Claims Browser

A single untuned LLM query can produce algorithms competitive with specialized methods for well-specified operations research problems

"for the well-specified operations problems we study, a single untuned LLM query can already produce algorithms competitive with specialized methods."

Frontier LLMs should be considered a serious empirical baseline for algorithm design in well-specified operations research problems

"These results suggest that frontier LLMs can be a serious empirical baseline for algorithm design in well-specified OR problems."

QuantumBoostNet, a hybrid classical-quantum architecture with a 10-qubit quantum circuit, consistently outperforms state-of-the-art classical and hybrid classical-quantum models in cardiac ultrasound view identification

"Extensive experiments indicate that, despite the limited number of qubits that can be simulated, QuantumBoostNet consistently outperforms state-of-the-art classical and hybrid classical-quantum models in cardiac ultrasound view identification, achieving a relative improvement over the best competitor."

Most state-of-the-art computer vision models perform well on standard benchmarks but often yield suboptimal results in specialized medical imaging tasks due to high levels of noise in the data

"Although computer vision has advanced significantly, most state-of-the-art models perform well on standard benchmarks but often yield suboptimal results in specialized medical imaging tasks due to the high level of noise present in the data."

Evolution Strategies have emerged as a memory-efficient post-training paradigm for LLM reasoning

"Evolution Strategies (ES) have recently emerged as a memory-efficient post-training paradigm for LLM reasoning."

QuantumBoostNet demonstrates superior performance on established image classification benchmarks and exhibits robustness to noise

"QuantumBoostNet also demonstrates superior performance on established image classification benchmarks and exhibits robustness to noise."

The findings support the continued development of hybrid classical-quantum models for specialized medical imaging applications

"These findings support the continued development of hybrid classical-quantum models for specialized medical imaging applications."

Double-difference designs used to audit LLM judge bias are not properly identified on bounded rating scales, confounding differential preference with differential attenuation

"We show that this endpoint is not identified on the scale that reports it. Each term of the double difference is censored by its own share, so the observed statistic confounds differential preference with differential attenuation"

A severity shift common to both responses can manufacture a spurious interaction effect whenever two responses censor it unequally due to unequal distances from scale bounds

"a severity shift common to both responses manufactures an interaction whenever the two censor it unequally, as unequal distances from the bounds make them, exactly where good stimuli place them"

In a pre-registered audit of a pedagogy judge with 990 calls, the effect of stated learner profile on scaffolding preference was null at +0.085 points

"The registered primary endpoint, the effect of a stated learner profile on the judge's scaffolding preference, is null: $+0.085$ points (95\% BCa $[-0.167, +0.353]$, $p = 0.684$)"

A nominally significant interaction effect of +0.378 in the audit is not actually identified as preference, with 79-85% of it reproducible from severity shift and scale floor alone without any differential preference

"The audit's one nominally significant interaction, $+0.378$ ($p = 0.002$), is not identified as preference: a construction containing zero differential preference reproduces 79 to 85\% of it from the observed severity shift and the scale floor alone"

The contribution of the censoring mechanism to observed interaction effects is measurable from an audit's own ratings and can be derived in closed form

"We derive the mechanism in closed form and show that its contribution is measurable from an audit's own ratings"

Existing propose-and-verify methods for agent harness adaptation waste rollouts on unrelated behaviors and allow aggregate scores to obscure specific regressions

"Existing propose-and-verify methods typically score every candidate on a fixed task set, wasting rollouts on unrelated behaviors and allowing aggregate scores to obscure specific regressions."

HarnessLens improves average held-out performance by 7.6-13.6% while consuming substantially less evaluation budget than competing baselines across three agent harnesses and four benchmarks

"Across three agent harnesses and four benchmarks, HarnessLens improves average held-out performance by 7.6-13.6% while consuming substantially less evaluation budget than competing baselines."

Behavior-aware verification with explicit attribution enables more reliable and sample-efficient harness evolution under constrained interaction budgets

"These results demonstrate that behavior-aware verification with explicit attribution enables more reliable and sample-efficient harness evolution under constrained interaction budgets."

Adapting agent harnesses requires costly verification

"Agent harnesses shape how language-model agents use instructions, tools, and runtime components, but adapting these harnesses requires costly verification."

Synchronous quantile temporal-difference learning (QTD) in tabular distributional reinforcement learning has a global finite-sample guarantee with leading last-iterate fluctuation of order Õ(T^(-a/2)/√(1-γ)) for stepsizes α_t=c(t+1)^(-a) with a∈(1/2,1), with no polynomial dependence on the number of quantiles.

"We establish a global finite-sample guarantee for synchronous quantile temporal-difference learning (QTD) in tabular distributional reinforcement learning."

The proof technique separates two stability mechanisms: a global comparison argument based on order monotonicity and W_∞ contraction brings an arbitrarily initialized iterate into a local neighborhood, where linearization and martingale analysis apply.

"The proof separates two stability mechanisms. A global comparison argument, based on the order monotonicity of reward cumulative distribution functions and the $W_\infty$ contraction of the distributional Bellman operator, brings an arbitrarily initialized iterate into a local neighborhood."