Machine Learning
Sage: Formalization with Semantic Correction
Thomas Hirtz, Farzad Jafarrahmani, Abdelmouksit Sagueni et al. (+3 more)
While neural
theorem provers have achieved impressive milestones in formal mathematics, they largely operate on the assumption that faithful Lean 4 formal statements are already provided. Translating informal natural
language into a formal
language is a critical data bottleneck plagued by an "illusion of rigor": standard type-checkers accept statements that compile but drop hypotheses, introduce vacuous truths, or subtly alter mathematical bounds. To resolve this, we introduce Sage (Semantic
Agent-Guided Formalization Engine), an agentic framework that replaces monolithic translation with a four-stage decomposed generation pipeline coupled with a dual-signal semantic correction loop. By pairing Lean 4 compiler diagnostics with multi-dimensional semantic feedback, our correction loop enforces mathematical fidelity alongside syntactic validity. By explicitly accounting for the gap between open-ended queries and declarative formal targets, our pipeline prevents models from achieving high formalization rates by guessing unverified answers (exhibiting a 70.9% answer leakage rate in monolithic baselines). Consequently, Sage suppresses leakage to 2.7% while achieving 73.3% pass@4 joint compilation and semantic fidelity on the Omni-MATH without proofs (compared to 42.0% for a fine-tuned Goedel-Formalizer-V2 baseline). Finally, on IMO-Unformalized, a novel frontier of 175 unformalized International Mathematical Olympiad problems, Sage demonstrates effective zero-shot generalization with 87.4% pass@4 verified fidelity compared to just 19.4% for the baseline, winning over 79% of blind pairwise evaluations.
Read the full abstract ▾
All authors Thomas Hirtz, Farzad Jafarrahmani, Abdelmouksit Sagueni, Xiang Zhou, Wenping Deng, Liang Zhang
Computation and Language
Don't Repeat Yourself: Self-Supervised Fine-Tuning for Coverage
Eric Fithian, Kirill Skobelev and X.Y. Han
In verifiable domains such as math and coding, finding one correct solution among many attempts can matter more than the pass rate of each attempt. Post-training can concentrate large
language model outputs around a few modes, while increasing sampling temperature has limited effectiveness. We introduce Don't Repeat Yourself Supervised Fine-Tuning (DRY-SFT), a post-training method that increases output diversity and coverage: the probability of at least one correct solution among many attempts. DRY-SFT has two stages. First, for each problem, sequentially generate K solutions, showing the model all prior attempts and asking for a different solution. Second, fine-tune on each attempt independently, removing prior attempts from the context. The process uses no reward, verifier, or correctness filter. On HumanEval+, MBPP+, and DS-1000, DRY-SFT raises pass@100 by 10.8, 12.5, and 12.4 percentage points, respectively, at a small cost to pass@1. Structural diversity, measured by abstract syntax tree edit distance among passing solutions, rises significantly on all three
benchmarks. DRY-SFT also solves 244 of 600 problems that the base model did not solve in the same 200 attempts. Across nine open-weight models, lower structural diversity of the base model significantly predicts larger DRY-SFT gains, indicating that the method is especially effective on more mode-collapsed models.
Read the full abstract ▾
Computation and Language
Verification of PETSc with CIVL using LLM-generated ACSL contracts and deterministic driver generation
Hansol Suh, Jan Hückelheim and Stephen Siegel
Parallel numerical libraries such as PETSc are widely used in science and engineering applications where wrong results can have costly consequences. Despite this, numerical libraries are rarely formally verified. One of the challenges is the need for an expert to hand-write a specification and manually apply a verification tool, often requiring the development of a harness or driver, all of which can contain additional bugs that lead to false positives or false negatives during verification. With recent advancements in large
language models (LLMs), it is tempting to generate such drivers and reference models automatically, but one-shot generation based on a simple
prompt is brittle and leads to additional unverified code that needs to be audited. In this paper, we present an approach to use LLMs in a limited setting to generate a small, human-certifiable ACSL contract from the function's documentation, combined with a deterministic toolchain that supports a restricted ACSL profile and generates a driver that uses the CIVL verifier to check the implementation against the contract and, when available, an existing reference model. We demonstrate the pipeline end-to-end on three PETSc functions: MatAXPY (reference model already exists), MatAYPX (no reference model, so the certified contract is the sole oracle), and the non-compressing mode of MatFilter (no reference model, with more complex, conditional behavior). With this pipeline, we were able to discover a bug in PETSc's MatAYPX function that was previously undiscovered and had been present in the code since 1997.
