GenAI4ELab/papercli-papers huggingface dataset:
Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows
Authors: Shuhao Cao; Francesco Brarda; Rui Peng Li; Yuanzhe Xi
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Recent advancements in operator-type neural networks have shown promising results in approximating the solutions of spatiotemporal Partial Differential Equations (PDEs). However, these neural networks often entail considerable training expenses, and may not always achieve the desired accuracy required in many scientific and engineering disciplines. In this paper, we propose a new learning framework to address these issues. A new spatiotemporal adaptation is proposed to generalize any Fourier Neural Operator (FNO) variant to learn maps between Bochner spaces, which can perform an arbitrary-length temporal super-resolution for the first time. To better exploit this capacity, a new paradigm is proposed to refine the commonly adopted end-to-end neural operator training and evaluations with the help from the wisdom from traditional numerical PDE theory and techniques. Specifically, in the learning problems for the turbulent flow modeled by the Navier-Stokes Equations (NSE), the proposed paradigm trains an FNO only for a few epochs. Then, only the newly proposed spatiotemporal spectral convolution layer is fine-tuned without the frequency truncation. The spectral fine-tuning loss function uses a negative Sobolev norm for the first time in operator learning, defined through a reliable functional-type a posteriori error estimator whose evaluation is exact thanks to the Parseval identity. Moreover, unlike the difficult nonconvex optimization problems in the end-to-end training, this fine-tuning loss is convex. Numerical experiments on commonly used NSE benchmarks demonstrate significant improvements in both computational efficiency and accuracy, compared to end-to-end evaluation and traditional numerical PDE solvers under certain conditions. The source code is publicly available at https://github.com/scaomath/torch-cfd.
Adaptive backtracking line search
Authors: Joao V. Cavalcanti; Laurent Lessard; Ashia C. Wilson
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Backtracking line search is foundational in numerical optimization. 
The basic idea is to adjust the step size of an algorithm by a {\em constant} factor until some chosen criterion (e.g. Armijo, Descent Lemma) is satisfied. 
We propose a novel way to adjust step sizes, replacing the constant factor used in regular backtracking with one that takes into account the degree to which the chosen criterion is violated, with no additional computational burden. This light-weight adjustment leads to significantly faster optimization, which we confirm by performing a variety of experiments on over fifteen real world datasets.
For convex problems, we prove adaptive backtracking requires no more adjustments to produce a feasible step size than regular backtracking does.
For nonconvex smooth problems, we prove adaptive backtracking enjoys the same guarantees of regular backtracking. %same lower bounds that step sizes produced by regular backtracking do.
Furthermore, we prove adaptive backtracking preserves the convergence rates of gradient descent and its accelerated variant.
Breach By A Thousand Leaks: Unsafe Information Leakage in 'Safe' AI Responses
Authors: David Glukhov; Ziwen Han; Ilia Shumailov; Vardan Papyan; Nicolas Papernot
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Vulnerability of Frontier language models to misuse has prompted the development of safety measures like filters and alignment training seeking to ensure safety through robustness to adversarially crafted prompts. We assert that robustness is fundamentally insufficient for ensuring safety goals due to inferential threats from dual-intent queries, with current defenses and evaluations failing to account for these risks. To quantify these risks, we introduce a new safety evaluation framework based on $\textit{impermissible information leakage}$ of model outputs and demonstrate how our proposed question-decomposition attack can extract dangerous knowledge from a censored LLM more effectively than traditional jailbreaking. Underlying our proposed evaluation method is a novel information-theoretic threat model of $\textit{inferential adversaries}$, distinguished from $\textit{security adversaries}$, such as jailbreaks, in that success involves inferring impermissible knowledge from victim outputs as opposed to forcing explicitly impermissible victim outputs. Through our information-theoretic framework, we show that ensuring safety against inferential adversaries requires defenses which bound impermissible information leakage, and, such defenses inevitably incur safety-utility trade-offs.
Biologically Constrained Barrel Cortex Model Integrates Whisker Inputs and Replicates Key Brain Network Dynamics
Authors: Tianfang Zhu; Dongli Hu; Jiandong Zhou; Kai Du; Anan LI
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: The brain's ability to transform sensory inputs into motor functions is central to neuroscience and crucial for the development of embodied intelligence. Sensory-motor integration involves complex neural circuits, diverse neuronal types, and intricate intercellular connections. Bridging the gap between biological realism and behavioral functionality presents a formidable challenge. In this study, we focus on the columnar structure of the superficial layers of mouse barrel cortex as a model system. We constructed a model comprising 4,218 neurons across 13 neuronal subtypes, with neural distribution and connection strengths constrained by anatomical experimental findings. A key innovation of our work is the development of an effective construction and training pipeline tailored for this biologically constrained model. Additionally, we converted an existing simulated whisker sweep dataset into a spiking-based format, enabling our network to be trained and tested on neural signals that more closely mimic those observed in biological systems. The results of object discrimination utilizing whisker signals demonstrate that our barrel cortex model, grounded in biological constraints, achieves a classification accuracy exceeds classical convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs), by an average of 8.6%, and is on par with recent spiking neural networks (SNNs) in performance. Interestingly, a whisker deprivation experiment, designed in accordance with neuroscience practices, further validates the perceptual capabilities of our model in behavioral tasks.
