6G is expected to push ultra-reliable low-latency communication (URLLC) toward stringent residual-error tar- gets for mission-critical services, where undetected errors and erasures carry fundamentally different costs. Block error rate (BLER) conflates block confusions (undetected errors) and block erasures, which have fundamentally different impacts on system reliability. To characterize the confusion rate analyt- ically, we consider an error-bounded decoder with spherical decision regions. We focus on square quadrature amplitude modulation (QAM) constellations in the finite blocklength (FBL) regime. The confusion rate is governed by the Euclidean distances between modulated codewords, whereas the code structure constrains their Hamming distances. To bridge the two metrics, we introduce a typical distance, which represents the Euclidean distance of a codeword pair at a given Hamming distance on average, and relate it to the Hamming distance through a per-symbol squared-distance coefficient. On this basis, we derive a lower bound on the block confusion rate for random codes and an upper bound for codebooks whose minimum Euclidean distance reaches the typical distance at the non-overlapping threshold. We further characterize how both bounds evolve with the average symbol energy and the block- length. Numerical results, validated by an exact computation for short codes, confirm that as the signal-to-noise ratio (SNR) or redundancy increases, the confusion rate falls many orders of magnitude below the reliability target, leaving detectable erasures as the dominant residual error.
Bin Han, Xintong Ling, Muxia Sun, Hans D. Schotten
A 6G service crosses a chain of stages from different vendors, and its contract binds class-conditioned guarantees at the terminal interface: in the packet relay chain studied here, a priority class is owed a timely-delivery rate and a loss ceiling. We define trustworthiness as conformance of the delivered per-class distribution of outcomes to the contract and develop the measurement structure this forces. Two results fix its form: a discrepancy that treats outcomes interchangeably cannot express contract preferences, and no single expectation-valued score that determines the contracted terminal expectations can be carried from stage to stage once two or more contracted indicators remain independent at some stage. The minimal record is one signed number per independent contracted indicator, which the downstream design data carry to the terminal exactly; classical scalar trust models are the single-indicator case. The terminal deviation splits exactly into per-stage contributions. Transition logs at a stage estimate its contribution without reconstructing its full deployed behavior, a stage at its design behavior is flagged at most at a declared rate whatever traffic it receives, and the estimates bracket each stage's share of the terminal deviation at explicit confidence. In the three-relay chain, a relay that either deprioritizes premium traffic or has degraded capacity violates the same clause with identical contracted evidence: the transition audit localizes the relay in 300 of 300 windows and brackets its share of the deviation, and a diagnostic separates the causes.
Wanshu Fan, Zheng Tong, Yang Liu, Jing Qin, Zhongbin Han, Haifan Gong, Congyu Liao, Xiaofeng Liu 等 9 位作者
Large language models and multimodal foundation models are enabling medical artificial intelligence (AI) systems to move beyond isolated prediction and undertake multistep clinical tasks that require planning, tool use, memory, iterative correction, and coordination among specialized agents. However, the scope of agentic AI in medicine remains unsettled, and current evaluation practices are not yet aligned with the requirements of clinical use. We conducted a scoping review with systematic evidence mapping across five electronic sources, screened 1,649 exportable records, and provisionally included 557 unique studies that met predefined criteria for goal-directed task execution, tool use, interaction with external resources, feedback-based refinement, or multi-agent collaboration. The included studies describe single agents that use external tools, workflows supported by retrieval and external knowledge, multimodal agents, and multi-agent systems applied to medical question answering, image interpretation, electronic health record analysis, drug safety, and clinical trial prediction. The evidence base remains dominated by public benchmarks, simulated settings, retrospective datasets, and small-scale expert evaluation. Process reliability, evidence traceability, uncertainty, safety, workflow impact, and external validity are evaluated less consistently. Clinical translation will depend on clearer definitions, reproducible evaluation, auditable oversight, interoperable system design, and prospective validation in real-world clinical workflows.
Chanhee Park, Jeongho Yoon, Sungbin Han, Hyeonseok Moon, Heuiseok Lim
Agentic tasks require a large language model to interact with the world, navigating information and gathering evidence across multiple steps with restricted resources. Due to this complexity, agentic task failures arise from various sources, and pinpointing these failure causes is essential to diagnose and improve agentic systems. Existing benchmarks, however, tend to focus on a single leaderboard score, leaving the underlying failure modes opaque. To fill this gap, we introduce AgentHop, a diagnostic benchmark of 1,011 multiple-choice questions paired with a controlled seven-tool sandbox under fixed token, turn, and tool-call constraints. AgentHop reveals model vulnerabilities by dissecting a single accuracy score along four axes of agent operation: retrieval, synthesis, tool-call, and resource management. Across 19 models, we find that behavior clusters by model family, with tool-call signatures revealing distinct family fingerprints: GPT models commit early, Anthropic and GLM checkpoints verify before committing, DeepSeek and Kimi over-search, and Gemini-3 Pro stays balanced. Decomposed axes further expose within-family structure: Claude Opus 4.6 and Sonnet 4.6 land within one accuracy point yet diverge on retrieval-versus-synthesis emphasis, with Opus retrieving more and Sonnet synthesizing better. We release the full benchmark set and the harness to support diagnostic agent benchmarking.