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Social Aspects · Accountability, Transparency, and Interpretability

Grace Luo, Jiahai Feng, Trevor Darrell, Alec Radford, Jacob Steinhardt

Existing approaches for manipulating neural network activations, such as PCA and SAEs, rely on strong assumptions about activation structure. We develop a generative approach that models activations with diffusion, that makes minimal assumptions and improves with data and model scale. We use this activation diffusion model to improve downstream tasks: for instance, post-processing interventions with its learned generative prior, allowing for more effective steering without sacrificing fluency. Furthermore, the activation diffusion model can be used as an encoder, with units that cover a broad range of human-interpretable concepts, as measured by scalar probing. We also characterize the scaling properties of our approach, training models with 0.5B to 3.3B parameters on one billion residual stream activations from the Llama model family. We see that the diffusion loss decreases smoothly and reliably as a function of compute, and serves as a good proxy for downstream steering and probing performance. Our method provides a scalable approach towards interpretability without requiring commitments to strong assumptions.

Theory · Online Learning and Bandits

Ibne Farabi Shihab, SANJEDA AKTER, Anuj Sharma

We study black-box optimization of Lipschitz functions under noisy evaluations. Existing adaptive discretization methods implicitly avoid suboptimal regions but do not provide explicit certificates of optimality or measurable progress guarantees. We introduce **Certificate-Guided Pruning (CGP)**, which maintains an explicit *active set* $A_t$ of potentially optimal points via confidence-adjusted Lipschitz envelopes. Any point outside $A_t$ is certifiably suboptimal with high probability, and under a margin condition with near-optimality dimension $\alpha$, we prove Vol $(A_t)$ shrinks at a controlled rate yielding sample complexity $Õ(\varepsilon^{-(2+\alpha)})$. We develop three extensions: CGP-Adaptive learns $L$ online with $O(\log T)$ overhead; CGP-TR scales to $d > 50$ via trust regions with local certificates; and CGP-Hybrid switches to GP refinement when local smoothness is detected. Experiments on 12 benchmarks ($d \in [2, 100]$) show CGP variants match or exceed strong baselines while providing principled stopping criteria via certificate volume.

Applications · Health / Medicine

Jialin Li, Zhuo Zhang, Cao Yue, Shuai Xiao, Guipeng Lan, Jiabao Wen, Jiachen Yang

The scarcity of high-quality imaging data for coronary angiography (CAG) stenosis limits the clinical translation of automated stenosis detection. Synthetic stenosis data provides a practical avenue to augment training sets, improving data quality, diversity, and distributional coverage, and enhancing detection precision and generalization. However, diffusion-based editing commonly relies on soft guidance in a noise-initialized reverse process, offering limited pixel-level precision and structure preservation. We propose the **OT-Bridge Editor**, which reframes localized editing as a constrained entropic optimal transport (OT) problem and leverages geometric information to steer the generation path, enabling stronger geometric control. Extensive experiments show that our synthesized angiograms consistently improve downstream stenosis detection, yielding substantial relative gains of 27.8% on the public ARCADE benchmark and 23.0% on our multi-center dataset, supported by consistent qualitative results.

Applications · Social Sciences

Jiahao Qiu, Fulian Xiao, Yimin Wang, Yuchen Mao, Yijia Chen, Xinzhe Juan, Siran Wang, Xuan Qi, Tongcheng Zhang, Zixin Yao 等

Recent advances in large language models (LLMs) have led to remarkable progress across various domains, yet their capabilities in the humanities, particularly history, remain underexplored. Historical reasoning poses unique challenges for LLMs, involving multimodal source interpretation, temporal inference, and cross-linguistic analysis. Existing general-purpose agents perform well on many current benchmarks but lack the domain expertise needed to address complex historical questions. To address this gap, we introduce HistBench, a new benchmark of 414 high-quality and carefully-reviewed questions stratified by difficulty and designed to evaluate LLM's capacity for historical reasoning. The tasks span a wide range of historical problems—from factual retrieval based on primary sources to interpretive analysis of manuscripts and images, to interdisciplinary challenges involving archaeology, linguistics, or cultural history. Furthermore, the benchmark dataset spans 29 ancient and modern languages and covers a wide range of historical periods and world regions. Finding the poor performance of LLMs and other agents on HistBench, we further present HistAgent, a history-specific agent equipped with carefully designed tools for OCR, translation, archival search, and image understanding in History. On HistBench, HistAgent based on GPT-4o achieves an accuracy of 27.54% pass@1 and 36.47% pass@2, significantly outperforming LLMs with online search and generalist agents, including GPT-4o (18.60%), DeepSeek-R1(14.49%), Grok 3(17.63%) and Open Deep Research by smolagents(20.29% pass@1 and 25.12% pass@2). These results highlight the limitations of existing LLMs and generalist agents and demonstrate the advantages of HistAgent for historical reasoning. Notably, HistAgent also achieves 60.00% pass@1 accuracy on the GAIA benchmark, showing that domain-specific customization doesn't hinder HistAgent's competitive performance on real-world general tasks.