Read the full abstract ▾
Machine Learning
3-D Emissions Mapping and Social Cost Estimation for US Domestic Aviation at West Coast Hubs
Hesam Shafiei Nia and Don MacKenzie
Existing aviation emissions inventories lack accurate trajectory data for high-resolution social cost and health impact assessment. This paper develops a 3-D emissions map by reconstructing flight trajectories for US west coast hubs to estimate regional environmental and near-airport health impacts. A physics-informed autoencoder (AE) is applied to ADS-B trajectory records for January 2025 covering US west coast hubs. The encoder combines a Convolutional Neural Network (CNN), a Bi-GRU, and a 3-D CNN with skip connection; the decoder is a Temporal Convolutional Network (TCN). It is benchmarked against a baseline-AE and cubic spline interpolation. Emissions are mapped via EUROCONTROL Base of Aircraft Data (BADA) performance tables and ICAO Engine Emissions Databank (EEDB) emission indices, with altitude corrections via Boeing Fuel Flow Method 2 (BFFM2). Social costs are quantified for all flight phases, with health impacts assessed for Landing and Takeoff cycles within 50 km of each hub. The proposed AE model outperforms both a TCN-AE and cubic spline interpolation across 5% to 50% missing rates. Monetizing the emissions inventory shows NOx produces a small net cooling effect in direct
climate forcing, while accounting for 99.7% of monetized air-quality and health cost despite being under 0.4% of CO2 by mass, making it the dominant health-cost driver. To our knowledge, this is among the first studies combining AE-based trajectory reconstruction with separate spatial-temporal feature encoding and altitude-based emissions modeling to produce a regional aviation emissions inventory for air quality,
climate impact and population exposure. The resulting emissions map and social cost estimates provide quantitative context for environmental impact assessment and near-airport health policy evaluation for US domestic aviation.
Read the full abstract ▾
Machine Learning
What does FFN compression change downstream? Same-state causal restoration in diffusion language models
Shaurya Omar
Diffusion language models (DLMs) enable flexible, parallel generation, but their iterative denoising remains computationally expensive, motivating increasingly aggressive compression. Existing compression objectives largely measure how well compressed computation approximates the original locally, but local error does not reveal which removed computations actually matter to the downstream denoising trajectory. We introduce Same-State Causal Restoration (SSR), which restores the original FFN on the exact current input reached by the compressed model and measures how the resulting trajectory changes. To our knowledge, this is the first direct measurement of the same-current-input closed-loop effect of removed FFN computation in DLM compression. Across LLaDA-8B-Instruct and Dream-v0-Instruct-7B, compressed-side state ranks this downstream effect substantially better than local NMSE at fixed denoising phase, while controlled interventions show that correction structure matters beyond magnitude. Using task-label-free calibration, SSR freezes a single restoration window for held-out inference. Under aggressive LLaDA compression, restoring only four transitions recovers 89.9% of the lost accuracy while retaining an estimated 36.8% whole-model MAC saving and outperforming an equal-budget local-error baseline. Dream further shows that restoring dense behavior and repairing the final task are distinct outcomes.
Read the full abstract ▾
Computation and Language
The Temporal Tug-of-War: Visualizing and Detecting RAG Conflicts in Diffusion Models via Trajectory Variance
Sravan Karthick T, Pranav Darshan, Pranav A et al. (+2 more)
Retrieval-Augmented Generation (RAG) introduces a specific failure mode in discrete
diffusion language models: when retrieved context contradicts parametric knowledge, the iterative denoising process becomes a visible battleground between competing knowledge sources. We identify temporal semantic divergence as an observable for detecting these conflicts and introduce the Trajectory Variance Score (TVS), a simple and interpretable measure of this divergence. TVS computes the mean pairwise cosine distance of answer
embeddings across independent
stochastic denoising trajectories, capturing the temporal tug of war between parametric and contextual attractors. Requiring as few as two parallel inference runs, TVS is computationally lightweight. Across four diverse
datasets (Synthetic, SciQ, PopQA, and CounterFact), a simple Logistic Regression classifier using TVS achieves $70.10\%$ accuracy and $0.7647$ AUROC on LLaDA. On Dream 7B, increasing the number of trajectories from two to five improves accuracy from $63.91\%$ to $69.62\%$. More complex sequential models provide only marginal improvements over the linear classifier. Evaluation across LLaDA and Dream 7B demonstrates that conflict-induced trajectory dynamics and their key properties transfer across distinct
diffusion architectures.
Read the full abstract ▾
All authors Sravan Karthick T, Pranav Darshan, Pranav A, Minal Moharir, Ivan P. Yamshchikov
Machine Learning
Autonomous Research Project Management as an Agent Skill: A Case Study in Exact Spectral Spatial Regression
Alexander Chen, Jeffrey Meng, Bram Hoex et al. (+1 more)
This work presents an end-to-end demonstration of autonomous machine learning research conducted by an
agent skill on consumer hardware. The demonstration evaluates an FFT-based Kernel Ridge Regression (KRR) solver for regular spatial grids using 2005 monthly NOAA Kaplan SST v2 anomaly fields on a $36 \times 72$ grid. This was autonomously executed by DeepSeek V4 Flash, orchestrated by our
agent skill suite within DeepSeek Harness (DSH). Experiments were executed on CPU-only hardware (Apple M2 Pro; 78.7 s solver time, 1.57 GB peak RSS). Long-horizon state was decoupled into a file-based epic- and issue-tracking substrate. Across 74 sub-
agent sessions, the
agent demonstrated closed-loop scientific resilience: routing two failed hypothesis review gates back to literature
retrieval, patching bootstrap indexing bugs, and executing with only four discrete human steering events. Finally, we reflect on autonomous research governance, arguing that scientific credibility requires inspectable state, falsifiable review gates, and transparent reporting of negative results, urging the machine learning community to favour
agent-accessible structured formats over static PDF manuscripts.
