arXiv:2511. 21731v2 Announce Type: replace-cross Abstract: We present the results of cognitive tests on conceptual combinations, performed using specific Large Language Models (LLMs) as test subjects.
arXiv:2603. 20381v2 Announce Type: replace-cross Abstract: Understanding the fundamental mechanisms governing the production of meaning in the processing of natural language is critical for designing safe, thoughtful, engaging, and empowering human-agent interactions.
arXiv:2406. 05335v3 Announce Type: replace-cross Abstract: Generation of text and speech in natural languages can be modeled as a stochastic process.
arXiv:2607. 17219v1 Announce Type: cross Abstract: Human survey respondents exhibit question-order effects that satisfy the QQ (quantum question) equality, an a priori, parameter-free prediction of the projective quantum question-order model.
The paper investigates neural scaling laws for RydbergGPT, an autoregressive transformer trained on qubit measurement data from Rydberg atom arrays. Near a critical point in the quantum system, the transformer’s loss scales with dataset size following a power‑law with a loss‑floor correction, whereas this relationship weakens away from criticality. By comparing entropy‑normalized mutual‑information two‑point functions of Rydberg data and natural‑language corpora, the authors find that near‑critical statistics resemble natural language more closely, suggesting that multi‑scale dependence underlies stable neural scaling and that scaling behaviour depends on the model–data pair.
arXiv:2608. 14691v1 Announce Type: new Abstract: Sequence models are conventionally distinguished by their backbone, the mechanism that routes information across positions, such as attention or recurrence.
arXiv:2601. 10588v2 Announce Type: replace-cross Abstract: Whether neural information processing is entirely classical or involves quantum-mechanical elements remains an open question.
arXiv:2511.00763v3 Announce Type: replace Abstract: We investigate the performance of large language models (LLMs) on repetitive deterministic prediction tasks and study how the sequence accuracy rat...
arXiv:2602. 18364v2 Announce Type: replace-cross Abstract: Maximum likelihood prediction (MLP) is a core task at the heart of modern large language models.
The paper argues that language operates with two parameters: amplitude, which measures how often words co‑occur, and phase, a signed relational factor that determines how co‑activated meanings combine and can reverse a meaning’s contribution. Unlike amplitude, phase is not captured by standard word embeddings or transformer attention weights and is indexed to individuals and dyadic interactions. The authors propose six empirical predictions to test phase’s role and suggest that future language models should incorporate agent‑indexed, phase‑bearing semantic states.
QEncodeBench evaluates whether large language models can translate classical constraint problems into verified quantum phase oracles. The benchmark measures the correctness of generated circuits using an adversarial self‑validated verifier that checks full solution‑set equivalence while enforcing resource limits. Results show that models lacking a reasoning mode perform poorly, whereas enabling native reasoning improves accuracy tenfold; semantic errors dominate, and neuro‑symbolic pipelines close most gaps by delegating critical composition to deterministic procedures.
QART is a quantum‑classical hybrid architecture that augments a language model with quantum encoding, CIM‑based QUBO optimization, and quantum decoding to improve long‑horizon reasoning. The authors claim that, under certain assumptions, QART can maintain a non‑zero probability of recovering an optimal reasoning path while traditional autoregressive LLMs see their acceptance probability drop to zero as cumulative risk grows. Experiments on six benchmarks with three backbone models show that QART outperforms the baselines in 14 of 15 pairings, with relative gains up to 84.0% on SciCode.