New research reveals that large language models possess surprisingly accurate numerical prediction capabilities encoded within their internal states, bypassing the need for traditional text generation.
A new machine learning framework intelligently samples radio propagation paths, dramatically speeding up simulations while maintaining accuracy.
A new model leverages multi-scale neighborhood awareness to improve the detection of fraudulent activity in complex network data.
![Trading activity in digitally-created “Biden Tokens” spiked around key political events, demonstrating how speculative fervor transforms symbolic capital into quantifiable market behavior and revealing the susceptibility of even novelty assets to emotionally-driven surges and declines-a pattern mirroring established financial bubbles driven by hope and fear rather than underlying value, as predictably encoded in [latex] P = f(E, S) [/latex], where price (P) is a function of events (E) and sentiment (S).](https://arxiv.org/html/2603.03152v1/2603.03152v1/biden_no_token_price_tx_vol_events.png)
New research examines how prediction markets process unexpected political events, revealing the interplay between shifting beliefs and immediate trading pressures.
![The shifting correlation between predictions in the Trump and Democrat-aligned forecasting markets demonstrates a dynamic relationship, revealing how consensus diverges and converges as events unfold-a pattern indicative of evolving perceptions rather than static alignment, and suggesting that even opposing systems momentarily share predictive signals before ultimately re-establishing independent trajectories-a phenomenon mirroring the inherent impermanence of all complex systems [latex] \Delta t \rightarrow \in fty [/latex].](https://arxiv.org/html/2603.03136v1/2603.03136v1/rolling_correlation_between_trump_and_dem_market.png)
A detailed analysis of transaction data from the blockchain-based prediction market Polymarket reveals how it evolved during the 2024 presidential election cycle.
![Model sensitivity, as measured by impulse responses at [latex]t-1[/latex], diverges across different optimization algorithms, highlighting the nuanced impact of each on system dynamics.](https://arxiv.org/html/2603.02620v1/2603.02620v1/figs/difference_adam_muon_t-1_transformer.jpeg)
New research reveals that different optimization algorithms can produce functionally different financial models with surprisingly similar predictive power.
A challenging new dataset, DeepResearch-9K, reveals significant limitations in current artificial intelligence systems when it comes to performing complex, multi-step research tasks.
A new study reveals that many third-party services offering access to powerful language models are riddled with inconsistencies and outright model substitutions, raising serious concerns about reliability and reproducibility.
This research details a method for building specialized training data to enhance the reasoning capabilities of artificial intelligence in complex financial scenarios.
![A transformation utilizing the score function maximizes the local Fisher information, enabling a detector-applied to the transformed data-to achieve a higher probability of detection, though estimates remain biased globally, while application to the original data yields globally unbiased estimates with a variance exceeding the Cramér-Rao lower bound [latex] CRLB [/latex].](https://arxiv.org/html/2603.01737v1/2603.01737v1/x2.png)
A new approach leverages neural networks to dramatically improve the detection of faint signals buried in complex, non-Gaussian noise.