Machine Learning · Cross-listed from cs.CR
Beyond Indistinguishability: Measuring Extraction Risk in LLM APIs
Ruixuan Liu, David Evans and Li Xiong
Indistinguishability properties such as differential
privacy bounds or low empirically measured membership inference are widely treated as proxies to show a model is sufficiently protected against broader memorization risks. However, we show that indistinguishability properties are neither sufficient nor necessary for preventing data extraction in LLM APIs. We formalize a
privacy-game separation between extraction and indistinguishability-based
privacy, showing that indistinguishability and inextractability are incomparable: upper-bounding distinguishability does not upper-bound extractability. To address this gap, we introduce $(l, b)$-inextractability as a definition that requires at least $2^b$ expected queries for any black-box adversary to induce the LLM API to emit a protected $l$-gram substring. We instantiate this via a worst-case extraction game and derive a rank-based extraction risk upper bound for targeted exact extraction, as well as extensions to cover untargeted and approximate extraction. The resulting
estimator captures the extraction risk over multiple attack trials and prefix adaptations. We show that it can provide a tight and efficient estimation for standard greedy extraction and an upper bound on the probabilistic extraction risk given any decoding configuration. We empirically evaluate extractability across different models, clarifying its connection to distinguishability, demonstrating its advantage over existing extraction risk
estimators, and providing actionable mitigation guidelines across model training, API access, and decoding configurations in LLM API deployment. Our code is publicly available at: https://github.com/Emory-AIMS/Inextractability.
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Machine Learning · Cross-listed from cs.AI
Shaden Alshammari, Kevin Wen, Abrar Zainal et al. (+5 more)
Mathematical problem solving remains a challenging test of
reasoning for large
language and
multimodal models, yet existing
benchmarks are limited in size,
language coverage, and task diversity. We introduce MathNet, a high-quality, large-scale,
multimodal, and
multilingual dataset of Olympiad-level math problems together with a
benchmark for evaluating mathematical
reasoning in generative models and mathematical
retrieval in
embedding-based systems. MathNet spans 47 countries, 17
languages, and two decades of competitions, comprising 30,676 expert-authored problems with solutions across diverse domains. In addition to the core
dataset, we construct a
retrieval benchmark consisting of mathematically equivalent and structurally similar problem pairs curated by human experts. MathNet supports three tasks: (i) Problem Solving, (ii) Math-Aware
Retrieval, and (iii)
Retrieval-Augmented Problem Solving. Experimental results show that even state-of-the-art
reasoning models (78.4% for Gemini-3.1-Pro and 69.3% for GPT-5) remain challenged, while
embedding models struggle to retrieve equivalent problems. We further show that
retrieval-augmented generation performance is highly sensitive to
retrieval quality; for example, DeepSeek-V3.2-Speciale achieves gains of up to 12%, obtaining the highest scores on the
benchmark. MathNet provides the largest high-quality Olympiad
dataset together with the first
benchmark for evaluating mathematical problem
retrieval, and we publicly release both the
dataset and
benchmark at https://mathnet.mit.edu.
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All authors Shaden Alshammari, Kevin Wen, Abrar Zainal, Mark Hamilton, Navid Safaei, Sultan Albarakati, William T. Freeman, Antonio Torralba
Machine Learning
Sessa: Selective State Space Attention
Liubomyr Horbatko
Modern sequence modeling is dominated by two families:
Transformers, whose self-
attention can access arbitrary elements of the visible sequence, and structured state-space models, which propagate information through an explicit recurrent state. These mechanisms face different limitations on long contexts: when
attention is diffuse, the influence of individual
tokens is diluted across the effective support, while recurrent state propagation can lose long-range sensitivity unless information is actively preserved. As a result, both mechanisms face challenges in preserving and selectively retrieving information over long contexts. We propose Sessa, a decoder that places
attention inside a recurrent feedback path. This creates many
attention-based paths through which past
tokens can influence future states, rather than relying on a single
attention read or a single recurrent chain. We prove that, under explicit assumptions and matched regimes, Sessa admits power-law
memory tails $O(\ell^{-\beta})$ for $0 < \beta < 1$, with slower decay than in the corresponding
Transformer and Mamba-style baselines. We further give an explicit construction that achieves this power-law rate. Under the same assumptions, Sessa is the only model class among those considered that realizes flexible selective
retrieval, including profiles whose influence does not decay with distance. Consistent with this theoretical advantage, across matched experiments, Sessa achieves the strongest performance on long-context
benchmarks while remaining competitive with
Transformer and Mamba-style baselines on short-context
language modeling.
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Machine Learning
Bounded Ratio Reinforcement Learning
Yunke Ao, Le Chen, Bruce D. Lee et al. (+5 more)
Proximal Policy Optimization (PPO) has become the predominant
algorithm for on-policy reinforcement learning due to its scalability and empirical robustness across domains. However, there is a significant disconnect between the underlying foundations of trust region methods and the heuristic clipped objective used in PPO. In this paper, we bridge this gap by introducing the Bounded Ratio Reinforcement Learning (BRRL) framework. We formulate a novel regularized and constrained policy optimization problem and derive its analytical optimal solution. We prove that this solution ensures monotonic performance improvement. To handle parameterized policy classes, we develop a policy optimization
algorithm called Bounded Policy Optimization (BPO) that minimizes an advantage-weighted divergence between the policy and the analytic optimal solution from BRRL. We further establish a lower bound on the expected performance of the resulting policy in terms of the BPO loss function. Notably, our framework also provides a new theoretical lens to interpret the success of the PPO loss, and connects trust region policy optimization and the Cross-
Entropy Method (CEM). We additionally extend BPO to Group-relative BPO (GBPO) for LLM fine-tuning. Empirical evaluations of BPO across MuJoCo, Atari, and complex IsaacLab environments (e.g.,
Humanoid locomotion), and of GBPO for LLM fine-tuning tasks, demonstrate that BPO and GBPO generally match or outperform PPO and GRPO in stability and final performance.