Social Aspects · Accountability, Transparency, and Interpretability

Georgios Milis, Yubin Qin, Yihan Wu, Heng Huang

As policy catches up with the capabilities of generative AI, watermarking is central to content provenance efforts. Inference-time watermarks for autoregressive models are unfit for continuous modalities due to discretization inconsistencies. Existing methods overcome this by finetuning the modality tokenizers, nullifying the watermark's training-free advantage. In this work, motivated by the vocabulary redundancy of discretization, we propose an elegant solution for powerful and robust watermarking of synthetic audio. We theoretically analyze the impact of token errors on watermark detection, and effectively mitigate them using a reduced vocabulary obtained via community detection. Thorough experiments showcase that our gradient-free method can boost detectability by several orders of magnitude, while also achieving built-in robustness to audio modifications. Broadly, we discover a new state-of-the-art for token-level watermarks in multimedia, which simply arises from the nature of discrete representation learning.

Deep Learning · Other Representation Learning

Ye Xiao, Ruikun Li, Zhenyu Yang, Andrey Vasnev, Junbin Gao

Existing parameter isolation-based methods in continual learning employ diverse designs to learn more tasks within a limited model capacity. However, most of their designs inevitably incur substantial computational overhead if their model capacity is enlarged to accommodate further tasks as the task stream continually grows, resulting in a significant efficiency bottleneck. In this paper, we propose a novel GNN framework with a biological neuron-inspired architecture, termed the capacity-agnostic GNN (CAGNN), to simultaneously overcome catastrophic forgetting and boost efficiency under capacity expansion. Unlike other methods that employ full network propagation, CAGNN leverages graph contextual information to support the construction of task-specific subnetworks and decouples subnetworks during both training and inference, while enabling effective knowledge transfer between tasks. Intensive experiments demonstrate CAGNN's superiority to the state of the art, in terms of effectiveness as well as computational efficiency.

Applications · Language, Speech and Dialog

Xiaoyu Yang, Yifan Yang, Zengrui Jin, Ziyun Cui, Wen Wu, Baoxiangli, Chao Zhang, Phil Woodland

Self-supervised learning (SSL) has significantly advanced acoustic representation learning. However, most existing models are optimised for either speech or audio event understanding, resulting in a persistent gap between these two domains. We address this gap with SPEAR (SPEech and Audio Representations), a self-supervised framework that distils complementary knowledge from a speech-focused SSL teacher and a general-audio SSL teacher into a single unified model. SPEAR applies multi-codebook vector quantisation to continuous teacher representations to produce fine-grained discrete tokens that capture both semantic and acoustic information. To effectively integrate these heterogeneous representations, SPEAR jointly predicts them given a masked input with an asymmetric pre-training loss. We further improve robustness in complex sound scenes through a novel token mixing mechanism. Extensive experiments demonstrate that SPEAR consistently outperforms existing unified speech and audio models. SPEAR establishes a new state-of-the-art on the SUPERB benchmark, surpassing WavLM Large on 12 of 15 tasks, while achieving competitive performance on the HEAR benchmark. These results position SPEAR as a versatile foundation for general-purpose speech and audio representation learning. The code and pre-trained models will be released.

Social Aspects · Accountability, Transparency, and Interpretability

David Chanin, Adrià Garriga-Alonso

Sparse Autoencoders (SAEs) extract features from LLM internal activations, meant to correspond to interpretable concepts. A core SAE training hyperparameter is L0: how many SAE features should fire per token on average. Existing work compares SAE algorithms using sparsity-reconstruction tradeoff plots, implying L0 is a free parameter with no single correct value aside from its effect on reconstruction. In this work we study the effect of L0 on SAEs, and show that if L0 is not set correctly, the SAE fails to disentangle the underlying features of the LLM. If L0 is too low, the SAE will mix correlated features to improve reconstruction. If L0 is too high, the SAE finds degenerate solutions that also mix features. Further, we present a proxy metric that can help guide the search for the correct L0 for an SAE on a given training distribution. We show that our method finds the correct L0 in toy models and coincides with peak sparse probing performance in LLM SAEs. We find that most commonly used SAEs have an L0 that is too low. Our work shows that L0 must be set correctly to train SAEs with correct features.