Read the full abstract ▾
All authors Alexander Chen, Jeffrey Meng, Bram Hoex, Tong Xie
Computer Vision
Towards Transparent Diagnostics: Investigating Architectural Trade-offs and Explainability in Malaria Detection
Suman Kunwar and Avishek Dangol
More than 80 countries have reported malaria cases with 610 thousand deaths and are projected to increase. Identifying malaria early and accurately helps save lives and effective way to diagnose malaria is through microscopic methods that are labor intensive and require experts with special equipment. Deep learning (DL) has shown promising results in medical diagnosis. Here, we explored various DL models: ResNet18, MobileNetV2, EfficientNet-B2, VGG19 and proposed model ResNet18+TTA (ResNet18 backbone with modified classification head and test time augmentation) for detecting malaria presence using blood smears taken from the NIH Malaria
dataset. Our experiment shows MobileNetV2 achieved 96.85 % accuracy with smallest model size (8.49 MB) and fastest inference (1.35 ms). The ResNet18+TTA model achieved 97.96 % accuracy, 0.996 AUC with longest inference time (13.32 ms). Larger architecture outputs a larger model size with moderate accuracy. Upon further
pruning, ResNet18+TTA model gained a slight improvement in accuracy and reduced inference time. GRAD-CAM, SHAP and LIME provide explainable AI (XAI) insights into model predictions, using explanation agreement and divergence to evaluate predictive reliability.
Read the full abstract ▾
Computer Vision
Devanagari Handwritten Character Recognition Using TrOCR: A Transformer-Based Model with Real-Time Web Deployment
Amrit Baskota, Samyam Budhathoki, Shubham Ghimire et al. (+3 more)
Devnagari is a one of the ancient
language of the Indian subcontinent consisting of 36 vowels, 14 consonants and 10 numerals. The accurate recognition of handwritten Devnagari characters is challenging due to high complexity of Devnagari scripts. This paper presents a method to fine tune the pre trained TrOCR model to accurately recognize Devanagari handwritten characters. The Methodology consists of a preprocessing mechanism where input images are standardized into RGB format, tokenized in batches and integrated with Hugging Face
Dataset. The pre-trained microsoft/trocr-base-handwritten model is fine-tuned over a
dataset of nearly 5000 character images that are uniformly partitioned in the ratio 8:1:1 for training, evaluation and testing. Further optimization is done is the training process through mixed precision training, gradient checkpointing, and early stopping mechanism. The model achieves a character error rate (CER) of 3.95% and a character-level accuracy of 96.05%, outperforming the previous CNN based models. A scalable web application is developed using Vite.js, Golang, and FastAPI which practically deploys the OCR model and serves character recognition task with a latency less than 5 seconds per request. This study demonstrates the use of TrOCR model to build a scalable handwritten Devnagari character recognition system and also a foundation to future research on Devnagari Script Recognition using
Transformers.
Read the full abstract ▾
All authors Amrit Baskota, Samyam Budhathoki, Shubham Ghimire, Abiskar Ghimire, Sarwesh Phuyal, Baskaran P
Machine Learning
Does Joint-Embedding Predictive Architecture Pretraining Help Time Series Forecasting?
Yutong Feng, Bowen Liao, See Kiong Ng et al. (+1 more)
Joint-
embedding predictive architectures (JEPA) have emerged as a promising self-supervised pretraining paradigm for time series, learning representations by predicting target
embeddings in latent space rather than reconstructing raw signals. Yet evidence on their benefits remains mixed, and most studies test only a single backbone or a narrow set of architectures, leaving unclear whether JEPA pretraining is a reliable improvement or one that depends heavily on the downstream model. We address this gap through a large scale evaluation of one JEPA instantiation across nine backbones and eleven
benchmarks spanning temporal and spatio-temporal
forecasting, the most extensive cross architecture assessment of JEPA for time series to date. We find that the benefit of this instantiation varies sharply across backbones, producing consistent gains for some architectures and consistent degradation for others, even on the same
dataset. This pattern holds across both task families, indicating the variability is a general property of this instantiation rather than a
dataset specific artifact worth accounting for when choosing a backbone in practice.
Read the full abstract ▾
All authors Yutong Feng, Bowen Liao, See Kiong Ng, Yuxuan Liang
Computer Vision
Toward AI-Assisted Poultry Coccidiosis Diagnosis: Evaluating Gemini and BiomedParse on Eimeria Microscopy Images
Ali Alsalama, Ahmed Kubba and Manar Abu Talib
Coccidiosis caused by Eimeria parasites is a major economic burden in poultry production, and effective control depends on accurate species-level diagnosis. This study evaluates whether a general-purpose
multimodal large
language model can support such diagnosis. Google Gemini was assessed on 4,225 mi- croscopy images covering the seven fowl-infecting Eimeria species under two prompting conditions, one without candidate labels and one with a predefined class list, and was further tested for pathology-report generation, while BiomedParse was examined for parasite
segmentation. Without candidate labels, the model produced broad and taxonomically inconsistent outputs. With candidate labels, overall accuracy reached only 14.9%, with a strong bias toward E. tenella at 74% and no correct classifications for E. acervulina, E. mitis and E. praecox. Generated treatment reports were coherent but unverified, and
segmentation was only partial. Current
multimodal models are therefore not yet reliable for standalone Eimeria diagnosis without domain-specific fine- tuning and expert validation.
