Machine Learning

81 papers

Every entry carries a structured overview and the reproduction evidence collected so far. A paper is listed here when it carries the category as its primary field or as a secondary classification. Both are shown on the paper page.

Latest in Machine Learning

Showing 41–60 of 81

Apr 22, 2026Formal Sciences260423.0047

DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories

Neemesh Yadav, Palakorn Achananuparp, Jing Jiang, Ee-Peng Lim

DialToM is a benchmark that tests whether LLMs truly understand Theory of Mind by evaluating both mental state prediction and the ability to forecast dialogue trajectories from those states. The study reveals that most LLMs can identify mental states but fail to use this understanding to predict realistic social interactions.

Apr 22, 2026Formal Sciences260423.0048

Unlocking the Forecasting Economy: A Suite of Datasets for the Full Lifecycle of Prediction Market: [Experiments \& Analysis]

Huaiyu Jia, Luofeng Zhou, Wentao Zhang, Lin William Cong +2 more

This paper introduces the first comprehensive dataset suite covering the complete lifecycle of decentralized prediction markets on Polymarket, integrating fragmented on-chain and off-chain data from October 2020 to March 2026. The unified dataset enables systematic analysis of market dynamics and supports downstream applications like outcome calibration and economic expectation reconstruction.

Apr 22, 2026Formal Sciences260423.0049

Scalable AI Inference: Performance Analysis and Optimization of AI Model Serving

Hung Cuong Pham, Fatih Gedikli

This paper evaluates and optimizes a BentoML-based AI inference system for scalable model serving, testing it under realistic workloads and applying multi-level optimizations to improve latency and throughput. The work bridges the gap between AI model research and practical deployment by providing concrete performance analysis and optimization strategies.

Apr 22, 2026Formal Sciences260423.0050

Calibrating conditional risk

Andrey Vasilyev, Yikai Wang, Xiaocheng Li, Guanting Chen

This paper introduces conditional risk calibration, the problem of estimating a model's expected loss conditional on input features, and shows it is equivalent to regression while being distinct from probability calibration. The work provides theoretical analysis and demonstrates practical applications in learning to defer frameworks.

Apr 20, 2026Formal Sciences260421.0038

MathNet: a Global Multimodal Benchmark for Mathematical Reasoning and Retrieval

Shaden Alshammari, Kevin Wen, Abrar Zainal, Mark Hamilton +4 more

MathNet is a large-scale, multilingual dataset of 30,676 Olympiad-level math problems from 47 countries spanning two decades, designed to benchmark both mathematical reasoning in generative models and mathematical retrieval in embedding systems. The benchmark reveals that even state-of-the-art models struggle with these problems, with top models achieving only 78.4% accuracy, and that retrieval quality significantly impacts retrieval-augmented generation performance.

Apr 20, 2026Formal Sciences260421.0039

Sessa: Selective State Space Attention

Liubomyr Horbatko

Sessa is a new sequence model that places attention inside a recurrent feedback path, enabling power-law memory decay instead of exponential or 1/length dilution. This architecture achieves superior long-context performance while remaining competitive on short sequences.

Apr 20, 2026Formal Sciences260421.0040

Bounded Ratio Reinforcement Learning

Yunke Ao, Le Chen, Bruce D. Lee, Assefa S. Wahd +4 more

This paper introduces Bounded Ratio Reinforcement Learning (BRRL), a theoretical framework that bridges the gap between trust region methods and PPO's clipped objective, leading to a new algorithm called Bounded Policy Optimization (BPO) that provides monotonic improvement guarantees while matching or exceeding PPO's performance. The framework also extends to Group-relative BPO (GBPO) for large language model fine-tuning.

Apr 20, 2026Formal Sciences260421.0041

When Can LLMs Learn to Reason with Weak Supervision?

Salman Rahman, Jingyan Shen, Anna Mordvina, Hamid Palangi +2 more

This paper investigates when reinforcement learning with verifiable rewards (RLVR) enables large language models to generalize under weak supervision (scarce data, noisy rewards, or self-supervised signals). The key finding is that models generalize when they exhibit prolonged pre-saturation training dynamics, which is predicted by reasoning faithfulness—the degree to which intermediate reasoning steps logically support final answers.

Apr 20, 2026Formal Sciences260421.0042

Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale

A. Sophia Koepke, Daniil Zverev, Shiry Ginosar, Alexei A. Efros

This paper challenges the Platonic Representation Hypothesis by showing that apparent alignment between vision and language models is an artifact of small-scale evaluation. When tested at scale with millions of samples and realistic many-to-many settings, cross-modal alignment degrades substantially, suggesting different modalities learn different representations of reality.

Apr 20, 2026Formal Sciences260421.0044

Latent Phase-Shift Rollback: Inference-Time Error Correction via Residual Stream Monitoring and KV-Cache Steering

Manan Gupta, Dhruv Kumar

LPSR is an inference-time error correction method that monitors internal model activations to detect reasoning mistakes, then rolls back generation and steers the model using cached corrections—no training required. It improves an 8B model's math accuracy from 28.8% to 44.0%, outperforming prompted self-correction and even larger 70B models.