Social Aspects · Robustness

Lucas D. Konrad, Nikolas Kuschnig

Identifying *most influential sets* (MIS) – size-$k$ subsets whose removal maximally changes a target estimand – is typically infeasible because it requires searching over $\binom{n}{k}$ subsets. We show that, for a broad class of estimands whose leave-set-out effect admits a linear-fractional form, the MIS problem reduces to a one-parameter sequence of top-$k$ selections. Using Dinkelbach's method, we obtain an efficient algorithm that runs in $O(n)$ per iteration and terminates in finitely many steps. We show that our approach returns globally optimal sets for univariate settings, such as average treatment effect estimation in randomized experiments. For partial linear models, we establish selection consistency under Neyman orthogonality and mild first-stage stability. We validate our method through simulations and real-world applications – recovering MIS that were previously computationally inaccessible.

Deep Learning · Generative Models and Autoencoders

Michael Hersche, Nicolas Menet, Ronan Tanios, Abbas Rahimi

Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models, offering sub-linear generation latency and bidirectional capabilities that are particularly appealing for code generation and editing. Achieving sub-linear latency in discrete DLMs requires predicting multiple tokens in parallel. However, standard DLMs sample tokens independently from conditional marginal distributions, failing to capture the joint dependencies among concurrently generated tokens. As a result, they often lead to syntactic inconsistencies and break multi-token structures. In this work, we introduce CoDiLA (Coherent Diffusion with Local Autoregression), a method that reconciles parallel sampling with local dependency modeling. Rather than forcing the DLM to resolve fine-grained syntax, CoDiLA delegates local decoding to a small, auxiliary AR model operating on the diffusion latents. This design allows for parallel block generation while ensuring sequential validity within each block and maintaining core DLM capabilities, including bidirectional modeling across blocks. We demonstrate that using a highly compact auxiliary AR model (e.g., 0.6B parameters) effectively eliminates coherence artifacts, establishing a new Pareto frontier for accuracy and speed in code generation benchmarks.

Applications · Health / Medicine

Zhengqiu Yu, Yueping Ding, Xiangrong Liu

Patient-level sepsis prediction in the ICU requires models that track how a patient’s condition evolves over time and integrate heterogeneous structured evidence from electronic health records. We present PathwayLLM, a trajectory-based framework that grounds prediction on temporal signals together with graph-structured and pathway-level clinical information derived from statistical dependency discovery. PathwayLLM follows a three-stage design. First, each observation window is encoded from multiple structured views, including physiological measurements, temporal dynamics, a heterogeneous patient–diagnosis–medication graph, and pathway signals constructed from discovered conditional independence structures among clinical variables. Second, these representations are provided to a pre-trained language model as auxiliary contextual embeddings so that risk prediction and evidence-conditioned text explanations can be learned jointly. Third, a Clinical Trajectory LSTM with Deterioration Attention aggregates window-level representations to highlight critical deterioration points and produce a patient-level risk score. On MIMIC-IV (15,410 ICU patients; 8.45% sepsis prevalence), PathwayLLM achieves AUROC 0.891 and AUPRC 0.724, outperforming strong time-series and pre-trained baselines. Ablation studies indicate that trajectory aggregation and structured clinical signals are key contributors, and clinician review suggests that the generated explanations are coherent, interpretable, and clinically relevant.

Theory · Game Theory

Shuang Cui, He Huang, Yu-e Sun, Chen Xue

Budget-feasible procurement auctions play a pivotal role in various AI-driven marketplaces, such as data acquisition and crowdsourcing, where a buyer with a limited budget seeks to procure services from strategic sellers with private costs. While numerous budget-feasible mechanisms have been proposed for the classic objective of maximizing the buyer's valuation, the more challenging and economically significant objective of social welfare maximization has only recently been studied, and existing approaches still sacrifice budget feasibility, thereby limiting their practical applicability. In this paper, we bridge this gap by proposing BFM-SWM, the first budget-feasible mechanism with provable approximation guarantees for submodular welfare maximization in procurement auctions. Our mechanism satisfies standard economic properties, including truthfulness, individual rationality, and non-negative auctioneer surplus. As a by-product, we develop BFM-VM, a variant tailored for valuation maximization, which achieves a deterministic approximation ratio of $1/(12+4\sqrt{3})$ for general submodular functions, substantially improving upon the best-known deterministic ratio of $1/64$ established by [Balkanski et al., SODA 2022], while reducing the running time from $\mathcal{O}(n^2\log n)$ to $\mathcal{O}(n\log n)$. Extensive experiments demonstrate the efficiency and effectiveness of our mechanisms.