Critically, it offers significant biological interpretability: post-training analysis reveals that neurons within our model exhibit firing characteristics and distribution patterns similar to those observed in the actual neuronal systems of the barrel cortex. This study advances our understanding of neural processing in the barrel cortex and exemplifies how integrating detailed biological structures into neural network models can enhance both scientific inquiry and artificial intelligence applications. The code is available at https://github.com/fun0515/RSNN_bfd.
The Complexity of Two-Team Polymatrix Games with Independent Adversaries
Authors: Alexandros Hollender; Gilbert Maystre; Sai Ganesh Nagarajan
Venue: ICLR 2025 (ICLR 2025 Oral)

Abstract: Adversarial multiplayer games are an important object of study in multiagent learning. In particular, polymatrix zero-sum games are a multiplayer setting where Nash equilibria are known to be efficiently computable. Towards understanding the limits of tractability in polymatrix games, we study the computation of Nash equilibria in such games where each pair of players plays either a zero-sum or a coordination game. We are particularly interested in the setting where players can be grouped into a small number of teams of identical interest. While the three-team version of the problem is known to be PPAD-complete, the complexity for two teams has remained open. Our main contribution is to prove that the two-team version remains hard, namely it is CLS-hard. Furthermore, we show that this lower bound is tight for the setting where one of the teams consists of multiple independent adversaries. On the way to obtaining our main result, we prove hardness of finding any stationary point in the simplest type of non-convex-concave min-max constrained optimization problem, namely for a class of bilinear polynomial objective functions.
Efficient Off-Policy Learning for High-Dimensional Action Spaces
Authors: Fabian Otto; Philipp Becker; Vien Anh Ngo; Gerhard Neumann
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Existing off-policy reinforcement learning algorithms often rely on an explicit state-action-value function representation, which can be problematic in high-dimensional action spaces due to the curse of dimensionality.
This reliance results in data inefficiency as maintaining a state-action-value function in such spaces is challenging. 
We present an efficient approach that utilizes only a state-value function as the critic for off-policy deep reinforcement learning.
This approach, which we refer to as Vlearn, effectively circumvents the limitations of existing methods by eliminating the necessity for an explicit state-action-value function. 
To this end, we leverage a weighted importance sampling loss for learning deep value functions from off-policy data. 
While this is common for linear methods, it has not been combined with deep value function networks. 
This transfer to deep methods is not straightforward and requires novel design choices such as robust policy updates, twin value function networks to avoid an optimization bias, and importance weight clipping.
We also present a novel analysis of the variance of our estimate compared to commonly used importance sampling estimators such as V-trace. 
Our approach improves sample complexity as well as final performance and ensures consistent and robust performance across various benchmark tasks.
Eliminating the state-action-value function in Vlearn facilitates a streamlined learning process, yielding high-return agents.
Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
Authors: Gabriele Dominici; Pietro Barbiero; Mateo Espinosa Zarlenga; Alberto Termine; Martin Gjoreski; Giuseppe Marra; Marc Langheinrich
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying the decisions of deep neural network (DNN) models. This leads to the inability to rely on and verify state-of-the-art DNN-based systems, especially in high-stakes scenarios. For this reason, circumventing causal opacity in DNNs represents a key open challenge at the intersection of deep learning, interpretability, and causality. This work addresses this gap by introducing Causal Concept Graph Models (Causal CGMs), a class of interpretable models whose decision-making process is causally transparent by design. Our experiments show that Causal CGMs can: (i) match the generalisation performance of causally opaque models, (ii) enable human-in-the-loop corrections to mispredicted intermediate reasoning steps, boosting not just downstream accuracy after corrections but also the reliability of the explanations provided for specific instances, and (iii) support the analysis of interventional and counterfactual scenarios, thereby improving the model's causal interpretability and supporting the effective verification of its reliability and fairness.