Read the full abstract ▾
Machine Learning
When Keywords Drop but Classifiers Hold: Soft Refusals under KV Cache Compression
Kang Chen, Xiuze Zhou, Hong Chen et al. (+1 more)
KV cache compression is widely used for long context LLM inference under
memory constraints, while deployed systems typically score refusals after generation with keyword filters or learned classifiers. Such monitors are intended to indicate whether a model declined a harmful request under the serving regime actually used. However, it remains unclear whether matched compression that preserves task accuracy also preserves agreement between lightweight lexical monitors and stronger refusal classifiers. We study this with a paired protocol on n=200 harmful
prompts with a long filler context: each
prompt is answered once under full retention and once under matched eviction after a shared prefill, and the same replies are scored by keyword heuristics, the HarmBench Llama-2-13B classifier, an auxiliary LLM judge, and humans on disagreements. On Qwen2.5-3B, keyword refusal falls from 98.0% to 80.5% (McNemar p~1e-8) while classifier refusal stays near ceiling (99.0%-99.5%) and MMLU accuracy is unchanged (50.0%); human labels predominantly follow the classifier, consistent with soft refusals. The gap is not universal and weakens under short fillers and paired SnapKV, so safety
auditing under compression should rely on several judges matched to the serving context rather than on keyword rates alone.
Read the full abstract ▾
All authors Kang Chen, Xiuze Zhou, Hong Chen, Yuanguo Lin
Computer Vision
A Comparative Transfer-Learning Study of CNN Backbones for Partial Face Recognition on the SoF Dataset
Ahmed Kubba, Ali Alsalama, Abdelrahman Abdalla et al. (+2 more)
Face recognition is widely deployed in surveillance, access control, and forensic workflows, yet accuracy degrades sharply once the face is occluded by accessories, foreground objects, or the frame edge. Because most faces in the wild are partial, robust partial face recognition (PFR) remains open. This paper compares three pretrained convolutional backbones, ResNet-50, VGG-16, and FaceNet, fine-tuned for PFR by transfer learning under identical preprocessing, splitting, and optimization protocols on the Specs-on-Faces (SoF)
dataset. All three arms use a common 160x160 input and a frozen backbone with a trainable head under a fixed epoch budget and no per-backbone hyperparameter search. The FaceNet configuration, denoted PFN (Partial FaceNet), substantially outperforms the other two, reaching 97.4% test accuracy with macro-averaged 87.04% precision, 84.61% recall, and 84.17% F1 over the 112 identity classes, the highest accuracy and recall reported on SoF.
Read the full abstract ▾
All authors Ahmed Kubba, Ali Alsalama, Abdelrahman Abdalla, Qassim Nasir, Manar Abu Talib
Computation and Language
An Evaluation of AI-Supported Evidence-Based Learning for Public Speaking Skill Development
Sashini Hettiarachchi, Shahbaz Siddeeq, Mika Saari et al. (+1 more)
Public speaking is an essential skill in academic and professional contexts, but it often causes anxiety. Although several AI-based
speech coaching tools exist, they typically provide generic feedback and lack model
speeches for learning. This study addresses these gaps by developing a system that incorporates
speech context into evaluation criteria, enabling more tailored feedback. It also generates improved model
speeches in both text and audio formats to support example-based learning. The system was evaluated in a pilot study with seven undergraduate students over one to three weeks. Participants used the system repeatedly and practiced the same
speech at least three times. Results showed improvements in public speaking performance for all participants. Filler word usage decreased, and anxiety levels dropped by 15.3% to 34.4%. Participants found the context-aware feedback and revised
speeches useful and confidence-building. These findings suggest that context-aware feedback and model
speeches can enhance public speaking skills and reduce anxiety. Future studies could explore
video-based analysis of physical behavior during
speeches.
Read the full abstract ▾
All authors Sashini Hettiarachchi, Shahbaz Siddeeq, Mika Saari, Pekka Abrahamsson
Machine Learning
Active Causal Discovery Benchmark: Evaluating LLM Agents Under Budgeted Interventions
Sagar Deb, Devam Shah and Ashwanth Krishnan
We introduce the Active Causal Discovery
Benchmark (ACDB), an SCM-grounded environment for evaluating whether LLM
agents recover causal graph structure from observations and budget-constrained hard interventions. ACDB pairs a linear-Gaussian world generator with a fixed observe-intervene-submit API and a three-layer scoring contract that separates skeleton recovery, DAG recovery, and intervention efficiency. On the current six-level ladder, PC with a greedy active orientation heuristic is the strongest non-oracle method (directed F1 42.7%, SHD 4.79), ahead of Claude Sonnet 4.6 raw active (31.7%, 7.25) and GPT-5.4 raw active (22.9%, 9.27). The most informative diagnostic is the precision-recall decomposition: PC under-commits with high precision, LLMs over-commit with lower precision, and statistical-tool access often increases abstention rather than useful intervention. A structure-blind random DAG baseline reaches 23.6% directed F1 on this dense v0 ladder; a density probe lowers this floor to 16.9%, motivating the v1 calibration pass. The current results should therefore be read as a
benchmark audit and calibration report, not as evidence that current LLMs solve active causal discovery.
