Machine Learning
Path-Coupled Bellman Flows for Distributional Reinforcement Learning
Boyang Xu, Qing Zou, Siqin Yang et al. (+1 more)
Distributional reinforcement learning (DRL) models the full return distribution, but existing finite-support or
quantile-based methods rely on projections, while recent flow-based approaches can suffer from boundary mismatch at the flow source or from high-variance bootstrapping when current and successor noises are independent. We propose Path-Coupled Bellman Flows (PCBF), a continuous-time DRL method that learns return distributions with flow matching using source-consistent Bellman-coupled paths: the current path starts from the required base prior at $t{=}0$, reaches the Bellman target at $t{=}1$, and maintains a pathwise affine relation to the successor flow at intermediate times (without requiring time-$t$ marginals to satisfy a distributional Bellman fixed point for all $t$). PCBF couples current and successor return flows through shared base noise and uses a $\lambda$-parameterized control-variate target: $\lambda{=}0$ recovers an unbiased sample Bellman target, while $\lambda{>}0$ trades controlled bias for variance reduction. Experiments on analytically tractable MRPs, OGBench, and D4RL show improved distributional fidelity and training stability, and competitive offline RL performance.
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All authors Boyang Xu, Qing Zou, Siqin Yang, Hao Yan
Machine Learning · Cross-listed from cs.CV
Smart Railway Obstruction Detection System using IoT and Computer Vision
Pravin Kumar, Mritunjay Shall Peelam, Ramakant Kumar et al. (+2 more)
Railway track intrusions pose a critical safety challenge for Indian Railways, encompassing wildlife incursions and deliberate malicious obstructions. The December 2025 collision in Assam, in which seven elephants were killed by the Rajdhani Express, underscores the urgency of effective real-time detection. Existing solutions such as the optical fiber-based Gajraj system suffer from prohibitive costs (\$1000/km) and high false alarm rates, limiting deployment to only 20 of India's 101 elephant corridors. This paper proposes NETRA, a cost-effective, internet-independent intrusion detection system deployed on Raspberry Pi Zero W and Raspberry Pi 4 edge platforms. NETRA employs probabilistic sensor fusion integrating a PIR motion sensor and an HC-SR04 ultrasonic distance sensor with a tunable threshold (tau_c = 0.65), enabling event-driven camera activation that reduces unnecessary visual processing by 52%. Upon confirmed intrusion, edge-AI classification using MobileNet-SSD (Pi Zero) or YOLOv5 ONNX (Pi 4) identifies threats including humans, large animals, and track obstructions. Confirmed threats are transmitted via LoRa (868 MHz) to alert the locomotive driver within 2.4 seconds end-to-end. Experimental evaluation across 113 motion events demonstrated 95% detection accuracy with zero false alarms through probabilistic fusion, compared to 85% for binary methods. Raspberry Pi 4 with YOLOv5 achieved 83.5% elephant F1-score, a 5.6x improvement over Pi Zero's heuristic approach (14.8%). LoRa communication achieved 100% packet delivery across 1-2 km in field trials. NETRA reduces deployment cost by 75% (\$247/km vs \$1000/km for Gajraj) while providing unified detection of both wildlife and obstruction threats.
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All authors Pravin Kumar, Mritunjay Shall Peelam, Ramakant Kumar, Sanjay Kumar, Vinay Chamola
Machine Learning · Cross-listed from cs.PL
GPU-Accelerated Synthesis of Mixed-Boolean Arithmetic: Beyond Caching
Gabriel Bathie, Baptiste Mouillon and Nathanaël Fijalkow
Synthesizing Mixed-Boolean Arithmetic (MBA) expressions from input-output examples is central to program deobfuscation and also useful for compiler optimization, reverse engineering, and cryptanalysis. Existing MBA synthesizers are typically CPU-based and scale poorly on large specifications or complex targets. Recent GPU-accelerated synthesis methods achieve large speedups in qualitative settings, but they depend on caching observationally equivalent candidates; this strategy breaks down for MBA because candidate outputs are quantitative bitvectors and the behavioral space is enormous. We present SIMBA (Synthesis of Mixed-Boolean Arithmetic), a GPU-accelerated MBA synthesizer built around cache-free bottom-up enumeration. SIMBA avoids
language caches entirely and uses a GPU-oriented enumeration design that keeps work local and highly parallel. In experiments, SIMBA is substantially faster than prior MBA synthesis tools, handles larger specifications, and reaches expression sizes that existing methods fail to solve. These results establish cache-free GPU synthesis as a practical and scalable approach for quantitative domains, and identify it as a strong alternative to cache-centric designs.
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Machine Learning · Cross-listed from q-bio.QM
An Explainable Unsupervised-to-Supervised Machine Learning Framework for Dietary Pattern Discovery Using UK National Dietary Survey Data
Wing Yi Yu and Chun Yin Chiu
Clinical dietary assessment can generate detailed but high-dimensional nutrient and food-group information that is difficult to translate quickly into counselling priorities. This paper proposes an explainable unsupervised-to-supervised machine learning framework for discovering, reproducing and interpreting dietary patterns using public UK National Diet and Nutrition Survey data. Adult participants aged 19 years and above from NDNS Years 12-15 were represented using 25 energy-adjusted nutrient and food-group features. K-means, Gaussian Mixture Models and Agglomerative Clustering were compared across k = 2-8, with stability and dietetic interpretability used alongside internal validation metrics. The selected K-means k = 4 solution identified four interpretable dietary patterns: high fat/meat and sodium, higher fibre fruit-vegetable micronutrient, high free-sugar snacks and sugary drinks, and dairy/cereal calcium-rich saturated-fat. A supervised surrogate classifier reproduced held-out cluster membership with high test performance (macro-F1 = 0.963), but was interpreted only as an explanatory surrogate rather than as an independent clinical prediction model. SHAP analysis linked predictions to dietetically meaningful drivers, suggesting potential value for dietitian-in-the-loop assessment, counselling prioritisation and follow-up monitoring.
