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Author

Qianli Wang

5 papers indexed here

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#natural language process... Preprint Jan 2026

eTracer: Polarity-Aware Evidence Grounding

This work formalizes polarity-aware evidence grounding: linking each response sentence to its contextual evidence with a supporting, contradicting, or irrelevant label, and proposes eTracer, a post-hoc framework whose context-aware signed scorer recovers the full evidence map in a single forward pass per response.

Bo-Hao Chu, Qianli Wang, Hendrik Damm et al. · 0 citations
#natural language process... Preprint Sep 2026

Locally Sound, Globally Insufficient: The Local-Global Gap in Multi-Hop Reasoning

Instead of post-hoc diagnosis, E-Closure is introduced to supervise three dependencies for reliable reasoning during training, combining generation supervision on supported original and counterfactual responses with bidirectional switching constraints.

Bo-Hao Chu, Hendrik Damm, Qianli Wang et al. · 0 citations
#artificial intelligence Preprint Sep 2026

From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness

Chain-of-thought (CoT) can sound plausible yet be unfaithful to the model's underlying reasoning. Most prior work probes CoT faithfulness through input--output behavior or input attributions, leaving internal computation largely underexplored. We instead cast faithfulness as internal concept grounding: Does a large lan...

Qianli Wang, Yilong Wang, Dennis Wei et al. · 0 citations
#machine learning Preprint May 2026

Judge Circuits Explain Format-Induced Inconsistency in LLM-as-a-Judge

By structurally decoupling abstract judging from output formatting, this work provides a mechanistic account of format-induced inconsistency on the open-weight models the authors study and implies that benchmark comparisons of judge reliability across formats partly measure the fragile formatting stage, and can underst...

Nils Feldhus, Tanja Baeumel, Elena Golimblevskaia et al. · 1 citation
#machine learning Preprint Sep 2026

Limitations of Automated Simulatability: LLM Simulators Can Bypass Explanations

This work qualitatively replicate and extend ConSim's ranking of explanation methods across the tested datasets, explanation families, and simulator LLMs, and identifies two limitations: when class names are meaningful, simulators can obtain high simulatability by solving the classification task directly, without relyi...

Antonin Poché, Fanny Jourdan, Nils Feldhus et al. · 0 citations

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