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Jiaheng Zhang

Jiaheng Zhang contributes to research discovery and scholarly infrastructure.

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Artificial Intelligence Computation and Language Cryptography and Security

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preprint2026arXiv

AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration

Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail and inform the next attempt, and lessons accumulate across cycles. Existing autonomous research systems often model this process as a linear pipeline: they rely on single-agent reasoning, stop when execution fails, and do not carry experience across runs. We present AutoResearchClaw, a multi-agent autonomous research pipeline built on five mechanisms: structured multi-agent debate for hypothesis generation and result analysis, a self-healing executor with a \textsc{Pivot}/\textsc{Refine} decision loop that transforms failures into information, verifiable result reporting that prevents fabricated numbers and hallucinated citations, human-in-the-loop collaboration with seven intervention modes spanning full autonomy to step-by-step oversight, and cross-run evolution that converts past mistakes into future safeguards. On ARC-Bench, a 25-topic experiment-stage benchmark, AutoResearchClaw outperforms AI Scientist v2 by 54.7%. A human-in-the-loop ablation across seven intervention modes reveals that precise, targeted collaboration at high-leverage decision points consistently outperforms both full autonomy and exhaustive step-by-step oversight. We position AutoResearchClaw as a research amplifier that augments rather than replaces human scientific judgment. Code is available at https://github.com/aiming-lab/AutoResearchClaw.

preprint2026arXiv

Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models

Diffusion Language Models (DLMs) offer a promising alternative for language modeling by enabling parallel decoding through iterative refinement. However, most DLMs rely on hard binary masking and discrete token assignments, which hinder the revision of early decisions and underutilize intermediate probabilistic representations. In this paper, we propose EvoToken-DLM, a novel diffusion-based language modeling approach that replaces hard binary masks with evolving soft token distributions. EvoToken-DLM enables a progressive transition from masked states to discrete outputs, supporting revisable decoding. To effectively support this evolution, we introduce continuous trajectory supervision, which aligns training objectives with iterative probabilistic updates. Extensive experiments across multiple benchmarks show that EvoToken-DLM consistently achieves superior performance, outperforming strong diffusion-based and masked DLM baselines. Project webpage: https://aim-uofa.github.io/EvoTokenDLM.

preprint2026arXiv

Making Theft Useless: Adulteration-Based Protection of Proprietary Knowledge Graphs in GraphRAG Systems

Graph Retrieval-Augmented Generation (GraphRAG) has emerged as a key technique for enhancing Large Language Models (LLMs) with proprietary Knowledge Graphs (KGs) in knowledge-intensive applications. As these KGs often represent an organization's highly valuable intellectual property (IP), they face a significant risk of theft for private use. In this scenario, attackers operate in isolated environments. This private-use threat renders passive defenses like watermarking ineffective, as they require output access for detection. Simultaneously, the low-latency demands of GraphRAG make strong encryption which incurs prohibitive overhead impractical. To address these challenges, we propose AURA, a novel framework based on Data Adulteration designed to make any stolen KG unusable to an adversary. Our framework pre-emptively injects plausible but false adulterants into the KG. For an attacker, these adulterants deteriorate the retrieved context and lead to factually incorrect responses. Conversely, for authorized users, a secret key enables the efficient filtering of all adulterants via encrypted metadata tags before they are passed to the LLM, ensuring query results remain completely accurate. Our evaluation demonstrates the effectiveness of this approach: AURA degrades the performance of unauthorized systems to an accuracy of just 5.3%, while maintaining 100% fidelity for authorized users with negligible overhead. Furthermore, AURA proves robust against various sanitization attempts, retaining 80.2% of its adulterants.

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