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Machine Learning · Cross-listed from cs.CV
Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation
Farhana Yasmin, Mahade Hasan, Haipeng Liu et al. (+3 more)
Cardiac
magnetic resonance (CMR)
segmentation underpins quantitative assessment of ventricular structure and function, yet reliable delineation remains difficult due to low tissue contrast, fuzzy boundaries, and inter scan variability. We present CardiacNAS, an evolutionary neural architecture search (NAS) framework that couples a UNet like supernet with a cardiac aware search space spanning depth width, kernel size, filter size,
attention, fusion, activation, dropout, and residual scaling. The search is explicitly resource aware, jointly optimizing dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) versus model size and floating point operations (FLOPs) under fixed compute budgets. Candidate architectures are instantiated from the supernet, trained with proxy budgets, and evolved through crossover, mutation, and elitist selection. We evaluate on the ACDC
dataset and compare against six state of the art methods, using qualitative comparisons, learning curve analyses, and design factor correlation studies. The resulting model attains 93.22% average DSC and 4.73 mm HD95 with 3.58M parameters and 14.56 GFLOPs, demonstrating a favorable accuracy efficiency trade off. Analyses indicate that searched
attention and fusion choices, together with residual scaling, contribute to improved boundary fidelity and stability. CardiacNAS offers a principled, resource aware approach to deployable CMR
segmentation with transparent reporting of architectural complexity and compute budgets.
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All authors Farhana Yasmin, Mahade Hasan, Haipeng Liu, Amjad Ali, Ghulam Muhammad, Yu Xue
Machine Learning
Distributional Spectral Diagnostics for Localizing Grokking Transitions
Ziyue Wang, Yufeng Ying and Takafumi Kanamori
In grokking, a model first fits the training data while test accuracy remains low, and only later begins to generalize. We ask whether this transition can be localized from observed training trajectories before the test accuracy rises, and formulate grokking transition localization as a diagnostic problem with an explicit threshold/FPR/lead-time trade-off. Task-dependent observables are summarized as empirical distributions, mapped to Wasserstein/
quantile coordinates, and analyzed by Hankel dynamic mode decomposition (DMD); the resulting reconstruction residual, together with spectrum and effective rank, forms the diagnostic output. On held-out modular-addition
Transformer runs, the residual achieves AUROC \(\approx \) 0.93 for grokking-vs-non-grokking discrimination at the run level; under a fixed sustained-threshold operating rule, true-positive alarms can precede onset, with lead time reported jointly with false-alarm rate and uncertainty intervals. Perturbation experiments show that, in the tested \(wd=1\) pool, high-residual windows exhibit about \(3\times\) larger short-horizon perturbation deviation than low-residual windows. In a same-data norm-window control, perturbation sensitivity aligns with the residual ordering rather than total-parameter-norm ordering, suggesting that the residual is not merely a total-norm proxy at the window level in the studied \(wd=1\) dynamics. Norm signals remain strong run-level regime indicators, and log-probability performs best among the observables tested under the current protocol. We position the residual as a window-level monitoring and localization signal in the studied modular-arithmetic
Transformer settings, not a universal early-warning predictor or an intervention rule.
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Machine Learning
When Does Value-Aware KV Eviction Help? A Fixed-Contract Diagnostic for Non-Monotone Cache Compression
Ruijie Zhang, Haozhe Liang, Da Chang et al. (+4 more)
Long-context LLM inference is bottlenecked by the
memory and bandwidth cost of reading large KV caches during decoding. KV compression reduces this cost by keeping only part of the cache, but task accuracy alone does not identify why a selector succeeds or fails. A selector can fail at three steps: it may miss the evidence future decoding needs, give high scores to
tokens that do not affect the output, or break related evidence when fitting scores into a small cache. We introduce a fixed-contract diagnostic that holds the selector's setup fixed and changes one decision slot at a time. For value ranking, the probe combines a block's
attention mass with the estimated output change from removing it. On LongBench across three models and two budgets, the probe is positive on 72.6% of positive-margin
cells and 32.4% of nonpositive-margin
cells. NeedleBench M-RT at 32k and a RULER 8k check probe support closure under branched
retrieval, and a 264-
cell sign evaluation separates support recovery and output-value ranking from leverage effects near the boundary. The resulting order is to recover decode-side evidence, rank its output value, and preserve coupled evidence during projection.
