Selected work

Representative Papers

Gaming Consensus: Coordinated Manipulation in Crowdsourced Fact-Checking

Jul 02, 2026

This study addresses the vulnerability of crowdsourced fact-checking systems to strategic manipulation, wherein coordinated users exploit voting mechanisms to fabricate cross-ideological consensus. The research demonstrates that “unhelpful” ratings can paradoxically inflate a note’s perceived usefulness, and that fewer than ten adversarial ratings suffice to push 10.7% of low-quality notes above the consensus threshold. To quantify this manipulation cost—a first in the literature—the authors integrate matrix factorization, theoretical modeling, and empirical analysis of historical data. Building on these insights, they propose targeted algorithmic interventions to mitigate such attacks. The proposed defenses have been deployed in X’s Community Notes system, significantly enhancing the robustness of its consensus mechanism against coordinated manipulation.

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DeployBench: Benchmarking LLM Agents for Research Artifact Deployment

Jun 03, 2026

This work addresses the frequent failure of research code deployment due to complex environment configurations, heterogeneous toolchains, system dependencies (e.g., GPU/CUDA), and legacy compatibility issues—challenges inadequately captured by existing benchmarks. To bridge this gap, we introduce DeployBench, the first systematic, multidimensional deployment benchmark encompassing 51 research artifacts across AI/ML, computer systems, and scientific computing. DeployBench evaluates the autonomous deployment capabilities of LLM agents through hidden validation pipelines that reproduce experiments and verify outputs. Built upon the OpenHands framework, our evaluation integrates four state-of-the-art LLMs and executes end-to-end deployment and validation in full system environments. Results reveal that even the best-performing agent achieves a success rate of only 7.8%–51.0%, with 63% of failures attributed to premature self-termination or misaligned validation objectives, exposing critical deficiencies in agents’ judgment of task completion.

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Almost-Orthogonality in Lp Spaces: A Case Study with Grok

May 06, 2026

This study investigates the validity of Carbery’s proposed sharpened form of the multilinear triangle inequality in $L^p$ spaces for $p > 2$. By constructing explicit counterexamples, the work disproves the universal validity of this inequality in the superquadratic regime and establishes that any such estimate can hold only if the exponent $c$ satisfies $c \leq p'$, where $p'$ denotes the conjugate exponent of $p$. Moreover, at the critical exponent $c = p'$, the inequality is proven to hold for all integer $p \geq 2$. In the three-function case, the authors obtain a sharp bound with an optimal exponent that improves upon the result of Carlen–Frank–Lieb. The analysis combines tools from functional analysis, $L^p$ space theory, and carefully designed counterexamples, with key lemmas aided by large language model–assisted derivations.

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TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment

Apr 13, 2026

This work addresses the limited fine-grained alignment between image patches and textual concepts in current vision-language foundation models. To enhance patch-text alignment, the authors propose a novel pretraining framework featuring several key innovations: patch-level knowledge distillation, an improved iBOT++ masked image modeling objective that incorporates unmasked patches directly into the loss computation, an optimized exponential moving average mechanism, and a multi-granularity synthetic caption sampling strategy. Built upon an efficient dual-encoder architecture, the method achieves state-of-the-art or competitive performance across nine task categories and twenty benchmark datasets, demonstrating its effectiveness in advancing visual representation learning.

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Test-Time Adaptation via Many-Shot Prompting: Benefits, Limits, and Pitfalls

Mar 06, 2026

This study systematically investigates the effectiveness and limitations of test-time adaptation methods that do not require updating model parameters in open-source large language models. Focusing on many-shot in-context learning (ICL), it integrates dynamic and reinforcement-based ICL prompting strategies to evaluate how the number, ordering, and selection mechanisms of examples influence performance across diverse tasks and model architectures. The findings reveal that many-shot prompting substantially improves performance on structured tasks with high information gain but is highly sensitive to example selection, whereas its benefits are limited in open-ended generation tasks. This work delineates the applicability boundaries and potential risks of prompt-based test-time adaptation, offering both theoretical grounding and practical guidance for real-world deployment.

