Artificial Intelligence · Cross-listed from cs.LG
Improving Access to Essential Medicines via Decision-Aware Machine Learning
Angel Tsai-Hsuan Chung, Jatu Abdulai, Patrick Bayoh et al. (+4 more)
A critical challenge in healthcare systems in low- and middle-income countries (LMICs) is the efficient and equitable allocation of scarce resources, particularly essential medicines. This problem is complicated by limited high-quality data, which restricts the applicability of traditional data-driven techniques. We propose a novel decision-aware machine learning framework for essential medicines allocation, which additionally leverages multi-task learning to ensure sample efficiency and catalytic priors to ensure equitable allocation. In collaboration with the Sierra Leone national government, we performed a staggered, nationwide deployment of our system as a decision support tool. Our econometric evaluation finds an estimated 19% increase in consumption of allocated products in treated districts, demonstrating its efficacy at improving access to essential medicines. Our tool was subsequently scaled nationwide, covering an estimated 2 million women and children under five. Our work demonstrates how machine learning methods can improve efficiency at very low cost in resource-constrained global health settings.
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All authors Angel Tsai-Hsuan Chung, Jatu Abdulai, Patrick Bayoh, Lawrence Sandi, Francis Smart, Hamsa Bastani, Osbert Bastani
Artificial Intelligence · Cross-listed from cs.LG
HypNO: A Graph-Based Neural Operator with Physics-Informed Message Passing for Hyperbolic Conservation Laws
Dimitrije Ždrale, Cassie An Jeng, Katie Wang et al. (+3 more)
We introduce HypNO, a graph-based neural operator for scalar
hyperbolic conservation laws. HypNO operates directly on a space-time graph of finite-volume
cells and uses adjacency-factored, physics-informed message passing to respect upwinding and
entropy admissibility near shocks. We
benchmark the architecture on the Lighthill-Whitham-Richards (LWR) and Aw-Rascle-Zhang (ARZ) traffic-flow models, a stress test for operator-learning methods because of their simultaneous global transport and shock formation. HypNO predicts solution snapshots accurately across a range of initial conditions while capturing the shocks and discontinuities of the solution.
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All authors Dimitrije Ždrale, Cassie An Jeng, Katie Wang, Sonia Vanier, Alexandre Bayen, Hossein Nick Zinat Matin
Artificial Intelligence · Cross-listed from cs.LG
From Atoms to Entropy: Optimal Noise Allocation for Diffusion Training in the Convex Regime
Luca Ambrogioni, Giulio Franzese, Alberto Foresti et al. (+7 more)
How should a
diffusion model decide which noise levels to train on, and how much? Despite the importance of this choice, current noise schedules are based largely on heuristics or empirical tuning. Here, we develop a general statistical framework for studying asymptotically optimal noise-level allocation in
diffusion training. Our first main result concerns the fully coupled regime, where information can spread between different time points. Under convexity or Polyak-Lojasiewicz-type assumptions, we show that the optimized training schedule admits an atomic minimizer, concentrated on finitely many noise levels. Our second main result specializes this framework to an idealized independent-learner regime, intended to model temporal specialization in neural networks. Under an additional feature-noise decoupling condition, a random-
matrix analysis leads to an information-theoretic proxy: the decoupled sampling density is proportional to the square root of the generative
entropy rate, the rate at which conditional
entropy grows along the forward process. We test these predictions in controlled settings where the coupled objective can be optimized directly, including Dirac mixtures, low-dimensional
manifolds, and MNIST. In these settings, the optimized schedules are consistently finite-support, while the smooth entropic proxy closely tracks the atomic optimum in neural-network models and breaks down mainly in the fully coupled parametric case, as the theory suggests. We then evaluate the entropic schedule in larger-scale experiments, where full schedule optimization is currently intractable. The results indicate that square-root
entropy scheduling can substantially improve training efficiency on discrete domains and remains competitive with standard EDM-style heuristics on continuous images.
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All authors Luca Ambrogioni, Giulio Franzese, Alberto Foresti, Gabriel Raya, Bac Nguyen, Georgios Batzolis, Yuhta Takida, Naoki Murata, Chieh-Hsin Lai, Yuki Mitsufuji
Artificial Intelligence · Cross-listed from cs.LG
Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modeling
Kyunghoon Hur, Eunjung Jeon, Hyun Woo Kim et al. (+2 more)
While deep learning has accelerated drug discovery, its impact on biomanufacturing has been considerably more limited. The reason is data scarcity. Bioreactor experiments are high-cost, take days to weeks, and are rarely shared in public form, leaving each research work with only a handful of experiments. The domain itself, however, is rich in prior knowledge. Biokinetic ordinary differential equation (ODE) models have described microbial growth for decades, yet how to inject this knowledge into a neural network has not been studied systematically. We present the first systematic study of how to inject this ODE knowledge into a neural network, comparing a data-level prior that pre-trains a generic decoder on simulated ODE curves against an architecture-level prior that embeds the ODE inside the decoder. Both consistently outperform no-prior baselines across 11
datasets and 7 microbial species. Our central finding is that the two are substitutable. A generic decoder pre-trained on simulation matches a fully bio-structured decoder trained on real data. Simulation pre-training therefore offers a simple, data-efficient recipe for deep learning under bioprocess data scarcity.