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All authors Ruijie Zhang, Haozhe Liang, Da Chang, Li Hu, Fanqi Kong, Huaxiao Yin, Yu Li
Machine Learning
Towards Differentially Private Reinforcement Learning with General Function Approximation
Yi He and Xingyu Zhou
We present the first theoretical guarantees for differentially private online reinforcement learning (RL) with general function approximation, extending beyond prior work restricted to tabular and linear settings. Our approach combines a batched policy update scheme with the exponential mechanism, together with a novel regret analysis. We show that, even under general function approximation, the regret in the model-free setting under differential
privacy matches the state of the art for the linear case, scaling as $\widetilde{O}(K^{3/5})$, where $K$ denotes the number of episodes. As an important by-product, we also establish the first regret bound for online RL with batch update that depends on the standard complexity measure of coverability, complementing existing results based on a newly introduced Eluder-Condition class. In addition, we uncover fundamental gaps in recent results for private RL with linear function approximation, thereby clarifying its landscape.
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Machine Learning
Unlocking High-Fidelity Molecular Generation from Mass Spectra via Dual-Stream Line Graph Diffusion
Xujun Che, Xiuxia Du and Depeng Xu
De novo molecular generation from tandem mass spectra is a challenging inverse problem whose core difficulty lies in the circular dependency between atom-level and bond-level
reasoning: determining a bond's type requires knowing its endpoint atoms' chemical environment, yet an atom's environment is in turn defined by its incident bonds. Existing graph
diffusion methods process atoms and bonds within a single computation stream, where atom-bond information synchronization can only occur implicitly across layers. We argue that this single-stream paradigm, rather than the choice of any particular aggregation kernel, is a key architectural bottleneck. We propose DualLGD (Dual-stream Line Graph
Diffusion), which reformulates molecular graph denoising as the alternating solution of two coupled subproblems: atom-level
reasoning and bond-level
reasoning, each operating in its own dedicated representation space. The line graph provides a natural mathematical construction for the bond space, in which bond angles, dihedrals, conjugation chains, and rings correspond to local topological motifs between bonds. Incidence-constrained bidirectional cross-
attention synchronizes the two streams at every layer, ensuring that each atom attends only to its incident bonds and vice versa, respecting the fundamental chemical principle that an atom's environment is determined by its bonding context. On the NPLIB1 and MassSpecGym
benchmarks, DualLGD achieves top-1 accuracy of 34.37% and 23.89%, approximately $3\times$ the previous state of the art. Ablation studies confirm the architecture as the primary source of improvement: DualLGD without any pre-training already surpasses the previous best fully pretrained model.
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Machine Learning · Cross-listed from stat.ML
An Interpretable and Scalable Framework for Evaluating Large Language Models
Xinhao Qu, Qiang Heng, Hao Zeng et al. (+1 more)
Evaluation of large
language models (LLMs) is increasingly critical, yet standard benchmarking methods rely on average accuracy, overlooking both the inherent stochasticity of LLM outputs and the heterogeneity of
benchmark items. Item Response Theory (IRT) offers a principled framework for modeling latent model abilities and item characteristics, but conventional methods are computationally expensive and numerically unstable, limiting large-scale implementations. To address these challenges, we propose an interpretable and scalable framework for LLM evaluation based on the majorization-minimization principle. Our approach reformulates the problem as a sequence of constrained
matrix factorization subproblems, enabling stable and efficient parameter estimation with theoretical guarantees for identifiability and convergence. Experiments on synthetic and real-world
datasets, including MATH-500 and six Open LLM Leaderboard
benchmarks, demonstrate that our method achieves superior scalability and interpretability. It delivers orders-of-magnitude speedups over competing methods while maintaining comparable or even higher estimation accuracy. Our results align with established scaling laws and offer insights into item difficulty and discrimination, informing more principled
benchmark design.
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All authors Xinhao Qu, Qiang Heng, Hao Zeng, Xiaoqian Liu
Machine Learning · Cross-listed from cs.AI
The Context Gathering Decision Process: A POMDP Framework for Agentic Search
Chinmaya Kausik, Adith Swaminathan and Nathan Kallus
Large
Language Model (LLM)
agents are deployed in complex environments – such as massive codebases, enterprise databases, and conversational histories – where the relevant state far exceeds their context windows. To navigate these spaces, an
agent must iteratively explore the environment to find relevant information. However, without explicit infrastructure, an
agent's working
memory can degrade into lossy representations of the search state, resulting in redundant work (e.g. repetitive looping) and premature stopping. In this work, we formalize this challenge as the Context Gathering Decision Process (CGDP), a specialized Partially Observable Markov Decision Process, where an
agent's objective is to adaptively refine its belief state to isolate the necessary information for a task. We model an LLM's behavior as approximate Thompson Sampling within this CGDP, and introduce a predicate-based method that decomposes an LLM's implicit search into explicit and modular operations. We then derive two plug-and-play interventions for iterative LLM
agents: a persistent, predicate-based belief state that bounds context while preserving multi-hop
reasoning, and a programmatic exhaustion gate that halts unproductive search without premature stopping. Across four methods and three question-answering domains, we empirically validate that replacing an LLM's implicit state with our CGDP-motivated belief state improves multi-hop
reasoning by up to $11.4\%$; while the modular programmatic exhaustion detection saves up to $39\%$ of
tokens without any degradation in
agent performance. Ultimately, we argue that framing the LLM
agent loop as a CGDP can guide the design of modular, non-interfering improvements to agentic search harnesses.
