We propose Re-FORC, an adaptive reward prediction method that, given a context, enables prediction of the expected future rewards as a function of the number of future thinking tokens. Re-FORC trains a lightweight adapter on reasoning models, demonstrating improved prediction with longer reasoning and larger models. Re-FORC enables: 1) early stopping of unpromising reasoning chains, reducing compute by 26\% while maintaining accuracy, 2) optimized model and thinking length selection that achieves 4\% higher accuracy at equal compute and 55\% less compute at equal accuracy compared to the largest model, 3) adaptive test-time scaling, which increases accuracy by 11\% in high compute regime, and 7\% in low compute regime. Re-FORC allows dynamic reasoning with length control via cost-per-token thresholds while estimating computation time upfront.
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Applications · Language, Speech and Dialog
AI-generated text increasingly blends with human writing, raising practical risks such as misinformation, academic misuse, and corpora contamination. While statistical detectors are appealing for efficiency and generalization, they suffer from two key limitations. (i) Boilerplate dominance, boilerplate tokens shared across human and LLM writing can overwhelm discriminative signals. (ii) Brittle point estimates, relying on a single probability score yields unstable decisions under adversarial manipulations. To address these issues, we propose Uncertainty, a multiscale uncertainty estimator that focuses on informative low-probability tokens, which more clearly expose distributional discrepancies. Locally, it alleviates boilerplate dominance by averaging the log-probabilities of low-probability tokens; globally, it reduces brittleness by capturing the distributional shape of this low-probability region via Rényi entropy. We further extend the estimator to Uncertainty++ via conditional independent sampling, yielding a more stable uncertainty estimation. Experiments on seven datasets and sixteen LLMs demonstrate high accuracy, generalization, and robustness. Our code is available at https://anonymous.4open.science/r/Uncertainty-8915.
Probabilistic Methods · Bayesian Models and Methods
Generative diffusion models can provide powerful priors for inverse problems in imaging, but existing implementations suffer from two key limitations: $(i)$ they learn only an implicit approximation of the prior density, and $(ii)$ they rely on crude likelihood approximations that introduce biases in the sampling. We address these challenges by introducing a new energy-based model trained using denoising score matching with a covariance-based regularization that enforces consistency across different inverse problems. Our approach learns explicit, normalized posterior densities for diverse linear inverse problems using a single model, while preserving the sampling capabilities of diffusion models. This enables new capabilities unavailable to score-based diffusion models: energy-guided adaptive sampling that adjusts schedules on-the-fly, unbiased MCMC correction with Metropolis-Hastings acceptance, and blind degradation estimation via Bayes rule. We validate our method on multiple datasets (MNIST, CelebA, ImageNet) and tasks (inpainting, deblurring), demonstrating competitive or superior performance to established baselines.
Probabilistic Methods · Everything Else
Item response theory (IRT) is widely used in areas such as recommender systems, education, psychology, and other fields. A popular model for IRT is the Rasch model. Under this model, if a user with ability $\theta$ performs a task with difficulty $\beta$ then its label $X \sim \text{Bernoulli} (1 / (1 + \exp(-(\theta - \beta)))$. Existing joint maximum likelihood estimation approaches for this problem do not perform well on small datasets and also lack theoretical guarantees. Recently, Nguyen and Zhang proposed a two step approach: (1) spectral method for estimation of task parameters, (2) likelihood optimization for estimation of user parameters. While this approach is theoretically sound, it is not computationally efficient. In this work, we propose an EM-based algorithm for joint estimation of item and user parameters by introducing Pólya-Gamma latent variables, which simplify the logistic log-likelihood. We show that our algorithm is both theoretically sound and consistently outperforms existing methods on synthetic and real-world datasets.
See First, Reason Later: Mutual Information-Guided Reinforcement Learning for Vision-Language Models
Deep Learning · Large Language Models
Vision-Language Models (VLMs) frequently suffer from visual perception errors and hallucinations that compromise answer accuracy in complex reasoning tasks. Reinforcement Learning with Verifiable Rewards (RLVR) offers a promising solution by optimizing policies using answer correctness signals. Despite their effectiveness, prevailing RLVR methods face two critical limitations. First, much of the sampling budget is wasted on trajectories doomed to fail due to early visual description errors. Second, sparse rewards cannot distinguish whether failures stem from visual perception or reasoning stages. We introduce MIRL, a decoupled framework that addresses both limitations by leveraging mutual information (MI) between generated descriptions and visual inputs as a cheap pre-screening signal. This enables intelligent budget allocation toward high-potential trajectories via forking, while decoupled training provides independent MI-based rewards for visual perception optimization, resolving reward blindness. Experiments on six vision-language reasoning benchmarks demonstrate that MIRL achieves 70.22\% average accuracy and successfully surpasses the performance of sampling 16 complete trajectories using only 10 pre-samples with top-6 selection (25\% fewer complete trajectories). Our code is available at: https://anonymous.4open.science/r/mirl-main/.