Read the full abstract ▾
Multiagent Systems · Cross-listed from eess.AS
OneVoice: An Intermediate Representation for Agentic Speech Pipelines
Vipul Charugundla, Dancheng Liu and Jinjun Xiong
Agentic
speech systems must exchange more than text, including speaker identity, timing, phonetic information, and behavioral annotations. Yet these signals are often produced in incompatible tool-specific formats, making
agent-to-
agent handoff fragile. We present OneVoice, a lightweight, JSON-native intermediate representation that provides a shared semantic structure for
speech pipelines. OneVoice organizes heterogeneous
speech evidence into validated session records with stable identifiers, layered transcripts, explicit timing relationships, and provenance information. We evaluate OneVoice in two complementary multi-
agent workflows covering event aggregation and acoustic-phonetic temporal linking across three
language models. Compared with implicit
agent-defined handoffs, OneVoice substantially improves aggregation reliability and the preservation of temporal relationships, demonstrating the value of an explicit
speech-specific representation for
agent communication.
Read the full abstract ▾
Computation and Language
A Benchmark Framework for Screening Automation in Systematic Reviews
Gauransh Kumar, Luciano Marchezan, Guillaume Genois et al. (+2 more)
Systematic reviews (SR) are essential for evidence-based research, but their screening phase is highly time-consuming and labor-intensive. Large
language models (LLMs) offer a promising opportunity to reduce this workload by assisting with article relevance classification. However, existing evaluation approaches often rely on traditional metrics that may be misleading for highly imbalanced SR screening
datasets. This paper presents a
benchmark dataset of $45\,064$ labeled entries for evaluating LLM performance in SR screening across 32 curated secondary studies. It proposes an evaluation framework that accounts for class imbalance, i.e., the natural prevalence of excluded articles relative to included articles in SRs. It also introduces PromptSR, a tool designed to support
prompt experimentation, experiment management, and result analysis for LLM-based screening. We also present a use case demonstrating the application of SRBench and PromptSR.
Read the full abstract ▾
All authors Gauransh Kumar, Luciano Marchezan, Guillaume Genois, Kévin Delcourt, Eugene Syriani
Computation and Language
Bootstrapping Conversational Recommendation Agents At Spotify: Synthetic Data Generation and Self-Improvement Loops
Enrico Palumbo, Alexandre Tamborrino, Victor Ode et al. (+15 more)
Conversational recommendation
agents are a new paradigm for content discovery, enabling users to express complex intents through natural
language (e.g., "recommend Italian indie artists I haven't heard before"). A central challenge in building such
agents is optimizing
agent planning – deciding how to select, sequence, and invoke tools – particularly in cold-start settings where real user interactions are not yet available. We introduce a pipeline for multi-turn synthetic data generation and a self-improvement loop to address this challenge. The synthetic data pipeline transforms single-turn
prompts into realistic multi-turn conversations, enabling systematic evaluation before launch. The self-improvement loop combines variance-based contrastive optimization with iterative refinement through a coding
agent, automatically identifying and fixing planning and tool-use errors. Our approach improves quality by +8% on top of a highly optimized manual
prompt. The system has been productionized and significantly accelerated iteration cycles for the launch of a conversational recommendation
agent at Spotify. Online A/B tests demonstrate its effectiveness, with +14% user listening, +5% increase in weekly active users, and a 5% reduction in skip rate compared to a prior experience supporting only session refinement. This work provides a practical framework for accelerating the development of conversational recommendation
agents in industry.
Read the full abstract ▾
All authors Enrico Palumbo, Alexandre Tamborrino, Victor Ode, Ben Lacker, Adrià Casas Escoda, Jeremy Hopple, Marcus Better, James Leoni, Hugo Galvão, Hugues Bouchard, Mounia Lalmas, José Luis Redondo García, Abenezer Abebe, Ann Clifton, Anton Blomberg, Henrik Lindström, Dani Doro, Christine Doig Cardet
Artificial Intelligence
TW3Cast: A Frozen Router of Lightly Fine-Tuned Foundation Models for Time-Series Forecasting on GIFT-Eval, Selected Entirely on the Training Split
Nathan Thierry and Andre-Louis Rochet
TW3Cast is a time-series
forecasting system that reaches position 3 of 130 entries on the GIFT-Eval
benchmark by mean MASE rank, as of 2026-09-14. The two entries above it belong to the leaderboard's agentic category, multi-step systems that use
agents or
language models to reason about, generate or select forecasts. TW3Cast runs no
agent and no
language model. Its selection is a table computed once on the training split and then frozen, and its experts are public foundation models lightly fine-tuned on those training splits. For each of the 97
dataset, frequency and horizon configurations, the table serves one of four modes: a specialist, which is a LoRA or full fine-tune of Chronos-2, TiRex or Toto whose training data was cleaned and enriched by explicit rules; a
quantile blend that contains a specialist; a blend of base models; or a selection tournament played on a backtest carved from the training split. Every decision in the table was taken on that backtest. A specialist is admitted the moment it beats the tournament there, so a candidate costs a few megabytes and minutes of GPU time, and a failed candidate changes nothing. Three guarded mechanisms protect the selection from its own biases: a dual accuracy and calibration criterion, an asymmetric margin against candidates that saw the series during training, and conservative per-window gates. The selection rules themselves were chosen inside a temporal meta-backtest. The best base model served alone reaches a mean MASE rank of 33.8, the tournament served on every configuration reaches 38.0, and the full router reaches 19.4. The routing table, the expert index, the pinned base-model revisions, the submitted score file and the dated snapshot of the public scores are released, and every leaderboard number in this paper regenerates from them by one script.