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All authors Kyunghoon Hur, Eunjung Jeon, Hyun Woo Kim, Gyubok Lee, Seongjun Yang
Artificial Intelligence · Cross-listed from cs.LG
Codec-Gauge: Learning Compression-Friendly Gauges for Transformer KV Caches
Yitao Jiang, Yaoqing Yang, Luyang Zhao et al. (+2 more)
Long-context
Transformer inference increasingly relies on KV-cache compression or quantization. Prior rotation and transform-coding results suggest that the channel basis of each key/value vector affects how faithfully a fixed backend preserves model behavior. We introduce Codec-Gauge, a post-training cache-coordinate layer that learns small orthogonal channel transforms around existing compression and quantization backends. Its frequency-distribution objective combines a
token-channel DCT spectral-centroid loss with a smooth rate proxy to concentrate KV energy in low-frequency codec-facing layouts. We evaluate actual compression and decompression using measured bytes and rolling compressed-history scoring. Across six models at $3$, $4$, and $6$ bits/value, learned gauges reduce zfp KL divergence by $44.0\%$ on average relative to raw coordinates and outperform random, Hadamard, DCT, and PCA/KLT controls. The same gauges improve quality preservation for block-uniform and KIVI-style quantization. Experiments on a 27B model and long-context task
prompts reproduce the quality trend, while serial storage and timing measurements validate the implemented compressed-cache paths. These results establish cache-coordinate geometry as a practical post-training variable for improving compression fidelity without changing model weights,
attention semantics, or backend coding rules.
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All authors Yitao Jiang, Yaoqing Yang, Luyang Zhao, Muhao Chen, Devin Balkcom
Artificial Intelligence · Cross-listed from cs.LG
ReliableTableQA:How Much Supervision Does Reliability Annotation Need?
Huei-Chung Hu, Hsin-Tai Wu and Koyo Kobayashi
We introduce ReliableTableQA, a framework for training an LLM to annotate the statistical reliability of tabular QA results, not whether the query is answerable, but whether the computed answer is statistically meaningful. In real enterprise analytics, a syntactically correct SQL query can return a value that is based on too small a sample, has an excessively wide confidence interval, or is too confounded to support action. Existing systems answer confidently in all such cases, a failure we quantify as the Unreliable Confident Answer Rate (UCAR). We contribute (1) a ten-category reliability taxonomy (R1-R10) covering hazards such as small-sample aggregates, multiple-comparison inflation, and distribution-tail mismatch; (2) a program-first data pipeline that generates 50,000 reliability-labeled training examples from a context-free grammar over public retail schemas, with schema-stratified SFT/GRPO splits; and (3) a controlled study of how much supervision calibrated reliability annotation actually requires. We find that a small, schema-stratified SFT set is remarkably sufficient: 200 examples raise reliability-flag F1 from 0.61 to 0.98 and parse rate from 0.52 to 1.00, drive UCAR to zero, and yield a model that generalizes to an unseen retail domain (Rel-F1 0.997 on held-out H&M). Against this strong SFT baseline, GRPO, commonly assumed to be essential, helps only when SFT is under-trained (+0.06-0.16 exact-flag-set match at 100 examples, in- and out-of-distribution) and provides no measurable benefit once SFT is adequate, a null result we confirm across a hard compound-flag slice, a strict exact-match metric, and out-of-distribution evaluation. Our findings reframe reliability annotation as a data-efficiency problem and delineate precisely when reinforcement fine-tuning does and does not pay off.
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Artificial Intelligence
AppWorld-UL: Benchmarking Diverse Agent-User Interactions for Tool-Use
Junzhi Chen, Harsh Trivedi, Jane Pan et al. (+4 more)
Tool-use
agents that address day-to-day digital tasks such as ordering groceries must not only operate applications, but also interact with the user, e.g., to ask clarification questions,
prompt for confirmation, and inform the user when the instruction is infeasible. However, current
benchmarks for evaluating
agent-user interactions do not capture the diversity of such interactions. Further, they operate in small environments with few, often non-state-changing, APIs. To address this gap, we introduce AppWorld-UL, a “user-in-the-loop”
benchmark of 516 challenging tasks requiring diverse
agent-user interactions. Building upon the AppWorld framework with 9 popular simulated apps like Amazon and Spotify, we systematically modify original tasks to introduce ambiguities and constraints that necessitate various types of
agent-user interaction. User behavior is simulated by an LLM prompted to respond with carefully designed knowledge boundaries, offering more reliable simulation than the unconstrained or overly rigid alternatives used in prior work. Our evaluation reveals that a state-of-the-art LLM, Claude Opus 4.7, achieves only 48.6% success on AppWorld-UL, and only 35.7% on the harder, compositional subset. On the stricter, scenario-level metric, compositional task performance drops to only 21.3%. Our analysis reveals that correct user-interaction is crucial for success. This demonstrates the
benchmark's difficulty and its potential to advance research on user-in-the-loop tool-use
agents.
