Publications

Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
Danny Reidenbach
Zhonglin Cao
Zuobai Zhang
Kieran Didi
Tomas Geffner
Guoqing Zhou
Jian Tang
Christian Dallago
Arash Vahdat
Emine Kucukbenli
Karsten Kreis
High-quality training datasets are crucial for the development of effective protein design models, but existing synthetic datasets often inc… (voir plus)lude unfavorable sequence-structure pairs, impairing generative model performance. We leverage ProteinMPNN, whose sequences are experimentally favorable as well as amenable to folding, together with structure prediction models to align high-quality synthetic structures with recoverable synthetic sequences. In that way, we create a new dataset designed specifically for training expressive, fully atomistic protein generators. By retraining La-Proteína, which models discrete residue type and side chain structure in a continuous latent space, on this dataset, we achieve new state-of-the-art results, with improvements of +54% in structural diversity and +27% in co-designability. To validate the broad utility of our approach, we further introduce Proteína-Atomística, a unified flow-based framework that jointly learns the distribution of protein backbone structure, discrete sequences, and atomistic side chains without latent variables. We again find that training on our new sequence-structure data dramatically boosts benchmark performance, improving Proteína-Atomística’s structural diversity by +73% and co-designability by +5%. Our work highlights the critical importance of aligned sequence-structure data for training high-performance de novo protein design models. All data will be publicly released.
2025-09-23
NeurIPS.cc/2025/Workshop/AI4Science (poster)
openreview.net
openreview.net
Localized-Attention-Guided Concept Erasure for Text-to-Image Diffusion Models
Zhuan Shi
Alireza Dehghanpour Farashah
Rik de Vries
Golnoosh Farnadi
2025-09-23
NeurIPS.cc/2025/Workshop/GenProCC (publié)
openreview.net
openreview.net
Source-free cross-modality medical image synthesis with diffusion priors
Jia Chen
Xin Wang
Jun Bai
Kai Yang
Xinrong Hu
Yue Li
2025-09-23
Journal of King Saud University Computer and Information Sciences (publié)
doi.org
Stochastic Chameleons: Irrelevant Context Hallucinations Reveal Class-Based (Mis)Generalization in LLMs
Ziling Cheng
Meng Cao
Marc-Antoine Rondeau
Jackie CK Cheung
The widespread success of LLMs on NLP benchmarks has been accompanied by concerns that LLMs function primarily as stochastic parrots that re… (voir plus)produce texts similar to what they saw during pre-training, often erroneously. But what is the nature of their errors, and do these errors exhibit any regularities? In this work, we examine irrelevant context hallucinations, in which models integrate misleading contextual cues into their predictions. Through behavioral analysis, we show that these errors result from a structured yet flawed mechanism that we term _class-based (mis)generalization_, in which models combine abstract class cues with features extracted from the query or context to derive answers. Furthermore, mechanistic interpretability experiments on Llama-3, Mistral, and Pythia across 39 factual recall relation types reveal that this behavior is reflected in the model's internal computations: (i) abstract class representations are constructed in lower layers before being refined into specific answers in higher layers, (ii) feature selection is governed by two competing circuits --- one prioritizing direct query-based reasoning, the other incorporating contextual cues --- whose relative influences determine the final output. Our findings provide a more nuanced perspective on the stochastic parrot argument: through form-based training, LLMs can exhibit generalization leveraging abstractions, albeit in unreliable ways based on contextual cues — what we term _stochastic chameleons_.
2025-09-23
colmweb.org/COLM/2025/Workshop/INTERPLAY (publié)
openreview.net
openreview.net
Beyond Naive Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs
Arjun Ashok
Andrew Robert Williams
Vincent Zhihao Zheng
Irina Rish
Nicolas Chapados
Étienne Marcotte
Valentina Zantedeschi
Alexandre Drouin
Forecasting in real-world settings requires models to integrate not only historical data but also relevant contextual information, often ava… (voir plus)ilable in textual form. While recent work has shown that large language models (LLMs) can be effective context-aided forecasters via naïve direct prompting, their full potential remains underexplored. We address this gap with 4 strategies, providing new insights into the zero-shot capabilities of LLMs in this setting. ReDP improves interpretability by eliciting explicit reasoning traces, allowing us to assess the model's reasoning over the context independently from its forecast accuracy. CorDP leverages LLMs solely to refine existing forecasts with context, enhancing their applicability in real-world forecasting pipelines. IC-DP proposes embedding historical examples of context-aided forecasting tasks in the prompt, substantially improving accuracy even for the largest models. Finally, RouteDP optimizes resource efficiency by using LLMs to estimate task difficulty, and routing the most challenging tasks to larger models. Evaluated on different kinds of context-aided forecasting tasks from the CiK benchmark, our strategies demonstrate distinct benefits over naïve prompting across LLMs of different sizes and families. These results open the door to further simple yet effective improvements in LLM-based context-aided forecasting.