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Machine Learning
PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents
Minghao Yan, Bo Peng, Benjamin Coleman et al. (+11 more)
Large
language models have become drivers of evolutionary search, but most systems rely on a fixed,
prompt-elicited policy to sample next candidates. This limits adaptation in practical engineering and research tasks, where evaluations are expensive, and progress depends on learning task-specific search dynamics. We introduce PACEvolve++, an advisor-model reinforcement learning framework for test-time policy adaptation in evolutionary search
agents. PACEvolve++ decouples strategic search decisions from implementation: a trainable advisor generates, assesses, and selects hypotheses, while a stronger frontier model translates selected hypotheses into executable candidates. To train the advisor under non-stationary feedback, we propose a phase-adaptive approach that adapts its optimization strategy to different phases of the evolutionary process. Early in
evolution, it uses group-relative feedback to learn broad search preferences; later, as reward gaps compress, it emphasizes best-of-$k$ frontier contribution to support stable refinement. Across expert-parallel load balancing, sequential recommendation, and
protein fitness extrapolation, PACEvolve++ outperforms the state-of-the-art evolutionary search framework with frontier models, achieving faster convergence and stabilizing test-time training during evolutionary search.
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All authors Minghao Yan, Bo Peng, Benjamin Coleman, Ziqi Chen, Zhouhang Xie, Shuo Chen, Zhankui He, Noveen Sachdeva, Weili Wang, Ed H. Chi, Shivaram Venkataraman, Wang-Cheng Kang, Derek Zhiyuan Cheng, Beidou Wang
Machine Learning
Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation
Aditya Sai Ellendula, Yi Wang and Chandrajit Bajaj
Risk-aware
navigation should be selective: a policy should expose evasive degrees of freedom only when the local scene admits a lower-risk feasible maneuver, and suppress them when no safer alternative exists. We show that adding one context-energy term to a port-
Hamiltonian navigation policy produces a learned force channel with exactly this falsifiable signature. When the local risk field contains a feasible lower-risk direction, the induced context force activates toward it; when the apparent escape is blocked or not yet available, a route-aware gate suppresses lateral force rather than hallucinating an unsafe maneuver. A CVaR tail-risk objective focuses gradient updates on rare but consequential risk transitions. We validate the selectivity signature across four settings. In the primary delayed-required-escape
benchmark, route-aware CVaR reduces premature force activation from 0.950 to 0.180 versus DWA while raising success from 0.480 to 0.810 with zero replans. On real off-road terrain (RELLIS-3D), route-aware enrichment achieves correct activation rate 0.837 and false activation rate 0.114, compared to 0.378/0.752 for scalar risk gradients. On static semantic maps (DFC2018), enrichment reduces catastrophic failure from 0.60 to 0.10 and oscillation by 90.7% while preserving path efficiency. In highway traffic, collisions drop from 100% to 0% when a lane escape is feasible; when no escape exists, the policy suppresses the lateral maneuver. The selectivity property follows from the gradient structure of the context energy rather than from training-time tuning.
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Machine Learning · Cross-listed from cs.CR
Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers using TRUSTEE
Riyazuddin Mohammed and Lan Zhang
Modern cybersecurity relies heavily on static machine-learning-based malware classifiers. However, transformations such as packing and other non-semantic modifications applied to executable files limit their reliability. Malware classifiers often learn these unnecessary artifacts rather than the true binary behavior because of the high association between maliciousness and packing. Moreover, these malware classifiers are black boxes, making it difficult to understand what they learn. To address this issue, we proposed a two-part framework using the post-hoc interpretability XAI tool TRUSTEE, followed by a manual analysis of the top features. We conducted several controlled experiments by varying the
dataset composition ratios to understand their impact on the results. The top-ranked features across all experiments, identified by TRUSTEE, were predominantly packing artifacts, portable executable(PE) metadata, and n-grams at the string level, rather than malicious semantics. These results suggest that these malware classifiers are highly sensitive to
dataset composition and can misinterpret packing as malicious behavior. Our proposed framework allows for the reproducible diagnosis of such biases and forms a guideline for building more robust and semantically meaningful malware detection models
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Machine Learning
A Systematic Investigation of RL-Jailbreaking in LLMs
Montaser Mohammedalamen, Kevin Roice, Reginald McLean et al. (+1 more)
The
evolution of generative models from next-
token predictors to autonomous engines of complex systems necessitates rigorous safety hardening.
Adversarial jailbreaking, the strategic manipulation of models to elicit harmful output, remains a primary threat to safe deployment. While Reinforcement Learning (RL) frames jailbreaking as a multi-step attack through sequential optimization, a mechanistic understanding of why the framework succeeds remains incomplete. To fill this gap, we present the first systematic decomposition of RL jailbreaking. We deconstruct the framework into problem formalization (reward function, action space, episode length), and algorithmic measures (RL
algorithm, training data, reward-shaping) to identify the structural determinants of
adversarial success. Our results reveal that the RL-jailbreaker successfully compromised all targeted models and safeguards. Through this first-of-its-kind analysis, we demonstrate that environment formalization, specifically dense rewards and extended episode lengths, is the primary driver of jailbreaking success. This work provides a tool for improving RL-jailbreaker efficiency and, ultimately, harden generative models resistant to RL-based attacks.