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All authors Yunke Ao, Le Chen, Bruce D. Lee, Assefa S. Wahd, Aline Czarnobai, Philipp Fürnstahl, Bernhard Schölkopf, Andreas Krause
Machine Learning
When Can LLMs Learn to Reason with Weak Supervision?
Salman Rahman, Jingyan Shen, Anna Mordvina et al. (+3 more)
Large
language models have achieved significant
reasoning improvements through reinforcement learning with verifiable rewards (RLVR). Yet as model capabilities grow, constructing high-quality reward signals becomes increasingly difficult, making it essential to understand when RLVR can succeed under weaker forms of supervision. We conduct a systematic empirical study across diverse model families and
reasoning domains under three weak supervision settings: scarce data, noisy rewards, and self-supervised proxy rewards. We find that generalization is governed by training reward saturation dynamics: models that generalize exhibit a prolonged pre-saturation phase during which training reward and downstream performance climb together, while models that saturate rapidly memorize rather than learn. We identify
reasoning faithfulness, defined as the extent to which intermediate steps logically support the final answer, as the pre-RL property that predicts which regime a model falls into, while output diversity alone is uninformative. Motivated by these findings, we disentangle the contributions of continual pre-training and supervised fine-tuning, finding that SFT on explicit
reasoning traces is necessary for generalization under weak supervision, while continual pre-training on domain data amplifies the effect. Applied together to Llama3.2-3B-Base, these interventions enable generalization across all three settings where the base model previously failed.
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All authors Salman Rahman, Jingyan Shen, Anna Mordvina, Hamid Palangi, Saadia Gabriel, Pavel Izmailov
Machine Learning · Cross-listed from cs.CV
Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale
A. Sophia Koepke, Daniil Zverev, Shiry Ginosar et al. (+1 more)
The Platonic Representation Hypothesis posits that neural networks trained on different modalities (e.g., text and images) converge toward a shared representation of reality. If true, this has significant implications for whether modality choice matters at all. In this paper, we show that the evidence for this claim is substantially weaker than subsequent work suggests. The mutual $k$-nearest-neighbor metric used on 1024 text-image pairs in the original study captures only coarse structure. To keep the
alignment from collapsing as one scales up the data, $k$ has to grow proportionally, undercutting the argument for fine-grained representational convergence. The reported increase in
alignment with
language model strength saturates for recent models. Moreover, the one-to-one text-image pairing favors
alignment, while
alignment decreases with non-bijective data. We further find that image and text representations indeed share coarse semantic structure, but neither stronger
language models nor richer captions yield fine-grained
alignment. Thus,
multimodal representations share coarse structure without evidence of convergence to a shared representation – arguably, full representational convergence would require fine-grained
alignment.
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All authors A. Sophia Koepke, Daniil Zverev, Shiry Ginosar, Alexei A. Efros
Machine Learning
A multimodal and temporal foundation model for virtual patient representations at healthcare system scale
Andrew Zhang, Tong Ding, Sophia J. Wagner et al. (+8 more)
Modern medicine generates vast
multimodal data across siloed systems, yet no existing model integrates the full breadth and temporal depth of the clinical record into a unified patient representation. We introduce Apollo, a
multimodal temporal foundation model trained and evaluated on over three decades of longitudinal hospital records from a major US hospital system, composed of 25 billion records from 7.2 million patients, representing 28 distinct medical modalities and 12 major medical specialties. Apollo learns a unified representation space integrating over 100 thousand unique medical events in our clinical vocabulary as well as images and clinical text. This "atlas of medical concepts" forms a computational substrate for modeling entire patient care journeys comprised of sequences of structured and unstructured events, which are compressed by Apollo into virtual patient representations. To assess the potential of these whole-patient representations, we created 322 prognosis and
retrieval tasks from a held-out test set of 1.4 million patients. We demonstrate the generalized clinical
forecasting potential of Apollo
embeddings, including predicting new disease onset risk up to five years in advance (95 tasks), disease progression (78 tasks), treatment response (59 tasks), risk of treatment-related adverse events (17 tasks), and hospital operations endpoints (12 tasks). Using feature attribution techniques, we show that model predictions align with clinically-interpretable
multimodal biomarkers. We evaluate semantic similarity search on 61
retrieval tasks, and moreover demonstrate the potential of Apollo as a
multimodal medical search engine using text and image queries. Together, these modeling capabilities establish the foundation for computable medicine, where the full context of patient care becomes accessible to computational
reasoning.