2025-09-22
BERT2S @ Neural Information Processing Systems (poster)
doi.org
openreview.net
Conscious Data Contribution via Community-Driven Chain-of-Thought Distillation
Lena Libon
Meghana Bhange
Rushabh Solanki
Elliot Creager
Ulrich Matchi Aïvodji
2025-09-22
NeurIPS.cc/2025/Workshop/ACA (présentation orale)
openreview.net
openreview.net
Context-Aware World Models for Task-Agnostic Control
Busra Tugce Gurbuz
Hafez Ghaemi
Christopher C. Pack
Shahab Bakhtiari
Eilif Benjamin Muller
2025-09-22
NeurIPS.cc/2025/Workshop/UniReps (publié)
openreview.net
openreview.net
Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy
Rushabh Solanki
Meghana Bhange
Ulrich Matchi Aïvodji
Elliot Creager
The integration of AI into daily life has generated considerable attention and excitement, while also raising concerns about automating algo… (voir plus)rithmic harms and re-entrenching existing social inequities. While top-down solutions such as regulatory policies and improved algorithm design are common, the fact that AI trains on social data creates an opportunity for a grassroots approach, Algorithmic Collective Action, where users deliberately modify the data they share to steer a platform's learning process in their favor. This paper considers how these efforts interact with a firm's use of a differentially private model to protect user data, motivated by the growing regulatory focus on privacy and data protection. In particular, we investigate how the use of Differentially Private Stochastic Gradient Descent (DPSGD) affects the collective’s ability to influence the learning process. Our findings show that while differential privacy contributes to the protection of individual data, it introduces challenges for effective algorithmic collective action. We characterize lower bounds on the success of these actions as a function of the collective's size and the firm's privacy parameters, verifying these trends experimentally by training deep neural network classifiers across several datasets.
2025-09-22
NeurIPS.cc/2025/Workshop/ACA (poster)
openreview.net
openreview.net
FEval-TTC: Fair Evaluation Protocol for Test-Time Compute
Pavel Rumiantsev
Soumyasundar Pal
Yingxue Zhang
Mark J. Coates
The performance of Large Language Models (LLMs) and the associated dollar costs of API calls can fluctuate over time, potentially invalidati… (voir plus)ng conclusions drawn in prior research. To address this, we propose a _**F**air **Eval**uation protocol for **T**est-**T**ime **C**ompute_ (FEval-TTC), designed to ensure consistent assessment of test-time compute (TTC) methods, regardless of such fluctuations. FEval-TTC focuses on evaluation of TTC methods that utilize underlying Chains-of-Thought (CoT). It supports evaluations across multiple LLMs on a diverse set of mathematical and commonsense reasoning datasets. The few-shot prompting and answer extraction processes are standardized across datasets, reducing both time and monetary overhead for researchers. Furthermore, we provide a cost modeling procedure that estimates both the token and dollar cost per query, facilitating equitable comparisons of prevalent TTC methods. We open-source FEval-TTC for public use at [anonymized code link](https://drive.google.com/file/d/1DUeteFA7lnx5MubuR0lh6OPN6XKfpqGC/view?usp=sharing).
2025-09-22
NeurIPS.cc/2025/Workshop/LLM_Evaluation (poster)
doi.org
openreview.net
How to Get Your LLM to Generate Challenging Problems for Evaluation
Arkil Patel
Siva Reddy
Dzmitry Bahdanau
The pace of evolution of Large Language Models (LLMs) necessitates new approaches for rigorous and comprehensive evaluation. Traditional hum… (voir plus)an annotation is increasingly impracticable due to the complexities and costs involved in generating high-quality, challenging problems, particularly for tasks such as long-context reasoning. Moreover, the rapid saturation of existing human-curated benchmarks by LLMs further necessitates the need to develop scalable and automatically renewable evaluation methodologies. In this work, we introduce **CHASE**, a unified framework to synthetically generate challenging problems using LLMs without human involvement. For a given task, our approach builds a hard problem in a bottom-up manner from simpler components. Moreover since we want to generate synthetic data for evaluation, our framework decomposes the generation process into independently verifiable sub-tasks, thereby ensuring a high level of quality and correctness. We implement CHASE to create evaluation benchmarks across three diverse domains: document-based question answering, repository-level code completion, and math reasoning. The performance of state-of-the-art LLMs on these synthetic benchmarks lies in the range of 40-60\% accuracy, thereby demonstrating the effectiveness of our framework at generating hard problems. Our experiments further reveal that the Gemini models significantly outperform other LLMs at long-context reasoning, and that the performance of all LLMs drastically drops by as much as 70\% when we scale up the context size to 50k tokens.
2025-09-22
NeurIPS.cc/2025/Workshop/LLM_Evaluation (poster)
doi.org
openreview.net
Inferring dynamical features from neural data through joint learning of latents factors and weights
Anirudh Gururaj Jamkhandi
Ali Korojy
Olivier Codol
Guillaume Lajoie
Matthew G Perich
Behavior arises from coordinated synaptic changes in recurrent neural populations. Inferring the underlying dynamics from limited, noisy, an… (voir plus)d high-dimensional neural recordings is a major challenge, as experimental data often provide only partial access to brain states. While data-driven recurrent neural networks (dRNNs) have been effective for modeling such dynamics, they are typically limited to single-task domains and struggle to generalize across behavioral conditions. Here, we propose a hierachical model that captures neural dynamics across multiple behavioral contexts by learning a shared embedding space over RNN weights. We demonstrate that our model captures diverse neural dynamics with a single, unified model using both simulated datasets of many tasks and neural recordings during monkey reaching. Using the learned task embeddings, we demonstrate accurate classification of dynamical regimes and generalization to unseen samples. Crucially, spectral analysis on the learnt weights provide valuable insights into network computations, highlighting the potential of joint embedding–weight learning for scalable inference of brain dynamics.
2025-09-22
NeurIPS.cc/2025/Workshop/NeurReps (poster)
openreview.net
openreview.net
Leveraging Parameter Space Symmetries for Reasoning Skill Transfer in LLMs
Stefan Horoi
Sangwoo Cho
Supriyo Chakraborty
Shi-Xiong Zhang
Sambit Sahu
Guy Wolf
Genta Indra Winata
2025-09-22
NeurIPS.cc/2025/Workshop/UniReps (publié)
openreview.net
openreview.net