arXiv subjects

Mo Hai

Publications and source records attributed to Mo Hai.

4 recordsLinked to original sources

arXiv2026

State-dependent error correlations shape voting thresholds in committees of AI agents

The aggregation benefit of a committee of artificial intelligence (AI) agents comes from complementary information across members. Classical voting guarantees assume independent errors. Language-model errors often co-occur on the same cases. We combine Sah-Stiglitz screening with error dependence that can differ between good and bad cases. In a homogeneous exchangeable Gaussian-copula model, shared errors create a positive asymptotic error floor for majority voting and can change the approval threshold that minimizes expected loss. We estimate a heterogeneous extension from 174,384 votes cast by 28 language models on four binary-screening benchmarks. Parameters estimated from odd-indexed items predicted committee loss on even-indexed items. For the sampled committee composition, the full-matrix dependence model increased identity-line R^2 from 0.840 under independence to 0.967. In a design-balanced analysis, cost-sensitive threshold selection under independence reduced scaled loss from 60.25 for majority to 52.50. Modeling dependence reduced it further to 50.77, an incremental improvement of 1.73 units (95% bootstrap CI, 0.68-2.33). The overall reduction from majority was 15.73% (95% bootstrap CI, 13.41-16.75%).

cs.CY↗

arXiv2026

Falsification-Based Verification of LLM-Generated Optimization Models: Sound Test Batteries and Their Detection Limits

Large language models now translate natural-language descriptions of decision problems into solver-ready optimization models, and they fail silently. A generated model often runs and still encodes the wrong problem, while standard evaluation compares optimal values against labeled answers that deployment does not provide. How to certify such a model without any reference is the question this paper addresses. We develop falsification-based verification. Every numeric quantity in a problem description plays a role that the text itself states, such as a capacity, a requirement, or a unit cost, and any correct model must respond to changes in these quantities as the stated roles dictate. From duality and sensitivity analysis we derive a battery of solver-based tests that are individually sound, so a violation certifies a faulty model and the false-positive rate is zero by design. We characterize the errors that no test of this kind can see, give conditions under which each canonical error class is detected with certainty, and prove that perturbation testers with tuned thresholds cannot be simultaneously sound and nontrivial. Across 326 ground-truth models, a synthetic family, and four public benchmarks with two generators, the battery flags 0.0% of faithful models while a threshold tester flags 54.9%; it detects 56.1% of core formulation errors, 70.0% under certified preconditions, and 40.4% of the errors that value-based scoring provably cannot see, and it reproduces the predicted detectability pattern including its blind spots. Every flag carries a machine-checkable certificate that localizes the defect, and a full audit costs about 25 millisecond-scale solver calls per model. Classical sensitivity analysis and duality thus offer a rigorous, label-free audit that complements existing evaluation of AI-generated optimization models.

cs.SE↗

arXiv2026

CASP: Learning-Augmented Offline Approximation with Verifiable Certificates and Bounded-Loss PAC Guarantees

Machine-learned predictions can speed up offline NP-hard optimization, but asking a predictor what to do amounts to asking it to solve the problem, and committing an unchecked prediction forfeits every worst-case guarantee. CASP (Certificate-Augmented Solution Pruning) instead asks which parts of the search space may be ignored, and accepts each answer only after a sound polynomial-time verifier has checked it, so correctness never depends on prediction quality. We develop the learning theory of this design. The verifier makes the induced loss class uniformly bounded, so certificate parameters are learnable from $\tilde O(\varepsilon^{-2}\log K)$ samples ($K$ the maximum instance size), whereas the unverified commitment class admits no distribution-free rate and, under cost spread $R$, none below $\Omega(R/\varepsilon^2)$. Filtering noisy predictions by verifiable confidence dominates the standard min-combiner, with a margin we compute in closed form, and the prediction stays useful even given the LP, because it breaks ties on degenerate optimal faces, where every symmetric LP policy, meaning one whose commitments depend on the instance only through the verifiable confidence values, provably stalls. Experiments on five problems test the theory's quantitative predictions. With trained predictors, unverified pruning loses up to $26%$ of the optimum under distribution shift, while the verified deployment of the same predictions loses nothing.

cs.LG↗

arXiv2023

A Multi-level Distillation based Dense Passage Retrieval Model

Ranker and retriever are two important components in dense passage retrieval. The retriever typically adopts a dual-encoder model, where queries and documents are separately input into two pre-trained models, and the vectors generated by the models are used for similarity calculation. The ranker often uses a cross-encoder model, where the concatenated query-document pairs are input into a pre-trained model to obtain word similarities. However, the dual-encoder model lacks interaction between queries and documents due to its independent encoding, while the cross-encoder model requires substantial computational cost for attention calculation, making it difficult to obtain real-time retrieval results. In this paper, we propose a dense retrieval model called MD2PR based on multi-level distillation. In this model, we distill the knowledge learned from the cross-encoder to the dual-encoder at both the sentence level and word level. Sentence-level distillation enhances the dual-encoder on capturing the themes and emotions of sentences. Word-level distillation improves the dual-encoder in analysis of word semantics and relationships. As a result, the dual-encoder can be used independently for subsequent encoding and retrieval, avoiding the significant computational cost associated with the participation of the cross-encoder. Furthermore, we propose a simple dynamic filtering method, which updates the threshold during multiple training iterations to ensure the effective identification of false negatives and thus obtains a more comprehensive semantic representation space. The experimental results over two standard datasets show our MD2PR outperforms 11 baseline models in terms of MRR and Recall metrics.

cs.IR↗