscale filtering operations. Our key insight is the recognition that these effective dynamics can be fit by a neural ordinary differential equation (NODE) having the same parameter space as the input PDE. The preceding filtering operations strongly regularize the phase space of the NODE, making TRENDy significantly more robust to noise compared to existing methods. We train TRENDy to predict the effective dynamics of synthetic and real data representing dynamics from across the physical and life sciences. We then demonstrate how we can automatically locate both Turing and Hopf bifurcations in unseen regions of parameter space. We finally apply our method to the analysis of spatial patterning of the ocellated lizard through development. We found that TRENDy's predicted effective state not only accurately predicts spatial changes over time but also identifies distinct pattern features unique to different anatomical regions, such as the tail, neck, and body--an insight that highlights the potential influence of surface geometry on reaction-diffusion mechanisms and their role in driving spatially varying pattern dynamics.
On Bits and Bandits: Quantifying the Regret-Information Trade-off
Authors: Itai Shufaro; Nadav Merlis; Nir Weinberger; Shie Mannor
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: In many sequential decision problems, an agent performs a repeated task. He then suffers regret and obtains information that he may use in the following rounds. However, sometimes the agent may also obtain information and avoid suffering regret by querying external sources. We study the trade-off between the information an agent accumulates and the regret it suffers. We invoke information-theoretic methods for obtaining regret lower bounds, that also allow us to easily re-derive several known lower bounds. We introduce the first Bayesian regret lower bounds that depend on the information an agent accumulates. We also prove regret upper bounds using the amount of information the agent accumulates. These bounds show that information measured in bits, can be traded off for regret, measured in reward. Finally, we demonstrate the utility of these bounds in improving the performance of a question-answering task with large language models, allowing us to obtain valuable insights.
SEBRA : Debiasing through Self-Guided Bias Ranking
Authors: Adarsh Kappiyath; Abhra Chaudhuri; AJAY KUMAR JAISWAL; Ziquan Liu; Yunpeng Li; Xiatian Zhu; Lu Yin
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Ranking samples by fine-grained estimates of spuriosity (the degree to which spurious cues are present) has recently been shown to significantly benefit bias mitigation, over the traditional binary biased-vs-unbiased partitioning of train sets. However, this spuriousity ranking comes with the requirement of human supervision. In this paper, we propose a debiasing framework based on our novel Self-Guided Bias Ranking (Sebra), that mitigates biases via an automatic ranking of data points by spuriosity within their respective classes. Sebra leverages a key local symmetry in Empirical Risk Minimization (ERM) training -- the ease of learning a sample via ERM inversely correlates with its spuriousity; the fewer spurious correlations a sample exhibits, the harder it is to learn, and vice versa. However, globally across iterations, ERM tends to deviate from this symmetry. Sebra dynamically steers ERM to correct this deviation, facilitating the sequential learning of attributes in increasing order of difficulty, ie, decreasing order of spuriosity. As a result, the sequence in which Sebra learns samples naturally provides spuriousity rankings. We use the resulting fine-grained bias characterization in a contrastive learning framework to mitigate biases from multiple sources. Extensive experiments show that Sebra consistently outperforms previous state-of-the-art unsupervised debiasing techniques across multiple standard benchmarks, including UrbanCars, BAR, and CelebA.
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning
Authors: Yan Scholten; Stephan Günnemann
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Conformal prediction provides model-agnostic and distribution-free uncertainty quantification through prediction sets that are guaranteed to include the ground truth with any user-specified probability. Yet, conformal prediction is not reliable under poisoning attacks where adversaries manipulate both training and calibration data, which can significantly alter prediction sets in practice. As a solution, we propose reliable prediction sets (RPS): the first efficient method for constructing conformal prediction sets with provable reliability guarantees under poisoning. To ensure reliability under training poisoning, we introduce smoothed score functions that reliably aggregate predictions of classifiers trained on distinct partitions of the training data. To ensure reliability under calibration poisoning, we construct multiple prediction sets, each calibrated on distinct subsets of the calibration data. We then aggregate them into a majority prediction set, which includes a class only if it appears in a majority of the individual sets. Both proposed aggregations mitigate the influence of datapoints in the training and calibration data on the final prediction set. We experimentally validate our approach on image classification tasks, achieving strong reliability while maintaining utility and preserving coverage on clean data. Overall, our approach represents an important step towards more trustworthy uncertainty quantification in the presence of data poisoning.