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Recent publications

Latest Papers

Gaming Consensus: Coordinated Manipulation in Crowdsourced Fact-Checking

Jul 02, 2026

This study addresses the vulnerability of crowdsourced fact-checking systems to strategic manipulation, wherein coordinated users exploit voting mechanisms to fabricate cross-ideological consensus. The research demonstrates that “unhelpful” ratings can paradoxically inflate a note’s perceived usefulness, and that fewer than ten adversarial ratings suffice to push 10.7% of low-quality notes above the consensus threshold. To quantify this manipulation cost—a first in the literature—the authors integrate matrix factorization, theoretical modeling, and empirical analysis of historical data. Building on these insights, they propose targeted algorithmic interventions to mitigate such attacks. The proposed defenses have been deployed in X’s Community Notes system, significantly enhancing the robustness of its consensus mechanism against coordinated manipulation.

0 citationsRead paper

DeployBench: Benchmarking LLM Agents for Research Artifact Deployment

Jun 03, 2026

This work addresses the frequent failure of research code deployment due to complex environment configurations, heterogeneous toolchains, system dependencies (e.g., GPU/CUDA), and legacy compatibility issues—challenges inadequately captured by existing benchmarks. To bridge this gap, we introduce DeployBench, the first systematic, multidimensional deployment benchmark encompassing 51 research artifacts across AI/ML, computer systems, and scientific computing. DeployBench evaluates the autonomous deployment capabilities of LLM agents through hidden validation pipelines that reproduce experiments and verify outputs. Built upon the OpenHands framework, our evaluation integrates four state-of-the-art LLMs and executes end-to-end deployment and validation in full system environments. Results reveal that even the best-performing agent achieves a success rate of only 7.8%–51.0%, with 63% of failures attributed to premature self-termination or misaligned validation objectives, exposing critical deficiencies in agents’ judgment of task completion.

0 citationsRead paper

Almost-Orthogonality in Lp Spaces: A Case Study with Grok

May 06, 2026

This study investigates the validity of Carbery’s proposed sharpened form of the multilinear triangle inequality in $L^p$ spaces for $p > 2$. By constructing explicit counterexamples, the work disproves the universal validity of this inequality in the superquadratic regime and establishes that any such estimate can hold only if the exponent $c$ satisfies $c \leq p'$, where $p'$ denotes the conjugate exponent of $p$. Moreover, at the critical exponent $c = p'$, the inequality is proven to hold for all integer $p \geq 2$. In the three-function case, the authors obtain a sharp bound with an optimal exponent that improves upon the result of Carlen–Frank–Lieb. The analysis combines tools from functional analysis, $L^p$ space theory, and carefully designed counterexamples, with key lemmas aided by large language model–assisted derivations.

0 citationsRead paper

TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment

Apr 13, 2026

This work addresses the limited fine-grained alignment between image patches and textual concepts in current vision-language foundation models. To enhance patch-text alignment, the authors propose a novel pretraining framework featuring several key innovations: patch-level knowledge distillation, an improved iBOT++ masked image modeling objective that incorporates unmasked patches directly into the loss computation, an optimized exponential moving average mechanism, and a multi-granularity synthetic caption sampling strategy. Built upon an efficient dual-encoder architecture, the method achieves state-of-the-art or competitive performance across nine task categories and twenty benchmark datasets, demonstrating its effectiveness in advancing visual representation learning.

0 citationsRead paper

Test-Time Adaptation via Many-Shot Prompting: Benefits, Limits, and Pitfalls

Mar 06, 2026

This study systematically investigates the effectiveness and limitations of test-time adaptation methods that do not require updating model parameters in open-source large language models. Focusing on many-shot in-context learning (ICL), it integrates dynamic and reinforcement-based ICL prompting strategies to evaluate how the number, ordering, and selection mechanisms of examples influence performance across diverse tasks and model architectures. The findings reveal that many-shot prompting substantially improves performance on structured tasks with high information gain but is highly sensitive to example selection, whereas its benefits are limited in open-ended generation tasks. This work delineates the applicability boundaries and potential risks of prompt-based test-time adaptation, offering both theoretical grounding and practical guidance for real-world deployment.

0 citationsRead paper