Read the full abstract ▾
Computer Vision · Cross-listed from eess.IV
Semantic-Guided Fusion Network for Multi-Source Remote Sensing Image Classification
Yuwei Zhao, Chuanzheng Gong, Baogui Huan et al. (+3 more)
Multi-source remote sensing image classification has attracted increasing
attention due to the complementary spectral, structural, and
geometric information. However, existing methods still suffer from two limitations: insufficient semantic contextual modeling and unreliable feature fusion caused by slight spatial misalignment. To address these issues, we propose a Semantic-Guided Fusion Network (SGFNet) for multi-source remote sensing image classification. Specifically, the Semantic Mixing Convolution Block (SMCB) is designed to dynamically generate semantic-aware convolution kernels according to contextual relationships among feature representations. In addition, the Frequency Modulated Fusion Block (FMFB) is introduced to perform cross-modal interaction in the frequency domain, which effectively alleviates the influence of slight spatial misalignment and improves complementary information fusion. Extensive experiments conducted on the Augsburg and Houston 2018
datasets demonstrate that the proposed SGFNet consistently outperforms several state-of-the-art methods. The codes are publicly available at https://github.com/oucailab/SGFNet .
Read the full abstract ▾
All authors Yuwei Zhao, Chuanzheng Gong, Baogui Huan, Feng Gao, Junyu Dong, Qian Du
Machine Learning
The Drift Contract: Spectral Updates for Depth-Robust Local Learning
Fabien Polly
Local learning trains each layer with its own auxiliary loss and no global backward pass, which makes layer updates structurally parallel. Two problems have kept it marginal: accuracy degrades as depth grows, and hyperparameters are fragile. We apply
Muon-style spectral update geometry (momentum orthogonalization with spectral step scaling) to per-layer local updates, an intersection not previously studied. On CIFAR-10 MLP
benchmarks with local linear heads, a single step-size setting is the best value in our tested grids from width 128 to 2048 and from depth 12 to 48, while local Adam requires re-tuning along both axes and still collapses at depth 48 (31.3 percent re-tuned per depth, 19 percent with its depth-12 setting transferred, vs 42.7 percent for the spectral update at its unchanged setting). At five seeds and width 512 the spectral update leads local Adam by a clear margin (48.9 +/- 0.5 vs 46.6 +/- 0.3). Prospectively specified controls attribute the transfer and most of the depth robustness to the spectral geometry itself rather than to any step-size rule on top of it. We additionally formulate the step size as a drift contract, lr = epsilon / RMS(input), which bounds each layer's weight-induced pre-activation change per step, conditioned on its current input. The contract yields a small gain over the best fixed learning rate where that baseline is measured, makes the step size interpretable, and provides a per-layer, input-conditioned drift bound that standard optimizers do not offer. We report one negative result: with RMSNorm and weight decay in the trunk, the stability benefit of spectral updates accrues to global rather than local training, so the local advantage concentrates precisely where normalization is absent.
Read the full abstract ▾
Computer Vision
AgroBench: A Reproducible Multimodal Benchmark for Weakly Supervised Crop Yield Learning from County Statistics and Pixel Observations
Udaiveer Singh, Rajiv Ranjan, Shashank Tamaskar et al. (+1 more)
Reliable agricultural yield statistics are typically reported at coarse administrative scales, whereas modern geospatial machine learning methods require spatially explicit, pixel level supervision. This mismatch has limited the development of large-scale
benchmarks for crop yield learning using
multimodal Earth observation data. A reproducible
benchmark, AgroBench, is presented for transforming publicly available U.S. county level crop yield statistics into weakly supervised pixel-level crop time series. Each crop pixel time series is paired with a county-level yield value as a weak supervisory signal rather than a directly measured pixel-level yield label. Our geospatial data generation pipeline integrates USDA crop yield statistics with crop-specific land cover masks, Sentinel 2 multispectral imagery, Sentinel-1 synthetic aperture radar observations, climatic variables, and terrain information to produce temporally aligned
multimodal sequences describing individual crop pixels throughout the growing season. The resulting
benchmark contains over 13 million observations from 788,654 unique crop pixels spanning 5,107 county year combinations across eight growing seasons (2017 to 2024) for five major U.S. crops. To facilitate standardized evaluation, we establish a crop yield prediction
benchmark using a Leave-One-Year-Out evaluation protocol and provide baseline results using representative machine learning models. By releasing the complete data generation pipeline,
benchmark dataset, and evaluation protocol, AgroBench provides a reproducible foundation for future research in weakly supervised learning,
multimodal remote sensing, spatiotemporal modeling, and geospatial foundation models for agriculture.
Read the full abstract ▾
All authors Udaiveer Singh, Rajiv Ranjan, Shashank Tamaskar, Dharmendra Saraswat
Computer Vision
SPARC: SuperPixel-Aware Region Contrastive Learning for Self-Supervised Dense Prediction
David Szczecina, Yuanpei Xiang, Jitao Hu et al. (+4 more)
Self-supervised learning (SSL) has become an effective approach for learning visual representations without manual annotations. Among SSL approaches, contrastive learning has been widely used for visual representation learning. However, existing contrastive SSL methods have focused primarily on image-level or pixel-level representation learning, while region-level representation learning remains less explored. We propose SPARC, a region-level contrastive learning framework that leverages superpixels to establish explicit correspondence between augmented image views. SPARC introduces a region contrastive branch that performs superpixel-based feature pooling and optimizes a region-level contrastive objective jointly with a global image-level objective. Under identical settings, SPARC consistently outperforms previous methods such as MoCo-v2 and DenseCL, achieving improvements of up to +9.79 mIoU for semantic
segmentation and +4.88 AP for object detection. Ablation studies further demonstrate that region-level objectives produce the strongest performance. Thus, region-level contrastive learning is an effective approach for improving self-supervised visual pretraining for dense prediction tasks. Code repository can be accessed at https://github.com/xRIPEIx/SPARC.