Lambda-Skip Connections: the architectural component that prevents Rank Collapse
Authors: Federico Arangath Joseph; Jerome Sieber; Melanie Zeilinger; Carmen Amo Alonso
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Rank collapse, a phenomenon where embedding vectors in sequence models
rapidly converge to a uniform token or equilibrium state, has recently gained at-
tention in the deep learning literature. This phenomenon leads to reduced expres-
sivity and potential training instabilities due to vanishing gradients. Empirical ev-
idence suggests that architectural components like skip connections, LayerNorm,
and MultiLayer Perceptrons (MLPs) play critical roles in mitigating rank collapse.
While this issue is well-documented for transformers, alternative sequence mod-
els, such as State Space Models (SSMs), which have recently gained prominence,
have not been thoroughly examined for similar vulnerabilities. This paper extends
the theory of rank collapse from transformers to SSMs using a unifying frame-
work that captures both architectures. We introduce a modification in the skip
connection component, termed lambda-skip connections, that provides guaran-
tees for rank collapse prevention. We present, via analytical results, a sufficient
condition to achieve the guarantee for all of the aforementioned architectures. We
also study the necessity of this condition via ablation studies and analytical exam-
ples. To our knowledge, this is the first study that provides a general guarantee to
prevent rank collapse, and that investigates rank collapse in the context of SSMs,
offering valuable understanding for both theoreticians and practitioners. Finally,
we validate our findings with experiments demonstrating the crucial role of archi-
tectural components in preventing rank collapse.
Foundation Models Secretly Understand Neural Network Weights: Enhancing Hypernetwork Architectures with Foundation Models
Authors: Jeffrey Gu; Serena Yeung-Levy
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large pre-trained models, or foundation models, have shown impressive performance when adapted to a variety of downstream tasks, often out-performing specialized models. Hypernetworks, neural networks that generate some or all of the parameters of another neural network, have become an increasingly important technique for conditioning and generalizing implicit neural representations (INRs), which represent signals or objects such as audio or 3D shapes using a neural network. However, despite the potential benefits of incorporating foundation models in hypernetwork methods, this research direction has not been investigated, likely due to the dissimilarity of the weight generation task with other visual tasks. To address this gap, we (1) show how foundation models can improve hypernetworks with Transformer-based architectures, (2) provide an empirical analysis of the benefits of foundation models for hypernetworks through the lens of the generalizable INR task, showing that leveraging foundation models improves performance, generalizability, and data efficiency across a variety of algorithms and modalities. We also provide further analysis in examining the design space of foundation model-based hypernetworks, including examining the choice of foundation models, algorithms, and the effect of scaling foundation models.
Mini-Monkey: Alleviating the Semantic Sawtooth Effect for Lightweight MLLMs via Complementary Image Pyramid
Authors: Mingxin Huang; Yuliang Liu; Dingkang Liang; Lianwen Jin; Xiang Bai
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Recently, scaling images to high resolution has received much attention in multimodal large language models (MLLMs). Most existing practices adopt a sliding-window-style cropping strategy to adapt to resolution increase. Such a cropping strategy, however, can easily cut off objects and connected regions, which introduces semantic discontinuity and therefore impedes MLLMs from recognizing small or irregularly shaped objects or text, leading to a phenomenon we call the semantic sawtooth effect. This effect is particularly evident in lightweight MLLMs. To address this issue, we introduce a Complementary Image Pyramid (CIP), a simple, effective, and plug-and-play solution designed to mitigate semantic discontinuity during high-resolution image processing. In particular, CIP dynamically constructs an image pyramid to provide complementary semantic information for the cropping-based MLLMs, enabling it rich acquire semantics at all levels. Furthermore, we introduce a Scale Compression Mechanism (SCM) to reduce the additional computational overhead by compressing the redundant visual tokens. Our experiments demonstrate that CIP can consistently enhance the performance across diverse architectures (e.g., MiniCPM-V-2, InternVL2, and LLaVA-OneVision), various model capacity (1B$\rightarrow$8B), and different usage configurations (training-free and fine-tuning). Leveraging the proposed CIP and SCM, we introduce a lightweight MLLM, Mini-Monkey, which achieves remarkable performance in both general multimodal understanding and document understanding. On the OCRBench, the 2B-version Mini-Monkey even surpasses the 8B model InternVL2-8B by 12 score. Additionally, training Mini-Monkey is cheap, requiring only eight RTX 3090 GPUs. Code and models are available at 
https://github.com/Yuliang-Liu/Monkey.