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All authors Andrew Zhang, Tong Ding, Sophia J. Wagner, Caiwei Tian, Ming Y. Lu, Rowland Pettit, Joshua E. Lewis, Alexandre Misrahi, Dandan Mo, Long Phi Le, Faisal Mahmood
Machine Learning · Cross-listed from stat.ML
Revisiting Active Sequential Prediction-Powered Mean Estimation
Maria-Eleni Sfyraki and Jun-Kun Wang
In this work, we revisit the problem of active sequential prediction-powered mean estimation, where at each round one must decide the query probability of the ground-truth label upon observing the
covariates of a sample. Furthermore, if the label is not queried, the prediction from a machine learning model is used instead. Prior work proposed an elegant scheme that determines the query probability by combining an uncertainty-based suggestion with a constant probability that encodes a soft constraint on the query probability. We explored different values of the mixing parameter and observed an intriguing empirical pattern: the smallest confidence width tends to occur when the weight on the constant probability is close to one, thereby reducing the influence of the uncertainty-based component. Motivated by this observation, we develop a non-asymptotic analysis of the
estimator and establish a data-dependent bound on its confidence interval. Our analysis further suggests that when a no-regret learning approach is used to determine the query probability and control this bound, the query probability converges to the constraint of the max value of the query probability when it is chosen obliviously to the current
covariates. We also conduct simulations that corroborate these theoretical findings.
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Machine Learning
Latent Phase-Shift Rollback: Inference-Time Error Correction via Residual Stream Monitoring and KV-Cache Steering
Manan Gupta and Dhruv Kumar
Large
language models frequently commit unrecoverable
reasoning errors mid-generation: once a wrong step is taken, subsequent
tokens compound the mistake rather than correct it. We introduce $\textbf{Latent Phase-Shift Rollback}$ (LPSR): at each generation step, we monitor the residual stream at a critical layer lcrit, detect abrupt directional reversals (phase shifts) via a cosine-similarity $+$
entropy dual gate, and respond by rolling back the KV-cache and injecting a pre-computed steering vector. No fine-tuning, gradient computation, or additional forward passes are required. LPSR achieves $\mathbf{44.0\%}$ on MATH-500 with an 8B model versus $28.8\%$ for standard AR ($+15.2$ pp; McNemar $\chi^2 = 66.96$, $p < 10^{-15}$). Critically, prompted self-correction, the most natural inference-time baseline, scores only $19.8\%$, below standard AR; LPSR exceeds it by $+24.2$ pp ($\chi^2 = 89.4$, $p \approx 0$). LPSR also outperforms Best-of-16 ($+7.8$ pp) at $5.4\times$ lower
token cost, and surpasses a standard 70B model ($35.2\%$) with $8.75\times$ fewer parameters at ${\sim}3\times$ the
token budget. A 32-layer sweep reveals a novel detection-correction dissociation: error-detection AUC peaks at layer~14 ($0.718$) but task accuracy peaks at layer~16 ($44.0\%$ vs.\ $29.2\%$), demonstrating that optimal monitoring depth differs for detection and correction.
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Machine Learning · Cross-listed from cs.AI
Benchmarking System Dynamics AI Assistants: Cloud Versus Local LLMs on CLD Extraction and Discussion
Terry Leitch
We present a systematic evaluation of large
language model families – spanning both proprietary cloud APIs and locally-hosted open-source models – on two purpose-built
benchmarks for System Dynamics AI assistance: the CLD Leaderboard (53 tests, structured causal loop diagram extraction) and the Discussion Leaderboard (interactive model discussion, feedback explanation, and model building coaching). On CLD extraction, cloud models achieve 77–89% overall pass rates; the best local model reaches 77% (Kimi~K2.5~GGUF~Q3, zero-shot engine), matching mid-tier cloud performance. On Discussion, the best local models achieve 50–100% on model building steps and 47–75% on feedback explanation, but only 0–50% on error fixing – a category dominated by long-context
prompts that expose
memory limits in local deployments. A central contribution of this paper is a systematic analysis of model type effects on performance: we compare
reasoning vs.\ instruction-tuned architectures, GGUF (llama.cpp) vs.\ MLX (mlx\_lm) backends, and quantization levels (Q3 / Q4\_K\_M / MLX-3bit / MLX-4bit / MLX-6bit) across the same underlying model families. We find that backend choice has larger practical impact than quantization level: mlx\_lm does not enforce JSON schema constraints, requiring explicit
prompt-level JSON instructions, while llama.cpp grammar-constrained sampling handles JSON reliably but causes indefinite generation on long-context
prompts for dense models. We document the full parameter sweep ($t$, $p$, $k$) for all local models, cleaned timing data (stuck requests excluded), and a practitioner guide for running 671B–123B parameter models on Apple~Silicon.
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Machine Learning · Cross-listed from q-bio.BM
ConforNets: Latents-Based Conformational Control in OpenFold3
Minji Lee, Colin Kalicki, Minkyu Jeon et al. (+3 more)
Models from the AlphaFold (AF) family reliably predict one dominant conformation for most well-ordered
proteins but struggle to capture biologically relevant alternate states. Several efforts have focused on eliciting greater conformational variability through ad hoc inference-time perturbations of AF models or their inputs. Despite their progress, these approaches remain inefficient and fail to consistently recover major conformational modes. Here, we investigate both the optimal location and manner-of-operation for perturbing latent representations in the AF3 architecture. We distill our findings in ConforNets: channel-wise affine transforms of the pre-Pairformer pair latents. Unlike previous methods, ConforNets globally modulate AF3 representations, making them reusable across
proteins. On unsupervised generation of alternate states, ConforNets achieve state-of-the-art success rates on all existing multi-state
benchmarks. On the novel supervised task of conformational transfer, ConforNets trained on one source
protein can induce a conserved conformational change across a
protein family. Collectively, these results introduce a mechanism for conformational control in AF3-based models.