Read the full abstract ▾
All authors David Szczecina, Yuanpei Xiang, Jitao Hu, David Clausi, Yuhao Chen, Jason Deglint, Paul Fieguth
Computation and Language
Understanding Reliability in LLM-based Human Behavior Simulation
Pei Wang, Lei Wang, Yuanzi Li et al. (+1 more)
Large
language models (LLMs) are increasingly used to simulate human survey responses and behavioral reactions, yet unreliable simulations can mislead social science conclusions. However, existing evaluations focus on end-to-end scores, leaving it unclear how different aspects of the simulation process interact to determine reliability. We propose ReliMap, which decomposes LLM-based human behavior simulation into three structured layers and evaluates reliability at both the individual level (R1) and population level (R2) across three configuration dimensions: model capacity, profile completeness, and population coverage. Through experiments across four simulation tasks and eleven LLMs, we find that all models exhibit substantial distributional bias without profile conditioning. Profile conditioning reduces this bias with diminishing returns. Larger models benefit more, and attribute informativeness matters more than quantity. Critically, R1 gains do not reliably transfer to R2–individual and population-level reliability can move in opposite directions. At the population layer, increasing coverage reduces variance but not systematic bias, with R2 stabilizing at around 50-100 individuals. These findings highlight that reliable simulation cannot be achieved by optimizing any single layer in isolation, but requires coordinated improvement across all three.
Read the full abstract ▾
All authors Pei Wang, Lei Wang, Yuanzi Li, Xu Chen
Hardware Architecture
Design-Space Exploration of Post-Quantum Cryptography Acceleration on an Open-Source RISC-V GPGPU
Hossam Hassan, Marc Xander Makkes, Chae Eun Lee et al. (+1 more)
Post-
quantum cryptography (PQC) acceleration has been studied for CPUs and FPGAs, yet GPGPUs - dominating high-throughput computing - remain unexplored for PQC hardware integration as a functional unit. We present what is, to our knowledge, the first such integration: the
Vortex Post-
Quantum Crypto Unit (PCU) on the open-source
Vortex RISC-V GPGPU. The PCU implements a 256-point NTT over Kyber (q=3329) and Dilithium (q=8380417)
moduli in 134 logic
cells (73 LUT4 + 61 CCU2C) - the smallest reported NTT accelerator by logic-
cell count, 2.8x smaller than the next smallest design. It achieves 228.78 MHz on
Lattice ECP5 and 299.8 MHz place-and-route on Xilinx Artix-7, and passes 253 functional tests across 7 suites. On
Vortex SimX, it achieves 42.6x acceleration over GPU ALU execution, projecting to roughly 36x speedup on RTL. Synthesized to ASAP7 7nm, the NTT datapath occupies 96.53 um^2 (post-route) at 702 MHz. This work establishes a foundation for integrating PQC hardware acceleration into general-purpose parallel computing.
Read the full abstract ▾
All authors Hossam Hassan, Marc Xander Makkes, Chae Eun Lee, Hyungmin Yoon
Machine Learning · Cross-listed from cond-mat.dis-nn
Gradient-estimator design overcomes trainability barriers in neural-network-based variational optimization
Yi-Ran Xue, Rui Wang, Baigeng Wang et al. (+1 more)
Neural networks provide expressive representations for scientific computing. However, even sufficiently expressive networks can suffer training failure in weak-gradient regimes, limiting their practical use in
quantum many-body physics and ab initio
quantum chemistry. Here we derive an unbiased direct gradient
estimator and introduce the adaptive minimum-variance phase (AMVP)
estimator for neural-network variational optimization. By improving the signal-to-noise ratio of weak gradients, these methods enable reliable scientific calculations where training previously failed, while substantially reducing computational cost. The framework enables compact networks to outperform larger and fine-tuned default standard-
estimator models with over an order of magnitude less GPU time on correlated flux models, and ultimately exceed the density
matrix renormalization group (DMRG) accuracy. It further achieves chemical accuracy in N$_2$ bond breaking and, for the first time, in heavy-element I$_2$ with explicit
spin-orbit coupling. These results demonstrate that gradient-
estimator design expands the capabilities of neural-network variational methods for accurate scientific computing.