General Machine Learning · Evaluation
Evaluating generative AI models is increasingly resource-intensive due to slow generation, expensive raters, and a rapidly growing landscape of models and benchmarks. We propose ProEval, a proactive evaluation framework that leverages transfer learning to efficiently estimate performance and identify failure cases. ProEval employs pre-trained Gaussian Processes (GPs) as surrogates for the performance score function, mapping model inputs to metrics such as error likelihood or safety violations. By framing performance estimation as Bayesian quadrature (BQ) and failure discovery as superlevel set sampling, we develop strategies that proactively select or synthesize the most informative inputs for testing. Theoretically, we prove that our pre-trained GP-based BQ estimator is unbiased and bounded. Empirically, extensive experiments on reasoning, safety alignment, and classification benchmarks demonstrate that ProEval is significantly more efficient than competitive baselines. It requires 10–100x fewer samples to achieve estimates within $\pm1\%$ of ground truth, while simultaneously revealing more diverse failure cases under a stricter evaluation budget.
Deep Learning · Graph Neural Networks
Hypergraphs are graph generalizations with key applications in domains such as healthcare, where strict data privacy requirements apply, or bioinformatics, where testing new compounds is costly. However, due to their combinatorial nature, hypergraph representations are often either intractable, or introduce major information loss. For this reason, research into hypergraph synthesis is limited, and state-of-the-art approaches yield poor generation quality in terms of overall structural patterns and graph-level validity. To address such shortcomings, we introduce SuperHype, an exact and tractable hypergraph diffusion model. The core of SuperHype is the projection of graph-superposition, a novel representation that embeds a hypergraph into a multilayer graph enabling a tractable representation with no loss of generalization. To generate new samples from such representations, we introduce a Graph-Superposition Transformer that treats the superposition as an interconnected sequence of layers. Moreover, we enhance the model’s performance with hypergraph specific auxiliary features and triplet aggregation of indirect node interactions. Our evaluation on five datasets shows that SuperHype generally reproduces local and global connectivity patterns with superior fidelity than state-of-the-art baselines.
Deep Learning · Large Language Models
The deployment of Large Language Models (LLMs) with extended context windows is increasingly constrained by the linear growth of Key-Value (KV) cache memory. Vector Quantization (VQ) is a key enabler for pushing KV cache storage toward the sub-1-bit regime; in particular, Residual Quantization (RQ) supports this goal via progressive refinement, sequentially encoding residuals with small codebooks across stages. Yet most VQ methods still rely on standard $\ell_2$ $K$-means as the core codebook-learning primitive. We identify a subtle high-dimensional issue: Euclidean centroid averaging can induce centroid shrinkage, and under an $\ell_2$ objective this shrinkage reduces the influence of angular alignment in the distortion term. This coupling can make directional preservation harder to maintain, hindering KV cache vector quantization methods from pushing into the sub-1-bit regime. To mitigate this coupling, we propose Gain-Shape $K$-means (GSKM), a drop-in replacement for $K$-means that improves directional fidelity over standard $K$-means while matching, and in some regimes improving, $\ell_2$ distortion. We build Gain-Shape Residual Quantization (GSRQ) by incorporating a weighted extension of GSKM into a RQ pipeline. On LLaMA-3-8B, GSRQ yields substantial improvements over strong KV cache quantization baselines across bit rates. At 1-bit, our method improves the average accuracy across LongBench tasks from 11.34 to 32.26, a gain of 20.92 percentage points over VQLLM.