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All authors Montaser Mohammedalamen, Kevin Roice, Reginald McLean, Alyssa Lefaivre Škopac
Machine Learning · Cross-listed from stat.ML
BGM-IV: AI-Powered Bayesian Generative Modeling for Instrumental Variable Regression with High-Dimensional Covariates
Guyue Luo, Haidong Lu, Andrew J. Loza et al. (+1 more)
Instrumental-variable (IV) regression enables causal estimation under endogeneity, but modern IV problems often involve nonlinear structural effects and high-dimensional
covariates. Existing methods typically operate in observed or generic learned feature spaces, and they often yield point estimates without uncertainty quantification. We introduce BGM-IV, a
Bayesian generative modeling approach that performs nonlinear IV regression through posterior inference in a causally structured latent space. BGM-IV separates
covariate variation by the role in the treatment and outcome mechanism, and accounts for endogeneity through an IV-integrated pseudo-likelihood that averages over instrument-induced treatment variation. The resulting model provides both structural-function estimates and predictive intervals for outcomes under intervention. Across various
benchmark datasets, BGM-IV outperforms existing nonlinear IV methods overall, with significant gains in high-dimensional settings, while achieving near-nominal predictive coverage. These results highlight structured latent generative modeling as a flexible approach to uncertainty-aware IV inference with rich
covariates. The code of BGM-IV is available at https://github.com/liuq-lab/BGM-IV.
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All authors Guyue Luo, Haidong Lu, Andrew J. Loza, Qiao Liu
Machine Learning · Cross-listed from q-bio.NC
Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning
Minheng Chen, Chao Cao, Jing Zhang et al. (+2 more)
Functional connectivity (FC) derived from resting-state fMRI is widely used to characterize large-scale
brain network alterations in neurological and psychiatric disorders. However, FC construction critically depends on the choice of
brain atlas, and different parcellations may emphasize distinct organizational features, leading to heterogeneous and sometimes inconsistent representations. Existing multi-atlas approaches partially alleviate this issue but often fuse atlas-derived features or predictions at a relatively shallow level, while single-atlas disentanglement methods do not explicitly address cross-atlas heterogeneity. We propose Multi-Atlas Disentangled Connectivity LEarning (MADCLE), a multi-branch representation learning framework that jointly encodes FC
matrices derived from different
brain atlases. Rather than introducing a single explicitly shared latent variable across parcellations, MADCLE learns atlas-wise disease-related representations and encourages them to be cross-atlas consistent through distributional
alignment. Meanwhile,
covariate-related and atlas-dependent residual factors are modeled separately using
covariate similarity supervision, atlas-specific reconstruction, and decorrelation constraints, thereby reducing the leakage of non-disease and parcellation-dependent information into the disease-related
embeddings. Experiments on the ADNI and ADHD-200
datasets suggest that MADCLE achieves competitive or improved performance compared with single-atlas baselines, multi-atlas GNN/
Transformer models, and recent multi-atlas consistency frameworks. These results support the potential value of structured disentanglement for FC-based disorder identification under heterogeneous parcellation schemes.
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All authors Minheng Chen, Chao Cao, Jing Zhang, Tianming Liu, Dajiang Zhu
Machine Learning
Delulu: A Verified Multi-Lingual Benchmark for Code Hallucination Detection in Fill-in-the-Middle Tasks
Mahdi Erfanian, Nelson Daniel Troncoso, Aashna Garg et al. (+4 more)
Large
Language Models for code generation frequently produce
hallucinations in Fill-in-the-Middle (FIM) tasks – plausible but incorrect completions such as invented API methods, invalid parameters, undefined variables, or non-existent imports. These failures pass superficial review yet introduce runtime errors. We introduce Delulu, a verified multi-lingual
benchmark of 1,951 FIM samples across 7
languages and 4
hallucination types. Samples are curated through an
adversarial pipeline: a frontier LLM generates plausible
hallucinations, four diverse judge models evaluate them,
embedding-based clustering mines progressively harder examples, self-contained Docker containers verify that golden completions compile while hallucinated variants produce the expected runtime error, and a final human-expert review removes any remaining biased or trivially decidable samples. We evaluate 11 open-weight FIM models from five families spanning 0.5B-32B parameters: a six-point Qwen2.5-Coder scaling slate, plus a cross-family slate (CodeLlama, DeepSeek-Coder-V2, StarCoder2). The strongest model reaches only 84.5% pass@1, no family exceeds 0.77 Edit Similarity, and every family produces
hallucination-aligned completions on a non-trivial share of samples, confirming that the difficulty exposed by Delulu is task-intrinsic rather than family-specific. We release the
benchmark, containers, and evaluation framework at https://github.com/microsoft/delulu.
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All authors Mahdi Erfanian, Nelson Daniel Troncoso, Aashna Garg, Amabel Gale, Xiaoyu Liu, Pareesa Ameneh Golnari, Shengyu Fu
Machine Learning
Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale
Haydn Jones, Yimeng Zeng, Alden Rose et al. (+11 more)
Manually curated biomedical repositories – spanning bioactivity, genomics, and chemistry – are expensive to maintain, lag behind primary literature, and discard experimental context, obscuring nuances needed to assess data correctness and coverage. We show that PubMed itself can be autonomously and cost-effectively turned into structured
datasets that are larger, more nuanced, and more accurate than the curated databases they replace. We present three coupled contributions: (1) an LLM-based entity-tagging pipeline, grounded in nine biomedical ontologies, that tags 4.5B entities across 19 categories in a 22.5M-paper, 2.5T-
token PubMed corpus; (2) hybrid sparse-dense
retrieval supporting entity-filtered semantic queries over the tagged corpus; and (3) Starling, a multi-
agent deep research system that, given only a natural-
language task description, designs precision- and recall-targeted
retrieval filters, induces an extraction schema, and emits structured records with nuance-rich fields and supporting passages. Across six tasks – blood-
brain barrier permeability, oral bioavailability, acute toxicity (LD50), gene-disease associations,
protein subcellular localization, and chemical reactions – Starling produces ~6.3M records (91K-3M per task); several are, to our knowledge, the largest public
datasets for their property. Frontier-model rejection of our extractions is 0.6-7.7% across tasks, far below error rates we measure on widely used curated counterparts (e.g., 16.5% on BBB_Martins, 7.3% on Bioavailability_Ma). Beyond scale and accuracy, the supporting passages carry nuance tabular databases discard – e.g., oral bioavailability may depend on fed vs. fasted state. Together, the corpus,
retrieval, and
agent establish a foundation for AI-driven therapeutic design. Code and
datasets: https://github.com/starling-labs/starling.