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All authors Junzhi Chen, Harsh Trivedi, Jane Pan, Michael JQ Zhang, Tejas Srinivasan, Niranjan Balasubramanian, Ashish Sabharwal
Artificial Intelligence · Cross-listed from cs.LG
A Graph Neural Network approach to zero-shot Digital Twins
Alicia Tierz, Icíar Alfaro, David González et al. (+1 more)
Traditional Predictive Digital Twins often remain geometrically rigid, requiring extensive retraining or fine-tuning whenever the underlying physical domain or boundary conditions change. To overcome this limitation, we present a novel framework for Zero-Shot Digital Twins that seamlessly couples real-time visual perception with a geometry-agnostic, physics-informed
reasoning engine. At the core of our architecture is the Thermodynamics-Informed Graph Neural Network architecture, a
Geometric Deep Learning solver grounded in a metriplectic thermodynamic formalism that enforces energy conservation and non-negative
entropy production locally through graph message passing. The framework integrates an auxiliary Graph Neural Network to infer unobservable fields (such as stress tensors or velocity and energy distributions) directly from sparse initial visual boundaries, mitigating numerical start-up transients. To bridge the sim-to-real gap, we implement a continuous closed-loop data assimilation mechanism; the pipeline tracks macroscopic deformations and free-surface fluid boundaries in real-time using deep
segmentation networks combined with sparse optical flow, dynamically correcting the autoregressive simulation rollout and eliminating numerical drift. To test the validity of our approach, we demonstrate the extreme generalization capabilities of our approach across two disparate physical regimes: the large deformations of a viscoelastic beam and the non-linear sloshing of a viscous fluid. In both scenarios, the unified framework instantiates physically accurate simulations on novel, unseen geometries without case-specific retraining, operating well within real-time latency budgets (approximately 25 ms per frame) and enabling the direct projection of latent mechanical variables via Augmented Reality.
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All authors Alicia Tierz, Icíar Alfaro, David González, Elías Cueto
Artificial Intelligence · Cross-listed from cs.LG
Grounding Investor Views: Neural Predicates in the Black-Litterman Model
Marcos Florencio
Portfolio construction under the Black-Litterman model requires investors to specify views on asset returns alongside explicit uncertainty estimates – a process that remains largely subjective and difficult to scale. We propose a formal approach in which neural predicates serve as a structured, probabilistic mechanism for view generation. In our formulation, structured financial analysis data is processed through a compositional hierarchy of neural predicates whose outputs – probability distributions over
market stances – are mapped to the pick
matrix $\mathbf{P}$, the view return vector $\mathbf{q}$, and the view uncertainty
matrix $\boldsymbol{\Omega}$ of the Black-Litterman model. View confidence is derived from predicate output distributions, providing a data-driven alternative to subjective uncertainty elicitation. The resulting approach is interpretable, in the sense that any portfolio weight can be traced back through the predicate's logical chain to the underlying data, and fully differentiable, enabling end-to-end learning.
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Artificial Intelligence · Cross-listed from cs.LG
Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development
Nadine Chang, Maying Shen, Jialiang Wang et al. (+2 more)
Many modern AI systems are designed to operate under diverse, open-ended, use-cases. To help generalize deployed systems, many deployed-system maintenance pipelines use a reactive AI flywheel that observes emerging feedback from user behavior (errors) and patches the model accordingly. However, when used as the primary maintenance mechanism, these flywheels often ignore the broader context of these errors within the system's objectives, failing to preempt potential future edge cases, which leads to more unnecessary flywheel iterations. Also, it is statistically increasingly difficult to collect remaining errors due to the long-tail nature of open-world use-cases. This position paper argues that a proactive test-driven flywheel is required to address reactive flywheel's limitations and to approach a generalizable system. We advocate for creating a "test space" to technically map feedback data to task objectives, evolving the flywheel from reactive to proactive. We augment our position by mathematically proving a proactive one achieves better long-term scaling with fewer iterations than the reactive flywheel.
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All authors Nadine Chang, Maying Shen, Jialiang Wang, Rafid Mahmood, Jose M. Alvarez
Artificial Intelligence
DynamicMCPBench: A Trace-Grounded, Effect-Scored Benchmark for LLM Agents over Live MCP Servers
Jerzy Kamiński, Ilya Galyukshev, Artem Kuznetsov et al. (+4 more)
Large
language model (LLM)
agents are increasingly deployed over Model Context Protocol (MCP) servers, yet the
benchmarks used to evaluate them score the final answer or a fixed "ground-truth" list of tools, both of which are fragile once the underlying data is live and stateful. We present DynamicMCPBench, a reusable framework rather than a fixed
dataset. A practitioner can run it on their own MCP servers to test models on their own tasks, or let it collect servers automatically to measure a model's general ability to solve agentic tasks. Given the servers and any set of models, it generates realistic goals, pursues each one live to record a successful trajectory, distills that trajectory into path-agnostic effect checkpoints, and scores an
agent on whether it reproduces those effects, never on the final answer. To show what the framework reveals, we run it at scale: 24 models over 121 servers and 750 tasks spread evenly over 15 task categories (50 each), where each category targets a distinct tool-use challenge of the generated questions. Each task is scored by pass^3: it counts as solved only if all three independent attempts succeed. Even the strongest
agents solve only about half of the tasks, 31% of tasks are solved by no model at all, and accuracy collapses as the required tool chain grows longer (from 39% on the shortest chains to 13% on the longest). A human validation study confirms the automatic scoring is reliable (chance-corrected agreement of 0.76). DynamicMCPBench thus turns
benchmark construction into something practitioners can rerun on their own servers and models, while exposing a consistent inability of current
agents to handle long, multi-step agentic tasks.