Applications · Computer Vision
While prior studies have successfully compressed vision Transformers (ViTs) through various pruning techniques, most have concentrated on width pruning to achieve significant reductions in model size. Depth pruning, which involves the removal of entire layers from a ViT, is notoriously difficult for accuracy recovery, although depth pruning usually leads to higher speedups of compressed ViTs. Consequently, existing joint approaches that incorporate both width and depth pruning have exhibited limited acceleration ratios due to the inefficiencies of previous depth pruning methods. In this work, we reveal that the failure of existing depth pruning methods lies in their neglect of heterogeneity between different layers. Through a comprehensive analysis of the heterogeneity, we introduce HetDPT, a method that handles heterogeneity during depth pruning while avoiding dimension mismatch. Comprehensive experiments on ImageNet1k, CIFAR-100, COCO, and ADE20K have validated our methods. HetDPT achieves a 1.58$\times$ speedup for DeiT-B while maintaining accuracy, and a 1.39$\times$ speedup for DeiT-S with nearly no accuracy degradation. Furthermore, when combined with width pruning, i.e., HetDPT+, our method sets a new state-of-the-art record in extreme ViT pruning. HetDPT+ enhances the acceleration ratio from 4.24$\times$ to 5.19$\times$ for the Isomorphic-Pruning-2.6G configuration while maintaining near-lossless accuracy.
Probabilistic Methods · Bayesian Models and Methods
Bayesian experimental design (BED) provides a principled framework for optimizing data collection by choosing experiments that are maximally informative about unknown parameters. However, existing methods cannot deal with the joint challenge of (a) *partially observable dynamical systems*, where only noisy and incomplete observations are available, and (b) *fully online inference*, which updates posterior distributions and selects designs sequentially in a computationally efficient manner. Under partial observability, dynamical systems are naturally modeled as state-space models (SSMs), where latent states mediate the link between parameters and data, making the likelihood---and thus information-theoretic objectives like the expected information gain (EIG)---intractable. We address these challenges by deriving new estimators of the EIG and its gradient that explicitly marginalize latent states, enabling scalable stochastic optimization in nonlinear SSMs. Our approach leverages nested particle filters for efficient online state-parameter inference with convergence guarantees. Applications to realistic models, such as the susceptible–infectious–recovered (SIR) and a moving source location task, show that our framework successfully handles both partial observability and online inference.
Deep Learning · Large Language Models
Recent reinforcement learning (RL) based large-thinking models demonstrate impressive expert-level abilities, i.e., software and math, but still rely heavily on verifiable rewards in specific domains, which places a significant bottleneck to extend the performance boundary of general reasoning capabilities. In this work, we propose PretrainZero, a reinforcement active learning framework built on the pretraining corpus to extend RL from domain-specific post-training to general pretraining. PretrainZero features the following characteristics: 1) Active pretraining: inspired by the active learning ability of humans, PretrainZero learns a unified reasoning policy to actively identify reasonable and informative contents from pretraining corpus, and reason to predict these contents by RL. 2) Self-supervised learning: without any verifiable labels, pretrained reward models, or supervised fine-tuning, we directly pretrain reasoners from $3\sim30$B base models on the general Wikipedia corpus using RL, significantly breaking the verification data-wall for general reasoning. 3) Verification scaling: by tackling increasingly challenging masked spans, PretrainZero substantially enhances the general reasoning abilities of pretrained base models. In reinforcement pretraining, PretrainZero improves Qwen3-4B-Base for 8.43, 5.96 and 10.60 on MMLU-Pro, SuperGPQA and math average benchmarks. In post-training, the pretrained models can also serve as reasoning foundation models for downstream RLVR tasks.
Reinforcement Learning · Batch/Offline
Offline goal-conditioned reinforcement learning remains challenging for long-horizon tasks. While hierarchical approaches mitigate this issue by decomposing tasks, most existing methods rely on separate high- and low-level networks and generate only a single intermediate subgoal, making them inadequate for complex tasks that require coordinating multiple intermediate decisions. To address this limitation, we draw inspiration from the chain-of-thought paradigm and propose the Chain-of-Goals Hierarchical Policy (CoGHP), a novel framework that reformulates hierarchical decision-making as autoregressive sequence modeling within a unified architecture. Given a state and a final goal, CoGHP autoregressively generates a sequence of latent subgoals followed by the primitive action, where each latent subgoal acts as a reasoning step that conditions subsequent predictions. To implement this efficiently, we pioneer the use of an MLP-Mixer backbone, which supports cross-token communication and captures structural relationships among state, goal, latent subgoals, and action. Across challenging navigation and manipulation benchmarks, CoGHP consistently outperforms strong offline baselines, demonstrating improved performance on long-horizon tasks.
