Bojie Li

Bojie Li

Modern datacenter RDMA is bottlenecked at the network interface, not the wire. A NIC running RoCE or InfiniBand holds per-connection state for every (application, remote-endpoint) pair - hundreds of megabytes at 1024-application fanout - and pays a four-traversal PCIe round trip on a 64-byte operation, inflating latency an order of magnitude beyond the wire. Both follow from the Queue Pair over PCIe abstraction RDMA inherits from InfiniBand. Huawei's Unified Bus (UB), a public 2025 specification, changes the abstraction: it decouples per-application endpoint state from per-host transport state so connection context grows additively, exposes ordering as opt-in, and reaches remote memory through native CPU load/store to an on-chip-bus controller. UB ships in Huawei's closed Ascend 950 silicon. OpenURMA is the first clean-room open implementation of UB's transport and transaction layers, realised at three tiers - synthesisable RTL on Alveo U50, a cycle-level two-node SystemC simulator, and a gem5 full-system scaffold - each with a matched OpenRoCE (RoCEv2 RC) baseline. The contribution is the implementation, harness, and controlled comparison closed silicon does not admit. On the canonical 64-byte remote fetch - LOAD on UB-spec Sec.8.3, READ on RoCEv2 RC - UB's load/store path delivers ~500 ns end-to-end, 4.37x below the matched baseline (2186 ns), sustains 2.80x higher throughput, and fits in ~14% of a U50's LUTs.

Bojie Li

Closed-source frontier labs do not disclose parameter counts, and the standard alternative -- inference economics -- carries $2\times$+ uncertainty from hardware, batching, and serving-stack assumptions external to the model. We exploit a tighter intrinsic bound: storing $F$ facts requires at least $F/$(bits per parameter) weights, so measuring how much a model \emph{knows} lower-bounds how many parameters it \emph{has}. We introduce \textbf{Incompressible Knowledge Probes (IKPs)}, a benchmark of 1{,}400 factual questions spanning 7 tiers of obscurity, designed to isolate knowledge that cannot be derived by reasoning or compressed by architectural improvements. We calibrate a log-linear mapping from IKP accuracy to parameter count on 89 open-weight models (135M--1,600B) spanning 19 vendors, achieving $R^2 = 0.917$; leave-one-out cross-validation confirms generalization (median fold error $1.59\times$, $68.5\%$ within $2\times$ and $87.6\%$ within $3\times$). For Mixture-of-Experts models, total parameters predict knowledge ($R^2 = 0.79$) far better than active parameters ($R^2 = 0.51$). We evaluate 188 models from 27 vendors and estimate effective knowledge capacity for all major proprietary frontier models; for heavily safety-tuned models the estimates are lower bounds, since refusal policy can hide tens of percentage points of "refused but known" capacity. The widely-reported saturation of reasoning benchmarks does not imply the end of scaling. Procedural capability compresses under the "Densing Law," but across 96 dated open-weight models the IKP time coefficient is $-0.0010$/month (95\% CI $[-0.0031, +0.0008]$) -- indistinguishable from zero, and rejecting the Densing prediction of $+0.0117$/month at $p < 10^{-15}$. Factual capacity continues to scale log-linearly with parameters across generations and across vendors.

Jiaying Meng, Bojie Li

Real-time multimodal agents transport raw audio and screenshots using networking stacks designed for human receivers, which optimize for perceptual fidelity and smooth playout. Yet agent models act as event-driven processors with no inherent sense of physical time, consuming task-relevant semantics rather than reconstructing signals in real time. This fundamental difference shifts the transport goal from the technical problem of signal fidelity (Shannon-Weaver Level A) to the semantic problem of meaning preservation (Level B). This mismatch imposes significant overhead. In visual pipelines, screenshot upload accounts for over 60% of end-to-end action latency on constrained uplinks, and in voice pipelines, conventional transport carries massive redundancy, sending 43-64x more data than needed to maintain task accuracy. We present Sema, a semantic transport system that combines discrete audio tokenizers with a hybrid screen representation (lossless accessibility-tree or OCR text, plus compact visual tokens) and bursty token delivery that eliminates jitter buffers. In simulations under emulated WAN conditions, Sema reduces uplink bandwidth by 64x for audio and 130-210x for screenshots while preserving task accuracy within 0.7 percentage points of the raw baseline.