Read the full abstract ▾
All authors Yi-Ran Xue, Rui Wang, Baigeng Wang, Chenan Wei
Systems and Control
Learning and Control Beyond Linearity: Towards a Non-asymptotic Theory for Bilinear Systems
Yahya Sattar, Yassir Jedra, Robin Strässer et al. (+3 more)
This tutorial provides a unified view of the emerging area of bilinear learning and control. Using linear systems as a
benchmark, it explains what fundamentally changes in the bilinear settings, how recent theory addresses finite-sample learning and control, and how these ideas connect to broader themes in nonlinear control, representation learning, and data-driven decision making. For learning, we emphasize tools that are particularly useful in the bilinear settings, such as one-sided Bernstein's inequality for dependent and heavy-tailed
covariates, blocking arguments, and martingale concentration for input-dependent noise. We then apply these tools to obtain finite-sample learning guarantees for fully observed bilinear systems, partially observed bilinear systems, and linear systems with bilinear observations. For control, we discuss quadratic control from bilinear observations, where the classical separation principle fails, and review tractable approaches based on belief-space receding horizon control. We also cover stabilization of bilinear dynamics under state feedback using semi-definite programming, LMI relaxations, sum-of-squares methods, and Koopman-based lifting. We conclude by discussing connections to reinforcement learning and machine learning, and some open problems in combined learning and control of bilinear systems.
Read the full abstract ▾
All authors Yahya Sattar, Yassir Jedra, Robin Strässer, Frank Allgöwer, Maryam Fazel, Sarah Dean
Hardware Architecture
KerColle: Unlocking Fine-Grained GPU Concurrency in Vision-Language-Action Models
Anna Li, Christina Giannoula and Nandita Vijaykumar
Vision-
Language-Action (VLA) models have emerged as foundational models for next-generation robotics. High VLA inference throughput is critical for meeting the control-rate requirements of
robots. VLA models comprise two phases, a
vision-
language model (VLM) and an action head, that can be decoupled and executed asynchronously and concurrently across independent
robot requests. Through a detailed characterization of four state-of-the-art VLAs, we observe that GPUs are severely underutilized in VLA inference as the Cooperative Thread Array (CTA) scheduler of GPU is unable to fully overlap the two independent phases of VLA execution. We identify that this inefficiency is caused by head-of-line blocking in the hardware thread-block dispatcher. We demonstrate that prior scheduling frameworks do not address the challenges posed by VLA concurrency. First, the independent phases across different
robot requests each comprise numerous kernels, and at any given time, there are different combinations of kernels that are executed in parallel. This makes static or ahead-of-time scheduling policies largely ineffective. Second, many of the action-head operators are short-running kernels and there are numerous such kernels. This leaves no headroom for online profiling or preemption-based mechanisms. To address these challenges, we present KerColle, a lightweight GPU scheduling framework that leverages online Streaming Multiprocessor (SM) utilization and individual kernel resource requirements to intelligently and dynamically co-schedule kernels to efficiently overlap the two phases of execution by (1) mitigating head-of-line blocking, and (2) co-scheduling kernels with complementary resource requirements. We demonstrate in simulation, across two GPU architectures, for 4 state-of-the-art VLA models, that KerColle delivers throughput gains of up to $28\%$.
Read the full abstract ▾
Computer Vision
Dimensionality reduction for AI based hyperspectral image classification based on XAI
Vladimir Zeljković, Branka Stojanović, Harald Ganster et al. (+1 more)
This research addresses the challenge of limited material recycling in wood recycling processes by leveraging artificial intelligence (AI)-based dimensionality reduction. Our study explores the application of convolutional neural networks (CNNs) in multi-channel hyperspectral imaging (HSI), extending beyond RGB channels to over 200 spectral channels. Dimensionality reduction within this context involves streamlining the feature space for AI system training and inference. Focusing on explainable AI (XAI) methods, this paper contributes to a broader research initiative, presenting a solution framework that enhances the sustainability and efficiency of wood recycling processes.
Read the full abstract ▾
All authors Vladimir Zeljković, Branka Stojanović, Harald Ganster, Aleksandar Nešković
Robotics
AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation
Jiadi You, Qize Yu, Yue Chen et al. (+11 more)
Generalizable
robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act. Action-labeled
robot videos directly supervise control but are costly and limited in diversity, whereas
egocentric human
videos capture diverse interactions but lack
robot actions and differ in embodiment and appearance. We introduce AffordanceWAM, an affordance-aware generative World Action Model that represents object-centric spatiotemporal affordance through Scalar Affordance and Affordance Heatmap, within the generated future World. This representation grounds visual prediction in task-relevantant objects and interaction regions for action generation, and provides shared interaction targets across human and
robot videos. Built on a pretrained
video diffusion Transformer, AffordanceWAM uses separately parameterized World and Action Experts, coupled through Masked Joint Self-
Attention, to jointly predict future RGB observations, Scalar Affordance fields, Affordance Heatmaps, and continuous
robot actions under a unified flow-matching objective. Human
videos supervise all three future-World streams, whereas
robot trajectories additionally provide action supervision, enabling transfer without human action labels or retargeting. Experiments on RoboCasa, CALVIN ABC$\rightarrow$D, and real-world manipulation demonstrate consistent gains over RGB-only and
robot-data-only baselines. Under fixed
robot supervision, RoboCasa performance improves monotonically as affordance-annotated human
video scales. These results support affordance as an effective interface for both
vision-
language-action learning and human-to-
robot transfer.
Read the full abstract ▾
All authors Jiadi You, Qize Yu, Yue Chen, Minghong Cai, Zhide Zhong, Yuran Wang, Bowen Ping, Jiaqi Liang, Zhenhao Shen, Haodong Yan, Yinchuan Li, Ruihai Wu, Xiaojuan Qi, Yingcong Chen