Applications · Robotics

Xiaoji Zheng, Ziyuan Yang, Yuhang PENG, Yanhao Chen, Yuanrong Tang, Gengyuan Liu, Bokui Chen, Jiangtao Gong

End-to-end autonomous driving models trained with imitation learning (IL) often generalize poorly, particularly in long-tail scenarios where expert demonstrations are sparse. Reinforcement learning (RL) can provide complementary reward signals, but applying RL in real-world autonomous driving is challenging in offline settings without simulators, where datasets consist almost exclusively of expert actions and lack behavioral diversity. We propose CoIRL-AD, a competitive dual-policy framework that integrates IL and RL under a unified offline training regime. CoIRL-AD decouples IL and RL into separate actors to alleviate objective conflicts between imitation and reward maximization, and introduces a competition-based mechanism that stabilizes learning and enables effective exploration while remaining anchored to expert behavior. Experiments on the nuScenes benchmark show a 27\% relative reduction in collision rate weighted by L2 error compared to strong baselines, with substantially larger gains on cross-city generalization (up to 77\%) and long-tail scenarios (up to 85\%), demonstrating that competitive integration of IL and RL significantly improves robustness in offline end-to-end autonomous driving. Code is available at: \url{https://anonymous.4open.science/r/drive-with-two-minds}.

Optimization · Large Scale, Parallel and Distributed

Ling Chen, Houming Wu, Wenjie Yu

Pipeline parallelism is essential for large-scale model training, but existing asynchronous approaches often degrade convergence due to parameter mismatch between forward and backward passes. We propose Asynchronous Multi-Directional Pipeline parallelism (AMDP) to mitigate this issue while sustaining high utilization. AMDP limits the first stage of each pipeline to process at most two minibatches before backpropagation, bounding the number of parameter updates between forward and backward passes. To alleviate the resulting pipeline bubbles, AMDP launches multiple concurrent pipelines and adapts their number according to pipeline depth. In addition, AMDP accumulates gradients across minibatches and applies them in a single update, ensuring that only a bounded number of minibatches experience parameter mismatch, limited to within one optimization step. Experiments on GPT- and BERT-style models demonstrate that AMDP significantly accelerates training while preserving convergence. The source code based on Megatron-LM is available at https://anonymous.4open.science/r/Megatron-AMDP-59A7.

Reinforcement Learning · Batch/Offline

Mahmoud Selim, Cristina Cipriani, Karl Johansson

Diffusion policies offer a powerful and expressive parameterization for continuous control. Yet, their integration with reinforcement learning remains conceptually and algorithmically challenging. In this work, we address this gap by introducing a noisy-space action-value (Q-)function that assigns values to diffusion latents through the distribution of executed actions induced by the denoising process. We show that this construction admits a precise semantic interpretation and derive a noisy-space policy gradient (NSPG) in which value estimates for noisy latents are computed exclusively using clean action-space values. Building on this result, we formulate a KL-regularized policy improvement over noisy latents and show that the resulting objective admits a diffusion-compatible regression form, avoiding backpropagation through the denoising process. Experiments on the D4RL benchmark demonstrate that semantically grounded value gradients provide a principled, effective foundation for training diffusion policies in offline reinforcement learning.

Reinforcement Learning · Everything Else

Yunhai Hu, Zining Liu, Xiangyang Yin, Tianhua Xia, BO BAO, Eric Sather, Vithursan Thangarasa, Sai Qian Zhang

Speculative reasoning has recently been proposed as a means to accelerate reasoning-intensive generation in large multimodal models, but its effectiveness is often constrained by misalignment between speculative drafts and target-verified reasoning. In this work, we introduce \textit{DREAM-R}, a framework that substantially improves the performance of speculative reasoning. At its core, DREAM-R employs \textit{Speculative Alignment Policy Optimization} (SAPO), a reinforcement-learning objective that trains draft models to generate reasoning steps that are both faithful to target trajectories and concise. We further propose a \textit{Threshold-based Verification Mechanism} (TBVM) that uses a ratio-based criterion to provide stable and interpretable acceptance of speculative steps only when positive evidence clearly dominates, thereby preventing error propagation. Building on these components, we develop a \textit{Fully Parallel Speculative Reasoning} (FPSR) framework that parallelizes draft generation, target-side reasoning, and verification across multi-step reasoning, enabling early stopping and clean fallback. Experiments on reasoning-heavy benchmarks demonstrate up to $2.49\times$ speedup while preserving target-model accuracy, yielding substantial efficiency gains without compromising reasoning quality.