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All authors Jerzy Kamiński, Ilya Galyukshev, Artem Kuznetsov, Sergey Chuprin, Kirill Redko, Aidar Shumbalov, Anna Kalyuzhnaya
Artificial Intelligence · Cross-listed from cs.LG
CLOE: Christoffel Loss Autoencoder for Anomaly Detection
Léa Billet, Louise Travé-Massuyès, Elodie Chanthery et al. (+1 more)
Semi-supervised anomaly detection plays a key role in diverse fields such as process monitoring, healthcare, and finance. However, lightweight methods often struggle with high-dimensional data and typically require careful tuning of multiple hyperparameters. Among existing approaches, Christoffel Function–based methods are attractive due to their simplicity, requiring at most a single hyperparameter. They also benefit from a well-established theoretical foundation that yields several interesting results for data science. However, their main limitation is poor scalability to high-dimensional settings. In this paper, we introduce CLOE, a new method that combines an autoencoder for dimensionality reduction with a Christoffel Function–based detector applied in the latent space. To better align representation learning with anomaly detection, we design a novel loss function that leverages the Christoffel Function to guide the autoencoder toward representations that better capture the support of the normal data distribution. We further propose a principled procedure to set the detection threshold and an efficient strategy to tune the single remaining hyperparameter. Experiments on multiple high-dimensional tabular anomaly detection
benchmarks demonstrate that CLOE achieves superior performance compared to existing methods, while preserving the lightweight and low-tuning advantages of Christoffel Function–based approaches.
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All authors Léa Billet, Louise Travé-Massuyès, Elodie Chanthery, Alexandre Gaffet
Artificial Intelligence · Cross-listed from cs.LG
Uncertainty-Aware Trust Estimation for Multi-LLM Systems via Structured Expert Judgement
Jiawei Zheng and Jiazhen Zhang
Large
Language Model (LLM) ensembles are increasingly used to improve reliability by combining predictions from multiple LLMs. However, existing aggregation methods typically assume that all models are equally trustworthy, overlooking differences in uncertainty quality. This assumption is poorly suited to heterogeneous LLMs, whose reliability and capability vary significantly, making naive aggregation vulnerable to unreliable or
adversarial experts. In this work, we formulate multi-LLM aggregation as a problem of uncertainty-aware trust estimation. We adapt structured expert judgment from decision theory, using context-aware calibration questions to estimate expert reliability based on the quality of its probabilistic predictions. Specifically, we employ Cooke-style log weighting, which penalises overconfident incorrect predictions and favours well-calibrated experts. We evaluate our approach on MMLU and MMLU-Pro across homogeneous, heterogeneous, and contaminated expert panels. Results show that while aggregation methods perform similarly in homogeneous settings, Cooke weighting becomes critical under heterogeneity and contamination. It achieves a superior accuracy-reliability balance and remains robust when unreliable experts are introduced. These findings suggest that Multi-LLM aggregation requires not just combining predictions, but calibrating trust under uncertainty.
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Artificial Intelligence
PromptPack: Scaling LLM Annotation Agents for Online Recommendation
Sebastian Koralewski, Merwan Barlier, Yulia Stolin et al. (+1 more)
Online recommendation platforms increasingly use Large
Language Models (LLMs) to extract structured features from ad creatives. While deploying a single-call LLM annotation
agent yields significant Click-Through Rate (CTR) improvements in our live production environment, per-creative prompting is prohibitively expensive to scale. The redundant system instructions sent in every request account for 94% of billed input
tokens. To break this cost bottleneck, we introduce PromptPack, a scalable, high-throughput LLM annotation
agent. PromptPack achieves this scale via in-context batching, combining a shared system
prompt, a strict XML structural envelope, and an output correction layer to ensure deterministic, pipeline-ready feature extraction across multiple creatives simultaneously. We evaluate PromptPack via an offline
retrieval benchmark using a downstream logistic-regression ranker. To deeply profile the
agent's behavior, we measure AUC and introduce Volume-Weighted Absolute Lift (VWAL), a novel metric capturing the signal quality of the generated features. Compared to our live, unbatched production baseline, PromptPack at batch size 20 cuts our LLM costs by 89% and accelerates throughput by 2.5x while fully preserving AUC.
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All authors Sebastian Koralewski, Merwan Barlier, Yulia Stolin, Blaž Škrlj
Artificial Intelligence
Evaluating and Guarding Citation Faithfulness in Agentic Scientific Synthesis
Taewan Goo, Junsik Kim, Kyulhee Han et al. (+3 more)
Agentic LLM systems such as OpenScholar and PaperQA2 read the scientific literature and return cited answers, and both they and their
benchmarks already check whether those citations hold, with a fixed attribution model or human graders. Neither audits the reliability of that check itself. We show it is not reliable, and that this matters. On identical
agent outputs the measured unsupported-citation rate ranges from about 3% to about 18% depending only on the verifier's strictness, and although verifiers agree on which citations are supported, they disagree on which to flag (negative-specific agreement 0.27 to 0.30), so no single flag set is trustworthy and cross-paper comparison is invalid without a named verifier and protocol. We present a gold-anchored evaluation protocol and a deployable guard that make this behavior measurable and bounded. The protocol validates the verifier, measures re-attribution, and calibrates a guarantee against human gold rather than another model's verdict; the verifier is a swappable instrument chosen on cost (recall 0.94 on the supported class, held out), and re-attribution is a commodity step where a deterministic BM25 matches the best open generator. The guard adds a split-conformal layer placing a distribution-free, finite-sample bound on truly unsupported citations that slip past a chosen flagging rule, a guarantee on catch rate rather than conclusion correctness. The bound holds on held-out gold, and we identify and quantify the condition governing its transfer to deployment, calibration-negative difficulty, with a concrete recalibration recipe, left untested by prior conformal-factuality work. Validated across four open 27-35B models and three agentic pipelines on public
benchmarks (SciFact, QASA, PubMedQA), with confidence intervals on every headline number, the protocol and guard ship as an open single-GPU kit.
