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Researcher profile

Peng Yu

Peng Yu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceComputation and Languagecond-mat.mtrl-sciMachine LearningMultiagent Systems

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Published work

3 published item(s)

preprint2026arXiv

Anomalously High Phonon Thermal Conductivity Driven by Weak Electron-Phonon Coupling in Weyl Semimetals TaAs and TaP

In conventional metals, thermal transport is governed by electrons, with phonon contributions often considered negligible. Here, through rigorous first-principles calculations, we uncover a phonon-dominated thermal transport regime in the Weyl semimetals TaAs and TaP. Remarkably, although TaP is metallic, its phonon thermal conductivity ($κ_{\text{ph}}$) reaches as high as 171 Wm$^{-1}$K$^{-1}$ at room temperature, surpassing its electronic counterpart by more than a factor of five. This anomalously high $κ_{\text{ph}}$ is enabled by the unique electronic and phononic band structures, characterized by the Weyl nodes near the Fermi level, together with acoustic phonon bunching and a wide frequency gap in the phonon spectrum, which collectively suppress phonon-electron and phonon-phonon scattering processes. Due to the substantial phonon contribution, the derived Lorenz number deviates strongly from the conventional Wiedemann-Franz law. We further show that the significance of phonon thermal transport is universal across topological semimetals. Our work provides deeper insight into thermal transport mechanisms in topological semimetals and extends the scope for discovering materials with high thermal conductivity.

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preprint2026arXiv

KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning

Autoregressive large language models (LLMs) pre-trained by next token prediction are inherently proficient in generative tasks. However, their performance on knowledge-driven tasks such as factual knowledge querying remains unsatisfactory. Knowledge graphs (KGs), as high-quality structured knowledge bases, can provide reliable knowledge for LLMs, potentially compensating for their knowledge deficiencies. Aligning LLMs with explicit, structured knowledge from KGs has been a challenge; previous attempts either failed to effectively align knowledge representations or compromised the generative capabilities of LLMs, leading to less-than-optimal outcomes. This paper proposes \textbf{KaLM}, a \textit{Knowledge-aligned Language Modeling} approach, which fine-tunes autoregressive LLMs to align with KG knowledge via the joint objective of explicit knowledge alignment and implicit knowledge alignment. The explicit knowledge alignment objective aims to directly optimize the knowledge representation of LLMs through dual-view knowledge graph contrastive learning. The implicit knowledge alignment objective focuses on incorporating textual patterns of knowledge into LLMs through triple completion language modeling. Notably, our method achieves a significant performance boost in evaluations of knowledge-driven tasks, specifically embedding-based knowledge graph completion and generation-based knowledge graph question answering.

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preprint2026arXiv

Not All Turns Matter: Credit Assignment for Multi-Turn Jailbreaking

Deploying LLMs in multi-turn dialogues facilitates jailbreak attacks that distribute harmful intent across seemingly benign turns. Recent training-based multi-turn jailbreak methods learn long-horizon attack strategies from interaction feedback, but often rely on coarse trajectory-level outcome signals that broadcast uniformly to every turn. However, we find that turn-level contributions in multi-turn jailbreaking are non-uniform, phase-dependent, and target-specific. Such coarse outcome supervision induces a credit assignment problem, leading to over-rewarding redundant turns in successful trajectories and under-crediting useful intermediate turns in failed ones. To address this, we propose TRACE, a turn-aware credit assignment framework for reinforcement learning (RL)-based multi-turn jailbreaking. For successful trajectories, TRACE estimates turn-level contributions via leave-one-turn-out semantic masking; for failed ones, TRACE assigns penalties based on prompt harmfulness and semantic relevance, with an additional local refusal-aware penalty. Furthermore, we reuse the attack-side credit signal for multi-turn defense alignment. Extensive experiments on open-source and closed-source targets show that TRACE achieves strong overall performance in effectiveness, transferability, and efficiency, yielding about a 25% relative improvement in attack success rate over the strongest RL baseline while also improving the safety-utility balance when reused for defense alignment.

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