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All authors Minji Lee, Colin Kalicki, Minkyu Jeon, Aymen Qabel, Alisia Fadini, Mohammed AlQuraishi
Machine Learning · Cross-listed from cs.CL
GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling
Alireza Dadgarnia, Soroush Tabesh, Mahdi Nikdan et al. (+4 more)
Quantization has become a standard tool for efficient LLM deployment, especially for local inference, where models are now routinely served at 2-3 bits per parameter. The state of the art is currently split into simple scalar quantization techniques, such as GPTQ or AWQ, which are widely deployed but plateau in accuracy at 3-4 bits per parameter (bpp), and "second-generation" vector- or trellis-quantized methods, such as QTIP, GPTVQ and AQLM, which push the accuracy frontier but are notoriously hard to implement and to scale. In this paper, we ask whether this gap is fundamental, or whether a carefully optimized $\textit{scalar}$ quantizer can recover most of it. We answer in the affirmative, by introducing GSQ (Gumbel-Softmax Quantization), a post-training scalar quantization method which jointly learns the per-coordinate grid assignments and the per-group scales using a Gumbel-Softmax relaxation of the discrete grid. GSQ matches the cardinality of the relaxation to the small number of levels available in the target bit-width regime (e.g., 3-8 levels for ternary and 3 bpp, respectively), making optimization tractable. Practically, on the standard Llama-3.1-8B/70B-Instruct models, GSQ closes most of the gap between scalar quantization and the QTIP frontier at 2 and 3 bits, while using a symmetric scalar grid with group-wise quantization, and thus remains compatible with existing scalar inference kernels. We further show that the same discrete-assignment optimization can be applied to practical GGUF K-Quant checkpoints: starting from publicly released GGUF models, GSQ improves accuracy while projecting the result back into the same deployment format. Finally, GSQ scales to trillion-scale Mixture-of-Experts models such as Kimi-K2.5, where vector-quantized methods are difficult to apply. The source code is publicly available at https://github.com/IST-DASLab/GSQ.
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All authors Alireza Dadgarnia, Soroush Tabesh, Mahdi Nikdan, Michael Helcig, Eldar Kurtic, Maximilian Kleinegger, Dan Alistarh
Machine Learning
A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work
Ran Ben-Basat, Yaniv Ben-Itzhak, Gal Mendelson et al. (+3 more)
This note clarifies the relationship between the recent TurboQuant work and the earlier DRIVE (NeurIPS 2021) and EDEN (ICML 2022) schemes. DRIVE is a 1-bit quantizer that EDEN extended to any $b>0$ bits per coordinate; we refer to them collectively as EDEN. First, TurboQuant$_{\text{mse}}$ is a special case of EDEN obtained by fixing EDEN's scalar scale parameter to $S=1$. EDEN supports both biased and unbiased quantization, each optimized by a different $S$ (chosen via methods described in the EDEN works). The fixed choice $S=1$ used by TurboQuant is generally suboptimal, although the optimal $S$ for biased EDEN converges to $1$ as the dimension grows; accordingly TurboQuant$_{\text{mse}}$ approaches EDEN's behavior for large $d$. Second, TurboQuant$_{\text{prod}}$ combines a biased $(b-1)$-bit EDEN step with an unbiased 1-bit QJL quantization of the residual. It is suboptimal in three ways: (1) its $(b-1)$-bit step uses the suboptimal $S=1$; (2) its 1-bit unbiased residual quantization has worse MSE than (unbiased) 1-bit EDEN; (3) chaining a biased $(b-1)$-bit step with a 1-bit unbiased residual step is inferior to unbiasedly quantizing the input directly with $b$-bit EDEN. Third, some of the analysis in the TurboQuant work mirrors that of the EDEN works: both exploit the connection between random rotations and the shifted Beta distribution, use the Lloyd-Max
algorithm, and note that Randomized Hadamard Transforms can replace uniform random rotations. Experiments support these claims: biased EDEN (with optimized $S$) is more accurate than TurboQuant$_{\text{mse}}$, and unbiased EDEN is markedly more accurate than TurboQuant$_{\text{prod}}$, often by more than a bit (e.g., 2-bit EDEN beats 3-bit TurboQuant$_{\text{prod}}$). We also repeat all accuracy experiments from the TurboQuant paper, showing that EDEN outperforms it in every setup we have tried.
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All authors Ran Ben-Basat, Yaniv Ben-Itzhak, Gal Mendelson, Michael Mitzenmacher, Amit Portnoy, Shay Vargaftik
Machine Learning
Physics-Informed Neural Networks for Biological $2\mathrm{D}{+}t$ Reaction-Diffusion Systems
William Lavery, Jodie A. Cochrane, Christian Olesen et al. (+3 more)
Physics-informed neural networks (PINNs) provide a powerful framework for learning governing equations of dynamical systems from data. Biologically-informed neural networks (BINNs) are a variant of PINNs that preserve the known differential operator structure (e.g., reaction-
diffusion) while learning constitutive terms via trainable neural subnetworks, enforced through soft residual penalties. Existing BINN studies are limited to $1\mathrm{D}{+}t$ reaction-
diffusion systems and focus on forward prediction, using the governing partial differential equation as a regulariser rather than an explicit identification target. Here, we extend BINNs to $2\mathrm{D}{+}t$ systems within a PINN framework that combines data preprocessing, BINN-based equation learning, and symbolic regression post-processing for closed-form equation discovery. We demonstrate the framework's real-world applicability by learning the governing equations of lung cancer
cell population dynamics from time-lapse microscopy data, recovering $2\mathrm{D}{+}t$ reaction-
diffusion models from experimental observations. The proposed framework is readily applicable to other spatio-temporal systems, providing a practical and interpretable tool for fast analytic equation discovery from data.