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
Authors: Lazar Atanackovic; Xi Zhang; Brandon Amos; Mathieu Blanchette; Leo J Lee; Yoshua Bengio; Alexander Tong; Kirill Neklyudov
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Numerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynamics of communicating cells or physical particles. Learning the dynamics of such systems is essential for predicting the temporal evolution of populations across novel samples and unseen environments. Flow-based models allow for learning these dynamics at the population level - they model the evolution of the entire distribution of samples. However, current flow-based models are limited to a single initial population and a set of predefined conditions which describe different dynamics. We argue that multiple processes in natural sciences have to be represented as vector fields on the Wasserstein manifold of probability densities. That is, the change of the population at any moment in time depends on the population itself due to the interactions between samples. In particular, this is crucial for personalized medicine where the development of diseases and their respective treatment response depend on the microenvironment of cells specific to each patient. We propose *Meta Flow Matching* (MFM), a practical approach to integrate along these vector fields on the Wasserstein manifold by amortizing the flow model over the initial populations. Namely, we embed the population of samples using a Graph Neural Network (GNN) and use these embeddings to train a Flow Matching model. This gives MFM the ability to generalize over the initial distributions, unlike previously proposed methods. We demonstrate the ability of MFM to improve the prediction of individual treatment responses on a large-scale multi-patient single-cell drug screen dataset.
JetFormer: An autoregressive generative model of raw images and text
Authors: Michael Tschannen; André Susano Pinto; Alexander Kolesnikov
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Removing modeling constraints and unifying architectures across domains has been a key driver of the recent progress in training large multimodal models. However, most of these models still rely on many separately trained components such as modality-specific encoders and decoders. In this work, we further streamline joint generative modeling of images and text. We propose an autoregressive decoder-only transformer---JetFormer---which is trained to directly maximize the likelihood of raw data, without relying on any separately pretrained components, and can understand and generate both text and images. Specifically, we leverage a normalizing flow model to obtain a soft-token image representation that is jointly trained with an autoregressive multimodal transformer. The normalizing flow model serves as both an image encoder for perception tasks and an image decoder for image generation tasks during inference. JetFormer achieves text-to-image generation quality competitive with recent VQVAE- and VAE-based baselines. These baselines rely on pretrained image autoencoders, which are trained with a complex mixture of losses, including perceptual ones. At the same time, JetFormer demonstrates robust image understanding capabilities. To the best of our knowledge, JetFormer is the first model that is capable of generating high-fidelity images and producing strong log-likelihood bounds.
MMQA: Evaluating LLMs with Multi-Table Multi-Hop Complex Questions
Authors: Jian Wu; Linyi Yang; Dongyuan Li; Yuliang Ji; Manabu Okumura; Yue Zhang
Venue: ICLR 2025 (ICLR 2025 Oral)

Abstract: While large language models (LLMs) have made strides in understanding tabular data, current tabular evaluation benchmarks, such as WikiTableQuestions and WikiSQL, are focus on single-table scenarios, which cannot necessarily reflect the complexity of real-world applications. To bridge this gap, we present a \textbf{M}ulti-table and 
Multi-hop Question Answering (MMQA) dataset to assess LLMs' understanding and reasoning capabilities in handling multi-table tasks. The MMQA dataset demands that models perform multiple inferences by drawing evidence from various tables, which are designed to be connected with each other and require models to identify and utilize relationships such as foreign and primary keys. Then, we introduce a comprehensive evaluation framework that tailors to assess LLMs' capabilities in several aspects including Multi-Table Retrieval, Text-to-SQL Generation, Multi-Table QA, Primary Key Selection, and Foreign Key Selection. 
Finally, we propose a novel multi-table retrieval method that achieves state-of-the-art (SOTA) performance on the MMQA dataset compared to several strong baselines. 
Our experiment results reveal that, compared with human performance, both open-source and commercial LLMs leave significant performance room for improvements in multi-table understanding and reasoning tasks. We believe that the MMQA benchmark will enhance and facilitate LLMs' multi-table capabilities in real-world scenarios.
Counterfactual Concept Bottleneck Models
Authors: Gabriele Dominici; Pietro Barbiero; Francesco Giannini; Martin Gjoreski; Giuseppe Marra; Marc Langheinrich
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Current deep learning models are not designed to simultaneously address three fundamental questions: predict class labels to solve a given classification task (the "What?"), simulate changes in the situation to evaluate how this impacts class predictions (the "How?"), and imagine how the scenario should change to result in different class predictions (the "Why not?"). While current approaches in causal representation learning and concept interpretability are designed to address some of these questions individually (such as Concept Bottleneck Models, which address both ``what'' and ``how'' questions), no current deep learning model is specifically built to answer all of them at the same time. To bridge this gap, we introduce CounterFactual Concept Bottleneck Models (CF-CBMs), a class of models designed to efficiently address the above queries all at once without the need to run post-hoc searches. Our experimental results demonstrate that CF-CBMs: achieve classification accuracy comparable to black-box models and existing CBMs (“What?”), rely on fewer important concepts leading to simpler explanations (“How?”), and produce interpretable, concept-based counterfactuals (“Why not?”). Additionally, we show that training the counterfactual generator jointly with the CBM leads to two key improvements: (i) it alters the model's decision-making process, making the model rely on fewer important concepts (leading to simpler explanations), and (ii) it significantly increases the causal effect of concept interventions on class predictions, making the model more responsive to these changes.