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All authors Taewan Goo, Junsik Kim, Kyulhee Han, GwonYul Jo, Jong-Soo Kim, Tae-Hyung Kim
Artificial Intelligence
ConfidenceBench: Evaluating Confidence Calibration in Large Language Models
Matthew ffrench-Constant, Daniel Yang, Xinmeng Huang et al. (+1 more)
Large
language models (LLMs) are increasingly deployed in settings where fluent but incorrect answers can be costly. In these settings, accuracy alone is insufficient: models must also know when they are likely to be wrong. We present ConfidenceBench, a calibration
benchmark that evaluates verbalized confidence estimates in 15 frontier LLMs using the Brier score, a proper scoring rule that incentivises truthful probability reporting. Confidence is elicited via prompting, requiring no access to model logits and making the framework applicable to both closed-source and open-source systems. The
benchmark comprises 200 private multiple-choice questions across four categories: spatial
reasoning, high-precision mathematics, word lookup, and unknowable questions. Across three independent runs, Claude Opus 4.6 and Gemini 3.1 Pro Preview achieve the lowest Brier scores, both reported as 0.103. Both substantially outperform the calibrated-random baseline of 0.1875, while Gemini 3.1 Flash-Lite scores 0.367, indicating severe miscalibration. Accuracy and calibration diverge substantially across model families: the most accurate model is not the best-calibrated, and several models perform worse than the calibrated-random Brier baseline despite reasonable accuracy. These results show that verbalized confidence calibration is a distinct and practically important axis of LLM reliability, complementary to standard accuracy-based evaluation.
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All authors Matthew ffrench-Constant, Daniel Yang, Xinmeng Huang, Sanyam Kapoor
Artificial Intelligence · Cross-listed from cs.CV
GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification
Md Faraz Kabir Khan, Saeed Anwar and Ghulam Mubashar Hassan
The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before. We introduce GenSyn10, a CIFAR-10-aligned synthetic image
dataset of 60,000 images (10 classes, 32$\times$32, 50k/10k split) generated using three architecturally diverse state-of-the-art models: FLUX.2-dev (Rectified Flow
Transformer), HunyuanImage-3.0 (MoE
Transformer), and Qwen-Image-2512 (
Multimodal Diffusion Transformer), to advance research in AI-generated image detection. A central challenge in this domain is that detectors perform well on known generators but degrade on unseen ones. GenSyn10 addresses this limitation by curating data from multiple contemporary architectures under a standardized generation protocol, enabling controlled and systematic evaluation of out-of-distribution (OOD) generalization to novel generators. Images are generated using a template-based
prompt engine and downsampled to ensure consistency. We evaluate 17 image classification models under a four-stage protocol: real-data baseline, zero-shot transfer, fine-tuning, and retention. Despite a measurable domain gap, CIFAR-10-trained models achieve up to 96.86% zero-shot accuracy on GenSyn10, increasing to 99.88% after fine-tuning. In binary real-vs-synthetic classification, fine-tuned models achieve 97-99.9% accuracy on seen generators but drop to 79-96% on images from an unseen generator, highlighting persistent limitations in OOD generalization. These results establish GenSyn10 as a controlled
benchmark for studying synthetic image detection beyond single-generator settings, supporting research on robustness, domain adaptation, and cross-generator generalization.
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Artificial Intelligence · Cross-listed from cs.PL
Volition Elicitation: Operational Semantics for People and Their Machines
Idit Keidar and Ehud Shapiro
The most prevalent distributed systems today include people and their personal machines (smartphones), and computations in them are driven by people's volitions. Volition-guarded multiagent atomic transactions were proposed as an abstract specification
language for such systems, in which a transaction has a precondition on the machine states of its participants and is guarded by the volitions of some of them. Here we develop an abstraction cascade that derives smartphone apps from such specifications and prove it correct down to the base programming
language, GLP. The cascade consists of: (1) communicating volitional
agents (CVA), which implement every local volition-guarded specification with simple guards, correctly, and completely for its strongly fair runs, by two-phase commit coordinated by the initiating
agent; (2) volition-guarded GLP (vGLP), an extension of GLP with volitional procedures, whose goals put an interactively-typed question to the person, into which that implementation compiles; and (3) GLP, which implements vGLP by eliciting the person's answers through the widgets of their interactive types, correctly, and completely for complete widgets. We demonstrate the approach on three grassroots platforms — a social graph, a social network, and currencies: a compiler carries the social graph and the currencies down the cascade to GLP programs that run with scripted persons, and earlier programs of the three run as mini-apps of one smartphone app.
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Artificial Intelligence · Cross-listed from cs.PL
Untrusted Authors, Trusted Answers: A Calculus of Fidelity-Graded Translations
Christoph Kirsch
To answer a question about a program, move the program to where the question is decidable. Every such move is a translation, and every translation is a place to be wrong. We study translation as a graph – many
languages, a few
reasoning targets, independently built routes of honestly different trustworthiness – and give it a calculus: pairs of
languages close commuting squares that are directional (exactness is the identity-
embedding special case of over-approximation), checkable per program, and composable, a route's contract being the componentwise meet of its hops' contracts – assurance class, direction, kept observables, measured cost. One asymmetry organizes trust: witness-carrying answers are self-certifying by replay at the source; universal answers are where grades, independent branches, and re-checked certificates earn their cost. The compositional core, lax
telescope included, is mechanized in Lean 4. hurdy-gurdy implements the calculus as two planes meeting in one registry. The use plane reads declarations and produces evidence-carrying answers; its builders and its intended player are both LLMs, untrusted by construction. The
evolution plane grows the graph: unmet questions are recorded as demand, pairs are recommended by evidence and registered by humans, and a ratchet keeps every prior verdict standing. Answers never write; growth never answers. Run indefinitely, the loop converges on every reducibly decidable question, at fidelity that only rises. We measure the July 2026 snapshot – per-construct conjoined coverage, dual-route branch agreement for two ISAs, source-level witness replay, certified unreachability re-validated by a formally verified checker, escape rates for the gate itself – and report the defects the architecture caught in its own authors' work.
