arXiv:2607. 23386v1 Announce Type: new Abstract: We document a failure class in frontier large language models -- exception chain collapse -- observed in eligibility evaluation under nested conditional rules of the form "A is required UNLESS B applies, UNLESS C overrides B".
By Paul Simpson, John Kozak, Lisa Doake
The paper introduces Bounded-RS, a protocol for testing whether neural networks are representationally systematic. It evaluates explanations by checking if the claimed representation is recoverable and causally used, then classifies the explanation into one of four states: Unsupported, Present-only, Causal-use, or Undetermined. Applied to a semantic‑parsing benchmark, the protocol finds that T5’s role‑filler Binding explanation is Causal‑use while its Geometry explanation is Unsupported, and it demonstrates high reliability across different configurations.
By Boyu Cao, Wenshuo Wang
The paper introduces BiasGuard‑D, a system that uses two adversarial language‑model agents from different families to exchange typed claim documents, with a third model quorum voting to adjudicate. A combined referential‑plus‑semantic invariant validates the verdict, and the iterative deliberation layer shifts remediation from stereotypes to hallucinations, improving right‑answer recovery by 49 percentage points on HaluEval QA. All claims, votes, and instructions are stored as typed records, providing an auditable provenance trail for operators under emerging AI regulations.
By Kardelen Subasi, Jaros{\l}aw A. Chudziak
The paper introduces $A^2E^2$, a diagnostic framework that decomposes confusion in fine‑grained aircraft detection into four distinct, measurable sources: affinity, heterogeneity, contested, and collapsed. By attributing each source to either aleatoric or epistemic uncertainty and within‑ or between‑class distinctions, the method provides actionable remedies and experimentally verifies that targeted interventions reduce the identified source without affecting irreducible ones. This turns passive confusion matrices into a concrete, validatable diagnosis that can guide model improvement.
By Hai Huang, Helmut Mayer
arXiv:2604. 27720v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly applied to medical visual question answering (Med-VQA), yet whether they can \emph{localize} the evidence behind their answers---a prerequisite for clinical auditability---is poorly characterized.
By Xupeng Chen, Binbin Shi, Chenqian Le, Qifu Yin, Lang Lin, Haowei Ni, Ran Gong, Panfeng Li
The paper investigates a failure mode in Graph-JEPA, a joint‑embedding predictive model trained on a large scientific‑reasoning graph. Despite achieving high linear‑probe accuracy and effective rank, the learned representation contains almost no usable instance information, as shown by retrieval metrics. The authors diagnose the issue to variance allocation in the objective, propose a repair that restores near‑perfect information recovery, and demonstrate that the problem persists even after repair, highlighting limitations in the evaluation metrics used.
By Gollam Rabby, S\"oren Auer