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Apple

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Selected work

Representative Papers

Conformal Thinking: Risk Control for Reasoning on a Compute Budget

Feb 03, 2026

This work addresses the key challenge of balancing inference accuracy and error risk control under limited computational budgets in large language model reasoning. The authors reformulate the computational budget allocation problem as an adaptive stopping decision task under explicit risk constraints and propose a dynamic termination mechanism based on dual confidence thresholds. By parameterizing a lower threshold to early-exit hopeless samples and integrating a distribution-free risk control method to optimize the stopping policy, the approach ensures rigorous adherence to user-specified risk limits. Furthermore, an efficiency loss metric is introduced to select the optimal exit strategy across multiple criteria. Experimental results demonstrate that the proposed method significantly improves computational efficiency across diverse tasks and models while strictly satisfying prescribed risk upper bounds.

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Stochastic CHAOS: Why Deterministic Inference Kills, and Distributional Variability Is the Heartbeat of Artifical Cognition

Jan 12, 2026

This work challenges the prevailing reliance on deterministic outputs in large language model (LLM) inference, which obscures inherent uncertainties, vulnerabilities, and safety risks while suppressing emergent capabilities and multi-path reasoning. To address this, the authors propose the “Stochastic CHAOS” framework, which treats output distribution variability as a core cognitive signal. By integrating multi-path sampling, non-deterministic attention mechanisms, and multi-sample evaluation, the framework systematically uncovers the true capabilities and latent risks of LLMs. Experimental results demonstrate that deterministic inference substantially underestimates both model performance and tail-end risks. In contrast, the proposed approach effectively recovers emergent phenomena, enhances reasoning accuracy, and reveals hidden safety hazards, thereby questioning conventional assumptions about reliability and reproducibility in LLM deployment.

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Voice Quality Dimensions as Interpretable Primitives for Speaking Style for Atypical Speech and Affect

May 27, 2025

This study addresses the challenge of modeling interpretable speech quality dimensions to characterize speaker styles in atypical and affective speech. We propose the first systematically defined set of seven phonatory quality primitives—e.g., clarity, glottalization, and breathiness—and train linear probes on frozen Wav2Vec 2.0 embeddings using the Speech Accessibility Project (SAP) dataset. Our method achieves strong interpretability and cross-domain generalizability: it attains state-of-the-art performance on SAP and, for the first time, enables zero-shot transfer across languages (English/Italian) and tasks (atypical vs. emotional speech), with average accuracy exceeding 82%. These results validate the framework’s efficacy as a universal, style-sensitive speech representation. The core contribution is the first interpretable, transferable, and multilingual-compatible speech quality dimension modeling framework.

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Scalable Private Search with Wally

Jun 10, 2024arXiv.org

To address the challenge of balancing privacy preservation and query efficiency in large-scale private database search, this paper proposes Wally—a scalable system achieving $(varepsilon,delta)$-differential privacy under high concurrency. Wally integrates four key techniques: (i) an adaptive dummy-query attenuation mechanism—where the number of dummy queries decreases with the number of honest users; (ii) Tor-based anonymous routing; (iii) randomized query submission timing; and (iv) lightweight homomorphic encryption. This design ensures that per-query privacy overhead asymptotically approaches zero as system scale increases. To our knowledge, Wally is the first system to enable efficient private search at the scale of ten million database entries and one million concurrent users. Experiments on a 3.2-million-entry dataset show that Wally achieves 7–28× higher QPS than Tiptoe while reducing communication overhead by 6.69–31×.

1 citationsRead paper
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