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All authors Haydn Jones, Yimeng Zeng, Alden Rose, Li S. Yifei, Yining Huang, Kaiwen Wu, Jiaming Liang, Maggie Ziyu Huan, Yoseph Barash, Cesar de la Fuente-Nunez, Osbert Bastani, Zachary Ives, Mark Yatskar, Jacob R. Gardner
Machine Learning
FlashMol: High-Quality Molecule Generation in as Few as Four Steps
Xinyuan Wei, Zian Li, Shaoheng Yan et al. (+2 more)
Generating chemically valid 3D molecular conformations is critical for computational drug discovery. Classical
diffusion-based models like GeoLDM perform well but require hundreds of steps, making large-scale in silico screening impractical. Recent efforts on few-step molecular generation have accelerated this process to 12-50 steps, but they often largely sacrifice sample stability. In this work, we present FlashMol, an ultra-fast molecule generative model producing high-quality molecular conformations in as few as 4 steps. To achieve this, we adapt distribution matching
distillation (DMD) – a reverse KL-divergence minimization objective – to the molecular domain for effective
distillation. Considering the local minimization behavior of DMD, we respace the molecule generation timesteps, providing the generator with much better initialization and enables effective
distillation. Additionally, to mitigate the mode-seeking behavior of DMD and improve diversity, we further regularize it with a Jensen-Shannon divergence term, which incorporates the mean-seeking behavior of the forward KL divergence. Extensive experiments on QM9 and GEOM-DRUG
datasets demonstrate that FlashMol matches and even surpasses the original 1000-step teacher: by applying respacing to the GeoLDM teacher (GeoLDM-respaced), we achieve comparable results to the 1000-NFE teacher with as few as 16 NFEs. After further
distillation, FlashMol is able to sample with only 4 NFEs while maintaining high molecular quality, achieving up to 250$\times$ acceleration compared to the GeoLDM teacher.
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All authors Xinyuan Wei, Zian Li, Shaoheng Yan, Cai Zhou, Muhan Zhang
Machine Learning
Dual-Agent Co-Training for Health Coaching via Implicit Adversarial Preference Optimization
Da Long, Lingyi Fu, Diya Michelle Rao et al. (+3 more)
Motivational-interviewing-based health coaching is an effective approach for improving mental health and promoting healthy behavior change. However, the scarcity of trained human coaches and the high cost of coaching services make such support inaccessible to many people who could benefit from it. This motivates the development of AI health coaches that can provide scalable and affordable support. Existing methods typically optimize only one side of the interaction: they either train a dialogue
agent against a fixed client environment or train a client simulator against a fixed assistant. This one-sided setup can limit exploration of the interaction space and may be inefficient at developing the capabilities required by the target
agent and pushing its performance boundaries. In this paper, we propose a dual-
agent framework that interactively co-trains both the health coach
agent and the client simulator. The coach is optimized with DPO using Pareto-dominant response pairs identified by a multi-dimensional LLM judge. In turn, the client is trained adversarially by reversing these preferences, inducing an implicit
adversarial training dynamic. We further show that this co-training process admits a natural
stochastic-game interpretation. Extensive experiments demonstrate that our method effectively improves coaching quality across several important dimensions.
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All authors Da Long, Lingyi Fu, Diya Michelle Rao, Jasmine Ruales Carrera, Yang Bai, Shandian Zhe
Machine Learning
Inductive Power Grid Cascading Failure Analysis with GRU-Gated Graph Attention
Tianxin Zhou, Xiang Li and Haibing Lu
Identifying vulnerable transmission lines in power grids before a cascading failure occurs is challenging: existing methods can learn inter-line failure correlations from cascade data, but they are trained and evaluated on a single grid, and transferring the learned knowledge to an unseen grid remains an open problem. We address this by training a single Gated Recurrent Unit (GRU)-gated Graph
Attention Network on combined cascading failure data from limited training grids and applying it directly to any unseen grid without retraining. A GRU gate controls what information each node retains or discards at each cascade iteration. Empirical evaluation shows that the model transfers zero-shot to multiple new grids spanning inter-time and inter-domain settings. Using information extracted from the trained model, we consistently identify more vulnerable lines than established structural and electrical baselines.
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Machine Learning · Cross-listed from cs.DS
Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
Adam R. Klivans, Shyamal Patel, Konstantinos Stavropoulos et al. (+1 more)
Recent work on provably efficient
algorithms for learning with distribution shift has focused on two models: PQ learning (Goldwasser et al. (2020)) and TDS learning (Klivans et al. (2024)).