Multi-objective antibody design with constrained preference optimization
Authors: Milong Ren; ZaiKai He; Haicang Zhang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Antibody design is crucial for developing therapies against diseases such as cancer and viral infections. Recent deep generative models have significantly advanced computational antibody design, particularly in enhancing binding affinity to target antigens. However, beyond binding affinity, antibodies should exhibit other favorable biophysical properties such as non-antigen binding specificity and low self-association, which are important for antibody developability and clinical safety. To address this challenge, we propose AbNovo, a framework that leverages constrained preference optimization for multi-objective antibody design. First, we pre-train an antigen-conditioned generative model for antibody structure and sequence co-design. Then, we fine-tune the model using binding affinity as a reward while enforcing explicit constraints on other biophysical properties. Specifically, we model the physical binding energy with continuous rewards rather than pairwise preferences and explore a primal-and-dual approach for constrained optimization. Additionally, we incorporate a structure-aware protein language model to mitigate the issue of limited training data. Evaluated on independent test sets, AbNovo outperforms existing methods in metrics of binding affinity such as Rosetta binding energy and evolutionary plausibility, as well as in metrics for other biophysical properties like stability and specificity.
NL-Eye: Abductive NLI For Images
Authors: Mor Ventura; Michael Toker; Nitay Calderon; Zorik Gekhman; Yonatan Bitton; Roi Reichart
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Will a Visual Language Model (VLM)-based bot warn us about slipping if it detects a wet floor? Recent VLMs have demonstrated impressive capabilities, yet their ability to infer outcomes and causes remains underexplored. To address this, we introduce NL-Eye, a benchmark designed to assess VLMs' visual abductive reasoning skills. NL-Eye adapts the abductive Natural Language Inference (NLI) task to the visual domain, requiring models to evaluate the plausibility of hypothesis images based on a premise image and explain their decisions. NL-Eye consists of 350 carefully curated triplet examples (1,050 images) spanning diverse reasoning categories: physical, functional, logical, emotional, cultural, and social. The data curation process involved two steps—writing textual descriptions and generating images using text-to-image models, both requiring substantial human involvement to ensure high-quality and challenging scenes. Our experiments show that VLMs struggle significantly on NL-Eye, often performing at random baseline levels, while humans excel in both plausibility prediction and explanation quality. This demonstrates a deficiency in the abductive reasoning capabilities of modern VLMs. NL-Eye represents a crucial step toward developing VLMs capable of robust multimodal reasoning for real-world applications, including accident-prevention bots and generated video verification.
COFlowNet: Conservative Constraints on Flows Enable High-Quality Candidate Generation
Authors: Yudong Zhang; Xuan Yu; Xu Wang; Zhaoyang Sun; Chen Zhang; Pengkun Wang; Yang Wang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Generative flow networks (GFlowNets) have been considered as powerful tools for generating candidates with desired properties. Given that evaluating the property of candidates can be complex and time-consuming, existing GFlowNets train proxy models for efficient online evaluation. However, the performance of proxy models is heavily dependent on the amount of data and is of considerable uncertainty. Therefore, it is of great interest that how to develop an offline GFlowNet that does not rely on online evaluation. Under the offline setting, the limited data results in an insufficient exploration of state space. The insufficient exploration means that offline GFlowNets can hardly generate satisfying candidates out of the distribution of training data. Therefore, it is critical to restrict the offline model to act in the distribution of training data. The distinctive training goal of GFlownets poses a unique challenge for making such restrictions. Tackling the challenge, we propose Conservative Offline GFlowNet (COFlowNet) in this paper. We define unsupported flow, edges containing unseen states in training data. Models can learn extremely little knowledge about unsupported flow from training data. By constraining the model from exploring unsupported flows, we restrict COFlowNet to explore as optimal trajectories on the training set as possible, thus generating better candidates. In order to improve the diversity of candidates, we further introduce a quantile version of unsupported flow restriction. Experimental results on several widely-used datasets validate the effectiveness of COFlowNet in generating high-scored and diverse candidates. All implementations are available at https://github.com/yuxuan9982/COflownet.
Curriculum-aware Training for Discriminating Molecular Property Prediction Models
Authors: Hansi Yang; Quanming Yao; James Kwok
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Despite their wide application across various fields, current molecular property prediction models struggle with the challenge of activity cliff, which refers to the situation where molecules with similar chemical structures display remarkable different properties. This phenomenon hinders existing models' ability to learn distinctive representations for molecules with similar chemical structures, and results in inaccurate predictions on molecules with activity cliff. To address this limitation, we first present empirical evidence demonstrating the ineffectiveness of standard training pipelines on molecules with activity cliff. We propose a novel approach that reformulates molecular property prediction as a node classification problem, introducing two innovative tasks at both the node and edge levels to improve learning outcomes for these challenging molecules with activity cliff. Our method is versatile, allowing seamless integration with a variety of base models, whether pre-trained or randomly initialized. Extensive evaluation across different molecular property prediction datasets validate the effectiveness of our approach.