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Artificial Intelligence · Cross-listed from stat.ME
Geometric mean-based pairwise comparison method with the reference values – statistical approach
Konrad Kułakowski, Kamil Pustelnik and Jacek Szybowski
For many years, pairwise comparison methods have been widely used for eliciting preferences and ranking alternatives in decision-making problems. These methods estimate priority weights from a pairwise comparison
matrix and are now standard tools in multi-criteria decision analysis. In this paper, we present a statistical view of the pairwise comparison method using reference values and the
geometric mean to calculate alternative priorities. Thanks to this approach, we can simultaneously capture the phenomenon of inconsistency in pairwise comparisons and the preference distance between different alternatives. In this paper, we define indicators that measure the quality of the obtained weight vector, which, thanks to the statistical approach, have an understandable interpretation.
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Artificial Intelligence
A Symbolic Neural CPU for Quantization-Simulated Writeback and Interpretable Program Execution
Jose Luis Lima de Jesus Silva
Neural networks can learn algorithmic input-output mappings, but trusting a learned executor requires more than a correct final answer because the state transitions that produce it are usually hidden. To make those transitions visible, we introduce a trace-supervised symbolic neural CPU, a factorized learned execution architecture that combines recurrent control, an explicit operation router over a fixed differentiable arithmetic-logic unit bank, destination-masked register writeback, complete trajectory supervision and matched fixed-point replay. The model exposes the selected operation, source and destination registers, register trajectory,
memory signals and writeback semantics at every step. On the principal 16-wide
benchmark, the non-quantized executor reproduces reference execution exactly, while the eight-bit quantization-simulated executor preserves the symbolic operation path through programs of 1,000 instructions. When the same execution is evaluated against a matched fixed-point replay, the residual numerical drift disappears, showing that it comes from a mismatch between continuous and low-precision reference semantics rather than from execution failure. We compare recurrent,
Transformer, temporal-convolution, temporal graph-inspired and state-space controllers, and the ablations show that operation-gate supervision is necessary for an inspectable execution path. Hidden-opcode
memory-pressure tasks expose the remaining limits in delayed state use and temporal binding. We also extend the interface with ValueMemory, hybrid adaptive leaky integrate-and-fire controllers, candidate-constrained symbolic control trained through behaviour cloning and actor-critic reinforcement learning, and an RV32I base-integer semantic bridge. Together, these results establish a trace-verifiable framework for interpretable, low-precision and controllable neural execution.
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Artificial Intelligence · Cross-listed from cs.CL
FindMyText: Robust, Scalable Detection of Text Containment in Large Web-Crawled Corpora
Lars Henry Berge Olsen, Pierre Lison, Martin Jullum et al. (+1 more)
We present FindMyText, an open-source Python package designed to efficiently assess whether a given text appears, in part or in full, within a text corpus. The tool builds on prior techniques for document fingerprinting, but extends them with a novel mechanism to explicitly capture sequences of matching fingerprints. By identifying such chains, the tool can more reliably detect near-verbatim copies of a given text rather than mere textual similarities. This makes FindMyText particularly suited for verifying the presence of copyrighted material in a corpus. Leveraging a distributed, disk-based indexing framework, the system scales to large web-crawled
datasets. Using a new
benchmark for evaluating text containment methods, we show that FindMyText outperforms alternative approaches across three
datasets (ArXiv papers, Wikipedia, and generic web content).
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All authors Lars Henry Berge Olsen, Pierre Lison, Martin Jullum, Mark Anderson
Artificial Intelligence
Who&When Pro: Can LLMs Really Attribute Failures in AI Agents?
Jiale Liu, Huajun Xi, Shaokun Zhang et al. (+6 more)
Automated failure attribution uses LLMs to identify where and why agentic systems fail. As
agents become more capable, their failures become subtler, making automated attribution increasingly important. We introduce Who&When Pro, a large-scale
benchmark for automated failure attribution in agentic systems. Using a strictly controlled pipeline that injects a failure only after exactly replaying a successful prefix, we construct 12,326 failed trajectories with golden labels across 3 modalities and 26
benchmarks covering various scenarios. Beyond benchmarking, we conduct extensive experiments and analyses, revealing systematic patterns in how models attribute failures across modalities, protocols, and model families, and providing empirical guidance for future automated failure attribution systems.