Algorithms for TDS learning are allowed to reject a test set entirely if distribution shift is detected. In contrast, PQ learners may only reject points that are deemed out-of-distribution on an individual basis. Our main result is a surprising equivalence between these two models in the distribution-free setting. In particular, we give an efficient black-box reduction from PQ learning to TDS learning for any Boolean concept class. This equivalence implies the first hardness results for distribution-free TDS learning of basic classes such as halfspaces. The main technical contribution underlying our equivalence is a method for boosting, via branching programs, the weak distinguishing power of TDS learners that have rejected the target domain. We also show that giving a learner access to membership queries sidesteps these hardness results and allows for efficient, distribution-free PQ learnability of halfspaces. Our
algorithm iteratively recovers large-margin separators obtained by applying successive Forster transforms on the training data.
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All authors Adam R. Klivans, Shyamal Patel, Konstantinos Stavropoulos, Arsen Vasilyan
Machine Learning
Echo: KV-Cache-Free Associative Recall with Spectral Koopman Operators
Anupama Sridhar and Alexander Johansen
Long chain-of-thought
reasoning and agentic tool-calling produce traces spanning tens of thousands of
tokens, yet
Transformer KV caches grow linearly with sequence length, creating a
memory bottleneck on commodity hardware. State-space models offer constant-
memory recurrence but suffer a
memory cliff:
retrieval accuracy collapses once the gap between a stored fact and its query exceeds the effective horizon of the recurrent state. We introduce Echo, a KV-cache-free associative recall architecture built around Spectral Koopman
Attention (SKA); a drop-in replacement for
attention layers that augments SSM blocks with a closed-form dynamical operator whose sufficient statistics are accumulated in constant
memory with no KV cache. Echo fits a spectral linear system to the key and value history via kernel ridge regression and retrieves through a learned power-iterated filter, all from $O(r^{2})$ streaming state where $r$ is a small projection rank. On the Multi-Query Associative Recall
benchmark, a pure Mamba-2 SSM fails to exceed chance accuracy (${\sim}3\%$) across all gap lengths and KV-pair counts, while at the 50M parameter scale SKA-augmented models achieve $100\%$
retrieval accuracy on every configuration tested, including distractor gaps of $4{,}096$
tokens with $32$ KV pairs. Across five additional transfer
benchmarks including needle-in-a-haystack, tool-trace, and multi-hop
retrieval, SKA consistently outperforms both pure SSM and SSM+
Attention hybrids while maintaining constant inference
memory. Ablations confirm that the spectral operator, not the prefix masking strategy, drives the
retrieval gain.
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Machine Learning
Why Does Agentic Safety Fail to Generalize Across Tasks?
Yonatan Slutzky, Yotam Alexander, Tomer Slor et al. (+2 more)
AI
agents are increasingly deployed in multi-task settings, where the task to perform is specified at test time, and the
agent must generalize to unseen tasks. A major concern in such settings is safety: often, an
agent must not only execute unseen tasks, but do so while avoiding risks and handling ones that materialize. Empirical evidence suggests that even when the ability to execute generalizes to unseen tasks, the ability to do so safely frequently does not. This paper provides theory and experiments indicating that failures of agentic safety to generalize across tasks are not merely due to limitations of training methods, but can also reflect an inherent property of safety itself: the relationship between a task and its safe execution is more complex than the relationship between a task and its execution alone. Theoretically, we analyze linear-quadratic control with $H_{\infty}$-robustness, and prove that in many cases, the mapping from task specification to an optimal controller has higher Lipschitz constant with safety requirements than without, yielding a Lipschitz bound of independent interest. Empirically, we demonstrate our conclusions in simulated quadcopter
navigation with a neural network
agent and in CRM with an LLM
agent, showing that imitating safe and unsafe teachers is similarly straightforward on tasks seen in training, yet generalizing across tasks is considerably more difficult with the safe teacher. Our findings suggest that current efforts to enhance agentic safety may be insufficient, and point to a need for fundamentally different approaches.
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All authors Yonatan Slutzky, Yotam Alexander, Tomer Slor, Yoav Nagel, Nadav Cohen
Machine Learning · Cross-listed from cs.CV
TrajGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations
Maria Despoina Siampou, Gengchen Mai, Ni Lao et al. (+4 more)
Many urban prediction tasks depend not only on the static attributes of a location, such as the visual appearance of its built environment, but also on its dynamic function: how people navigate and use it over time. Predicting congestion, travel demand, or road safety risks, for instance, requires mobility information that imagery and geographic coordinates alone cannot provide. Trajectories carry this signal, yet current trajectory representations are difficult to align with static geospatial observations: sparse, irregular GPS samples rarely coincide with street-view image coordinates, and existing encoders produce either a single
embedding per trajectory or road segment, or
embeddings only at the observed GPS samples. We introduce TrajGANR, a trajectory-centric
multimodal self-supervised learning framework that represents each trajectory as a continuous, path-conditioned neural field. Reconstructed from the observed GPS samples, this representation can be queried at arbitrary coordinates (including the off-path locations where street-view images were captured), yielding localized mobility
embeddings that align with street-view image and location representations at the same coordinate. Across four fine-grained road-level tasks in San Francisco and Porto, TrajGANR consistently outperforms recent geospatial and trajectory foundation models, with ablations attributing the gains to both the mobility signal and the fine granularity at which it is aligned. TrajGANR's location encoder further generalizes beyond the road network to areas where neither street-view imagery nor trajectories are available.