Apr 20, 2026Formal Sciences260421.0045

Benchmarking System Dynamics AI Assistants: Cloud Versus Local LLMs on CLD Extraction and Discussion

Terry Leitch

This paper benchmarks cloud and local large language models on two System Dynamics tasks: extracting causal loop diagrams and providing interactive coaching. The best local models match mid-tier cloud performance on diagram extraction (77%) but struggle with long-context error-fixing tasks, with backend implementation choices mattering more than quantization levels.

Apr 20, 2026Formal Sciences260421.0046

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling

Alireza Dadgarnia, Soroush Tabesh, Mahdi Nikdan, Michael Helcig +2 more

GSQ is a new scalar quantization method for large language models that uses Gumbel-Softmax relaxation to jointly optimize grid assignments and scales, achieving accuracy comparable to complex vector quantization methods while remaining compatible with existing inference kernels. It successfully quantizes models to 2-3 bits per parameter and scales to trillion-parameter mixture-of-experts models.

Apr 20, 2026Formal Sciences260421.0047

A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work

Ran Ben-Basat, Yaniv Ben-Itzhak, Gal Mendelson, Michael Mitzenmacher +2 more

This note demonstrates that TurboQuant, a recent quantization method, is a suboptimal special case of the earlier EDEN/DRIVE quantization schemes. EDEN consistently outperforms TurboQuant across all tested scenarios, often by more than one bit of precision.

Apr 20, 2026Formal Sciences260421.0048

Physics-Informed Neural Networks for Biological $2\mathrm{D}{+}t$ Reaction-Diffusion Systems

William Lavery, Jodie A. Cochrane, Christian Olesen, Dagim S. Tadele +2 more

This paper extends biologically-informed neural networks (BINNs) from 1D to 2D spatial domains for learning reaction-diffusion equations from data, combining neural network training with symbolic regression to discover closed-form equations. The method is demonstrated on real lung cancer cell microscopy data, successfully recovering interpretable 2D+time reaction-diffusion models.

Apr 20, 2026Formal Sciences260421.0050

Wasserstein Distributionally Robust Risk-Sensitive Estimation via Conditional Value-at-Risk

Feras Al Taha, Eilyan Bitar

This paper develops a distributionally robust estimation method that minimizes worst-case conditional value-at-risk (CVaR) of estimation error when the true distribution is uncertain but lies within a Wasserstein ball. The method can be computed via tractable semidefinite programming and outperforms existing approaches on electricity price forecasting.

Apr 20, 2026Formal Sciences260421.0052

IDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem

Aniruddha Adiga, Jingyuan Chou, Anshul Chiranth, Bryan Lewis +6 more

IDOBE is a standardized benchmark dataset containing over 10,000 infectious disease outbreak segments from a century of surveillance data across 13 diseases, designed to evaluate and compare epidemic forecasting methods. The authors test 11 baseline models and find MLP-based methods perform most robustly, with statistical methods excelling in pre-peak phases.

Apr 20, 2026Formal Sciences260421.0054

Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data

Zhenwen Liang, Yujun Zhou, Sidi Lu, Xiangliang Zhang +2 more

This paper addresses a critical problem in reinforcement learning for large language models: when base models are already very accurate on training benchmarks, standard RL methods fail because there aren't enough errors to learn from, causing models to collapse into repetitive solutions. The authors propose CUTS, a novel sampling strategy that maintains solution diversity even when models are highly accurate, improving generalization on challenging out-of-domain math problems by up to 15.1%.

Apr 20, 2026Formal Sciences260421.0055

Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting

Worachit Amnuaypongsa, Yotsapat Suparanonrat, Pana Wanitchollakit, Jitkomut Songsiri

This paper presents a neural network framework that simultaneously generates point forecasts and prediction intervals for multi-step time series forecasting, using multi-objective optimization to automatically balance forecast accuracy and interval sharpness while guaranteeing non-crossing intervals and target coverage. The method eliminates manual hyperparameter tuning and demonstrates superior performance on solar irradiance forecasting compared to existing approaches.

Apr 20, 2026Formal Sciences260421.0056

Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD

Nicholas Thumiger, Andrea Bartezzaghi, Mattia Rigotti, Cezary Skura +4 more

This paper introduces GIST, a neural network surrogate that predicts race-car aerodynamics 10,000× faster than traditional CFD simulations while maintaining accuracy suitable for early-stage design. The work includes a new high-fidelity dataset of LMP2 race-car aerodynamics validated by industry experts at Dallara, enabling interactive design exploration in motorsport.

Apr 20, 2026Formal Sciences260421.0059

Multi-Scale Reversible Chaos Game Representation: A Unified Framework for Sequence Classification

Sarwan Ali, Taslim Murad

MS-RCGR is a new method that converts biological sequences (DNA/protein) into multi-resolution geometric images without losing information, enabling better classification through traditional ML, computer vision, or hybrid approaches. The method consistently improves performance across different analysis paradigms and achieves best results when combined with protein language models.