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All authors William Lavery, Jodie A. Cochrane, Christian Olesen, Dagim S. Tadele, John T. Nardini, Sara Hamis
Machine Learning · Cross-listed from stat.ML
FUSE: Ensembling Verifiers with Zero Labeled Data
Joonhyuk Lee, Virginia Ma, Sarah Zhao et al. (+4 more)
Verification of model outputs is rapidly emerging as a key primitive for both training and real-world deployment of large
language models (LLMs). In practice, this often involves using imperfect LLM judges and reward models since ground truth acquisition can be time-consuming and expensive. We introduce Fully Unsupervised Score Ensembling (FUSE), a method for improving verification quality by ensembling verifiers without access to ground truth correctness labels. The key idea behind FUSE is to control conditional dependencies between verifiers in a manner that improves the unsupervised performance of a class of spectral
algorithms from the ensembling literature. Despite requiring zero ground truth labels, FUSE typically matches or improves upon semi-supervised alternatives in test-time scaling experiments with diverse sets of generator models, verifiers, and
benchmarks. In particular, we validate our method on both conventional academic
benchmarks such as GPQA Diamond and on frontier, unsaturated
benchmarks such as Humanity's Last Exam and IMO Shortlist questions.
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All authors Joonhyuk Lee, Virginia Ma, Sarah Zhao, Yash Nair, Asher Spector, Regev Cohen, Emmanuel J. Candès
Machine Learning
Wasserstein Distributionally Robust Risk-Sensitive Estimation via Conditional Value-at-Risk
Feras Al Taha and Eilyan Bitar
We propose a distributionally robust approach to risk-sensitive estimation of an unknown signal x from an observed signal y. The observation and unknown signal are modeled as random vectors whose joint probability distribution is unknown, but assumed to belong to a given type-2 Wasserstein ball of distributions, termed the ambiguity set. The performance of an
estimator is measured according to the conditional value-at-risk (CVaR) of the squared estimation error. Within this framework, we study the problem of computing affine
estimators that minimize the worst-case CVaR over all distributions in the given ambiguity set. As our main result, we show that, when the nominal distribution at the center of the Wasserstein ball is finitely supported, such
estimators can be exactly computed by solving a tractable semidefinite program. We evaluate the proposed
estimators on a wholesale electricity price
forecasting task using real
market data and show that they deliver lower out-of-sample CVaR of squared error compared to existing methods.
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Machine Learning · Cross-listed from math.AP
Duality for the Adversarial Total Variation
Leon Bungert and Lucas Schmitt
Adversarial training of binary classifiers can be reformulated as regularized risk minimization involving a nonlocal total variation. Building on this perspective, we establish a characterization of the subdifferential of this total variation using duality techniques. To achieve this, we derive a dual representation of the nonlocal total variation and a related integration-by-parts formula, involving a nonlocal gradient and divergence. We provide such duality statements both in the space of continuous functions vanishing at infinity on proper metric spaces and for the space of essentially bounded functions on separable metric spaces. Furthermore, under some additional conditions we provide characterizations of the subdifferential in these settings.
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Machine Learning
IDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem
Aniruddha Adiga, Jingyuan Chou, Anshul Chiranth et al. (+7 more)
Epidemic
forecasting has become an integral part of real-time infectious disease outbreak response. While collaborative ensembles composed of statistical and machine learning models have become the norm for real-time
forecasting, standardized
benchmark datasets for evaluating such methods are lacking. Further, there is limited understanding on performance of these methods for novel outbreaks with limited historical data. In this paper, we propose IDOBE, a curated collection of epidemiological time series focused on outbreak
forecasting. IDOBE compiles from multiple data repositories spanning over a century of surveillance and across U.S. states and global locations. We perform derivative-based
segmentation to generate over 10,000 outbreaks covering multiple outcomes such as cases and hospitalizations for 13 diseases. We consider a variety of information-theoretic and distributional measures to quantify the epidemiological diversity of the
dataset. Finally, we perform multi-horizon short-term
forecasting (1- to 4-week-ahead) through the progression of the outbreak using 11 baseline models and report on their performance. In addition to standard metrics such as NMSE and MAPE for point forecasts, we include probabilistic scoring rules such as Normalized Weighted Interval Score (NWIS) to quantify the performance. We find that MLP-based methods have the most robust performance, with statistical methods having a slight edge during the pre-peak phase. IDOBE
dataset along with baselines are released publicly on https://github.com/NSSAC/IDOBE to enable standardized, reproducible benchmarking of outbreak
forecasting methods.
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All authors Aniruddha Adiga, Jingyuan Chou, Anshul Chiranth, Bryan Lewis, Ana I. Bento, Shaun Truelove, Geoffrey Fox, Madhav Marathe, Harry Hochheiser, Srini Venkatramanan
Machine Learning · Cross-listed from cs.CV
UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models
Jiaqi Wang, Haoge Deng, Ting Pan et al. (+5 more)
Uniform Discrete
Diffusion Model (UDM) has recently emerged as a promising paradigm for discrete generative modeling; however, its integration with reinforcement learning remains largely unexplored. We observe that naively applying GRPO to UDM leads to training instability and marginal performance gains. To address this, we propose UDM-GRPO, the first framework to integrate UDM with RL. Our method is guided by two key insights: (i) treating the final clean sample as the action provides more accurate and stable optimization signals; and (ii) reconstructing trajectories via the
diffusion forward process better aligns probability paths with the pretraining distribution. Additionally, we introduce two strategies, Reduced-Step and CFG-Free, to further improve training efficiency. UDM-GRPO significantly improves base model performance across multiple T2I tasks. Notably, GenEval accuracy improves from $69\%$ to $96\%$ and PickScore increases from $20.46$ to $23.81$, achieving state-of-the-art performance in both continuous and discrete settings. On the OCR
benchmark, accuracy rises from $8\%$ to $57\%$, further validating the generalization ability of our method. Code is available at https://github.com/Yovecent/UDM-GRPO.