General Machine Learning · Representation Learning
The Platonic Representation Hypothesis posits that learned representations from models trained on different modalities converge to a shared latent structure of the world. However, this hypothesis has largely been examined in vision and language, and it remains unclear whether time series participate in such convergence. We first examine this in a trimodal setting and find that independently pretrained time series, vision, and language encoders exhibit near-orthogonal geometry in the absence of explicit coupling. We then apply post-hoc alignment by training projection heads over frozen encoders using contrastive learning, and analyze the resulting representations with respect to geometry, scaling behavior, and dependence on information density and input modality characteristics. Our investigation reveals that overall alignment in contrastive representation spaces improves with model size, but this alignment is asymmetric: time series align more strongly with visual representations than with text, and images can act as effective intermediaries between time series and language. We further see that richer textual descriptions improve alignment only up to a threshold; training on denser captions does not lead to further improvement. Analogous effects are observed for visual representations. Our findings shed light on considerations for building multimodal systems involving non-conventional data modalities beyond vision and language.
Deep Learning · Foundation Models
Time Series Foundation Models (TSFMs) leverage extensive pretraining to accurately predict unseen time series during inference, without the need for task-specific fine-tuning. Through large-scale evaluations on standard benchmarks, we find that leading transformer-based TSFMs exhibit redundant components in their intermediate layers. We introduce a set of tools for mechanistic interpretability of TSFMs, including ablations of specific components and direct logit attribution on the residual stream. Our findings are consistent across several leading TSFMs with diverse architectures, and across a diverse set of real-world and synthetic time-series datasets. We discover that all models in our study are robust to ablations of entire layers. Furthermore, we develop a theoretical framework framing transformers as kernel regressors, motivating a purely intrinsic strategy for ablating heads based on the stable rank of the per-head projection matrices. Using this approach, we uncover the specific heads responsible for degenerate phenomena widely observed in TSFMs, such as parroting of motifs from the context and seasonality bias. Our study sheds light on the universal properties of this emerging class of architectures for continuous-time sequence modeling.
Reinforcement Learning · Deep RL
We study a simple yet principled modification of classical Q-learning that clips the value estimate in the Bellman backup by a threshold $\lambda$. The resulting algorithm, clipped Q-learning, is motivated by a key theoretical insight: the clipped Bellman backup is an unbiased one-sample estimation of a robust Bellman operator arising naturally from a transition-regularized MDP framework. This formulation corresponds to optimizing performance against a specific class of adversarial dynamics perturbations at the test time that reallocate transition probability mass away from high-value states, thereby inducing conservative but stable decision making. Under this interpretation, clipped Q-learning can be viewed as tracking the fixed point of the robust Bellman equation and learning policies that hedge against adversarial dynamics shifts at test time. We analyze two clipped Q-learning variants with an optimistic exploration bonus and establish polynomial regret guarantees, demonstrating statistical efficiency. Beyond the tabular setting, our framework suggests that value clipping is a modular mechanism that can be incorporated into general value-based RL algorithms with function approximation. As a proof of concept, we evaluate a clipped Double DQN algorithm on a control task and observe robustness improvements consistent with our theoretical predictions.
Reinforcement Learning · Policy Search
Aligning large language models (LLMs) with human preferences is commonly done via reinforcement learning from human feedback (RLHF) with Proximal Policy Optimization (PPO) or, more simply, via Direct Preference Optimization (DPO). While DPO is stable and RL-free, it treats preferences as flat winner vs. loser signals and is sensitive to noisy or brittle preferences arising from fragile chains of thought. We propose TUR-DPO, a topology- and uncertainty-aware variant of DPO that rewards how answers are derived, not only what they say, by eliciting lightweight reasoning topologies and combining semantic faithfulness, utility, and topology quality into a calibrated uncertainty signal. A small learnable reward is factorized over these signals and incorporated into an uncertainty-weighted DPO objective that remains RL-free and relies only on a fixed or moving reference policy. Empirically, across open 7–8B models and benchmarks spanning mathematical reasoning, factual question answering, summarization, and helpful/harmless dialogue, TUR-DPO improves judge win-rates, faithfulness, and calibration relative to DPO while preserving training simplicity and avoiding online rollouts. We further observe consistent gains in multimodal and long-context settings, and show that TUR-DPO matches or exceeds PPO on reasoning-centric tasks while maintaining operational simplicity.