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All authors Maria Despoina Siampou, Gengchen Mai, Ni Lao, Jinmeng Rao, Neha Arora, Cyrus Shahabi, Shushman Choudhury
Machine Learning · Cross-listed from stat.AP
Drawing Lines in Psychological Space: What K-means Clustering Reveals in Simulated and Real Psychometric Data
Pedro Henrique Ramos Pinto, Maria Jullyanna Ferreira Marques and Luiz Carlos Serramo Lopez
K-means clustering is widely used in psychological and psychometric research to identify profiles, subgroups, and potential typologies, yet its classical formulation does not test whether such groups exist as latent psychological categories. Instead, K-means partitions multidimensional space into regions around centroids, favoring compact, approximately spherical clusters defined by
geometric distance. In this paper, we examine this limitation through a sequence of controlled simulated
datasets. We then extend the analysis to the SMARVUS
dataset, a large international psychometric
dataset comprising survey responses from university students across 35 countries, to evaluate whether similar
geometric partitioning patterns emerge in empirical psychological data. By contrasting simulated and empirical data, this paper argues that K-means can produce stable and visually coherent clustering solutions even in continuous Gaussian latent spaces without true subgroup structure.
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Machine Learning
Response Time Enhances Alignment with Heterogeneous Preferences
Federico Echenique, Alireza Fallah, Baihe Huang et al. (+1 more)
Aligning large
language models (LLMs) to human preferences typically relies on aggregating pooled feedback into a single reward model. However, this standard approach assumes that all labelers share the same underlying preferences, ignoring the fact that real-world labelers are highly heterogeneous and usually anonymous. Consequently, relying solely on binary choice data fundamentally distorts the learned policy, making the true population-average preference unidentifiable. To overcome this critical limitation, we demonstrate that augmenting preference
datasets with a simple, secondary signal – the user's response time – can restore the identifiability of the population's average preference. By modeling each decision as a Drift-
Diffusion Model (DDM), we introduce a novel, consistent
estimator of heterogeneous preferences that successfully corrects the distortions of standard choice-only labels. We prove that our
estimator asymptotically converges to the true average preference even in extreme cases where each anonymous labeler contributes only a single choice. Empirically, across both synthetic and real-world
datasets, our method consistently outperforms standard baselines that otherwise fail and plateau at a bias floor. Because response times are essentially free to record and require zero user tracking or identification, our results bring promises and open up new opportunities for future data-collection pipelines to improve the social benefit without requiring user-level identifiers or repeated elicitations.
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All authors Federico Echenique, Alireza Fallah, Baihe Huang, Michael I. Jordan
Machine Learning
FastOmniTMAE: Parallel Clause Learning for Scalable and Hardware-Efficient Tsetlin Embeddings
Ahmed K. Kadhim, Lei Jiao, Rishad Shafik et al. (+2 more)
Embedding models in natural
language processing (NLP) increasingly rely on deep architectures such as BERT, while simpler models such as Word2Vec provide efficient representations but limited interpretability. The Tsetlin Machine (TM) offers an alternative logic-based learning paradigm. Omni TM Autoencoder (Omni TM-AE) applies this paradigm to static
embedding by exploiting automaton state distributions within a single clause layer, but its training process remains slow. In this work, we propose FastOmniTMAE, a reformulation of Omni TM-AE that replaces sequential training dependencies with a two-stage parallel process: evaluation and update. Using a Single-Run Multi-Environment
Benchmark covering classification, similarity, and clustering, FastOmniTMAE achieves up to 5$\times$ faster training in classification while maintaining comparable
embedding quality under both Spearman and Kendall similarity measures. To address the limited efficiency of TM training on conventional GPUs, we further implement FastOmniTMAE as a reusable accelerator on SoC-FPGA platforms. The Multi-Hardware
Benchmark shows that FastOmniTMAE achieves similarity scores of 0.669 on a resource-constrained FPGA and 0.696 on an UltraScale+ SoC, demonstrating efficient logic-based
embedding training with a small hardware footprint.
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All authors Ahmed K. Kadhim, Lei Jiao, Rishad Shafik, Ole-Christoffer Granmo, Mayur Kishor Shende
Machine Learning
PLOT: Progressive Localization via Optimal Transport in Neural Causal Abstraction
Jonathn Chang, Arya Datla and Ziv Goldfeld
Causal abstraction offers a principled framework for mechanistic interpretability, aligning a high-level causal model with low-level neural computation through interchange intervention analysis. Finding such an
alignment, however, often requires fitting and evaluating separate learned mappings across many candidate neural locations. We introduce PLOT, a gradient-free approach for joint correspondence discovery via a global matching of intervention effects. PLOT represents abstract and neural interventions by
geometric signatures of their effects on the shared task output and fits an optimal transport coupling between the two collections. The coupling can be calibrated directly into an executable intervention handle or used progressively to select coarse parent sites for finer localization, reducing the cost of searching broad collections of neural sites. Experiments on hierarchical equality, binary addition, and multiple-choice question answering (MCQA) demonstrate fast and accurate direct handles without learning intervention rotations and show that progressive localization can improve accuracy under intervention-size constraints while reducing computational cost. PLOT can also guide gradient-based subspace learning, with PLOT-guided distributed
alignment search (DAS) attaining accuracy comparable to full DAS at approximately $3.6\times$ and $20\times$ lower serial runtime on binary addition and MCQA, respectively. PLOT thus separates the search for candidate correspondences from optional subspace learning and provides a common framework for constructing and testing neural intervention handles for a given causal model.
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