FairMT-Bench: Benchmarking Fairness for Multi-turn Dialogue in Conversational LLMs
Authors: Zhiting Fan; Ruizhe Chen; Tianxiang Hu; Zuozhu Liu
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: The increasing deployment of large language model (LLM)-based chatbots has raised concerns regarding fairness. Fairness issues in LLMs may result in serious consequences, such as bias amplification, discrimination, and harm to minority groups. Many efforts are dedicated to evaluating and mitigating biases in LLMs. However, existing fairness benchmarks mainly focus on single-turn dialogues, while multi-turn scenarios, which better reflect real-world conversations, pose greater challenges due to conversational complexity and risk for bias accumulation. In this paper, we introduce a comprehensive benchmark for fairness of LLMs in multi-turn scenarios, **FairMT-Bench**. Specifically, We propose a task taxonomy to evaluate fairness of LLMs cross three stages: context understanding, interaction fairness, and fairness trade-offs, each comprising two tasks. To ensure coverage of diverse bias types and attributes, our multi-turn dialogue dataset FairMT-10K is constructed by integrating data from established fairness benchmarks. For evaluation, we employ GPT-4 along with bias classifiers like Llama-Guard-3, and human annotators to ensure robustness. Our experiments and analysis on FairMT-10K reveal that in multi-turn dialogue scenarios, LLMs are more prone to generating biased responses, showing significant variation in performance across different tasks and models. Based on these findings, we develop a more challenging dataset, FairMT-1K, and test 15 current state-of-the-art (SOTA) LLMs on this dataset. The results highlight the current state of fairness in LLMs and demonstrate the value of this benchmark for evaluating fairness of LLMs in more realistic multi-turn dialogue contexts. This underscores the need for future works to enhance LLM fairness and incorporate FairMT-1K in such efforts. Our code and dataset are available at https://github.com/FanZT6/FairMT-bench.
Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models
Authors: Biao Yi; Tiansheng Huang; Sishuo Chen; Tong Li; Zheli Liu; Zhixuan Chu; Yiming Li
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Backdoor unalignment attacks against Large Language Models (LLMs) enable the stealthy compromise of safety alignment using a hidden trigger while evading normal safety auditing. These attacks pose significant threats to the applications of LLMs in the real-world Large Language Model as a Service (LLMaaS) setting, where the deployed model is a fully black-box system that can only interact through text. Furthermore, the sample-dependent nature of the attack target exacerbates the threat. Instead of outputting a fixed label, the backdoored LLM follows the semantics of any malicious command with the hidden trigger, significantly expanding the target space. In this paper, we introduce BEAT, a black-box defense that detects triggered samples during inference to deactivate the backdoor. It is motivated by an intriguing observation (dubbed the **probe concatenate effect**), where concatenated triggered samples significantly reduce the refusal rate of the backdoored LLM towards a malicious probe, while non-triggered samples have little effect. Specifically, BEAT identifies whether an input is triggered by measuring the degree of distortion in the output distribution of the probe before and after concatenation with the input. Our method addresses the challenges of sample-dependent targets from an opposite perspective. It captures the impact of the trigger on the refusal signal (which is sample-independent) instead of sample-specific successful attack behaviors. It overcomes black-box access limitations by using multiple sampling to approximate the output distribution. 
Extensive experiments are conducted on various backdoor attacks and LLMs (including the closed-source GPT-3.5-turbo), verifying the effectiveness and efficiency of our defense. 
Besides, we also preliminarily verify that BEAT can effectively defend against popular jailbreak attacks, as they can be regarded as "natural backdoors". Our source code is available at https://github.com/clearloveclearlove/BEAT.
SigDiffusions: Score-Based Diffusion Models for Time Series via Log-Signature Embeddings
Authors: Barbora Barancikova; Zhuoyue Huang; Cristopher Salvi
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Score-based diffusion models have recently emerged as state-of-the-art generative
models for a variety of data modalities. Nonetheless, it remains unclear how to
adapt these models to generate long multivariate time series. Viewing a time
series as the discretisation of an underlying continuous process, we introduce
SigDiffusion, a novel diffusion model operating on log-signature embeddings
of the data. The forward and backward processes gradually perturb and denoise
log-signatures while preserving their algebraic structure. To recover a signal from
its log-signature, we provide new closed-form inversion formulae expressing the
coefficients obtained by expanding the signal in a given basis (e.g. Fourier or
orthogonal polynomials) as explicit polynomial functions of the log-signature.
Finally, we show that combining SigDiffusions with these inversion formulae
results in high-quality long time series generation, competitive with the current
state-of-the-art on various datasets of synthetic and real-world examples.