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All authors Jiaqi Wang, Haoge Deng, Ting Pan, Yang Liu, Chengyuan Wang, Fan Zhang, Yonggang Qi, Xinlong Wang
Machine Learning · Cross-listed from math.OC
Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks
Jun Chen, Umberto Biccari and Junmin Wang
We propose a computational framework for replacing the repeated numerical solution of differential Riccati equations in finite-horizon Linear Quadratic Regulator (LQR) problems by a learned operator surrogate. Instead of solving a nonlinear
matrix-valued differential equation for each new system instance, we construct offline an approximation of the associated solution operator mapping time-dependent system parameters to the Riccati trajectory. The resulting model enables fast online evaluation of approximate optimal feedbacks across a wide class of systems, thereby shifting the computational burden from repeated numerical integration to a one-time learning stage. From a theoretical perspective, we establish control-theoretic guarantees for this operator-based approximation. In particular, we derive bounds quantifying how operator approximation errors propagate to feedback performance, trajectory accuracy, and cost suboptimality, and we prove that exponential stability of the closed-loop system is preserved under sufficiently accurate operator approximation. These results provide a framework to assess the reliability of data-driven approximations in optimal control. On the computational side, we design tailored DeepONet architectures for
matrix-valued, time-dependent problems and introduce a progressive learning strategy to address scalability with respect to the system dimension. Numerical experiments on both time-invariant and time-varying LQR problems demonstrate that the proposed approach achieves high accuracy and strong generalization across a wide range of system configurations, while delivering substantial computational speedups compared to classical solvers. The method offers an effective and scalable alternative for parametric and real-time optimal control applications.
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Machine Learning
Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data
Zhenwen Liang, Yujun Zhou, Sidi Lu et al. (+3 more)
Reinforcement Learning (RL) enhances LLM
reasoning, yet a paradox emerges as models scale: strong base models saturate standard
benchmarks (e.g., MATH), yielding correct but homogeneous solutions. In such environments, the lack of failure cases causes the advantage signal in group-relative
algorithms (e.g., GRPO) to vanish, driving policies into mode collapse. To address this, we propose Constrained Uniform Top-K Sampling (CUTS), a parameter-free decoding strategy enforcing structure-preserving exploration. Unlike standard sampling that follows model biases, CUTS flattens the local optimization landscape by sampling uniformly from constrained high-confidence candidates. We integrate this into Mixed-CUTS, a training framework synergizing exploitative and exploratory rollouts to amplify intra-group advantage variance. Experiments on Qwen3 models demonstrate that our approach prevents policy degeneration and significantly boosts out-of-domain generalization. Notably, Mixed-CUTS improves Pass@1 accuracy on the challenging AIME25
benchmark by up to 15.1% over standard GRPO, validating that maintaining diversity within the semantic
manifold is critical for rigorous
reasoning.
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All authors Zhenwen Liang, Yujun Zhou, Sidi Lu, Xiangliang Zhang, Haitao Mi, Dong Yu
Machine Learning
Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting
Worachit Amnuaypongsa, Yotsapat Suparanonrat, Pana Wanitchollakit et al. (+1 more)
This paper proposes a multi-step probabilistic
forecasting framework using a single neural-network based model to generate simultaneous point and interval forecasts. Our approach ensures non-crossing prediction intervals (PIs) through a model structure design that strictly satisfy a target coverage probability (PICP) while maximizing sharpness. Unlike existing methods that rely on manual weight tuning for scalarized loss functions, we treat point and PI
forecasting as a multi-objective optimization problem, utilizing multi-gradient descent to adaptively select optimal weights. Key innovations include a new PI loss function based on an extended log-barrier with an adaptive hyperparameter to guarantee the coverage, a hybrid architecture featuring a shared temporal model with horizon-specific submodels, and a training strategy. The proposed loss is scale-independent and universally applicable; combined with our training
algorithm, the framework eliminates trial-and-error hyperparameter tuning for balancing multiple objectives. Validated by an intra-day
solar irradiance
forecasting application, results demonstrate that our proposed loss consistently outperforms those in current literature by achieving target coverage with the narrowest PI widths. Furthermore, when compared against LSTM encoder-decoder and
Transformer architectures–including those augmented with Chronos foundation models–our method remains highly competitive and can be seamlessly adapted to any deep learning structure.