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All authors Jiale Liu, Huajun Xi, Shaokun Zhang, Yifan Zeng, Tianwei Yue, Chi Wang, Jian Kang, Qingyun Wu, Huazheng Wang
Artificial Intelligence · Cross-listed from cs.GT
Beyond Bayesian Nash: Learning Minimax-Regret Equilibria for Adversarial Team Games under Asymmetric Information
Naman Aggarwal and Jonathan P. How
Adversarial team games (ATGs) with asymmetric information, such as
adversarial path-finding, goal search, and reachability games on graphs, require strategies that are robust to hidden opponent types, such as a hidden goal flag, and to deception. Under asymmetric information, deception is seen as strategic shifts in the type distribution such that the omniscient opponent can collude with Nature and condition its play on the observed type. Existing risk-neutral solution concepts, such as
Bayesian Nash
equilibrium (BNE), are sensitive to distribution shifts, while distributionally robust approaches provide guarantees only within a prescribed ambiguity set. To address these limitations, we introduce Probabilistically Robust Minimax-Regret
Equilibrium (PR-MRE), a novel
equilibrium concept that combines the distribution-free robustness of minimax-regret
reasoning with probabilistic information from a nominal type distribution. PR-MRE minimizes worst-case regret over a high-confidence subset of the type space, providing protection against strategic redistribution of probability mass while avoiding the conservatism of fully distribution-free approaches. We show that, for finite normal-form
Bayesian games, approximate PR-MRE computation can be formulated as a robust bilinear program and derive a tractable semidefinite relaxation. We then adapt this relaxation into a novel meta-solver within a robust double-oracle framework, PRMRE-PSRO, enabling population-based learning of approximate PR-MRE strategies via deep reinforcement learning best responses. Experiments on graph-structured
adversarial team games demonstrate that PR-MRE discovers strategies with substantially improved worst-case performance across hidden types compared to risk-neutral
Bayesian Nash
equilibrium solutions, resulting in more robust behavior under strategic distribution shifts.
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Artificial Intelligence · Cross-listed from cs.IR
An LLM-powered Agentic Recommendation System for Connected TV Content Discovery
Lei Shi, Di Wang, Harry Tran et al. (+23 more)
Recommendation systems, from traditional multi-stage to recent unified generative architectures, face challenges in incorporating diverse contextual signals, such as trending topics, breaking news, cultural events, and cross-surface user activities, into their ranking pipelines. These systems are designed to consume structured behavioral signals with consistent schemas, and lack the
reasoning capability to naturally process unstructured or heterogeneously formatted contextual information. Incorporating such signals typically requires feature engineering, bespoke data pipelines, and carefully tuned heuristics. In this paper, we present an LLM-powered agentic recommendation system designed for Connected TV (CTV) content discovery that addresses these limitations. Our system leverages the
reasoning capabilities of large
language models to naturally process and synthesize diverse signals across varying schemas and structures, eliminating much of the manual integration inherent in traditional ranking and
retrieval systems. Recognizing that current LLM-based solutions still fall short of traditional machine learning models in several recommendation tasks, including
retrieval efficiency, personalization precision, and scalability, we adopt an agentic architecture that orchestrates specialized components, allowing each sub-task to be handled by the most suitable method, whether LLM-based or traditional ML. The main contribution of this work is our engineering approach to successfully overcoming the practical limitations of enabling LLM for recommendation, particularly inference latency. We share insights from our work and discuss the trade-offs and lessons learned in building a hybrid system that combines the flexibility of LLMs with the performance of established recommendation techniques.
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All authors Lei Shi, Di Wang, Harry Tran, Helsing Xu, Yuchen Lu, Dhara Ghodasara, Wilson Chaney, Xueting Liao, Guohao Xiao, Jerry Yu, Huayu Ding, Reza Mirghaderi, David Fan, Qi Guo, Chongguang He, Warren Wang, Warren Deng, Mingze Gao, Shike Mei, Shuo Tang, Zhe Zhang, Jianming He, Abhishek Kumar, Haotian Wu, Hamed Firooz, Li Li
Artificial Intelligence · Cross-listed from cs.SD
A Production-Oriented Framework for Evaluation of SFX Generation
Mélodie Desbos, Yara Bahram, Eric Granger et al. (+1 more)
Industrial sound design requires audio generation systems that not only produce realistic audio, but also preserve the perceptual identity of a reference, support controllable variation, and remain efficient for practical workflows. Existing evaluations are usually tied to text-to-audio (TTA), unconditional, or task-specific settings, limiting assessment for reference-guided sound effects (SFX) variation. To address this gap, we present a production-oriented evaluation framework for structured comparison of heterogeneous audio generation and editing methods. Our framework identifies nine production requirements and explicitly accounts for differences in model capabilities, enabling comparison under a common production objective. A two-stage protocol is introduced: (1) a reference-guided audio-to-audio (ATA) variation task, in which all methods are evaluated under the same ESC-50 SFX adaptation setup, and (2) capability-specific analyses of native operations such as SFX morphing, temporal and energy
alignment, inpainting, and targeted editing. This framework combines objective metrics (including FAD, ImageBind-based reference
alignment, and diversity across generated variants), together with a human study of perceptual identity preservation and transient diagnosis. Our study reveals complementary strengths and trade-offs across baselines for different production needs. Among the full-generation baselines evaluated under a shared ATA setting, AudioX provides the strongest overall trade-off between reference
alignment and diversity while still supporting SFX morphing. Other baselines remain most suitable for specific editing operations. Our framework establishes a structured evaluation and decision protocol for reference-guided SFX variation and provides a practical basis for designing future unified industrial audio generation pipelines. Audio demos are on the accompanying web page.
