Decentralized finance exposes supervisors to fast-moving, networked credit risks that are difficult to inspect directly from raw on-chain records. We present DeXposure-Claw, an end-to-end decision-support system that converts forecasts of future exposure networks into bounded, auditable review tickets. DeXposure-FM produces future graph distributions. Deterministic monitors and stress scenarios organize forecast signals into a typed EvidenceBundle, and an LLM turns this record into ranked tickets with evidence-linked rationales. Data-health and confidence controls restrict intervention-level outputs when the supporting evidence is insufficient, while supervisors retain final authority. DeXposure-Bench evaluates the resulting workflow across forecasting, warning, calibration, stress analysis, ticket construction, and degraded-input behavior. The release schema separates forecast evidence, queue position, and control state for supervisory review. Retained legacy tickets do not contain every designed gate field. Code: https://github.com/EVIEHub/DeXposure-Claw/tree/finnlp-2026-code-snapshot-v1.

Significant progress has been made in aligning LLMs with target value functions. We argue that, even when an LLM has been well aligned in (post-)training, it may still fail to maximise the aligned value in reasoning. We mathematically formalise this gap as rational value risk: the utility discrepancy between a model’s deployed reasoning strategy and its rational counterpart whose responses maximise utility in the steepest direction. The estimation error of rational value risk is further decomposed into three components from bounded prompts, bounded responses, and imperfect verifiers. Extensive experiments are conducted, covering models Llama-3.1, Qwen-2.5, Tülu-3 families (7B-72B), GPT-5.2, GPT-5.5, and DeepSeek-V4, and benchmarks UltraFeedback, AlpacaEval, GSM8K, MATH, HumanEval, and MathArena. The results validate that (1) rational value risk is widespread; (2) value alignment can reduce, but cannot avoid, it; (3) self-consistency can improve rationality; and (4) a longer chain of thought improves rationality but with diminishing returns. The code is at https://github.com/EVIEHub/LLM-Rationality.

Multi-agent debate improves the reliability of large language models (LLMs) through iterative peer critiques. However, fixed topologies often introduce persistent positional biases, amplify unreliable agents, and cause high sensitivity to role assignments. We introduce Permutation-Equivariant Adaptive Routing Multi-Agent Debate (PEAR), an inference-time protocol that dynamically reconfigures communication roles and sparse topologies across consecutive debate rounds. By strategically switching agent-to-role assignments based on evolving agent states, PEAR prevents any agent from permanently occupying a privileged network position or distributes influence more evenly across the debate. We theoretically characterize PEAR as an equivariant sparse router: it preserves accuracy under agent relabeling while reducing routing complexity and improving generalization. Comprehensive empirical evaluations across four reasoning benchmarks and six diverse LLM backbones demonstrate PEAR significantly improves average accuracy over the strongest debate baselines. The code is at https://github.com/EVIEHub/PEAR.