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All authors Worachit Amnuaypongsa, Yotsapat Suparanonrat, Pana Wanitchollakit, Jitkomut Songsiri
Machine Learning
Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD
Nicholas Thumiger, Andrea Bartezzaghi, Mattia Rigotti et al. (+5 more)
Computational Fluid Dynamics (CFD) is central to race-car aerodynamic development, yet its cost – tens of thousands of core-hours per high-fidelity evaluation – severely limits the design space exploration feasible within realistic budgets. AI-based surrogate models promise to alleviate this bottleneck, but progress has been constrained by the limited complexity of public
datasets, which are dominated by smoothed passenger-car shapes that fail to exercise surrogates on the thin, complex, highly loaded components governing motorsport performance. This work presents three primary contributions. First, we introduce a high-fidelity RANS
dataset built on a parametric LMP2-class CAD model and spanning six operating conditions (map points) covering straight-line and cornering regimes, generated and validated by aerodynamics experts at Dallara to preserve features relevant to industrial motorsport. Second, we present the Gauge-Invariant Spectral
Transformer (GIST), a graph-based neural operator whose spectral
embeddings encode mesh connectivity to enhance predictions on tightly packed, complex geometries. GIST guarantees discretization invariance and scales linearly with mesh size, achieving state-of-the-art accuracy on both public
benchmarks and the proposed race-car
dataset. Third, we demonstrate that GIST achieves a level of predictive accuracy suitable for early-stage aerodynamic design, providing a first validation of the concept of interactive design-space exploration – where engineers query a surrogate in place of the CFD solver – within industrial motorsport workflows.
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All authors Nicholas Thumiger, Andrea Bartezzaghi, Mattia Rigotti, Cezary Skura, Thomas Frick, Elisa Serioli, Fabrizio Arbucci, A. Cristiano I. Malossi
Machine Learning · Cross-listed from eess.SY
Safe Control using Learned Safety Filters and Adaptive Conformal Inference
Sacha Huriot, Ihab Tabbara and Hussein Sibai
Safety filters have been shown to be effective tools to ensure the safety of control systems with unsafe nominal policies. To address scalability challenges in traditional synthesis methods, learning-based approaches have been proposed for designing safety filters for systems with high-dimensional state and control spaces. However, the inevitable errors in the decisions of these models raise concerns about their reliability and the safety guarantees they offer. This paper presents Adaptive Conformal Filtering (ACoFi), a method that combines learned Hamilton-Jacobi reachability-based safety filters with adaptive conformal inference. Under ACoFi, the filter dynamically adjusts its switching criteria based on the observed errors in its predictions of the safety of actions. The range of possible safety values of the nominal policy's output is used to quantify uncertainty in safety assessment. The filter switches from the nominal policy to the learned safe one when that range suggests it might be unsafe. We show that ACoFi guarantees that the rate of incorrectly quantifying uncertainty in the predicted safety of the nominal policy is asymptotically upper bounded by a user-defined parameter. This gives a soft safety guarantee rather than a hard safety guarantee. We evaluate ACoFi in a Dubins car simulation and a Safety Gymnasium environment, empirically demonstrating that it significantly outperforms the baseline method that uses a fixed switching threshold by achieving higher learned safety values and fewer safety violations, especially in out-of-distribution scenarios.
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Machine Learning · Cross-listed from math.NA
Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle
Abdeladhim Tahimi
This paper is a step-by-step, self-contained guide to the complete training cycle of a Physics-Informed Neural Network (PINN) – a topic that existing tutorials and guides typically delegate to automatic differentiation libraries without exposing the underlying
algebra. Using a first-order initial value problem with a known analytical solution as a running example, we walk through every stage of the process: forward propagation of both the network output and its temporal derivative, evaluation of a composite loss function built from the ODE residual and the initial condition, backpropagation of gradients – with particular
attention to the product rule that arises in hidden layers – and a gradient descent parameter update. Every calculation is presented with explicit, verifiable numerical values using a 1-3-3-1 multilayer perceptron with two hidden layers and 22 trainable parameters. From these concrete examples, we derive general recursive formulas – expressed as sensitivity propagation relations – that extend the gradient computation to networks of arbitrary depth, and we connect these formulas to the automatic differentiation engines used in practice. The trained network is then validated against the exact solution, achieving a relative $L^2$ error of $4.290 \times 10^{-4}$ using only the physics-informed loss, without any data from the true solution. A companion Jupyter/PyTorch notebook reproduces every manual calculation and the full training pipeline, providing mutual validation between hand-derived and machine-computed gradients.
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Machine Learning
Multi-Scale Reversible Chaos Game Representation: A Unified Framework for Sequence Classification
Sarwan Ali and Taslim Murad
Biological classification with interpretability remains a challenging task. For this, we introduce a novel encoding framework, Multi-Scale Reversible Chaos Game Representation (MS-RCGR), that transforms biological sequences into multi-resolution
geometric representations with guaranteed reversibility. Unlike traditional sequence encoding methods, MS-RCGR employs rational arithmetic and hierarchical k-mer decomposition to generate scale-invariant features that preserve complete sequence information while enabling diverse analytical approaches. Our framework bridges three distinct paradigms for sequence analysis: (1) traditional machine learning using extracted
geometric features, (2) computer
vision models operating on CGR-generated images, and (3) hybrid approaches combining
protein language model
embeddings with CGR features. Through comprehensive experiments on synthetic DNA and
protein datasets encompassing seven distinct sequence classes, we demonstrate that MS-RCGR features consistently enhance classification performance across all paradigms. Notably, our hybrid approach combining pre-trained
language model
embeddings (ESM2, ProtT5) with MS-RCGR features achieves superior performance compared to either method alone. The reversibility property of our encoding ensures no information loss during transformation, while multi-scale analysis captures patterns ranging from individual nucleotides to complex motif structures. Our results indicate that MS-RCGR provides a flexible, interpretable, and high-performing foundation for biological sequence analysis.