Applications · Time Series

Manrui Jiang, Jingru Huang, Yong Chen, Chen Zhang

Forecasting multivariate hidden Markov processes is challenging due to nonlinear and nonstationary observations, latent state transitions, and cross-sequence dependencies. While deep learning methods achieve strong predictive accuracy, they typically lack explicit state modeling, whereas Hidden Markov Models (HMMs) provide interpretable latent states but struggle with complex nonlinear emissions and scalability. To address these limitations, we propose DRL-STAF, a Deep Reinforcement Learning based STate-Aware Forecasting framework that jointly predicts next-step observations and estimates the corresponding hidden states for complex multivariate hidden Markov processes. Specifically, DRL-STAF models complex nonlinear emissions using deep neural networks and estimates hidden state transitions via reinforcement learning, avoiding predefined transition structures and enabling flexible adaptation to diverse and high-order dynamics. In particular, DRL-STAF remains effective when typical HMM-based methods suffer from state-space explosion. Extensive experiments demonstrate that DRL-STAF consistently outperforms HMM variants, standalone deep learning models, and existing DL–HMM hybrids in both forecasting accuracy and hidden state estimation.

Social Aspects · Accountability, Transparency, and Interpretability

Guancheng Zhou, Yisi Luo, Zhengfu He, Zhenyu Jin, Xuyang Ge, Wentao Shu, Deyu Meng, Xipeng Qiu

Most current paradigms in visual mechanistic interpretability (MI) remain confined to interpreting internal units of the vision model via heuristic methods (e.g., top-$K$ activation retrieval or optimization with regularization). In this work, we establish a theoretical distributional view for visual MI, which models the influence of a feature activation on the natural image distribution, thereby formulating a Kullback-Leibler (KL)-minimal optimization problem to model the MI task. Under this framework, statistical biases are identified within previous MI paradigms, which reveal that they may either be perceptually uninterpretable to humans (i.e., deviate from the natural image distribution), or mechanistically unfaithful to the vision models (i.e., unable to activate model features). To resolve the biases under the distributional view, we propose a model with a KL-minimal soft-constraint principle for visual MI that theoretically balances interpretability and faithfulness. We realize this principle via energy-guided diffusion posterior sampling. Extensive experiments validate the theoretical soundness of the proposed distributional view and demonstrate the practical effectiveness of our paradigm on the DINOv3 vision model.

Social Aspects · Privacy

Zhenlong Liu, Wenyu Jiang, Feng Zhou, Hongxin Wei

Membership Inference Attacks (MIAs) aim to estimate whether a specific data point was used in the training of a given model. Existing state-of-the-art attacks typically rely on training multiple reference models to approximate the conditional score distribution for individual data points, which leads to significant computational overhead and limits their practical applicability. In this work, we propose a novel approach -- Bayesian Membership Inference Attack (BMIA), which performs conditional attack through Bayesian sampling. Specifically, we apply Laplace approximation to a single reference model to obtain a posterior over model parameters, enabling direct estimation of the conditional score distribution. Theoretically, we demonstrate that Bayesian sampling reduces intra-model variance, thereby improving attack power. This insight naturally motivates the multi-reference variant that further enhances performance when additional reference models are available. Extensive experiments across image, text, and tabular datasets indicate that our method achieves state-of-the-art performance in both effectiveness and efficiency.

Deep Learning · Large Language Models

Xin Qiu, Yulu Gan, Conor Hayes, Qiyao Liang, Yinggan XU, Roberto Dailey, Elliot Meyerson, Babak Hodjat, Risto Miikkulainen

Fine-tuning large language models (LLMs) for downstream tasks is an essential stage of modern AI deployment. Reinforcement learning (RL) has emerged as the dominant fine-tuning paradigm, underpinning many state-of-the-art LLMs. In contrast, evolution strategies (ES) has largely been overlooked due to the widespread belief that it does not scale to modern model sizes. This paper overturns this assumption by demonstrating the first successful application of ES to full-parameter fine-tuning of LLMs at the billion-parameter scale, without dimensionality reduction. ES can indeed search over extremely high-dimensional parameter spaces and outperform established RL implementations across multiple axes, including improved tolerance to long-horizon and delayed rewards, robustness across diverse base LLMs, reduced susceptibility to reward hacking, and improved training stability. These findings suggest that ES is not merely a viable alternative to RL, but a fundamentally different and powerful backpropagation-free post-training paradigm that opens a new direction for LLM fine-tuning beyond current RL-based approaches.