Reinforcement Learning · Batch/Offline
There is growing interest in utilizing flow-based models as decision-making policies in reinforcement learning due to their high expressive capacity. However, effectively leveraging this expressivity for value maximization remains challenging, as naive gradient-based optimization requires backpropagating through numerical solvers and often leads to instability. Existing approaches typically address this issue by restricting the expressive capacity of flow-based policies, resulting in a trade-off between optimization stability and representational flexibility. To resolve this, we introduce **Q-Flow**, a framework that leverages the deterministic nature of flow dynamics to explicitly propagate terminal trajectory value to intermediate latent states along the policy-induced flow. This formulation enables stable policy optimization using intermediate value gradients without unrolling the numerical solver, effectively bridging the gap between stability and expressivity. We evaluate Q-Flow in the offline learning setting on the challenging OGBench suite, where it consistently outperforms state-of-the-art baselines by an average of **10.6** percentage points, while also enabling stable online adaptation within the same framework.
State-space models (SSMs) offer efficient sequence modeling but show a large performance gap compared to Transformers on benchmarks that require in-context retrieval. This gap has been linked to a small set of attention heads, called Gather-and-Aggregate (G&A), which SSMs struggle to implement and are believed to drive the disparity. Leveraging this insight, we propose retrieval-aware distillation, a strategy that converts a pretrained Transformer into a hybrid student by preserving only these retrieval-critical components. We identify essential attention heads via ablation on a synthetic retrieval task and distill the rest into recurrent heads, resulting in a model with non-uniform attention placement tailored to retrieval demands. We empirically show that preserving just 2% of attention heads enables the hybrid model to recover teacher-level performance (10 retrieval-critical heads in a 1B model), reducing memory overhead by up to 6x compared to prior distillation methods that retain 30–50% of heads. Furthermore, we show that large recurrent states in SSMs often compensate for missing retrieval. Once retrieval is handled by these specific heads, the SSM backbone can be significantly simplified while maintaining performance, even with an 8x reduction in state dimension. Overall, the results show that strategically concentrating attention can close the Transformer–SSM gap with a fraction of the memory cost.
Applications · Health / Medicine
Interval-censored data present unique challenges in statistical analysis due to the partial observability of event times within known intervals, requiring assumptions about the censoring mechanism. This paper explores the theoretical relationship between two foundational assumptions: independent monitoring and non-informative censoring. We demonstrate that these assumptions are equivalent for Case-1 interval-censored data, but not for Case-$K$ interval-censored data, where $K \geq 2$, through a synthetic dataset example. Additionally, we propose the first strictly proper scoring rule and calibration metric specifically designed for interval-censored data under the constant-sum assumption and the non-informative censoring assumption, respectively. Our empirical evaluations on real-world datasets show that a neural network model trained with our scoring rule is competitive with established statistical baselines, offering enhanced flexibility. These contributions provide significant advancements in the theoretical understanding and practical analysis of interval-censored data.
Deep Learning · Attention Mechanisms
Large language models have achieved remarkable success in recent years, primarily due to self-attention. However, traditional Softmax attention suffers from numerical instability and reduced performance as the number of inference tokens increases. This work addresses these issues by proposing a new design principle for attention, viewing it as a two-stage process. The first stage (normalisation) refines standard attention by replacing Softmax with the more numerically stable Softplus followed by $l_{1}$-normalisation. Furthermore, we introduce a dynamic scale factor based on invariance entropy. We show that this novel attention mechanism outperforms conventional Softmax attention, and state-of-the-art Softmax-free alternatives. Our second proposal is to introduce a second processing stage (sharpening) which consists of a re-weighting mechanism that amplifies significant attentional weights while diminishing weaker ones. This enables the model to concentrate more effectively on relevant tokens, mitigating the attention sink phenomenon, and fundamentally improving length extrapolation. This novel, two-stage, replacement for self-attention is shown to ensure numerical stability and dramatically improve length extrapolation, maintaining a nearly constant validation loss at 16$\times$ the training length while achieving superior results on challenging long-context retrieval tasks and standard downstream benchmarks. Furthermore, symbolic regression experiments demonstrate that our method enables models to recover Newton's gravitational law from orbital trajectory sequences, providing evidence that appropriate attention mechanisms are crucial in order for foundation models to develop genuine physical world models.