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Machine Learning
Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts
Jacob Morrison, Sanjay Adhikesaven, Akshita Bhagia et al. (+3 more)
Extending a fully post-trained
language model with new domain capabilities is fundamentally limited by monolithic training paradigms: retraining from scratch is expensive and scales poorly, while continued training often degrades existing capabilities. We present BAR (Branch-Adapt-Route), which trains independent domain experts, each through its own mid-training, supervised finetuning, and reinforcement learning pipeline, and composes them via a Mixture-of-Experts architecture with lightweight router training. Unlike retraining approaches that mix all domains and require full reprocessing for any update (with cost scaling quadratically), BAR enables updating individual experts independently with linear cost scaling and no degradation to existing domains. At the 7B scale, with experts for math, code, tool use, and safety, BAR achieves an overall score of 49.1 (averaged across 7 evaluation categories), matching or exceeding re-training baselines (47.8 without mid-training, 50.5 with). We further show that modular training provides a structural advantage: by isolating each domain, it avoids the catastrophic forgetting that occurs when late-stage RL degrades capabilities from earlier training stages, while significantly reducing the cost and complexity of updating or adding a domain. Together, these results suggest that decoupled, expert-based training is a scalable alternative to monolithic retraining for extending
language models.
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All authors Jacob Morrison, Sanjay Adhikesaven, Akshita Bhagia, Matei Zaharia, Noah A. Smith, Sewon Min
Machine Learning
NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
Enshu Liu, Xuefei Ning, Yu Wang et al. (+1 more)
Discrete
diffusion language models (dLLMs) have recently emerged as a promising alternative to traditional autoregressive approaches, offering the flexibility to generate
tokens in arbitrary orders and the potential of parallel decoding. However, existing heuristic sampling strategies remain inefficient: they choose only a small part of
tokens to sample at each step, leaving substantial room for improvement. In this work, we study the problem of
token sampling order optimization and demonstrate its significant potential for acceleration. Specifically, we find that fully leveraging correct predictions at each step can reduce the number of sampling iterations by an order of magnitude without compromising accuracy. Based on this, we propose Neural Indicator Sampling (NI Sampling), a general sampling order optimization framework that utilize a neural indicator to decide which
tokens should be sampled at each step. We further propose a novel trajectory-preserving objective to train the indicator. Experiments on LLaDA and Dream models across multiple
benchmarks show that our method achieves up to 14.3$\times$ acceleration over full-step sampling with negligible performance drop, and consistently outperforms confidence threshold sampling in the accuracy-step trade-off. Code is available at https://github.com/imagination-research/NI-Sampling.
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All authors Enshu Liu, Xuefei Ning, Yu Wang, Zinan Lin
Machine Learning · Cross-listed from cs.CV
Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation
Tianshi Cao, Jiawei Ren, Yuxuan Zhang et al. (+12 more)
Closed-loop simulation is a core component of autonomous vehicle (AV) development, enabling scalable testing, training, and safety validation before real-world deployment. Neural scene reconstruction converts driving logs into interactive 3D environments for simulation, but it does not produce complete 3D object assets required for
agent manipulation and large-viewpoint novel-view synthesis. To address this challenge, we present Asset Harvester, an image-to-3D model and end-to-end pipeline that converts sparse, in-the-wild object observations from real driving logs into complete, simulation-ready assets. Rather than relying on a single model component, we developed a system-level design for real-world AV data that combines large-scale curation of object-centric training tuples, geometry-aware preprocessing across heterogeneous sensors, and a robust training recipe that couples sparse-view-conditioned multiview generation with 3D Gaussian lifting. Within this system, SparseViewDiT is explicitly designed to address limited-angle views and other real-world data challenges. Together with hybrid data curation, augmentation, and self-
distillation, this system enables scalable conversion of sparse AV object observations into reusable 3D assets.
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All authors Tianshi Cao, Jiawei Ren, Yuxuan Zhang, Jaewoo Seo, Jiahui Huang, Shikhar Solanki, Haotian Zhang, Mingfei Guo, Haithem Turki, Muxingzi Li, Yue Zhu, Sipeng Zhang, Zan Gojcic, Sanja Fidler, Kangxue Yin
Machine Learning
Partner-aware Peptide-Protein Interaction Prediction and Target-conditioned Peptide Generation
Chupei Tang, Junxiao Kong, Moyu Tang et al. (+5 more)
Motivation: Peptide-
protein interactions (PepPIs) are central to cellular regulation and peptide therapeutics, but experimental characterization remains too slow for large-scale screening. Existing methods usually emphasize either interaction prediction or peptide generation, leaving candidate prioritization, residue-level interpretation, and target-conditioned expansion insufficiently integrated. Results: We present an integrated framework for early-stage peptide screening that combines a partner-aware prediction and localization model (ConGA-PepPI) with a target-conditioned generative model (TC-PepGen). ConGA-PepPI uses asymmetric encoding, bidirectional cross-
attention, and progressive transfer from pair prediction to binding-site localization, while TC-PepGen preserves target information throughout autoregressive decoding via layerwise conditioning. In five-fold cross-validation, ConGA-PepPI achieved 0.839 accuracy and 0.921 AUROC, with binding-site AUPR values of 0.601 on the
protein side and 0.950 on the peptide side, and remained competitive on external
benchmarks. Under a controlled length-conditioned
benchmark, 40.39% of TC-PepGen peptides exceeded native templates in AlphaFold 3 ipTM, and unconstrained generation retained evidence of target-conditioned signal.
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All authors Chupei Tang, Junxiao Kong, Moyu Tang, Di Wang, Jixiu Zhai, Ronghao Xie, Shangkun Sima, Tianchi Lu