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All authors Mélodie Desbos, Yara Bahram, Eric Granger, Mohammadhadi Shateri
Artificial Intelligence
TopoExplore: Homology as an Exploration Signal
Jason Carlson
Exploration signals in reinforcement learning are currently computed from what an
agent has seen: visitation counts, density estimates, or a model's prediction error at individual states. None of these report the holes in the visitation space. To capture them, we compute the persistent homology of the
agent's own archive of visited states during exploration and turn each detected class (an enclosed region the archive surrounds but has not entered) into a selection bonus concentrated on the archived states from which entry is possible, gated by an attempt counter that retires candidate entrances and prunes sealed structures. The method, TopoExplore, is Go-Explore plus one additive term, so the comparison with Go-Explore isolates that term. On the 189 held-out worlds of the open-source TopoGym
benchmark of topologically varied hard-exploration environments, TopoExplore finds the goal in 167 worlds against 152 for Go-Explore and, where both find it, with a median 126k steps against 186k. On an environment that stress-tests far-apart chambers that create holes in the archive space, TopoExplore enters all six or eight chambers across 5 seeds, against 1 and 2 of 5 seeds for Go-Explore. On Montezuma's Revenge, built over the room graph, the term helps whenever the archive surrounds a room it has not entered, puts 33-45% of the selections on the surrounding rooms while it is active, and enters the room sooner in five of six cases; such enclosures are rare on this game, so the score is unchanged. Beyond discrete, restorable state spaces, the same signal drives an
embodied explorer that walks to every target it selects and explores from depth alone; a
vision-
language-action
navigation policy fine-tuned on its trajectories succeeds 58% more often on unseen scenes than one fine-tuned on shortest-path oracle demonstrations.
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Artificial Intelligence · Cross-listed from cs.LG
Learning in Curved Weight Space:Exponential-Linear Weight Reparameterization for Improved Optimization
Ethan Smith
Many neural networks operations have a multiplicative nature rather than additive: halving or doubling a norm are analogous relatively but require unequal optimization distances when taking linear steps. Adaptive optimizers such as Adam normalize updates per coordinate, but update steps remain additive; weights with very different magnitudes receive similarly sized absolute changes, producing very different relative perturbations. We introduce \method (\methodshort), a weight reparameterization for neural networks that combines a sign-aware symmetric-exponential pathway with an identity-like linear pathway. The symmetric-exponential pathway is near-linear for small raw weights but increasingly curved at larger magnitudes. Additive updates in logarithmic space map to magnitude-proportional changes in effective weight space. The linear pathway provides a direct route through the transform that we hypothesize stabilizes optimization, while learnable scale,
curvature, and offset parameters control balance between pathways and the
curvature of the exponential pathway. These components create a curved parameter-space geometry that empirically improves speed of loss descent over standard linear parameterization. We also identify a useful mismatched initialization: raw weights are chosen so a symmetric version of the transform matches Xavier statistics, but training uses an asymmetric forward transform that leaves positive weights at full strength while making negative weights smaller in magnitude; in small-model ablations, this improves early optimization and may act as a form of
symmetry breaking. We train
transformers on OpenWebText over nine width$\times$depth configurations, \methodshort reaches matched validation loss in 1.32–1.49$\times$ fewer training steps, with the largest widths seeing the biggest gains.
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Artificial Intelligence · Cross-listed from cs.CL
Context-Aware Subtitle Translation on CPU: Deployment-Driven Training, Evaluation, and Latency Design
Tsz-To Wong and Shyang-En Weng
We study English-to-Traditional-Chinese (Taiwan) subtitle translation for a free browser extension, where the model must run on the viewer's CPU in about 500 ms per subtitle. We train two models built on the LMT-60 base models, Sotto-1.7B and the deployed Sotto-0.6B, with a task-specific vocabulary, continued pretraining, supervised fine-tuning, weight interpolation, logit
distillation and on-policy preference optimization. Because reference translations of subtitles are unreliable, we evaluate by pairwise comparison against one fixed baseline, Google Translate, with each pair judged in both orders by a panel of two LLM judges. On 1,820 held-out subtitles, Sotto-1.7B has a win rate of 0.576 against the baseline and wins head-to-head against four open translation models, and is slightly below a 27B model. We split latency into prefill and decoding: idle-time prefill, which prefills the known part of the
prompt while waiting for the next subtitle, cuts mean end-to-end latency by 31% on a CPU, and the task-specific vocabulary encodes the same Chinese text in 28% fewer
tokens. The deployed model stays within 500 ms for every subtitle in our latency test. The code is available at https://github.com/aiden1020/localsubs .
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Artificial Intelligence · Cross-listed from cs.LG
A Foundation Model for Multimodal Event Sequences in Financial Applications
Nikita Rusakov, Vladislav Meshkov, Konstantin Zorin et al. (+4 more)
Predictive modeling is a core component of modern financial services, where a wide range of tasks are traditionally addressed using separate models trained on manually engineered tabular features. This task-specific approach limits reuse and makes it difficult to fully exploit heterogeneous data sources such as transaction histories and digital interaction signals. In this paper, we present an approach based on pretraining a foundation
transformer model on
multimodal sequences of user events. Events from multiple data sources are unified into a single chronological sequence, enabling early fusion of heterogeneous modalities and learning of general-purpose representations via a next-event prediction objective. These representations are combined with existing engineered user features, on top of which lightweight neural models are trained for multiple downstream tasks. The proposed system outperforms traditional task-specific models while reducing development overhead. The approach was deployed in production at one of the biggest banks in Eastern Europe, resulting in measurable improvements in business metrics.
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All authors Nikita Rusakov, Vladislav Meshkov, Konstantin Zorin, Gleb Zaripov, Alexander Uglov, Alexey Vasilev, Anton Klenitskiy